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  • Model scope and methods

    STEM is an energy systems model, based on the TIMES open source modelling framework, representing the entire Swiss energy system by all essential current and future energy technologies from the primary energy supply over the processing, conversion, transport, distribution of energy carriers (e.g. gas, electricity, hydrogen, biofuels, e-fuels) to the end-use sectors and the energy service demands. These demands are linked to exogenous underlying drivers like population and GDP growth. STEM includes more than 90 energy service demands for industry, services, residential and transport sectors. STEM identifies the least-cost combination of technologies and fuels to meet the energy service demands in future, while fulfilling other technical, environmental and policy constraints (e.g. CO2 mitigation policy). STEM has a high level of technology detail to ensure feasibility of future energy pathways from an engineering perspective. It has a century-long horizon to analyse long-term goals. It also has a high intra-annual resolution to account for temporal variations in energy demand and supply.

  • Temporal dimension

    Base year: 2015 (to be updated to 2019 in the context of SURE)
    Time Horizon: 2020 – 2050 (with possibility to be extended until 2100)
    Time periods/steps: currently 4 time periods of 10 years each (2020, 2030, 2040, 2050)
    Intra-annual resolution: 288 timeslices (or typical operating hours) in a year, representing 12 average days at hourly resolution organised in 4 seasons x 3 days/season (Working day, Saturday, Sunday)

  • Spatial dimension

    National model, i.e. NUTS-1 for Switzerland. For electricity grid a pseudo regional NUTS-2 representation is used with 15 grid nodes (7 Swiss nodes/regions, 4 nodes for existing nuclear power plants, 4 import/export nodes for the neighbouring countries).
    The model covers all sectors of the Swiss energy system from imports/exports, to energy conversion (e.g. electricity, hydrogen, synthetic fuels, biofuels) and energy use in industry, residential, services and transport sectors.

     

  • Representation of economic, energy and climate policies:

    STEM can represent several energy and climate policies, such as:

    • Energy efficiency directives and standards (e.g. eco-designs, building standards, labeling)
    • Renewable technologies financial supports (e.g. subsidies)
    • Phase-out of existing nuclear power plants
    • Emission trade schemes and carbon markets
    • Emissions standards in different sectors (e.g. buildings, vehicles)
    • Energy and emissions taxation (per fuel/emission and sector)
    • Emissions ceilings and targets
    • Biofuels directives and standards
    • Technology-specific or specific conversion pathways supporting schemes (e.g. e-fuels)
    • Energy market regulations and agreements
    • Infrastructure development supporting measures

  • Representation of shocks

    STEM currently is more suitable to capture long-term effects of shocks with a nation-wide impact. As a technology rich bottom up model it can quantify different types of shocks as long as these have an effect on the deployment or operation of energy supply and demand technologies.

    STEM is more suitable for shocks lasting/or having an effect that spans from months to years (in its current version). Short-term shocks can be quantified but at a coarse detail.

    STEM cannot quantify shocks outside Switzerland, but it can consider their impact as boundary conditions. Also, STEM is weak in quantifying shocks occurring at sub-national scales, unless if their impact is extrapolated at the national scale.

    Examples of shocks that can be modeled with the current version of STEM include:

    •  Transient events: rapid technology diffusion, drop-in technology costs, energy trade disruptions, energy supply/demand disruptions
    • Disruptive events: low/high population/economic development, changes in consumer preferences and technology acceptance, digitalisation in energy demand and supply

  • Representation/Quantification of Indicators

    Examples of indicators that can be quantified by STEM (directly, from the model’s output) include:

    • Energy and process related CO2 emissions
    • Physical investment in energy supply and demand technologies
    • Energy consumption by energy carrier in different end use sectors
    • Investment expenditures in energy supply and demand technologies
    • System cost (CAPEX, OPEX, fuel costs) in different sectors for different years
    • Energy costs to consumers
    • End-use prices (approximation via marginal cost of energy commodities)
    • Net Import dependency
    • CO2 intensity in industry, services, residential and transport, and the whole energy system
    • Energy intensity per demand sector and in the whole energy system, per GDP or capita
    • Shares of renewables in energy supply and in final energy consumption
    • Energy saved due to conservation and efficiency improvements

  • List of key model parameters (required input data)

    Parameter short description

    Unit (optional)

    Technical parameters related to technologies:

    Efficiency

    %

    Availability factor

    %

    Consumption of energy per unit of activity

    PJ

    Shares of fuels per unit of activity

    %

    Technical life time

    Years

    Construction lead in time and dismantling times

    Years

    Amounts of commodities consumed during construction

    PJ

    Amounts of commodities released during dismantling

    PJ

    Contribution of technology to peak and ancillary markets

    %

    Ramping rate

    %

    Part load efficiency losses

    %

    Minimum online/offline times

    Hours

    Maintenance cycles and duration

    Hours

    Maximum number of cycles (for storages)

    Maximum capacity

    GW, veh

    Economic and policy parameters related to technologies

    Investment costs

    CHF/kW, CHF/veh

    Annual fixed O&M costs

    CHF/kW, CHF/veh

    Variable O&M costs (other than fuel costs)

    CHF/PJ

    Taxes and subsidies

    CHF/kW, CHF/PJ

    Construction and dismantling costs

    CHF/kW

    Economic lifetime

    Years

    Discount rate or hurdle rate

    %

    Technical parameters attached to commodities (fuels, emissions)

    Efficiency of transmission/distribution

    %

    Projected energy service demand (for demand commodities only)

    Energy service demand load curve

    % per timeslice

    Resource potential

    PJ, Mt CO2 seq

    Resource load curve (e.g. wind/solar/water inflow profiles=

    % per timeslice

    Economic and policy parameters attached commodities (fuels, emissions)

    Gross production bounds (minimum, maximum)

    PJ, Mt CO2

    Net production bounds (minimum, maximum)

    PJ, Mt CO2

    Max/min imports per region

    PJ

    Max/min exports per region

    PJ

    Taxes and subsidies

    CHF/GJ, CHF/tCO2

    Delivery and variable costs

    CHF/GJ

    Energy prices (for imported commodities only)

    CHF/GJ

  • List of key model output variables (reporting data)

    Output variable short description

    Unit

    Investment capacity per year, technology and sector

    GW, veh

    Early retired capacity per year, technology and sector

    GW, veh

    Total scrapped capacity per year, technology and sector

    GW, veh

    Total installed capacity per year, technology and sector

    GW, veh

    Activity level per year, technology and sector

    PJ, vkm

    Energy consumption per year, timeslice, commodity, technology and sector

    PJ

    Energy production per year, timeslice, commodity, technology and sector

    PJ

    Emissions per year, timeslice, pollutant, technology and sector

    Mt CO2eq

    Input to storage per year, timeslice, commodity, storage technology and sector

    PJ

    Output from storage per year, timeslice, commodity, storage technology and sector

    PJ

    Imports from international trade per year, importing region, timeslice, commodity

    PJ

    Exports from international trade per year, exporting region, timeslice, commodity

    PJ

    Energy service demand per year, timeslice, sector and end-use

    PJ

    Commodity load profiles per year, timeslice, commodity, sector, end-use

    PJ

    CAPEX per year and technology

    MCHF

    FIXOM per year and technology

    MCHF

    VAROM per year and technology

    MCHF

    Construction costs per year and technology

    MCHF

    Dismantling costs per year and technology

    MCHF

    Import/export costs pear year and neighbouring region

    MCHF

    Delivery costs per year and commodity

    MCHF

    Taxes and subsidies per year and technology/commodity

    MCHF

    Investment expenditures per year and technology

    MCHF

    Marginal cost of commodity (fuel, emissions)

    CHF/GJ, CHF/tCO2

  • List of selected key model parameters mentioned above, which are subject to high uncertainty with impact on robustness of results

    Parameter short description

    Influencing factor or reason for uncertainty

    “Willingness to pay” parameter to reflect “uneconomic” decisions in the past

    Behavioural aspects in decision making might change in the future

    Overall potential of non-site-specific renewable energy carriers (e.g., biomass or biogas) for heating in buildings

    Each sector has own cost curve for using such resources. The allocation of resources to the specific demand sectors is relevant for robustness of results.

    STEM has been used in quantifying Net-Zero pathways for Switzerland in the SCCER Joint Activity Scenarios and Modelling. Detailed report and scenario results can be found here:
    https://www.psi.ch/en/eem/projects/sccer-joint-activity-scenarios-and-modelling

  • List of selected key model parameters mentioned above, which are subject to high uncertainty with impact on robustness of results

    Parameter short description

    Influencing factor or reason for uncertainty

    Investment cost of technologies

    Technology costs influencing the uptake of technologies and the overall abatement cost. Because are taken exogenously from literature or studies, consistency among the costs of technologies in different sectors needs to be ensured (especially in a context of sector coupling). Moreover, the projections of costs of the new technologies (e.g. CC(U)S, H2-related technologies, batteries) show high variability in literature.

    Resource potential

    Domestic resource potentials influencing the costs for mitigation. For many resources, their potential shows high variability in literature. A particular case is the sequestration of CO2, which besides the technical uncertainty is also subject to social acceptance.

    Projected energy service demand

    Energy service demands depend on socioeconomic conditions, which are evaluated outside STEM (e.g. population, GDP, structure of economy, societal values and norms, consumer energy behaviour). These socioeconomic conditions are surrounded by enormous uncertainty, especially when one attempts projections in 30-40 years from now.

    Max/min Imports and Exports

    STEM is a national model in scope, and international trade is not represented endogenously. The exogenous assumptions regarding the availability to import zero carbon energy carriers (e.g. biofuels, e-fuels, etc.) are highly uncertainty and depend not only on economic conditions (e.g. prices) but also on geopolitical conditions. The availability of imports highly influences the deployment of domestic options and costs of mitigation.

    Energy prices for imported commodities

    Besides the availability of imports, the energy prices of zero carbon fuels are also surrounded by high uncertainty, influencing the deployment of domestic options and the mitigation costs.

    Discount rate and hurdle rates

    STEM takes decisions based on the NPV of the total system costs depending on a social discount rate and possibly on other technology-specific hurdle rates. These discount and hurdle rates depending on the socioeconomic conditions, which are highly uncertain when projections for the next 30-40 years are made, and highly influence the uptake of technologies (including the timing of investments).

  • List of key model variables mentioned above, with sensitivity to a particular model instance

    Variable short description

    Influencing factor or reason for uncertainty

    Investment capacity per year, technology and sector

    Investment decisions depend not only on technology production costs but also on integration and system costs for this technology, which account, among others, for flexibility and security of supply. These costs are highly influenced by the scenario assumptions, e.g. costs of technologies, availability of resources, discount rates, policies, etc. Besides, the deployment of technology is also dependent on social acceptance which is also subject to scenario assumptions.

    Activity level per year, technology and sector

    The activity level (i.e. production or level of use) of a technology is subject to its installed capacity and technical constraints, as well as it is subject to its coordinated integration with other technologies. All these factors are correlated with the investment decisions and hence, the activity level variable inherits the uncertainties described above.

    CAPEX, FIXOM, VAROM, costs per technology

    The technology costs depend on investment and operational decisions, and as such they are characterised by the same uncertainties as the investment and activity variables.

    Marginal cost of commodities

    The marginal cost of commodities, often used as a proxy for the “price” of the commodity in a competitive market, is subject not only to uncertainties related to investment and activity variables of the different technologies in the model, but also to the various specific-scenario constraints imposed by the scenario story line.

  • Linkages with other modelling tools (quantitative or qualitative) within SURE

    Modelling framework

    Direction (From, To)

    Exchanged data

    Comment

    BSM

    From

    Differential investment level per year, technology, and sector

    Possibly by new/existing buildings, multi-family/single family

    BSM

    From

    Refurbishment rate per building element and building period

    BSM

    From

    Total installed capacity per year, technology, and sector (for buildings and services)

    BSM

    From

    Refurbishment costs per year and building element

    BSM

    From

    Projected energy service demand (buildings, services)

    BSM

    From

    Energy service demand load curve

    EXPANSE

    From

    Local constraints on tech investment

    It needs confirmation with the EXPANSE team and further clarification

    GRID

    From

    Grid loading indicators

    To get a proxy of congestion issues that need to be accounted in STEM runs

    GEM-E3

    From

    Gross Domestic Product

    GEM-E3

    From

    Sectoral Production (for each economic activity)

    GEM-E3

    From

    Private Consumption

    BSM

    To

    Marginal cost of energy commodities in STEM

    It can be perhaps used as a proxy for prices for energy commodities needed in BSM

    BSM

    To

    Investment capacity per year, technology and sector

    For all sectors not covered by BSM, the developments of which are needed to be considered in the decisions taken by BSM

    BSM

    To

    Activity level per year, technology and sector

    BSM

    To

    Total installed capacity per year, technology and sector

    EXPANSE

    To

    Investment capacity per year, technology and sector

    Needs confirmation with the EXPANSE team, but perhaps some exchange is needed here to ensure consistency

    EXPANSE

    To

    Total installed capacity per year, technology and sector

    GRID

    To

    Time profiles for production (fixed and flexible) and demand

    GEM-E3

    To

    Investment capacity per year, technology and sector

    GEM-E3

    To

    Activity level per year, technology and sector

    GEM-E3

    To

    Total installed capacity per year, technology and sector

    LCA

    To

    Activity level per year, technology and sector

    LCA

    To

    Total installed capacity per year, technology and sector

    LCA

    To

    Marginal cost of energy commodities in STEM

    It can be perhaps used as a proxy for prices for energy commodities

    Flexi-TI

    To

    Marginal cost of energy commodities in STEM

    It can be perhaps used as a proxy for prices for energy commodities

    Flexi-TI

    To

    Investment capacity per year, technology and sector

    For all sectors and technologies not covered by Flexi-Ti and the developments of which are needed to be considered in the model. Also, to ensure a kind of consistency with national developments

    Flexi-TI

    To

    Activity level per year, technology and sector

    Flexi-TI

    To

    Total installed capacity per year, technology and sector

Building Stock Model of Switzerland (BSM)

TEP Energy GmbH

Giacomo Catenazzi

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  • Model scope and methods

    The Building Stock Model is a simulation model, representing the complete Swiss building stock on single building level. It is used to calculate and estimate the todays and future energy demand from buildings and related services and appliances. The model is based on Swiss, cantonal and communal statistics, the Swiss 3-D mapping, results of surveys, studies and potential analyses, data from the Buildings and Housing Register (GWR) and the Company Register (STATENT). In addition, energy consumption data and waste heat sources, zone plans as well as maps of site uses, grid-bound energy supplies such as gas, district and local heating networks and renewable energies are used. Key features are the integration of GIS-based data in terms of e.g., resource potentials, grid spanning algorithms as well as techno-economic parameters into the analysis for various types of future energy demand scenarios.

  • Temporal dimension

    Base year: 2018/2019 where applicable (to be updated to 2020 in the context of SURE)

    Time Horizon: 2020 – 2060 (with the possibility to be extended to 2100)

    Time periods/steps
    : annual resolution

    Intra-annual resolution
    : depending on the appliance on hourly level (temperature depending) or by hourly resolution organised in 4 seasons x 3 days/season (Working day, Saturday, Sunday) -> hourly load profiles can be provided for electric appliances.

  • Spatial dimension

    GIS-based single building level if needed, generally aggregated on cantonal or Swiss level for different building sectors, branches, and typologies:

    • Residential sector: Single and multi-family houses
    • Tertiary sector: 8 branches of tertiary sector (Noga-Codes) plus agricultural sector

  • Representation of policies

    BSM can represent several energy and climate policies, such as:

    • Energy efficiency directives and standards (e.g., eco-designs, building standards, labelling), potentially on cantonal level
    • Renewable technologies financial supports (e.g., subsidies)
    • Phase-out of heating technologies
    • Emissions standards in different sectors (e.g., heating in buildings)
    • Energy and emissions taxation (per fuel and emission)
    • Indirect emissions ceilings and targets
    • Biofuel’s directives and standards
    • Technology-specific or specific conversion pathways supporting schemes (e.g., e-fuels)
    • Local constraints on renewable resources and potentials,
    • Infrastructure development supporting measures

  • Representation of shocks

    BSM currently is more suitable to capture long-term effects of shocks with a nation-wide or cantonal impact (e.g., cantonal building regulations). As a technology rich bottom-up model, it can quantify different types of shocks if these influence the deployment or operation of energy demand technologies.

    BSM is more suitable for shocks lasting/or having an effect that spans over years (in its current version). Short-term shocks on sub-annual level cannot be quantified now.

    BSM cannot quantify shocks outside Switzerland and can only consider their impact as boundary conditions in terms of energy carrier prices.

    Examples of shocks that can be modelled with the current version of BSM include:

    • Transient events: rapid demand technology diffusion (e.g., heating systems), drop in technology costs, energy potentials limitation (e.g., ground source heating without regeneration)
    • Disruptive events: low/high population/economic development, changes in consumer preferences and technology acceptance, digitalisation in energy demand, cold or hot years.
    • Policy failure on cantonal/Swiss level (e.g., acceptance of new regulations)
    • Demographic: densification of building stock in urban or non-urban areas

  • Representation/Quantification of Indicators

    Examples of indicators that can be quantified by BSM (directly, from the model’s output) include:

    • Energy and process related CO2 emissions per energy reference area (ERA) or demand driver (e.g., full-time equivalents, building typology)
    • Physical investment in demand technologies and insulation materials
    • Energy consumption by energy carrier in different end use sectors
    • Investment expenditures in energy demand technologies, heating grid infrastructure (local and district heating networks) and refurbishment measures
    • System cost (CAPEX, OPEX, fuel costs) in different sectors for different years
    • Energy costs to consumers
    • CO2 intensity of buildings
    • Energy intensity per building typology and energy reference area (kWh/m2)
    • Shares of renewables in energy demand
    • Energy saved due to conservation and efficiency improvements
    • Useful and final energy demand differentiation
    • Cost and savings of efficiency measures

  • List of key model parameters (required input data)

    Parameter short description

    Unit (optional)

    Technical parameters related to technology

    Efficiency

    %

    Full load hours per year

    hours

    Consumption of energy per unit of activity

    GJ

    Shares of fuels per unit of activity

    %

    Technical lifetime of heating systems and building elements

    years

    Capacity installed

    kW

    Economic and policy parameters related to technologies

    Investment costs

    CHF/m2 and CHF/kW

    Annual fixed O&M costs

    CHF/kW

    Taxes and subsidies

    CHF/GJ

    Economic lifetime

    years

    Discount rate or hurdle rate

    %

    GDP and population/employment development

    Mio CHF / Mio empl.

    Technical parameters attached to commodities (fuels, emissions)

    Projected energy service demand (for demand commodities only)

    GJ

    Energy service demand load curve

    GJ/hr

    Resource load curve (e.g., heating and cooling degree days)

    HDD & CDD

    Economic and policy parameters attached commodities (fuels, emissions)

    employment

    Full time equivalent

    Specific energy demand per employee

    GJ/FTE

    Socio-economic parameters related to decision finding

    Willingness to pay

  • List of key model output variables (reporting data)

    Variable short description

    Unit (optional)

    Output variable short description

    Differential investment level per year, technology, and sector (scenario comparison)

    Refurbishment rate per building element and building period

    Total installed heating capacity per year, technology, and sector

    kW/MW

    Activity level per year, technology, and sector

    Energy consumption per year, time-slice, commodity, technology, and sector

    GJ

    Emissions per year, time-slice, pollutant, technology, and sector

    Mt CO2-eq

    Energy service demand per year, time-slice, sector and end-use

    CAPEX per year and technology

    Mio CHF

    FIXOM per year and technology

    Mio CHF

    VAROM per year and technology

    Mio CHF

    Any other comments that should be known about the model and its usage/applicability
    BSM has been used in quantifying Net-Zero pathways for Switzerland for the energy perspectives 2050+ scenarios and modelling, for the tertiary buildings and the agricultural sector. Detailed report and scenario results can be found here:
    Swiss Energy Perspectives – TEP Energy (tep-energy.ch)

  • List of selected key model parameters mentioned above, which are subject to high uncertainty with impact on robustness of results

    Parameter short description

    Influencing factor or reason for uncertainty

    “Willingness to pay” parameter to reflect “uneconomic” decisions in the past

    Behavioural aspects in decision making might change in the future

    Overall potential of non-site-specific renewable energy carriers (e.g., biomass or biogas) for heating in buildings

    Each sector has own cost curve for using such resources. The allocation of resources to the specific demand sectors is relevant for robustness of results.

  • List of key model variables mentioned above, with sensitivity to a particular model instance

    Variable short description

    Influencing factor or reason for uncertainty

    Energy carrier price relation (e.g., ratio electricity price to district heating price)

    The development of some energy carrier prices is highly uncertain compared to other energy carriers (e.g., price of district heating or biomass-based heat supply). The differential between main energy carriers (e.g., natural gas, electricity, and renewable alternatives) is affecting the choice of heating system investments.

  • Linkages with other modelling tools (quantitative or qualitative) within SURE

    Modelling framework

    Direction (From, To)

    Exchanged data

    Comment

    From TIMES to BSM

    Energy carrier prices (electricity, oil, gas, biomass, district heating)

    From E3M to TEP

    Economic activity (number of employees per sector/sub-sector)

    Effects on willingness to pay

    This is not an established link, but if we get additional insights from socio-economic modelling on the willingness to pay, we could discuss adjusting assumptions

    UniGE to TEP

    The use of solar thermal potentials and heat pumps

    We should align assumptions and results

    From TIMES to TEP and vice versa

    Potentials and use of district heat / waste heat from industries / data centres

    Exchange in both directions

    TI use case

    Bi-directional

    Exchange on potentials and results for the use case of canton Tessin

EXploration of PAtterns in Near-optimal energy ScEnarios

University of Geneva

Prof. Evelina Trutnevyte

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  • Model scope and methods

    EXPANSE is a spatially-explicit, bottom-up, technology-rich, single-year optimization model of the Swiss electricity system. The model accounts for up to hourly operation and long-term capacity planning of electricity generation, storage, and transmission. EXPANSE has three key features. First, the model is spatially resolved at the level of 2’172 Swiss municipalities. Second, EXPANSE applies Modeling to Generate Alternatives (MGA) to compute many alternative scenarios of different spatial configurations of the electricity system with cost-optimal and near-optimal costs. Third, EXPANSE quantifies regional impacts associated with the capacity and operation of electricity system infrastructure, including investment and divestment needs, job gains and losses, and regional impacts on electricity prices, greenhouse gas emissions, particulate matter emissions, and land use.

    In SWEET SURE, EXPANSE is used in combination with statistical modeling of spatial growth of new renewable electricity installations in Switzerland. This statistical approach uses spatial data on past installations since 2008 and other technical and socio-economic indicators to calculate forward-looking projections of new installations. This statistical modeling hence helps improving the realism of EXPANSE spatial scenarios.

  • Temporal dimension

    EXPANSE is a single-year model for the years 2035 and 2050 with up to hourly representation of electricity generation, storage, and transmission. Time steps can be flexibly changed in the settings in order to account for the computational tractability of modeling many scenarios at high spatial resolutions.

  • Spatial dimension

    EXPANSE represents electricity demand and electricity generation for 2’172 individual Swiss municipalities. This can be flexibly changed to cantonal or country level as well. Electricity storage and transmission is represented by eight clustered transmission grid nodes and this can be flexibly changed to higher or lower numbers of transmission grid nodes.

     

  • Representation of policies

    • Technology-focused: e.g., nuclear phase-out
    • Subsidies and taxes: e.g., carbon tax, feed-in tariffs
    • Resource-focused: e.g., minimum generation targets from renewable resources
    • Environment-focused: e.g., greenhouse gas emission targets
    • Society-focused: e.g., no reduction in jobs in the electricity sector
    • Demand-focused: e.g., reduced electricity demand from higher efficiency

  • Representation of shocks

    • Natural shocks: e.g., effects of extreme weather on hourly solar and wind resource availability and the hourly operation of the electricity system
    • Technical shocks: e.g., outages of individual elements of the electricity system (e.g., individual transmission lines or power plants) and hourly security of supply
    • Economic shocks: e.g., effects of international technology or fuel prices on the hourly and regional operation of the electricity system

  • Representation/Quantification of Indicators

    • direct jobs in electricity sector jobs
    • electricity curtailment (e.g., hourly curtailment of wind electricity)
    • electricity prices (locational marginal prices)
    • greenhouse gas emissions
    • investment and divestment
    • land use
    • particulate matter emissions
    • total system costs
    • transmission line loading

  • List of key model parameters (required input data):

    Parameter short description

    Unit (optional)

    Technology characterization (e.g., capacity factor, efficiency)

    Technology and fuel costs (e.g., capital and operation costs)

    Renewable energy resources potential

    International energy prices (e.g., natural gas, oil, electricity import)

    Policies (e.g., emission targets, nuclear phase-out)

    Electricity demand

    Discount rate

    Slack (cost deviation of near-optimal scenarios from cost-optimal scenario)

  • List of key model output variables (reporting data):

    Variable short description

    Unit (optional)

    Installed capacity of electricity generation, storage, and transmission

    MW

    Hourly operation of electricity generation, storage, and transmission

    MWh

    Primary energy demand in the electricity sector

    MWh

    Fuel use in the electricity sector

    MWh

    Renewable electricity generation

    MWh

    International electricity trade volume: import and exports

    MWh

    Electricity system costs (fuel, investment, etc.)

    CHF

    CO2 emissions

    Mt CO2

    Direct employment

    Jobs

    Particulate matter emissions

    t PM10-eq

    Electricity prices

    CHF/MWh

    Land use

    km2

  • List of selected key model parameters mentioned above, which are subject to high uncertainty with impact on robustness of results

    Parameter short description

    Influencing factor or reason for uncertainty

    Investment costs of renewable electricity technologies

    All cost assumptions are drivers of uncertainty in optimization-based EXPANSE. In the case of renewable electricity technologies, there is a certain convergence of investment costs of multiple technologies, making the results sensitive to minor differences in costs.

    Investment costs of storage technologies

    All cost assumptions are drivers of uncertainty in optimization-based EXPANSE. In the case of storage technologies, investment costs of new technologies, like batteries, hydrogen technologies etc., are especially uncertainty.

    Technical and realistic potentials of renewable electricity technologies

    In scenarios with high-levels of renewable generation, technical and realistic potential of technologies, like solar PV or wind power, contribute a lot to uncertainty because these technologies are increasingly cheaper and hence some scenarios show their high deployment.

    Technical and realistic potentials of new storage technologies

    In scenarios with high-levels of renewable generation, the potential to integrate variable generation is dependent on the availability of storage. See the previous point.

    Uptake rates of renewable generation and storage

    For shorter-term scenarios, e.g. 2030-2035, the full potential of renewable technologies and storage is unlikely to be used, hence making the annual uptake rates into important uncertainties.

    Electricity prices abroad

    All cost assumptions are drivers of uncertainty in optimization-based EXPANSE. In the case of electricity import/export, the results depend on the difference between the domestic and foreign prices.

    Electricity demand and load curves

    EXPANSE assumes electricity demand and respective load curves exogenously, conducting scenario analysis of electricity demand and adoption of electric vehicles and heat pumps outside the model. Yet, the electricity demand drives the composition of the generation and import/export.

    Slack

    This is an EXPANSE-specific parameter that defines an acceptable deviation in costs from the cost-optimal solution.

    Policy assumptions

    While some policies are part of the scenario analysis, e.g. year of nuclear phase out, there are other types of policies that remain outside the model, e.g. R&D investment, global climate commitments, cantonal policies etc.

  • List of key model variables mentioned above, with sensitivity to a particular model instance

    Variable short description

    Influencing factor or reason for uncertainty

    Investment costs, technical and realistic potentials of storage technologies

    In some scenarios with high-shares of variable renewable generation, the results are particularly sensitive to the availability and costs of new storage technologies, like batteries and hydrogen.

  • Linkages with other modelling tools (quantitative or qualitative) within SURE

    Modelling framework

    Direction (From, To)

    Exchanged data

    Comment

    STEP

    From

    Investment capacity per year, technology and sector

    Electricity demand changes due to sector coupling

    To be discussed

    STEP

    To

    Local constraints on technology investment

    To be discussed

    GRID

    From

    Datasets for validation of transmission grid modelling

    To be discussed

    GRID

    To

    Hourly electricity generation in shock scenarios, related to extreme weather

    To be discussed

  • Any other comments that should be known about the model and its usage/applicability

    EXPANSE model references:

    J. Müller, E. Trutnevyte, Spatial projections of solar PV installations at subnational level: Accuracy testing of regression models. Applied Energy 265, 114747 (2020).

    J.-P. Sasse, E. Trutnevyte, Regional impacts of electricity system transition in Central Europe until 2035. Nat. Commun. 11, 4972 (2020).

    J.-P. Sasse, E. Trutnevyte, Distributional trade-offs between regionally equitable and cost-efficient allocation of renewable electricity generation. Appl. Energy 254, 113724 (2019).

    E. Trutnevyte, M. Stauffacher, M. Schlegel, R. W. Scholz, Context-Specific Energy Strategies: Coupling Energy System Visions with Feasible Implementation Scenarios. Environ. Sci. Technol. 46, 9240–9248 (2012).

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  • Model scope and methods

    GEM-E3[1] is a large scale multi-sectoral CGE economic model that covers all countries aggregated to 56 regions. The key innovations of the model relate to the explicit representation of the financial sector, semi-endogenous dynamics based on R&D induced technical progress and knowledge spillovers, the representation of multiple households (the model represents 460 households distinguished by income group), unemployment in the labour market and endogenous formation of labour skills. The model is calibrated to a wide range of datasets comprising of IO tables, financial accounting matrices, institutional transactions, energy balances, GHG inventories, bilateral trade matrices, investment matrices and household budget surveys. All countries in the model are linked through endogenous bilateral trade transactions identifying origin and destination. Particular focus is placed on the representation of the energy system where specialized bottom-up modules of the power generation, buildings and transport sectors have been developed.  The model is recursive dynamic coupled with a forward-looking expectations mechanism

    [1] For a detailed technical presentation of the model its manual is available at: http://e3modelling.gr/modelling-tools/gem-e3/

  • Temporal dimension

    Base year: 2015

    Time Horizon: 2020 – 2100

    Time periods/steps: 5 yr (option for yearly time steps until 2030)

  • Spatial dimension

    The model covers 47 Countries/Regions. For each EU27 member state and Switzerland a separate regional satellite module is used for NUTS2 (7 regions for Switzerland).  Each economy is fully represented through an aggregation of 67 economic activities.

  • Representation of policies

    The following key policies are represented in the model:

    1. Fiscal policies (VAT, Direct-Indirect taxes, Subsidies, Duties)
    2. ETS market and recycling options for ETS revenues
    3. Carbon border adjustment policies
    4. RES targets
    5. Energy efficiency targets (industry, household)
    6. CO2 Standards in transport
    7. Social Policies (unemployment benefits, support schemes for low-income households)
    8. GHG emission reduction targets for different emission reduction clubs (country/region/economic activity)
    9. Feed in Tariffs
    10. R&D subsidies
    11. Adoption of alternative financing schemes (debt / interest rate by economic agent)
    12. Trade Integration

    International transportation costs

  • Representation of shocks

    The model can simulate economic shocks and capture their impact through time and among countries/sectors and regions. All countries and economic activities of the model are captured through endogenous bilateral trade transactions.

    The shocks can relate to

    1. Changes in technical progress that are exogenously specified (i.e. accelerated technical progress in a specific sector with mass diffusion and productivity effects in other sectors)
    2. Destruction of capital stock (natural disaster etc.)
    3. Changes in Population
    4. Transient events like the impact of COVID-19 onto different economic activites
    5. Changes in consumer behaviour / consumption pattern (car pooling, diet etc)
    6. Changes in international trade

  • Representation/Quantification of Indicators

    The most important results, provided by GEM-E3 are: Full Input-Output tables for each country/region identified in the model, dynamic projections in constant values and deflators of national accounts by country, employment by economic activity and by skill and unemployment rates, capital, interest rates and investment by country and sector, private and public consumption, bilateral trade flows, consumption matrices by product and investment matrix by ownership branch,  GHG emissions by country, sector and fuel and detailed energy system projections (energy demand by sector and fuel, power generation mix, deployment of transport technologies, energy efficiency improvements)

  • List of key model parameters (required input data):

    Parameter short description Unit (optional)
    Population in m. persons
    Investment (by economic activity) b. $
    Fossil Fuel Prices $/toe
    Capital Costs of clean energy technologies $/unit

  • List of key model output variables (reporting data):

    Variable short description Unit (optional)
    Gross Domestic Product b. $
    Investment b. $
    Public Consumption b. $
    Private Consumption b. $
    Exports b. $
    Imports b. $
    Balance of Trade % of GDP
    Employment in m. persons
    Population (in m. persons) in m. persons
    Labour Force (in m. persons) in m. persons
    Working age population (in m. persons) in m. persons
    Unemployment %
    Wages and Salaries (m. €/yr)
    Average Wage rate (€/hour)
    Sectoral Production (for each economic activity) b. $
    Sectoral Employment (for each economic activity) b. $

  • List of selected key model parameters mentioned above, which are subject to high uncertainty with impact on robustness of results

  • List of key model variables mentioned above, with sensitivity to a particular model instance

  • Linkages with other modelling tools (quantitative or qualitative) within SURE

  • Any other comments that should be known about the model and its usage/applicability

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FlexiTI

SUPSI

Jalomi Maayan Tardif

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  • Model scope and methods

    FlexiTI is an energy system model based on the System Dynamics approach for simulating socio-technical transitions. The model currently focuses on the electric system and on the diffusion of four distributed technologies in the residential sector that can impact the amount of flexibility that could be available within the territorial boundaries of a DSO. These technologies are: photovoltaics, heat-pumps, stationary batteries, and electric vehicles. The model considers archetype residential buildings, that are characterized in terms of four floor areas, four thermal performance levels, and four electrification levels. The adoption rate of the technologies changes endogenously over the years as a function of their perceived economic, social, environmental benefits. The purpose of the model was to mirror socio-political trends and simulate their direct impact and their side effects on the adoption or dismissal of technologies. Such information is meant to support the DSO in mid-long-term planning or resources and pre-emptive testing of innovative business models that could be applied along with the probable shifts in energy demand and supply.

  • Temporal dimension

    Base year: 2015

    Time Horizon: 2020-2050

    Time periods/steps: currently 4 time periods for each year (these are to be updated in the context of SURE)

    Intra-annual resolution: 4 seasons, 3-hour bins, no variation in terms of week-weekend (these are to be updated in the context of SURE)

  • Spatial dimension

    Regional model (NUTS-2/3) – the model was first built based on Cantonal statistics but was then adapted to the specific territory serviced by a DSO.

  • Representation of policies

    FlexiTI can integrate several energy and climate policies, such as:

    • Energy efficiency directives and standards
    • Renewable technologies financial supports
    • Phase-out of existing nuclear power plants
    • Energy and emissions taxation (per fuel/emission and sector)
    • Emissions ceilings and targets
    • Energy market regulations and agreements

    Infrastructure development supporting measures

  • Representation of shocks

    FlexiTI is based on a top-down approach and, in its current form, could evaluate the response of the system to economic and socio-political shocks within Ticino. FlexiTI cannot evaluate the impact of shocks occurring within Ticino on higher spatial levels (CH, International) or adjacent territories (other Cantons, other DSOs). For example, FlexiTI could be used to model transient events: changes in user and supplier choice parameters and effects on technology diffusion; gradual and/or drastic changes in cost of technologies due to technology constant/changing learning parameter, business models, policies.

  • Representation/Quantification of Indicators

    Examples of indicators that can be quantified by the current FlexiTI model include:

    • Distribution of residential building stock by archetypes
    • Distribution of vehicle stock by drive-train (currently only ICE and BEV)
    • Shares of renewables in energy supply and in final energy consumption in residential sector
    • Range of energy self-sufficiency in the residential sector
    • Electricity cost to consumers
    • CO2 intensity in residential sector and partially in the transport sector

  • List of key model parameters (required input data):

    Technical parameters related to supply and conversion technologies:
      Self-consumption %
    Average, maximum, minimum capacity kWh (or kWh/kW)
    Technical lifetime Years
    Factor of growth 1/year
    Typical installed capacity ex: kW per building archetype
    emissions ex: gCO2eq./km
    Typical resource availability ex: kWh/hr, kW/m2
    Economic and policy parameters related to technologies
      Investment costs CHF/kW
    Annual fixed O&M costs CHF/kW
    Variable O&M costs (other than fuel costs) CHF/kW or CHF/kWh
    Taxes and subsidies CHF/kW
    Learning rate Dmnl
    Discount rate Dmnl
    Technical parameters related to demand:
    Building stock, archetypes, and shares Units, kWh/m2/year, %
    Vehicle stock, drive-train, and shares Units, fuels, %
    Typical usage patterns ex: kWh/hr, refuelling/recharging
    Social parameters related to user choice
    Social, environmental, and economic weight in choice %
    Willingness to consider/participate Dmnl
    List of key model output variables (reporting data): Unit (optional)
    Total installed capacity, per year and technology GW
    Total decommissioned capacity, per year and technology GW
    Energy consumption per year, per user type GWh
    Energy production per year, per user type GWh
    Investment in capacity, per year and technology CHF
    Taxes and subsidies per year and technology CHF

  • List of key model output variables (reporting data):

  • List of selected key model parameters mentioned above, which are subject to high uncertainty with impact on robustness of results

  • List of key model variables mentioned above, with sensitivity to a particular model instance

  • Linkages with other modelling tools (quantitative or qualitative) within SURE

  • Any other comments that should be known about the model and its usage/applicability

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  • Model scope and methods

    This model ensures the consistent modeling of energy grids (electricity, gas, heat) suitable for the modeling approaches and complexities of the different scenario analysis tools used in other SURE-work packages. Model inputs are production and demand from different energy carriers, distributed temporarily and spatially over the entire scope of investigation. The degree to which the different energy grids are represented depends on the application (e.g. for SURE scenario assessments, regional studies, case studies), non-crucial parts of the grid will be aggregated. The methodology performs either a simulates the system state if all productions and demands are determined, or optimizes the system state, if some of the production/load is flexible. Model outputs are a representation of the overall grid state for all temporal and spatial elements of the analysis. The output will be further assessed in the grid-security tool.

  • Temporal dimension

    Base year of the models is 2020 with appropriate extensions for planned developments (e.g. electricity grid expansion). The model is applied to representative time series for study years of the development path. Important: The intra-annual resolution is always hourly or sub-hourly, since the grid security assessment requires to simulate the actual powers/pressures at critical time-instances, not averaged values.

     

  • Spatial dimension

    The models have an inherent spatial dimension, which is the locations of grid elements connecting different geographic regions. Depending on the application this can be countries (SURE scenario assessments), cantons (regional studies) or communities (maybe some of the case studies). Important: Depending on the application and data source, available profiles may lack the spatial granularity (e.g. be only available on a regional or country level) and may be disaggregated before application to the grid model.

  • Representation of policies

    This is mostly a technical assessment tool. However strategic energy grid investments can be a part of the development paths and may have a policy dimension to be considered. In general, any grid upgrade within Europe (for electricity and gas) is a highly political and contested issue.

  • Representation of shocks

    Shocks from the production and demand profiles will be processed with this model, as generated by the Swiss TIMES Energy Systems Model. Shocks on the grid infrastructure (e.g. loss of grid-elements or entire sub-grids) will be assessed within the model.

  • Representation/Quantification of Indicators

    The model computes the nominal grid loading for each spatial and temporal element of the grid. From there, grid loading indicators are derived.

    Resilience indicators (grid security, recovery capability) are represented by a separate tool (SURE-GRID-STRESS).

  • List of key model parameters (required input data):

    Parameter short description Unit (optional)
    Time profiles for production (fixed and flexible) and demand with spatial characteristics. MW

  • List of key model output variables (reporting data):

    Variable short description Unit (optional)
    grid loading indicators [% of capacity]

  • List of selected key model parameters mentioned above, which are subject to high uncertainty with impact on robustness of results

  • List of key model variables mentioned above, with sensitivity to a particular model instance

  • Linkages with other modelling tools (quantitative or qualitative) within SURE

    Linkages are established with the Swiss TIMES Energy Systems Model (main coupling), the Building Stock Model of Switzerland and potentially also Expanse.

  • Any other comments that should be known about the model and its usage/applicability

SURE-GRIDSTRESS

ETHZ-FEN (Research Center for Energy Networks)

Alexander Fuchs, Turhan Demiray

In Development, based on grid related models and security assessment

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  • Model scope and methods

    This model assesses the energy grid security Model inputs are (i) grid state simulations and optimization results from the SURE-GRID model, and (ii) stress/shock scenarios of the energy grids. The model performs a security assessment (e.g. of temporary or long-term grid outages/impairments) to quantify the grid security and grid resilience level. Model outputs are grid security and grid resilience indicators.

  • Temporal dimension

    Base year of the models is 2020 with appropriate extensions for planned developments (e.g. electricity grid expansion). The model is applied to representative time series for study years of the development path. Important: The intra-annual resolution is always hourly or sub-hourly, since the grid security assessment requires to simulate the actual powers/pressures at critical time-instances, not averaged values.

     

  • Spatial dimension

    As in the SURE-GRID model, the spatiality varies depending on the use case / subproject within SURE. In addition, the stresses themselves have a spatial component. In the case of the scenario assessment, the focus of the stress analysis will be on Switzerland, while stresses can also be triggered in other parts of the European energy system.

     

  • Representation of policies

    Policy aspects enter the model as inputs, e.g. (i) the Swiss participation or non-participation in energy market agreements (ii) policy-based stresses (e.g. consumption patterns, e-mobility usages etc.)

  • Representation of shocks

    Shocks on the grid infrastructure include (i) the short-term loss of grid-elements or entire sub-grids (established in electricity grids as N-1 assessment and frequency reserve mechanisms). (ii) unexpected changes in the load balance (e.g. unplanned absence of the wind and solar PV production “Dunkelflaute” with little other flexible generation) (ii) the long-term stress on the grid infrastructure (permanent loss of components due to lack of maintenance).

  • Representation/Quantification of Indicators

    Security indicators: Worst Grid loading under expected short-term stresses over a given time period Resilience indicators: Ability of the grids to adopt to unexpected and long-term stresses.

  • List of key model parameters (required input data):

    Parameter short description Unit (optional)
    Grid state sequences from SURE-GRID MW, pressures, …
    Grid stress scenarios List of outages and stresses

  • List of key model output variables (reporting data):

    List of key model output variables (reporting data):

     

    Variable short description Unit (optional)
    Security indicators: [% of capacity]
    Resilience indicators (binary, mean time to recovery, outage probabilities mixed

  • List of selected key model parameters mentioned above, which are subject to high uncertainty with impact on robustness of results

  • List of key model variables mentioned above, with sensitivity to a particular model instance

  • Any other comments that should be known about the model and its usage/applicability

    See grid related models in https://www.fen.ethz.ch/projekte/systembetrieb.html

    https://doi.org/10.1109/PTC.2015.7232811

    https://doi.org/10.1109/TPWRS.2017.2789187

Multicriteria Decision Analysis framework for integrated assessment

Paul Scherrer Institute

Peter Burgherr, Eleftherios Siskos

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  • Model scope and methods

    The Multicriteria Decision Analysis (MCDA) methodological framework provides a number of powerful tools to support decision making in complex real life decision and evaluation problems. As part of its interactive modular procedure,

    • the exact objective of the assessment is specified and outlined,
    • the alternative actions under evaluation are defined,
    • the different evaluation criteria are selected and modelled,
    • the preference model is developed and applied, and
    • the decision makers (stakeholders) are supported in reaching the final decision.

    MCDA can provide support to a single decision maker or a group of stakeholders, accommodating their subjective preference input in the carefully built preference model, with a view to reaching a personalized evaluation of the alternative actions. The whole process can be complemented with additional post-evaluation mechanisms, such as robustness analysis, stability testing and consultation procedures.

    As part of the SWEET-SURE project, the MCDA framework will perform a holistic evaluation of the different Swiss energy pathways till 2050, considering the potential occurrence of various transient and disruptive shocks. Ultimately, resilience and sustainability will be assessed for all possible future cases, which are modelled within the frame of the SWEET-SURE, leading to an overall performance ranking of the pathways.

  • Temporal dimension

    Time Horizon: 2020-2050 (with potential extension until 2060)

    Time periods/steps: Currently four temporal evaluation points (2020, 2030, 2040, 2050)

  • Spatial dimension

    The MCDA model will evaluate the pathways specifically for the case of Switzerland as a whole.

  • Representation of policies

    MCDA will represent the policies that will be accounted for by the SWEET-SURE models, in the form of the overall energy pathways till 2050.

  • Representation of shocks

    The MCDA framework will accommodate the shocks that will be modeled and quantified by the SWEET-SURE models. Such shocks include

    • Transient events: rapid technology diffusion, drop-in technology costs, energy trade disruptions, energy supply/demand disruptions

    Disruptive events: low/high population/economic development, changes in consumer preferences and technology acceptance, digitalisation in energy demand and supply.

  • Representation/Quantification of Indicators

    The indicators that are measured and quantified within the scope of the MCDA framework are in their nature composite and subjective/personalized. They include:

    • Resilience and sustainability performance of the pathways
    • Overall performance of the pathways
    • Robustness indicators, focusing on the uncertainty and imprecision of the model parameters and the preference information of the stakeholders
    • Consensus indicators among the diversified stakeholder groups
    • Additional decision making indicators, analyzing the behavior and characteristics of the different stakeholder groups

  • List of key model parameters (required input data):

    The MCDA evaluation system requires two kind of input data for its development and implementation, as follows:

    • On one hand, the evaluation criteria require specific input from the SWEET-SURE models and certain other reliable national and international databases, for their processing and quantification. Such input data, include quantitative parameters, such as economic, technological and societal data, as well as non-quantifiable indicators, namely, regulatory control, socio-political acceptance, risk of failure.
    • On the other hand, preference information need to be elicited from the stakeholders, in order to quantify and frame the intra-criteria parameters and other subjective MCDA parameters, such as the criteria trade-offs, vetoes, and aspiration levels. The preference information elicitation is performed within the framework of the MCDA integrated assessment.

  • List of key model output variables (reporting data):

    The outputs of the MCDA framework consist of the actual evaluation and ranking results of the pathways, together with their individual performance in the pillars of resilience and sustainability. The consensus indicators of the different stakeholder groups, participating and expressing their preference information in the assessment procedure, together with their individual decision making profiles, are also key outputs of the post-evaluation MCDA procedure. All this produced data, will be reported together with a series of corresponding policy recommendations for Switzerland.

  • List of selected key model parameters mentioned above, which are subject to high uncertainty with impact on robustness of results

    Apart from the model input data, the uncertainty of which is appraised and analyzed in the corresponding modeling tools, the uncertainty and imprecision of the preference input, elicited by the stakeholders, plays a key role in the robustness of the MCDA results. Trying to maintain a relatively low cognitive burden, together with an optimal information gain, the stakeholders will be asked to answer appropriate and carefully drafted elicitation questions. In case the robustness of the MCDA model does not reach acceptable levels, additional input will be asked from the stakeholders, until the acquisition of satisfactory results.

  • List of key model variables mentioned above, with sensitivity to a particular model instance

  • Linkages with other modelling tools (quantitative or qualitative) within SURE

    The MCDA framework settles linkages with the quantitative models of SWEET-SURE for the acquisition of the required data that will frame the evaluation criteria of the integrated assessment MCDA model. It has not been decided yet, which evaluation criteria will be selected for the evaluation of the pathways and, therefore, which modeling tools will provide data for them.

  • Any other comments that should be known about the model and its usage/applicability

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  • Model scope and methods

    Premise interprets outputs from economy-energy models and integrate them into life-cycle assessment (LCA) databases, to produce a prospective LCA database coherent with the scenarios considered within SWEET-SURE.

  • Temporal dimension

    The temporal dimension of the output produced by premise (a prospective LCA database) aligns with the temporal dimension of the economy-energy model scenarios used as inputs.

  • Spatial dimension

    Process-based LCA databases, such as ecoinvent, have a very disaggregated sectoral resolution (to the country level, or lower), which may not always match with the resolution of the model scenarios it reads from. A mapping work is necessary in most cases.

  • Representation/Quantification of Indicators

    The LCA databases produced by premise, based on the projections from economy-energy models, allows to produce sustainability-related indicators for the whole Swiss energy system, such as:
    * environmental and climate indicators: greenhouse gas emissions
    * resource indicators: land use, water use, minerals depletion, primary energy use
    * ecosystems damage

  • List of key model output variables (reporting data)

    The indicators listed above are reported across each scenario and time-step.

  • Linkages with other modelling tools (quantitative or qualitative) within SURE

    The indicators reported above provide inputs for the MCDA tool developed under WP1.