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Paul Scherrer Institute
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)
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.
STEM can represent several energy and climate policies, such as:
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:
Examples of indicators that can be quantified by STEM (directly, from the model’s output) include:
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 |
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 |
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
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). |
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. |
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 |
TEP Energy GmbH
Ulrich Reiter
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.
GIS-based single building level if needed, generally aggregated on cantonal or Swiss level for different building sectors, branches, and typologies:
BSM can represent several energy and climate policies, such as:
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:
Examples of indicators that can be quantified by BSM (directly, from the model’s output) include:
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 |
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)
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. |
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. |
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 |
University of Geneva
Prof. Evelina Trutnevyte
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.
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Parameter short description |
Unit (optional) |
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Technology characterization (e.g., capacity factor, efficiency) |
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Technology and fuel costs (e.g., capital and operation costs) |
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Renewable energy resources potential |
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International energy prices (e.g., natural gas, oil, electricity import) |
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Policies (e.g., emission targets, nuclear phase-out) |
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Electricity demand |
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Discount rate |
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Slack (cost deviation of near-optimal scenarios from cost-optimal scenario) |
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Variable short description |
Unit (optional) |
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Installed capacity of electricity generation, storage, and transmission |
MW |
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Hourly operation of electricity generation, storage, and transmission |
MWh |
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Primary energy demand in the electricity sector |
MWh |
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Fuel use in the electricity sector |
MWh |
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Renewable electricity generation |
MWh |
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International electricity trade volume: import and exports |
MWh |
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Electricity system costs (fuel, investment, etc.) |
CHF |
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CO2 emissions |
Mt CO2 |
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Direct employment |
Jobs |
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Particulate matter emissions |
t PM10-eq |
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Electricity prices |
CHF/MWh |
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Land use |
km2 |
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Parameter short description |
Influencing factor or reason for uncertainty |
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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. |
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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. |
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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. |
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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. |
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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. |
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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. |
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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. |
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Slack |
This is an EXPANSE-specific parameter that defines an acceptable deviation in costs from the cost-optimal solution. |
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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. |
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Variable short description |
Influencing factor or reason for uncertainty |
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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. |
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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 |
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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 |
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).
E3-Modelling
Leonidas Paroussos,
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/
Base year: 2015
Time Horizon: 2020 – 2100
Time periods/steps: 5 yr (option for yearly time steps until 2030)
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.
The following key policies are represented in the model:
International transportation costs
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
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).
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 |
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. $ |
SUPSI
Jalomi Maayan Tardif
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)
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.
FlexiTI can integrate several energy and climate policies, such as:
Infrastructure development supporting measures
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.
Examples of indicators that can be quantified by the current FlexiTI model include:
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 |
ETHZ-FEN (Research Center for Energy Networks)
Alexander Fuchs, Turhan Demiray
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).
Parameter short description | Unit (optional) |
Time profiles for production (fixed and flexible) and demand with spatial characteristics. | MW |
Variable short description | Unit (optional) |
grid loading indicators | [% of capacity] |
ETHZ-FEN (Research Center for Energy Networks)
Alexander Fuchs, Turhan Demiray
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):
| |
Variable short description | Unit (optional) |
Security indicators: | [% of capacity] |
Resilience indicators (binary, mean time to recovery, outage probabilities | mixed |
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
Paul Scherrer Institute
Peter Burgherr, Eleftherios Siskos
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,
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.
Time Horizon: 2020-2050 (with potential extension until 2060)
Time periods/steps: Currently four temporal evaluation points (2020, 2030, 2040, 2050)
The MCDA model will evaluate the pathways specifically for the case of Switzerland as a whole.
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.
The MCDA framework will accommodate the shocks that will be modeled and quantified by the SWEET-SURE models. Such shocks include:
Disruptive events: low/high population/economic development, changes in consumer preferences and technology acceptance, digitalisation in energy demand and supply.
The indicators that are measured and quantified within the scope of the MCDA framework are in their nature composite and subjective/personalized. They include:
The MCDA evaluation system requires two kind of input data for its development and implementation, as follows:
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.
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.
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.
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