Helsinki energy decision 2015

This page is a knowledge crystal of subtype assessment. The page identifier is Op_en7237
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Main message:

Question: 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. 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. 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.

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?

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.

Show details

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, Olli the City of Helsinki, Helen energy company Uusi energiapolitiikka group Energiaremontti 2015 Boundaries Time: 2015 - 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 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.

1. BAU: Only small, essential renovations are made to current power plants to stay within new emission limits.
2. 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.
3. Hanasaari shutdown: The Hanasaari powerplant is shut down, demolished and apartment buildings are built in its place.
4. Hanasaari 40 bio: The Hanasaari power plant is renovated to burn 40% wood-based fuels and 60% coal.
5. Salmisaari 40 bio: The Salmisaari power plants are renovated to burn 40% wood-based fuels and 60% coal.
6. Biofueled heat production units: Salmisaari oilfueled heat plant is shut down and new biofuel burning heat plants are built in Salmisaari and Vuorsaari.
7. Loviisa nuclear CHP: A third nuclear power plant is built in Loviisa and the heat is used for district heating in Helsinki.
8. Neste excess heat: The excess heat from the Neste's oil refinery in Porvoo is used for district heating in Helsinki.
9. 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.
10. Large heat pumps: Big heat pumps are installed to draw heat from the Baltic sea or deep from the ground to produce district heating.
11. Energy saving: With huge energy saving campaigns and by renovating buildings to be more energy efficient the amount of required energy is decreased significantly.

Show details

Decisions(-) Obs Decision_maker Decision Option Variable Cell Change Unit Amount Description 1 Builders EnergySavingPolicy BAU efficiencyShares Add 0 2 Builders EnergySavingPolicy Energy saving moderate efficiencyShares Add 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 BAU renovationRate Multiply 1 /a 1 Assumes BAU renovation rate = 1%/a for buildings >30 a old 8 Building owner EnergySavingPolicy Energy saving moderate renovationRate Multiply 1 /a 2 9 Building owner EnergySavingPolicy Energy saving total renovationRate Multiply 1 /a 4 10 Building owner EnergySavingPolicy BAU renovationShares Add fraction 0 11 Building owner EnergySavingPolicy Energy saving moderate renovationShares Add fraction 0 12 Building owner EnergySavingPolicy Energy saving total renovationShares Renovation:Windows Add fraction -0.25 13 Building owner EnergySavingPolicy Energy saving total renovationShares Renovation:Sheath reform Add fraction 0.25 14 Helen PlantPolicy BAU plantParameters Plant:Vuosaari C biofuel,Loviisa nuclear heat,Neste oil refinery heat;Parameter:Max Replace MW 0 15 Helen PlantPolicy BAU plantParameters Plant:Vuosaari C biofuel,Loviisa nuclear heat,Neste oil refinery heat;Parameter:Investment cost Replace M€ 0 16 Helen PlantPolicy Biofuels and process heat plantParameters Plant:Vuosaari C biofuel,Loviisa nuclear heat,Neste oil refinery heat;Parameter:Max Identity MW 0 17 Helen PlantPolicy Biofuels and process heat energyProcess Plant:Hanasaari;Fuel:Biofuel;Time:>=2018 Add MJ/MJ -0.4 18 Helen PlantPolicy Biofuels and process heat energyProcess Plant:Hanasaari;Fuel:Coal;Time:>=2018 Add MJ/MJ 0.4 19 Helen PlantPolicy Biofuels and process heat plantParameters Plant:Hanasaari;Time:>=2018;Time:<=2060;Parameter:Max Replace MW 640 20 Helen PlantPolicy Biofuels and process heat plantParameters Plant:Hanasaari;Time:>=2018;Parameter:Investment cost Replace M€ 100 21 Helen PlantPolicy BAU energyProcess Identity MJ/MJ 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.
• Plant policy: Choose an optimal selection of power plant infrastructure
  • BAU: Old plants remain as planned but Vuosaari C biofuel, Loviisa nuclear heat, and Neste oil refinery heat are not built.
  • Biofuels and process heat: Old plants remain as planned. In addition, Vuosaari C biofuel, Loviisa nuclear heat, and Neste oil refinery heat are built.

←--#: . If you have ideas about options that should be modelled, please just contact us and we'll try and adjust the model accordingly. --Jouni (talk) 08:54, 15 July 2015 (UTC) (type: truth; paradigms: science: defence)

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^st August 2015 at noon.

Answer

Current results:
Model run 29.7.2015 Full run with lots of graphs.

• What happens if you shut down both fossil and biofuel plants?
• What happens if you shut down coal and gas power plants?
• What happens if you shut down both coal-fired power plants?

Choose power plants to shut down in 2035 (default is all active). This will become PlantPolicy: Custom.: Biofueled heat production units CHP diesel generators Deep-drill heat Hanasaari Household air heat pumps Household air heat pumps for cooling Household geothermal heat Household solar Katri Vala cooling Katri Vala heat Kellosaari back-up plant Kymijoki River plants Loviisa nuclear heat Neste oil refinery heat Powerplant museum in Vanhakaupunki Salmisaari A&B Salmisaari biofuel renovation Sea heat pump Sea heat pump for cooling Small-scale wood burning Smaller gas heat plants around Helsinki Smaller fuel oil heat plants around Helsinki Suvilahti power storage Suvilahti solar Vuosaari A Vuosaari B Vuosaari C biofuel Wind mills Choose power plants to be renovated (PlantPolicy: Custom): Hanasaari biofuel renovation + Show code- Hide code #Smaller fuel oil heat plants around Helsinki Smaller gas heat plants around Helsinki library(OpasnetUtils) library(ggplot2) openv.setN(0) # use medians instead of whole sampled distributions BS <- 24 # b # Download all pre-calculated inputs, e.g. building stock. objects.latest("Op_en7237", code_name = "intermediates") # [[Helsinki energy decision 2015]] #plants <- union(as.character(unique(plantParameters@data$Plant)), as.character(unique(energyProcess@data$Plant))) shutdown <- c(shutdown, "None") customdecisions <- data.frame( Obs = NA, Decision_maker = "Helen", Decision = "PlantPolicy", Option = "Custom", Variable = c("plantParameters", "energyProcess"), Cell = c(paste("Plant:", paste(shutdown, collapse = ","), ";Parameter:Max;Time:>=2035"), ""), Change = c("Replace", "Identity"), Unit = NA, Result = 0 ) decisions <- rbind(decisions, customdecisions) if ("Hanasaari" %in% renovation) { customdecisions <- data.frame( Obs = NA, Decision_maker = "Helen", Decision = "PlantPolicy", Option = "Custom", Variable = "plantParameters", Cell = c("Plant:Hanasaari;Time:>=2018;Time:<=2060;Parameter:Max", "Plant:Hanasaari;Time:>=2018;Parameter:Investment cost"), Change = "Replace", Unit = NA, Result = 100 ) decisions <- rbind(decisions, customdecisions) customdecisions <- data.frame( Obs = NA, Decision_maker = "Helen", Decision = "PlantPolicy", Option = "Custom", Variable = "energyProcess", Cell = c("Plant:Hanasaari;Fuel:Biofuel;Time:>=2018", "Plant:Hanasaari;Fuel:Coal;Time:>=2018"), Change = "Add", Unit = NA, Result = c(-0.40, 0.40) ) decisions <- rbind(decisions, customdecisions) } DecisionTableParser(decisions) cat("Plants that will be shut down in the option Plant policy/Custom are:", shutdown, "\n") fuelUse <- EvalOutput(fuelUse) fuelUse$Fuel <- factor( fuelUse$Fuel, levels = c( "Biofuel", "Coal", "Fuel oil", "Gas", "Light oil", "Wood", "Electricity", "Heat", "Cooling" ), ordered = TRUE ) # Consumption in energyBalance is negative, therefore multiply by -1. #fuelUse <- energyBalance * temperdays * -1 * 3600 * 24 # From MW -> MJ /a. # This is used for continuous energy use. #fuelUse <- oapply(fuelUse, cols = c("Temperature", "Burner", "Consumable"), FUN = sum) #emissions <- EvalOutput(emissions) # Calculate exposure based on average iF. #exposure <- EvalOutput(exposure) #exposure@output <- exposure@output[exposure@output$Area == "Average" , ] #exposure <- oapply(exposure, cols = c("Plant", "Emission_site", "Emission_height", "Area"), FUN = sum) #totcases <- EvalOutput(totcases) #DALYs <- EvalOutput(DALYs) ############################ Output tables and graphs #oprint(decisions) #oprint(plantParameters[plantParameters$Plant == "Loviisa nuclear heat" & plantParameters$Parameter == "Max", ]) #debug #oprint(head(buildings@output)) #oprint(head(nondynsupply@output)) #oprint(head(energyUse@output)) #oprint(head(EnergyConsumerDemand@output)) #oprint(head(energy@output)) #oprint(head(energyBalance@output)) #oprint(head(EnergyNetworkOptim@output)) #oprint(head(emissions@output)) #oprint(head(exposure@output)) #oprint(head(totcases@output)) #oprint(head(DALYs@output)) ####################### Post-processing and graphs eb <-EnergyNetworkOptim[EnergyNetworkOptim$Process_variable_type == "Activity",] colnames(eb@output)[colnames(eb@output) == "Process_variable_name"] <- "Plant" eb$Process_variable_type <- NULL ebtemp <- eb ebtemp@output <- subset(eb@output, Time %in% c("2035") & EnergySavingPolicy == "BAU" & PlantPolicy == "BAU") ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 7) ggplot( ebtemp@output, aes(x = Plant, weight = EnergyNetworkOptimResult, fill = Plant) ) + geom_bar() + facet_wrap( ~ Temperature) + theme_gray(base_size = BS) + theme(axis.text.x = element_blank()) + # Turn text and adjust to right labs( title = "Power plant activity by temperature daily optim \nPlant policy = BAU, Year = 2035", #x = "Plant", y = "Power output daily average (MW)" ) ebtemp <- eb ebtemp@output <- subset(eb@output, Time %in% c("2035") & EnergySavingPolicy == "BAU" & PlantPolicy == "Custom") ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 7) ggplot(ebtemp@output, aes(x = Plant, weight = EnergyNetworkOptimResult, fill = Plant))+geom_bar()+ facet_wrap( ~ Temperature) + theme_gray(base_size = BS) + theme(axis.text.x = element_blank()) + # Turn text and adjust to right labs( title = "Power plant activity by temperature daily optim \nPlant policy = Custom, Year = 2035", #x = "Plant", y = "Power output daily average (MW)" ) fu <- fuelUse #result(fu) <- round(result(fu), 12) # Round results smaller than 1e-12 to 0 fu$Burner <- NULL #fu <- truncateIndex(fuelUse / 3.6E+6, cols = "Plant", bins = 7) # From MJ/a -> GWh/a futemp <- fu / 3.6E+6 # From MJ/a -> GWh/a futemp@output <- subset(fu@output, Time %in% c("2015", "2035", "2065") & PlantPolicy == "BAU") futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) ggplot(futemp@output, aes(x = Fuel, fill = Plant)) + geom_bar( data = subset(futemp@output, fuelUseResult > 0), aes(weight = -1 * fuelUseResult), position="stack" ) + geom_bar( data = subset(futemp@output, fuelUseResult < 0), aes(weight = -1 * fuelUseResult), position="stack" ) + geom_hline(aes(yintercept=0)) + facet_grid(Time ~ EnergySavingPolicy) + theme_gray(base_size = BS) + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) + # Turn text and adjust to right labs( title = "Energy commodity flows \n Plant policy = BAU", y = "Total annual energy (GWh/a)" ) futemp <- fu / 3.6E+6 # From MJ/a -> GWh/a futemp@output <- subset(fu@output, Time %in% c("2015", "2035", "2065") & PlantPolicy == "Custom") futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) ggplot(futemp@output, aes(x = Fuel, fill = Plant)) + geom_bar( data = subset(futemp@output, fuelUseResult > 0), aes(weight = -1 * fuelUseResult), position="stack" ) + geom_bar( data = subset(futemp@output, fuelUseResult < 0), aes(weight = -1 * fuelUseResult), position="stack" ) + geom_hline(aes(yintercept=0)) + facet_grid(Time ~ EnergySavingPolicy) + theme_gray(base_size = BS) + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) + # Turn text and adjust to right labs( title = "Energy commodity flows \n Plant policy = Custom", y = "Total annual energy (GWh/a)" ) futemp <- fu * 1E-6 # From MJ/a -> TJ/a futemp@output <- subset(fu@output, Fuel %in% c("Heat")) futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) futemp@output$Time <- as.numeric(as.character(futemp@output$Time)) ggplot(futemp@output, aes(x = Time, fill = Plant)) + geom_bar( data = subset(futemp@output, fuelUseResult > 0), aes(weight = -1 * fuelUseResult), position="stack", binwidth = 5 ) + geom_bar( data = subset(futemp@output, fuelUseResult < 0), aes(weight = -1 * fuelUseResult), position="stack", binwidth = 5 ) + geom_hline(aes(yintercept=0)) + #geom_bar(binwidth = 5)+ facet_grid(EnergySavingPolicy ~ PlantPolicy) + theme_gray(base_size = BS) + labs( title = "District heat flow", #x = "Time", y = "Total annual energy (TJ/a)" ) futemp <- fu * 1E-6 # From MJ/a -> TJ/a futemp@output <- subset(fu@output, Fuel %in% c("Electricity")) futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) futemp@output$Time <- as.numeric(as.character(futemp@output$Time)) ggplot(futemp@output, aes(x = Time, fill = Plant)) + geom_bar( data = subset(futemp@output, fuelUseResult > 0), aes(weight = -1 * fuelUseResult), position="stack", binwidth = 5 ) + geom_bar( data = subset(futemp@output, fuelUseResult < 0), aes(weight = -1 * fuelUseResult), position="stack", binwidth = 5 ) + geom_hline(aes(yintercept=0)) + #geom_bar(binwidth = 5)+ facet_grid(EnergySavingPolicy ~ PlantPolicy) + theme_gray(base_size = BS) + labs( title = "Electricity flow", #x = "Time", y = "Total annual energy (TJ/a)" ) futemp <- fu * 1E-6 # From MJ/a -> TJ/a futemp@output <- subset(fu@output, Fuel %in% c("Cooling")) futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) futemp@output$Time <- as.numeric(as.character(futemp@output$Time)) ggplot(futemp@output, aes(x = Time, fill = Plant)) + geom_bar( data = subset(futemp@output, fuelUseResult > 0), aes(weight = -1 * fuelUseResult), position="stack", binwidth = 5 ) + geom_bar( data = subset(futemp@output, fuelUseResult < 0), aes(weight = -1 * fuelUseResult), position="stack", binwidth = 5 ) + geom_hline(aes(yintercept=0)) + #geom_bar(binwidth = 5)+ facet_grid(EnergySavingPolicy ~ PlantPolicy) + theme_gray(base_size = BS) + labs( title = "Cooling flow", #x = "Time", y = "Total annual energy (TJ/a)" )

Results

• 

  Daily average expenditure and production of energy in the city of Helsinki depending on the outside temperature for business-as-usual (BAU) scenario and year 2035.

• 

  Total annual expenditure and production of energy in the city of Helsinki in years 2015, 2035 and 2065 depending on the energy policy adopted.

• 

  Current data and estimation of future building stock of Helsinki by heating type.

• 

  Energy used in heating in Helsinki by energy saving policy and different energy renovations that are done to buildings older than 30 a.

Rationale

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, 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) (type: truth; paradigms: science: defence)
energyUse Baseline energy consumption [2] ←--#: . Update this to use U values and ambient temperatures. --Jouni (talk) 12:22, 24 May 2015 (UTC) (type: truth; paradigms: science: defence) 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 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) (type: truth; paradigms: science: comment)
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) (type: truth; paradigms: science: comment)
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	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) (type: truth; paradigms: science: 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. --Jouni (talk) 12:22, 24 May 2015 (UTC) (type: truth; paradigms: science: comment)	Fuel_type.
emissionFactors Emission factors for burning processes	emissions per unit of energy produced (g / J or similar unit)	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) (type: truth; paradigms: science: attack) ⇤--#: . Rather use iFs based on Piltti? --Jouni (talk) 12:22, 24 May 2015 (UTC) (type: truth; paradigms: science: attack) 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) (type: truth; paradigms: science: comment)
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) (type: truth; paradigms: science: comment)	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 ?	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.

• Other parts of model: Helsinki energy consumption Energy use of buildings

Calculations

• Model run 2.7.2015 with intermediate result tables but a problem with totcases.
• Model run 5.7.2015
• Model run 6.7.2015 with corrected energyBalance model.
• Model run 7.7.2015
• Model run 8.7.2015 with daily heat optimising
• Model run 9.7.2015 with also consumer electricity and cooling
• Model run 9.7.2015 attempt to adjust decisions to plausible
• Model run 11.7.2015 updated emission factors and decision options
• Model run 12.7.2015
• Model run 15.7.2015 the whole model works but there seems to be a unit conversion error or similar from energy balance on.
• Model run 15.7.2015 long-term plant activity and fuel use added
• Model run 16.7.2015 used to store intermediate results for Answer code.
• New model initial run 23.7.2015
• Model run 24.7.2015
• Model run 27.7.2015
• Model run 27.7.2015 with shorter names and clearer graphs.
• Model run 29.7.2015

+ Show code- Hide code ## 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 = "energyBalancetest") # [[Energy balance]] objects.latest("Op_en5141", code_name = "EnergyNetworkOptim") # [[Energy balance]] ### 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 = "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. #########################i-------------------------------------------------- ################# Actual model and some intermediate processing. DecisionTableParser(decisions) # Remove previous decisions, if any. forgetDecisions <- function() { for(i in ls(envir = openv)) { if("dec_check" %in% names(openv[[i]])) openv[[i]]$dec_check <- FALSE } return(cat("Decisions were forgotten.\n")) } forgetDecisions() renovationRate <- EvalOutput(renovationRate) * 10 # Rates for 10-year periods buildings <- EvalOutput(buildings) buildings@output$EnergySavingPolicy <- factor( buildings@output$EnergySavingPolicy, levels = c("BAU", "Energy saving moderate", "Energy saving total"), ordered = TRUE ) #energyUse <- EvalOutput(energyUse) # energy <- energyUse * 1E-3 / 365.25 / 24 # kWh /a -> MW This is used for annual energy consumption #energy <- energyUse / 1E+6 # W -> MW # This is used for continuous energy use. #energyBalance <- EvalOutput(energyBalance) #EnergyConsumerDemand <- EvalOutput(EnergyConsumerDemand) fuelUse <- EvalOutput(fuelUse) fuelUse$Fuel <- factor( fuelUse$Fuel, levels = c( "Biofuel", "Coal", "Fuel oil", "Gas", "Light oil", "Wood", "Electricity", "Heat", "Cooling" ), ordered = TRUE ) # Consumption in energyBalance is negative, therefore multiply by -1. #fuelUse <- energyBalance * temperdays * -1 * 3600 * 24 # From MW -> MJ /a. # This is used for continuous energy use. #fuelUse <- oapply(fuelUse, cols = c("Temperature", "Burner", "Consumable"), FUN = sum) emissions <- EvalOutput(emissions) # Calculate exposure based on average iF. exposure <- EvalOutput(exposure) exposure@output <- exposure@output[exposure@output$Area == "Average" , ] exposure <- oapply(exposure, cols = c("Plant", "Emission_site", "Emission_height", "Area"), FUN = sum) totcases <- EvalOutput(totcases) DALYs <- EvalOutput(DALYs) ############################ Output tables and graphs oprint(head(buildings@output)) #oprint(head(nondynsupply@output)) #oprint(head(energyUse@output)) oprint(head(EnergyConsumerDemand@output)) #oprint(head(energy@output)) #oprint(head(energyBalance@output)) oprint(head(EnergyNetworkOptim@output)) oprint(head(emissions@output)) oprint(head(exposure@output)) oprint(head(totcases@output)) oprint(head(DALYs@output)) ####################### Post-processing and graphs bui <- oapply(buildings * 1E-6, cols = c("City_area", "buildingsSource"), FUN = sum) bui <- truncateIndex(bui, cols = "Heating", bins = 4)@output ggplot(subset(bui, EnergySavingPolicy == "BAU"), aes(x = Time, weight = buildingsResult, fill = Heating)) + geom_bar(binwidth = 5) + theme_gray(base_size = BS) + labs( title = "Building stock in Helsinki by heating", x = "Time", y = "Floor area (M m2)" ) if(figstofile) ggsave("Figure6.eps", width = 8, height = 7) ggplot(bui, aes(x = Time, weight = buildingsResult, fill = Efficiency)) + geom_bar(binwidth = 5) + facet_grid(. ~ EnergySavingPolicy) + theme_gray(base_size = BS) + labs( title = "Building stock in Helsinki by efficiency policy", x = "Time", y = "Floor area (M m2)" ) ggplot(bui, aes(x = Time, weight = buildingsResult, fill = Renovation)) + geom_bar(binwidth = 5) + theme_gray(base_size = BS) + facet_grid(. ~ EnergySavingPolicy) + labs( title = "Building stock in Helsinki", x = "Time", y = "Floor area (M m2)" ) ggplot(subset(bui, EnergySavingPolicy == "BAU"), aes(x = Time, weight = buildingsResult, fill = Building)) + geom_bar(binwidth = 5) + theme_gray(base_size = BS) + labs( title = "Building stock in Helsinki", x = "Time", y = "Floor area (M m2)" ) ggplot(buildings@output, aes(x = Time, weight = buildingsResult, fill = Efficiency))+geom_bar(binwidth = 5)+ facet_grid(EnergySavingPolicy ~ Renovation) emissions@output$Time <- as.numeric(as.character(emissions@output$Time)) # Plot energy need and emissions hea <- EnergyConsumerDemand * temperdays * 24 * 1E-9 # From W -> GWh /a. hea@output <- hea@output[hea@output$Consumable == "Heating" , ] hea <- oapply(hea, cols = c("City_area", "buildingsSource"), FUN = sum) hea <- truncateIndex(hea, cols = "Heating", bins = 4)@output ggplot(hea, aes(x = Time, weight = Result, fill = Renovation)) + geom_bar(binwidth = 5) + facet_wrap( ~ EnergySavingPolicy) + theme_gray(base_size = BS) + labs( title = "Energy used in heating in Helsinki", x = "Time", y = "Heating energy (GWh /a)" ) eb <-EnergyNetworkOptim[EnergyNetworkOptim$Process_variable_type == "Activity",] colnames(eb@output)[colnames(eb@output) == "Process_variable_name"] <- "Plant" eb$Process_variable_type <- NULL ebtemp <- eb ebtemp@output <- subset(eb@output, Time %in% c("2035") & EnergySavingPolicy == "BAU" & PlantPolicy == "BAU") ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 7) ggplot( ebtemp@output, aes(x = Plant, weight = EnergyNetworkOptimResult, fill = Plant) ) + geom_bar() + facet_wrap( ~ Temperature) + theme_gray(base_size = BS) + theme(axis.text.x = element_blank()) + # Turn text and adjust to right labs( title = "Power plant activity by temperature daily optim \nPlant policy = BAU, Year = 2035", #x = "Plant", y = "Power output daily average (MW)" ) ebtemp <- eb ebtemp@output <- subset(eb@output, Time %in% c("2035") & EnergySavingPolicy == "BAU" & PlantPolicy == "Biofuels and process heat") ebtemp <- truncateIndex(ebtemp, cols = "Plant", bins = 7) ggplot(ebtemp@output, aes(x = Plant, weight = EnergyNetworkOptimResult, fill = Plant))+geom_bar()+ facet_wrap( ~ Temperature) + theme_gray(base_size = BS) + theme(axis.text.x = element_blank()) + # Turn text and adjust to right labs( title = "Power plant activity by temperature daily optim \nPlant policy = Biofuels and process heat option, Year = 2035", #x = "Plant", y = "Power output daily average (MW)" ) fu <- fuelUse #result(fu) <- round(result(fu), 12) # Round results smaller than 1e-12 to 0 fu$Burner <- NULL #fu <- truncateIndex(fuelUse / 3.6E+6, cols = "Plant", bins = 7) # From MJ/a -> GWh/a futemp <- fu / 3.6E+6 # From MJ/a -> GWh/a futemp@output <- subset(fu@output, Time %in% c("2015", "2035", "2065") & PlantPolicy == "BAU") futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) ggplot(futemp@output, aes(x = Fuel, fill = Plant)) + geom_bar( data = subset(futemp@output, fuelUseResult > 0), aes(weight = -1 * fuelUseResult), position="stack" ) + geom_bar( data = subset(futemp@output, fuelUseResult < 0), aes(weight = -1 * fuelUseResult), position="stack" ) + geom_hline(aes(yintercept=0)) + facet_grid(Time ~ EnergySavingPolicy) + theme_gray(base_size = BS) + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) + # Turn text and adjust to right labs( title = "Energy commodity flows \n Plant policy = BAU", y = "Total annual energy (GWh/a)" ) futemp <- fu / 3.6E+6 # From MJ/a -> GWh/a futemp@output <- subset(fu@output, Time %in% c("2015", "2035", "2065") & PlantPolicy == "Biofuels and process heat") futemp <- truncateIndex(futemp, cols = "Plant", bins = 7) ggplot(futemp@output, aes(x = Fuel, fill = Plant)) + geom_bar( data = subset(futemp@output, fuelUseResult > 0), aes(weight = -1 * fuelUseResult), position="stack" ) + geom_bar( data = subset(futemp@output, fuelUseResult < 0), aes(weight = -1 * fuelUseResult), position="stack" ) + geom_hline(aes(yintercept=0)) + facet_grid(Time ~ EnergySavingPolicy) + theme_gray(base_size = BS) + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) + # Turn text and adjust to right labs( title = "Energy commodity flows \n Plant policy = Biofuels and process heat", y = "Total annual energy (GWh/a)" ) futemp <- fu * 1E-6 # From MJ/a -> TJ/a futemp@output <- subset(fu@output, Fuel %in% c("Heat")) futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) futemp@output$Time <- as.numeric(as.character(futemp@output$Time)) ggplot(futemp@output, aes(x = Time, fill = Plant)) + geom_bar( data = subset(futemp@output, fuelUseResult > 0), aes(weight = -1 * fuelUseResult), position="stack", binwidth = 5 ) + geom_bar( data = subset(futemp@output, fuelUseResult < 0), aes(weight = -1 * fuelUseResult), position="stack", binwidth = 5 ) + geom_hline(aes(yintercept=0)) + #geom_bar(binwidth = 5)+ facet_grid(EnergySavingPolicy ~ PlantPolicy) + theme_gray(base_size = BS) + labs( title = "District heat flow", #x = "Time", y = "Total annual energy (TJ/a)" ) futemp <- fu * 1E-6 # From MJ/a -> TJ/a futemp@output <- subset(fu@output, Fuel %in% c("Electricity")) futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) futemp@output$Time <- as.numeric(as.character(futemp@output$Time)) ggplot(futemp@output, aes(x = Time, fill = Plant)) + geom_bar( data = subset(futemp@output, fuelUseResult > 0), aes(weight = -1 * fuelUseResult), position="stack", binwidth = 5 ) + geom_bar( data = subset(futemp@output, fuelUseResult < 0), aes(weight = -1 * fuelUseResult), position="stack", binwidth = 5 ) + geom_hline(aes(yintercept=0)) + #geom_bar(binwidth = 5)+ facet_grid(EnergySavingPolicy ~ PlantPolicy) + theme_gray(base_size = BS) + labs( title = "Electricity flow", #x = "Time", y = "Total annual energy (TJ/a)" ) futemp <- fu * 1E-6 # From MJ/a -> TJ/a futemp@output <- subset(fu@output, Fuel %in% c("Cooling")) futemp <- truncateIndex(futemp, cols = "Plant", bins = 10) futemp@output$Time <- as.numeric(as.character(futemp@output$Time)) ggplot(futemp@output, aes(x = Time, fill = Plant)) + geom_bar( data = subset(futemp@output, fuelUseResult > 0), aes(weight = -1 * fuelUseResult), position="stack", binwidth = 5 ) + geom_bar( data = subset(futemp@output, fuelUseResult < 0), aes(weight = -1 * fuelUseResult), position="stack", binwidth = 5 ) + geom_hline(aes(yintercept=0)) + #geom_bar(binwidth = 5)+ facet_grid(EnergySavingPolicy ~ PlantPolicy) + theme_gray(base_size = BS) + labs( title = "Cooling flow", #x = "Time", y = "Total annual energy (TJ/a)" ) emis <- truncateIndex(emissions, cols = "Emission_site", bins = 5)@output ggplot(subset(emis, PlantPolicy == "BAU"), aes(x = Time, weight = emissionsResult, fill = Fuel)) + geom_bar(binwidth = 5) + facet_grid(Pollutant ~ EnergySavingPolicy, scale = "free_y") + theme_gray(base_size = BS) + labs( title = "Emissions from heating in Helsinki", x = "Time (Plant policy = BAU)", y = "Emissions (ton /a)" ) ggplot(subset(emis, PlantPolicy == "Biofuels and process heat"), aes(x = Time, weight = emissionsResult, fill = Emission_site)) + geom_bar(binwidth = 5) + facet_grid(Pollutant ~ EnergySavingPolicy, scale = "free_y") + theme_gray(base_size = BS) + labs( title = "Emissions from heating in Helsinki", x = "Time (Plant policy = Biofuels and process heat)", y = "Emissions (ton /a)" ) ggplot(subset(emis, EnergySavingPolicy == "BAU"), aes(x = Time, weight = emissionsResult, fill = Fuel)) + geom_bar(binwidth = 5) + facet_grid(Pollutant ~ PlantPolicy, scale = "free_y") + theme_gray(base_size = BS) + labs( title = "Emissions from heating in Helsinki", x = "Time (Energy saving policy = BAU)", y = "Emissions (ton /a)" ) ggplot(exposure@output, aes(x = Time, weight = exposureResult, fill = Fuel)) + geom_bar(binwidth = 5) + facet_grid(EnergySavingPolicy ~ Exposure_agent) + theme_gray(base_size = BS) + labs( title = "Exposure to PM2.5 from heating in Helsinki", x = "Time", y = "Average PM2.5 (µg/m3)" ) ggplot(exposure@output, aes(x = Time, weight = exposureResult, fill = Fuel)) + geom_bar(binwidth = 5) + facet_grid(EnergySavingPolicy ~ .) + theme_gray(base_size = BS) + labs( title = "Exposure to PM2.5 from heating in Helsinki", x = "Time", y = "Average PM2.5 (µg/m3)" ) ggplot(subset(totcases@output, PlantPolicy == "BAU"), aes(x = Time, weight = totcasesResult, fill = Fuel))+geom_bar(binwidth = 5) + facet_grid(Response ~ EnergySavingPolicy) + theme_gray(base_size = BS) + labs( title = "Health effects of PM2.5 from heating in Helsinki", x = "Time (Plant policy = BAU)", y = "Health effects (deaths /a)" ) if(figstofile) ggsave("Figure8.eps", width = 11, height = 7) cat("Total DALYs/a by different combinations of policy options.\n") temp <- DALYs temp@output <- subset( temp@output, as.character(Time) %in% c("2015", "2035") & 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 ) ggplot(subset(DALYs@output, Response == "Total mortality"), aes(x = Time, weight = DALYsResult, fill = Fuel))+geom_bar(binwidth = 5) + facet_grid(EnergySavingPolicy ~ PlantPolicy) + theme_gray(base_size = BS) + labs( title = "Health effects in DALYs of PM2.5 from heating in Helsinki", x = "Time", y = "Health effects (DALY /a)" ) ggplot(subset(DALYs@output, Time == 2035), aes(x = EnergySavingPolicy, weight = DALYsResult, fill = Response))+geom_bar() + theme_gray(base_size = BS) + labs( title = "Health effects in DALYs of PM2.5 from heating in Helsinki 2030", x = "Biofuel policy in district heating", y = "Health effects (DALY /a)" ) 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() # Super short version objects.store( BS, buildings, decisions, EnergyConsumerDemand, EnergyConsumerDemandTotal, EnergyFlowHeat, EnergyNetworkDemand, EnergyNetworkOptim, energyProcess, fuelPrice, fuelUse, plantParameters, temperdays, truncateIndex ) #objects.store( # bgexposure, BS, buildings, BW, changeBuildings, DALYs, decisions, disincidence, # dose, dummy, DW, emissionFactors, emissionLocations, emissions, EnergyConsumerDemand, # EnergyConsumerDemandTotal, EnergyFlowHeat, EnergyFlowOther, EnergyNetworkDemand, EnergyNetworkOptim, # energyProcess, ERF, exposure, findrest, forgetDecisions, # frexposed, fuelPrice, fuelShares, fuelSharesgeneric, fuelUse, heatingShares, iF, # L, nondynsupply, nontemperene, obstime, plantParameters, population, # renovationRate, renovationRatio, renovationShares, requiredName, RR, stockBuildings, # temperatures, temperdays, temperene, testforrow, threshold, timelylimit, # totcases, truncateIndex #) #cat(" # bgexposure, BS, buildings, BW, changeBuildings, DALYs, decisions, disincidence, # dose, dummy, DW, emissionFactors, emissionLocations, emissions, EnergyConsumerDemand, # EnergyConsumerDemandTotal, EnergyFlowHeat, EnergyFlowOther, EnergyNetworkDemand, EnergyNetworkOptim, # energyProcess, energyUse, ERF, exposure, findrest, forgetDecisions, # frexposed, fuelPrice, fuelShares, fuelSharesgeneric, fuelUse, heatingShares, iF, # L, nondynsupply, nontemperene, obstime, plantParameters, population, # renovationRate, renovationRatio, renovationShares, requiredName, RR, stockBuildings, # temperatures, temperdays, temperene, testforrow, threshold, timelylimit, # totcases, truncateIndex.\n") } koord <- opbase.data("Op_en7115", subset = "Locations of city areas") koord <- tidy(koord, widecol = "Location") colnames(koord)[colnames(koord) == "City_area"] <- "Emission_site" koord$Result <- 1 koord <- Ovariable("koord", output = koord, marginal = colnames(koord) != "Result") emis <- emissions * koord emis@output <- subset(emis@output, EnergySavingPolicy == "BAU" & Pollutant == "PM2.5") emis <- oapply(emis, INDEX = c("Emission_site", "N", "E"), FUN = sum) MyRmap( ova2spat( emis, coord = c("E", "N"), proj4string = "+init=epsg:3067" ), # National Land Survey Finland uses ETRS89 / ETRS-TM35FIN #http://spatialreference.org/ref/epsg/etrs89-etrs-tm35fin/ plotvar = "Result", legend_title = "PM2.5 emissions (ton/a)", numbins = 4, pch = 19, cex = sqrt(result(emis)) * 3 ) # Map saved manually to .eps with width = 1280, height = 960 px.

See also

• Entsoe Statistical fact sheet 2014
• Timo Korpela: Pienkuluttaja on tuulisähkön maksumies
• 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??)
• http://energia.fi/sites/default/files/sahkomarkkinakatsaus_20150701.pdf
• https://www.entsoe.eu/Documents/Publications/Statistics/Factsheet/entsoe_sfs2014_web.pdf
• http://www.nordpoolspot.com/How-does-it-work/The-market-members/Producers/
• http://energia.fi/sites/default/files/et_labelling_suositus2013_final.pdf
• http://www.vtt.fi/inf/pdf/technology/2014/T165.pdf
• http://www.hel.fi/static/ymk/julkaisut/julkaisu-07-14.pdf
• https://www.tem.fi/files/36292/Energia-_ja_ilmastostrategia_nettijulkaisu_ENGLANNINKIELINEN.pdf
• Climate change policies in Helsinki: Related assessment on reducing GHG emissions in Helsinki.
• Climate change policies and health in Kuopio: Related assessment on reducing GHG emissions in Kuopio.
• Making sense of climate science denial a web course by University of Queensland
• HS: Kivihiilikielto lässähtänee visioksi 22,6,2015
• Pienistä pellettimääristä suuriin pellettimääriin Sofia Grönroos syyskuu 8, 2014
• Uusi hajautettu malli hiilidioksidipäästöjen vähentämiseen Helen 17.6.2015
• Modeling and visualization of residential sector energy consumption and greenhouse gas emissions. Journal of Cleaner Production, Volume 81, 15 October 2014, Pages 70-80. Maija K. Mattinen, Juhani Heljo, Jaakko Vihola, Antti Kurvinen, Suvi Lehtoranta, Ari Nissinen. [3]
• An assessment of greenhouse gas emissions and material flows caused by the Finnish economy using the ENVIMAT model. Jyri Seppälä, Ilmo Mäenpää, Sirkka Koskela, Tuomas Mattila, Ari Nissinen, Juha-Matti Katajajuuri, Tiina Härmä, Marja-Riitta Korhonen, Merja Saarinen, Yrjö Virtanen doi:10.1016/j.jclepro.2011.04.021 [4]
• Journal of Cleaner Production Volume 19, Issue 16, November 2011, Pages 1865–1875. Promoting Transformation towards Sustainable Consumption and Production in a Resource and Energy Intensive Economy - the Case of Finland. Industrial symbiosis and the policy instruments of sustainable consumption and production. Suvi Lehtoranta, Ari Nissinen, Tuomas Mattila, Matti Melanen. doi:10.1016/j.jclepro.2011.04.002
• Journal of Cleaner Production. Available online 16 September 2014 In Press, Corrected Proof. Combinations of policy instruments to decrease the climate impacts of housing, passenger transport and food in Finland. Ari Nissinen, Eva Heiskanen, Adriaan Perrels, Elina Berghäll, Virpi Liesimaa, Maija K. Mattinen, doi:10.1016/j.jclepro.2014.08.095
• Facebook: Uusi energiapolitiikka
• National Energy and Climate Strategy. Government Report to Parliament on 20 March 2013 [5] [6]
• Open district heating as a new business model
• Stockholm - a new generation of energy networks
• National Energy and Climate Strategy Government Report to Parliament on 20 March 2013
• Helsingin 30 % päästövähennysselvitys Kasvihuonekaasupäästöjen kehitys ja vähentämisen kustannustehokkaat toimenpiteet
• #Climathon A global 24-hour hackathon-style climate change event organised by Climate-KIC has kicked off simultaneously in major cities around the world on 18 June 2015.
  • The winning team of Helsinki Climathon
• Heleniltä uusi hajautettu malli hiilidioksidipäästöjen vähentämiseen Blogi 17.6.2015
• Helsingin Energian biopolttoaineiden käytön lisääminen, Helsinki YVA 2013
• Population of Finland
• Climate change policies and health in Kuopio DALY weights etc
• Halme et al. 2014 Kasvua ja työllisyyttä uudella energiapolitiikalla
• Domestic energy use map of Britain [http://www.undertheraedar.com/2011/09/energy-consumption-in-london.html Energy consumption in London [7]
• Energiatodistusrekisteri (Helsingistä yli 300 rakennusta)
• Building energy use in New York City
• Niko Wirgentius: Cooling as a part of smart energy system in Stockholm [8]
• Parhaat energiakäytännöt
• HS 17.6.2015: Helsingin kivihiilikasat jäävät historiaan – Hanasaareen uusi asuinalue jo 2020-luvulla
• Erkka Ryynänen, Laura Oja ja Iivo Vehviläinen (Gaia Consulting Oy), Olli-Pekka Pietiläinen, Riina Antikainen ja Pasi Tainio (Suomen ympäristökeskus). Helsingin 30 % päästövähennysselvitys Kasvihuonekaasupäästöjen kehitys ja vähentämisen kustannustehokkaat

toimenpiteet Helsingin kaupungin ympäristökeskuksen julkaisuja 7/2014 [9]←--#: . Kustannus- ja päästövähenemätiedot pitäisi tästä kerätä taulukkoon. --Jouni (talk) 13:29, 19 June 2015 (UTC) (type: truth; paradigms: science: defence)

• National Energy and Climate Strategy. Government Report to Parliament on 20 March 2013 [10] [11]
• Energy balance in Kuopio: Data on the amounts of energy produced, consumed, imported, and exported in Kuopio.
• Soininvaara 2010: Helsingin lämmitys
• Assessment of small-scale energy production in Helsinki Metropolitan area
• Helsingin Energian biopolttoaineiden käytön lisääminen, Helsinki YVA 2013
• Tekniikka ja talous 2015: Yllätyskortti Helsingin energiapelissä.
• Helsingin rakennusvalvonnan ohjeet
• 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

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) (type: truth; paradigms: science: defence)