Helsinki energy production

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Question

What is the amount of energy produced (including distributed production) in Helsinki? Where is it produced (-> emissions)? Which processes are used in its production?

Rationale

Energy balance

Amount produced is determined largely by the energy balance in Helsinki and Helsinki energy consumption. Energy produced and fuels used by of all Helen's power plants.

Heat, power and cooling processes(MJ /MJ)

Obs	Plant	Electricity	Heat	Cooling	Coal	Gas	Fuel oil	Biofuel	Description
1	Hanasaari	0.31	0.60	0	-1	0	0	0	Assume 91 % efficiency. Capacity: electricity 220 MW heat 420 MW Loss 64 MW
2	Hanasaari biofuel renovation	0.31	0.60	0	-0.6	0	0	-0.4
3	Vuosaari A&B	0.47	0.44	0	0	-1	0	0	Capacity: electricity 630 MW heat 580 MW loss 121 MW
4	Vuosaari C biofuel	0.47	0.44	0	0	0	0	-1
5	Salmisaari A&B	0.32	0.59	0	-1	0	0	0	Capacity: electricity 160 MW heat 300 MW loss 46 MW
6	Salmisaari biofuel renovation	0.32	0.59	0	0	0	0	-1
7	Smaller gas heat plants around Helsinki	0	0.91	0	0	-1	0	0
8	Smaller fuel oil heat plants around Helsinki	0	0.91	0	0	0	-1	0
9	Katri Vala heat	-0.35	1	0	0	0	0	0	Heat from cleaned waste water and district heating network's returing water.
10	Katri Vala cooling	-0.55	0	1	0	0	0	0	District cooling produced by absorption (?) heat pumps.
11	Kymijoki River's plants	1	0	0	0	0	0	0	Hydropower
12	Powerplant museum in Vanhakaupunki	1	0	0	0	0	0	0	Hydropower
13	Kellosaari back-up plant	0.35	0	0	0	0	-1	0
14	Suvilahti power storage	1	0	0	0	0	0	0
15	Loviisa nuclear heat	-0.1	1	0	0	0	0	0
16	Neste oil refinery heat	-0.1	1	0	0	0	0	0
17	Household heat pumps	-0.4	1	0	0	0	0	0	The efficiency of heat pumps is largely dependent on outside air temperature. While they can easily reach very high efficiencies (5x) in a warm temperature, their efficiency as a heat source drops dramatically at low temperatures.
18	Deep-drill heat	-0.1	1	0	0	0	0	0
19	Sea heat pump	-0.2	1	0	0	0	0	0
20	Small-scale wood burning	0	0.5	0	0	0	0	-1
21	Air conditioning	-0.4	0	1	0	0	0	0
22	CHP diesel generators	0.3	0.4	0	0	0	-1	0

Plant parameters(MW,MW,M€,M€ /a,€ /MWh)

Obs	Year	Plant	Min	Max	Investment cost	Management cost	Operation cost	Description
1	2015	Hanasaari	0	640	100	10	4
2	2015	Hanasaari biofuel renovation	0	640	100	10	4
3	2015	Vuosaari A&B	0	1331	100	10	5
4	2015	Vuosaari C biofuel	0	1331	100	10	5
5	2015	Salmisaari A&B	0	506	100	10	4
6	2015	Salmisaari biofuel renovation	0	506	100	10	4
7	2015	Smaller heat plants around Helsinki	0	2015	100	10	10
8	2015	Katri Vala heat	0	90	100	10	3
9	2015	Katri Vala cooling	0	60	100	10	3
10	2015	Kymijoki River's plants	0	60	100	10	10	water
11	2015	Powerplant museum in Vanhakaupunki	0	0.2	100	10	0	water
12	2015	Kellosaari back-up plant	0	120	100	10	20	oil
13	2015	Suvilahti solar	0	0.34	100	10	5
14	2015	Suvilahti power storage	-1.2	1.2	100	10	5	electricity storage 0.6 MWh
15	2015	Wind mills	0	4	100	10	5
16	2015	Loviisa nuclear heat	0	3000	100	10	5
17	2015	Neste oil refinery heat	0	2000	100	10	5
18	2015	Household heat pumps	0	300	100	10	5
19	2015	Deep-drill heat	0	500	100	10	7
20	2015	Sea heat pump	0	500	100	10	4
21	2015	Small-scale wood burning	0	500	100	10	5
22	2015	Air conditioning	0	500	100	10	5
23	2015	CHP diesel generators	0	500	100	10	5

Non-adjustable energy production(MW)

Obs	Plant	Fuel	2015	2020	2030	2040	2050	2060
1	Suvilahti solar	Electricity	0	0	0	0	0	0
2	Wind mills	Electricity	0	0	0	0	0	0

⇤# : How to model non-adjustable energy production exactly? Probably needs a submodel instead of a t2b table. --Jouni (talk) 07:30, 27 June 2015 (UTC)

--# : Should air conditioning be in process matrix or in non-adjustable energy production? --Jouni (talk) 07:30, 27 June 2015 (UTC)

These equations aim to reflect the energy production facilities and capabilities.

Note! Maintenance cost only contains costs that do not depend on activity. Operational cost contains costs that depend on activity but NOT fuel costs; those are calculated separately based on energy produced.

+ Show code- Hide code ## This is a code Op_en7311 (no name) on page Helsinki energy production. library(OpasnetUtils) energyProcess <- Ovariable("energyProcess", ddata = "Op_en7311", subset = "Heat, power and cooling processes") plantParameters <- Ovariable("plantParameters", ddata = "Op_en7311", subset = "Plant parameters") objects.latest("Op_en4151", code_name="fuelPrice") requiredEnergy <- Ovariable("requiredEnergy", data = data.frame( RenovationPolicy = c("BAU", "Active"), Result = c(3000, 2500) )) requiredName <- "Heat" energyBalance <- Ovariable("energyBalance", dependencies = data.frame( Name = c( "energyProcess", # Matric showing inputs and outputs the processes of each plant. "fuelPrice", # Prices of fuels (by Time) "plantParameters", # Capacities and costs of running plants "requiredEnergy", # Energy demand that must be met by production plus all other energy #demand and supply that is not centrally controlled. "requiredName" # Fuel name for the energy type that is balanced for inputs and outputs. ), Ident = c( NA, "Op_en4151/fuelPrice", NA, NA, NA ) ), formula = function(...) { # First, define the optimising function optfunction <- function( x, # Variables to optimise (activities of power plants). requiredEnergy, # energy demand that must be met by supply (scalar) unitprice, # vector of prices per activity unit (by plant). unitRequired # vector of required energy produced per activity uniit (by plant). ) { out <- sum(x * unitprice) + (requiredEnergy - sum(x * unitRequired))^2 * 1E6 return(out) } # Operational costs per unit of activity. Fuel price is excluded and calculated elsewhere. operationCost <- plantParameters operationCost@output <- operationCost@output[operationCost@output$Parameter == "Operation cost" , ] operationCost <- unkeep(operationCost, cols = "Parameter") # Costs per unit of activity by plant. Fuel price included. Change into vector. unitprice <- oapply(energyProcess * fuelPrice * -1, cols = "Fuel", FUN = sum) + operationCost colnames(unitprice@output)[colnames(unitprice@output) == "plantParametersResult"] <- "unitpriceResult" unitprice@output$unitpriceResult <- result(unitprice) # Save result into another column # Required energy produced per unit activity by plant. Change into vector. unitRequired <- energyProcess unitRequired@output <- unitRequired@output[unitRequired@output$Fuel == requiredName , ] unitRequired <- unkeep(unitRequired, cols = "Fuel") # Put all inputs into one ovariable and sort by Plant. Note: important outputs in columns # unitpriceResult, requiredEnergyResult, energyProcessResult, and Result. temp <- (unitprice * unitRequired * requiredEnergy * 0 + unkeep(plantParameters, prevresults = TRUE, sources = TRUE) ) indices <- unique(temp@output[temp@marginal & !colnames(temp@output) %in% c("Plant", "Parameter")]) out <- data.frame() for(i in 1:nrow(indices)) { temp2 <- merge(indices[i , , drop = FALSE], temp@output) temp2 <- temp2[order(temp2$Plant) , ] temp3 <- optim( par = temp2[temp2$Parameter == "Min" , "Result"], # from plantParameters fn = optfunction, lower = temp2[temp2$Parameter == "Min" , "Result"], # from plantParameters upper = temp2[temp2$Parameter == "Max" , "Result"], # from plantParameters unitprice = temp2[temp2$Parameter == "Min" , "unitpriceResult"], # from unitprice requiredEnergy = temp2[temp2$Parameter == "Min" , "requiredEnergyResult"][1], # from requiredEnergy unitRequired = temp2[temp2$Parameter == "Min" , "energyProcessResult"], # from energyProcess method = "L-BFGS-B" ) out <- rbind(out, data.frame( indices[i , , drop = FALSE], Plant = temp2$Plant[temp2$Parameter == "Min"], Result = temp3$par )) } return(out) } ) energyBalance <- EvalOutput(energyBalance) test <- energyBalance * energyProcess oprint(oapply(test, cols = "Plant", FUN = sum))

Prices of fuels in heat production are used as direct inputs in the optimising.

Heating

Fuel use by heating type

Only district heating is Helsinki specific, rest should be taken from Emission factors for burning processes.

Fuel use by heating type(-)

Obs	Heating	Burner	Fuel	Fraction	Description
1	District	Large fluidized bed	Coal	0.6
2	District	Large fluidized bed	Gas	0.4
3	District	Large fluidized bed	Wood	0

+ Show code- Hide code ## This code is Op_en7311/fuelShares on page [[Helsinki energy production]] library(OpasnetUtils) fuelShares <- Ovariable("fuelShares", dependencies = data.frame( Name = "fuelSharesgeneric", Ident = "Op_en2791/fuelSharesgeneric" # [[Emission factors for burning processes]] ), formula = function(...) { dat <- EvalOutput(Ovariable("dat", ddata = "Op_en7311.fuel_use_by_heating_type")) # [[Helsinki energy production]] colnames(dat@output) <- gsub("[ \\.]", "_", colnames(dat@output)) out <- combine(fuelSharesgeneric, dat, name = "fuelShares") out <- out * Ovariable(output = data.frame(Time = 1900:2080, Result = 1), marginal = c(TRUE, FALSE)) out <- unkeep(out, prevresults = TRUE, sources = TRUE) return(out) } ) objects.store(fuelShares) cat("Object fuelShares stored.\n")

Emission locations

Emission location and height by heating type.

Emission locations(-)

Obs	Heating	Emission_site	Emission_height	Dummy
1	District	010	High
2	Electricity	010	High
3	Geothermal	010	High
4	Oil	At site of consumption	Ground
5	Wood	At site of consumption	Ground
6	Gas	At site of consumption	Ground
7	Coal	At site of consumption	Ground

This code creates technical ovariables emissionLocations and heatingShares that are needed to run the Building model and its ovariables buildings and heatingEnergy.

+ Show code- Hide code ## This code is Op_en7311/emissionLocations [[Helsinki energy production]] library(OpasnetUtils) emissionLocations <- Ovariable("emissionLocations", ddata = "Op_en7311", subset = "Emission locations") colnames(emissionLocations@data) <- gsub("[ \\.]", "_", colnames(emissionLocations@data)) emissionLocations@data$emissionLocationsResult <- 1 heatingShares <- 1 # This is already in the Basel data. objects.store(emissionLocations, heatingShares) cat("Objects emissionsLocations, heatingShares stored.\n")

Production and emission statistics

Helsingin Energia energy sold in 2013 (GWh)

Electricity	7145
District heat and steam	6807
District cooling	116

Calculations

+ Show code- Hide code library(OpasnetUtils) emissionLocations <- Ovariable("emissionLocations", ddata = "Op_en7311/Emission_locations") result(emissionLocations) <- 1 objects.store( emissionLocations ) cat("Objects emissionLocations stored.\n")

See also

• The model for small-scale energy production in Helsinki Metropolitan area is made by Gabi 4.3 - life cycle assessment software. It is a versatile program which can be used for life cycle assessment, cost analysis, efficiency analysis, Global Reporting Initiative-reports (GRI) and companies annual reports.