Helsinki energy decision 2015 methods

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Helsinki energy decision 2015 model

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.

Choose power plants you want to build (or keep running if they already exist) (the default selection is Helen's bio). This will become PlantPolicy: Custom.:
Biofuel heat plants
CHP diesel generators
Data center heat
Deep-drill heat
Hanasaari
Household air heat pumps
Household air conditioning
Household geothermal heat
Katri Vala cooling
Katri Vala heat
Kellosaari back-up plant
Loviisa nuclear heat
Neste oil refinery heat
Salmisaari A&B
Salmisaari biofuel renovation
Sea heat pump
Sea heat pump for cooling
Small gas heat plants
Small fuel oil heat plants
Small-scale wood burning
Vuosaari A
Vuosaari B
Vuosaari C biofuel

Choose power plants to be renovated (PlantPolicy: Custom):
Hanasaari biofuel renovation

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Rationale

Causal diagram for the assessment.


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(-)
ObsNameIdentTokenLatestGetDescription
1buildingsOp_en6289/buildingstestOp_en5488/EnergyConsumerDemand[[Building model]] buildings # Generic building model
2changeBuildingsOp_en7115/changeBuildingsOp_en6289/buildingstest
3demolitionRateOp_en7115/demolitionRateOp_en6289/buildingstest
4efficiencySharesOp_en5488/efficiencySharesOp_en6289/buildingstest
5emissionLocationsOp_en7311/emissionLocationsPerPlantOp_en2791/emissionstest[[Helsinki energy production]] emissionLocations, used by[[Emission factors for burning processes]] emissions
6energyProcessOp_en7311/energyProcessOp_en5141/EnergyNetworkOptim[[Helsinki energy production]] energyProcess, used by [[Energy balance]] EnergyConsumerDemandTotal
7exposureOp_en5813/exposure [[Intake fractions of PM]] exposure # uses Humbert iF as default.
8fuelSharesOp_en7311/fuelSharesOp_en2791/emissionFactors[[Helsinki energy production]] fuelShares, used by ([[Emission factors for burning processes]] emissionFactors?)
9plantParametersOp_en7311/plantParametersOp_en3283/totalCost[[Helsinki energy production]] plantParameters, used by [[Economic impacts]] plantCost
10renovationRateOp_en7115/renovationRateOp_en6289/buildingstest[[Building stock in Helsinki]] renovationRate
11Op_en7115/renovationRate[[Building stock in Helsinki]] renovationRate case-specific adjustment in formula
12renovationSharesOp_en7115/renovationSharesOp_en6289/buildingstest
13stockBuildingsOp_en7115/stockBuildingsOp_en6289/buildingstest
14temperaturesOp_en2959/temperaturesOp_en5488/EnergyConsumerDemand [[Outdoor air temperature in Finland]], used by [[Energy use of buildings]] EnergyConsumerDemand
15temperdaysOp_en2959/temperaturesOp_en5488/EnergyConsumerDemand [[Outdoor air temperature in Finland]]

Calculations

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Building stock in Helsinki

Question

What is the building stock in Helsinki and its projected future?

Answer

  • Current building stock in Helsinki by heating type.

    Current building stock in Helsinki by heating type.

  • Projected building stock based on 2015 data and urban plans.

    Projected building stock based on 2015 data and urban plans.

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Rationale

This part contains the data needed for calculations about the building stock in Helsinki. It shows the different building and heating types in Helsinki, and how much and what kind of renovations are done for the existing building stock in a year, including how much and how old building stock is demolished. This data is used in further calculations in the model.

There is also some other important data that wasn't used in the model's calculations. These include more accurate renovation statistics for residential buildings, U-value changes for renovations and thermal transmittance of different parts of residential buildings. This data is found under Data not used.

Carbon neutral Helsinki 2035

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Building stock

These tables are based on FACTA database classifications and their interpretation for assessments. This data is used for modelling. The data is large and can be seen from the Opasnet Base. Technical parts on this page are hidden for readability. Building types should match Energy use of buildings#Baseline energy consumption.



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Construction and demolition

It is assumed that construction occurs at a constant rate so that there is an increase of 42% in 2050 compared to 2013. Energy efficiency comes from Energy use of buildings.

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Fraction of houses demolished per year.

Demolition rate(% /a)
ObsAgeRate
100
2501
310001

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Heating type conversion

The fraction of heating types in the building stock reflects the situation at the moment of construction and not currently. The heating type conversion corrects this by changing a fraction of heating methods to a different one at different timepoints. Cumulative fraction, other timepoints will be interpolated.

Yearly_heating_converted_factor(m2/m2)
ObsHeating_fromHeating_toTimeResult
1OilGeothermal20050
2OilGeothermal20150.5
3OilGeothermal20251

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Renovations

Estimates from Laura Perez and Stephan Trüeb, unibas.ch N:\YMAL\Projects\Urgenche\WP9 Basel\Energy_scenarios_Basel_update.docx

Fraction of houses renovated per year(%)
ObsAgeResultDescription
100Estimates from Laura Perez and Stephan Trüeb
2200Assumption Result applies to buildings older than the value in the Age column.
3251
4301
5501
61001
710001

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Popularity of renovation types(%)
ObsRenovationFractionDescription
1None0
2Windows65
3Technical systems30
4Sheath reform5
5General0

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Building model

Question

How to estimate the size of the building stock of a city, including heating properties, renovations etc? The situation is followed over time, and different policies can be implemented.

Answer

Causal diagram of the building model. The actual model is up to the yellow node Building stock, and the rest is an example how the result can be used in models downstream.

The building model follows the development of a city's or area's building stock over time. The output of the model is the floor area (or volume, depending on the input data) of the building stock of a city at specified timepoints, classified by energy efficiency, heating type, and optionally by other case-specific characteristics. The model functions as part of Opasnet's modeling environment and it is coded using R. It uses specific R objects called ovariables. The model can also be downloaded and run on one's own computer.

The model is given data about the building stock of a certain city or area during a certain period of time. The data can be described with very different levels of precision depending on the situation and what kind of information is needed. Some kind of data on the energy efficiency and heating type is necessary, but even rough estimates suffice. Then again, if there is sufficient data, the model can analyse even individual buildings.

In addition to that, the model can describe changes in the building stock, i.e. construction of new buildings and demolishing of old ones. Data on the heating- and energy efficiencies of new and demolished buildings is required at the same level of precision as that of other buildings. This data is used to calculate how construction and demolishing change the building stock's size and heating types.

The model takes into account the energy renovation of existing buildings. They are analysed using two variables: firstly, what fraction of the building stock is energy renovated yearly and secondly, what type of renovation it is. This information, too, can be rough or precise and detailed. It can describe the whole building stock with a single number or be specific data on the time, the building's age, use or other background information.

For examples of model use, see Helsinki energy decision 2015, Building stock in Kuopio and Climate change policies and health in Kuopio.

The overall equation in the model is this:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle B_{t,h,e,r} = \int\int (Bs_{c,t,a} Hs_h Es_e + Bc_{c,h,e,t,a}) Rr_a Rs_{r,t} O)\mathrm{d}c \mathrm{d}a}

  • B = buildings, floor area of buildings in specified groups
  • Bs = stockBuildings, floor area of the current buildings
  • Bc = changeBuildings, floor area of constructed and demolished (as negative areas) buildings
  • Hs = heatingShares, fractions of different heating types in a group of buildings
  • Es = efficiencyShares, fractions of different efficiency classes in a group of buildings
  • Rr = renovationRate, fraction of buildings renovated per year
  • Rs = renovationShares, fractions of different renovation types performed when buildings are renovated
  • O = obstime, timepoints for which the building stock is calculated.
  • Indices required (also other indices are possible)
    • t = Obsyear, time of observation. This is renamed Time on the output data.
    • c = Construction year (the index is named 'Time' in the input data), time when the building was built.
    • a = Age, age of building at a timepoint. This is calculated as a = t - b.
    • h = Heating, primary heating type of a building
    • e = Efficiency, efficiency class of building when built
    • r = Renovation, type of renovation done to a non-renovated building (currently, you can only renovate a building once)

The model is iterative across the Obsyear index so that renovations performed at one timepoint are inherited to the next timepoint, and that situation is the starting point for renovations in that timepoint.

Rationale

Inputs and calculations

Variables in the building model
Variable Measure Indices Missing data
stockBuildings (case-specific data from the user) e.g. Building stock in Helsinki or Building stock in Kuopio Amount of building stock (typically in floor-m2) at given timepoints. Required indices: Time (time the building was built. If not known, present year can be used for all buildings.) Typical indices: City_area, Building (building type) You must give either stockBuildings, heatingShares, and efficiencyShares or changeBuildings or both. For missing data, use 0.
heatingShares (case-specific data from the user) Fractions of heating types. Should sum up to 1 within each group defined by optional indices. Required indices: Heating. Typical indices: Time, Building If no data, use 1 as a placeholder.
efficiencyShares (case-specific data from the user) Fraction of energy efficiency types. Should sum up to 1 for each group defined by other indices. Required indices: Efficiency. Typical indices: Time, Building. If no data, use 1 as default.
changeBuildings (case-specific data from the user) Construction or demolition rate as floor-m2 at given timepoints. Required indices: Obsyear, Time, Efficiency, Heating. If both stockBuildings and changeBuildings are used, changeBuildings should have all indices in stockBuildings, heatingShares, and efficiencyShares. Typical indices: Building, City_area. If the data is only in stockBuildings, use 0 here.
renovationShares (case-specific data from the user) Fraction of renovation types when renovation is done. Should sum to 1 for each group defined by other indices. Required indices: Renovation, Obsyear. Obsyear is the time when the renovation is done If no data, use 1 as default.
renovationRate (case-specific data from user. You can also use fairly generic data from Building stock in Helsinki or Building stock in Kuopio.) Rate of renovation (fraction per time unit). Required indices: Age (the time difference between construction and renovation, i.e. Obsyear - Time for each building). If no data, use 0.
obstime (assessment-specific years of interest) The years to be used in output. The only index Obsyear contains the years to look at; Result is 1. Required indices: Obsyear. Typical indices: other indices are not allowed. -

This code defines the generic building model object called buildings. Other objects needed can be found from case-specific pages, see table above.

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Energy use of buildings

Question

How to model the use of energy of buildings based on either annual consumption per floor area, or energy efficiency per floor area per indoor-outdoor temperature difference?

Answer

Example of consumer energy demand calculations: energy need in Helsinki from the assessment Helsinki energy decision 2015.

An example code for fetching and using the variables.

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Rationale

Input

Variables needed to calculate the EnergyConsumerDemand. Note that there are several different methods available, and temperature data is not needed in an annual energy version.
Dependencies Measure Indices Missing data
buildings (from the model). Floor area of the building stock to be heated Typical indices: Building, Heating, Efficiency, Renovation You can use value 1 to calculate energy need per 1 m2 floor area.
temperene (fairly generic data for a given cultural and climatic area, e.g. from Energy use of buildings) Energy need per floor area and indoor-outdoor temperature difference (W /m2 /K) Required indices: Consumable, Fuel (Commodity). Typical indices: Building, Heating. if this data is missing, you can only calculate building stock but nothing further.
nontemperene (fairly generic data for a given cultural and climatic area, e.g. from Energy use of buildings) Energy need for hot water and other non-temperature-dependent activities Required indices: Consumable, Fuel (Commodity). Use 0 to calculate energy demand excluding non-heating energy use.
temperatures (location-specific data) Average outdoor temperatures for particular temperature bins. Reuired indices: Temperature. If missing, use the annual energy version.
temperdays (location-specific data) Number of days per year for particular temperature bins. Required indices: Temperature. If missing, use the annual energy version.
efficiencyRatio (fairly generic data for a cultural and climatic area, e.g. from Energy use of buildings) Relative energy consumption compared with the efficiency group Old. Required indices: Efficiency. Typical indices: Time, Building. If no data, use 1 as default.
renovationRatio (fairly generic data for a cultural and climatic area, e.g. from Energy use of buildings) Relative energy consumption compared with the Renovation location None. Required indices: Renovation. Typical indices: Building. If no data, use 1 as default.


Temperature-dependent calculations

The code below assumes energy consumption factors relative to floor area (W /m2 /K). Local temperature data must be given in either individual or aggregated way. Individual way has temperature data for all timepoints (e.g. days or hours) of the given year, and heatingTime = 1. Aggregated way has a specific Temperature index (e.g. very cold, cold, cool etc) in both ovariables temperature and heatingTime. The ovariable temperature tells what is the actual temperature when it is "very cold", and heatingTime tells how many hours it is "very cold" during the year.

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle Q_{e,r,t} = \sum_b (B_{b,e,r} U_b (17 - T_t) E_e R_r + W_b) t_t,}

where

  • Q = Energy used for heating and cooling (kWh /a)
  • B = floor area of a building stock indexed by renovation and efficiency (m2)
  • U = energy consumption factor per floor area for a building type (W /m2 /K)
  • T = temperature outside (assumes that no heating is needed if outside temperature is 17 degrees Celsius)
  • E = relative efficiency of a building stock based on energy class when built (no unit)
  • R = relative efficiency of a building stock based on energy class after renovated (no unit)
  • W = heating need of hot water (W)
  • t = time spent in a particular outdoor temperature (h /a)
  • indices used:
    • b = building type
    • e = efficiency class of building
    • r = renovation class of building
    • t = ambient temperature class


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Baseline energy consumption

Heat reflects the energy need for heating in situations where the outdoor temperature is below 17 °C. Cooling reflects the cooling need (measured as thermal energy, not electricity!) in situations where the outdoor temperature is above 24 °C. This is not a U value, because it is about energy use per floor area, not about heat loss through building structures per m2. For estimating temperene, we take the total energy consumption in Helsinki and divide that with the total floor area and average temperature difference, see Helsinki energy consumption#U values based on overall data.

Energy use per area and temperature difference(W /K /m2)
ObsConsumableFuelEnergy flowDescription
1HeatingHeat1.66See Helsinki energy consumption: 6921.65/24/365/38990000/(17-4.8)*1E+9
2District coolingCooling0.3Guesswork. This uses centralised system.
3Electric coolingElectricity0.3Guesswork. This uses apartment-specific appliances.

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Temperature-independent energy consumption per floor area.

Temperature-independent energy use per area(W /m2)
ObsConsumableFuelEnergy intensityDescription
1Consumer electricityElectricity5Assumes 50 kWh /m2 /a (see below)
2Hot waterHeat4Assumes that hot water is ca. 20 % of energy need of heating: 6921.65/24/365/38990000*1E+9*0.2

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Energy efficiency in heating

What is the relative energy consumption of different efficiency classes compared with Old? This table tells that with some background information about heat (in kWh/m2/a), electricity, and water consumption.

Energy use by energy class of building(ratio)
ObsEfficiencyRatioHeatUser electricityWaterDescription
1Traditional1.2-1.4200Guesstimate
2Old115030Pöyry 2011 s.28
3New0.4-0.5705040Pöyry 2011 s.32 (2010 SRMK)
4Low-energy0.2-0.25355040Personal communication
5Passive0.1-0.1617.5 - 255040Pöyry 2011 s.33; Personal communication

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Energy efficiency of buildings when they are built(%)
ObsEfficiencyConstructedFractionDescription
1Traditional1800-1944100
2Old1945-1994100
3New1995-2019100
4New2020-202910-20
5Low-energy2020-2029The rest of energy class
6Passive2020-202925-35
7New2030-20395-10
8Low-energy2030-203920-50
9Passive2030-2039The rest of energy class
10New2040-20700-5
11Low-energy2040-207010-30
12Passive2040-2070The rest of energy class
  • Old: old buildings to be renovated (or in need of renovation)
  • New: normal new buildings (no current need of renovation)
  • Low-energy: buildings consuming about half of the energy of a new building
  • Passive: buildings consuming a quarter or less of the energy of a new building
  • Chinese green building system: [2] [3]

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Impact of renovations

Energy saving potential of different renovations(ratio,kWh/m2/a)
ObsRenovationRelativeAbsoluteRenovation detailsDescription
1Windows0.8525New windows and doorsPöyry 2011
2Technical systems0.5075New windows, sealing of building's sheath, improvement of building's technical systemsPöyry 2011
3Sheath reform0.35100New windows, sealing of building's sheath, improvement of building's technical systems, significant reform of building's sheathPöyry 2011
4General0.85-General renovationPöyry 2011
5None10Renovation not done

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Helsinki energy consumption

Question

How much energy is consumed and to what purposes in Helsinki?

Answer

Energy consumed for heating, cooling, hot water, and consumer electricity in Helsinki. Note: the future consumption is based on Energy saving total scenario from the assessment Helsinki energy decision 2015, not business-as-usual.

There is no answer code here, because the U value 1.661 W /m2 /K is directly used in Energy use of buildings#Baseline energy consumption.

Rationale

U values based on overall data

The total heat consumption by district-heated buildings is 6921.65 GWh in 2013 (see below). We can derive the total energy efficiency value expressed as W /m2 /K for floor area and temperature difference between indoors and outdoors. The typical energy efficiency calculations (using the so called U value) assume that outdoor 17 °C is thermoneutral and lower values require heating. The total floor area of district-heated buildings is 38990000 m2 in 2015 according to the Helsinki energy decision 2015 model. The annual average temperature in Helsinki is 4.8 °C [4] and during heating season Sep-May 1.4 C (Opasnet data). Therefore the energy efficiency value (approximate U value) is

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle U = \frac{6921.65 GWh/a /(24 h / d \times 365 d/a)}{38990000 m^2 (17 K - 4.8 K)} = 1.661 \frac{W}{m^2 K}}

Energy consumption statistics

Total energy consumption in Helsinki in 2013 (GWh) [1]
Adjusted for temperature Not adjusted for temperature
District heating 6921.65 6461.00
Separate heating 303.89 284.01
Electric heating 339.23 316.65
Consumer electricity 3988.10 3988.10
Private cars 1294.06 1294.06
Other road traffic 794.33 794.33
Trains 111.16 111.16
Ships 432.12 432.12
Industry and machinery 147.60 147.60
Total 14332.14 13829.03


Helsinki energy production

Question

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

Answer

Energy production capacity in Helsinki. The different scenarios are based on Helsinki energy decision 2015.

This code is used to fetch the ovariables on this page for modelling.

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Rationale

This page contains data about the heat plants in Helsinki. It tells, how much and what type of energy a plant produces per unit of fuel, how much the plants cost and the locations of the power plant emissions. This data is then further used in the model.

Amount produced is determined largely by the energy balance in Helsinki and Helsinki energy consumption. The maximum energy produced and fuels used by of all Helen's power plants can be found here: https://www.helen.fi/kotitalouksille/neuvoa-ja-tietoa/tietoa-meista/energiantuotanto/voimalaitokset/

Energy processes

Heat, power and cooling processes(MJ /MJ)
ObsPlantBurnerElectricityElectricity_taxedHeatCoolingCoalGasFuel oilBiofuelDescription
1Biofuel heat plantsLarge fluidized bed000.85-0.910000-1
2CHP diesel generatorsDiesel engine0.300.3-0.5000-10Efficiency not known well in practice
3Data center heatNone0-0.27 - -0.23100000Same as Neste without transport of heat
4Deep-drill heatNone0-0.4 - -0.1100000Experimental technology
5HanasaariLarge fluidized bed0.3100.600-1000Assume 91 % efficiency. Capacity: electricity 220 MW heat 420 MW Loss 64 MW
6Household air heat pumpsNone0-0.7 - -0.2100000The efficiency of heat pumps is largely dependent on outside air temperature, it's feasible for a household air heat pump to reach COP 5 at 10 °C and COP 1.5 at -25 °C.
7Household air conditioningNone0-0.7 - -0.2010000
8Household geothermal heatNone0-0.36 - -0.31100000Motiva 2014
9Katri Vala coolingNone0-0.36 - -0.31010000District cooling produced by absorption (?) heat pumps. Same as heat pumps for heating, Motiva 2014.
10Katri Vala heatNone0-0.36 - -0.31100000Heat from cleaned waste water and district heating network's returning water. Motiva 2014
11Kellosaari back-up plantLarge fluidized bed0.3 - 0.500000-10Only produces electric power
12Kymijoki River's plantsNone10000000Hydropower
13Loviisa nuclear heatNone0-0.4 - -0.1100000Assumes that for each MWh heat produced, 0.1-0.2 MWh electricity is lost in either production or when heat is pumped to Helsinki.
14Neste oil refinery heatNone0-0.31 - -0.27100000Motiva 2014
15Salmisaari A&BLarge fluidized bed0.3200.590-1000Capacity: electricity 160 MW heat 300 MW loss 46 MW
16Sea heat pumpNone0-0.36 - -0.31100000Motiva 2014
17Sea heat pump for coolingNone0-0.36 - -0.31010000Assuming the same as for heating
18Small-scale wood burningHousehold000.5 - 0.90000-1
19Small gas heat plantsLarge fluidized bed000.9100-100
20Small fuel oil heat plantsLarge fluidized bed000.91000-10
21Suvilahti power storageNone10000000
22Suvilahti solarNone10000000
23Vanhakaupunki museumNone10000000Hydropower
24Vuosaari ALarge fluidized bed0.45500.45500-100Capacity: electricity 160 MW heat 160 MW loss 30 MW
25Vuosaari BLarge fluidized bed0.500.4100-100Capacity: electricity 500 MW heat 424 MW loss 90 MW
26Vuosaari C biofuelLarge fluidized bed0.4700.440000-1
27Wind millsNone10000000

Notes about the data in the table:

  • Household air heat pumps data from heat pump comparison[2]
  • Household geothermal heat data from Energy Department of the United States: Geothermal Heat Pumps[3]
  • Small-scale wood burning data from Energy Department of the United States: Wood and Pellet Heating[4]
  • Loss of thermal energy through distribution is around 10 %. From Norwegian Water Resources and Energy Directorate: Energy in Norway.[5]
  • Sustainable Energy Technology at Work: Use of waste heat from refining industry, Sweden.[6]
  • Chalmers University of Technology: Towards a Sustainable Oil Refinery, Pre-study for larger co-operation projects[7]
  • CHP diesel generators are regular diesel generators, but they are located in apartment houses and operated centrally. This way, it is possible to produce electricity when needed and use the excess heat, instead of district heat, to warm up the hot water of the house.
  • Motiva estimates for heat pumps processes and costs for heating:[8]
    • Mechanical heat pumps usually have COP (coefficient of performance, thermal output energy per electric input energy needed) is 2.5 - 7.5.
    • In district heating, mechanical heat pumps have typically COP around 3.
    • Absorption heat pumps have COP typically 1.5 - 1.8. They do not use much electricity but they need either hot water or steam to operate. Therefore, they are not suitable for producing district heat from warm water with temperatures in the range of 25 - 30 °C (Neste) or 10-15 °C (sea heat).
    • The report uses these values for energy prices (€/MWh): bought electricity 50, process steam 25, wood chip 20, district heating 40, own excess heat 0.
    • The investment cost of a heat pump system (ominaiskustannus) in the cases described in this report were 0.47-0.73 M€/MWth for mechanical heat pumps and 0.072 - 0.102 M€/MWth for absorption heat pumps. These values do not include the pipelines needed, which may vary a lot; in these cases the pipeline costs were 0.1 - 2.5 times the cost of the heat pump.
    • The energy efficiency is theoretically COP = Tout / (Tout - Tin), and the actual COP values are typically 65 - 75 % of that. If we assume that we want 95 °C district heat out, we get
      • for sea heat pumps: COP = 368 K / (368 K - 283 K) = 4.3 ideally and in practice 2.8 - 3.2. Electricity needed per 1 MWh output: 0.31 - 0.36 MWh.
      • Neste process heat: COP = 368 K / (368 K - 303 K) = 5.7 ideally and in practice 3.7 - 4.2. Electricity needed per 1 MWh output: 0.23 - 0.27 MWh (plus what is needed for pumping the heat for 25 km, say + 0.04 MWh)

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Plant specifications

These equations below aim to reflect the energy production facilities and capabilities. The min and max values tell about the range of energy production of the plant, and the cost values tell the costs of building and running the powerplant.

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

Plant parameters(MW,MW,M€,M€ /a,€ /MWh)
ObsYears_activePlantMinMaxInvestment costManagement costOperation costDescription
12017-2070Biofuel heat plants0100-300360104-12biofuels (pellets, wood chips and possibly biochar)
22025-2070CHP diesel generators0144114411Assuming all of Helsinki's apartment houses were fitted with 100 kW generators.
32025-2080Deep-drill heat0300300-9009.640Investment cost from ETSAP
41965-2040Hanasaari064009.6895% coal, 5% pellets. Assume cost of running and maintenance in coal plants 15€/kW (Sähköenergian kustannusrakenne)
52010-2060Household air heat pumps0112200-300105Assuming all of Helsinki's detached and row houses were fitted with air heat pumps
62010-2060Household air conditioning067150-200105
72016-2060Household geothermal heat0335380-450105Assuming all of Helsinki's detached and row houses were fitted with geothermal heat pumps
82020-2035Household solar0105220-25055Assuming 700000 m2 suitable for solar panels.
92010-2070Katri Vala cooling0600103waste water. Max from Helen
102005-2065Katri Vala heat0900103waste water. Max from Helen
111980-2050Kellosaari back-up plant012001020oil
121980-2070Kymijoki River's plants0600101-4hydropower
132022-2080Loviisa nuclear heat01800-2600400-4000105Investment cost includes energy tunnel (double of Neste) but NOT building cost of plant. Some estimate for typical district heat pipes on ground is 2 M€/km; this is clearly a minimum for this project.
142020-2060Neste oil refinery heat0300200-500105
151975-2050Salmisaari A&B050607.6895% coal, 5% pellets
162020-2070Sea heat pump0225280104
172020-2070Sea heat pump for cooling0225280104
181980-2070Small-scale wood burning7878010Assuming 70% of Helsinki's detached and row houses have a working fireplace. Operation costs for consumer assumed to be 0.
191980-2070Small gas heat plants0600055
201980-2070Small fuel oil heat plants01600055
212015-2040Suvilahti power storage-1.21.2100105electricity storage 0.6 MWh
222013-2070Suvilahti solar00.340105
231880-2070Vanhakaupunki museum00.20100water
241991-2070Vuosaari A0320055natural gas
251998-2070Vuosaari B0924055natural gas
262018-2070Vuosaari C biofuel0133165010980-100% biofuels, rest coal
272017-2060Wind mills010120.07-0.157-13upper limit from EWEA-report: The economics of wind energy
282016-2070Data center heat015070.5-109.550Investment cost 0.47-0.73 M€/MWth based on Motiva 2014. Cooling is needed anyway, so assumes operation costs to be 0.

Notes:

  • Neste excess heat in Opasnet
  • Helens’s windpower [9]
  • Suvilahti solar [10]
  • Loviisan sanomat: Loviisan ydinvoimalan tehoja aiotaan nostaa 52 megawattia. [11]
  • Loviisa 3 periaatepäätös [12]
  • Sähköenergian kustannusrakenne [13]
  • European Wind Energy Association (EWEA): The economics of wind energy [14]
  • Operation costs (€/MWh) of nuclear, wind, coal, and wood based biomass [15]
  • Sea heat capacity and cost estimated using case Drammen. [16] [17][18]
  • Cost of household solar estimated using [5] and [6]
  • Deep drill heat
    • Energy Technology Systems Analysis Programme (ETSAP)[19]
  • Small heat plants' capacities [20]

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Non-adjustable energy production(MW)
ObsPlantBurnerFuel201520252035204520552065
1Suvilahti solarNoneElectricity5510101010
2Wind millsNoneElectricity5510101010

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Fuel availability

Wood

The byproducts of forest industry make up the bulk of fuel wood, and its quantity is almost completely dependent of the production of the forest industry's main products. Therefore it makes sense to calculate the amount of fuel wood usable in the future using the predictions about the volume of forest industry's production in coming years.

For example, the maximum potential production of woodchips is calibrated so, that it will reach 25 TWh in year 2020, and it is expected slowly increase to 33 TWh by year 2050. The production potential for firewood (for small scale heating) is expected to remain about the same at just under 60 PJ. The import of wood fuels is estimated to be 3 TWh at most. [21]

Fuel use by heating type

Helsinki-specific data about connections between Heating and fuel usage. Generic data should be taken from Energy balance. Because all Helsinki-specific data is given in the energyProcess table, this only contains dummy data.

Fuel use by heating type(-)
ObsHeatingBurnerFuelFractionDescription
1DummyNoneCoal0

This R code creates an ovariable for calculating the shares of different fuels used in heating processes.

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Fuel data from HSY

Data downloaded from [7] on 27 Nov 2018.



Emission locations

Emission locations per plant. The values of emission sites are based on locations of city areas.

Emission locations per plant(-)
ObsPlantEmission siteEmission heightDescription
1Biofuel heat plants010Low
2CHP diesel generators010Ground
3Deep-drill heat010
4Hanasaari010High
5Household air heat pumps010
6Household air conditioning010
7Household geothermal heat010
8Household solar010
9Katri Vala cooling010
10Katri Vala heat010
11Kellosaari back-up plant010High
12Kymijoki River's plants010
13Loviisa nuclear heat010
14Neste oil refinery heat010High
15Salmisaari A&B010High
16Sea heat pump010
17Sea heat pump for cooling010
18Small-scale wood burning010Ground
19Small gas heat plants010Low
20Small fuel oil heat plants010Low
21Suvilahti power storage010
22Suvilahti solar010
23Vanhakaupunki museum010High
24Vuosaari A010High
25Vuosaari B010High
26Vuosaari C biofuel010High
27Wind mills010
28Data center heat010
29UnidentifiedAt site of consumptionGround

This R code creates an ovariable for emission locations per plant.

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Prices of fuels in heat production

Question

What are prices of fuel used in heat production in own heating systems in apartments and in plants in Finland?

Answer

Prices of fuels in heat production without tax.

An example code for fetching and running the ovariable.

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Rationale

This page contains prices for electricity, district heating, liquid fuels and consumer prices of hard coal, natural gas and domestic fuels in heat production in Finland. all data is based on knowledge of Statistics Finland In the end of data section there will be also data for maintenance and investment costs!!

This code calculates the price of fuels including tax.

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Prices of fuels without tax

A previous version was based on several info sources [8]. However, this resulted in inconsistent prices between fuels. Now the table is simplified and made more robust using IEA estimates VTT-R-03704-14_YDINPAP_(1) page 8, with the cost of accuracy. There should be an expert panel discussing the interdependencies of fuel prices to get the correct order.

Fuel prices(€ /MWh)
ObsFuel198519952005201520252035204520552065Description
1Coal8.59.4113.78201515151515
2Gas9.111.616.242.94040404040
3Natural gas9.111.616.242.94040404040Same as gas.
4Oil14.32532525461687582
5Crude oil14.32532525461687582Same as oil.
6Fuel oil14.32532525461687582Same as oil.
7Heavy oil14.32532525461687582Same as oil.
8Light oil162835555764727986Slightly higher than oil.
9Bio91011212835455776
10Biofuel91011212835455765Same as bio.
11Peat6.689.8172128354550
12Electricity404040404255586065
13Heat30.830.840.453.996363656568
14Cooling30.830.840.453.996363656568Same as heat

All prices are in 2015 euros.

Coal, Gas/Natural Gas, Light oil and Bio/Biofuel and Peat data from Statistics Finland[22]. Bio/Biofuel stands for wood chips.

Oil prices from U.S. Energy Information Administration[23]. All values converted first to 2015 dollars (inflation correction) and then to 2015 euros using the current (21.7.2015) exchange ratio of 1 $ = 0.923 €. Price per barrel then converted to price per MWh presuming that the energy released by burning one barrel is about 5.8 x 106 BTU = 1.7 MWh, U.S. Internal Revenue Service[24].

Compound Annual Growth Rates (CAGR) for Coal, Gas/Natural Gas, Oil, Crude oil, Fuel oil, Heavy oil and Light oil from U.S. Energy Information Administration's "Annual Energy Outlook 2015 with projections to 2040" [25]. CAGR for Bio/Biofuel and Peat estimated to be 2 %, based on the price history.

Price of electricity [9].D↷

District heating data is based on the price for an apartment building (volume 10 000 m3, energy need 450 MWh/a). Data from Statistics Finland [10]. We assume that the price of district cooling is the same (although district cooling has been available only for a few years).

Uncertainties are assumed in the same way for all fuels: a certain percentage up and down from the best estimate, varying by year. The following uncertainties were used: 2025 5 %, 2035 10 %, 2045 15 %, 2055 20 %, 2065 30 %. It should be noted that uncertainties are different for different fuels, but we were unable to estimate fuel-specific uncertainties.

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Taxes for different fuels

Taxes and fees for fuel and energy production[26]
Taxes and fees from Petrol (snt/l) Diesel (snt/l) Light fuel oil (snt/l) Heavy fuel oil (snt/kg) Coal (€/t) Natural gas (€/MWh) Peat (€/MWh)
1.1.1990 21,53 16,82 0,34 0,34 2,69 0,17 0,34
1.1.1991 26,57 17,49 0,35 0,35 2,83 0,18 0,35
1.1.1992 28,26 17,49 0,35 0,35 2,83 0,18 0,35
1.8.1992 31,62 17,49 0,35 0,35 2,83 0,18 0,35
1.1.1993 39,52 19,17 1,41 1,12 5,61 0,35 0,70
1.7.1993 39,52 16,65 1,41 1,12 5,61 0,35 0,70
1.1.1994 40,05 17,29 2,05 1,98 11,30 1,09 0,35
1.1.1995 45,12 27,50 3,02 3,12 19,53 0,94 0,59
1.1.1996 51,85 27,50 3,02 3,12 19,53 0,94 0,59
1.1.1997 51,85 27,50 4,88 3,72 28,42 1,19 0,71
1.4.1997 51,85 27,50 4,88 3,72 28,42 1,19 0,71
1.1.1998 55,22 30,02 5,50 4,34 33,40 1,40 0,82
1.9.1998 55,22 30,02 6,37 5,40 41,37 1,73 1,51
1.1.2003 58,08 31,59 6,71 5,68 43,52 1,82 1,59
1.7.2005 58,08 31,59 6,71 5,68 43,52 1,82 -
1.1.2007 58,08 31,59 6,71 5,68 43,52 1,82 -
1.1.2008 62,02 36,05 8,35 6,42 49,32 2,016 -
1.1.2011 62,02 36,05 15,70 18,51 126,91 8,94 1,90
1.1.2012 64,36 46,60 15,70 18,51 126,91 8,940 1,90
1.1.2013 64,36 46,60 15,99 18,93 131,53 11,38 4,90
1.1.2014 66,61 49,31 15,99 18,93 131,53 11,38 4,90
1.1.2015 66,61 49,31 15,99 18,93 153,24 15,36 3,40
Energy content tax
1.1.2011 50,36 - 7,70 8,79 54,54 3,00 -
1.1.2012 50,36 30,70 7,70 8,79 54,54 3,00 -
1.1.2013 50,36 30,70 6,65 7,59 47,10 4,45 -
1.1.2015 50,36 30,70 6,65 7,59 47,10 6,65 -
Carbondioxide tax
1.1.2011 11,66 - 8,00 9,72 72,37 5,94 -
1.1.2012 14,00 15,90 8,00 9,72 72,37 5,94 -
1.1.2013 14,00 15,90 9,34 11,34 84,43 6,93 -
1.1.2014 16,25 18,61 9,34 11,34 84,43 6,93 -
1.1.2015 16,25 18,61 9,34 11,34 106,14 8,71 -
Energy tax
1.1.2011 - - - - - - 1,90
1.1.2013 - - - - - - 4,90
1.1.2014 - - - - - - 4,90
1.1.2015 - - - - - - 3,40
Maintenance and supply sercurity fees
1.7.1984 0,72 0,39 0,39 0,32 1,48 - -
1.1.1997 0,68 0,35 0,35 0,28 1,18 0,084 -
Oil pollution fees
1.1.1990 0,28 0,031 0,031 0,037 - - -
1.1.2005 0,038 0,042 0,042 0,050 - - -
1.1.2010 0,113 0,126 0,126 0,150 - - -

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Energy balance

Question

What is energy balance and how is it modelled?

Answer

Summing up the amount of energy produced and subtracting the amount of energy consumed within a time period gives the energy balance. Since the electricity grid and district heat network lack significant storage mechanics, the balance has to be virtually zero over short periods. When considering the balance of a particular area (e.g. Helsinki), we can make the assumption that electricity can be imported and exported in international markets. The energy in the district heat network, however, has to be produced locally. This sets up the non-trivial problem of optimising production so that there are no significant deficits as well as minimising losses and maximising profits. This problem is solved (to some extent) by market forces in the real world.

In Opasnet, there are two different ways to calculate energy balance. Our most recent energy balance model uses linear programming tools to solve an optimum for the activity of a given set of production units in simulated instances created by the main model. The main model is responsible for the decision making aspects, while the energy balance optimisation only functions as an approximation of real world market mechanics. This version was used in Helsinki energy decision 2015.

The previous version was based on setting up a set of linear equations describing the inputs, outputs, and shares of different energy and plant processes. This approach is less flexible, because it does not use an optimising function and everything must be described as linear (or piecewise linear). However, this approach was successfully used in Energy balance in Kuopio and Energy balance in Suzhou.

This code is an example how the energy balance model is used in a city case. The data comes from Helsinki energy decision 2015.

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Rationale

Energy balance with linear programming

The linear programming problem is set up as follows.

For each production unit: let xi be activity of the plant. Lets also have variables yj for deficits and excesses for each type of energy produced.

The objective function is the function we are optimising. Each production unit has a unit profit per activity denoted by ai which is determined by the amount of different input commodities (e.g. coal) per amount of different output commodities (i.e. electricity and heat) and their market prices. Also, lets say we want to make sure that district heat demand is always met when possible and have a large penalty factor for each unit of heat demand not met (1 M€ in the model). In addition, it must be noted that excess district heat becomes wasted so it counts as loss. Let these deficit and excess related losses be denoted by bj. The whole objective function then becomes: sum(xiai) + sum(yjbj).

The values of variables are constrained by equalities and inequalities: the sum of production of a commodity is equal to its demand minus deficit plus excess, activity is constrained by the maximum capacity and all variables are non-negative by definition. This can be efficiently solved by computers for each given instance. Production wind-up and wind-down is ignored, since time continuity is not considered. As a consequence fuel limits (e.g. diminishing hydropower capacity) are not modelled completely either.

Ovariables like EnergyNetworkOptim below are used in Helsinki energy decision 2015. Prices of fuels in heat production are used as direct inputs in the optimising.

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Fuel use and fuel shares in generic processes

There is an alternative way for calculating fuel use. It is based on the idea that for each heating type, there is a constant share of fuels used. For some heating types, this is generic and is shown on this page. For some others, the constant is case-specific and is determined on a case-specific page.

The table below contains connections of heating types and fuel usage in generic situations. There may be case-specific differences, which must be handled separately.

Fuel use in different heating types(-)
ObsHeatingBurnerFuelFractionDescription
1WoodHouseholdWood1
2OilHouseholdLight oil1
3GasHouseholdGas1
4Heating oilHouseholdLight oil1
5CoalHouseholdCoal1
6Other sourcesHouseholdOther sources1
7No energy sourceHouseholdOther sources1
8GeothermalGridElectricity0.3Geothermal does not sum up to 1 because more heat is produced than electricity consumed.
9Centrifuge, hydro-extractorGridElectricity0.3Not quite clear what this is but presumably a heat pump.
10Solar heater/ collectorGridElectricity0.1Use only; life-cycle impacts omitted.
11ElectricityGridElectricity1
12DistrictUndefinedHeat1

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Emission factors for burning processes

Question

What are the emission factors for burning processes and how to estimate emissions based on them? The focus is on the situation in Finland.

Answer

Example of the use of emission factors: CO2 and fine particle emissions in Helsinki. Scenarios are based on Helsinki energy decision 2015.

An example code for downloading and using the variable.

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Rationale

HSY emission factors

This section describes the use of HSY emission factors used for CO2 emission estimates. This should be merged with the other data, but it is first described as such.

These emission factors are derived from the Helsinki Region Environmental Services (HSY) climate data [11] on 27th Nov 2018. Emission factors were initially calculated for every entry in the data file (4180 rows in total). Then, emission sectors were compared along timeline for each city separately. We noticed that several sectors shared practically the same emission factors with few differences, which are not plausible as real differences (Table Sector classification). So, we took a mean of the values to represent all sectors in the same EFclass group.

Then, we compared results from different cities. There were minor changes that may be due to some real differences in data, and also a some changes that looked like artefact. However, there were two sectors, namely district heating and fuels that clearly differed between cities in a plausible way. Therefore, we used city-specific emission factors for these two sectors and those of Helsinki for all other sectors (because Helsinki seemed to have the least amount of artefact in the data).

Sector classification(-)
ObsSectorSektoriEFclassPKluokkadummy
1metrometrotelectricitysähkö1
2tramsraitiovaunutelectricitysähkö1
3local trainslähijunatelectricitysähkö1
4consumer electricitykulutussähköelectricitysähkö1
5passenger shipsmatkustajalaivatshipslaivat1
6cargo shipsrahtilaivatshipslaivat1
7buseslinja-autotdiesel machinesdieselkoneet1
8vanspakettiautotdiesel machinesdieselkoneet1
9machinerytyökoneetdiesel machinesdieselkoneet1
10truckskuorma-autotdiesel machinesdieselkoneet1
11leasure boatshuviveneetboatsveneet1
12professional boatsammattiveneetboatsveneet1
13district heatingkaukolämpödistrict heatingkaukolämpö1
14oil heatingöljylämmitysoil heatingöljylämmitys1
15electric heatingsähkölämmityselectric heatingsähkölämmitys1
16geothermal heatingmaalämpögeothermal heatingmaalämpö1
17fuelspolttoaineetfuelspolttoaineet1
18processesprosessitprocessesprosessit1
19private carshenkilöautotprivate carshenkilöautot1
20motor cyclesmoottoripyörätmotor cyclesmoottoripyörät1
Sector hierarchy(-)
ObsClassSubclassLuokkaAlaluokkadummy
1heatingdistrict heatinglämmityskaukolämpö1
2heatingoil heatinglämmitysöljylämmitys1
3heatingelectric heatinglämmityssähkölämmitys1
4heatinggeothermal heatinglämmitysmaalämpö1
5electricityconsumer electricitysähkökulutussähkö1
6transportroad transportliikennetieliikenne1
7transportrail transportliikenneraideliikenne1
8transportshippingliikennelaivaliikenne1
9industry and machinerymachineryteollisuus ja työkoneettyökoneet1
10industry and machinaryfuelsteollisuus ja työkoneetpolttoaineet1
11industry and machineryprocessesteollisuus ja työkoneetprosessit1
12waste managementlandfilljätteiden käsittelykaatopaikka1
13waste managementbiowaste compostingjätteiden käsittelybiojätteen kompostointi1
14waste managementwaste water treatmentjätteiden käsittelyjäteveden käsittely1
15waste managementwaste water sludge compostingjätteiden käsittelyjätevesilietteen kompostointi1
16agriculturefieldsmaatalouspellot1
17agriculturefarm animalsmaatalouskotieläimet1
18road transportprivate carstieliikennehenkilöautot1
19road transportmotor cyclestieliikennemoottoripyörät1
20road transportvanstieliikennepakettiautot1
21road transporttruckstieliikennekuorma-autot1
22road transportbusestieliikennelinja-autot1
23rail transportlocal trainsraideliikennelähijunat1
24rail transportmetroraideliikennemetrot1
25rail transporttramsraideliikenneraitiovaunut1
26shippingleasure boatslaivaliikennehuviveneet1
27shippingprofessional boatslaivaliikenneammattiveneet1
28shippingpassenger shipslaivaliikennematkustajalaivat1
29shippingcargo shipslaivaliikennerahtilaivat1

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Emission factors in Helsinki(ton/GWh)
ObsSector201520302035
1Consumer electricity121.570.645
2Electric heating234.2138.588.3
3District heating189.7128.849.1
4Natural gas198198198
5Light fuel oil261261261
6Coal341341341

The data above comes from Gaia report [12].

Inputs and calculations

Variables needed for calculating emissions.
Dependencies Measure Indices Missing data
fuelUse (from Energy balance or other relevant source) Amount of fuel used per timepoint. Required indices: Fuel. Typical indices: Plant
emissionsLocations (case-specific knowledge from e.g. Helsinki energy production) Tells how where emissions occur and from how high a stack. Required indices: - . Typical indices: Plant
emissionFactors (generic information, but may be cultural differences. E.g. Emission factors for burning processes ## emissions per unit of energy produced (g / J or similar unit) Required indices: Pollutant, Fuel. Typical indices: Burner.

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Emission factors for heating

Emission factors of energy production(mg /MJ)
ObsBurnerFuelPM2.5CO2directCO2tradeCO2eqDescription
1HouseholdWood140 (65.8-263)7420008333Other stoves and ovens. Karvosenoja et al. 2008
2HouseholdBiofuel140 (65.8-263)7420008333Other stoves and ovens. Karvosenoja et al. 2008
3HouseholdLight oil0-1074200-872227420087222Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
4HouseholdOil0-1074200-872227420087222Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
5HouseholdOther sources0-10742007420074200Same as oil.
6HouseholdCoal0-1074200-872227420087222
7HouseholdGeothermal0-1074200-872227420087222
8HouseholdGas0-3556505565055650For PM2.5: one third of that of oil. For CO2: 3/4 of that of oil.
9HouseholdFuel oil0-1074200-872227420087222Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
10DomesticWood140 (65.8-263)7420008333Other stoves and ovens. Karvosenoja et al. 2008 Just repeat the previous rows to match different wording of burners.
11DomesticBiofuel140 (65.8-263)7420008333Other stoves and ovens. Karvosenoja et al. 2008
12DomesticLight oil0-1074200-872227420087222Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
13DomesticOil0-1074200-872227420087222Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
14DomesticOther sources0-10742007420074200Same as oil.
15DomesticCoal0-1074200-872227420087222
16DomesticGeothermal0-1074200-872227420087222
17DomesticGas0-3556505565055650For PM2.5: one third of that of oil. For CO2: 3/4 of that of oil.
18DomesticFuel oil0-1074200-872227420087222Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
19Diesel engineFuel oil0-1074200-872227420087222Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
20Diesel engineLight oil0-1074200-872227420087222
21Diesel engineBiofuel0-1074200-872227420087222
22Large fluidized bedGas0-3556505565055650For PM2.5: one third of that of oil. For CO2: 3/4 of that of oil.
23Large fluidized bedCoal2-20106000106000106000Same as peat.
24Large fluidized bedWood2-2074200074200Leijupoltto 100-300 MW Emission factors for burning processes. Karvosenoja et al., 2008
25Large fluidized bedBiofuel2-2074200074200Leijupoltto 100-300 MW Emission factors for burning processes. Karvosenoja et al., 2008
26Large fluidized bedWaste2-20742000-50000CO2trade same as wood. CO2eq is guesswork but it is negative because without burning it would produce methane in landfill
27Large fluidized bedPeat2-20106000106000107500Leijupoltto 100-300 MW Emission factors for burning processes. Peat 382 kg /MWh
28Large fluidized bedHeavy oil8-2291111-10600010600091111Leijupoltto 100-300 MW Emission factors for burning processes. Peat 382 kg /MWh
29Large fluidized bedFuel oil8-2291111-10600010600091111Leijupoltto 100-300 MW Emission factors for burning processes. Peat 382 kg /MWh
30GridElectricity1-10530002120005300050 % of large-scale burning (because of nuclear and hydro). Heavy oil 279 kg /MWh. Officially, electricity is not CHP but requires a double amount of coal to produce it.
31NoneElectricity_taxed1-10530002120005300050 % of large-scale burning (because of nuclear and hydro). Heavy oil 279 kg /MWh. Officially, electricity is not CHP but requires a double amount of coal to produce it. These emissions are assumed when power plants buy electricity from the grid.
32NoneElectricity0000We might want to keep these locations in the model, but we assume that emissions are zero.
33NoneHeat0000We might want to keep these locations in the model, but we assume that emissions are zero.
34NoneCooling0000We might want to keep these locations in the model, but we assume that emissions are zero.
  • Large fluidized bed (Peat) CO2-eq value from Väisänen, Sanni: Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat — Estimation of various production chains by using LCA methodology[27]
  • Other CO2-eq values from EKOREM: Sähkölämmitys ja lämpöpumput sähkönkäyttäjinä ja päästöjen aiheuttajina Suomessa.
  • Classes of climate emissions:
    CO2direct
    Direct CO2 emissions from the stack
    CO2trade
    CO2 emissions as they are defined in the emission trade. Non-trade sectors have emission 0.
    CO2eq
    CO2 emissions as equivalents (i.e. includes methane, N2O and other climate emissions based on life cycle impacts.

In Finland there are about 700 kettles that have under 5MW fuel power. Same amount is between 5 to 50 MW kettles and over 50 MW kettles there are 200 in Finland. One heating power plant can have several kettles. Many 5-50 MW power plants have also less than 5 MW a kettle. [28]

See further discussions about emission factors of wood burning and other topics on the discussion page.D↷

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Intake fractions of fine particles


Question

How to calculate exposure based on intake fractions of airborne particulate matter for different emission sources and locations?

Answer

Intake fraction (iF) is the fraction of an emission that is ultimately breathed by someone in the target population. With fine particles, it is often in the range of one in a million, but variation is large. It can be used as a shortcut for calculating exposures in a situation where actual atmospheric fate and transport modelling is not feasible. For fine particles, there is fairly good understanding of the magnitudes of intake fractions in different situations. [29] Therefore, they have been successfully used in many assessments.

Intake fraction is defined as

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle iF = \frac{c * P * BR}{E},} where

  • iF = intake fraction (unitless after proper unit conversions)
  • c = exposure concentratíon of the population (µg/m3)
  • P = population size
  • BR = breating rate, usually a nominal value 20 m3/d is used
  • E = emission of fine particles (g/s)

In an assessment, exposure concentration c is solved from the equation and used as exposure in health impact modelling.

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Rationale

Inputs and calculations

Variables used to calculate exposure in an assessment model (in µg/m3 in ambient air average concentration).
Variable Measure Indices Missing data
emissions (from the model) is in ton /a Required indices: - . Typical indices: Time, City_area, Exposure_agent, Emission_height.
iF (generic data but depends on population density, emission height etc) conc (g /m3) * pop (#) * BR (m3 /s) / emis (g /s) <=> conc = emis * iF / BR / pop # conc is the exposure concentration Required indices: - . Typical indices: Emission_height, Area
population Amount of population exposed. Required indices: - . Typical indices: Time, Area


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Data

These data come from [29]

Pollutants:

  • PM10-2.5: Primary PM10 - primary PM2.5
  • PM2.5: Primary PM2.5
  • SO2: Secondary PM2.5 derived from SO2 (in practice, SO_4)
  • NOx: Secondary PM2.5 derived from NOx (in practice, NO_3)
  • NH3: Secondary PM2.5 derived from NH3 (in practice, NH4)
Intake fractions of PM(ppm)
ObsPollutantEmission heightUrbanRuralRemoteAverageDescription
1PM10-2.5High8.80.70.045.0
2PM10-2.5Low131.10.047.5
3PM10-2.5Ground403.70.0423
4PM10-2.5Average373.40.0421
5PM2.5High111.60.16.8
6PM2.5Low152.00.16.8
7PM2.5Ground443.80.125
8PM2.5Average262.60.115
9SO20.990.790.050.89
10NOx0.20.170.010.18
11NH31.71.70.11.7

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Exposure-response functions of environmental pollutants

Question

What are the exposure-response functions (ERF) of environmental pollutants that do not have own pages in Opasnet?

Answer

Exposure-response function (ERF) is a mathematical quantitative description of the relationship between an exposure to an agent and the health responses it causes in the human body. How to actually estimate the responses based on ERF is described in detail on page Health impact assessment. Relevant example results can be found from here (in Finnish).

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Rationale

Data

Helsinki energy decision 2015 methods: Difference between revisions(-)
ObsExposure agentResponseSubgroupExposureER functionScalingExposure unitThresholdERFDescription
1Indoor radonLung cancer morbidityAnnual average indoor air concentrationRRNoneBq /m301.0016 (1.0005-1.0031)
2Chlorination byproductsBladder cancer morbidityConcentration in ingested waterRRNoneµg /l01.0039 (1.00053-1.00722)
3Chlorination byproductsBladder cancer morbidityConcentration in ingested waterRRNonenetrev /l01.000029 (1-1.000072)
4ArsenicBladder cancer morbidityConcentration in ingested waterRRNoneµg /l01.002 (0.999-1.006)
5FormaldehydeAsthma morbidityAge:<14Annual average indoor concentrationRRNoneµg /m301.0140743178
6FormaldehydeAsthma morbidityAge:>=14Annual average indoor concentrationRRNoneµg /m301
7Dampness damageAsthma morbidityYes/no moisture damageRRNone%01.37 (1.23-1.53)
8Dampness damageLower respiratory symptoms morbidityYes/no moisture damageRRNone%01.5 (1.38-1.86)
9Dampness damageUpper respiratory symptoms morbidityYes/no moisture damageRRNone%01.7 (1.44-2)
10FluorideFluorosisAge:<14Concentration in ingested waterURNone00.125
11FluorideFluorosisAge:>=14Concentration in ingested waterURNone00
12LeadDecrease of IQ below 70 pointsIntake level from foodURNone ?00.025
13LeadIncreased blood pressureIntake level from foodURNone? 00.025
14LeadIQ lossAge:Age 1Blood concentrationURNoneIQ l /ug240.039 (0.024 - 0.053)Lanphear et al 2005 https://doi.org/10.1289/ehp.7688 using the first increment from 24 to 100 ug/l. Assumes threshold at 24 ug/l although this is strong assumption
15AflatoxinLiver cancerHepatitis:Hepatitis B-Intake via foodURNone# /(ng /kg /d /100000py)00.01 (0.002 - 0.03)WHO. Is this per year or per lifetime?
16AflatoxinLiver cancerHepatitis:Hepatitis B+Intake via foodURNone# /(ng /kg /d /100000py)00.3 (0.01 - 0.5)WHO. Is this per year or per lifetime?
17FormaldehydeNasal cancer morbidityAnnual average indoor concentrationURNoneµg /m300.000013
18BenzeneLeukemia morbidityAnnual average indoor concentrationURNone µg /m300.000005
19QuatzdustSilicosis morbidityIndoor air concentrationURNonemg /m300.125; 0.125; 0.25
20Asbestos at workLung cancer and mesothelioma morbidityIndoor air concentrationURNone 00.05 (0.01-0.1)
21Noise at workHearing damageNoise levelURNone 0570Medium noise (80-85 dB)
22Noise at workHearing damageNoise levelURNone 01320Loud noise (>85 dB)

Ozone and PM2.5 moved to ERF of outdoor air pollution.

Calculations

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External cost

External costs are costs that are not included in the price of a product but still cause negative (or sometimes positive) impacts to the society or stakeholders. The market theory says that if external costs cannot be included in prices e.g. using taxation, the market process will lead to outcomes that deviate from the societal optimum.

Question

What are important external costs in environmental health?

Answer

Health impacts and climate impacts are often not considered in pricing, so they remain as external costs. The code below is used to fetch the variable for models.

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Rationale

External costs in environmental health(€/DALY,€/ton)
ObsCostResultDescription
1Health50000-150000Euros lost per DALY (disability-adjusted life year)
2Climate15-75Euros lost per ton of CO2(equivalent) emitted

Numbers are rough estimates based on typical health impact assessments and common target prices of carbon trade.

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References

  1. ↑ Helsingin ympäristötilasto. Helsingin kaupungin ympäristökeskus. http://www.helsinginymparistotilasto.fi/
  2. ↑ Scanoffice.fi: VTT:n testiraportit - Ilmalämpöpumppuvertailu. http://www.scanoffice.fi/fi/tuotteet/tuoteryhmat/ilmalampopumput/raportit-ja-sertifikaatit/vttn-testiraportit
  3. ↑ Energy.gov: Geothermal heat pumps. U.S. department of energy. http://energy.gov/energysaver/geothermal-heat-pumps
  4. ↑ Energy.gov: Wood and pellet heating. U.S. department of energy http://energy.gov/energysaver/wood-and-pellet-heating
  5. ↑ Norwegian Water Resources and Energy Directorate: Energy in Norway, an brief annual presentation, 2009. http://www.nve.no/global/energi/analyser/energi%20i%20norge%20folder/energy%20in%20norway%202009%20edition.pdf
  6. ↑ Sustainable Energy Technology at Work -project: Use of waste heat from refining industry, Sweden. Preem AB, H Samuelsson. http://www.setatwork.eu/database/products/R179.htm
  7. ↑ Berntsson T, Persson Elmeroth L, Algehed J, Hektor E, Franck PÅ, Åsblad A, Johnsson F, Lyngfelt A, Gevert B, Richards T: Towards a Sustainable Oil Refinery - Pre-study for larger co-operation projects. Chalmers Energy Centre (CEC) Report 2008:1. Chalmers University of Technology. http://publications.lib.chalmers.se/records/fulltext/69752.pdf
  8. ↑ Ilkka Maaskola, Matti Kataikko: Ylijäämälämmön taloudellinen hyödyntäminen. Lämpöpumppu- ja ORC-sovellukset. Motiva, Helsinki, 2014. http://www.motiva.fi/files/10217/Ylijaamalammon_taloudellinen_hyodyntaminen_Lampopumppu-_ja_ORC-sovellukset.pdf
  9. ↑ Helen: Lisäämme tuulivoimalla tuotetun energian määrää. https://www.helen.fi/kotitalouksille/neuvoa-ja-tietoa/vastuullisuus/hiilineutraali-tulevaisuus/lisaa-tuulivoimaa/
  10. ↑ Helen: Aurinkovoiman tuotanto on käynnistynyt Suvilahdessa. 10.3.2015 https://www.helen.fi/uutiset/2015/aurinkovoiman-tuotanto-on-kaynnistynyt-helsingin-suvilahdessa/
  11. ↑ Loviisan sanomat: Loviisan ydinvoimalan tehoja aiotaan nostaa 52 megawattia. 13.1.2012 http://www.loviisansanomat.net/lue.php?id=5361
  12. ↑ Valtioneuvoston periaatepäätös Loviisa 3 -ydinvoimalasta. 6.5.2010 https://www.tem.fi/files/26809/PAP_FPH_LO3.pdf
  13. ↑ Lähdeaho Marika, Meskanen Jukka, Yrjänäinen Heli: Sähköenergian kustannusrakenne: vertailuna vesivoima, hiilivoima ja ydinvoima. Seminaarityö. Tampere university of technology. http://www.tut.fi/smg/tp/kurssit/SMG-4050/seminaarit07/sahkoenergian_kustannusrakenne.pdf
  14. ↑ Krohn S (editor), Morthorst PE, Awerbuch S: The Economics of Wind Energy. European Wind Energy Association (EWEA). March 2009 [http://www.ewea.org/fileadmin/files/library/publications/reports/Economics_of_Wind_Energy.pdf
  15. ↑ Vainio Tuukka: Sähkön tuotantokustannusvertailu. Aalto-yliopisto, Insinööritieteiden korkeakoulu, energiatekniikan laitos. 2011 https://aaltodoc.aalto.fi/bitstream/handle/123456789/4969/isbn9789526041353.pdf?sequence=1
  16. ↑ Hawkings, Will: An affordable district heating system in Norway. Heat Pupms Today. 10.3.2014 http://www.heatpumps.media/features/an-affjordable-district-heating-system-in-norway
  17. ↑ Kenneth Hoffmann MSc, David Forbes Pearson MInstR: Ammonia Heat Pumps for District Heating in Norway – a case study. The Institute of Refrigeration (IOR). 2011 http://www.ammonia21.com/web/assets/link/Hoffman7thApril2011London%20colour.pdf
  18. ↑ European Heat Pump Association: The World's Largest “Natural” District Heat Pump. 6.3.2015 http://www.ehpa.org/about/news/article/the-worlds-largest-natural-district-heat-pump/
  19. ↑ Lako, Paul: Geothermal heat and power. Energy technology systems analysis programme, IEA. 2010. http://www.etsap.org/E-techDS/PDF/E06-geoth_energy-GS-gct.pdf
  20. ↑ Helen Oy: Lämpölaitosten turvallisuustiedote. 17.6.2015 https://www.helen.fi/globalassets/ymparisto/turvallisuustiedote-lampolaitokset.pdf
  21. ↑ Lehtilä A, Koljonen T, Airaksinen M, Tuominen P, Järvi T, Laurikko J, Similä L, Grandell L: Energiajärjestelmien kehityspolut kohti vähähiilistä yhteiskuntaa. Low Carbon Finland 2050 -platform. VTT. 2014. http://en.opasnet.org/en-opwiki/images/d/d1/Low_Carbon_Finland_Platform.pdf
  22. ↑ Tilastokeskus (Statistics Finland). List of tables under the topic "Price of energy". http://pxweb2.stat.fi/database/statfin/ene/ehi/ehi_fi.asp
  23. ↑ U.S. Energy Information Administration: Spot prices for crude oil and petroleum products. http://www.eia.gov/dnav/pet/PET_PRI_SPT_S1_A.htm
  24. ↑ Internal revenue service: Nonconventional Source Fuel Credit, Inflation Adjustment Factor, and Reference Price http://www.irs.gov/pub/irs-drop/n-99-18.pdf
  25. ↑ U.S. Energy Information Administration: Annual Energy Outlook 2015 - With Projections to 2040. 2015. http://www.eia.gov/forecasts/aeo/pdf/0383(2015).pdf
  26. ↑ Tilastokeskus (Statistics Finland): Energian hinnat 2015, 1. vuosineljännes. (Energy prices 2015, 1. quarter) http://tilastokeskus.fi/til/ehi/2015/01/
  27. ↑ Väisänen S: Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat - Estimation of various production chains by using LCA methodology. Lappeenranta University of Technology. 2014. http://www.doria.fi/bitstream/handle/10024/94404/isbn9789522655578.pdf?sequence=2
  28. ↑ http://www.ymparisto.fi/download.asp?contentid=3706 ⇤--#: . Link broken. I simply don't know what this is supposed to be. --~~~~ (type: truth; paradigms: science: attack)
  29. ↑ 29.0 29.1 Sebastien Humbert, Julian D. Marshall, Shanna Shaked, Joseph V. Spadaro, Yurika Nishioka, Philipp Preiss, Thomas E. McKone, Arpad Horvath, and Olivier Jolliet. Intake Fraction for Particulate Matter: Recommendations for Life Cycle Impact Assessment (2011). Environmental Science and Technology, 45, 4808-4816.
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