Helsinki energy decision 2015

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 (token 144638929414). 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

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.
Total annual expenditure and production of district heat energy in the city of Helsinki in 2035 after energy saving total policy.
Current data and estimation of future building stock of Helsinki by heating type.
Energy used in heating, consumer electricity, and hot water in Helsinki in the energy saving total policy.
Fuel prices used in optimising.
Energy production capacity in Helsinki by plant policy
Incomes and costs of energy production in Helsinki by power plant. Note that the y axes are on different scales.
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.
Flow of district heat in Helsinki. The supply is always matched with the demand.
Health effects of PM2.5 from heating in Helsinki.
Emissions of CO2 and PM2.5 from heating in Helsinki.

Conclusions

See summary in the beginning.

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: 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) (type: truth; paradigms: science: defence)
energyUse Baseline energy consumption [3] ←--#: . 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 ^[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) (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^[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) (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) ^[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) (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 ^[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.

Case-specific ovariables

Name is the name of ovariable that has case-specific rather than default content. Ident is the indentifier of the code that defines the case-specific ovariable. Token is the same as Ident but it uses a specific version of the code rather than the newest version. Latest is the code for an ovariable whose dependencies will be changed, i.e. who has the case-specific ovariable as parent. Get is the same as Latest but a specific version rather than the newest version is fetched.

Case-specific ovariables(-)
Obs	Name	Ident	Latest	Description
1	buildings	Op_en6289/buildingstest	Op_en5488/EnergyConsumerDemand	[[Building model]] buildings # Generic building model
2	changeBuildings	Op_en7115/changeBuildings	Op_en6289/buildingstest
3	demolitionRate	Op_en7115/demolitionRate	Op_en6289/buildingstest
4	efficiencyShares	Op_en5488/efficiencyShares	Op_en6289/buildingstest
5	emissionLocations	Op_en7311/emissionLocationsPerPlant	Op_en2791/emissionstest	[[Helsinki energy production]] emissionLocations, used by[[Emission factors for burning processes]] emissions
6	energyProcess	Op_en7311/energyProcess	Op_en5141/EnergyNetworkOptim	[[Helsinki energy production]] energyProcess, used by [[Energy balance]] EnergyConsumerDemandTotal
7	exposure	Op_en5813/exposure		[[Intake fractions of PM]] exposure # uses Humbert iF as default.
8	fuelShares	Op_en7311/fuelShares	Op_en2791/emissionFactors	[[Helsinki energy production]] fuelShares, used by ([[Emission factors for burning processes]] emissionFactors?)
9	plantParameters	Op_en7311/plantParameters	Op_en3283/totalCost	[[Helsinki energy production]] plantParameters, used by [[Economic impacts]] plantCost
10	renovationRate	Op_en7115/renovationRate	Op_en6289/buildingstest	[[Building stock in Helsinki]] renovationRate
11			Op_en7115/renovationRate	[[Building stock in Helsinki]] renovationRate case-specific adjustment in formula
12	renovationShares	Op_en7115/renovationShares	Op_en6289/buildingstest
13	stockBuildings	Op_en7115/stockBuildings	Op_en6289/buildingstest
14	temperatures	Op_en2959/temperatures	Op_en5488/EnergyConsumerDemand	[[Outdoor air temperature in Finland]], used by [[Energy use of buildings]] EnergyConsumerDemand
15	temperdays	Op_en2959/temperatures	Op_en5488/EnergyConsumerDemand	[[Outdoor air temperature in Finland]]

Calculations

Model run 25.10.2015. Archived version with several updates
Model run 17.12.2018 for HNH2035 [4]

+ 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 <- TRUE

########################## 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
if(FALSE) {

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)"
		)
} # END if(FALSE)

Preference order

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

+ Show code - Hide code

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

[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

Helsinki energy decision 2015

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

Case-specific ovariables

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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