Difference between revisions of "Helsinki energy decision 2015"

Latest revision as of 13:39, 27 January 2016

Main message:

Helsinki will make a large energy decision during fall 2015. it will impact energy production for decades. An important decision requires systematic information. How do we guarantee a continuous and sufficient supply of district heat and electricity in Helsinki around the year for the next fifty years in such a way that the impacts on costs, health, climate change, and sustainable growth are as beneficial as possible? How can decentralised energy production and energy efficiency renovations improve the situation? The latter question was asked by the City Council, and another assessment (coordinated by the city of Helsinki and Helen energy company) will answer that soon.

Answer:

National Institute for Health and Welfare (THL) performed an open assessment on the Opasnet web-workspace during summer and fall 2015 aiming at shared understanding. We utilised knowledge crystals, i.e. regurlarly updated collaborative online answers to specific research questions. Based on them, we built a model called Sofia to assess impacts and implementability of several different energy options in Helsinki. An assessment report| has been published in Finnish.

Sofia can estimate the building stock and energy consumption based on average energy efficiency, floor area, and ambient temperature. Sofia is an energy balance model that optimises costs of energy production while ensuring the supply of district heating in Helsinki for each day. The situation is followed from 1985 to 2065. Sofia looks also at other heat, fuel, and electric power consumption, but not those of industry and traffic.

Based on balanced energy production Sofia estimates the fuel and other costs and greenhouse gas and fine particle emissions from power plants and other energy processes. The model can assess several scenarios and thus compare the overall impacts of different actions, such as energy renovations or new power plants.

Sofia's main conclusion is that the energy solutions seem to fall into two categories: those that produce electric power as a side stream of district heat, like in Hanasaari coal plant and the suggested Vuosaari C biofuel plant; and those that do not produce or even consume a large amount of electricity, like decentralised biofuel heat plants or sea heat pumps. Based on current discussions, the self-sufficiency of electric power in Helsinki seems to be disappearing and probably changing into a deep dependency of outside electricity. The development of the Nordic electric market determines whether this will be a problem or not in the future.

Unfortunately, the electric market is under strong transition, and for example the wind power tariffes complicate the situation, making future predictions of this crucial issue even more difficult. Local heat production solutions are in a great need of a clear and systematic national electric policy. Otherwise, the previously very efficient combined heat and power production will be replaced with something much less efficient in Finland in the future.

If we forget the question about self-sufficiency of electric power and focus only on district heat, there are several cost-effective solutions available. Excess heat from different processes such as data centres or Neste oil refinery seem to be more cost-effective than e.g. Vuosaari C biofuel plant. Also small and large heat pumps that take heat from the environment are cost-effective. District heat from Loviisa 3 nuclear power plant is also cost-effective in our estimates, but we were not able to fully charaterise uncertainties in the investment cost; and we must remember that the decision to build or not to build the reactor is not in the hands of Helsinki.

A major problem with the process heat solutions is that they consume electric power exactly when the demand for power is the largest and price the highest. We were not able to assess the variability of the price of electricity in this assessment, but it is substantial and might change conclusions. Also for this reason, there is a need for a national energy balance model with hourly resolution including eletricity, industry, and traffic.

Health and climate impacts are important in every single option we looked at, together ranging between a fifth and a fourth of all costs, health and climate comprising a half of that each. Surprisingly, differences between policy options in this respect were small even if the reputation of the climate-friendliness of the options was very different. According to Sofia, this is due to several reasons. All large power plants filter fine particles out very effectively, and therefore there are no large differences in health impacts; a clear exception is small-scale wood burning in houses, which has several times larger health problems than any other option despite its marginal role in energy production. Also, the life cycle emissions of fine particles and greenhouse gases is also important: even if the direct emissions from a heat pump are zero, the electric power imported has produced emissions somewhere. Biofuels are considered climate neutral in emission trade, but they still produce emissions during their life cycle. Therefore, even a fuel switch does not produce the expected results.

Of course, the conclusions based on the model depend on the goodness of the data used. There are several uncertainties that should have been clarified had there been more time to work on the topic. Especially the fuel prices are very difficult to predict into the future. Also the fuel taxes are very high for some fuels but not for others. Effectively, the society decides, using taxes, which solutions make sense on the city level. This is a third reason why there should be a national, long-term, and clear energy policy - and tax policy supporting it - based on detailed, open energy balance modelling the best available information.

Scope

Question

Helsinki must be able to provide its residents with reliable, climate-friendly and cost-effective district heating and electricity every day all year round for the decades to come. When this energy balance is assessed, what options does Helsinki have for main energy solutions? What kind of impacts do these options have in terms of

climate mitigation,
stability (fuel availability etc),
cost to the city and citizens,
environment,
biofuel use,
national energy balance,
domestic source,
health?^[1]

Intended use and users

Helsinki City Council will make a major decision in autumn 2015 about renovating old power plants, building a new one, or some other option replacing the need of the old power plants. Therefore, the City Council is the major user of the assessment. There are also secondary uses, such as informing national energy discussion and demonstrating the usefulness of an open combined energy balance and building model.

Participants

The work is coordinated by Jouni Tuomisto from THL / Impact Assessment Unit. Their motivation is to contribute to the decision-making process by bringing quantitative results to help assess the health and other impacts of different options in a transparent manner. Participants that we hope get involved when they are informed about the assessment include

THL: Jouni, Pauli, Teemu, Matleena, Julia
the City of Helsinki,
Helen energy company
Uusi energiapolitiikka group
Energiaremontti 2015

Boundaries

Time: 1985 - 2065
Energy need estimated for Helsinki.
Main focus is on local heat and power need. Energy balance estimated for Helsinki (electricity nationally).
Health impacts estimated for the regional area (ca. 300 km radius)
Impacts are assessed separately for the citizen, the city, Helen energy company, and Finland.
Transport is not looked at although it is an important energy consumer. This is because there is no interaction with heating except via city structure, and there are no resources to look at that in this assessment. Electric cars would have an interaction with electricity production, but that applies to the total electricity market area (Finland, partly Scandinavia) and is too complex to look at.

Decisions and scenarios

Main article: Helsinki energy decision options 2015

The two options in the official decision preparation as of May 2015 are i) Hanasaari shutdown and Vuosaari C, and ii) Hanasaari 40 bio and Salmisaari 40 bio. However, also other options have been suggested, and also they are evaluated at least superficially.

BAU: Only small, essential renovations are made to current power plants to stay within new emission limits.
Vuosaari C: A new power plant is built in Vuosaari with the capacity to burn 100 % wood-based fuel or any combination of wood-based fuels and coal.
Hanasaari shutdown: The Hanasaari powerplant is shut down, demolished and apartment buildings are built in its place.
Hanasaari 40 bio: The Hanasaari power plant is renovated to burn 40% wood-based fuels and 60% coal.
Salmisaari 40 bio: The Salmisaari power plants are renovated to burn 40% wood-based fuels and 60% coal.
Biofueled heat production units: Salmisaari oilfueled heat plant is shut down and new biofuel burning heat plants are built in Salmisaari and Vuorsaari.
Loviisa nuclear CHP: A third nuclear power plant is built in Loviisa and the heat is used for district heating in Helsinki.
Neste excess heat: The excess heat from the Neste's oil refinery in Porvoo is used for district heating in Helsinki.
Decentralised energy production: The amount of decentralised energy production is increased as much as possible. Practically this means building a lot more solar panels, geothermal power, small-scale wood burning and wind mills around Helsinki.
Large heat pumps: Big heat pumps are installed to draw heat from the Baltic sea or deep from the ground to produce district heating.
Energy saving: With huge energy saving campaigns and by renovating buildings to be more energy efficient the amount of required energy is decreased significantly.

	BAU	Process heat	Helen proposition	Zero investment	Carbon neutral 2050	CHP bio	Distributed and sea
Biofuel heat plants	No	No	Yes	No	Yes	No	No
CHP diesel generators	No	No	No	No	No	No	Yes
Data center heat	No	Yes	No	No	No	No	Yes
Deep-drill heat	No	No	No	No	No	No	Yes
Hanasaari	renovated for biofuels	Yes	No	Yes	No	No	No
Household air heat pumps	Yes	No	Yes	Yes	Yes	Yes	Yes
Household air conditioning	Yes	No	Yes	Yes	Yes	Yes	Yes
Household geothermal heat	Yes	No	Yes	Yes	Yes	Yes	Yes
Household solar	Yes	No	Yes	Yes	Yes	Yes	Yes
Katri Vala cooling	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Katri Vala heat	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Kellosaari back-up plant	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Kymijoki River's plants	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Loviisa nuclear heat	No	Yes	No	No	No	No	No
Neste oil refinery heat	No	Yes	No	No	Yes	No	No
Salmisaari A&B	renovated for biofuels	Yes	Yes	Yes	No	renovated for biofuels	Yes
Sea heat pump	No	No	No	No	Yes	No	Yes
Sea heat pump for cooling	No	No	No	No	No	No	Yes
Small-scale wood burning	No	No	No	No	No	No	No
Small gas heat plants	Yes	Yes	Yes	Yes	No	Yes	Yes
Small fuel oil heat plants	Yes	Yes	Yes	Yes	No	Yes	Yes
Suvilahti power storage	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Vanhakaupunki museum	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Vuosaari A	Yes	Yes	Yes	Yes	No	Yes	Yes
Vuosaari B	Yes	Yes	Yes	Yes	No	Yes	Yes
Vuosaari C biofuel	No	No	No	No	Yes	Yes	No
Wind mills	No	No	No	No	Yes	Yes	Yes

Show details

Decisions(-)
Obs	Decision_maker	Decision	Option	Variable	Cell	Change	Unit	Amount	Description
1	Builders	EnergySavingPolicy	BAU	efficiencyShares		Identity		0
2	Builders	EnergySavingPolicy	Energy saving moderate	efficiencyShares		Identity		0
3	Builders	EnergySavingPolicy	Energy saving total	efficiencyShares	Efficiency:Passive;Time:2025,2035	Add	fraction	0.25	All input must be given in units that are used in respective ovariables.
4	Builders	EnergySavingPolicy	Energy saving total	efficiencyShares	Efficiency:Passive;Time:2045,2055,2065	Add	fraction	0.1
5	Builders	EnergySavingPolicy	Energy saving total	efficiencyShares	Efficiency:Low-energy;Time:2025,2035	Add	fraction	-0.25
6	Builders	EnergySavingPolicy	Energy saving total	efficiencyShares	Efficiency:Low-energy;Time:2045,2055,2065	Add	fraction	-0.1
7	Building owner	EnergySavingPolicy	WWF energy saving	efficiencyShares		Identity	fraction	0	Energy efficiency in future buildings is so close to BAU that it is not changed.
8	Building owner	EnergySavingPolicy	BAU	renovationRate		Multiply	1 /a	1	Assumes BAU renovation rate = 1%/a for buildings >30 a old
9	Building owner	EnergySavingPolicy	Energy saving moderate	renovationRate		Multiply	1 /a	2
10	Building owner	EnergySavingPolicy	Energy saving total	renovationRate		Multiply	1 /a	4
11	Building owner	EnergySavingPolicy	WWF energy saving	renovationRate		Multiply	1 /a	2.5
12	Building owner	EnergySavingPolicy	BAU	demolitionRate		Replace	1 /a	0
13	Building owner	EnergySavingPolicy	Energy saving moderate	demolitionRate		Replace	1 /a	0
14	Building owner	EnergySavingPolicy	Energy saving total	demolitionRate		Replace	1 /a	0
15	Building owner	EnergySavingPolicy	WWF energy saving	demolitionRate		Identity	1 /a	0
16	Building owner	EnergySavingPolicy	BAU	renovationShares		Add	fraction	0
17	Building owner	EnergySavingPolicy	Energy saving moderate	renovationShares		Add	fraction	0
18	Building owner	EnergySavingPolicy	Energy saving total	renovationShares	Renovation:Windows	Add	fraction	-0.25
19	Building owner	EnergySavingPolicy	Energy saving total	renovationShares	Renovation:Sheath reform	Add	fraction	0.25
20	Building owner	EnergySavingPolicy	WWF energy saving	renovationShares	Renovation:Sheath reform	Add	fraction	0.1
21	Building owner	EnergySavingPolicy	WWF energy saving	renovationShares	Renovation:Windows	Add	fraction	-0.1
22	Helen	PlantPolicy	BAU	plantParameters	Plant:Biofuel heat plants,CHP diesel generators,Data center heat,Deep-drill heat,Loviisa nuclear heat,Neste oil refinery heat,Sea heat pump,Sea heat pump for cooling,Small-scale wood burning,Vuosaari C biofuel,Wind mills;Parameter:Min,Max,Investment cost,Management cost	Replace	MW,MW,M€,M€/a	0
23	Helen	PlantPolicy	BAU	energyProcess	Plant:Hanasaari;Fuel:Biofuel;Time:>=2018	Add	MJ/MJ	-0.4
24	Helen	PlantPolicy	BAU	energyProcess	Plant:Hanasaari;Fuel:Coal;Time:>=2018	Add	MJ/MJ	0.4
25	Helen	PlantPolicy	BAU	plantParameters	Plant:Hanasaari;Time:>=2018;Time:<=2060;Parameter:Max	Replace	MW	640
26	Helen	PlantPolicy	BAU	plantParameters	Plant:Hanasaari;Time:>=2018;Parameter:Investment cost	Replace	M€	100
27	Helen	PlantPolicy	BAU	energyProcess	Plant:Salmisaari A&B;Fuel:Biofuel;Time:>=2018	Add	MJ/MJ	-0.4
28	Helen	PlantPolicy	BAU	energyProcess	Plant:Salmisaari A&B;Fuel:Coal;Time:>=2018	Add	MJ/MJ	0.4
29	Helen	PlantPolicy	BAU	plantParameters	Plant:Salmisaari A&B;Time:>=2018;Time:<=2070;Parameter:Max	Replace	MW	506
30	Helen	PlantPolicy	BAU	plantParameters	Plant:Salmisaari A&B;Time:>=2018;Parameter:Investment cost	Replace	M€	100
31	Helen	PlantPolicy	Process heat	energyProcess		Identity	MJ/MJ	0
32	Helen	PlantPolicy	Process heat	plantParameters	Plant:Biofuel heat plants,CHP diesel generators,Deep-drill heat,Household air heat pumps,Household air conditioning,Household geothermal heat,Household solar,Sea heat pump,Sea heat pump for cooling,Small-scale wood burning,Wind mills;Parameter:Min,Max,Investment cost,Management cost	Replace	MW,MW,M€,M€/a	0
33	Helen	PlantPolicy	Helen proposition	energyProcess		Identity	MJ/MJ	0
34	Helen	PlantPolicy	Helen proposition	plantParameters	Plant:CHP diesel generators,Data center heat,Deep-drill heat,Loviisa nuclear heat,Neste oil refinery heat,Sea heat pump,Sea heat pump for cooling,Small-scale wood burning,Vuosaari C biofuel,Wind mills;Parameter:Min,Max,Investment cost,Management cost	Replace	MW,MW,M€,M€/a	0
35	Helen	PlantPolicy	Helen proposition	plantParameters	Plant:Small fuel oil heat plants;Parameter:Max	Add	MJ/MJ	-300-100
36	Helen	PlantPolicy	Helen proposition	plantParameters	Plant:Hanasaari;Time:>=2020;Parameter:Min,Max,Investment cost,Management cost	Replace	MW,MW,M€,M€/a	0
37	Helen	PlantPolicy	Zero investment	energyProcess		Identity	MJ/MJ	0
38	Helen	PlantPolicy	Zero investment	plantParameters	Plant:Biofuel heat plants,CHP diesel generators,Data center heat,Deep-drill heat,Sea heat pump,Sea heat pump for cooling,Small-scale wood burning,Suvilahti power storage,Loviisa nuclear heat,Neste oil refinery heat,Vuosaari C biofuel,Wind mills;Parameter:Min,Max,Investment cost,Management cost	Replace	MW,MW,M€,M€/a	0
39	Helen	PlantPolicy	Carbon neutral 2050	energyProcess		Identity	MJ/MJ	0
40	Helen	PlantPolicy	Carbon neutral 2050	plantParameters	Plant:CHP diesel generators,Data center heat,Deep-drill heat,Loviisa nuclear heat,Sea heat pump for cooling,Small-scale wood burning;Parameter:Min,Max,Investment cost,Management cost	Replace	MW,MW,M€,M€/a	0
41	Helen	PlantPolicy	Carbon neutral 2050	plantParameters	Plant:Salmisaari A&B,Small gas heat plants,Small fuel oil heat plants,Vuosaari A,Vuosaari B;Time:>=2030;Parameter:Min,Max,Investment cost,Management cost	Replace	MW,MW,M€,M€/a	0	Fossil fuel plants
42	Helen	PlantPolicy	Carbon neutral 2050	plantParameters	Plant:Hanasaari;Time:>=2023;Parameter:Max	Replace	MW	0	As described in Helen Vuosaari C proposition
43	Helen	PlantPolicy	CHP bio	plantParameters	Plant:Biofuel heat plants,CHP diesel generators,Data center heat,Deep-drill heat,Loviisa nuclear heat,Neste oil refinery heat,Sea heat pump,Sea heat pump for cooling,Small-scale wood burning;Parameter:Min,Max,Investment cost,Management cost	Replace	MW,MW,M€,M€/a	0
44	Helen	PlantPolicy	CHP bio	plantParameters	Plant:Hanasaari;Time:>=2020;Parameter:Min,Max,Investment cost,Management cost	Replace	MW,MW,M€,M€/a	0
45	Helen	PlantPolicy	CHP bio	energyProcess	Plant:Salmisaari A&B;Fuel:Biofuel;Time:>=2018	Add	MJ/MJ	-0.4
46	Helen	PlantPolicy	CHP bio	energyProcess	Plant:Salmisaari A&B;Fuel:Coal;Time:>=2018	Add	MJ/MJ	0.4
47	Helen	PlantPolicy	CHP bio	plantParameters	Plant:Salmisaari A&B;Time:>=2018;Time:<=2070;Parameter:Max	Replace	MW	506
48	Helen	PlantPolicy	CHP bio	plantParameters	Plant:Salmisaari A&B;Time:>=2018;Parameter:Investment cost	Replace	M€	100
49	Helen	PlantPolicy	Distributed and sea	energyProcess		Identity	MJ/MJ	0
50	Helen	PlantPolicy	Distributed and sea	plantParameters	Plant:Biofuel heat plants,Loviisa nuclear heat,Neste oil refinery heat,Small-scale wood burning,Vuosaari C;Parameter:Min,Max,Investment cost,Management cost	Replace	MW,MW,M€,M€/a	0
51	Helen	PlantPolicy	Distributed and sea	plantParameters	Plant:Hanasaari;Time:>=2020;Parameter:Min,Max,Investment cost,Management cost	Replace	MW,MW,M€,M€/a	0

There are two policies that are currently implemented in the model. Each has several options:

Energy saving policy: take several actions that reduce the energy demand of the building stock.
- BAU: business as usual, e.g. renovate 1 % of buildings per year if age > 30 a.
- Energy saving moderate: renovate 2 %/a
- Energy saving total: renovate 4 %/a, in addition increase the share of passive buildings up by 25 %-units since 2025, and add the share of sheath reform renovations up by 25 %-units.
- WWF energy saving: energy saving according to WWF energy saving plan published 8th October 2015. It is based on efficient energy saving actions on buildings and consequental possibility to reduce coal energy. The savings are based on increased energy renovations (2.5 % per year), more effective renovations in 10 % of renovation cases, and demolition of old buildings (1 % per year).^[2]
Plant policy: Choose an optimal selection of power plant infrastructure. These poicy options are the main focus of the assessment. There is a separate table describing these options.

Timing

The assessment started in May 2015. First draft results are expected before midsummer 2015. Final results should be available well before the City Council makes the decision in autumn, which means that results should exist by September 15th, 2015. There will be a public meeting to present the near-final results and discuss their implications. The meeting was held in THL, Helsinki on 11^st September 2015 at noon. The final report was published on 27th October 2015.

Answer

Model with user interface

The final results results can be found from model run 1.11.2015. It is the final archived version in English. Objects were stored, so you can download the whole assessment to R in your own computer.

Model run 26.10.2015 graphs in Finnish 20.11.2015 additional graphs
Intermediate results are available at section #See also.

Results

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Daily average production of district heat energy in the city of Helsinki depending on the outside temperature for energy saving total scenario and year 2035.
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Total annual expenditure and production of district heat energy in the city of Helsinki in 2035 after energy saving total policy.
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Current data and estimation of future building stock of Helsinki by heating type.
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Energy used in heating, consumer electricity, and hot water in Helsinki in the energy saving total policy.
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Fuel prices used in optimising.
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Energy production capacity in Helsinki by plant policy
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Incomes and costs of energy production in Helsinki by power plant. Note that the y axes are on different scales.
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Flow of electric power in Helsinki by plant policy. Many policies result in severe electricity demand from outside the city. This is possibly not a problem, but the decision maker should be aware of such side effect.
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Flow of district heat in Helsinki. The supply is always matched with the demand.
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Health effects of PM2.5 from heating in Helsinki.
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Emissions of CO2 and PM2.5 from heating in Helsinki.

Conclusions

See summary in the beginning.

Rationale

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Causal diagram for the assessment.

Stakeholders

The impacts are assessed and valued from the point of view of the following stakeholders:

The city of Helsinki
Helen Oy energy company
A citizen of Helsinki
Finland
Global view

Dependencies

List of key pages used in model

Helsinki energy decision 2015: Main page of assessment.
Helsinki energy decision options 2015: More detailed information on the different options available.
Building model: Method of estimating the size of the building stock of a city, including heating properties and renovations.
- Building stock in Helsinki: Data on the building stock in Helsinki and its projected future.
Energy use of buildings: Method of estimating energy need based on building stock and outdoor temperature.
- Helsinki energy consumption: Data on the energy consumption of buildings.
Energy balance: Method of calculating energy balance.
- Helsinki energy production: Data on the energy production in Helsinki.
- Energy balance in Helsinki: Data on the amounts of energy produced, consumed, imported, and exported in Helsinki.
Emission factors for burning processes: Data on the emission factors for burning processes in Finland.
Prices of fuels in heat production: Data on market prices of fuels used in Helsinki

Other models used in the Helsinki assessment (but are not in the core of this assessment)

Burden of disease in Finland: Data on disease burden.
Exposure-response function: Description of ERF, a mathematical construct used to describe a probability of different responses to a given exposure.
Health impact assessment: Method of estimating the health impacts of a particular event or policy.
- ERFs of environmental pollutants: Data on the exposure-response function of several environmental pollutants that do not have their own pages.
- Exposures in Finland: Data on the typical exposure levels of common pollutants in Finland.
- Disease risk: Data on the incidence or prevalence rates of different diseases in Finland.
- Population attributable fraction: Method of calculating the fraction of disease that would disappear if the exposure to that agent would disappear.
Population of Helsinki metropolitan area: Data on the total population of Helsinki metropolitan area.
Intake fractions of PM: Data on the intake fractions of airborne particulate matter for different emission sources and locations.
Exposure to PM2.5 in Finland: Method of estimating exposure to fine particles (PM2.5) in the Finnish population.
Emission assessment of small-scale energy production in the Helsinki metropolitan area: Assessment evaluating the greenhouse gas emissions and emission trading costs of small-scale (output less than 50 MW) energy production in the Helsinki metropolitan area.
OpasnetUtils/Drafts

**Variables in the assessment model**
Variable	Measure	Indices
buildings (from the Building model)
stockBuildings From statistics data in OB	Building stock in floor-m2 at given timepoints: Stock details in Opasnet Base	Time (Valmistusmisaika), City_area (Sijainti), Building (Käyttötarkoitus), Heating (Lämmitystapa, Polttoaine), Efficiency (Varusteena koneellinen ilmanvaihto, Perusparannus)
heatingShares = 1 Building stock in Helsinki	Fractions of heating types. Should sum up to 1 within each group defined by optional indices. Data is already in stockBuildings, so we use 1 here.	Heating, Time, Building
efficiencyShares Energy efficiency of new buildings in the future. OpasnetBase	Fraction of energy efficiency types of new buildings in the future. Should sum up to 1 for each group defined by other indices. Fractions of heating and energy efficiency: from Facta data and Energiatodistusrekisteri This can be used to derive energy classes and the U values for buildings in Helsinki.	Efficiency, Time, Building.
changeBuildings = stockBuildings * 0.02 /a	Construction or demolition rate as floor-m2 at given timepoints. Implemented in code.	Time, Efficiency, Heating. changeBuildings should have all indices in stockBuildings, heatingShares, and efficiencyShares.
renovationShares Guesstimate data in OB	Fraction of renovation types when renovation is done. Should sum to 1 for each group defined by other indices.	Renovation, Startyear. Startyear is the time when the renovation is done, and it must be different than the Time index.
renovationRate Guesstimate data in OB	Rate of renovation (fraction per time unit).	Age (the time difference between construction and renovation, i.e. Startyear - Time for each building).
obstime Ten-year intervals 1980 - 2060.	one-column data.frame about the years to be used in output.	Startyear
heating_before = FALSE	If TRUE, heatingShares contains data and is added to buildings before obstime.
efficiency_before = TRUE	If TRUE, efficiencyShares contains data and is added to buildings before obstime.
heatingEnergy: Energy consumption from the Building model ←# : Move code to Heating consumption of buildings --Jouni (talk) 03:48, 9 June 2015 (UTC)
energyUse Baseline energy consumption [3] ←# : Update this to use U values and ambient temperatures. --Jouni (talk) 12:22, 24 May 2015 (UTC) Also use Building stock in Helsinki, Table 6.	Energy consumption per floor area (kWh / m2 /a) (U: W/m2/K * T: K * Area: m2)	Building, Heating.
efficiencyRatio Energy use of buildings#Energy efficiency in heating) Opasnet Base	Relative energy consumption compared with the efficiency group Old.	Efficiency.
renovationRatio Impact of renovations Opasnet Base	Relative energy consumption compared with the Renovation location None.	Renovation.
energyBalance Energy balance in Helsinki
heatingEnergy (see above)
consumerElectricity: from Baseline energy consumption Opasnet Base based on floor area	Amount of consumer electricity use (MW) calculated based on either amount of people or floor-m2	Hour (time of day), others?
otherEnergy Helsinki energy consumption	Other energy consumption (industry, transport, other): from statistics of Helsinki Helsingin ympäristötilasto. Thermal energy need in Helsinki Metropolitan Area	Time
windElectricity Check ^[1] about possibilities and Wind pattern Tuuliatlas about useful wind mill sites in Helsinki.	Amount of wind power produced (MW) by wind type. We also need amounts of hours with different wind patterns per year.	Hour (do we need this, is wind power production dependent on time of day?), Weather (temperature and weather types), maybe Wind (Windy, Normal, Calm)
solarElectricity	Amount of solar energy produced (MW) in certain conditions. Data source?	Hour, Month, Weather, maybe Sun (Sunny, Cloudy, Dark) --# : Have to decide which of these is used. --Jouni (talk) 17:27, 24 May 2015 (UTC)
airpumpHeat	This means air-driven heat pumps installed in individual homes and run based on ambient temperature. The efficiency of air heat pump reduces with very cold weather. We assume that the heat pump will be used for cooling as well, if it is installed. (We assume that cooling is not used in apartments without heat pump; cooling is otherwise only used in offices etc).	Weather, Hour, maybe Temperature. --# : Should we have index for day/night so that daily storage of heat/cool can be assessed? --Jouni (talk) 17:27, 24 May 2015 (UTC)
energyProduction Helsinki energy production	Energy production based on need: basically, we need to know a) what power plants exist, b) what fuels they use and what (heat, power or both) they produce, c) what emissions they produce, d) what is their maximum capacity, e) at what demand level are they turned on. District heating production units in Helsinki metropolitan area Geothermal heat pumps: this means both individual geothermal heat pumps and district heat pumps for heating and cooling, such as the one in Katri Vala's park. Its efficiency is not reduced with very cold weather. See District cooling by Helen, en:Absorption refrigerator, en:District cooling.
emissions (from the model) (emissions in mass per time) Emissions of energy production (ton/a). Emission assessment of small-scale energy production in the Helsinki metropolitan area
energyBalance (from the model; see above)
fuelShares Energy balance in Helsinki^[3]	Tells how much of fuel is used for a certain neating energy need. --# : Should this be on page Energy balance in Helsinki or, like in Kuopio and Basel, a separate table? --Jouni (talk) 17:27, 24 May 2015 (UTC) --# : Use shares of different fuels. Currently this data is on page Emission factors for burning processes Table Fuel use in different heating types. However, this is clearly case-specific data and should be on a case-specific page. This should be done retrospectively to Kuopio and Basel as well. --Jouni (talk) 12:22, 24 May 2015 (UTC)	Fuel_type.
emissionFactors Emission factors for burning processes	emissions per unit of energy produced (g / J or similar unit) ^[4]	Exposure_agent, Emission_height.
exposure is in ug/m3 in ambient air average concentration. Exposure to PM2.5 in Finland
emissions	(from the model; see above) is in ton /a	Time, City_area, Exposure_agent, Emission_height.
iF (generic data but depends on population density, emission height etc) Intake fractions of PM.	conc (g /m3) * pop (#) * BR (m3 /s) / emis (g /s) <=> conc = emis * iF / BR / pop # conc is the exposure ⇤# : This is NOT the right page, but there are some pages such as Exposure to PM2.5 in Finland that should be merged. --Jouni (talk) 15:45, 17 May 2015 (UTC) ⇤# : Rather use iFs based on Piltti? --Jouni (talk) 12:22, 24 May 2015 (UTC) Fate and dispersion: is embedded in intake fractions and is not separately needed. Rename? Time use and activity: not explicitly needed? Remove? With transport it would make a difference.	Emission_height, Pollutant, Area
population Population of Helsinki metropolitan area	Amount of population exposed. Data from Statistics Finland?	Time, Area, Age, Sex
ambientTemperature: Weather in Helsinki (also contains solar radiation and wind patterns)	Ambient temperatures. --# : We actually need hourly temperatures. How do we manage this in the model? Temperatures should match indices Sun, Wind, Hour, and Month. --Jouni (talk) 17:27, 24 May 2015 (UTC)
Health impact assessment
exposure (see above)
Disease risk	Incidences of diseases of interest. --# : What if prevalence values would be more useful? --Jouni (talk) 17:27, 24 May 2015 (UTC)	Age, Sex, Response
ERF Exposure-response function	Exposure-response functions of all relevant exposure agents
Burden of disease in Finland
Other human health effects, indoor environment, and well-being is not probably handled here? Remove or keep as an important placeholder?
Costs: Where do we get this? From Vuosaari YVA? From ^[1]?	Costs of activities (energy production mainly) but also health impacts.	Time

Analyses

Cost-benefit analysis of different options. Costs considered: capital and operational costs of energy production, climate costs (CO2e emissions converted to euros), health (DALYs converted to euros).
Total capacity availability and feasibility (applies especially to decentralised option).
Temporal heat and power demand and supply (hourly resolution).

Indices

Temporal: Time (5-year observation periods)
Temperature (3-degree-Celsius intervals for ambient daily average temperatures. It is assumed that heating is not needed above 17 C and cooling is not needed below 24 C. Hot water need is independent of ambient temperature.
Decisions: EnergySavingPolicy contains options that reduce the energy need of the building stock and other consumption. PlantPolicy contains options about which energy plants to build (or demolish).
Stakeholder (Citizen, City, Helen, Finland)
Spatial: City area (summed up after energy need)
Health: Response (any disease that is linked to Exposure agents emitted)
Emission, exposure: Pollutant or Exposure agent (any agent that is emitted by energy production)
Energy production: Burner (type of burner used in the facility where energy is produced), Fuel (type of fuel used in energy production), Heating (type of heating in the building).
Buildings: Building [use type], Heating, Constructed, City area, Renovation, Efficiency.

Calculations

Model run 25.10.2015. Archived version with several updates

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## This code is Op_en7237/intermediates on page [[Helsinki energy decision 2015]]
library(OpasnetUtils)
library(ggplot2)
library(rgdal)
library(maptools)
library(RColorBrewer)
library(classInt)
library(RgoogleMaps)

### Technical parameters

openv.setN(0) # use medians instead of whole sampled distributions
BS <- 24 # base_size = font size in graphs
figstofile <- FALSE
saveobjects <- FALSE

########################## Case-specific data and submodels

### Decisions
decisions <- opbase.data("Op_en7237", subset = "Decisions") # [[Helsinki energy decision 2015]]

oprint(decisions)

### Energy production in Helsinki

objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] findrest
objects.latest("Op_en7311", code_name = "energyProcess") # [[Helsinki energy production]]
objects.latest("Op_en7311", code_name = "plantParameters") # [[Helsinki energy production]]
objects.latest("Op_en4151", code_name = "fuelPrice") # [[Prices of fuels in heat production]]

objects.latest("Op_en5141", code_name = "EnergyNetworkOptim") # [[Energy balance]] incl EnergyNetworkCost

### Building data in Helsinki

# Observation years must be defined for an assessment.
obstime <- Ovariable("obstime", data = data.frame(Obsyear = factor(seq(1985, 2065, 10), ordered = TRUE), Result = 1))
heatingShares <- 1 # Shares of different heating types in the building stock (already in the building data)
heating_before <- FALSE # Should heatingShares be calculated before renovate and timepoints (or after)? 
efficiency_before <- TRUE # Should efficiencyShares be calculated before renovate and timepoints (or after)?

objects.latest("Op_en7115", code_name = "stockBuildings") # [[Building stock in Helsinki]]
objects.latest("Op_en7115", code_name = "changeBuildings") # [[Building stock in Helsinki]]
objects.latest("Op_en7115", code_name = "demolitionRate") # [[Building stock in Helsinki]]
objects.latest("Op_en7115", code_name = "renovationRate") # [[Building stock in Helsinki]]
objects.latest("Op_en7115", code_name = "renovationShares") # [[Building stock in Helsinki]]
objects.latest("Op_en5488", code_name = "efficiencyShares") # [[Energy use of buildings]] 

objects.latest("Op_en6289", code_name = "buildingstest") # [[Building model]] # Generic building model.

## Energy use 
#objects.latest("Op_en5488", code_name = "energyUseTemperature")        # [[Energy use of buildings]] temperature version of energyUse
objects.latest("Op_en5488", code_name = "temperene")        # [[Energy use of buildings]] temperature version of energyUse
objects.latest("Op_en5488", code_name = "nontemperene")        # [[Energy use of buildings]] temperature version of energyUse
objects.latest("Op_en2959", code_name = "temperatures") # [[Outdoor air temperature in Finland]]
#objects.latest("Op_en5488", code_name = "energyUseTemperature") # [[Energy use of buildings]]
objects.latest("Op_en5488", code_name = "EnergyConsumerDemand") # [[Energy use of buildings]]

requiredName <- "Heat" # Name of the fuel type that must match for input and output

### Energy and emissions
objects.latest("Op_en7311", code_name = "emissionLocationsPerPlant") # [[Helsinki energy production]] also heatingShares
objects.latest("Op_en2791", code_name = "emissionFactors") # [[Emission factors for burning processes]] 
objects.latest("Op_en2791", code_name = "emissionstest") # [[Emission factors for burning processes]]
objects.latest("Op_en7311", code_name = "fuelShares") # [[Helsinki energy production]]
objects.latest("Op_en7311", code_name = "nondynsupply") # [[Helsinki energy production]]
timelylimit <- 10000 # The largest production (MW) available at each plant (used to describe time-varying limits).

## Exposure

objects.latest("Op_en5813", code_name = "exposure") # [[Intake fractions of PM]] uses Humbert iF as default.

## Health assessment

objects.latest('Op_en2261', code_name = 'totcases') # [[Health impact assessment]] totcases and dependencies. 
objects.latest('Op_en5461', code_name = 'DALYs') # [[Climate change policies and health in Kuopio]] DALYs, DW, L

population <- 623732 # Contains only the Helsinki city, i.e. assumes no exposure outside city. (Wikipedia)
# Note: the population size does NOT affect the health impact if the exposure-response function in linear.
# However, it DOES affect exposure estimates.

# DALYshortcut is for a quicker health impact calculations, as it directly uses the Helsinki conditions.

objects.latest("Op_en7237", code_name = "DALYshortcut") # [[Helsinki energy decision 2015]]

objects.latest("Op_en7379", code_name = "externalities") # [[External cost]]
objects.latest("Op_en3283", code_name = "totalCost") # [[Economic impacts]]
objects.latest("Op_en3283", code_name = "EAC") # [[Economic impacts]]

################################# Actual model and some intermediate processing.

DecisionTableParser(decisions)

# Remove previous decisions, if any. 

forgetDecisions()

renovationRate <- EvalOutput(renovationRate) * 10 # Rates for 10-year periods

buildings <- EvalOutput(buildings)

buildings$EnergySavingPolicy <- factor(
		buildings$EnergySavingPolicy,
		levels = c("BAU", "Energy saving moderate", "Energy saving total", "WWF energy saving"),
		ordered = TRUE
)

EnergyConsumerDemand <- EvalOutput(EnergyConsumerDemand)

EnergyNetworkDemand <- EvalOutput(EnergyNetworkDemand)

EnergyNetworkDemand@output <- rbind(
	EnergyNetworkDemand@output,
	data.frame(
		Time = rep(c(2025, 2035, 2045, 2055, 2065), each = 4),
		EnergySavingPolicy = rep(c("BAU", "Energy saving moderate", "Energy saving total", "WWF energy saving"), times = 5),
		Temperature = "(-18,-15]",
		EnergyConsumerDemandSource = "Formula",
		EnergyConsumerDemandTotalSource = "Formula",
		Fuel = "Cooling",
		fuelSharesSource = "Formula",
		EnergyNetworkDemandResult = 0,
		EnergyNetworkDemandSource = "Formula"
	)
)

########################### SAVE OBJECTS

if(saveobjects) {
	
	# Clean decisions and previous results not wanted/needed by the half-model
	
	energyProcess@output <- data.frame()
	plantParameters@output <- data.frame()
	EnergyNetworkOptim@output <- data.frame()
	#EnergyConsumerDemand@output <- data.frame()
	
	totcases@output <- data.frame()
	DALYs@output <- data.frame()
	exposure@output <- data.frame()
	rm(saveobjects, dictionary) # Remove technical objects that may be updated independently of the model
	
	objects.store(list = ls()) # Save all objects of the global environment.
	cat("All objects stored for later use:\n", paste(ls(), collapse = ", "), "\n")
}

############################ Output tables and graphs

fuelUse <- EvalOutput(fuelUse)

fuelUse$Fuel <- factor(
	fuelUse$Fuel, levels = c(
		"Biofuel",
		"Coal",
		"Fuel oil",
		"Gas",
		"Light oil",
		"Wood",
		"Electricity",
		"Electricity_taxed",
		"Heat",
		"Cooling"
	), ordered = TRUE
)

# Calculate exposure based on average iF.
exposure <- EvalOutput(exposure)
exposure <- exposure[exposure$Area == "Average" , ]
exposure <- oapply(exposure, cols = c("Plant", "Emission_site", "Emission_height", "Area"), FUN = sum)
totcases <- EvalOutput(totcases)
DALYs <- EvalOutput(DALYs)
EnergyNetworkCost <- EvalOutput(EnergyNetworkCost)

cat("Total DALYs/a by different combinations of policy options.\n")

temp <- DALYs[as.character(DALYs$Time) %in% c("2015", "2035", "2065") & DALYs$Response == "Total mortality" , ]

oprint(
		oapply(temp, INDEX = c("Time", "EnergySavingPolicy", "PlantPolicy"), FUN = sum),
		caption = "Table 1: Total DALYs/a by different combinations of policy options.",
		caption.placement = "top",
		include.rownames = FALSE
)

####################### Post-processing and graphs

bui <- oapply(buildings * 1E-6, cols = c("City_area", "buildingsSource"), FUN = sum)
bui <- truncateIndex(bui, cols = "Heating", bins = 4)

oggplot(bui[bui$EnergySavingPolicy == "BAU" , ], x = "Time", fill = "Heating", binwidth = 5) + 
		labs(
				title = "Building stock in Helsinki by heating",
				y = "Floor area (M m2)"
		)

oggplot(bui, x = "Time", fill = "Efficiency", binwidth = 5) + 
		facet_grid(. ~ EnergySavingPolicy) + 
		labs(
				title = "Building stock in Helsinki by efficiency policy",
				y = "Floor area (M m2)"
		)

oggplot(bui, x = "Time", fill = "Renovation", binwidth = 5) + 
		facet_grid(. ~ EnergySavingPolicy) +
		labs(
				title = "Building stock in Helsinki by renovation policy",
				y = "Floor area (M m2)"
		)

oggplot(bui[bui$EnergySavingPolicy == "BAU" , ], x = "Time", fill = "Building", binwidth = 5) + 
		labs(
				title = "Building stock in Helsinki",
				y = "Floor area (M m2)"
		)

oggplot(buildings, x = "Time", fill = "Efficiency", binwidth = 5)+
		facet_grid(EnergySavingPolicy ~ Renovation) + 
		labs(
				title = "Renovation of buildings by policy and efficiency",
				y = "Floor area (M m2)"
		)

emissions$Time <- as.numeric(as.character(emissions$Time))

# Plot energy need and emissions

hea <- EnergyConsumerDemand * temperdays * 24 * 1E-9 # From W -> GWh /a.
hea <- hea[hea$Consumable == "Heating" , ]
hea <- oapply(hea, cols = c("City_area", "buildingsSource"), FUN = sum)
hea <- truncateIndex(hea, cols = "Heating", bins = 4)

oggplot(hea, x = "Time", fill = "Renovation", binwidth = 5) + 
		facet_wrap( ~ EnergySavingPolicy) + 
		labs(
				title = "Energy used in heating in Helsinki",
				x = "Time",
				y = "Heating energy (GWh /a)"
		)

eb <-EnergyNetworkOptim[EnergyNetworkOptim$Process_variable_type == "Activity",]
colnames(eb@output)[colnames(eb@output) == "Process_variable_name"] <- "Plant"
eb$Process_variable_type <- NULL

ebtemp <- eb[eb$Time %in% c("2035") & eb$EnergySavingPolicy == "BAU" & eb$PlantPolicy == "BAU" , ]
ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 7)

oggplot(ebtemp, x = "Plant", fill = "Plant") + facet_wrap( ~ Temperature) +
		theme(axis.text.x = element_blank()) + # Turn text and adjust to right
		labs(
				title = "Power plant activity by temperature daily optim \nPlant policy = BAU, Year = 2035",
				y = "Power output daily average (MW)"
		)

fu <- fuelUse
fu$Burner <- NULL
fu <- fu / 3.6E+6 # From MJ/a -> GWh/a
fu$Time <- as.numeric(as.character(fu$Time))

futemp <- fu[fu$Time %in% c("2015", "2035", "2065") & fu$PlantPolicy == "BAU" , ]
futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) * -1
oggplot(futemp, x = "Fuel", fill = "Plant", turnx = TRUE) + facet_grid(Time ~ EnergySavingPolicy) +
		labs(
				title = "Energy commodity flows \n Plant policy = BAU",
				y = "Total annual energy (GWh/a)"
		)

futemp <- fu[fu$Fuel %in% c("Heat") , ]
futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1
oggplot(futemp, x = "Time", fill = "Plant", binwidth = 5) + facet_grid(EnergySavingPolicy ~ PlantPolicy) +
		labs(
				title = "District heat flow",
				y = "Total annual energy (GWh/a)"
		)

futemp <- fu[fu$Fuel %in% c("Electricity") , ]
futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1
oggplot(futemp, x = "Time", fill = "Plant", binwidth = 5) + facet_grid(EnergySavingPolicy ~ PlantPolicy) +
		labs(
				title = "Electricity flow",
				y = "Total annual energy (GWh/a)"
		)

oggplot(emissions[emissions$EnergySavingPolicy == "BAU" , ], x = "Time", fill = "Fuel", binwidth = 5) + 
		facet_grid(Pollutant ~ PlantPolicy, scale = "free_y") +
		labs(
				title = "Emissions from heating in Helsinki",
				x = "Time (Energy saving policy = BAU)",
				y = "Emissions (ton /a)"
		)

oggplot(exposure[exposure$Exposure_agent == "PM2.5" , ], x = "Time", fill = "Fuel", binwidth = 5) + 	
		facet_grid(EnergySavingPolicy ~ PlantPolicy) +
		labs(
				title = "Exposure to PM2.5 from heating in Helsinki",
				x = "Time",
				y = "Average PM2.5 (µg/m3)"
		)

oggplot(DALYs, x = "Time", fill = "Fuel", binwidth = 5) + facet_grid(EnergySavingPolicy ~ PlantPolicy) +
		labs(
				title = "Health effects in DALYs of PM2.5 from heating in Helsinki",
				y = "Health effects (DALY /a)"
		)

Preference order

This code should be used for new model runs. It is newer but not fully adjusted for its purpose yet.

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# This code is Op_en7237/finalresults on page [[Helsinki energy decision 2015]]
library(OpasnetUtils)
library(ggplot2)

openv.setN(0) # use medians instead of whole sampled distributions

# Download all pre-calculated inputs, e.g. building stock.
objects.latest("Op_en7237", code_name = "intermediates") # [[Helsinki energy decision 2015]]
objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] oggplot 
objects.latest("Op_en7392", code_name = "translate") # [[OpasnetUtils/Translate]] translate
objects.latest("Op_en7392", code_name = "dictionary") # [[OpasnetUtils/Translate]] dictionary
BS <- 24 # base_size for graph fonts
saveobjects <- FALSE
language <- "Finnish"
fi <- language == "Finnish"
EnergyNetworkDemand <- EnergyNetworkDemand[EnergyNetworkDemand$EnergySavingPolicy == "Energy saving total" , ]

decisions <- opbase.data("Op_en7237", subset = "Decisions") # [[Helsinki energy decision 2015]]
decisions$Obs <- NULL
temp <- decisions[decisions$Decision== "PlantPolicy",][1,]
decisions <- decisions[decisions$Decision!= "PlantPolicy" , ] # & decisions$Option == "Energy saving total" , ]
temp$Option <- "All1"
temp$Variable <- "plantParameters"
temp$Cell <- ""
temp$Change <- "Identity"
decisions <- rbind(decisions, temp)

allplants <- list(
	Loviisa2 = c(
		'Sea heat pump for cooling',
		'Household air conditioning',
		'Household solar',
		"Kymijoki River's plants",
		'Small-scale wood burning',
		'Suvilahti power storage',
		'Suvilahti solar',
		'Vanhakaupunki museum',
		'Wind mills'
	),
	DataAndSea3 = c(
		'Loviisa nuclear heat'
	),
	NesteAndDeep4 = c(
		'Data center heat',
		'Sea heat pump',
		'Household air heat pumps',
		'Household geothermal heat'
	),
	Existing5 = c(
		'Katri Vala heat',
		'Neste oil refinery heat',
		'Vuosaari C biofuel',
		'Deep-drill heat'
	),
	Backup6 = c(
		'Biofuel heat plants',
		'CHP diesel generators',
		'Hanasaari',
		'Salmisaari A&B',
		'Vuosaari A',
		'Vuosaari B'
	),
	Lowcost = c(
		'Katri Vala cooling',
		'Kellosaari back-up plant',
		'Small fuel oil heat plants',
		'Small gas heat plants'
	)
)

cheapest <- c(
	'Loviisa nuclear heat',
	'Data center heat',
	'Household air heat pumps',
	'Katri Vala heat',
	'Neste oil refinery heat',
	'Small fuel oil heat plants'
)

existing <- c( # Major existing plants are only closed at 2026 if they are closed
		"Kymijoki River's plants",
		'Small-scale wood burning',
		'Katri Vala heat',
		'Hanasaari',
		'Salmisaari A&B',
		'Vuosaari A',
		'Vuosaari B',
		'Kellosaari back-up plant',
		'Small fuel oil heat plants',
		'Small gas heat plants'
)
for(i in 1:6) {
	shutdown <- unlist(allplants[1:i])
	if(i == 6) shutdown <- unlist(allplants)[!unlist(allplants) %in% cheapest]

temp <- data.frame(
	Decision_maker = "Helen", 
	Decision = "PlantPolicy", 
	Option = names(allplants[i]), 
	Variable = c("plantParameters"),
	Cell = c(
		paste("Plant:", paste(shutdown[!shutdown %in% existing], collapse = ","), ";Parameter:Max,Min,Investment cost;Time:>=2016", sep = ""),
		paste("Plant:", paste(shutdown[shutdown %in% existing], collapse = ","), ";Parameter:Max,Min,Investment cost;Time:>=2026", sep = "")
	),
	Change = c("Replace"),
	Unit = NA,
	Result = 0
)
decisions <- rbind(decisions, temp)
}

DecisionTableParser(decisions)

oprint(decisions)

oprint(data.frame(
	Shutdown_order = c(
		"Plants are shut down in this order (most cost efficient first):",
		paste(allplants[[1]], collapse = ", "),
		paste(allplants[[2]], collapse = ", "),
		paste(allplants[[3]], collapse = ", "),
		paste(allplants[[4]], collapse = ", "),
		paste(allplants[[5]], collapse = ", "),
		paste(allplants[[6]], collapse = ", "),
		paste(c("All but the cheapest", cheapest), collapse = ", ")
	)
))

#EnergyNetworkDemand <- EvalOutput(EnergyNetworkDemand)

oprint(oapply(EnergyNetworkDemand, INDEX = c("Time", "Fuel"), FUN = length)@output)

oprint(EnergyNetworkDemand@output)

EnergyNetworkOptim <- EvalOutput(EnergyNetworkOptim)

#EnergyNetworkDemand@output <- rbind(
#	EnergyNetworkDemand@output,
#	data.frame(
#		Time = rep(c(2025, 2035, 2045, 2055, 2065), each = 4),
#		EnergySavingPolicy = rep(c("BAU", "Energy saving moderate", "Energy saving total", "WWF energy saving"), times = 5),
#		Temperature = "(-18,-15]",
#		EnergyConsumerDemandSource = "Formula",
#		EnergyConsumerDemandTotalSource = "Formula",
#		Fuel = "Cooling",
#		fuelSharesSource = "Formula",
#		EnergyNetworkDemandResult = 0,
#		EnergyNetworkDemandSource = "Formula"
#	)
#)

fuelUse <- EvalOutput(fuelUse)

fuelUse$Fuel <- factor(
	fuelUse$Fuel, levels = c(
		"Biofuel",
		"Coal",
		"Fuel oil",
		"Gas",
		"Light oil",
		"Wood",
		"Electricity",
		"Electricity_taxed",
		"Heat",
		"Cooling"
	), ordered = TRUE
)

DALY <- EvalOutput(DALYs)
DALYs <- unkeep(DALY, cols = c("Age", "Sex", "Population"))
DALYs <- oapply(DALYs, cols = c("Emission_site", "Emission_height", "Area"), FUN = sum)
DALYs <- DALYs[DALYs$Response == "Total mortality" , ]

EnergyNetworkCost <- EvalOutput(EnergyNetworkCost)

EnergyNetworkCost$Time <- as.numeric(as.character(EnergyNetworkCost$Time))

totalCost <- EvalOutput(totalCost)
totalCost@output$Time <- as.numeric(as.character(totalCost@output$Time))
totalCost <- unkeep(totalCost[totalCost$Time >= 2015 & totalCost$Time <=2065 , ], sources = TRUE)

# Net present value and effective annual cost

discount <- 0.03
times <- c(2015, 2065)
EAC <- EvalOutput(EAC)

if(saveobjects) {
	objects.store(list = ls())
	cat("All objects stored for later use:\n", paste(ls(), collapse = ", "), "\n")
}

############## POST_PROCESSING AND GRAPHS, VERSION FROM PERFERENCE ANALYSIS

cat(translate("NOTE! In all graphs and tables, the Total energy saving policy is assumed unless otherwise noted\n"))
cat(translate("Total DALYs/a by different combinations of policy options.\n"))

temp <- DALYs[as.character(DALYs$Time) %in% c("2015", "2035") & DALYs$Response == "Total mortality" , ]

oprint(
	translate(oapply(temp, INDEX = c("Time", "EnergySavingPolicy", "PlantPolicy"), FUN = sum)),
	caption = translate("Table 1: Total DALYs/a by different combinations of policy options."),
	caption.placement = "top",
	include.rownames = FALSE
)

bui <- oapply(buildings * 1E-6, cols = c("City_area", "buildingsSource"), FUN = sum)
bui <- truncateIndex(bui, cols = "Heating", bins = 4)

oggplot(bui[bui$EnergySavingPolicy == "BAU" , ], x = "Time", fill = "Heating", binwidth = 5) + 
		labs(
				title = translate("Building stock in Helsinki by heating"),
				y = translate("Floor area (M m2)")
		)

oggplot(bui, x = "Time", fill = "Efficiency", binwidth = 5) + 
		{if(fi) facet_wrap(~ Energiansäästöpolitiikka) else facet_wrap(~ EnergySavingPolicy)} + 
		labs(
				title = translate("Building stock in Helsinki by efficiency policy"),
				y = translate("Floor area (M m2)")
		)

oggplot(bui, x = "Time", fill = "Renovation", binwidth = 5) + 
		{if(fi) facet_wrap(~ Energiansäästöpolitiikka) else facet_wrap(~ EnergySavingPolicy)} +
		labs(
				title = translate("Building stock in Helsinki by renovation policy"),
				y = translate("Floor area (M m2)")
		)

oggplot(bui[bui$EnergySavingPolicy == "BAU" , ], x = "Time", fill = "Building", binwidth = 5) + 
	labs(
		title = translate("Building stock in Helsinki"),
		y = translate("Floor area (M m2)")
	)

oggplot(buildings, x = "Time", fill = "Efficiency", binwidth = 5)+
	{if(fi) facet_grid(Energiansäästöpolitiikka ~ Korjaukset) else facet_grid(EnergySavingPolicy ~ Renovation)} + 
	labs(
		title = translate("Renovation of buildings by policy and efficiency"),
		y = translate("Floor area (M m2)")
	)

# Contains also other buildings than district heating and other energy than heating
hea <- EnergyConsumerDemandTotal * temperdays * 24 * 1E-3 # MW -> GWh
hea <- hea[hea$EnergySavingPolicy == "Energy saving total" & !hea$Consumable %in% c("District cooling", "Electric cooling") , ]
hea$Time <- as.numeric(as.character(hea$Time))

oggplot(truncateIndex(hea, cols = "Temperature", bins = 7), x = "Time", fill = "Temperature", binwidth = 5) + 
{if(fi) facet_wrap(~ Hyödyke) else facet_wrap(~ Consumable)} +
labs(
	title = translate("Energy consumption in all buildings"),
	y = translate("Total energy (GWh /a)")
)

hea2 <- EnergyNetworkDemand * temperdays * 24 / 1000 # MW -> GWh
hea2$Time <- as.numeric(as.character(hea2$Time))

oggplot(hea2, x = "Time", fill = "Fuel", binwidth = 5) + 
{if(fi) facet_wrap(~ Energiansäästöpolitiikka) else facet_wrap(~ EnergySavingPolicy)} +
labs(
	title = translate("Energy demand in the network"),
	fill = translate("Consumable"),
	y = translate("Total energy (GWh /a)")
)

eb <-EnergyNetworkOptim[EnergyNetworkOptim$Process_variable_type == "Activity",]
eb <- eb[eb$EnergySavingPolicy == "Energy saving total" , ]
colnames(eb@output)[colnames(eb@output) == "Process_variable_name"] <- "Plant"
eb$Process_variable_type <- NULL

ebtemp <- eb[eb$Time %in% c("2035") & eb$PlantPolicy == "BAU" & eb$Temperature != "(-18,-15]" , ]
ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 7)

oggplot(ebtemp, x = "Temperature", fill = "Plant", turnx = TRUE) +
	labs(
		title = translate("Power plant activity by temperature daily optim \nPlant policy = BAU, Year = 2035"),
		x = translate("Temperature of the day"),
		y = translate("Average daily activity (MW)")
	)

ebtemp <- eb[eb$Time %in% c("2035") & eb$Temperature != "(-18,-15]" , ]
ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 10)
oggplot(ebtemp, x = "Temperature", fill = "Plant", turnx = TRUE) + 
	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap(~ PlantPolicy)} +
	labs(
		title = translate("Power plant activity by temperature daily optim in 2035"),
		x = translate("Temperature of the day"),
		y = translate("Average daily activity (MW)")
	)

ebtemp <- eb[eb$Time %in% c("2005") & eb$PlantPolicy == "BAU" & eb$Temperature == "(0,3]" , ]
ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 10)

oggplot(ebtemp, x = "Plant", fill = "Plant", turnx = TRUE) + 
	{if(fi) facet_wrap(~ Lämpötila) else facet_wrap( ~ Temperature)} +
	theme(axis.text.x = element_blank()) + # Turn text and adjust to right
	labs(
		title = translate("Power plant activity by temperature daily optim \nPlant policy = BAU, Year = 2005"),
		y = translate("Average daily activity (MW)")
	)

fu <- fuelUse / 3.6E+6 # From MJ/a -> GWh/a
fu <- fu[fu$EnergySavingPolicy == "Energy saving total" , ]
fu$Burner <- NULL
fu$Time <- as.numeric(as.character(fu$Time))

futemp <- fu[fu$Time %in% c("2015", "2035", "2065") & fu$PlantPolicy == "BAU" , ]
futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) * -1

oggplot(futemp, x = "Fuel", fill = "Plant", turnx = TRUE) + 
	{if(fi) facet_grid(Aika ~ Energiansäästöpolitiikka) else facet_grid(Time ~ EnergySavingPolicy)} +
	labs(
		title = translate("Energy commodity flows \n Plant policy = BAU"),
		y = translate("Total annual energy (GWh/a)")
	)

futemp <- fu[fu$Time %in% c("2005") & fu$PlantPolicy == "BAU" , ]
futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) * -1

oggplot(futemp, x = "Fuel", fill = "Plant", turnx = TRUE) +
	labs(
		title = translate("Energy commodity flows in 2005 \n Plant policy = BAU"),
		y = translate("Total annual energy (GWh/a)")
	)

futemp <- fu[fu$Fuel %in% c("Heat") , ]
futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1

oggplot(futemp,
	 x = "Time", fill = "Plant", binwidth = 5) + 
	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap(~ PlantPolicy)} +
	labs(
		title = translate("District heat flow"),
		y = translate("Total annual energy (GWh/a)")
	)

futemp <- fu[fu$Fuel %in% c("Electricity") , ]
futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1

oggplot(futemp, x = "Time", fill = "Plant", binwidth = 5) + 
	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap(~ PlantPolicy)} +
	labs(
		title = translate("Electricity flow"),
		y = translate("Total annual energy (GWh/a)")
	)

emis <- truncateIndex(emissions, cols = "Emission_site", bins = 5)
emis <- emis[emis$EnergySavingPolicy == "Energy saving total" , ]
emis$Time <- as.numeric(as.character(emis$Time))
oggplot(emis, x = "Time", fill = "Fuel", binwidth = 5) + 
	{if(fi) facet_grid(Saaste ~ Voimalapolitiikka, scale = "free_y") else facet_grid(Pollutant ~ PlantPolicy, scale = "free_y")} +
	labs(
		title = translate("Emissions from heating in Helsinki"),
		y = translate("Emissions (ton /a)")
	)

da <- DALYs[DALYs$EnergySavingPolicy == "Energy saving total" , ]
da$Time <- as.numeric(as.character(da$Time))
oggplot(da, x = "Time", fill = "Fuel", binwidth = 5) + 
	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap(~ PlantPolicy)} +
	labs(
		title = translate("Health effects of PM2.5 from heating in Helsinki"),
		y = translate("Health effects (DALY /a)")
	)

fp <- fuelPrice[fuelPrice$Fuel %in% c(
	"Biofuel",
	"Coal",
	"Electricity_taxed",
	"Fuel oil",
	"Heat",
	"Light oil",
	"Natural gas",
	"Peat"
) , ]
fp$Time <- as.numeric(as.character(fp$Time))
levels(fp$Fuel)[levels(fp$Fuel) == "Electricity_taxed"] <- "Electricity"

ggplot(translate(fp@output), if(fi) {
	aes(x = Aika, y = fuelPriceResult, colour = Polttoaine, group = Polttoaine)
} else {
	aes(x = Time, y = fuelPriceResult, colour = Fuel, group = Fuel)
}) +
	geom_line(size = 2)+theme_gray(base_size = BS) +
	labs(
		title = translate("Fuel prices (with tax)"),
		y = translate("Price (Eur/MWh)")
	)

tc <- truncateIndex(totalCost, cols = "Plant", bins = 11) / 10 * -1 # Yearly benefits (costs are negative)
tc <- tc[tc$EnergySavingPolicy == "Energy saving total" , ]

oggplot(tc, x = "Time", fill = "Cost", binwidth = 10) + 
	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap( ~ PlantPolicy)} +
	labs(
		y = translate("Yearly cash flow (Meur)"),
		title = translate("Total benefits and costs of energy production")
	)

oggplot(tc, x = "Time", fill = "Plant", binwidth = 10) + 
	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap(~ PlantPolicy)} +
	labs(
		y = translate("Yearly cash flow (Meur)"),
		title = translate("Total benefits and costs of energy production")
	)

eac <- EAC[EAC$EnergySavingPolicy == "Energy saving total" , ] * -1

BS <- 14 # Plot the next two graphs with smaller font because they are busy graphs.

oggplot(eac, x = "PlantPolicy", fill = "Cost", turnx = TRUE) +
	{if(fi) facet_wrap(~ Voimala, scale = "free_y") else facet_wrap(~ Plant, scale = "free_y")} +
	labs(
		title = translate("Incomes and costs by plant"), 
		y = translate("Effective annual cash flow (Meur/a)")
	)

oggplot(eac, x = "PlantPolicy", fill = "Cost", turnx = TRUE)+
	{if(fi) facet_wrap(~ Voimala) else facet_wrap(~ Plant)} +
	labs(
		title = translate("Incomes and costs by plant"), 
		y = translate("Effective annual cash flow (Meur/a)")
	)

BS <- 24
eac <- truncateIndex(eac, cols = "Plant", bins = 11)

oggplot(eac, x = "PlantPolicy", fill = "Plant", turnx = TRUE)+
	labs(
		title = translate("Incomes and costs by plant policy"), 
		y = translate("Effective annual cash flow (Meur/a)")
	)

oggplot(eac, x = "PlantPolicy", fill = "Cost", turnx = TRUE)+
	labs(
		title = translate("Incomes and costs by plant policy"), 
		y = translate("Effective annual cash flow (Meur/a)")
	)

temp <- truncateIndex(plantParameters[plantParameters$Parameter == "Max" , ], cols = "Plant", bins = 11)
temp <- temp[temp$Time >= 2000 & temp$Time <=2070 , ]
oggplot(temp, x = "Time", fill = "Plant", binwidth = 1) + 
	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap(~ PlantPolicy)} +
	labs(
		title = translate("Energy production capacity by plant policy"), 
		y = translate("Maximum capacity (MW)")
	)

odag() #Plots a directed acyclic graph of ovariables used in the model.
# This causes an internal error, so it must be the last row of the model.

Model run 22.10.2015 with updated fuel prices. This is NOT used for the assessment report, because the scenarios were based on the previous fuel prices.

This code was used for prioritizing before fuel prices were updated.

+ Show code - Hide code

# This code is Op_en7237/preferenceorder on page [[Helsinki energy decision 2015]]
library(OpasnetUtils)
library(ggplot2)

openv.setN(0) # use medians instead of whole sampled distributions
BS <- 14 # base_size for graph fonts
objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]]
saveobjects <- FALSE

# Download all pre-calculated inputs, e.g. building stock.
#objects.latest("Op_en7237", code_name = "intermediates") # [[Helsinki energy decision 2015]]

decisions <- opbase.data("Op_en7237", subset = "Decisions") # [[Helsinki energy decision 2015]]
decisions$Obs <- NULL
temp <- decisions[decisions$Decision== "PlantPolicy",][1,]
decisions <- decisions[decisions$Decision!= "PlantPolicy" , ] # & decisions$Option == "Energy saving total" , ]
temp$Option <- "All1"
temp$Variable <- "plantParameters"
temp$Cell <- ""
temp$Change <- "Identity"
decisions <- rbind(decisions, temp)

allplants <- list(
	Loviisa2 = c(
		'Sea heat pump for cooling',
		'Household air conditioning',
		'Household solar',
		"Kymijoki River's plants",
		'Small-scale wood burning',
		'Suvilahti power storage',
		'Suvilahti solar',
		'Vanhakaupunki museum',
		'Wind mills'
	),
	DataAndSea3 = c(
		'Loviisa nuclear heat'
	),
	NesteAndDeep4 = c(
		'Data center heat',
		'Sea heat pump',
		'Household air heat pumps',
		'Household geothermal heat'
	),
	Existing5 = c(
		'Katri Vala heat',
		'Neste oil refinery heat',
		'Vuosaari C biofuel',
		'Deep-drill heat'
	),
	Backup6 = c(
		'Biofuel heat plants',
		'CHP diesel generators',
		'Hanasaari',
		'Salmisaari A&B',
		'Vuosaari A',
		'Vuosaari B'
	),
	Lowcost = c(
		'Katri Vala cooling',
		'Kellosaari back-up plant',
		'Small fuel oil heat plants',
		'Small gas heat plants'
	)
)

cheapest <- c(
	'Loviisa nuclear heat',
	'Data center heat',
	'Household air heat pumps',
	'Katri Vala heat',
	'Neste oil refinery heat',
	'Small fuel oil heat plants'
)

existing <- c( # Major existing plants are only closed at 2026 if they are closed
		"Kymijoki River's plants",
		'Small-scale wood burning',
		'Katri Vala heat',
		'Hanasaari',
		'Salmisaari A&B',
		'Vuosaari A',
		'Vuosaari B',
		'Kellosaari back-up plant',
		'Small fuel oil heat plants',
		'Small gas heat plants'
)
for(i in 1:6) {
	shutdown <- unlist(allplants[1:i])
	if(i == 6) shutdown <- unlist(allplants)[!unlist(allplants) %in% cheapest]

temp <- data.frame(
	Decision_maker = "Helen", 
	Decision = "PlantPolicy", 
	Option = names(allplants[i]), 
	Variable = c("plantParameters"),
	Cell = c(
		paste("Plant:", paste(shutdown[!shutdown %in% existing], collapse = ","), ";Parameter:Max,Min,Investment cost;Time:>=2016", sep = ""),
		paste("Plant:", paste(shutdown[shutdown %in% existing], collapse = ","), ";Parameter:Max,Min,Investment cost;Time:>=2026", sep = "")
	),
	Change = c("Replace"),
	Unit = NA,
	Result = 0
)
decisions <- rbind(decisions, temp)
}

DecisionTableParser(decisions)

oprint(decisions)

oprint(data.frame(
	Shutdown_order = c(
		"Plants are shut down in this order (most cost efficient first):",
		paste(allplants[[1]], collapse = ", "),
		paste(allplants[[2]], collapse = ", "),
		paste(allplants[[3]], collapse = ", "),
		paste(allplants[[4]], collapse = ", "),
		paste(allplants[[5]], collapse = ", "),
		paste(allplants[[6]], collapse = ", "),
		paste(c("All but the cheapest", cheapest), collapse = ", ")
	)
))

### Energy production in Helsinki

objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] findrest
objects.latest("Op_en7311", code_name = "energyProcess") # [[Helsinki energy production]]
objects.latest("Op_en7311", code_name = "plantParameters") # [[Helsinki energy production]]
objects.latest("Op_en4151", code_name = "fuelPrice") # [[Prices of fuels in heat production]]

objects.latest("Op_en5141", code_name = "EnergyNetworkOptim") # [[Energy balance]] incl EnergyNetworkCost

### Building data in Helsinki

# Observation years must be defined for an assessment.
obstime <- Ovariable("obstime", data = data.frame(Obsyear = factor(seq(1985, 2065, 10), ordered = TRUE), Result = 1))
heatingShares <- 1 # Shares of different heating types in the building stock (already in the building data)
heating_before <- FALSE # Should heatingShares be calculated before renovate and timepoints (or after)? 
efficiency_before <- TRUE # Should efficiencyShares be calculated before renovate and timepoints (or after)?

objects.latest("Op_en7115", code_name = "stockBuildings") # [[Building stock in Helsinki]]
objects.latest("Op_en7115", code_name = "changeBuildings") # [[Building stock in Helsinki]]
objects.latest("Op_en7115", code_name = "demolitionRate") # [[Building stock in Helsinki]]
objects.latest("Op_en7115", code_name = "renovationRate") # [[Building stock in Helsinki]]
objects.latest("Op_en7115", code_name = "renovationShares") # [[Building stock in Helsinki]]
objects.latest("Op_en5488", code_name = "efficiencyShares") # [[Energy use of buildings]] 

objects.latest("Op_en6289", code_name = "buildingstest") # [[Building model]] # Generic building model.

## Energy use 
##objects.latest("Op_en5488", code_name = "energyUseTemperature")        # [[Energy use of buildings]] temperature version of energyUse
objects.latest("Op_en5488", code_name = "temperene")        # [[Energy use of buildings]] temperature version of energyUse
objects.latest("Op_en5488", code_name = "nontemperene")        # [[Energy use of buildings]] temperature version of energyUse
objects.latest("Op_en7317", code_name = "temperatures") # [[Helsinki energy consumption]]
##objects.latest("Op_en5488", code_name = "energyUseTemperature") # [[Energy use of buildings]]
objects.latest("Op_en5488", code_name = "EnergyConsumerDemand") # [[Energy use of buildings]]

requiredName <- "Heat" # Name of the fuel type that must match for input and output

### Energy and emissions
objects.latest("Op_en7311", code_name = "emissionLocationsPerPlant") # [[Helsinki energy production]] also heatingShares
objects.latest("Op_en2791", code_name = "emissionFactors") # [[Emission factors for burning processes]] 
objects.latest("Op_en2791", code_name = "emissionstest") # [[Emission factors for burning processes]]
objects.latest("Op_en7311", code_name = "fuelShares") # [[Helsinki energy production]]
objects.latest("Op_en7311", code_name = "nondynsupply") # [[Helsinki energy production]]
timelylimit <- 10000 # The largest production (MW) available at each plant (used to describe time-varying limits).

## Exposure

objects.latest("Op_en5813", code_name = "exposure") # [[Intake fractions of PM]] uses Humbert iF as default.

## Health assessment

objects.latest('Op_en2261', code_name = 'totcases') # [[Health impact assessment]] totcases and dependencies. 
objects.latest('Op_en5461', code_name = 'DALYs') # [[Climate change policies and health in Kuopio]] DALYs, DW, L

population <- 623732 # Contains only the Helsinki city, i.e. assumes no exposure outside city. (Wikipedia)
# Note: the population size does NOT affect the health impact if the exposure-response function in linear.
# However, it DOES affect exposure estimates.

# DALYshortcut is for a quicker health impact calculations, as it directly uses the Helsinki conditions.

objects.latest("Op_en7237", code_name = "DALYshortcut") # [[Helsinki energy decision 2015]]

objects.latest("Op_en7379", code_name = "externalities") # [[External cost]]
objects.latest("Op_en3283", code_name = "totalCost") # [[Economic impacts]]
objects.latest("Op_en3283", code_name = "EAC") # [[Economic impacts]]

################################# Actual model and some intermediate processing.

DecisionTableParser(decisions)

# Remove previous decisions, if any. 

forgetDecisions()

renovationRate <- EvalOutput(renovationRate) * 10 # Rates for 10-year periods

buildings <- EvalOutput(buildings)

buildings <- buildings[buildings$EnergySavingPolicy == "Energy saving total" , ]
cat("Only energy saving total considered in this run.\n")

EnergyConsumerDemand <- EvalOutput(EnergyConsumerDemand)

fuelUse <- EvalOutput(fuelUse)

fuelUse$Fuel <- factor(
	fuelUse$Fuel, levels = c(
		"Biofuel",
		"Coal",
		"Fuel oil",
		"Gas",
		"Light oil",
		"Wood",
		"Electricity",
		"Electricity_taxed",
		"Heat",
		"Cooling"
	), ordered = TRUE
)

DALY <- EvalOutput(DALYs)
DALYs <- unkeep(DALY, cols = c("Age", "Sex", "Population"))
DALYs <- oapply(DALYs, cols = c("Emission_site", "Emission_height", "Area"), FUN = sum)
DALYs <- DALYs[DALYs$Response == "Total mortality" , ]

EnergyNetworkCost <- EvalOutput(EnergyNetworkCost)

EnergyNetworkCost$Time <- as.numeric(as.character(EnergyNetworkCost$Time))

totalCost <- EvalOutput(totalCost)
totalCost@output$Time <- as.numeric(as.character(totalCost@output$Time))
totalCost <- unkeep(totalCost[totalCost$Time >= 2015 & totalCost$Time <=2065 , ], sources = TRUE)

# Net present value and effective annual cost

discount <- 0.03
times <- c(2015, 2065)
EAC <- EvalOutput(EAC)

if(saveobjects) {
	objects.store(list = ls())
	cat("All objects stored for later use:\n", paste(ls(), collapse = ", "), "\n")
}

####################### Post-processing and graphs

cat("Total DALYs/a by different combinations of policy options.\n")

temp <- DALYs[as.character(DALYs$Time) %in% c("2015", "2035") & DALYs$Response == "Total mortality" , ]

oprint(
		oapply(temp, INDEX = c("Time", "EnergySavingPolicy", "PlantPolicy"), FUN = sum),
		caption = "Table 1: Total DALYs/a by different combinations of policy options.",
		caption.placement = "top",
		include.rownames = FALSE
)

BS <- 14

eac <- EAC * -1

oggplot(eac, x = "PlantPolicy", fill = "Cost", turnx = TRUE)+
	facet_wrap(~ Plant, scale = "free_y") +
	labs(title = "Incomes and costs by plant, total energy saving", y = "Effective annual cash flow (Meur/a)")

oggplot(eac, x = "PlantPolicy", fill = "Cost", turnx = TRUE)+
	facet_wrap(~ Plant)+
	labs(title = "Incomes and costs by plant, total energy saving", y = "Effective annual cash flow (Meur/a)")

BS <- 24

eb <-EnergyNetworkOptim[EnergyNetworkOptim$Process_variable_type == "Activity",]
colnames(eb@output)[colnames(eb@output) == "Process_variable_name"] <- "Plant"
eb$Process_variable_type <- NULL

ebtemp <- eb[eb$Time %in% c("2035") & eb$PlantPolicy == "All1" , ]
ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 7)

oggplot(ebtemp, x = "Temperature", fill = "Plant") +
	labs(
		title = "Power plant activity by temperature daily optim \nPlant policy = All1, Year = 2035",
		x = "Temperature of the day",
		y = "Power output daily average (MW)"
	)

ebtemp <- eb[eb$Time %in% c("2035") , ]
ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 10)
oggplot(ebtemp, x = "Temperature", fill = "Plant") + facet_wrap(~ PlantPolicy)
	labs(
		title = "Power plant activity by temperature daily optim \nmoderate energy saving in 2035",
		x = "Temperature of the day",
		y = "Power plant activity daily average (MW)"
	)

ebtemp <- eb[eb$Time %in% c("2005") & eb$PlantPolicy == "All1" & eb$Temperature == "(0,3]" , ]
ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 10)

oggplot(ebtemp, x = "Plant", fill = "Plant") + facet_wrap( ~ Temperature) +
		theme(axis.text.x = element_blank()) + # Turn text and adjust to right
		labs(
				title = "Power plant activity by temperature daily optim \nPlant policy = All1, Year = 2005",
				y = "Power output daily average (MW)"
		)

fu <- fuelUse / 3.6E+6 # From MJ/a -> GWh/a
fu$Burner <- NULL
fu$Time <- as.numeric(as.character(fu$Time))

futemp <- fu[fu$Time %in% c("2015", "2035", "2065") & fu$PlantPolicy == "All1" , ]
futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) * -1

oggplot(futemp, x = "Fuel", fill = "Plant", turnx = TRUE) + facet_grid(Time ~ EnergySavingPolicy) +
	labs(
		title = "Energy commodity flows \n Plant policy = All1",
		y = "Total annual energy (GWh/a)"
	)

futemp <- fu[fu$Time %in% c("2005") & fu$PlantPolicy == "All1" , ]
futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) * -1

oggplot(futemp, x = "Fuel", fill = "Plant", turnx = TRUE) +
		labs(
				title = "Energy commodity flows in 2015 \n Plant policy = All1, Energy saving policy == total",
				y = "Total annual energy (GWh/a)"
		)

futemp <- fu[fu$Fuel %in% c("Heat") , ]
futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1

oggplot(futemp,
	 x = "Time", fill = "Plant", binwidth = 5) + facet_wrap(~ PlantPolicy) +
		labs(
				title = "District heat flow",
				y = "Total annual energy (GWh/a)"
		)

futemp <- fu[fu$Fuel %in% c("Electricity") , ]
futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1

oggplot(futemp, x = "Time", fill = "Plant", binwidth = 5) + facet_wrap(~ PlantPolicy) +
		labs(
				title = "Electricity flow",
				y = "Total annual energy (GWh/a)"
		)

emis <- truncateIndex(emissions, cols = "Emission_site", bins = 5)
oggplot(emis, x = "Time", fill = "Fuel", binwidth = 5) + facet_grid(Pollutant ~ PlantPolicy, scale = "free_y") +
		labs(
				title = "Emissions from heating in Helsinki",
				y = "Emissions (ton /a)"
		)

oggplot(DALYs, x = "Time", fill = "Fuel", binwidth = 5) + facet_wrap(~ PlantPolicy) +
		labs(
				title = "Health effects in DALYs of PM2.5 from heating in Helsinki",
				y = "Health effects (DALY /a)"
		)

fp <- fuelPrice[fuelPrice$Fuel %in% c(
	"Biofuel",
	"Coal",
	"Electricity_taxed",
	"Fuel oil",
	"Heat",
	"Light oil",
	"Natural gas",
	"Peat"
) , ]
fp$Time <- as.numeric(as.character(fp$Time))
levels(fp$Fuel)[levels(fp$Fuel) == "Electricity_taxed"] <- "Electricity"

ggplot(fp@output, aes(x = Time, y = fuelPriceResult, colour = Fuel, group = Fuel))+
	geom_line(size = 2)+theme_gray(base_size = BS) +
	labs(
		title = "Fuel prices (with tax)",
		y = "Price (Eur / MWh)"
	)

tc <- truncateIndex(totalCost, cols = "Plant", bins = 11) / 10 # Yearly costs

oggplot(tc, x = "Time", fill = "Cost", binwidth = 10) + facet_wrap( ~ PlantPolicy) +
	labs(
		y = "Yearly costs (Meur)",
		title = "Total costs of energy production"
	)

oggplot(tc, x = "Time", fill = "Plant", binwidth = 10) + facet_wrap( ~ PlantPolicy) +
	labs(
		y = "Yearly costs (Meur)",
		title = "Total costs of energy production"
	)

eb <-EnergyNetworkOptim[EnergyNetworkOptim$Process_variable_type == "Activity",]
colnames(eb@output)[colnames(eb@output) == "Process_variable_name"] <- "Plant"
eb$Process_variable_type <- NULL

ebtemp <- eb[eb$Time %in% c("2035") , ]
ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 11)

oggplot(ebtemp, x = "Temperature", fill = "Plant", turnx = TRUE) + facet_wrap(~ PlantPolicy) +
	labs(
		title = "Power plant activity by temperature daily optim in 2035",
		x = "Temperature of the day",
		y = "Average daily activity (MW)"
	)

eac <- truncateIndex(EAC * -1, cols = "Plant", bins = 11)

oggplot(eac, x = "PlantPolicy", fill = "Plant", turnx = TRUE)+
	labs(title = "Incomes and costs by plant policy\ntotal energy saving", y = "Effective annual cash flow (Meur/a)")

oggplot(eac, x = "PlantPolicy", fill = "Cost", turnx = TRUE)+
	labs(title = "Incomes and costs by plant policy\ntotal energy saving", y = "Effective annual cash flow (Meur/a)")

temp <- truncateIndex(plantParameters[plantParameters$Parameter == "Max" , ], cols = "Plant", bins = 11)
temp <- temp[temp$Time >= 2000 & temp$Time <=2070 , ]
oggplot(temp, x = "Time", fill = "Plant", binwidth = 1) + facet_wrap(~ PlantPolicy)+
labs(title = "Energy production capacity by plant policy", y = "Maximum capacity (MW)")

DALY shortcut

This code creates a ready-made DALYs estimate for 1 ug/m^3 PM2.5. It is much quicker to calculate health impacts of case-specific emissions with this ovariable, if there is a large emissions ovariable and if the exposure is such that proportionality can be assumed in health impacts.

+ Show code - Hide code

library(OpasnetUtils)

openv.setN(1000)

objects.latest("Op_en5813", code_name = "exposure") # [[Intake fractions of PM]] uses Humbert iF as default.
objects.latest('Op_en2261', code_name = 'totcases') # [[Health impact assessment]] totcases and dependencies. 
objects.latest('Op_en5461', code_name = 'DALYs') # [[Climate change policies and health in Kuopio]] DALYs, DW, L

emissions <- Ovariable(output = data.frame(Pollutant = "PM2.5", Result = 1))
population <- 623732

exposure <- EvalOutput(exposure) 
exposure <- exposure[exposure$Area == "Average" , ]
exposure <- oapply(exposure, cols = c("Plant", "Emission_site", "Emission_height", "Area"), FUN = sum)
totcases <- EvalOutput(totcases)
DALYs <- EvalOutput(DALYs)
DALYs$Pollutant <- "PM2.5" # Why do you have this line? The Pollutant is "PM2.5" already. To get rid of other levels?

DALYper1 <- unkeep(DALYs, sources = TRUE, prevresults = TRUE)
DALYper1$Result <- DALYper1$DALYsResult
DALYper1$DALYsResult <- NULL
DALYper1@name <- ""

# Short version of DALYs
DALYs <- Ovariable(
	"DALYs",
	dependencies = data.frame(Name = c("DALYper1", "emissions")), 
	formula = function(...) {
		if(!"Iter" %in% colnames(emissions@output) & "Iter" %in% colnames(DALYper1@output)) {
			out <- oapply(DALYper1, cols = "Iter", FUN = mean)
		} else {
			out <- DALYper1
		}
		out <- emissions * out
		return(out)
	}
)

objects.store(DALYper1, DALYs)

Keywords

Energy, renewable energy, nuclear energy, fossil energy, wood pellets, power plants, district heating, decentralised energy production, centralised energy production, cost-effectiveness, Helsinki, climate change, health effects, fine particles, biofuels, energy saving.

References

↑ ^1.0 ^1.1 ^1.2 Halme, Minna; Hukkinen, Janne; Korppi-Tommola, Jouko; Linnanen, Lassi; Liski, Matti; Lovio, Raimo; Lund, Peter; Luukkanen, Jyrki; Nokso-Koivisto, Oskari; Partanen, Jarmo; Wilenius, Markku. Kasvua ja työllisyyttä uudella energiapolitiikalla. Jyväskylän yliopiston julkaisuja 2014. [1]
↑ WWF. Helsingin energiapäätös. Energiansäästö on polttoainevaihtoehdoista paras. WWF, 8.10.2015 http://wwf.fi/mediabank/7784.pdf [2]
↑ Jáchym Judl, Sirkka Koskela, Timo Korpela, Niko Karvosenoja, Anna Häyrinen, Jari Rantsi. Net environmental impacts of low-share wood pellet co-combustion in an existing coal-fired CHP (combined heat and power) production in Helsinki, Finland. Energy 77 (2014) 844-851. doi:10.1016/j.energy.2014.09.068
↑ Sanni Väisänen: Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat — Estimation of various production chains by using LCA methodology

Related files

Assessment of small-scale energy production in Helsinki Metropolitan area ←# : This contains all Pasi's assessment's data tables and is very useful! --Jouni (talk) 17:05, 14 May 2015 (UTC)

[professors-1] 1.0 ^1.1 ^1.2 Halme, Minna; Hukkinen, Janne; Korppi-Tommola, Jouko; Linnanen, Lassi; Liski, Matti; Lovio, Raimo; Lund, Peter; Luukkanen, Jyrki; Nokso-Koivisto, Oskari; Partanen, Jarmo; Wilenius, Markku. Kasvua ja työllisyyttä uudella energiapolitiikalla. Jyväskylän yliopiston julkaisuja 2014. [1]

[wwf-2] WWF. Helsingin energiapäätös. Energiansäästö on polttoainevaihtoehdoista paras. WWF, 8.10.2015 http://wwf.fi/mediabank/7784.pdf [2]

[judl-3] Jáchym Judl, Sirkka Koskela, Timo Korpela, Niko Karvosenoja, Anna Häyrinen, Jari Rantsi. Net environmental impacts of low-share wood pellet co-combustion in an existing coal-fired CHP (combined heat and power) production in Helsinki, Finland. Energy 77 (2014) 844-851. doi:10.1016/j.energy.2014.09.068

[4] Sanni Väisänen: Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat — Estimation of various production chains by using LCA methodology

[1]

[2]

[3]

[4]

In English
Assessment	Main page \| Helsinki energy decision options 2015
Helsinki data	Building stock in Helsinki \| Helsinki energy production \| Helsinki energy consumption \| Energy use of buildings \| Emission factors for burning processes \| Prices of fuels in heat production \| External cost
Models	Building model \| Energy balance \| Health impact assessment \| Economic impacts
Related assessments	Climate change policies in Helsinki \| Climate change policies and health in Kuopio \| Climate change policies in Basel
In Finnish
Yhteenveto	Helsingin energiapäätös 2015 \| Helsingin energiapäätöksen vaihtoehdot 2015 \| Helsingin energiapäätökseen liittyviä arvoja \| Helsingin energiapäätös 2015.pptx

@@ Line 1: / Line 1: @@
+<noinclude>
 [[heande:Helsinki energy decision 2015]]
 [[op_fi:Helsingin_energiapäätös_2015]]
@@ Line 7: / Line 8: @@
 {{summary box
-| question = This website contains an on-going impact assessment to support the decision making on how energy should be produced in Helsinki in the future. The City of Helsinki is making a major decision about power plants in 2015, and it will affect the energy supply in Helsinki for decades. Political discussion is active, but quantitative, analytical assessments are scarce, especially those that are also looking at health and climate impacts. How to continuously supply energy for the heating and electricity needs of Helsinki in all circumstances all year round for the next fifty years? An energy balance assessment is needed with several output indicators, such as cost, climate emissions, health, supply security, and domestic growth. How can decentralised energy supply and energy efficiency renovations improve the situation? The latter question was asked by the city council in spring 2015, and an other assessment (coordinated by the City of Helsinki and Helen energy company) is ongoing to also answer that question by September 2015.
+|question = Helsinki will make a large energy decision during fall 2015. it will impact energy production for decades. An important decision requires systematic information. How do we guarantee a continuous and sufficient supply of district heat and electricity in Helsinki around the year for the next fifty years in such a way that the impacts on costs, health, climate change, and sustainable growth are as beneficial as possible? How can decentralised energy production and energy efficiency renovations improve the situation? The latter question was asked by the City Council, and another assessment (coordinated by the city of Helsinki and Helen energy company) will answer that soon.
-| answer = The National Institute of Health and Welfare [[THL]] is performing an [[open assessment]] in Opasnet web-workspace during summer 2015. We are developing an assessment model (called ''Sofia'') to estimate feasibility and impacts of several suggested solutions to the Helsinki energy decision. Sofia is an energy balance model that optimises the costs of energy production in a way that the district heat demand is met every day with appropriate supply from the power plants in Helsinki. The situation is followed from 1985 to 2065. Sofia is looking also at other heat and electricity consumption, but not traffic or industry. Based on the balanced energy supply, Sofia estimates the fuel and other costs as well as greenhouse gas and fine particle emissions for power plants and other energy processes. The model can handle several scenarios that can be compared with each other to see the impacts of different actions (such as building renovations or new power plants) on the overall situation.
+|answer = National Institute for Health and Welfare (THL) performed an open assessment on the Opasnet web-workspace during summer and fall 2015 aiming at shared understanding. We utilised knowledge crystals, i.e. regurlarly updated collaborative online answers to specific research questions. Based on them, we built a model called ''Sofia'' to assess impacts and implementability of several different energy options in Helsinki. [[:op_fi:File:Helsingin energiapäätös 2015 raportti.pdf|An assessment report|]] has been published in Finnish.
-Sofia is able to calculate the building stock and its energy use based on average energy intensity per floor area and outdoor temperature. It also finds the optimal use of existing power plants for different situations for the whole timespan of the assessment. The model suggests how the energy demand could be covered in the future when different impacts are considered. Production costs are optimised, but also other criteria could be used. Currently, Sofia suggests the use of Vuosaari A&B gas plant as the main energy source of energy, but if Vuosaari C biofuel plant were built, the importance of Vuosaari A&B would diminish. Such guidance is naturally based on the data that Sofia currently has, and her recommendations are likely to change when more and more precise data is collected. According to the principles of open assessment, we invite anybody who is interested in Helsinki's energy decision to participate and browse through the pages of this assessment. All data and all parts of the model are openly available in machine-readable format. All comments, criticism, and update suggestions are welcome.
+Sofia can estimate the building stock and energy consumption based on average energy efficiency, floor area, and ambient temperature. Sofia is an energy balance model that optimises costs of energy production while ensuring the supply of district heating in Helsinki for each day. The situation is followed from 1985 to 2065. Sofia looks also at other heat, fuel, and electric power consumption, but not those of industry and traffic.
+Based on balanced energy production Sofia estimates the fuel and other costs and greenhouse gas and fine particle emissions from power plants and other energy processes. The model can assess several scenarios and thus compare the overall impacts of different actions, such as energy renovations or new power plants.
+Sofia's main conclusion is that the energy solutions seem to fall into two categories: those that produce electric power as a side stream of district heat, like in Hanasaari coal plant and the suggested Vuosaari C biofuel plant; and those that do not produce or even consume a large amount of electricity, like decentralised biofuel heat plants or sea heat pumps. Based on current discussions, the self-sufficiency of electric power in Helsinki seems to be disappearing and probably changing into a deep dependency of outside electricity. The development of the Nordic electric market determines whether this will be a problem or not in the future.
+Unfortunately, the electric market is under strong transition, and for example the wind power tariffes complicate the situation, making future predictions of this crucial issue even more difficult. Local heat production solutions are in a great need of a clear and systematic national electric policy. Otherwise, the previously very efficient combined heat and power production will be replaced with something much less efficient in Finland in the future.
+If we forget the question about self-sufficiency of electric power and focus only on district heat, there are several cost-effective solutions available. Excess heat from different processes such as data centres or Neste oil refinery seem to be more cost-effective than e.g. Vuosaari C biofuel plant. Also small and large heat pumps that take heat from the environment are cost-effective. District heat from Loviisa 3 nuclear power plant is also cost-effective in our estimates, but we were not able to fully charaterise uncertainties in the investment cost; and we must remember that the decision to build or not to build the reactor is not in the hands of Helsinki.
+A major problem with the process heat solutions is that they consume electric power exactly when the demand for power is the largest and price the highest. We were not able to assess the variability of the price of electricity in this assessment, but it is substantial and might change conclusions. Also for this reason, there is a need for a national energy balance model with hourly resolution including eletricity, industry, and traffic.
+Health and climate impacts are important in every single option we looked at, together ranging between a fifth and a fourth of all costs, health and climate comprising a half of that each. Surprisingly, differences between policy options in this respect were small even if the reputation of the climate-friendliness of the options was very different. According to Sofia, this is due to several reasons. All large power plants filter fine particles out very effectively, and therefore there are no large differences in health impacts; a clear exception is small-scale wood burning in houses, which has several times larger health problems than any other option despite its marginal role in energy production. Also, the life cycle emissions of fine particles and greenhouse gases is also important: even if the direct emissions from a heat pump are zero, the electric power imported has produced emissions somewhere. Biofuels are considered climate neutral in emission trade, but they still produce emissions during their life cycle. Therefore, even a fuel switch does not produce the expected results.
+Of course, the conclusions based on the model depend on the goodness of the data used. There are several uncertainties that should have been clarified had there been more time to work on the topic. Especially the fuel prices are very difficult to predict into the future. Also the fuel taxes are very high for some fuels but not for others. Effectively, the society decides, using taxes, which solutions make sense on the city level. This is a third reason why there should be a national, long-term, and clear energy policy - and tax policy supporting it - based on detailed, open energy balance modelling the best available information.
 }}
@@ Line 31: / Line 46: @@
 Helsinki City Council will make a major decision in autumn 2015 about renovating old power plants, building a new one, or some other option replacing the need of the old power plants. Therefore, the City Council is the major user of the assessment. There are also secondary uses, such as informing national energy discussion and demonstrating the usefulness of an open combined [[energy balance]] and [[building model]].
-{{hidden|1 =
-<br/>
 === Participants ===
 The work is coordinated by [[User:Jouni|Jouni Tuomisto]] from [[THL]] / Impact Assessment Unit. Their motivation is to contribute to the decision-making process by bringing quantitative results to help assess the health and other impacts of different options in a transparent manner. Participants that we hope get involved when they are informed about the assessment include
-* THL: Jouni, Pauli, Teemu, Matleena, Julia, Olli
+* THL: Jouni, Pauli, Teemu, Matleena, Julia
 * the City of Helsinki,
 * Helen energy company
@@ Line 44: / Line 57: @@
 === Boundaries ===
-* Time: 2015 - 2065
+* Time: 1985 - 2065
 * Energy need estimated for Helsinki.
 * Main focus is on local heat and power need. Energy balance estimated for Helsinki (electricity nationally).
@@ Line 50: / Line 63: @@
 * Impacts are assessed separately for the citizen, the city, Helen energy company, and Finland.
 * Transport is not looked at although it is an important energy consumer. This is because there is no interaction with heating except via city structure, and there are no resources to look at that in this assessment. Electric cars would have an interaction with electricity production, but that applies to the total electricity market area (Finland, partly Scandinavia) and is too complex to look at.
-}}
 === Decisions and scenarios ===
@@ Line 56: / Line 68: @@
 :''Main article: [[Helsinki energy decision options 2015]]
-The two options in the official decision preparation are Hanasaari shutdown and Vuosaari, and Hanasaari 40 bio and Salmisaari 40 bio. However, also other options have been suggested, and also they are evaluated at least superficially.
+The two options in the official decision preparation as of May 2015 are i) Hanasaari shutdown and Vuosaari C, and ii) Hanasaari 40 bio and Salmisaari 40 bio. However, also other options have been suggested, and also they are evaluated at least superficially.
 # '''BAU:''' Only small, essential renovations are made to current power plants to stay within new emission limits.
 # '''Vuosaari C:''' A new power plant is built in Vuosaari with the capacity to burn 100 % wood-based fuel or any combination of wood-based fuels and coal.
@@ Line 107: / Line 119: @@
 || Loviisa nuclear heat || {{no}}|| {{yes}}|| {{no}}|| {{no}}|| {{no}}|| {{no}}|| {{no}}
 |----
-|| Neste oil refinery heat || {{no}}|| {{yes}}|| {{no}}|| {{no}}|| {{no}}|| {{no}}|| {{no}}
+|| Neste oil refinery heat || {{no}}|| {{yes}}|| {{no}}|| {{no}}|| {{yes}}|| {{no}}|| {{no}}
 |----
 || Salmisaari A&amp;B || renovated for biofuels|| {{yes}}|| {{yes}}|| {{yes}}|| {{no}}|| renovated for biofuels|| {{yes}}
 |----
-|| Sea heat pump || {{no}}|| {{no}}|| {{no}}|| {{no}}|| {{no}}|| {{no}}|| {{yes}}
+|| Sea heat pump || {{no}}|| {{no}}|| {{no}}|| {{no}}|| {{yes}}|| {{no}}|| {{yes}}
 |----
 || Sea heat pump for cooling || {{no}}|| {{no}}|| {{no}}|| {{no}}|| {{no}}|| {{no}}|| {{yes}}
@@ Line 138: / Line 150: @@
 <t2b name="Decisions" index='Decision_maker,Decision,Option,Variable,Cell,Change,Unit' obs='Amount' desc='Description' unit='-'>
-Builders|EnergySavingPolicy|BAU|efficiencyShares||Add||0|
+Builders|EnergySavingPolicy|BAU|efficiencyShares||Identity||0|
-Builders|EnergySavingPolicy|Energy saving moderate|efficiencyShares||Add||0|
+Builders|EnergySavingPolicy|Energy saving moderate|efficiencyShares||Identity||0|
 Builders|EnergySavingPolicy|Energy saving total|efficiencyShares|Efficiency:Passive;Time:2025,2035|Add|fraction|0.25|All input must be given in units that are used in respective ovariables.
 Builders|EnergySavingPolicy|Energy saving total|efficiencyShares|Efficiency:Passive;Time:2045,2055,2065|Add|fraction|0.1|
 Builders|EnergySavingPolicy|Energy saving total|efficiencyShares|Efficiency:Low-energy;Time:2025,2035|Add|fraction|-0.25|
 Builders|EnergySavingPolicy|Energy saving total|efficiencyShares|Efficiency:Low-energy;Time:2045,2055,2065|Add|fraction|-0.1|
+Building owner|EnergySavingPolicy|WWF energy saving|efficiencyShares||Identity|fraction|0|Energy efficiency in future buildings is so close to BAU that it is not changed.
 Building owner|EnergySavingPolicy|BAU|renovationRate||Multiply|1 /a|1|Assumes BAU renovation rate = 1%/a for buildings >30 a old
 Building owner|EnergySavingPolicy|Energy saving moderate|renovationRate||Multiply|1 /a|2|
 Building owner|EnergySavingPolicy|Energy saving total|renovationRate||Multiply|1 /a|4|
+Building owner|EnergySavingPolicy|WWF energy saving|renovationRate||Multiply|1 /a|2.5|
+Building owner|EnergySavingPolicy|BAU|demolitionRate||Replace|1 /a|0|
+Building owner|EnergySavingPolicy|Energy saving moderate|demolitionRate||Replace|1 /a|0|
+Building owner|EnergySavingPolicy|Energy saving total|demolitionRate||Replace|1 /a|0|
+Building owner|EnergySavingPolicy|WWF energy saving|demolitionRate||Identity|1 /a|0|
 Building owner|EnergySavingPolicy|BAU|renovationShares||Add|fraction|0|
 Building owner|EnergySavingPolicy|Energy saving moderate|renovationShares||Add|fraction|0|
 Building owner|EnergySavingPolicy|Energy saving total|renovationShares|Renovation:Windows|Add|fraction|-0.25|
 Building owner|EnergySavingPolicy|Energy saving total|renovationShares|Renovation:Sheath reform|Add|fraction|0.25|
+Building owner|EnergySavingPolicy|WWF energy saving|renovationShares|Renovation:Sheath reform|Add|fraction|0.1|
+Building owner|EnergySavingPolicy|WWF energy saving|renovationShares|Renovation:Windows|Add|fraction|-0.1|
 Helen|PlantPolicy|BAU|plantParameters|Plant:Biofuel heat plants,CHP diesel generators,Data center heat,Deep-drill heat,Loviisa nuclear heat,Neste oil refinery heat,Sea heat pump,Sea heat pump for cooling,Small-scale wood burning,Vuosaari C biofuel,Wind mills;Parameter:Min,Max,Investment cost,Management cost|Replace|MW,MW,M€,M€/a|0|
 Helen|PlantPolicy|BAU|energyProcess|Plant:Hanasaari;Fuel:Biofuel;Time:>=2018|Add|MJ/MJ|-0.4|
@@ Line 169: / Line 189: @@
 Helen|PlantPolicy|Zero investment|plantParameters|Plant:Biofuel heat plants,CHP diesel generators,Data center heat,Deep-drill heat,Sea heat pump,Sea heat pump for cooling,Small-scale wood burning,Suvilahti power storage,Loviisa nuclear heat,Neste oil refinery heat,Vuosaari C biofuel,Wind mills;Parameter:Min,Max,Investment cost,Management cost|Replace|MW,MW,M€,M€/a|0|
 Helen|PlantPolicy|Carbon neutral 2050|energyProcess||Identity|MJ/MJ|0|
-Helen|PlantPolicy|Carbon neutral 2050|plantParameters|Plant:CHP diesel generators,Data center heat,Deep-drill heat,Loviisa nuclear heat,Neste oil refinery heat,Sea heat pump,Sea heat pump for cooling,Small-scale wood burning;Parameter:Min,Max,Investment cost,Management cost|Replace|MW,MW,M€,M€/a|0|
+Helen|PlantPolicy|Carbon neutral 2050|plantParameters|Plant:CHP diesel generators,Data center heat,Deep-drill heat,Loviisa nuclear heat,Sea heat pump for cooling,Small-scale wood burning;Parameter:Min,Max,Investment cost,Management cost|Replace|MW,MW,M€,M€/a|0|
 Helen|PlantPolicy|Carbon neutral 2050|plantParameters|Plant:Salmisaari A&B,Small gas heat plants,Small fuel oil heat plants,Vuosaari A,Vuosaari B;Time:>=2030;Parameter:Min,Max,Investment cost,Management cost|Replace|MW,MW,M€,M€/a|0|Fossil fuel plants
 Helen|PlantPolicy|Carbon neutral 2050|plantParameters|Plant:Hanasaari;Time:>=2023;Parameter:Max|Replace|MW|0|As described in Helen Vuosaari C proposition
@@ Line 189: / Line 209: @@
 ** Energy saving moderate: renovate 2 %/a
 ** Energy saving total: renovate 4 %/a, in addition increase the share of passive buildings up by 25 %-units since 2025, and add the share of sheath reform renovations up by 25 %-units.
-* Plant policy: Choose an optimal selection of power plant infrastructure
+** WWF energy saving: energy saving according to WWF energy saving plan published 8th October 2015. It is based on efficient energy saving actions on buildings and consequental possibility to reduce coal energy. The savings are based on increased energy renovations (2.5 % per year), more effective renovations in 10 % of renovation cases, and demolition of old buildings (1 % per year).<ref name="wwf">WWF. Helsingin energiapäätös. Energiansäästö on polttoainevaihtoehdoista paras. WWF, 8.10.2015 http://wwf.fi/mediabank/7784.pdf [http://wwf.fi/wwf-suomi/viestinta/uutiset-ja-tiedotteet/WWF--Hajautettu-malli-energiatehokkuuden-lisayksella-vahvistettuna-on-paras-ratkaisu-Helsingille-2584.a]</ref>
-** BAU: Old plants remain as planned but Vuosaari C biofuel, Loviisa nuclear heat, and Neste oil refinery heat are not built.
+* Plant policy: Choose an optimal selection of power plant infrastructure. These poicy options are the main focus of the assessment. There is a separate table describing these options.
-** Biofuels and process heat: Old plants remain as planned. In addition, Vuosaari C biofuel, Loviisa nuclear heat, and Neste oil refinery heat are built.
-{{defend|# |If you have ideas about options that should be modelled, please just contact us and we'll try and adjust the model accordingly.|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 08:54, 15 July 2015 (UTC)}}
 === Timing ===
-The assessment started in May 2015. First draft results are expected before midsummer 2015. Final results should be available well before the City Council makes the decision in autumn, which means that results should exist by September 15th, 2015. There will be a public meeting to present the near-final results and discuss their implications. The meeting is held in THL, Helsinki on 31<sup>st</sup> August 2015 at noon.
+The assessment started in May 2015. First draft results are expected before midsummer 2015. Final results should be available well before the City Council makes the decision in autumn, which means that results should exist by September 15th, 2015. There will be a public meeting to present the near-final results and discuss their implications. The meeting was held in THL, Helsinki on 11<sup>st</sup> September 2015 at noon. The final report was published on 27th October 2015.
 == Answer ==
+</noinclude>
-Current results:
+=== Model with user interface ===
-* 4.10.2015 with Finnish texts [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=gaNmmSYSVCVgu7bI]
-* 10.9.2015 model run for the seminar 11.9.: [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=Lxpx944xwD5V0DBw Preference model] [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=JOI0ZXiISnNkUNK8 model part 1]  [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=k7zUPAo436wEbFAg model part 2]
-** An additional model run 25.9.2015 to show a directed acyclic graph of the model ovariables [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=ufoXJLoL8bJSDdki].
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=W9GaPYU04pdgLVtD Model run 9.9.2015] with old and new graphs. ''Custom'' option is the same as ''Cheapest'' in the preference analysis
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=eSpi8FBqfKbl7NKD Model run 6.9.2015]
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=E2jFkLL7QuCFQ0Tt Model run 5.9.2015]
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=d3bp5VSYGCagxhWl Model run 4.9.2015] Cost calculations are up to profit including external costs.
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=9PE7a4B1mBhUjlcR Model run 29.7.2015] Full run with lots of graphs.
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=4klHVl14kKW0RqDF What happens if you shut down both fossil and biofuel plants?]
+The final results results can be found from [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=Z33GxBqiKH0MHC0B model run 1.11.2015]. It is the final archived version in English. Objects were stored, so you can download the whole assessment to R in your own computer.
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=lJf2oQkm1wjHOqtE What happens if you shut down coal and gas power plants?]
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=3gnGOAq9MXJYpTz0 Model run 26.10.2015] graphs in Finnish [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=pW8GFqqo6bPNkb6s 20.11.2015 additional graphs]
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=9c3Otq1qJIO19qzx What happens if you shut down both coal-fired power plants?]
+* Intermediate results are available at section [[#See also]].
-<rcode label="Run energy balance model" graphics=1 variables="
+<rcode name="finalresults" label="Run energy balance model" graphics=1 store=1 variables="
 	name:noshutdown|description:Choose power plants you want to build (or keep running if they already exist) (the default selection is Helen's bio). This will become PlantPolicy: Custom.|type:checkbox|options:
 		'Biofuel heat plants';Biofuel heat plants;
@@ Line 236: / Line 246: @@
 		'Small gas heat plants';Small gas heat plants;
 		'Small fuel oil heat plants';Small fuel oil heat plants;
+		'Small-scale wood burning';Small-scale wood burning;
 		'Vuosaari A';Vuosaari A;
 		'Vuosaari B';Vuosaari B;
-		'Vuosaari C biofuel';Vuosaari C biofuel|default:'Biofuel heat plants';'Household air conditioning';'Katri Vala cooling';'Katri Vala heat';'Kellosaari back-up plant';'Salmisaari A&B';'Small fuel oil heat plants';'Small gas heat plants';'Suvilahti power storage';'Suvilahti solar';'Vanhakaupunki museum';'Vuosaari A';'Vuosaari B';'Vuosaari C biofuel'|
+		'Vuosaari C biofuel';Vuosaari C biofuel|default:'Biofuel heat plants';'Household air conditioning';'Katri Vala cooling';'Katri Vala heat';'Kellosaari back-up plant';'Salmisaari A&B';'Small fuel oil heat plants';'Small gas heat plants';'Small-scale wood burning';'Suvilahti power storage';'Suvilahti solar';'Vanhakaupunki museum';'Vuosaari A';'Vuosaari B';'Vuosaari C biofuel'|
 	name:renovation|description:Choose power plants to be renovated (PlantPolicy: Custom)|type:checkbox|options:
 		'Hanasaari';Hanasaari biofuel renovation
 ">
+# This code is Op_en7237/finalresults on page [[Helsinki energy decision 2015]]
 library(OpasnetUtils)
 library(ggplot2)
 openv.setN(0) # use medians instead of whole sampled distributions
-BS <- 24 # base_size for graph fonts
-saveobjects <- FALSE
-language <- "Finnish"
-fi <- language == "Finnish"
-dictionary <- tidy(opbase.data("Op_en7392.dictionary"))
-objects.latest("Op_en7392", code_name = "translate") # [[OpasnetUtils/Translate]] translate
 # Download all pre-calculated inputs, e.g. building stock.
 objects.latest("Op_en7237", code_name = "intermediates") # [[Helsinki energy decision 2015]]
 objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] oggplot
+objects.latest("Op_en7392", code_name = "translate") # [[OpasnetUtils/Translate]] translate
+objects.latest("Op_en7392", code_name = "dictionary") # [[OpasnetUtils/Translate]] dictionary
+BSbase <- 24 # base_size for graph fonts. Use 12 if you use savefig to save svg files.
+BS <- BSbase
+saveobjects <- FALSE
+savefigs <- FALSE
+language <- "Finnish"
+fi <- language == "Finnish"
-#plants <- union(as.character(unique(plantParameters@data$Plant)), as.character(unique(energyProcess@data$Plant)))
+savefig <- function(
+	file,
+	path = "N:/YMAL/Publications/2015/Helsingin energiapäätös/Kuvat/",
+	save = if(exists("savefigs")) savefigs else FALSE,
+	type = "svg",
+	height = 18,
+	width = 24,
+	units = "cm"
+) {
+	if(save) {
+		ggsave(paste(path, file, ".", type, sep = ""), height = height, width = width, units = units)
+	}
+}
 allplants <- c(
@@ Line 342: / Line 367: @@
 	)
 ))
+EnergyNetworkDemand <- EvalOutput(EnergyNetworkDemand)
+EnergyNetworkDemand@output <- rbind(
+	EnergyNetworkDemand@output,
+	data.frame(
+		Time = rep(c(2025, 2035, 2045, 2055, 2065), each = 4),
+		EnergySavingPolicy = rep(c("BAU", "Energy saving moderate", "Energy saving total", "WWF energy saving"), times = 5),
+		Temperature = "(-18,-15]",
+		EnergyConsumerDemandSource = "Formula",
+		EnergyConsumerDemandTotalSource = "Formula",
+		Fuel = "Cooling",
+		fuelSharesSource = "Formula",
+		EnergyNetworkDemandResult = 0,
+		EnergyNetworkDemandSource = "Formula"
+	)
+)
+#cat("All energy taxes are assumed zero.\n")
+#objects.latest("Op_en4151", code_name = "fuelTax")
+#fuelTax <- EvalOutput(fuelTax)
+#result(fuelTax) <- 0
 fuelUse <- EvalOutput(fuelUse)
@@ Line 386: / Line 433: @@
 ############## POST_PROCESSING AND GRAPHS, VERSION FROM PERFERENCE ANALYSIS
-cat("Total DALYs/a by different combinations of policy options.\n")
+cat(translate("NOTE! In all graphs and tables, the Total energy saving policy is assumed unless otherwise noted\n"))
+cat(translate("Total DALYs/a by different combinations of policy options.\n"))
 temp <- DALYs[as.character(DALYs$Time) %in% c("2015", "2035") & DALYs$Response == "Total mortality" , ]
 oprint(
-		oapply(temp, INDEX = c("Time", "EnergySavingPolicy", "PlantPolicy"), FUN = sum),
+	translate(oapply(temp, INDEX = c("Time", "EnergySavingPolicy", "PlantPolicy"), FUN = sum)),
-		caption = "Table 1: Total DALYs/a by different combinations of policy options.",
+	caption = translate("Table 1: Total DALYs/a by different combinations of policy options."),
-		caption.placement = "top",
+	caption.placement = "top",
-		include.rownames = FALSE
+	include.rownames = FALSE
 )
-BS <- 14
+bui <- oapply(buildings * 1E-6, cols = c("City_area", "buildingsSource"), FUN = sum)
+bui <- truncateIndex(bui, cols = "Heating", bins = 4)
+oggplot(bui[bui$EnergySavingPolicy == "BAU" , ], x = "Time", fill = "Heating", binwidth = 5) +
+		labs(
+				title = translate("Building stock in Helsinki by heating"),
+				y = translate("Floor area (M m2)")
+		)
-eac <- EAC[EAC$EnergySavingPolicy == "Energy saving total" , ] * -1
+savefig("Rakennuskannan koko Helsingissä")
-oprint(head(translate(eac)@output))
+oggplot(bui, x = "Time", fill = "Efficiency", binwidth = 5) +
+		{if(fi) facet_wrap(~ Energiansäästöpolitiikka) else facet_wrap(~ EnergySavingPolicy)} +
+		labs(
+				title = translate("Building stock in Helsinki by efficiency policy"),
+				y = translate("Floor area (M m2)")
+		)
-oprint(head(dictionary))
+oggplot(bui, x = "Time", fill = "Renovation", binwidth = 5) +
+		{if(fi) facet_wrap(~ Energiansäästöpolitiikka) else facet_wrap(~ EnergySavingPolicy)} +
+		labs(
+				title = translate("Building stock in Helsinki by renovation policy"),
+				y = translate("Floor area (M m2)")
+		)
-oggplot(eac, x = "PlantPolicy", fill = "Cost", turnx = TRUE) +
+oggplot(bui[bui$EnergySavingPolicy == "BAU" , ], x = "Time", fill = "Building", binwidth = 5) +
-	if(fi) facet_wrap(~ Voimala, scale = "free_y") else facet_wrap(~ Plant, scale = "free_y") +
 	labs(
-		title = translate("Incomes and costs by plant, total energy saving"),
+		title = translate("Building stock in Helsinki"),
-		y = translate("Effective annual cash flow (Meur/a)")
+		y = translate("Floor area (M m2)")
 	)
-oggplot(eac, x = "PlantPolicy", fill = "Cost", turnx = TRUE)+
+oggplot(buildings, x = "Time", fill = "Efficiency", binwidth = 5)+
-	if(fi) facet_wrap(~ Voimala) else facet_wrap(~ Plant) +
+	{if(fi) facet_grid(Energiansäästöpolitiikka ~ Korjaukset) else facet_grid(EnergySavingPolicy ~ Renovation)} +
 	labs(
-		title = translate("Incomes and costs by plant, total energy saving"),
+		title = translate("Renovation of buildings by policy and efficiency"),
-		y = translate("Effective annual cash flow (Meur/a)")
+		y = translate("Floor area (M m2)")
 	)
-BS <- 24
+# Contains also other buildings than district heating and other energy than heating
+hea <- EnergyConsumerDemandTotal * temperdays * 24 * 1E-3 # MW -> GWh
+hea$Time <- as.numeric(as.character(hea$Time))
+temp <- hea[hea$EnergySavingPolicy == "Energy saving total" & !hea$Consumable %in% c("District cooling", "Electric cooling") , ]
+oggplot(truncateIndex(temp, cols = "Temperature", bins = 7), x = "Time", fill = "Temperature", binwidth = 5) +
+{if(fi) facet_wrap(~ Hyödyke) else facet_wrap(~ Consumable)} +
+labs(
+	title = translate("Energy consumption in all buildings"),
+	y = translate("Total energy (GWh /a)")
+)
+temp <- hea[!hea$Consumable %in% c("District cooling", "Electric cooling") , ]
+oggplot(temp, x = "Time", fill = "Consumable", binwidth = 5) +
+{if(fi) facet_wrap(~ Energiansäästöpolitiikka) else facet_wrap(~ EnergySavingPolicy)} +
+labs(
+	title = translate("Energy consumption in all buildings"),
+	y = translate("Total energy (GWh /a)")
+)
+savefig("Helsingin vuotuinen energiantarve")
+oggplot(hea, x = "Time", fill = "Consumable", binwidth = 5) +
+{if(fi) facet_wrap(~ Energiansäästöpolitiikka) else facet_wrap(~ EnergySavingPolicy)} +
+labs(
+	title = translate("Energy consumption in all buildings"),
+	y = translate("Total energy (GWh /a)")
+)
+hea2 <- EnergyNetworkDemand * temperdays * 24 / 1000 # MW -> GWh
+hea2$Time <- as.numeric(as.character(hea2$Time))
+oggplot(hea2, x = "Time", fill = "Fuel", binwidth = 5) +
+{if(fi) facet_wrap(~ Energiansäästöpolitiikka) else facet_wrap(~ EnergySavingPolicy)} +
+labs(
+	title = translate("Energy demand in the network"),
+	fill = translate("Consumable"),
+	y = translate("Total energy (GWh /a)")
+)
+savefig("Energiankulutus verkossa Helsingissä")
 eb <-EnergyNetworkOptim[EnergyNetworkOptim$Process_variable_type == "Activity",]
@@ Line 426: / Line 530: @@
 eb$Process_variable_type <- NULL
-ebtemp <- eb[eb$Time %in% c("2035") & eb$PlantPolicy == "BAU" , ]
+ebtemp <- eb[eb$Time %in% c("2035") & eb$PlantPolicy == "BAU" & eb$Temperature != "(-18,-15]" , ]
 ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 7)
@@ Line 433: / Line 537: @@
 		title = translate("Power plant activity by temperature daily optim \nPlant policy = BAU, Year = 2035"),
 		x = translate("Temperature of the day"),
-		y = translate("Power output daily average (MW)")
+		y = translate("Average daily activity (MW)")
 	)
-ebtemp <- eb[eb$Time %in% c("2035") , ]
+ebtemp <- eb[eb$Time %in% c("2035") & eb$Temperature != "(-18,-15]" , ]
 ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 10)
 oggplot(ebtemp, x = "Temperature", fill = "Plant", turnx = TRUE) +
-	if(fi) facet_wrap(~ Voimalapäätös) else facet_wrap(~ PlantPolicy) +
+	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap(~ PlantPolicy)} +
 	labs(
-		title = translate("Power plant activity by temperature daily optim \nmoderate energy saving in 2035"),
+		title = translate("Power plant activity by temperature daily optim in 2035"),
 		x = translate("Temperature of the day"),
-		y = translate("Power plant activity daily average (MW)")
+		y = translate("Average daily activity (MW)")
 	)
+savefig("Helsingin päivittäinen kaukolämpötase")
 ebtemp <- eb[eb$Time %in% c("2005") & eb$PlantPolicy == "BAU" & eb$Temperature == "(0,3]" , ]
 ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 10)
-oggplot(ebtemp, x = "Plant", fill = "Plant") +
+oggplot(ebtemp, x = "Plant", fill = "Plant", turnx = TRUE) +
-	if(fi) facet_wrap(~ Lämpötila) else facet_wrap( ~ Temperature) +
+	{if(fi) facet_wrap(~ Lämpötila) else facet_wrap( ~ Temperature)} +
 	theme(axis.text.x = element_blank()) + # Turn text and adjust to right
 	labs(
 		title = translate("Power plant activity by temperature daily optim \nPlant policy = BAU, Year = 2005"),
-		y = translate("Power output daily average (MW)")
+		y = translate("Average daily activity (MW)")
 	)
@@ Line 466: / Line 572: @@
 oggplot(futemp, x = "Fuel", fill = "Plant", turnx = TRUE) +
-	if(fi) facet_grid(Aika ~ Energiansäästöpäätös) else facet_grid(Time ~ EnergySavingPolicy) +
+	{if(fi) facet_grid(Aika ~ Energiansäästöpolitiikka) else facet_grid(Time ~ EnergySavingPolicy)} +
 	labs(
 		title = translate("Energy commodity flows \n Plant policy = BAU"),
@@ Line 477: / Line 583: @@
 oggplot(futemp, x = "Fuel", fill = "Plant", turnx = TRUE) +
 	labs(
-		title = translate("Energy commodity flows in 2015 \n Plant policy = BAU, Energy saving policy == total"),
+		title = translate("Energy commodity flows in 2005 \n Plant policy = BAU"),
 		y = translate("Total annual energy (GWh/a)")
 	)
@@ Line 486: / Line 592: @@
 oggplot(futemp,
 	 x = "Time", fill = "Plant", binwidth = 5) +
-	if(fi) facet_wrap(~ Voimalapäätös) else facet_wrap(~ PlantPolicy) +
+	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap(~ PlantPolicy)} +
 	labs(
 		title = translate("District heat flow"),
@@ Line 492: / Line 598: @@
 	)
-futemp <- fu[fu$Fuel %in% c("Electricity") , ]
+savefig("Helsingin vuotuinen kaukolämpötase")
-futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1
+futemp <- fu[fu$Fuel %in% c("Electricity") & fu$Plant != "Kymijoki River's plants", ]
+futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) * -1
-oggplot(futemp, x = "Time", fill = "Plant", binwidth = 5) + facet_wrap(~ PlantPolicy) +
+# Does not contain plants outside Helsinki: Kymijoki River's plants, a share of Olkiluoto nuclear plant.
+oggplot(futemp, x = "Time", fill = "Plant", binwidth = 5) +
+	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap(~ PlantPolicy)} +
 	labs(
 		title = translate("Electricity flow"),
 		y = translate("Total annual energy (GWh/a)")
 	)
+savefig("Helsingin vuotuinen sähkötase")
 emis <- truncateIndex(emissions, cols = "Emission_site", bins = 5)
-emis <- emis[emis$EnergySavingPolicy == "Energy saving total" , ]
+emis <- emis[emis$EnergySavingPolicy == "Energy saving total" & emis$Fuel != "Electricity" , ]
+levels(emis$Fuel)[levels(emis$Fuel) == "Electricity_taxed"] <- "Electricity bought"
+emis$Time <- as.numeric(as.character(emis$Time))
 oggplot(emis, x = "Time", fill = "Fuel", binwidth = 5) +
-	if(fi) facet_grid(Saaste ~ Voimalapäätös, scale = "free_y") else facet_grid(Pollutant ~ PlantPolicy, scale = "free_y") +
+	{if(fi) facet_grid(Saaste ~ Voimalapolitiikka, scale = "free_y") else facet_grid(Pollutant ~ PlantPolicy, scale = "free_y")} +
 	labs(
 		title = translate("Emissions from heating in Helsinki"),
 		y = translate("Emissions (ton /a)")
-	)
+	) +
+	scale_x_continuous(breaks = c(2000, 2050))
-da <- DALYs[DALYs$EnergySavingPolicy == "Energy saving total" , ]
+savefig("Helsingin energiantuotannon päästöt")
+da <- DALYs[DALYs$EnergySavingPolicy == "Energy saving total" & DALYs$Fuel != "Electricity" , ]
+levels(da$Fuel)[levels(da$Fuel) == "Electricity_taxed"] <- "Electricity bought"
+da$Time <- as.numeric(as.character(da$Time))
 oggplot(da, x = "Time", fill = "Fuel", binwidth = 5) +
-	if(fi) facet_wrap(~ Voimalapäätös) else facet_wrap(~ PlantPolicy) +
+	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap(~ PlantPolicy)} +
 	labs(
-		title = translate("Health effects in DALYs of PM2.5 from heating in Helsinki"),
+		title = translate("Health effects of PM2.5 from heating in Helsinki"),
 		y = translate("Health effects (DALY /a)")
 	)
+savefig("Helsingin energiantuotannon terveysvaikutukset")
 fp <- fuelPrice[fuelPrice$Fuel %in% c(
@@ Line 532: / Line 652: @@
 levels(fp$Fuel)[levels(fp$Fuel) == "Electricity_taxed"] <- "Electricity"
-ggplot(fp@output, aes(x = Time, y = fuelPriceResult, colour = Fuel, group = Fuel))+
+ggplot(translate(fp@output), if(fi) {
+	aes(x = Aika, y = fuelPriceResult, colour = Polttoaine, group = Polttoaine)
+} else {
+	aes(x = Time, y = fuelPriceResult, colour = Fuel, group = Fuel)
+}) +
 	geom_line(size = 2)+theme_gray(base_size = BS) +
 	labs(
 		title = translate("Fuel prices (with tax)"),
-		y = translate("Price (Eur / MWh)")
+		y = translate("Price (Eur/MWh)")
 	)
-tc <- truncateIndex(totalCost, cols = "Plant", bins = 11) / 10 # Yearly costs
+savefig("Polttoaineiden verolliset hinnat")
+tc <- truncateIndex(totalCost, cols = "Plant", bins = 11) / 10 * -1 # Yearly benefits (costs are negative)
 tc <- tc[tc$EnergySavingPolicy == "Energy saving total" , ]
 oggplot(tc, x = "Time", fill = "Cost", binwidth = 10) +
-	if(fi) facet_wrap(~ Voimalapäätös) else facet_wrap( ~ PlantPolicy) +
+	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap( ~ PlantPolicy)} +
 	labs(
-		y = translate("Yearly costs (Meur)"),
+		y = translate("Yearly cash flow (Meur)"),
-		title = translate("Total costs of energy production")
+		title = translate("Total benefits and costs of energy production")
-	)
+	)+
+	scale_x_continuous(breaks = c(2000, 2020, 2040, 2060))
-oggplot(tc, x = "Time", fill = "Plant", binwidth = 10) + facet_wrap( ~ PlantPolicy) +
+savefig("Energiantuotannon kokonaiskustannus Helsingissä kustannuksittain")
-	labs(
-		y = "Yearly costs (Meur)",
-		title = "Total costs of energy production"
-	)
-eb <-EnergyNetworkOptim[EnergyNetworkOptim$Process_variable_type == "Activity",]
+oggplot(tc, x = "Time", fill = "Plant", binwidth = 10) +
-eb <- eb[eb$EnergySavingPolicy == "Energy saving total" , ]
+	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap(~ PlantPolicy)} +
-colnames(eb@output)[colnames(eb@output) == "Process_variable_name"] <- "Plant"
-eb$Process_variable_type <- NULL
-ebtemp <- eb[eb$Time %in% c("2035") , ]
-ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 11)
-oggplot(ebtemp, x = "Temperature", fill = "Plant", turnx = TRUE) + facet_wrap(~ PlantPolicy) +
 	labs(
-		title = "Power plant activity by temperature daily optim in 2035",
+		y = translate("Yearly cash flow (Meur)"),
-		x = "Temperature of the day",
+		title = translate("Total benefits and costs of energy production")
-		y = "Average daily activity (MW)"
+	)+
-	)
+	scale_x_continuous(breaks = c(2000, 2020, 2040, 2060))
-eac <- truncateIndex(EAC * -1, cols = "Plant", bins = 11)
+savefig("Energiantuotannon kokonaiskustannus Helsingissä voimaloittain")
-eac <- eac[eac$EnergySavingPolicy == "Energy saving total" , ]
-oggplot(eac, x = "PlantPolicy", fill = "Plant", turnx = TRUE)+
+eac <- EAC[EAC$EnergySavingPolicy == "Energy saving total" , ] * -1
-	labs(title = "Incomes and costs by plant policy\ntotal energy saving", y = "Effective annual cash flow (Meur/a)")
-oggplot(eac, x = "PlantPolicy", fill = "Cost", turnx = TRUE)+
+BS <- BSbase * 0.7 # Plot the next two graphs with smaller font because they are busy graphs.
-	labs(title = "Incomes and costs by plant policy\ntotal energy saving", y = "Effective annual cash flow (Meur/a)")
-temp <- truncateIndex(plantParameters[plantParameters$Parameter == "Max" , ], cols = "Plant", bins = 11)
+eac2 <- eac[!eac$Plant %in% c(
-temp <- temp[temp$Time >= 2000 & temp$Time <=2070 , ]
+	'Household air conditioning',
-oggplot(temp, x = "Time", fill = "Plant", binwidth = 1) + facet_wrap(~ PlantPolicy)+
+	'Household solar',
-labs(title = "Energy production capacity by plant policy", y = "Maximum capacity (MW)")
+	'Katri Vala cooling',
+	'Kellosaari back-up plant',
+	'Sea heat pump for cooling',
+	'Small-scale wood burning',
+	'Suvilahti power storage',
+	'Suvilahti solar',
+	'Vanhakaupunki museum',
+	'Wind mills'
+) , ]
-####################### Post-processing and graphs ORIGINAL
+oggplot(eac2, x = "PlantPolicy", fill = "Cost", turnx = TRUE) +
+	{if(fi) facet_wrap(~ Voimala, scale = "free_y") else facet_wrap(~ Plant, scale = "free_y")} +
+	labs(
+		title = translate("Incomes and costs by plant"),
+		y = translate("Effective annual cash flow (Meur/a)")
+	)
-cat("Total DALYs/a by different combinations of policy options.\n")
+savefig("Helsingin voimalaitosten kustannustehokkuus")
-temp <- DALYs[as.character(DALYs$Time) %in% c("2015", "2035") & DALYs$Response == "Total mortality" , ]
+oggplot(eac2, x = "PlantPolicy", fill = "Cost", turnx = TRUE)+
+	{if(fi) facet_wrap(~ Voimala) else facet_wrap(~ Plant)} +
+	labs(
+		title = translate("Incomes and costs by plant"),
+		y = translate("Effective annual cash flow (Meur/a)")
+	)
-oprint(
+savefig("Helsingin voimalaitosten kustannustehokkuus yhtenäisasteikolla")
-		oapply(temp, INDEX = c("Time", "EnergySavingPolicy", "PlantPolicy"), FUN = sum),
-		caption = "Table 1: Total DALYs/a by different combinations of policy options.",
-		caption.placement = "top",
-		include.rownames = FALSE
-)
-eb <-EnergyNetworkOptim[EnergyNetworkOptim$Process_variable_type == "Activity",]
+BS <- BSbase
-colnames(eb@output)[colnames(eb@output) == "Process_variable_name"] <- "Plant"
+eac <- truncateIndex(eac, cols = "Plant", bins = 11)
-eb$Process_variable_type <- NULL
-ebtemp <- eb[eb$Time %in% c("2035") & eb$EnergySavingPolicy == "BAU" & eb$PlantPolicy == "BAU" , ]
+oggplot(eac, x = "PlantPolicy", fill = "Plant", turnx = TRUE)+
-ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 7)
+	labs(
+		title = translate("Incomes and costs by plant policy"),
+		y = translate("Effective annual cash flow (Meur/a)")
+	)
-oggplot(ebtemp, x = "Temperature", fill = "Plant") +
+oggplot(eac, x = "PlantPolicy", fill = "Cost", turnx = TRUE)+
 	labs(
-		title = "Power plant activity by temperature daily optim \nPlant policy = BAU, Year = 2035",
+		title = translate("Incomes and costs by plant policy"),
-		x = "Temperature of the day",
+		y = translate("Effective annual cash flow (Meur/a)")
-		y = "Power output daily average (MW)"
 	)
-ebtemp <- eb[eb$Time %in% c("2035") & eb$EnergySavingPolicy == "Energy saving moderate" , ]
+savefig("Teholliset tulot ja menot energiantuotannosta Helsingissä kustannuksittain")
-ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 10)
-oggplot(ebtemp, x = "Temperature", fill = "Plant") + facet_wrap(~ PlantPolicy)
+temp <- truncateIndex(plantParameters[plantParameters$Parameter == "Max" , ], cols = "Plant", bins = 11)
+temp <- temp[temp$Time >= 2000 & temp$Time <=2070 , ]
+oggplot(temp, x = "Time", fill = "Plant", binwidth = 1) +
+	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap(~ PlantPolicy)} +
 	labs(
-		title = "Power plant activity by temperature daily optim \nmoderate energy saving in 2035",
+		title = translate("Energy production capacity by plant policy"),
-		x = "Temperature of the day",
+		y = translate("Maximum capacity (MW)")
-		y = "Power plant activity daily average (MW)"
 	)
-ebtemp <- eb[eb$Time %in% c("2005") & eb$EnergySavingPolicy == "BAU" & eb$PlantPolicy == "BAU" & eb$Temperature == "(0,3]" , ]
+savefig("Energiantuotantokapasiteetin kehitys Helsingissä")
-ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 10)
-oggplot(ebtemp, x = "Plant", fill = "Plant") + facet_wrap( ~ Temperature) +
+odag() #Plots a directed acyclic graph of ovariables used in the model.
-		theme(axis.text.x = element_blank()) + # Turn text and adjust to right
+# This causes an internal error, so it must be the last row of the model.
-		labs(
-				title = "Power plant activity by temperature daily optim \nPlant policy = BAU, Year = 2005",
-				y = "Power output daily average (MW)"
-		)
-oggplot(plantParameters[plantParameters$Parameter == "Max" , ], x = "Time", fill = "Plant") + facet_wrap(~ PlantPolicy) +
+</rcode>
-labs(title = "Total energy production capacity", y = "Max capacity (MW)")
+<noinclude>
-fu <- fuelUse / 3.6E+6 # From MJ/a -> GWh/a
+=== Results ===
-fu$Burner <- NULL
-fu$Time <- as.numeric(as.character(fu$Time))
-futemp <- fu[fu$Time %in% c("2015", "2035", "2065") & fu$PlantPolicy == "BAU" , ]
+<gallery widths=400px heights=400px>
-futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) * -1
+File:Daily energy balance in Helsinki.png|Daily average production of district heat energy in the city of Helsinki depending on the outside temperature for energy saving total scenario and year 2035.
+File:Yearly energy balance in Helsinki.png|Total annual expenditure and production of district heat energy in the city of Helsinki in 2035 after energy saving total policy.
+File:Building stock in Helsinki by heating.png|Current data and estimation of future building stock of Helsinki by heating type.
+File:Energy used in heating in Helsinki.png|Energy used in heating, consumer electricity, and hot water in Helsinki in the energy saving total policy.
+File:fuelprices.png|Fuel prices used in optimising.
+File:Energy production capacity in Helsinki.png|Energy production capacity in Helsinki by plant policy
+File:Incomes and costs of energy production in Helsinki.png|Incomes and costs of energy production in Helsinki by power plant. Note that the y axes are on different scales.
+File:Flow of electric power in Helsinki.png|Flow of electric power in Helsinki by plant policy. Many policies result in severe electricity demand from outside the city. This is possibly not a problem, but the decision maker should be aware of such side effect.
+File:District heat flow in Helsinki.png|Flow of district heat in Helsinki. The supply is always matched with the demand.
+File:Health effects of PM2.5 from heating in Helsinki.png|Health effects of PM2.5 from heating in Helsinki.
+File:Emissions from heating in Helsinki.png|Emissions of CO2 and PM2.5 from heating in Helsinki.
+</gallery>
-oggplot(futemp, x = "Fuel", fill = "Plant", turnx = TRUE) + facet_grid(Time ~ EnergySavingPolicy) +
+=== Conclusions ===
-	labs(
-		title = "Energy commodity flows \n Plant policy = BAU",
-		y = "Total annual energy (GWh/a)"
-	)
-#oggplot(fu, x = "Time", fill = "Plant", binwidth = 10)+facet_grid(PlantPolicy ~ EnergySavingPolicy)
+See summary in the beginning.
-#oggplot(fu, x = "Time", fill = "Fuel")+facet_grid(PlantPolicy ~ EnergySavingPolicy)
+</noinclude>
-futemp <- fu[fu$Time %in% c("2005") & fu$PlantPolicy == "BAU" & fu$EnergySavingPolicy == "BAU" , ]
+== Rationale ==
-futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) * -1
+[[image:Helsinki energy decision 2015.png|thumb|400px|Causal diagram for the assessment.]]
+<noinclude>
-oggplot(futemp, x = "Fuel", fill = "Plant", turnx = TRUE) +
+=== Stakeholders ===
-		labs(
-				title = "Energy commodity flows in 2015 \n Plant policy = BAU, Energy saving policy == BAU",
-				y = "Total annual energy (GWh/a)"
-		)
-futemp <- fu[fu$Fuel %in% c("Heat") , ]
+The impacts are assessed and valued from the point of view of the following stakeholders:
-futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1
+* The city of Helsinki
+* Helen Oy energy company
+* A citizen of Helsinki
+* Finland
+* Global view
-oggplot(futemp, x = "Time", fill = "Plant", binwidth = 5) + facet_grid(EnergySavingPolicy ~ PlantPolicy) +
+=== Dependencies ===
-		labs(
-				title = "District heat flow",
-				y = "Total annual energy (GWh/a)"
-		)
-oggplot(futemp[futemp$EnergySavingPolicy == "Energy saving moderate" , ],
+;List of key pages used in model
-	 x = "Time", fill = "Plant", binwidth = 5) + facet_wrap(~ PlantPolicy) +
+* [[Helsinki energy decision 2015]]: Main page of assessment.
-		labs(
+* [[Helsinki energy decision options 2015]]: More detailed information on the different options available.
-				title = "District heat flow",
+* [[Building model]]: Method of estimating the size of the building stock of a city, including heating properties and renovations.
-				y = "Total annual energy (GWh/a)"
+** [[Building stock in Helsinki]]: Data on the building stock in Helsinki and its projected future.
-		)
+* [[Energy use of buildings]]: Method of estimating energy need based on building stock and outdoor temperature.
+** [[Helsinki energy consumption]]: Data on the energy consumption of buildings.
+* [[Energy balance]]: Method of calculating energy balance.
+** [[Helsinki energy production]]: Data on the energy production in Helsinki.
+** [[Energy balance in Helsinki]]: Data on the amounts of energy produced, consumed, imported, and exported in Helsinki.
+* [[Emission factors for burning processes]]: Data on the emission factors for burning processes in Finland.
+* [[Prices of fuels in heat production]]: Data on market prices of fuels used in Helsinki
-futemp <- fu[fu$Fuel %in% c("Electricity") & fu$EnergySavingPolicy == "Energy saving moderate" , ]
-futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1
-oggplot(futemp, x = "Time", fill = "Plant", binwidth = 5) + facet_wrap(~ PlantPolicy) +
+;Other models used in the Helsinki assessment (but are not in the core of this assessment)
-		labs(
+* [[Burden of disease in Finland]]: Data on disease burden.
-				title = "Electricity flow",
+* [[Exposure-response function]]: Description of ERF, a mathematical construct used to describe a probability of different responses to a given exposure.
-				y = "Total annual energy (GWh/a)"
+* [[Health impact assessment]]: Method of estimating the health impacts of a particular event or policy.
-		)
+** [[ERFs of environmental pollutants]]: Data on the exposure-response function of several environmental pollutants that do not have their own pages.
+** [[Exposures in Finland]]: Data on the typical exposure levels of common pollutants in Finland.
+** [[Disease risk]]: Data on the incidence or prevalence rates of different diseases in Finland.
+** [[Population attributable fraction]]: Method of calculating the fraction of disease that would disappear if the exposure to that agent would disappear.
+* [[Population of Helsinki metropolitan area]]: Data on the total population of Helsinki metropolitan area.
+* [[Intake fractions of PM]]: Data on the intake fractions of airborne particulate matter for different emission sources and locations.
+* [[Exposure to PM2.5 in Finland]]: Method of estimating exposure to fine particles (PM2.5) in the Finnish population.
+* [[Emission assessment of small-scale energy production in the Helsinki metropolitan area]]: Assessment evaluating the greenhouse gas emissions and emission trading costs of small-scale (output less than 50 MW) energy production in the Helsinki metropolitan area.
+* [[OpasnetUtils/Drafts]]
-#futemp <- fu[fu$Fuel %in% c("Cooling") , ]
-#futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1
-#oggplot(futemp, x = "Time", fill = "Plant", binwidth = 5) + #facet_grid(EnergySavingPolicy ~ PlantPolicy) +
+{| {{prettytable}} class="wikitable collapsible collapsed"
-#		labs(
+|+'''Variables in the assessment model
-#				title = "Cooling flow",
+! Variable || Measure || Indices
-#				y = "Total annual energy (GWh/a)"
+|----
-#		)
+| colspan="3"| buildings (from the [[Building model]])
+|----
-emis <- truncateIndex(emissions, cols = "Emission_site", bins = 5)
+| stockBuildings [[Building stock in Helsinki#Building stock|From statistics]] [http://en.opasnet.org/w/Special:Opasnet_Base?id=op_en7115.stock_details data in OB]
-emis <- emis[emis$EnergySavingPolicy == "BAU" , ]
+| Building stock in floor-m2 at given timepoints: Stock details in Opasnet Base
-oggplot(emis, x = "Time", fill = "Fuel", binwidth = 5) + facet_grid(Pollutant ~ PlantPolicy, scale = "free_y") +
+| Time (Valmistusmisaika), City_area (Sijainti), Building (Käyttötarkoitus), Heating (Lämmitystapa, Polttoaine), Efficiency (Varusteena koneellinen ilmanvaihto, Perusparannus)
-		labs(
+|----
-				title = "Emissions from heating in Helsinki",
+| heatingShares = 1 [[Building stock in Helsinki]]
-				x = "Time (Energy saving policy = BAU)",
+| Fractions of heating types. Should sum up to 1 within each group defined by optional indices. Data is already in stockBuildings, so we use 1 here.
-				y = "Emissions (ton /a)"
+| Heating, Time, Building
-		)
+|----
+| efficiencyShares [[Energy use of buildings#Energy efficiency in heating|Energy efficiency of new buildings in the future]]. [http://en.opasnet.org/w/Special:Opasnet_Base?id=op_en5488.energy_efficiency_of_new_buildings_in_the_future OpasnetBase]
-oggplot(DALYs, x = "Time", fill = "Fuel", binwidth = 5) + facet_grid(EnergySavingPolicy ~ PlantPolicy) +
+| Fraction of energy efficiency types of new buildings in the future. Should sum up to 1 for each group defined by other indices. Fractions of heating and energy efficiency: from Facta data and [https://www.energiatodistusrekisteri.fi/public_html?command=browse&s=todistushaku_section Energiatodistusrekisteri] This can be used to derive energy classes and the U values for buildings in Helsinki.
-		labs(
+| Efficiency, Time, Building.
-				title = "Health effects in DALYs of PM2.5 from heating in Helsinki",
+|----
-				y = "Health effects (DALY /a)"
+| changeBuildings = stockBuildings * 0.02 /a
-		)
+| Construction or demolition rate as floor-m2 at given timepoints. Implemented in code.
+| Time, Efficiency, Heating. changeBuildings should have all indices in stockBuildings, heatingShares, and efficiencyShares.
-fp <- fuelPrice[fuelPrice$Fuel %in% c(
+|----
-	"Biofuel",
+| renovationShares [[Building stock in Helsinki#Renovations|Guesstimate]] [http://en.opasnet.org/w/Special:Opasnet_Base?id=op_en7115.popularity_of_renovation_types data in OB]
-	"Coal",
+| Fraction of renovation types when renovation is done. Should sum to 1 for each group defined by other indices.
-	"Electricity_taxed",
+| Renovation, Startyear. Startyear is the time when the renovation is done, and it must be different than the Time index.
-	"Fuel oil",
+|----
-	"Heat",
+| renovationRate [[Building stock in Helsinki#Renovations|Guesstimate]] [http://en.opasnet.org/w/Special:Opasnet_Base?id=op_en7115.fraction_of_houses_renovated_per_year data in OB]
-	"Light oil",
+| Rate of renovation (fraction per time unit).
-	"Natural gas",
+| Age (the time difference between construction and renovation, i.e. Startyear - Time for each building).
-	"Peat"
+|----
-) , ]
+| obstime Ten-year intervals 1980 - 2060.
-fp$Time <- as.numeric(as.character(fp$Time))
+| one-column data.frame about the years to be used in output.
-levels(fp$Fuel)[levels(fp$Fuel) == "Electricity_taxed"] <- "Electricity"
+| Startyear
+|----
-ggplot(fp@output, aes(x = Time, y = fuelPriceResult, colour = Fuel, group = Fuel))+
+| heating_before = FALSE
-	geom_line(size = 2)+theme_gray(base_size = BS) +
+| If TRUE, heatingShares contains data and is added to buildings before obstime.
-	labs(
+|
-		title = "Fuel prices (with tax)",
+|----
-		y = "Price (Eur / MWh)"
+| efficiency_before = TRUE
-	)
+| If TRUE, efficiencyShares contains data and is added to buildings before obstime.
+|
-tc <- truncateIndex(totalCost[totalCost$EnergySavingPolicy == "Energy saving moderate" , ], cols = "Plant", bins = 10) / 10 # Yearly costs
+|----
+|colspan="3"| heatingEnergy: Energy consumption from the [[Building model]] {{defend|# |Move code to [[Heating consumption of buildings]]|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 03:48, 9 June 2015 (UTC)}}
-oggplot(tc, x = "Time", fill = "Cost", binwidth = 10) + facet_wrap( ~ PlantPolicy) +
+|----
-	labs(
+| energyUse [[Energy use of buildings#Baseline energy consumption|Baseline energy consumption]] [http://en.opasnet.org/w/Special:Opasnet_Base?id=op_en5488.baseline_energy_consumption_per_area_unit] {{defend|# |Update this to use U values and ambient temperatures.|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 12:22, 24 May 2015 (UTC)}} Also use [[Building stock in Helsinki]], Table 6.
-		y = "Yearly costs (Meur)",
+| Energy consumption per floor area (kWh / m2 /a) (U: W/m2/K * T: K * Area: m2)
-		title = "Total costs of energy production"
+| Building, Heating.
-	)
+|----
+| efficiencyRatio  [[Energy use of buildings#Energy efficiency in heating]]) [http://en.opasnet.org/w/Special:Opasnet_Base?id=op_en5488.energy_use_by_energy_class_of_building Opasnet Base]
-oggplot(tc, x = "Time", fill = "Plant", binwidth = 10) + facet_grid(~ PlantPolicy) +
+| Relative energy consumption compared with the efficiency group Old.
-	labs(
+| Efficiency.
-		y = "Yearly costs (Meur)",
+|----
-		title = "Total costs of energy production"
+| renovationRatio [[Energy use of buildings#Impact of renovations|Impact of renovations]] [http://en.opasnet.org/w/Special:Opasnet_Base?id=op_en5488.energy_saving_potential_of_different_renovations Opasnet Base]
-	)
+| Relative energy consumption compared with the Renovation location None.
+| Renovation.
-pr <- oapply(totalCost[totalCost$EnergySavingPolicy == "Energy saving moderate" , ], cols = c("Cost"), FUN = sum) / -10 # Values per year
+|----
-oggplot(pr, x = "Time", fill = "Plant", binwidth = 10)+facet_wrap(~ PlantPolicy)+
+|colspan="3"| energyBalance [[Energy balance in Helsinki]]
-	labs(title = "Total costs with moderate energy saving", y = "Cost Meur")
+|----
+| heatingEnergy (see above)
-oggplot(truncateIndex(EAC, cols = "Plant", bins = 10), x = "PlantPolicy", fill = "Cost")
+|
-BS <- 14
+|
-oggplot(EAC[EAC$PlantPolicy == "BAU" , ], x = "EnergySavingPolicy", fill = "Cost")+facet_wrap(~ Plant, scale = "free_y")
+|----
-oggplot(EAC[EAC$EnergySavingPolicy == "Energy saving moderate" , ], x = "PlantPolicy", fill = "Cost", turnx = TRUE)+
+| consumerElectricity: from [[Energy use of buildings#Baseline energy consumption|Baseline energy consumption]] [http://en.opasnet.org/w/Special:Opasnet_Base?id=op_en5488.baseline_energy_consumption_per_area_unit Opasnet Base] based on floor area
-	facet_wrap(~ Plant, scale = "free_y") +
+| Amount of consumer electricity use (MW) calculated based on either amount of people or floor-m2
-	labs(title = "Costs and incomes by plant\nmoderate energy saving", y = "Cash flow (Meur)")
+| Hour (time of day), others?
+|----
-oggplot(EAC[EAC$EnergySavingPolicy == "Energy saving moderate" , ], x = "PlantPolicy", fill = "Cost")+facet_wrap(~ Plant)+
+| otherEnergy [[Helsinki energy consumption]]
-	facet_wrap(~ Plant, scale = "free_y") +
+| Other energy consumption (industry, transport, other): from statistics of Helsinki [http://www.helsinginymparistotilasto.fi/ Helsingin ympäristötilasto]. [[Thermal energy need in Helsinki Metropolitan Area]]
-	labs(title = "Costs and incomes by plant\nmoderate energy saving", y = "Cash flow (Meur)")
+| Time
+|----
-oggplot(plantParameters[plantParameters$Parameter == "Max" , ], x = "Time", fill = "Plant", binwidth = 10) + facet_grid(PlantPolicy ~ .)+
+| windElectricity Check <ref name="professors"/> about possibilities and Wind pattern [http://www.tuuliatlas.fi/fi/index.html Tuuliatlas] about useful wind mill sites in Helsinki.
-labs(title = "Energy production capacity by plant policy", y = "Maximum capacity (MW)")
+| Amount of wind power produced (MW) by wind type. We also need amounts of hours with different wind patterns per year.
+| Hour (do we need this, is wind power production dependent on time of day?), Weather (temperature and weather types), maybe Wind (Windy, Normal, Calm)
-odag() #Plots a directed acyclic graph of ovariables used in the model.
+|----
-# This causes an internal error, so it must be the last row of the model.
+| solarElectricity
+| Amount of solar energy produced (MW) in certain conditions. Data source?
-</rcode>
+| Hour, Month, Weather, maybe Sun (Sunny, Cloudy, Dark) {{comment|# |Have to decide which of these is used.|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 17:27, 24 May 2015 (UTC)}}
+|----
-=== Results ===
+| airpumpHeat
+| This means air-driven heat pumps installed in individual homes and run based on ambient temperature. The efficiency of air heat pump reduces with very cold weather. We assume that the heat pump will be used for cooling as well, if it is installed. (We assume that cooling is not used in apartments without heat pump; cooling is otherwise only used in offices etc).
-<gallery widths=400px heights=400px>
+| Weather, Hour, maybe Temperature. {{comment|# |Should we have index for day/night so that daily storage of heat/cool can be assessed?|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 17:27, 24 May 2015 (UTC)}}
-File:Daily energy balance in Helsinki.png|Daily average production of energy in the city of Helsinki depending on the outside temperature for business-as-usual (BAU) scenario and year 2035.
+|----
-File:Yearly energy balance in Helsinki.png|Total annual expenditure and production of energy in the city of Helsinki in years 2015, 2035 and 2065 depending on the energy policy adopted.
+| energyProduction [[Helsinki energy production]]
-File:Building stock in Helsinki by heating.png|Current data and estimation of future building stock of Helsinki by heating type.
+| Energy production based on need: basically, we need to know a) what power plants exist, b) what fuels they use and what (heat, power or both) they produce, c) what emissions they produce, d) what is their maximum capacity, e) at what demand level are they turned on.
-File:Energy used in heating in Helsinki.png|Energy used in heating in Helsinki by energy saving policy and different energy renovations that are done to buildings older than 30 a.
+* [[District heating production units in Helsinki metropolitan area]]
-</gallery>
+* Geothermal heat pumps: this means both individual geothermal heat pumps and district heat pumps for heating and cooling, such as the one in Katri Vala's park. Its efficiency is not reduced with very cold weather. See [https://www.helen.fi/en/households/services/district-cooling/ District cooling by Helen], [[:en:Absorption refrigerator]], [[:en:District cooling]].
+|
-[[File:fuelprices.png|600px]]
+|----
+| colspan="3"| emissions (from the model) (emissions in mass per time) Emissions of energy production (ton/a). [[Emission assessment of small-scale energy production in the Helsinki metropolitan area]]
-Development of prices of power plant fuels over time.
+|----
+| energyBalance (from the model; see above)
-=== Conclusions ===
+|
+|
-== Rationale ==
+|----
-[[image:Helsinki energy decision 2015.png|thumb|400px|Causal diagram for the assessment.]]
+| fuelShares [[Energy balance in Helsinki]]<ref name="judl">Jáchym Judl, Sirkka Koskela, Timo Korpela, Niko Karvosenoja, Anna Häyrinen, Jari Rantsi. Net environmental impacts of low-share wood pellet co-combustion in an existing coal-fired CHP (combined heat and power) production in Helsinki, Finland. Energy 77 (2014) 844-851. {{doi|10.1016/j.energy.2014.09.068}}</ref>
+| Tells how much of fuel is used for a certain neating energy need. {{comment|# |Should this be on page [[Energy balance in Helsinki]] or, like in Kuopio and Basel, a separate table?|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 17:27, 24 May 2015 (UTC)}}
-=== Stakeholders ===
+{{comment|# | Use shares of different fuels. Currently this data is on page [[Emission factors for burning processes]] Table Fuel use in different heating types. However, this is clearly case-specific data and should be on a case-specific page. This should be done retrospectively to Kuopio and Basel as well.|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 12:22, 24 May 2015 (UTC)}}
+| Fuel_type.
-The impacts are assessed and valued from the point of view of the following stakeholders:
-* The city of Helsinki
-* Helen Oy energy company
-* A citizen of Helsinki
-* Finland
-* Global view
-=== Dependencies ===
-;List of key pages used in model
-* [[Helsinki energy decision 2015]]: Main page of assessment.
-* [[Helsinki energy decision options 2015]]: More detailed information on the different options available.
-* [[Building model]]: Method of estimating the size of the building stock of a city, including heating properties and renovations.
-** [[Building stock in Helsinki]]: Data on the building stock in Helsinki and its projected future.
-* [[Energy use of buildings]]: Method of estimating energy need based on building stock and outdoor temperature.
-** [[Helsinki energy consumption]]: Data on the energy consumption of buildings.
-* [[Energy balance]]: Method of calculating energy balance.
-** [[Helsinki energy production]]: Data on the energy production in Helsinki.
-** [[Energy balance in Helsinki]]: Data on the amounts of energy produced, consumed, imported, and exported in Helsinki.
-* [[Emission factors for burning processes]]: Data on the emission factors for burning processes in Finland.
-* [[Prices of fuels in heat production]]: Data on market prices of fuels used in Helsinki
-;Other models used in the Helsinki assessment (but are not in the core of this assessment)
-* [[Burden of disease in Finland]]: Data on disease burden.
-* [[Exposure-response function]]: Description of ERF, a mathematical construct used to describe a probability of different responses to a given exposure.
-* [[Health impact assessment]]: Method of estimating the health impacts of a particular event or policy.
-** [[ERFs of environmental pollutants]]: Data on the exposure-response function of several environmental pollutants that do not have their own pages.
-** [[Exposures in Finland]]: Data on the typical exposure levels of common pollutants in Finland.
-** [[Disease risk]]: Data on the incidence or prevalence rates of different diseases in Finland.
-** [[Population attributable fraction]]: Method of calculating the fraction of disease that would disappear if the exposure to that agent would disappear.
-* [[Population of Helsinki metropolitan area]]: Data on the total population of Helsinki metropolitan area.
-* [[Intake fractions of PM]]: Data on the intake fractions of airborne particulate matter for different emission sources and locations.
-* [[Exposure to PM2.5 in Finland]]: Method of estimating exposure to fine particles (PM2.5) in the Finnish population.
-* [[Emission assessment of small-scale energy production in the Helsinki metropolitan area]]: Assessment evaluating the greenhouse gas emissions and emission trading costs of small-scale (output less than 50 MW) energy production in the Helsinki metropolitan area.
-* [[OpasnetUtils/Drafts]]
-{| {{prettytable}} class="wikitable collapsible collapsed"
-|+'''Variables in the assessment model
-! Variable || Measure || Indices
 |----
-| colspan="3"| buildings (from the [[Building model]])
+| emissionFactors [[Emission factors for burning processes]]
+| emissions per unit of energy produced (g / J or similar unit) <ref>[http://www.doria.fi/bitstream/handle/10024/94404/isbn9789522655578.pdf?sequence=2 Sanni Väisänen]: Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat — Estimation of various production chains by using LCA methodology</ref>
+| Exposure_agent,  Emission_height.
 |----
-| stockBuildings [[Building stock in Helsinki#Building stock|From statistics]] [http://en.opasnet.org/w/Special:Opasnet_Base?id=op_en7115.stock_details data in OB]
+| colspan="3"|exposure is in ug/m3 in ambient air average concentration. [[Exposure to PM2.5 in Finland]]
-| Building stock in floor-m2 at given timepoints: Stock details in Opasnet Base
-| Time (Valmistusmisaika), City_area (Sijainti), Building (Käyttötarkoitus), Heating (Lämmitystapa, Polttoaine), Efficiency (Varusteena koneellinen ilmanvaihto, Perusparannus)
 |----
-| heatingShares = 1 [[Building stock in Helsinki]]
+| emissions
-| Fractions of heating types. Should sum up to 1 within each group defined by optional indices. Data is already in stockBuildings, so we use 1 here.
+| (from the model; see above) is in ton /a
-| Heating, Time, Building
+| Time, City_area, Exposure_agent, Emission_height.
 |----
-| efficiencyShares [[Energy use of buildings#Energy efficiency in heating|Energy efficiency of new buildings in the future]]. [http://en.opasnet.org/w/Special:Opasnet_Base?id=op_en5488.energy_efficiency_of_new_buildings_in_the_future OpasnetBase]
+| iF (generic data but depends on population density, emission height etc) [[Intake fractions of PM]].
-| Fraction of energy efficiency types of new buildings in the future. Should sum up to 1 for each group defined by other indices. Fractions of heating and energy efficiency: from Facta data and [https://www.energiatodistusrekisteri.fi/public_html?command=browse&s=todistushaku_section Energiatodistusrekisteri] This can be used to derive energy classes and the U values for buildings in Helsinki.
+| conc (g /m3) * pop (#) * BR (m3 /s) / emis (g /s) <=> conc = emis * iF / BR / pop # conc is the exposure
-| Efficiency, Time, Building.
+{{attack|# |This is NOT the right page, but there are some pages such as [[Exposure to PM2.5 in Finland]] that should be merged. |--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 15:45, 17 May 2015 (UTC)}} {{attack|# |Rather use iFs based on Piltti?|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 12:22, 24 May 2015 (UTC)}}
+** Fate and dispersion: is embedded in intake fractions and is not separately needed. Rename?
+** Time use and activity: not explicitly needed? Remove? With transport it would make a difference.
+| Emission_height, Pollutant, Area
 |----
-| changeBuildings = stockBuildings * 0.02 /a
+| population [[Population of Helsinki metropolitan area]]
-| Construction or demolition rate as floor-m2 at given timepoints. Implemented in code.
+| Amount of population exposed. Data from Statistics Finland?
-| Time, Efficiency, Heating. changeBuildings should have all indices in stockBuildings, heatingShares, and efficiencyShares.
+| Time, Area, Age, Sex
 |----
-| renovationShares [[Building stock in Helsinki#Renovations|Guesstimate]] [http://en.opasnet.org/w/Special:Opasnet_Base?id=op_en7115.popularity_of_renovation_types data in OB]
+| ambientTemperature: [[Weather in Helsinki]] (also contains solar radiation and wind patterns)
-| Fraction of renovation types when renovation is done. Should sum to 1 for each group defined by other indices.
+| Ambient temperatures. {{comment|# |We actually need hourly temperatures. How do we manage this in the model? Temperatures should match indices Sun, Wind, Hour, and Month.|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 17:27, 24 May 2015 (UTC)}}
-| Renovation, Startyear. Startyear is the time when the renovation is done, and it must be different than the Time index.
+|
+|----
+|colspan="3"| [[Health impact assessment]]
 |----
-| renovationRate [[Building stock in Helsinki#Renovations|Guesstimate]] [http://en.opasnet.org/w/Special:Opasnet_Base?id=op_en7115.fraction_of_houses_renovated_per_year data in OB]
+| exposure (see above)
-| Rate of renovation (fraction per time unit).
+|
-| Age (the time difference between construction and renovation, i.e. Startyear - Time for each building).
+|
 |----
-| obstime Ten-year intervals 1980 - 2060.
+| [[Disease risk]]
-| one-column data.frame about the years to be used in output.
+| Incidences of diseases of interest. {{comment|# |What if prevalence values would be more useful?|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 17:27, 24 May 2015 (UTC)}}
-| Startyear
+| Age, Sex, Response
 |----
-| heating_before = FALSE
+| ERF [[Exposure-response function]]
-| If TRUE, heatingShares contains data and is added to buildings before obstime.
+| Exposure-response functions of all relevant exposure agents
 |
 |----
-| efficiency_before = TRUE
+| [[Burden of disease in Finland]]
-| If TRUE, efficiencyShares contains data and is added to buildings before obstime.
+|
 |
 |----
-|colspan="3"| heatingEnergy: Energy consumption from the [[Building model]] {{defend|# |Move code to [[Heating consumption of buildings]]|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 03:48, 9 June 2015 (UTC)}}
+| Other human health effects, indoor environment, and well-being is not probably handled here? Remove or keep as an important placeholder?
+|
+|
 |----
-| energyUse [[Energy use of buildings#Baseline energy consumption|Baseline energy consumption]] [http://en.opasnet.org/w/Special:Opasnet_Base?id=op_en5488.baseline_energy_consumption_per_area_unit] {{defend|# |Update this to use U values and ambient temperatures.|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 12:22, 24 May 2015 (UTC)}} Also use [[Building stock in Helsinki]], Table 6.
+| Costs: Where do we get this? From Vuosaari YVA? From <ref name="professors"/>?
-| Energy consumption per floor area (kWh / m2 /a) (U: W/m2/K * T: K * Area: m2)
+| Costs of activities (energy production mainly) but also health impacts.
-| Building, Heating.
-|----
-| efficiencyRatio  [[Energy use of buildings#Energy efficiency in heating]]) [http://en.opasnet.org/w/Special:Opasnet_Base?id=op_en5488.energy_use_by_energy_class_of_building Opasnet Base]
-| Relative energy consumption compared with the efficiency group Old.
-| Efficiency.
-|----
-| renovationRatio [[Energy use of buildings#Impact of renovations|Impact of renovations]] [http://en.opasnet.org/w/Special:Opasnet_Base?id=op_en5488.energy_saving_potential_of_different_renovations Opasnet Base]
-| Relative energy consumption compared with the Renovation location None.
-| Renovation.
-|----
-|colspan="3"| energyBalance [[Energy balance in Helsinki]]
-|----
-| heatingEnergy (see above)
-|
-|
-|----
-| consumerElectricity: from [[Energy use of buildings#Baseline energy consumption|Baseline energy consumption]] [http://en.opasnet.org/w/Special:Opasnet_Base?id=op_en5488.baseline_energy_consumption_per_area_unit Opasnet Base] based on floor area
-| Amount of consumer electricity use (MW) calculated based on either amount of people or floor-m2
-| Hour (time of day), others?
-|----
-| otherEnergy [[Helsinki energy consumption]]
-| Other energy consumption (industry, transport, other): from statistics of Helsinki [http://www.helsinginymparistotilasto.fi/ Helsingin ympäristötilasto]. [[Thermal energy need in Helsinki Metropolitan Area]]
 | Time
-|----
+|}
-| windElectricity Check <ref name="professors"/> about possibilities and Wind pattern [http://www.tuuliatlas.fi/fi/index.html Tuuliatlas] about useful wind mill sites in Helsinki.
-| Amount of wind power produced (MW) by wind type. We also need amounts of hours with different wind patterns per year.
+=== Analyses ===
-| Hour (do we need this, is wind power production dependent on time of day?), Weather (temperature and weather types), maybe Wind (Windy, Normal, Calm)
-|----
+* Cost-benefit analysis of different options. Costs considered: capital and operational costs of energy production, climate costs (CO2e emissions converted to euros), health (DALYs converted to euros).
-| solarElectricity
+* Total capacity availability and feasibility (applies especially to decentralised option).
-| Amount of solar energy produced (MW) in certain conditions. Data source?
+* Temporal heat and power demand and supply (hourly resolution).
-| Hour, Month, Weather, maybe Sun (Sunny, Cloudy, Dark) {{comment|# |Have to decide which of these is used.|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 17:27, 24 May 2015 (UTC)}}
-|----
+=== Indices ===
-| airpumpHeat
-| This means air-driven heat pumps installed in individual homes and run based on ambient temperature. The efficiency of air heat pump reduces with very cold weather. We assume that the heat pump will be used for cooling as well, if it is installed. (We assume that cooling is not used in apartments without heat pump; cooling is otherwise only used in offices etc).
+* Temporal:  Time (5-year observation periods)
-| Weather, Hour, maybe Temperature. {{comment|# |Should we have index for day/night so that daily storage of heat/cool can be assessed?|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 17:27, 24 May 2015 (UTC)}}
+* Temperature (3-degree-Celsius intervals for ambient daily average temperatures. It is assumed that heating is not needed above 17 C and cooling is not needed below 24 C. Hot water need is independent of ambient temperature.
-|----
+* Decisions: EnergySavingPolicy contains options that reduce the energy need of the building stock and other consumption. PlantPolicy contains options about which energy plants to build (or demolish).
-| energyProduction [[Helsinki energy production]]
+* Stakeholder (Citizen, City, Helen, Finland)
-| Energy production based on need: basically, we need to know a) what power plants exist, b) what fuels they use and what (heat, power or both) they produce, c) what emissions they produce, d) what is their maximum capacity, e) at what demand level are they turned on.
+* Spatial: City area (summed up after energy need)
-* [[District heating production units in Helsinki metropolitan area]]
+* Health: Response (any disease that is linked to Exposure agents emitted)
-* Geothermal heat pumps: this means both individual geothermal heat pumps and district heat pumps for heating and cooling, such as the one in Katri Vala's park. Its efficiency is not reduced with very cold weather. See [https://www.helen.fi/en/households/services/district-cooling/ District cooling by Helen], [[:en:Absorption refrigerator]], [[:en:District cooling]].
+* Emission, exposure: Pollutant or Exposure agent (any agent that is emitted by energy production)
-|
+* Energy production: Burner (type of burner used in the facility where energy is produced), Fuel (type of fuel used in energy production), Heating (type of heating in the building).
-|----
+* Buildings: Building [use type], Heating, Constructed, City area, Renovation, Efficiency.
-| colspan="3"| emissions (from the model) (emissions in mass per time) Emissions of energy production (ton/a). [[Emission assessment of small-scale energy production in the Helsinki metropolitan area]]
+</noinclude>
-|----
-| energyBalance (from the model; see above)
+=== Calculations ===
-|
-|
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=N5yJnBbYuXKXcWAO Model run 25.10.2015]. Archived version with several updates
-|----
-| fuelShares [[Energy balance in Helsinki]]<ref name="judl">Jáchym Judl, Sirkka Koskela, Timo Korpela, Niko Karvosenoja, Anna Häyrinen, Jari Rantsi. Net environmental impacts of low-share wood pellet co-combustion in an existing coal-fired CHP (combined heat and power) production in Helsinki, Finland. Energy 77 (2014) 844-851. {{doi|10.1016/j.energy.2014.09.068}}</ref>
+<rcode name="intermediates" label="Store intermediates (for developers only)" graphics=1 store=1>
-| Tells how much of fuel is used for a certain neating energy need. {{comment|# |Should this be on page [[Energy balance in Helsinki]] or, like in Kuopio and Basel, a separate table?|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 17:27, 24 May 2015 (UTC)}}
+## This code is Op_en7237/intermediates on page [[Helsinki energy decision 2015]]
-{{comment|# | Use shares of different fuels. Currently this data is on page [[Emission factors for burning processes]] Table Fuel use in different heating types. However, this is clearly case-specific data and should be on a case-specific page. This should be done retrospectively to Kuopio and Basel as well.|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 12:22, 24 May 2015 (UTC)}}
+library(OpasnetUtils)
-| Fuel_type.
+library(ggplot2)
-|----
+library(rgdal)
-| emissionFactors [[Emission factors for burning processes]]
+library(maptools)
-| emissions per unit of energy produced (g / J or similar unit) <ref>[http://www.doria.fi/bitstream/handle/10024/94404/isbn9789522655578.pdf?sequence=2 Sanni Väisänen]: Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat — Estimation of various production chains by using LCA methodology</ref>
+library(RColorBrewer)
-| Exposure_agent,  Emission_height.
+library(classInt)
-|----
+library(RgoogleMaps)
-| colspan="3"|exposure is in ug/m3 in ambient air average concentration. [[Exposure to PM2.5 in Finland]]
-|----
+### Technical parameters
-| emissions
-| (from the model; see above) is in ton /a
+openv.setN(0) # use medians instead of whole sampled distributions
-| Time, City_area, Exposure_agent, Emission_height.
+BS <- 24 # base_size = font size in graphs
-|----
+figstofile <- FALSE
-| iF (generic data but depends on population density, emission height etc) [[Intake fractions of PM]].
+saveobjects <- FALSE
-| conc (g /m3) * pop (#) * BR (m3 /s) / emis (g /s) <=> conc = emis * iF / BR / pop # conc is the exposure
-{{attack|# |This is NOT the right page, but there are some pages such as [[Exposure to PM2.5 in Finland]] that should be merged. |--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 15:45, 17 May 2015 (UTC)}} {{attack|# |Rather use iFs based on Piltti?|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 12:22, 24 May 2015 (UTC)}}
+########################## Case-specific data and submodels
-** Fate and dispersion: is embedded in intake fractions and is not separately needed. Rename?
-** Time use and activity: not explicitly needed? Remove? With transport it would make a difference.
+### Decisions
-| Emission_height, Pollutant, Area
+decisions <- opbase.data("Op_en7237", subset = "Decisions") # [[Helsinki energy decision 2015]]
-|----
-| population [[Population of Helsinki metropolitan area]]
+oprint(decisions)
-| Amount of population exposed. Data from Statistics Finland?
-| Time, Area, Age, Sex
+### Energy production in Helsinki
-|----
-| ambientTemperature: [[Weather in Helsinki]] (also contains solar radiation and wind patterns)
+objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] findrest
-| Ambient temperatures. {{comment|# |We actually need hourly temperatures. How do we manage this in the model? Temperatures should match indices Sun, Wind, Hour, and Month.|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 17:27, 24 May 2015 (UTC)}}
+objects.latest("Op_en7311", code_name = "energyProcess") # [[Helsinki energy production]]
-|
+objects.latest("Op_en7311", code_name = "plantParameters") # [[Helsinki energy production]]
-|----
+objects.latest("Op_en4151", code_name = "fuelPrice") # [[Prices of fuels in heat production]]
-|colspan="3"| [[Health impact assessment]]
-|----
+objects.latest("Op_en5141", code_name = "EnergyNetworkOptim") # [[Energy balance]] incl EnergyNetworkCost
-| exposure (see above)
-|
+### Building data in Helsinki
-|
-|----
+# Observation years must be defined for an assessment.
-| [[Disease risk]]
+obstime <- Ovariable("obstime", data = data.frame(Obsyear = factor(seq(1985, 2065, 10), ordered = TRUE), Result = 1))
-| Incidences of diseases of interest. {{comment|# |What if prevalence values would be more useful?|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 17:27, 24 May 2015 (UTC)}}
+heatingShares <- 1 # Shares of different heating types in the building stock (already in the building data)
-| Age, Sex, Response
+heating_before <- FALSE # Should heatingShares be calculated before renovate and timepoints (or after)?
-|----
+efficiency_before <- TRUE # Should efficiencyShares be calculated before renovate and timepoints (or after)?
-| ERF [[Exposure-response function]]
-| Exposure-response functions of all relevant exposure agents
+objects.latest("Op_en7115", code_name = "stockBuildings") # [[Building stock in Helsinki]]
-|
+objects.latest("Op_en7115", code_name = "changeBuildings") # [[Building stock in Helsinki]]
-|----
+objects.latest("Op_en7115", code_name = "demolitionRate") # [[Building stock in Helsinki]]
-| [[Burden of disease in Finland]]
+objects.latest("Op_en7115", code_name = "renovationRate") # [[Building stock in Helsinki]]
-|
+objects.latest("Op_en7115", code_name = "renovationShares") # [[Building stock in Helsinki]]
-|
+objects.latest("Op_en5488", code_name = "efficiencyShares") # [[Energy use of buildings]]
-|----
-| Other human health effects, indoor environment, and well-being is not probably handled here? Remove or keep as an important placeholder?
-|
-|
-|----
-| Costs: Where do we get this? From Vuosaari YVA? From <ref name="professors"/>?
-| Costs of activities (energy production mainly) but also health impacts.
-| Time
-|}
-=== Analyses ===
+objects.latest("Op_en6289", code_name = "buildingstest") # [[Building model]] # Generic building model.
-* Cost-benefit analysis of different options. Costs considered: capital and operational costs of energy production, climate costs (CO2e emissions converted to euros), health (DALYs converted to euros).
+## Energy use
-* Total capacity availability and feasibility (applies especially to decentralised option).
+#objects.latest("Op_en5488", code_name = "energyUseTemperature")        # [[Energy use of buildings]] temperature version of energyUse
-* Temporal heat and power demand and supply (hourly resolution).
+objects.latest("Op_en5488", code_name = "temperene")        # [[Energy use of buildings]] temperature version of energyUse
+objects.latest("Op_en5488", code_name = "nontemperene")        # [[Energy use of buildings]] temperature version of energyUse
+objects.latest("Op_en2959", code_name = "temperatures") # [[Outdoor air temperature in Finland]]
+#objects.latest("Op_en5488", code_name = "energyUseTemperature") # [[Energy use of buildings]]
+objects.latest("Op_en5488", code_name = "EnergyConsumerDemand") # [[Energy use of buildings]]
-=== Indices ===
+requiredName <- "Heat" # Name of the fuel type that must match for input and output
-* Temporal:  Time (5-year observation periods)
+### Energy and emissions
-* Temperature (3-degree-Celsius intervals for ambient daily average temperatures. It is assumed that heating is not needed above 17 C and cooling is not needed below 24 C. Hot water need is independent of ambient temperature.
+objects.latest("Op_en7311", code_name = "emissionLocationsPerPlant") # [[Helsinki energy production]] also heatingShares
-* Decisions: EnergySavingPolicy contains options that reduce the energy need of the building stock and other consumption. PlantPolicy contains options about which energy plants to build (or demolish).
+objects.latest("Op_en2791", code_name = "emissionFactors") # [[Emission factors for burning processes]]
-* Stakeholder (Citizen, City, Helen, Finland)
+objects.latest("Op_en2791", code_name = "emissionstest") # [[Emission factors for burning processes]]
-* Spatial: City area (summed up after energy need)
+objects.latest("Op_en7311", code_name = "fuelShares") # [[Helsinki energy production]]
-* Health: Response (any disease that is linked to Exposure agents emitted)
+objects.latest("Op_en7311", code_name = "nondynsupply") # [[Helsinki energy production]]
-* Emission, exposure: Pollutant or Exposure agent (any agent that is emitted by energy production)
+timelylimit <- 10000 # The largest production (MW) available at each plant (used to describe time-varying limits).
-* Energy production: Burner (type of burner used in the facility where energy is produced), Fuel (type of fuel used in energy production), Heating (type of heating in the building).
-* Buildings: Building [use type], Heating, Constructed, City area, Renovation, Efficiency.
-=== Calculations ===
+## Exposure
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=vjTT6BjPtWIkLcAl Model run 2.7.2015] with intermediate result tables but a problem with totcases.
+objects.latest("Op_en5813", code_name = "exposure") # [[Intake fractions of PM]] uses Humbert iF as default.
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=pP2FsmdaijIfcz80 Model run 5.7.2015]
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=any4I2Y1gXfWtxER Model run 6.7.2015] with corrected energyBalance model.
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=xmzcj8gvOZsYpScd Model run 7.7.2015]
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=PNSmPRBuFdxysbRO Model run 8.7.2015] with daily heat optimising
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=x0t1a3M44AeiWg4F Model run 9.7.2015] with also consumer electricity and cooling
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=KTNmnTh1Ai1FXOW2 Model run 9.7.2015] attempt to adjust decisions to plausible
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=VXYsYzhq5wihX8g0 Model run 11.7.2015] updated emission factors and decision options
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=oABWo0x0u6zsA8ZY Model run 12.7.2015]
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=ixOkV2SnjMo1Pne1 Model run 15.7.2015] the whole model works but there seems to be a unit conversion error or similar from energy balance on.
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=kZuzQaO3mnpkl8Wz Model run 15.7.2015] long-term plant activity and fuel use added
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=uqEh554tIfmDKjvW Model run 16.7.2015] used to store intermediate results for Answer code.
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=2EIjgeJ0OnJjfJcZ New model initial run 23.7.2015]
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=IxmleCN7hW80URAd Model run 24.7.2015]
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=GQGqRQUwrTX7lzzi Model run 27.7.2015]
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=ejNvy1EscdzooTxP Model run 27.7.2015] with shorter names and clearer graphs.
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=9PE7a4B1mBhUjlcR Model run 29.7.2015]
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=6KeCs0rS7dpdrtHQ Model run 14.8.2015] Total construction corrected but oil heating not yet. Objects saved.
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=yc7vpnJErCPYLoSJ Model run 16.8.2016] With first cost estimates
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=HrRkCq1EyAd0V7bj Model run 16.8.2015] Better cost estimates. Objects stored.
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=POPfXqpcyS9s7Adf Model run 17.8.2015] Incremental oil heating conversion to geothermal implemented.
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=5uDBil9PcGEsv9LJ Model run 29.8.2015] Fixed plot scaling
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=goJdvrDzXp3Toedb Model run 2.9.2015] with updated process and price parameters.
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=NTkgZ2NfWGeQhdMM Model run 4.9.2015] BAU and Helen's bio decision updated.
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=8hJVMdh8aAy1E0sV Model run 5.9.2015] with technical improvements
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=wCUnFoiRLYePywae] some bugs fixed, no the whole model works
-Name intermediates temporarily removed so than nobody accidentally runs a version that does not store objects.
+## Health assessment
-<rcode graphics=1 store=1>
+objects.latest('Op_en2261', code_name = 'totcases') # [[Health impact assessment]] totcases and dependencies.
-## This code is Op_en7237/intermediates on page [[Helsinki energy decision 2015]]
+objects.latest('Op_en5461', code_name = 'DALYs') # [[Climate change policies and health in Kuopio]] DALYs, DW, L
-library(OpasnetUtils)
-library(ggplot2)
-library(rgdal)
-library(maptools)
-library(RColorBrewer)
-library(classInt)
-library(RgoogleMaps)
-### Technical parameters
+population <- 623732 # Contains only the Helsinki city, i.e. assumes no exposure outside city. (Wikipedia)
+# Note: the population size does NOT affect the health impact if the exposure-response function in linear.
+# However, it DOES affect exposure estimates.
-openv.setN(0) # use medians instead of whole sampled distributions
+# DALYshortcut is for a quicker health impact calculations, as it directly uses the Helsinki conditions.
-BS <- 24 # base_size = font size in graphs
-figstofile <- FALSE
-saveobjects <- FALSE
-########################## Case-specific data and submodels
+objects.latest("Op_en7237", code_name = "DALYshortcut") # [[Helsinki energy decision 2015]]
-### Decisions
+objects.latest("Op_en7379", code_name = "externalities") # [[External cost]]
-decisions <- opbase.data("Op_en7237", subset = "Decisions") # [[Helsinki energy decision 2015]]
+objects.latest("Op_en3283", code_name = "totalCost") # [[Economic impacts]]
+objects.latest("Op_en3283", code_name = "EAC") # [[Economic impacts]]
-oprint(decisions)
+################################# Actual model and some intermediate processing.
-### Energy production in Helsinki
+DecisionTableParser(decisions)
+# Remove previous decisions, if any.
-objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] findrest
+forgetDecisions()
-objects.latest("Op_en7311", code_name = "energyProcess") # [[Helsinki energy production]]
-objects.latest("Op_en7311", code_name = "plantParameters") # [[Helsinki energy production]]
-objects.latest("Op_en4151", code_name = "fuelPrice") # [[Prices of fuels in heat production]]
-objects.latest("Op_en5141", code_name = "EnergyNetworkOptim") # [[Energy balance]] incl EnergyNetworkCost
+renovationRate <- EvalOutput(renovationRate) * 10 # Rates for 10-year periods
-### Building data in Helsinki
+buildings <- EvalOutput(buildings)
-# Observation years must be defined for an assessment.
+buildings$EnergySavingPolicy <- factor(
-obstime <- Ovariable("obstime", data = data.frame(Obsyear = factor(seq(1985, 2065, 10), ordered = TRUE), Result = 1))
+		buildings$EnergySavingPolicy,
-heatingShares <- 1 # Shares of different heating types in the building stock (already in the building data)
+		levels = c("BAU", "Energy saving moderate", "Energy saving total", "WWF energy saving"),
-heating_before <- FALSE # Should heatingShares be calculated before renovate and timepoints (or after)?
+		ordered = TRUE
-efficiency_before <- TRUE # Should efficiencyShares be calculated before renovate and timepoints (or after)?
+)
-objects.latest("Op_en7115", code_name = "stockBuildings") # [[Building stock in Helsinki]]
+EnergyConsumerDemand <- EvalOutput(EnergyConsumerDemand)
-objects.latest("Op_en7115", code_name = "changeBuildings") # [[Building stock in Helsinki]]
-objects.latest("Op_en7115", code_name = "renovationRate") # [[Building stock in Helsinki]]
-objects.latest("Op_en7115", code_name = "renovationShares") # [[Building stock in Helsinki]]
-objects.latest("Op_en5488", code_name = "efficiencyShares") # [[Energy use of buildings]]
-objects.latest("Op_en6289", code_name = "buildingstest") # [[Building model]] # Generic building model.
+EnergyNetworkDemand <- EvalOutput(EnergyNetworkDemand)
-## Energy use
+EnergyNetworkDemand@output <- rbind(
-#objects.latest("Op_en5488", code_name = "energyUseTemperature")        # [[Energy use of buildings]] temperature version of energyUse
+	EnergyNetworkDemand@output,
-objects.latest("Op_en5488", code_name = "temperene")        # [[Energy use of buildings]] temperature version of energyUse
+	data.frame(
-objects.latest("Op_en5488", code_name = "nontemperene")        # [[Energy use of buildings]] temperature version of energyUse
+		Time = rep(c(2025, 2035, 2045, 2055, 2065), each = 4),
-objects.latest("Op_en7317", code_name = "temperatures") # [[Helsinki energy consumption]]
+		EnergySavingPolicy = rep(c("BAU", "Energy saving moderate", "Energy saving total", "WWF energy saving"), times = 5),
-#objects.latest("Op_en5488", code_name = "energyUseTemperature") # [[Energy use of buildings]]
+		Temperature = "(-18,-15]",
-objects.latest("Op_en5488", code_name = "EnergyConsumerDemand") # [[Energy use of buildings]]
+		EnergyConsumerDemandSource = "Formula",
+		EnergyConsumerDemandTotalSource = "Formula",
+		Fuel = "Cooling",
+		fuelSharesSource = "Formula",
+		EnergyNetworkDemandResult = 0,
+		EnergyNetworkDemandSource = "Formula"
+	)
+)
-requiredName <- "Heat" # Name of the fuel type that must match for input and output
+########################### SAVE OBJECTS
-### Energy and emissions
+if(saveobjects) {
-objects.latest("Op_en7311", code_name = "emissionLocationsPerPlant") # [[Helsinki energy production]] also heatingShares
-objects.latest("Op_en2791", code_name = "emissionFactors") # [[Emission factors for burning processes]]
+	# Clean decisions and previous results not wanted/needed by the half-model
-objects.latest("Op_en2791", code_name = "emissionstest") # [[Emission factors for burning processes]]
-objects.latest("Op_en7311", code_name = "fuelShares") # [[Helsinki energy production]]
+	energyProcess@output <- data.frame()
-objects.latest("Op_en7311", code_name = "nondynsupply") # [[Helsinki energy production]]
+	plantParameters@output <- data.frame()
-timelylimit <- 10000 # The largest production (MW) available at each plant (used to describe time-varying limits).
+	EnergyNetworkOptim@output <- data.frame()
+	#EnergyConsumerDemand@output <- data.frame()
-## Exposure
+	totcases@output <- data.frame()
+	DALYs@output <- data.frame()
+	exposure@output <- data.frame()
+	rm(saveobjects, dictionary) # Remove technical objects that may be updated independently of the model
+	objects.store(list = ls()) # Save all objects of the global environment.
+	cat("All objects stored for later use:\n", paste(ls(), collapse = ", "), "\n")
+}
-objects.latest("Op_en5813", code_name = "exposure") # [[Intake fractions of PM]] uses Humbert iF as default.
+############################ Output tables and graphs
-## Health assessment
+fuelUse <- EvalOutput(fuelUse)
-objects.latest('Op_en2261', code_name = 'totcases') # [[Health impact assessment]] totcases and dependencies.
+fuelUse$Fuel <- factor(
-objects.latest('Op_en5461', code_name = 'DALYs') # [[Climate change policies and health in Kuopio]] DALYs, DW, L
+	fuelUse$Fuel, levels = c(
+		"Biofuel",
+		"Coal",
+		"Fuel oil",
+		"Gas",
+		"Light oil",
+		"Wood",
+		"Electricity",
+		"Electricity_taxed",
+		"Heat",
+		"Cooling"
+	), ordered = TRUE
+)
-population <- 623732 # Contains only the Helsinki city, i.e. assumes no exposure outside city. (Wikipedia)
+# Calculate exposure based on average iF.
-# Note: the population size does NOT affect the health impact if the exposure-response function in linear.
+exposure <- EvalOutput(exposure)
-# However, it DOES affect exposure estimates.
+exposure <- exposure[exposure$Area == "Average" , ]
+exposure <- oapply(exposure, cols = c("Plant", "Emission_site", "Emission_height", "Area"), FUN = sum)
+totcases <- EvalOutput(totcases)
+DALYs <- EvalOutput(DALYs)
+EnergyNetworkCost <- EvalOutput(EnergyNetworkCost)
-# DALYshortcut is for a quicker health impact calculations, as it directly uses the Helsinki conditions.
+cat("Total DALYs/a by different combinations of policy options.\n")
-objects.latest("Op_en7237", code_name = "DALYshortcut") # [[Helsinki energy decision 2015]]
+temp <- DALYs[as.character(DALYs$Time) %in% c("2015", "2035", "2065") & DALYs$Response == "Total mortality" , ]
-objects.latest("Op_en7379", code_name = "externalities") # [[External cost]]
+oprint(
-objects.latest("Op_en3283", code_name = "totalCost") # [[Economic impacts]]
+		oapply(temp, INDEX = c("Time", "EnergySavingPolicy", "PlantPolicy"), FUN = sum),
-objects.latest("Op_en3283", code_name = "EAC") # [[Economic impacts]]
+		caption = "Table 1: Total DALYs/a by different combinations of policy options.",
+		caption.placement = "top",
+		include.rownames = FALSE
+)
-################################# Actual model and some intermediate processing.
+####################### Post-processing and graphs
-DecisionTableParser(decisions)
+bui <- oapply(buildings * 1E-6, cols = c("City_area", "buildingsSource"), FUN = sum)
+bui <- truncateIndex(bui, cols = "Heating", bins = 4)
-# Remove previous decisions, if any.
+oggplot(bui[bui$EnergySavingPolicy == "BAU" , ], x = "Time", fill = "Heating", binwidth = 5) +
+		labs(
-forgetDecisions()
+				title = "Building stock in Helsinki by heating",
+				y = "Floor area (M m2)"
+		)
-renovationRate <- EvalOutput(renovationRate) * 10 # Rates for 10-year periods
+oggplot(bui, x = "Time", fill = "Efficiency", binwidth = 5) +
+		facet_grid(. ~ EnergySavingPolicy) +
+		labs(
+				title = "Building stock in Helsinki by efficiency policy",
+				y = "Floor area (M m2)"
+		)
-buildings <- EvalOutput(buildings)
+oggplot(bui, x = "Time", fill = "Renovation", binwidth = 5) +
+		facet_grid(. ~ EnergySavingPolicy) +
+		labs(
+				title = "Building stock in Helsinki by renovation policy",
+				y = "Floor area (M m2)"
+		)
-buildings$EnergySavingPolicy <- factor(
+oggplot(bui[bui$EnergySavingPolicy == "BAU" , ], x = "Time", fill = "Building", binwidth = 5) +
-		buildings$EnergySavingPolicy,
+		labs(
-		levels = c("BAU", "Energy saving moderate", "Energy saving total"),
+				title = "Building stock in Helsinki",
-		ordered = TRUE
+				y = "Floor area (M m2)"
-)
+		)
-EnergyConsumerDemand <- EvalOutput(EnergyConsumerDemand)
+oggplot(buildings, x = "Time", fill = "Efficiency", binwidth = 5)+
+		facet_grid(EnergySavingPolicy ~ Renovation) +
+		labs(
+				title = "Renovation of buildings by policy and efficiency",
+				y = "Floor area (M m2)"
+		)
-########################### SAVE OBJECTS
+emissions$Time <- as.numeric(as.character(emissions$Time))
-if(saveobjects) {
+# Plot energy need and emissions
-	# Clean decisions and previous results not wanted/needed by the half-model
-	energyProcess@output <- data.frame()
-	plantParameters@output <- data.frame()
-	EnergyNetworkOptim@output <- data.frame()
-	#EnergyConsumerDemand@output <- data.frame()
-	totcases@output <- data.frame()
-	DALYs@output <- data.frame()
-	exposure@output <- data.frame()
-	objects.store(list = ls()) # Save all objects of the global environment.
-	cat("All objects of the global environment are stored.\n")
-}
-############################ Output tables and graphs
+hea <- EnergyConsumerDemand * temperdays * 24 * 1E-9 # From W -> GWh /a.
+hea <- hea[hea$Consumable == "Heating" , ]
+hea <- oapply(hea, cols = c("City_area", "buildingsSource"), FUN = sum)
+hea <- truncateIndex(hea, cols = "Heating", bins = 4)
-fuelUse <- EvalOutput(fuelUse)
+oggplot(hea, x = "Time", fill = "Renovation", binwidth = 5) +
+		facet_wrap( ~ EnergySavingPolicy) +
+		labs(
+				title = "Energy used in heating in Helsinki",
+				x = "Time",
+				y = "Heating energy (GWh /a)"
+		)
+eb <-EnergyNetworkOptim[EnergyNetworkOptim$Process_variable_type == "Activity",]
+colnames(eb@output)[colnames(eb@output) == "Process_variable_name"] <- "Plant"
+eb$Process_variable_type <- NULL
-fuelUse$Fuel <- factor(
+ebtemp <- eb[eb$Time %in% c("2035") & eb$EnergySavingPolicy == "BAU" & eb$PlantPolicy == "BAU" , ]
-	fuelUse$Fuel, levels = c(
+ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 7)
-		"Biofuel",
-		"Coal",
-		"Fuel oil",
-		"Gas",
-		"Light oil",
-		"Wood",
-		"Electricity",
-		"Electricity_taxed",
-		"Heat",
-		"Cooling"
-	), ordered = TRUE
-)
-# Calculate exposure based on average iF.
+oggplot(ebtemp, x = "Plant", fill = "Plant") + facet_wrap( ~ Temperature) +
-exposure <- EvalOutput(exposure)
+		theme(axis.text.x = element_blank()) + # Turn text and adjust to right
-exposure <- exposure[exposure$Area == "Average" , ]
+		labs(
-exposure <- oapply(exposure, cols = c("Plant", "Emission_site", "Emission_height", "Area"), FUN = sum)
+				title = "Power plant activity by temperature daily optim \nPlant policy = BAU, Year = 2035",
-totcases <- EvalOutput(totcases)
+				y = "Power output daily average (MW)"
-DALYs <- EvalOutput(DALYs)
+		)
-EnergyNetworkCost <- EvalOutput(EnergyNetworkCost)
-#oprint(head(buildings@output))
+fu <- fuelUse
-#oprint(head(EnergyConsumerDemand@output))
+fu$Burner <- NULL
-#oprint(head(EnergyNetworkOptim@output))
+fu <- fu / 3.6E+6 # From MJ/a -> GWh/a
-#oprint(head(emissions@output))
+fu$Time <- as.numeric(as.character(fu$Time))
-#oprint(head(exposure@output))
-#oprint(head(totcases@output))
-#oprint(head(DALYs@output))
-cat("Total DALYs/a by different combinations of policy options.\n")
+futemp <- fu[fu$Time %in% c("2015", "2035", "2065") & fu$PlantPolicy == "BAU" , ]
+futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) * -1
+oggplot(futemp, x = "Fuel", fill = "Plant", turnx = TRUE) + facet_grid(Time ~ EnergySavingPolicy) +
+		labs(
+				title = "Energy commodity flows \n Plant policy = BAU",
+				y = "Total annual energy (GWh/a)"
+		)
-temp <- DALYs[as.character(DALYs$Time) %in% c("2015", "2035", "2065") & DALYs$Response == "Total mortality" , ]
+futemp <- fu[fu$Fuel %in% c("Heat") , ]
+futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1
-oprint(
+oggplot(futemp, x = "Time", fill = "Plant", binwidth = 5) + facet_grid(EnergySavingPolicy ~ PlantPolicy) +
-		oapply(temp, INDEX = c("Time", "EnergySavingPolicy", "PlantPolicy"), FUN = sum),
-		caption = "Table 1: Total DALYs/a by different combinations of policy options.",
-		caption.placement = "top",
-		include.rownames = FALSE
-)
-####################### Post-processing and graphs
-bui <- oapply(buildings * 1E-6, cols = c("City_area", "buildingsSource"), FUN = sum)
-bui <- truncateIndex(bui, cols = "Heating", bins = 4)
-oggplot(bui[bui$EnergySavingPolicy == "BAU" , ], x = "Time", fill = "Heating", binwidth = 5) +
 		labs(
-				title = "Building stock in Helsinki by heating",
+				title = "District heat flow",
-				y = "Floor area (M m2)"
+				y = "Total annual energy (GWh/a)"
 		)
-oggplot(bui, x = "Time", fill = "Efficiency", binwidth = 5) +
+futemp <- fu[fu$Fuel %in% c("Electricity") , ]
-		facet_grid(. ~ EnergySavingPolicy) +
+futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1
+oggplot(futemp, x = "Time", fill = "Plant", binwidth = 5) + facet_grid(EnergySavingPolicy ~ PlantPolicy) +
 		labs(
-				title = "Building stock in Helsinki by efficiency policy",
+				title = "Electricity flow",
-				y = "Floor area (M m2)"
+				y = "Total annual energy (GWh/a)"
 		)
-oggplot(bui, x = "Time", fill = "Renovation", binwidth = 5) +
+oggplot(emissions[emissions$EnergySavingPolicy == "BAU" , ], x = "Time", fill = "Fuel", binwidth = 5) +
-		facet_grid(. ~ EnergySavingPolicy) +
+		facet_grid(Pollutant ~ PlantPolicy, scale = "free_y") +
 		labs(
-				title = "Building stock in Helsinki by renovation policy",
+				title = "Emissions from heating in Helsinki",
-				y = "Floor area (M m2)"
+				x = "Time (Energy saving policy = BAU)",
+				y = "Emissions (ton /a)"
 		)
-oggplot(bui[bui$EnergySavingPolicy == "BAU" , ], x = "Time", fill = "Building", binwidth = 5) +
+oggplot(exposure[exposure$Exposure_agent == "PM2.5" , ], x = "Time", fill = "Fuel", binwidth = 5) +
+		facet_grid(EnergySavingPolicy ~ PlantPolicy) +
 		labs(
-				title = "Building stock in Helsinki",
+				title = "Exposure to PM2.5 from heating in Helsinki",
-				y = "Floor area (M m2)"
+				x = "Time",
+				y = "Average PM2.5 (µg/m3)"
 		)
-oggplot(buildings, x = "Time", fill = "Efficiency", binwidth = 5)+
+oggplot(DALYs, x = "Time", fill = "Fuel", binwidth = 5) + facet_grid(EnergySavingPolicy ~ PlantPolicy) +
-		facet_grid(EnergySavingPolicy ~ Renovation) +
 		labs(
-				title = "Renovation of buildings by policy and efficiency",
+				title = "Health effects in DALYs of PM2.5 from heating in Helsinki",
-				y = "Floor area (M m2)"
+				y = "Health effects (DALY /a)"
 		)
-emissions$Time <- as.numeric(as.character(emissions$Time))
+</rcode>
+<noinclude>
-# Plot energy need and emissions
+==== Preference order ====
-hea <- EnergyConsumerDemand * temperdays * 24 * 1E-9 # From W -> GWh /a.
+'''This code should be used for new model runs. It is newer but not fully adjusted for its purpose yet.
-hea <- hea[hea$Consumable == "Heating" , ]
-hea <- oapply(hea, cols = c("City_area", "buildingsSource"), FUN = sum)
-hea <- truncateIndex(hea, cols = "Heating", bins = 4)
-oggplot(hea, x = "Time", fill = "Renovation", binwidth = 5) +
+<rcode name="preferenceorder" graphics=1>
-		facet_wrap( ~ EnergySavingPolicy) +
+# This code is Op_en7237/finalresults on page [[Helsinki energy decision 2015]]
-		labs(
+library(OpasnetUtils)
-				title = "Energy used in heating in Helsinki",
+library(ggplot2)
-				x = "Time",
-				y = "Heating energy (GWh /a)"
-		)
-eb <-EnergyNetworkOptim[EnergyNetworkOptim$Process_variable_type == "Activity",]
+openv.setN(0) # use medians instead of whole sampled distributions
-colnames(eb@output)[colnames(eb@output) == "Process_variable_name"] <- "Plant"
-eb$Process_variable_type <- NULL
-ebtemp <- eb[eb$Time %in% c("2035") & eb$EnergySavingPolicy == "BAU" & eb$PlantPolicy == "BAU" , ]
+# Download all pre-calculated inputs, e.g. building stock.
-ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 7)
+objects.latest("Op_en7237", code_name = "intermediates") # [[Helsinki energy decision 2015]]
+objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] oggplot
+objects.latest("Op_en7392", code_name = "translate") # [[OpasnetUtils/Translate]] translate
+objects.latest("Op_en7392", code_name = "dictionary") # [[OpasnetUtils/Translate]] dictionary
+BS <- 24 # base_size for graph fonts
+saveobjects <- FALSE
+language <- "Finnish"
+fi <- language == "Finnish"
+EnergyNetworkDemand <- EnergyNetworkDemand[EnergyNetworkDemand$EnergySavingPolicy == "Energy saving total" , ]
-oggplot(ebtemp, x = "Plant", fill = "Plant") + facet_wrap( ~ Temperature) +
+decisions <- opbase.data("Op_en7237", subset = "Decisions") # [[Helsinki energy decision 2015]]
-		theme(axis.text.x = element_blank()) + # Turn text and adjust to right
+decisions$Obs <- NULL
-		labs(
+temp <- decisions[decisions$Decision== "PlantPolicy",][1,]
-				title = "Power plant activity by temperature daily optim \nPlant policy = BAU, Year = 2035",
+decisions <- decisions[decisions$Decision!= "PlantPolicy" , ] # & decisions$Option == "Energy saving total" , ]
-				y = "Power output daily average (MW)"
+temp$Option <- "All1"
-		)
+temp$Variable <- "plantParameters"
+temp$Cell <- ""
+temp$Change <- "Identity"
+decisions <- rbind(decisions, temp)
-fu <- fuelUse
+allplants <- list(
-fu$Burner <- NULL
+	Loviisa2 = c(
-fu <- fu / 3.6E+6 # From MJ/a -> GWh/a
+		'Sea heat pump for cooling',
-fu$Time <- as.numeric(as.character(fu$Time))
+		'Household air conditioning',
+		'Household solar',
-futemp <- fu[fu$Time %in% c("2015", "2035", "2065") & fu$PlantPolicy == "BAU" , ]
+		"Kymijoki River's plants",
-futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) * -1
+		'Small-scale wood burning',
-oggplot(futemp, x = "Fuel", fill = "Plant", turnx = TRUE) + facet_grid(Time ~ EnergySavingPolicy) +
+		'Suvilahti power storage',
-		labs(
+		'Suvilahti solar',
-				title = "Energy commodity flows \n Plant policy = BAU",
+		'Vanhakaupunki museum',
-				y = "Total annual energy (GWh/a)"
+		'Wind mills'
-		)
+	),
+	DataAndSea3 = c(
-futemp <- fu[fu$Fuel %in% c("Heat") , ]
+		'Loviisa nuclear heat'
-futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1
+	),
-oggplot(futemp, x = "Time", fill = "Plant", binwidth = 5) + facet_grid(EnergySavingPolicy ~ PlantPolicy) +
+	NesteAndDeep4 = c(
-		labs(
+		'Data center heat',
-				title = "District heat flow",
+		'Sea heat pump',
-				y = "Total annual energy (GWh/a)"
+		'Household air heat pumps',
-		)
+		'Household geothermal heat'
+	),
-futemp <- fu[fu$Fuel %in% c("Electricity") , ]
+	Existing5 = c(
-futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1
+		'Katri Vala heat',
-oggplot(futemp, x = "Time", fill = "Plant", binwidth = 5) + facet_grid(EnergySavingPolicy ~ PlantPolicy) +
+		'Neste oil refinery heat',
-		labs(
+		'Vuosaari C biofuel',
-				title = "Electricity flow",
+		'Deep-drill heat'
-				y = "Total annual energy (GWh/a)"
+	),
-		)
+	Backup6 = c(
+		'Biofuel heat plants',
+		'CHP diesel generators',
+		'Hanasaari',
+		'Salmisaari A&B',
+		'Vuosaari A',
+		'Vuosaari B'
+	),
+	Lowcost = c(
+		'Katri Vala cooling',
+		'Kellosaari back-up plant',
+		'Small fuel oil heat plants',
+		'Small gas heat plants'
+	)
+)
-oggplot(emissions[emissions$EnergySavingPolicy == "BAU" , ], x = "Time", fill = "Fuel", binwidth = 5) +
+cheapest <- c(
-		facet_grid(Pollutant ~ PlantPolicy, scale = "free_y") +
+	'Loviisa nuclear heat',
-		labs(
+	'Data center heat',
-				title = "Emissions from heating in Helsinki",
+	'Household air heat pumps',
-				x = "Time (Energy saving policy = BAU)",
+	'Katri Vala heat',
-				y = "Emissions (ton /a)"
+	'Neste oil refinery heat',
-		)
+	'Small fuel oil heat plants'
+)
-oggplot(exposure[exposure$Exposure_agent == "PM2.5" , ], x = "Time", fill = "Fuel", binwidth = 5) +
+existing <- c( # Major existing plants are only closed at 2026 if they are closed
-		facet_grid(EnergySavingPolicy ~ PlantPolicy) +
+		"Kymijoki River's plants",
-		labs(
+		'Small-scale wood burning',
-				title = "Exposure to PM2.5 from heating in Helsinki",
+		'Katri Vala heat',
-				x = "Time",
+		'Hanasaari',
-				y = "Average PM2.5 (µg/m3)"
+		'Salmisaari A&B',
-		)
+		'Vuosaari A',
+		'Vuosaari B',
+		'Kellosaari back-up plant',
+		'Small fuel oil heat plants',
+		'Small gas heat plants'
+)
+for(i in 1:6) {
+	shutdown <- unlist(allplants[1:i])
+	if(i == 6) shutdown <- unlist(allplants)[!unlist(allplants) %in% cheapest]
-oggplot(DALYs, x = "Time", fill = "Fuel", binwidth = 5) + facet_grid(EnergySavingPolicy ~ PlantPolicy) +
+temp <- data.frame(
-		labs(
+	Decision_maker = "Helen",
-				title = "Health effects in DALYs of PM2.5 from heating in Helsinki",
+	Decision = "PlantPolicy",
-				y = "Health effects (DALY /a)"
+	Option = names(allplants[i]),
-		)
+	Variable = c("plantParameters"),
+	Cell = c(
+		paste("Plant:", paste(shutdown[!shutdown %in% existing], collapse = ","), ";Parameter:Max,Min,Investment cost;Time:>=2016", sep = ""),
+		paste("Plant:", paste(shutdown[shutdown %in% existing], collapse = ","), ";Parameter:Max,Min,Investment cost;Time:>=2026", sep = "")
+	),
+	Change = c("Replace"),
+	Unit = NA,
+	Result = 0
+)
+decisions <- rbind(decisions, temp)
+}
-</rcode>
+DecisionTableParser(decisions)
-==== Preference order ====
+oprint(decisions)
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=LwgipxX9IFCdAIRI Model run 8.9.2015]
+oprint(data.frame(
-* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=6XrzKix5Cbp0Ol9S Model run 9.9.2015] with much more graphs
+	Shutdown_order = c(
+		"Plants are shut down in this order (most cost efficient first):",
-<rcode graphics=1>
+		paste(allplants[[1]], collapse = ", "),
-library(OpasnetUtils)
+		paste(allplants[[2]], collapse = ", "),
-library(ggplot2)
+		paste(allplants[[3]], collapse = ", "),
+		paste(allplants[[4]], collapse = ", "),
+		paste(allplants[[5]], collapse = ", "),
+		paste(allplants[[6]], collapse = ", "),
+		paste(c("All but the cheapest", cheapest), collapse = ", ")
+	)
+))
+#EnergyNetworkDemand <- EvalOutput(EnergyNetworkDemand)
+oprint(oapply(EnergyNetworkDemand, INDEX = c("Time", "Fuel"), FUN = length)@output)
-openv.setN(0) # use medians instead of whole sampled distributions
+oprint(EnergyNetworkDemand@output)
-BS <- 14 # base_size for graph fonts
-objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]]
-saveobjects <- FALSE
-# Download all pre-calculated inputs, e.g. building stock.
+EnergyNetworkOptim <- EvalOutput(EnergyNetworkOptim)
-#objects.latest("Op_en7237", code_name = "intermediates") # [[Helsinki energy decision 2015]]
-decisions <- opbase.data("Op_en7237", subset = "Decisions") # [[Helsinki energy decision 2015]]
+#EnergyNetworkDemand@output <- rbind(
-decisions$Obs <- NULL
+#	EnergyNetworkDemand@output,
-temp <- decisions[decisions$Decision== "PlantPolicy",][1,]
+#	data.frame(
-decisions <- decisions[decisions$Decision!= "PlantPolicy" , ] # & decisions$Option == "Energy saving total" , ]
+#		Time = rep(c(2025, 2035, 2045, 2055, 2065), each = 4),
-temp$Option <- "All1"
+#		EnergySavingPolicy = rep(c("BAU", "Energy saving moderate", "Energy saving total", "WWF energy saving"), times = 5),
-temp$Variable <- "plantParameters"
+#		Temperature = "(-18,-15]",
-temp$Cell <- ""
+#		EnergyConsumerDemandSource = "Formula",
-temp$Change <- "Identity"
+#		EnergyConsumerDemandTotalSource = "Formula",
-decisions <- rbind(decisions, temp)
+#		Fuel = "Cooling",
+#		fuelSharesSource = "Formula",
+#		EnergyNetworkDemandResult = 0,
+#		EnergyNetworkDemandSource = "Formula"
+#	)
+#)
-allplants <- list(
+fuelUse <- EvalOutput(fuelUse)
-	Loviisa2 = c(
-		'Sea heat pump for cooling',
+fuelUse$Fuel <- factor(
-		'Household air conditioning',
+	fuelUse$Fuel, levels = c(
-		'Household solar',
+		"Biofuel",
-		"Kymijoki River's plants",
+		"Coal",
-		'Small-scale wood burning',
+		"Fuel oil",
-		'Suvilahti power storage',
+		"Gas",
-		'Suvilahti solar',
+		"Light oil",
-		'Vanhakaupunki museum',
+		"Wood",
-		'Wind mills'
+		"Electricity",
-	),
+		"Electricity_taxed",
-	DataAndSea3 = c(
+		"Heat",
-		'Loviisa nuclear heat'
+		"Cooling"
-	),
+	), ordered = TRUE
-	NesteAndDeep4 = c(
-		'Data center heat',
-		'Sea heat pump',
-		'Household air heat pumps',
-		'Household geothermal heat'
-	),
-	Existing5 = c(
-		'Katri Vala heat',
-		'Neste oil refinery heat',
-		'Vuosaari C biofuel',
-		'Deep-drill heat'
-	),
-	Backup6 = c(
-		'Biofuel heat plants',
-		'CHP diesel generators',
-		'Hanasaari',
-		'Salmisaari A&B',
-		'Vuosaari A',
-		'Vuosaari B'
-	),
-	Lowcost = c(
-		'Katri Vala cooling',
-		'Kellosaari back-up plant',
-		'Small fuel oil heat plants',
-		'Small gas heat plants'
-	)
 )
-cheapest <- c(
+DALY <- EvalOutput(DALYs)
-	'Loviisa nuclear heat',
+DALYs <- unkeep(DALY, cols = c("Age", "Sex", "Population"))
-	'Data center heat',
+DALYs <- oapply(DALYs, cols = c("Emission_site", "Emission_height", "Area"), FUN = sum)
-	'Household air heat pumps',
+DALYs <- DALYs[DALYs$Response == "Total mortality" , ]
-	'Katri Vala heat',
-	'Neste oil refinery heat',
+EnergyNetworkCost <- EvalOutput(EnergyNetworkCost)
-	'Small fuel oil heat plants'
-)
-existing <- c( # Major existing plants are only closed at 2026 if they are closed
+EnergyNetworkCost$Time <- as.numeric(as.character(EnergyNetworkCost$Time))
-		"Kymijoki River's plants",
-		'Small-scale wood burning',
-		'Katri Vala heat',
-		'Hanasaari',
-		'Salmisaari A&B',
-		'Vuosaari A',
-		'Vuosaari B',
-		'Kellosaari back-up plant',
-		'Small fuel oil heat plants',
-		'Small gas heat plants'
-)
-for(i in 1:6) {
-	shutdown <- unlist(allplants[1:i])
-	if(i == 6) shutdown <- unlist(allplants)[!unlist(allplants) %in% cheapest]
-temp <- data.frame(
+totalCost <- EvalOutput(totalCost)
-	Decision_maker = "Helen",
+totalCost@output$Time <- as.numeric(as.character(totalCost@output$Time))
-	Decision = "PlantPolicy",
+totalCost <- unkeep(totalCost[totalCost$Time >= 2015 & totalCost$Time <=2065 , ], sources = TRUE)
-	Option = names(allplants[i]),
-	Variable = c("plantParameters"),
+# Net present value and effective annual cost
-	Cell = c(
-		paste("Plant:", paste(shutdown[!shutdown %in% existing], collapse = ","), ";Parameter:Max,Min,Investment cost;Time:>=2016", sep = ""),
+discount <- 0.03
-		paste("Plant:", paste(shutdown[shutdown %in% existing], collapse = ","), ";Parameter:Max,Min,Investment cost;Time:>=2026", sep = "")
+times <- c(2015, 2065)
-	),
+EAC <- EvalOutput(EAC)
-	Change = c("Replace"),
-	Unit = NA,
+if(saveobjects) {
-	Result = 0
+	objects.store(list = ls())
-)
+	cat("All objects stored for later use:\n", paste(ls(), collapse = ", "), "\n")
-decisions <- rbind(decisions, temp)
 }
-DecisionTableParser(decisions)
+############## POST_PROCESSING AND GRAPHS, VERSION FROM PERFERENCE ANALYSIS
-oprint(decisions)
+cat(translate("NOTE! In all graphs and tables, the Total energy saving policy is assumed unless otherwise noted\n"))
+cat(translate("Total DALYs/a by different combinations of policy options.\n"))
-oprint(data.frame(
+temp <- DALYs[as.character(DALYs$Time) %in% c("2015", "2035") & DALYs$Response == "Total mortality" , ]
-	Shutdown_order = c(
-		"Plants are shut down in this order (most cost efficient first):",
-		paste(allplants[[1]], collapse = ", "),
-		paste(allplants[[2]], collapse = ", "),
-		paste(allplants[[3]], collapse = ", "),
-		paste(allplants[[4]], collapse = ", "),
-		paste(allplants[[5]], collapse = ", "),
-		paste(allplants[[6]], collapse = ", "),
-		paste(c("All but the cheapest", cheapest), collapse = ", ")
-	)
-))
-### Energy production in Helsinki
+oprint(
+	translate(oapply(temp, INDEX = c("Time", "EnergySavingPolicy", "PlantPolicy"), FUN = sum)),
+	caption = translate("Table 1: Total DALYs/a by different combinations of policy options."),
+	caption.placement = "top",
+	include.rownames = FALSE
+)
-objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] findrest
+bui <- oapply(buildings * 1E-6, cols = c("City_area", "buildingsSource"), FUN = sum)
-objects.latest("Op_en7311", code_name = "energyProcess") # [[Helsinki energy production]]
+bui <- truncateIndex(bui, cols = "Heating", bins = 4)
-objects.latest("Op_en7311", code_name = "plantParameters") # [[Helsinki energy production]]
-objects.latest("Op_en4151", code_name = "fuelPrice") # [[Prices of fuels in heat production]]
-objects.latest("Op_en5141", code_name = "EnergyNetworkOptim") # [[Energy balance]] incl EnergyNetworkCost
+oggplot(bui[bui$EnergySavingPolicy == "BAU" , ], x = "Time", fill = "Heating", binwidth = 5) +
+		labs(
+				title = translate("Building stock in Helsinki by heating"),
+				y = translate("Floor area (M m2)")
+		)
-### Building data in Helsinki
+oggplot(bui, x = "Time", fill = "Efficiency", binwidth = 5) +
+		{if(fi) facet_wrap(~ Energiansäästöpolitiikka) else facet_wrap(~ EnergySavingPolicy)} +
+		labs(
+				title = translate("Building stock in Helsinki by efficiency policy"),
+				y = translate("Floor area (M m2)")
+		)
-# Observation years must be defined for an assessment.
+oggplot(bui, x = "Time", fill = "Renovation", binwidth = 5) +
-obstime <- Ovariable("obstime", data = data.frame(Obsyear = factor(seq(1985, 2065, 10), ordered = TRUE), Result = 1))
+		{if(fi) facet_wrap(~ Energiansäästöpolitiikka) else facet_wrap(~ EnergySavingPolicy)} +
-heatingShares <- 1 # Shares of different heating types in the building stock (already in the building data)
+		labs(
-heating_before <- FALSE # Should heatingShares be calculated before renovate and timepoints (or after)?
+				title = translate("Building stock in Helsinki by renovation policy"),
-efficiency_before <- TRUE # Should efficiencyShares be calculated before renovate and timepoints (or after)?
+				y = translate("Floor area (M m2)")
+		)
-objects.latest("Op_en7115", code_name = "stockBuildings") # [[Building stock in Helsinki]]
+oggplot(bui[bui$EnergySavingPolicy == "BAU" , ], x = "Time", fill = "Building", binwidth = 5) +
-objects.latest("Op_en7115", code_name = "changeBuildings") # [[Building stock in Helsinki]]
+	labs(
-objects.latest("Op_en7115", code_name = "renovationRate") # [[Building stock in Helsinki]]
+		title = translate("Building stock in Helsinki"),
-objects.latest("Op_en7115", code_name = "renovationShares") # [[Building stock in Helsinki]]
+		y = translate("Floor area (M m2)")
-objects.latest("Op_en5488", code_name = "efficiencyShares") # [[Energy use of buildings]]
+	)
-objects.latest("Op_en6289", code_name = "buildingstest") # [[Building model]] # Generic building model.
+oggplot(buildings, x = "Time", fill = "Efficiency", binwidth = 5)+
+	{if(fi) facet_grid(Energiansäästöpolitiikka ~ Korjaukset) else facet_grid(EnergySavingPolicy ~ Renovation)} +
+	labs(
+		title = translate("Renovation of buildings by policy and efficiency"),
+		y = translate("Floor area (M m2)")
+	)
-## Energy use
+# Contains also other buildings than district heating and other energy than heating
-##objects.latest("Op_en5488", code_name = "energyUseTemperature")        # [[Energy use of buildings]] temperature version of energyUse
+hea <- EnergyConsumerDemandTotal * temperdays * 24 * 1E-3 # MW -> GWh
-objects.latest("Op_en5488", code_name = "temperene")        # [[Energy use of buildings]] temperature version of energyUse
+hea <- hea[hea$EnergySavingPolicy == "Energy saving total" & !hea$Consumable %in% c("District cooling", "Electric cooling") , ]
-objects.latest("Op_en5488", code_name = "nontemperene")        # [[Energy use of buildings]] temperature version of energyUse
+hea$Time <- as.numeric(as.character(hea$Time))
-objects.latest("Op_en7317", code_name = "temperatures") # [[Helsinki energy consumption]]
-##objects.latest("Op_en5488", code_name = "energyUseTemperature") # [[Energy use of buildings]]
-objects.latest("Op_en5488", code_name = "EnergyConsumerDemand") # [[Energy use of buildings]]
-requiredName <- "Heat" # Name of the fuel type that must match for input and output
+oggplot(truncateIndex(hea, cols = "Temperature", bins = 7), x = "Time", fill = "Temperature", binwidth = 5) +
+{if(fi) facet_wrap(~ Hyödyke) else facet_wrap(~ Consumable)} +
+labs(
+	title = translate("Energy consumption in all buildings"),
+	y = translate("Total energy (GWh /a)")
+)
-### Energy and emissions
+hea2 <- EnergyNetworkDemand * temperdays * 24 / 1000 # MW -> GWh
-objects.latest("Op_en7311", code_name = "emissionLocationsPerPlant") # [[Helsinki energy production]] also heatingShares
+hea2$Time <- as.numeric(as.character(hea2$Time))
-objects.latest("Op_en2791", code_name = "emissionFactors") # [[Emission factors for burning processes]]
-objects.latest("Op_en2791", code_name = "emissionstest") # [[Emission factors for burning processes]]
-objects.latest("Op_en7311", code_name = "fuelShares") # [[Helsinki energy production]]
-objects.latest("Op_en7311", code_name = "nondynsupply") # [[Helsinki energy production]]
-timelylimit <- 10000 # The largest production (MW) available at each plant (used to describe time-varying limits).
-## Exposure
+oggplot(hea2, x = "Time", fill = "Fuel", binwidth = 5) +
+{if(fi) facet_wrap(~ Energiansäästöpolitiikka) else facet_wrap(~ EnergySavingPolicy)} +
+labs(
+	title = translate("Energy demand in the network"),
+	fill = translate("Consumable"),
+	y = translate("Total energy (GWh /a)")
+)
-objects.latest("Op_en5813", code_name = "exposure") # [[Intake fractions of PM]] uses Humbert iF as default.
+eb <-EnergyNetworkOptim[EnergyNetworkOptim$Process_variable_type == "Activity",]
+eb <- eb[eb$EnergySavingPolicy == "Energy saving total" , ]
+colnames(eb@output)[colnames(eb@output) == "Process_variable_name"] <- "Plant"
+eb$Process_variable_type <- NULL
-## Health assessment
+ebtemp <- eb[eb$Time %in% c("2035") & eb$PlantPolicy == "BAU" & eb$Temperature != "(-18,-15]" , ]
+ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 7)
-objects.latest('Op_en2261', code_name = 'totcases') # [[Health impact assessment]] totcases and dependencies.
+oggplot(ebtemp, x = "Temperature", fill = "Plant", turnx = TRUE) +
-objects.latest('Op_en5461', code_name = 'DALYs') # [[Climate change policies and health in Kuopio]] DALYs, DW, L
+	labs(
+		title = translate("Power plant activity by temperature daily optim \nPlant policy = BAU, Year = 2035"),
+		x = translate("Temperature of the day"),
+		y = translate("Average daily activity (MW)")
+	)
-population <- 623732 # Contains only the Helsinki city, i.e. assumes no exposure outside city. (Wikipedia)
+ebtemp <- eb[eb$Time %in% c("2035") & eb$Temperature != "(-18,-15]" , ]
-# Note: the population size does NOT affect the health impact if the exposure-response function in linear.
+ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 10)
-# However, it DOES affect exposure estimates.
+oggplot(ebtemp, x = "Temperature", fill = "Plant", turnx = TRUE) +
+	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap(~ PlantPolicy)} +
+	labs(
+		title = translate("Power plant activity by temperature daily optim in 2035"),
+		x = translate("Temperature of the day"),
+		y = translate("Average daily activity (MW)")
+	)
-# DALYshortcut is for a quicker health impact calculations, as it directly uses the Helsinki conditions.
+ebtemp <- eb[eb$Time %in% c("2005") & eb$PlantPolicy == "BAU" & eb$Temperature == "(0,3]" , ]
+ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 10)
-objects.latest("Op_en7237", code_name = "DALYshortcut") # [[Helsinki energy decision 2015]]
+oggplot(ebtemp, x = "Plant", fill = "Plant", turnx = TRUE) +
+	{if(fi) facet_wrap(~ Lämpötila) else facet_wrap( ~ Temperature)} +
+	theme(axis.text.x = element_blank()) + # Turn text and adjust to right
+	labs(
+		title = translate("Power plant activity by temperature daily optim \nPlant policy = BAU, Year = 2005"),
+		y = translate("Average daily activity (MW)")
+	)
-objects.latest("Op_en7379", code_name = "externalities") # [[External cost]]
+fu <- fuelUse / 3.6E+6 # From MJ/a -> GWh/a
-objects.latest("Op_en3283", code_name = "totalCost") # [[Economic impacts]]
+fu <- fu[fu$EnergySavingPolicy == "Energy saving total" , ]
-objects.latest("Op_en3283", code_name = "EAC") # [[Economic impacts]]
+fu$Burner <- NULL
+fu$Time <- as.numeric(as.character(fu$Time))
-################################# Actual model and some intermediate processing.
+futemp <- fu[fu$Time %in% c("2015", "2035", "2065") & fu$PlantPolicy == "BAU" , ]
+futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) * -1
-DecisionTableParser(decisions)
+oggplot(futemp, x = "Fuel", fill = "Plant", turnx = TRUE) +
+	{if(fi) facet_grid(Aika ~ Energiansäästöpolitiikka) else facet_grid(Time ~ EnergySavingPolicy)} +
+	labs(
+		title = translate("Energy commodity flows \n Plant policy = BAU"),
+		y = translate("Total annual energy (GWh/a)")
+	)
-# Remove previous decisions, if any.
+futemp <- fu[fu$Time %in% c("2005") & fu$PlantPolicy == "BAU" , ]
+futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) * -1
-forgetDecisions()
+oggplot(futemp, x = "Fuel", fill = "Plant", turnx = TRUE) +
+	labs(
+		title = translate("Energy commodity flows in 2005 \n Plant policy = BAU"),
+		y = translate("Total annual energy (GWh/a)")
+	)
-renovationRate <- EvalOutput(renovationRate) * 10 # Rates for 10-year periods
+futemp <- fu[fu$Fuel %in% c("Heat") , ]
+futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1
-buildings <- EvalOutput(buildings)
+oggplot(futemp,
+	 x = "Time", fill = "Plant", binwidth = 5) +
+	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap(~ PlantPolicy)} +
+	labs(
+		title = translate("District heat flow"),
+		y = translate("Total annual energy (GWh/a)")
+	)
-buildings <- buildings[buildings$EnergySavingPolicy == "Energy saving total" , ]
+futemp <- fu[fu$Fuel %in% c("Electricity") , ]
-cat("Only energy saving total considered in this run.\n")
+futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1
-EnergyConsumerDemand <- EvalOutput(EnergyConsumerDemand)
+oggplot(futemp, x = "Time", fill = "Plant", binwidth = 5) +
+	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap(~ PlantPolicy)} +
+	labs(
+		title = translate("Electricity flow"),
+		y = translate("Total annual energy (GWh/a)")
+	)
-fuelUse <- EvalOutput(fuelUse)
+emis <- truncateIndex(emissions, cols = "Emission_site", bins = 5)
+emis <- emis[emis$EnergySavingPolicy == "Energy saving total" , ]
-fuelUse$Fuel <- factor(
+emis$Time <- as.numeric(as.character(emis$Time))
-	fuelUse$Fuel, levels = c(
+oggplot(emis, x = "Time", fill = "Fuel", binwidth = 5) +
-		"Biofuel",
+	{if(fi) facet_grid(Saaste ~ Voimalapolitiikka, scale = "free_y") else facet_grid(Pollutant ~ PlantPolicy, scale = "free_y")} +
-		"Coal",
+	labs(
-		"Fuel oil",
+		title = translate("Emissions from heating in Helsinki"),
-		"Gas",
+		y = translate("Emissions (ton /a)")
-		"Light oil",
+	)
-		"Wood",
-		"Electricity",
-		"Electricity_taxed",
-		"Heat",
-		"Cooling"
-	), ordered = TRUE
-)
-DALY <- EvalOutput(DALYs)
+da <- DALYs[DALYs$EnergySavingPolicy == "Energy saving total" , ]
-DALYs <- unkeep(DALY, cols = c("Age", "Sex", "Population"))
+da$Time <- as.numeric(as.character(da$Time))
-DALYs <- oapply(DALYs, cols = c("Emission_site", "Emission_height", "Area"), FUN = sum)
+oggplot(da, x = "Time", fill = "Fuel", binwidth = 5) +
-DALYs <- DALYs[DALYs$Response == "Total mortality" , ]
+	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap(~ PlantPolicy)} +
+	labs(
+		title = translate("Health effects of PM2.5 from heating in Helsinki"),
+		y = translate("Health effects (DALY /a)")
+	)
-EnergyNetworkCost <- EvalOutput(EnergyNetworkCost)
+fp <- fuelPrice[fuelPrice$Fuel %in% c(
+	"Biofuel",
+	"Coal",
+	"Electricity_taxed",
+	"Fuel oil",
+	"Heat",
+	"Light oil",
+	"Natural gas",
+	"Peat"
+) , ]
+fp$Time <- as.numeric(as.character(fp$Time))
+levels(fp$Fuel)[levels(fp$Fuel) == "Electricity_taxed"] <- "Electricity"
-EnergyNetworkCost$Time <- as.numeric(as.character(EnergyNetworkCost$Time))
+ggplot(translate(fp@output), if(fi) {
+	aes(x = Aika, y = fuelPriceResult, colour = Polttoaine, group = Polttoaine)
+} else {
+	aes(x = Time, y = fuelPriceResult, colour = Fuel, group = Fuel)
+}) +
+	geom_line(size = 2)+theme_gray(base_size = BS) +
+	labs(
+		title = translate("Fuel prices (with tax)"),
+		y = translate("Price (Eur/MWh)")
+	)
-totalCost <- EvalOutput(totalCost)
+tc <- truncateIndex(totalCost, cols = "Plant", bins = 11) / 10 * -1 # Yearly benefits (costs are negative)
-totalCost@output$Time <- as.numeric(as.character(totalCost@output$Time))
+tc <- tc[tc$EnergySavingPolicy == "Energy saving total" , ]
-totalCost <- unkeep(totalCost[totalCost$Time >= 2015 & totalCost$Time <=2065 , ], sources = TRUE)
-# Net present value and effective annual cost
+oggplot(tc, x = "Time", fill = "Cost", binwidth = 10) +
+	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap( ~ PlantPolicy)} +
+	labs(
+		y = translate("Yearly cash flow (Meur)"),
+		title = translate("Total benefits and costs of energy production")
+	)
-discount <- 0.03
+oggplot(tc, x = "Time", fill = "Plant", binwidth = 10) +
-times <- c(2015, 2065)
+	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap(~ PlantPolicy)} +
-EAC <- EvalOutput(EAC)
+	labs(
+		y = translate("Yearly cash flow (Meur)"),
+		title = translate("Total benefits and costs of energy production")
+	)
-if(saveobjects) {
+eac <- EAC[EAC$EnergySavingPolicy == "Energy saving total" , ] * -1
-	objects.store(list = ls())
-	cat("All objects stored for later use:\n", paste(ls(), collapse = ", "), "\n")
-}
-####################### Post-processing and graphs
+BS <- 14 # Plot the next two graphs with smaller font because they are busy graphs.
-cat("Total DALYs/a by different combinations of policy options.\n")
+oggplot(eac, x = "PlantPolicy", fill = "Cost", turnx = TRUE) +
+	{if(fi) facet_wrap(~ Voimala, scale = "free_y") else facet_wrap(~ Plant, scale = "free_y")} +
+	labs(
+		title = translate("Incomes and costs by plant"),
+		y = translate("Effective annual cash flow (Meur/a)")
+	)
-temp <- DALYs[as.character(DALYs$Time) %in% c("2015", "2035") & DALYs$Response == "Total mortality" , ]
+oggplot(eac, x = "PlantPolicy", fill = "Cost", turnx = TRUE)+
+	{if(fi) facet_wrap(~ Voimala) else facet_wrap(~ Plant)} +
+	labs(
+		title = translate("Incomes and costs by plant"),
+		y = translate("Effective annual cash flow (Meur/a)")
+	)
-oprint(
+BS <- 24
-		oapply(temp, INDEX = c("Time", "EnergySavingPolicy", "PlantPolicy"), FUN = sum),
+eac <- truncateIndex(eac, cols = "Plant", bins = 11)
-		caption = "Table 1: Total DALYs/a by different combinations of policy options.",
-		caption.placement = "top",
-		include.rownames = FALSE
-)
-BS <- 14
+oggplot(eac, x = "PlantPolicy", fill = "Plant", turnx = TRUE)+
+	labs(
+		title = translate("Incomes and costs by plant policy"),
+		y = translate("Effective annual cash flow (Meur/a)")
+	)
-eac <- EAC * -1
+oggplot(eac, x = "PlantPolicy", fill = "Cost", turnx = TRUE)+
+	labs(
+		title = translate("Incomes and costs by plant policy"),
+		y = translate("Effective annual cash flow (Meur/a)")
+	)
-oggplot(eac, x = "PlantPolicy", fill = "Cost", turnx = TRUE)+
+temp <- truncateIndex(plantParameters[plantParameters$Parameter == "Max" , ], cols = "Plant", bins = 11)
-	facet_wrap(~ Plant, scale = "free_y") +
+temp <- temp[temp$Time >= 2000 & temp$Time <=2070 , ]
-	labs(title = "Incomes and costs by plant, total energy saving", y = "Effective annual cash flow (Meur/a)")
+oggplot(temp, x = "Time", fill = "Plant", binwidth = 1) +
+	{if(fi) facet_wrap(~ Voimalapolitiikka) else facet_wrap(~ PlantPolicy)} +
+	labs(
+		title = translate("Energy production capacity by plant policy"),
+		y = translate("Maximum capacity (MW)")
+	)
-oggplot(eac, x = "PlantPolicy", fill = "Cost", turnx = TRUE)+
+odag() #Plots a directed acyclic graph of ovariables used in the model.
-	facet_wrap(~ Plant)+
+# This causes an internal error, so it must be the last row of the model.
-	labs(title = "Incomes and costs by plant, total energy saving", y = "Effective annual cash flow (Meur/a)")
+</rcode>
-BS <- 24
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=mJ2PgbC7TBP6w6RA Model run 22.10.2015] with updated fuel prices. This is NOT used for the assessment report, because the scenarios were based on the previous fuel prices.
-eb <-EnergyNetworkOptim[EnergyNetworkOptim$Process_variable_type == "Activity",]
+'''This code was used for prioritizing before fuel prices were updated.
-colnames(eb@output)[colnames(eb@output) == "Process_variable_name"] <- "Plant"
-eb$Process_variable_type <- NULL
-ebtemp <- eb[eb$Time %in% c("2035") & eb$PlantPolicy == "All1" , ]
+<rcode name="preferenceorder" graphics=1>
-ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 7)
+# This code is Op_en7237/preferenceorder on page [[Helsinki energy decision 2015]]
+library(OpasnetUtils)
+library(ggplot2)
-oggplot(ebtemp, x = "Temperature", fill = "Plant") +
+openv.setN(0) # use medians instead of whole sampled distributions
-	labs(
+BS <- 14 # base_size for graph fonts
-		title = "Power plant activity by temperature daily optim \nPlant policy = All1, Year = 2035",
+objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]]
-		x = "Temperature of the day",
+saveobjects <- FALSE
-		y = "Power output daily average (MW)"
-	)
-ebtemp <- eb[eb$Time %in% c("2035") , ]
+# Download all pre-calculated inputs, e.g. building stock.
-ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 10)
+#objects.latest("Op_en7237", code_name = "intermediates") # [[Helsinki energy decision 2015]]
-oggplot(ebtemp, x = "Temperature", fill = "Plant") + facet_wrap(~ PlantPolicy)
-	labs(
-		title = "Power plant activity by temperature daily optim \nmoderate energy saving in 2035",
-		x = "Temperature of the day",
-		y = "Power plant activity daily average (MW)"
-	)
-ebtemp <- eb[eb$Time %in% c("2005") & eb$PlantPolicy == "All1" & eb$Temperature == "(0,3]" , ]
+decisions <- opbase.data("Op_en7237", subset = "Decisions") # [[Helsinki energy decision 2015]]
-ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 10)
+decisions$Obs <- NULL
+temp <- decisions[decisions$Decision== "PlantPolicy",][1,]
+decisions <- decisions[decisions$Decision!= "PlantPolicy" , ] # & decisions$Option == "Energy saving total" , ]
+temp$Option <- "All1"
+temp$Variable <- "plantParameters"
+temp$Cell <- ""
+temp$Change <- "Identity"
+decisions <- rbind(decisions, temp)
-oggplot(ebtemp, x = "Plant", fill = "Plant") + facet_wrap( ~ Temperature) +
+allplants <- list(
-		theme(axis.text.x = element_blank()) + # Turn text and adjust to right
+	Loviisa2 = c(
-		labs(
+		'Sea heat pump for cooling',
-				title = "Power plant activity by temperature daily optim \nPlant policy = All1, Year = 2005",
+		'Household air conditioning',
-				y = "Power output daily average (MW)"
+		'Household solar',
-		)
+		"Kymijoki River's plants",
+		'Small-scale wood burning',
-fu <- fuelUse / 3.6E+6 # From MJ/a -> GWh/a
+		'Suvilahti power storage',
-fu$Burner <- NULL
+		'Suvilahti solar',
-fu$Time <- as.numeric(as.character(fu$Time))
+		'Vanhakaupunki museum',
+		'Wind mills'
-futemp <- fu[fu$Time %in% c("2015", "2035", "2065") & fu$PlantPolicy == "All1" , ]
+	),
-futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) * -1
+	DataAndSea3 = c(
+		'Loviisa nuclear heat'
-oggplot(futemp, x = "Fuel", fill = "Plant", turnx = TRUE) + facet_grid(Time ~ EnergySavingPolicy) +
+	),
-	labs(
+	NesteAndDeep4 = c(
-		title = "Energy commodity flows \n Plant policy = All1",
+		'Data center heat',
-		y = "Total annual energy (GWh/a)"
+		'Sea heat pump',
+		'Household air heat pumps',
+		'Household geothermal heat'
+	),
+	Existing5 = c(
+		'Katri Vala heat',
+		'Neste oil refinery heat',
+		'Vuosaari C biofuel',
+		'Deep-drill heat'
+	),
+	Backup6 = c(
+		'Biofuel heat plants',
+		'CHP diesel generators',
+		'Hanasaari',
+		'Salmisaari A&B',
+		'Vuosaari A',
+		'Vuosaari B'
+	),
+	Lowcost = c(
+		'Katri Vala cooling',
+		'Kellosaari back-up plant',
+		'Small fuel oil heat plants',
+		'Small gas heat plants'
 	)
+)
-futemp <- fu[fu$Time %in% c("2005") & fu$PlantPolicy == "All1" , ]
+cheapest <- c(
-futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) * -1
+	'Loviisa nuclear heat',
+	'Data center heat',
+	'Household air heat pumps',
+	'Katri Vala heat',
+	'Neste oil refinery heat',
+	'Small fuel oil heat plants'
+)
-oggplot(futemp, x = "Fuel", fill = "Plant", turnx = TRUE) +
+existing <- c( # Major existing plants are only closed at 2026 if they are closed
-		labs(
+		"Kymijoki River's plants",
-				title = "Energy commodity flows in 2015 \n Plant policy = All1, Energy saving policy == total",
+		'Small-scale wood burning',
-				y = "Total annual energy (GWh/a)"
+		'Katri Vala heat',
-		)
+		'Hanasaari',
+		'Salmisaari A&B',
+		'Vuosaari A',
+		'Vuosaari B',
+		'Kellosaari back-up plant',
+		'Small fuel oil heat plants',
+		'Small gas heat plants'
+)
+for(i in 1:6) {
+	shutdown <- unlist(allplants[1:i])
+	if(i == 6) shutdown <- unlist(allplants)[!unlist(allplants) %in% cheapest]
-futemp <- fu[fu$Fuel %in% c("Heat") , ]
+temp <- data.frame(
-futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1
+	Decision_maker = "Helen",
+	Decision = "PlantPolicy",
+	Option = names(allplants[i]),
+	Variable = c("plantParameters"),
+	Cell = c(
+		paste("Plant:", paste(shutdown[!shutdown %in% existing], collapse = ","), ";Parameter:Max,Min,Investment cost;Time:>=2016", sep = ""),
+		paste("Plant:", paste(shutdown[shutdown %in% existing], collapse = ","), ";Parameter:Max,Min,Investment cost;Time:>=2026", sep = "")
+	),
+	Change = c("Replace"),
+	Unit = NA,
+	Result = 0
+)
+decisions <- rbind(decisions, temp)
+}
-oggplot(futemp,
+DecisionTableParser(decisions)
-	 x = "Time", fill = "Plant", binwidth = 5) + facet_wrap(~ PlantPolicy) +
-		labs(
-				title = "District heat flow",
-				y = "Total annual energy (GWh/a)"
-		)
-futemp <- fu[fu$Fuel %in% c("Electricity") , ]
+oprint(decisions)
-futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1
-oggplot(futemp, x = "Time", fill = "Plant", binwidth = 5) + facet_wrap(~ PlantPolicy) +
+oprint(data.frame(
-		labs(
+	Shutdown_order = c(
-				title = "Electricity flow",
+		"Plants are shut down in this order (most cost efficient first):",
-				y = "Total annual energy (GWh/a)"
+		paste(allplants[[1]], collapse = ", "),
-		)
+		paste(allplants[[2]], collapse = ", "),
+		paste(allplants[[3]], collapse = ", "),
+		paste(allplants[[4]], collapse = ", "),
+		paste(allplants[[5]], collapse = ", "),
+		paste(allplants[[6]], collapse = ", "),
+		paste(c("All but the cheapest", cheapest), collapse = ", ")
+	)
+))
-emis <- truncateIndex(emissions, cols = "Emission_site", bins = 5)
+### Energy production in Helsinki
-oggplot(emis, x = "Time", fill = "Fuel", binwidth = 5) + facet_grid(Pollutant ~ PlantPolicy, scale = "free_y") +
-		labs(
-				title = "Emissions from heating in Helsinki",
-				y = "Emissions (ton /a)"
-		)
-oggplot(DALYs, x = "Time", fill = "Fuel", binwidth = 5) + facet_wrap(~ PlantPolicy) +
+objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] findrest
-		labs(
+objects.latest("Op_en7311", code_name = "energyProcess") # [[Helsinki energy production]]
-				title = "Health effects in DALYs of PM2.5 from heating in Helsinki",
+objects.latest("Op_en7311", code_name = "plantParameters") # [[Helsinki energy production]]
-				y = "Health effects (DALY /a)"
+objects.latest("Op_en4151", code_name = "fuelPrice") # [[Prices of fuels in heat production]]
-		)
-fp <- fuelPrice[fuelPrice$Fuel %in% c(
+objects.latest("Op_en5141", code_name = "EnergyNetworkOptim") # [[Energy balance]] incl EnergyNetworkCost
-	"Biofuel",
-	"Coal",
-	"Electricity_taxed",
-	"Fuel oil",
-	"Heat",
-	"Light oil",
-	"Natural gas",
-	"Peat"
-) , ]
-fp$Time <- as.numeric(as.character(fp$Time))
-levels(fp$Fuel)[levels(fp$Fuel) == "Electricity_taxed"] <- "Electricity"
-ggplot(fp@output, aes(x = Time, y = fuelPriceResult, colour = Fuel, group = Fuel))+
+### Building data in Helsinki
-	geom_line(size = 2)+theme_gray(base_size = BS) +
-	labs(
-		title = "Fuel prices (with tax)",
-		y = "Price (Eur / MWh)"
-	)
-tc <- truncateIndex(totalCost, cols = "Plant", bins = 11) / 10 # Yearly costs
+# Observation years must be defined for an assessment.
+obstime <- Ovariable("obstime", data = data.frame(Obsyear = factor(seq(1985, 2065, 10), ordered = TRUE), Result = 1))
+heatingShares <- 1 # Shares of different heating types in the building stock (already in the building data)
+heating_before <- FALSE # Should heatingShares be calculated before renovate and timepoints (or after)?
+efficiency_before <- TRUE # Should efficiencyShares be calculated before renovate and timepoints (or after)?
-oggplot(tc, x = "Time", fill = "Cost", binwidth = 10) + facet_wrap( ~ PlantPolicy) +
+objects.latest("Op_en7115", code_name = "stockBuildings") # [[Building stock in Helsinki]]
-	labs(
+objects.latest("Op_en7115", code_name = "changeBuildings") # [[Building stock in Helsinki]]
-		y = "Yearly costs (Meur)",
+objects.latest("Op_en7115", code_name = "demolitionRate") # [[Building stock in Helsinki]]
-		title = "Total costs of energy production"
+objects.latest("Op_en7115", code_name = "renovationRate") # [[Building stock in Helsinki]]
-	)
+objects.latest("Op_en7115", code_name = "renovationShares") # [[Building stock in Helsinki]]
+objects.latest("Op_en5488", code_name = "efficiencyShares") # [[Energy use of buildings]]
-oggplot(tc, x = "Time", fill = "Plant", binwidth = 10) + facet_wrap( ~ PlantPolicy) +
+objects.latest("Op_en6289", code_name = "buildingstest") # [[Building model]] # Generic building model.
-	labs(
-		y = "Yearly costs (Meur)",
-		title = "Total costs of energy production"
-	)
-eb <-EnergyNetworkOptim[EnergyNetworkOptim$Process_variable_type == "Activity",]
+## Energy use
-colnames(eb@output)[colnames(eb@output) == "Process_variable_name"] <- "Plant"
+##objects.latest("Op_en5488", code_name = "energyUseTemperature")        # [[Energy use of buildings]] temperature version of energyUse
-eb$Process_variable_type <- NULL
+objects.latest("Op_en5488", code_name = "temperene")        # [[Energy use of buildings]] temperature version of energyUse
+objects.latest("Op_en5488", code_name = "nontemperene")        # [[Energy use of buildings]] temperature version of energyUse
+objects.latest("Op_en7317", code_name = "temperatures") # [[Helsinki energy consumption]]
+##objects.latest("Op_en5488", code_name = "energyUseTemperature") # [[Energy use of buildings]]
+objects.latest("Op_en5488", code_name = "EnergyConsumerDemand") # [[Energy use of buildings]]
-ebtemp <- eb[eb$Time %in% c("2035") , ]
+requiredName <- "Heat" # Name of the fuel type that must match for input and output
-ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 11)
-oggplot(ebtemp, x = "Temperature", fill = "Plant", turnx = TRUE) + facet_wrap(~ PlantPolicy) +
+### Energy and emissions
-	labs(
+objects.latest("Op_en7311", code_name = "emissionLocationsPerPlant") # [[Helsinki energy production]] also heatingShares
-		title = "Power plant activity by temperature daily optim in 2035",
+objects.latest("Op_en2791", code_name = "emissionFactors") # [[Emission factors for burning processes]]
-		x = "Temperature of the day",
+objects.latest("Op_en2791", code_name = "emissionstest") # [[Emission factors for burning processes]]
-		y = "Average daily activity (MW)"
+objects.latest("Op_en7311", code_name = "fuelShares") # [[Helsinki energy production]]
-	)
+objects.latest("Op_en7311", code_name = "nondynsupply") # [[Helsinki energy production]]
+timelylimit <- 10000 # The largest production (MW) available at each plant (used to describe time-varying limits).
-eac <- truncateIndex(EAC * -1, cols = "Plant", bins = 11)
+## Exposure
-oggplot(eac, x = "PlantPolicy", fill = "Plant", turnx = TRUE)+
+objects.latest("Op_en5813", code_name = "exposure") # [[Intake fractions of PM]] uses Humbert iF as default.
-	labs(title = "Incomes and costs by plant policy\ntotal energy saving", y = "Effective annual cash flow (Meur/a)")
-oggplot(eac, x = "PlantPolicy", fill = "Cost", turnx = TRUE)+
+## Health assessment
-	labs(title = "Incomes and costs by plant policy\ntotal energy saving", y = "Effective annual cash flow (Meur/a)")
-temp <- truncateIndex(plantParameters[plantParameters$Parameter == "Max" , ], cols = "Plant", bins = 11)
+objects.latest('Op_en2261', code_name = 'totcases') # [[Health impact assessment]] totcases and dependencies.
-temp <- temp[temp$Time >= 2000 & temp$Time <=2070 , ]
+objects.latest('Op_en5461', code_name = 'DALYs') # [[Climate change policies and health in Kuopio]] DALYs, DW, L
-oggplot(temp, x = "Time", fill = "Plant", binwidth = 1) + facet_wrap(~ PlantPolicy)+
-labs(title = "Energy production capacity by plant policy", y = "Maximum capacity (MW)")
-</rcode>
+population <- 623732 # Contains only the Helsinki city, i.e. assumes no exposure outside city. (Wikipedia)
+# Note: the population size does NOT affect the health impact if the exposure-response function in linear.
+# However, it DOES affect exposure estimates.
+# DALYshortcut is for a quicker health impact calculations, as it directly uses the Helsinki conditions.
-==== DALY shortcut ====
+objects.latest("Op_en7237", code_name = "DALYshortcut") # [[Helsinki energy decision 2015]]
-This code creates a ready-made DALYs estimate for 1 ug/m^3 PM2.5. It is much quicker to calculate health impacts of case-specific emissions with this ovariable, if there is a large emissions ovariable and if the exposure is such that proportionality can be assumed in health impacts.
+objects.latest("Op_en7379", code_name = "externalities") # [[External cost]]
+objects.latest("Op_en3283", code_name = "totalCost") # [[Economic impacts]]
+objects.latest("Op_en3283", code_name = "EAC") # [[Economic impacts]]
-<rcode name="DALYshortcut" label="Initiate DALYshortcut(developers only)" embed=0>
+################################# Actual model and some intermediate processing.
-library(OpasnetUtils)
-openv.setN(1000)
+DecisionTableParser(decisions)
-objects.latest("Op_en5813", code_name = "exposure") # [[Intake fractions of PM]] uses Humbert iF as default.
+# Remove previous decisions, if any.
-objects.latest('Op_en2261', code_name = 'totcases') # [[Health impact assessment]] totcases and dependencies.
-objects.latest('Op_en5461', code_name = 'DALYs') # [[Climate change policies and health in Kuopio]] DALYs, DW, L
-emissions <- Ovariable(output = data.frame(Pollutant = "PM2.5", Result = 1))
+forgetDecisions()
-population <- 623732
-exposure <- EvalOutput(exposure)
+renovationRate <- EvalOutput(renovationRate) * 10 # Rates for 10-year periods
-exposure <- exposure[exposure$Area == "Average" , ]
-exposure <- oapply(exposure, cols = c("Plant", "Emission_site", "Emission_height", "Area"), FUN = sum)
-totcases <- EvalOutput(totcases)
-DALYs <- EvalOutput(DALYs)
-DALYs$Pollutant <- "PM2.5" # Why do you have this line? The Pollutant is "PM2.5" already. To get rid of other levels?
-DALYper1 <- unkeep(DALYs, sources = TRUE, prevresults = TRUE)
+buildings <- EvalOutput(buildings)
-DALYper1$Result <- DALYper1$DALYsResult
-DALYper1$DALYsResult <- NULL
+buildings <- buildings[buildings$EnergySavingPolicy == "Energy saving total" , ]
-DALYper1@name <- ""
+cat("Only energy saving total considered in this run.\n")
-# Short version of DALYs
+EnergyConsumerDemand <- EvalOutput(EnergyConsumerDemand)
-DALYs <- Ovariable(
-	"DALYs",
+fuelUse <- EvalOutput(fuelUse)
-	dependencies = data.frame(Name = c("DALYper1", "emissions")),
-	formula = function(...) {
+fuelUse$Fuel <- factor(
-		if(!"Iter" %in% colnames(emissions@output) & "Iter" %in% colnames(DALYper1@output)) {
+	fuelUse$Fuel, levels = c(
-			out <- oapply(DALYper1, cols = "Iter", FUN = mean)
+		"Biofuel",
-		} else {
+		"Coal",
-			out <- DALYper1
+		"Fuel oil",
-		}
+		"Gas",
-		out <- emissions * out
+		"Light oil",
-		return(out)
+		"Wood",
-	}
+		"Electricity",
-)
+		"Electricity_taxed",
+		"Heat",
-objects.store(DALYper1, DALYs)
+		"Cooling"
+	), ordered = TRUE
-</rcode>
+)
-== See also ==
+DALY <- EvalOutput(DALYs)
+DALYs <- unkeep(DALY, cols = c("Age", "Sex", "Population"))
-{{assessment|moderator=Jouni|status=ongoing}}
+DALYs <- oapply(DALYs, cols = c("Emission_site", "Emission_height", "Area"), FUN = sum)
-{{Helsinki energy decision 2015}}
+DALYs <- DALYs[DALYs$Response == "Total mortality" , ]
-* [https://www.entsoe.eu/Documents/Publications/Statistics/Factsheet/entsoe_sfs2014_web.pdf Entsoe Statistical fact sheet 2014]
+EnergyNetworkCost <- EvalOutput(EnergyNetworkCost)
-* [http://www.talouselama.fi/Tebatti/kysymykset/pienkuluttaja+on+tuulisahkon+maksumies/a2238751 Timo Korpela: Pienkuluttaja on tuulisähkön maksumies]
-* [http://www.hs.fi/mielipide/a1427173539858 Timo Korpela: Helsingin energiaratkaisu tulee harkita tarkoin]
+EnergyNetworkCost$Time <- as.numeric(as.character(EnergyNetworkCost$Time))
+totalCost <- EvalOutput(totalCost)
+totalCost@output$Time <- as.numeric(as.character(totalCost@output$Time))
+totalCost <- unkeep(totalCost[totalCost$Time >= 2015 & totalCost$Time <=2065 , ], sources = TRUE)
+# Net present value and effective annual cost
+discount <- 0.03
+times <- c(2015, 2065)
+EAC <- EvalOutput(EAC)
+if(saveobjects) {
+	objects.store(list = ls())
+	cat("All objects stored for later use:\n", paste(ls(), collapse = ", "), "\n")
+}
+####################### Post-processing and graphs
+cat("Total DALYs/a by different combinations of policy options.\n")
+temp <- DALYs[as.character(DALYs$Time) %in% c("2015", "2035") & DALYs$Response == "Total mortality" , ]
+oprint(
+		oapply(temp, INDEX = c("Time", "EnergySavingPolicy", "PlantPolicy"), FUN = sum),
+		caption = "Table 1: Total DALYs/a by different combinations of policy options.",
+		caption.placement = "top",
+		include.rownames = FALSE
+)
+BS <- 14
+eac <- EAC * -1
+oggplot(eac, x = "PlantPolicy", fill = "Cost", turnx = TRUE)+
+	facet_wrap(~ Plant, scale = "free_y") +
+	labs(title = "Incomes and costs by plant, total energy saving", y = "Effective annual cash flow (Meur/a)")
+oggplot(eac, x = "PlantPolicy", fill = "Cost", turnx = TRUE)+
+	facet_wrap(~ Plant)+
+	labs(title = "Incomes and costs by plant, total energy saving", y = "Effective annual cash flow (Meur/a)")
+BS <- 24
+eb <-EnergyNetworkOptim[EnergyNetworkOptim$Process_variable_type == "Activity",]
+colnames(eb@output)[colnames(eb@output) == "Process_variable_name"] <- "Plant"
+eb$Process_variable_type <- NULL
+ebtemp <- eb[eb$Time %in% c("2035") & eb$PlantPolicy == "All1" , ]
+ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 7)
+oggplot(ebtemp, x = "Temperature", fill = "Plant") +
+	labs(
+		title = "Power plant activity by temperature daily optim \nPlant policy = All1, Year = 2035",
+		x = "Temperature of the day",
+		y = "Power output daily average (MW)"
+	)
+ebtemp <- eb[eb$Time %in% c("2035") , ]
+ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 10)
+oggplot(ebtemp, x = "Temperature", fill = "Plant") + facet_wrap(~ PlantPolicy)
+	labs(
+		title = "Power plant activity by temperature daily optim \nmoderate energy saving in 2035",
+		x = "Temperature of the day",
+		y = "Power plant activity daily average (MW)"
+	)
+ebtemp <- eb[eb$Time %in% c("2005") & eb$PlantPolicy == "All1" & eb$Temperature == "(0,3]" , ]
+ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 10)
+oggplot(ebtemp, x = "Plant", fill = "Plant") + facet_wrap( ~ Temperature) +
+		theme(axis.text.x = element_blank()) + # Turn text and adjust to right
+		labs(
+				title = "Power plant activity by temperature daily optim \nPlant policy = All1, Year = 2005",
+				y = "Power output daily average (MW)"
+		)
+fu <- fuelUse / 3.6E+6 # From MJ/a -> GWh/a
+fu$Burner <- NULL
+fu$Time <- as.numeric(as.character(fu$Time))
+futemp <- fu[fu$Time %in% c("2015", "2035", "2065") & fu$PlantPolicy == "All1" , ]
+futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) * -1
+oggplot(futemp, x = "Fuel", fill = "Plant", turnx = TRUE) + facet_grid(Time ~ EnergySavingPolicy) +
+	labs(
+		title = "Energy commodity flows \n Plant policy = All1",
+		y = "Total annual energy (GWh/a)"
+	)
+futemp <- fu[fu$Time %in% c("2005") & fu$PlantPolicy == "All1" , ]
+futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) * -1
+oggplot(futemp, x = "Fuel", fill = "Plant", turnx = TRUE) +
+		labs(
+				title = "Energy commodity flows in 2015 \n Plant policy = All1, Energy saving policy == total",
+				y = "Total annual energy (GWh/a)"
+		)
+futemp <- fu[fu$Fuel %in% c("Heat") , ]
+futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1
+oggplot(futemp,
+	 x = "Time", fill = "Plant", binwidth = 5) + facet_wrap(~ PlantPolicy) +
+		labs(
+				title = "District heat flow",
+				y = "Total annual energy (GWh/a)"
+		)
+futemp <- fu[fu$Fuel %in% c("Electricity") , ]
+futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) * -1
+oggplot(futemp, x = "Time", fill = "Plant", binwidth = 5) + facet_wrap(~ PlantPolicy) +
+		labs(
+				title = "Electricity flow",
+				y = "Total annual energy (GWh/a)"
+		)
+emis <- truncateIndex(emissions, cols = "Emission_site", bins = 5)
+oggplot(emis, x = "Time", fill = "Fuel", binwidth = 5) + facet_grid(Pollutant ~ PlantPolicy, scale = "free_y") +
+		labs(
+				title = "Emissions from heating in Helsinki",
+				y = "Emissions (ton /a)"
+		)
+oggplot(DALYs, x = "Time", fill = "Fuel", binwidth = 5) + facet_wrap(~ PlantPolicy) +
+		labs(
+				title = "Health effects in DALYs of PM2.5 from heating in Helsinki",
+				y = "Health effects (DALY /a)"
+		)
+fp <- fuelPrice[fuelPrice$Fuel %in% c(
+	"Biofuel",
+	"Coal",
+	"Electricity_taxed",
+	"Fuel oil",
+	"Heat",
+	"Light oil",
+	"Natural gas",
+	"Peat"
+) , ]
+fp$Time <- as.numeric(as.character(fp$Time))
+levels(fp$Fuel)[levels(fp$Fuel) == "Electricity_taxed"] <- "Electricity"
+ggplot(fp@output, aes(x = Time, y = fuelPriceResult, colour = Fuel, group = Fuel))+
+	geom_line(size = 2)+theme_gray(base_size = BS) +
+	labs(
+		title = "Fuel prices (with tax)",
+		y = "Price (Eur / MWh)"
+	)
+tc <- truncateIndex(totalCost, cols = "Plant", bins = 11) / 10 # Yearly costs
+oggplot(tc, x = "Time", fill = "Cost", binwidth = 10) + facet_wrap( ~ PlantPolicy) +
+	labs(
+		y = "Yearly costs (Meur)",
+		title = "Total costs of energy production"
+	)
+oggplot(tc, x = "Time", fill = "Plant", binwidth = 10) + facet_wrap( ~ PlantPolicy) +
+	labs(
+		y = "Yearly costs (Meur)",
+		title = "Total costs of energy production"
+	)
+eb <-EnergyNetworkOptim[EnergyNetworkOptim$Process_variable_type == "Activity",]
+colnames(eb@output)[colnames(eb@output) == "Process_variable_name"] <- "Plant"
+eb$Process_variable_type <- NULL
+ebtemp <- eb[eb$Time %in% c("2035") , ]
+ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 11)
+oggplot(ebtemp, x = "Temperature", fill = "Plant", turnx = TRUE) + facet_wrap(~ PlantPolicy) +
+	labs(
+		title = "Power plant activity by temperature daily optim in 2035",
+		x = "Temperature of the day",
+		y = "Average daily activity (MW)"
+	)
+eac <- truncateIndex(EAC * -1, cols = "Plant", bins = 11)
+oggplot(eac, x = "PlantPolicy", fill = "Plant", turnx = TRUE)+
+	labs(title = "Incomes and costs by plant policy\ntotal energy saving", y = "Effective annual cash flow (Meur/a)")
+oggplot(eac, x = "PlantPolicy", fill = "Cost", turnx = TRUE)+
+	labs(title = "Incomes and costs by plant policy\ntotal energy saving", y = "Effective annual cash flow (Meur/a)")
+temp <- truncateIndex(plantParameters[plantParameters$Parameter == "Max" , ], cols = "Plant", bins = 11)
+temp <- temp[temp$Time >= 2000 & temp$Time <=2070 , ]
+oggplot(temp, x = "Time", fill = "Plant", binwidth = 1) + facet_wrap(~ PlantPolicy)+
+labs(title = "Energy production capacity by plant policy", y = "Maximum capacity (MW)")
+</rcode>
+==== DALY shortcut ====
+This code creates a ready-made DALYs estimate for 1 ug/m^3 PM2.5. It is much quicker to calculate health impacts of case-specific emissions with this ovariable, if there is a large emissions ovariable and if the exposure is such that proportionality can be assumed in health impacts.
+<rcode name="DALYshortcut" label="Initiate DALYshortcut(developers only)" embed=0>
+library(OpasnetUtils)
+openv.setN(1000)
+objects.latest("Op_en5813", code_name = "exposure") # [[Intake fractions of PM]] uses Humbert iF as default.
+objects.latest('Op_en2261', code_name = 'totcases') # [[Health impact assessment]] totcases and dependencies.
+objects.latest('Op_en5461', code_name = 'DALYs') # [[Climate change policies and health in Kuopio]] DALYs, DW, L
+emissions <- Ovariable(output = data.frame(Pollutant = "PM2.5", Result = 1))
+population <- 623732
+exposure <- EvalOutput(exposure)
+exposure <- exposure[exposure$Area == "Average" , ]
+exposure <- oapply(exposure, cols = c("Plant", "Emission_site", "Emission_height", "Area"), FUN = sum)
+totcases <- EvalOutput(totcases)
+DALYs <- EvalOutput(DALYs)
+DALYs$Pollutant <- "PM2.5" # Why do you have this line? The Pollutant is "PM2.5" already. To get rid of other levels?
+DALYper1 <- unkeep(DALYs, sources = TRUE, prevresults = TRUE)
+DALYper1$Result <- DALYper1$DALYsResult
+DALYper1$DALYsResult <- NULL
+DALYper1@name <- ""
+# Short version of DALYs
+DALYs <- Ovariable(
+	"DALYs",
+	dependencies = data.frame(Name = c("DALYper1", "emissions")),
+	formula = function(...) {
+		if(!"Iter" %in% colnames(emissions@output) & "Iter" %in% colnames(DALYper1@output)) {
+			out <- oapply(DALYper1, cols = "Iter", FUN = mean)
+		} else {
+			out <- DALYper1
+		}
+		out <- emissions * out
+		return(out)
+	}
+)
+objects.store(DALYper1, DALYs)
+</rcode>
+== See also ==
+{{assessment|moderator=Jouni|status=ongoing}}
+{{Helsinki energy decision 2015}}
+{| class="wikitable collapsible collapsed"
+! Previous model runs and links
+|----
+|
+=== Previous model runs ===
+==== Final model ====
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=pKBjzl8RJnwvvrHA Model run 26.10.2015] nuisance power plants removed from plant-wise cash flow graphs.
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=nZL5q8sr2yvOi2xN Model run 25.10.2015] benefits always positive, costs negative
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=oap5s3pqqrZz9eAP Model run 22.10.2015] contains also energy consumption graphs but objects were not stored.
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=NN5k0XgmmQBsjWb3 Model run 22.10.2015] with Finnish texts and new fuel prices. Objects stored for later examination.
+* 5.10.2015 with Finnish texts [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=UcIL6G6OmFwk1sOg]
+* 10.9.2015 model run for the seminar 11.9.: [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=Lxpx944xwD5V0DBw Preference model] [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=JOI0ZXiISnNkUNK8 model part 1]  [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=k7zUPAo436wEbFAg model part 2]
+** An additional model run 25.9.2015 to show a directed acyclic graph of the model ovariables [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=ufoXJLoL8bJSDdki].
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=W9GaPYU04pdgLVtD Model run 9.9.2015] with old and new graphs. ''Custom'' option is the same as ''Cheapest'' in the preference analysis
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=eSpi8FBqfKbl7NKD Model run 6.9.2015]
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=E2jFkLL7QuCFQ0Tt Model run 5.9.2015]
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=d3bp5VSYGCagxhWl Model run 4.9.2015] Cost calculations are up to profit including external costs.
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=9PE7a4B1mBhUjlcR Model run 29.7.2015] Full run with lots of graphs.
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=4klHVl14kKW0RqDF What happens if you shut down both fossil and biofuel plants?]
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=lJf2oQkm1wjHOqtE What happens if you shut down coal and gas power plants?]
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=9c3Otq1qJIO19qzx What happens if you shut down both coal-fired power plants?]
+==== Intermediate calculations without plant optimising ====
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=vjTT6BjPtWIkLcAl Model run 2.7.2015] with intermediate result tables but a problem with totcases.
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=pP2FsmdaijIfcz80 Model run 5.7.2015]
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=any4I2Y1gXfWtxER Model run 6.7.2015] with corrected energyBalance model.
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=xmzcj8gvOZsYpScd Model run 7.7.2015]
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=PNSmPRBuFdxysbRO Model run 8.7.2015] with daily heat optimising
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=x0t1a3M44AeiWg4F Model run 9.7.2015] with also consumer electricity and cooling
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=KTNmnTh1Ai1FXOW2 Model run 9.7.2015] attempt to adjust decisions to plausible
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=VXYsYzhq5wihX8g0 Model run 11.7.2015] updated emission factors and decision options
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=oABWo0x0u6zsA8ZY Model run 12.7.2015]
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=ixOkV2SnjMo1Pne1 Model run 15.7.2015] the whole model works but there seems to be a unit conversion error or similar from energy balance on.
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=kZuzQaO3mnpkl8Wz Model run 15.7.2015] long-term plant activity and fuel use added
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=uqEh554tIfmDKjvW Model run 16.7.2015] used to store intermediate results for Answer code.
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=2EIjgeJ0OnJjfJcZ New model initial run 23.7.2015]
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=IxmleCN7hW80URAd Model run 24.7.2015]
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=GQGqRQUwrTX7lzzi Model run 27.7.2015]
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=ejNvy1EscdzooTxP Model run 27.7.2015] with shorter names and clearer graphs.
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=9PE7a4B1mBhUjlcR Model run 29.7.2015]
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=6KeCs0rS7dpdrtHQ Model run 14.8.2015] Total construction corrected but oil heating not yet. Objects saved.
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=yc7vpnJErCPYLoSJ Model run 16.8.2016] With first cost estimates
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=HrRkCq1EyAd0V7bj Model run 16.8.2015] Better cost estimates. Objects stored.
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=POPfXqpcyS9s7Adf Model run 17.8.2015] Incremental oil heating conversion to geothermal implemented.
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=5uDBil9PcGEsv9LJ Model run 29.8.2015] Fixed plot scaling
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=goJdvrDzXp3Toedb Model run 2.9.2015] with updated process and price parameters.
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=NTkgZ2NfWGeQhdMM Model run 4.9.2015] BAU and Helen's bio decision updated.
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=8hJVMdh8aAy1E0sV Model run 5.9.2015] with technical improvements
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=wCUnFoiRLYePywae] some bugs fixed, no the whole model works
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=4jqvkC2hjuJQcvPH Model run 12.10.2015] New temperature data from FMI and WWF energy saving policy (from 8.10.2015 report) added
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=64BJmkBocy0ZEKEu Model run 22.10.2015] with updated fuel prices (no graphs in this run)
+==== Preference order calculations ====
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=LwgipxX9IFCdAIRI Model run 8.9.2015]
+* [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=6XrzKix5Cbp0Ol9S Model run 9.9.2015] with much more graphs
+=== Relevant links ===
+* [https://www.entsoe.eu/Documents/Publications/Statistics/Factsheet/entsoe_sfs2014_web.pdf Entsoe Statistical fact sheet 2014]
+* [http://www.talouselama.fi/Tebatti/kysymykset/pienkuluttaja+on+tuulisahkon+maksumies/a2238751 Timo Korpela: Pienkuluttaja on tuulisähkön maksumies]
+* [http://www.hs.fi/mielipide/a1427173539858 Timo Korpela: Helsingin energiaratkaisu tulee harkita tarkoin]
 * http://energia.fi/sahkomarkkinat/sahkon-hinta-ja-sopimukset/sahkon-alkupera
 * http://energia.fi/julkaisut/sahkomarkkinavisio-2030 (ehkä vähän vanha, 2011??)
@@ Line 1,847: / Line 2,355: @@
 * [http://www.hel.fi/www/rakvv/fi/tietopankki/ohjeet/ Helsingin rakennusvalvonnan ohjeet]
 * [[:op_fi:Tiedosto:Helsingin_30_prosentin_päästövähennysselvitys.pdf|Helsingin 30 % päästövähennysselvitys]]
+|}
 == Keywords ==
-Energy, renewable energy, nuclear energy, fossil energy, wood pellets, power plants, district heating, decentralised energy production, centralised energy production, cost-effectiveness
+Energy, renewable energy, nuclear energy, fossil energy, wood pellets, power plants, district heating, decentralised energy production, centralised energy production, cost-effectiveness, Helsinki, climate change, health effects, fine particles, biofuels, energy saving.
 == References ==
@@ Line 1,859: / Line 2,369: @@
 * {{#l:Assessment of small-scale energy production in Helsinki Metropolitan area.zip}} {{defend|# |This contains all Pasi's assessment's data tables and is very useful!|--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 17:05, 14 May 2015 (UTC)}}
+</noinclude>

Difference between revisions of "Helsinki energy decision 2015"

Latest revision as of 13:39, 27 January 2016

Contents

Scope

Question

Intended use and users

Participants

Boundaries

Decisions and scenarios

Timing

Answer

Model with user interface

Results

Conclusions

Rationale

Stakeholders

Dependencies

Analyses

Indices

Calculations

Preference order

DALY shortcut

See also

Previous model runs

Final model

Intermediate calculations without plant optimising

Preference order calculations

Relevant links

Keywords

References

Related files

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