The EXPANSE model
Short overview
EXPANSE is a bottom-up, technology-rich electricity system model. Two versions of the model exist depending on the research question: (1) spatially-explicit, single-year model at NUTS-2 or NUTS-3 spatial resolution and with hourly time step for Europe; and (2) national long-range capacity expansion model with in-depth uncertainty analysis. The unique feature of EXPANSE is that it applies Modelling to Generate Alternatives method (MGA) to compute and analyze large numbers of cost-optimal and near-optimal scenarios with only a single set of assumptions. The principle of MGA is to relax the cost-optimality assumption and instead define an acceptable range of total system costs to search for scenarios within this acceptable range. Thus, EXPANSE tackles the common critiques to other bottom-up technology-rich models by not only focusing on costs as the sole driver of the energy transition. In this way, EXPANSE also allows to better tackle structural uncertainty, reduce modeler’s bias, and provides various alternative scenarios for policymakers.
Based on the exogenous assumption, such as technology parameters and costs, electricity demand, or maximum acceptable increase in total system cost, EXPANSE generates a wanted set of maximally-different scenarios that are within the pre-defined level of acceptable costs. For each of these computer-generated scenarios, EXPANSE assesses the implications on the key decision-relevant outcomes for European regions, such as technology capacity and operation, cost, employment, greenhouse gas and particulate matter emissions, land use, regional inequality, and so on. EXPANSE is then combined with Monte-Carlo technique to quantify the associated uncertainties.
The role of EXPANSE is to provide more realistic what-if scenarios of the electricity sector transition that is driven by costs as well as other socio-technical factors.
Key features of the EXPANSE model
Key features- models cost-optimal and near-optimal scenarios of the electricity sector transition by applying state-of-the-art MGA methodology
- quantifies various environmental and economic impacts at NUTS-2 or NUTS-3 spatial resolution (e.g., employment, land use, particulate matter emissions)
- conducts in-depth analysis of parametric and structural uncertainties
Spatially-explicit EXPANSE has a nodal representation of up to 1'440 NUTS-3 regions for 36 European countries (EU-27, UK, CH, NO, IS, AL, BA, ME, MK, RS). Long-range EXPANSE represents 31 European countries at national level.
Climate module & emissions granularity
EXPANSE quantifies direct greenhouse gas and particulate matter emissions from the electricity sector with high spatial resolution at NUTS-3 in Europe. EXPANSE does not include a climate module.
Socioeconomic dimensions
Electricity prices, system costs and employment
EXPANSE estimates the total and regionally-explicit electricity prices and costs of the electricity system, including generation, transmission, and storage. It also quantifies regional employment impacts of the electricity sector based on bottom-up calculation associated to each unit of installed capacity.
Mitigation/adaptation measures and technologies
EXPANSE is a technology-rich model that includes most conventional and renewable electricity generation, storage, and transmission technologies. National renewable electricity targets, emission constraints, and national policy instruments can be used by the EXPANSE model to include and assess mitigation measures.
Economic rationale and model solution
The principle of MGA is to remove the objective function of cost optimization and to convert it into a constraint on acceptable total system costs. EXPANSE then uses MGA to sample a wanted number of scenarios in this space: from a handful of maximally-different scenarios to thousands of scenarios for analysis of parametric and structural uncertainty. The focus on near-optimal scenarios allows EXPANSE to represent the socio-technical energy transition more realistically than done in conventional cost-optimization models. The EXPANSE scenarios within the near-optimal space may also be more desirable by decision makers than the cost-optimal scenarios due to various objectives that are not part of the model.
Key parameters
Key parameters include socioeconomic parameters (e.g., electricity demand based on population and GDP growth), energy technology characteristics (e.g., costs, efficiencies, emissions, employment, land use), renewable resource availabilities (e.g., wind speed, solar irradiation, biomass resources, hydropower inflow), and policies (e.g. national renewable electricity targets, emission constraints).
Policy questions and SDGs
Key policies that can be addressed
National renewable electricity targets, emission constraints, feed-in tariffs and subsidies, carbon prices.
Implications for other SDGs
- SDG1: Distributional impacts from changes in electricity cost and employment in the electricity sector.
- SDG3: Changes in particulate matter emissions from the electricity sector, which could lead to changes in human health.
- SDG7: Share of renewable electricity per NUTS-3 region.
- SDG8: Changes in employment in the electricity sector.
- SDG10: Changes in regional inequalites from the electricity sector, in terms of infrastructure deployment, regional costs, employment, greenhouse gas and particulate emissions, and land use.
- SDG13: Regional implications of implementing national renewable electricity system targets to reach European climate targets.
- SDG15: Land use impacts of the electricity sector
Model presentation
Video
Slides
Download slides in pdfRecent use cases
| Paper DOI | Paper Title | Key findings |
|---|---|---|
| https://doi.org/10.1038/s41467-020-18812-y | Regional impacts of electricity system transition in Central Europe until 2035 | Achieving current electricity sector targets in Central Europe (Austria, Denmark, France, Germany, Poland and Switzerland) will redistribute regional benefits and burdens at sub-national level. Limiting emerging regional inequalities would foster the implementation success. We model one hundred scenarios of electricity generation, storage and transmission for 2035 in these countries for 650 regions and quantify associated regional impacts on system costs, employment, greenhouse gas and particulate matter emissions, and land use. We highlight tradeoffs among the scenarios that minimize system costs, maximize regional equality, and maximize renewable electricity generation. Here, we show that these three aims have vastly different implementation pathways as well as associated regional impacts and cannot be optimized simultaneously. Minimizing system costs leads to spatially-concentrated impacts. Maximizing regional equality of system costs has higher, but more evenly distributed impacts. Maximizing renewable electricity generation contributes to minimizing regional inequalities, although comes at higher costs and land use impacts. |
| https://doi.org/10.1016/j.apenergy.2019.113724 | Distributional trade-offs between regionally equitable and cost-efficient allocation of renewable electricity generation | Decentralized renewable electricity generation (DREG) has been growing at an unprecedented pace, yet the appropriate spatial allocation and associated regional equity implications remain underinvestigated. In this study, we quantify the trade-offs between cost-efficient (least-cost) and regionally equitable DREG allocation in terms of electricity generation costs, investment needs, and DREG capacity requirements. Using the case of the ambitious and publicly-approved Swiss Energy Strategy 2050, we set up a bottom-up, technology-rich electricity system model EXPANSE with Modeling to Generate Alternatives at a spatial resolution of 2’258 Swiss municipalities. In order to measure regional equity implication, we adapt the concepts of the Lorenz curve and the Gini coefficient. We find a significant trade-off by 2035 in Switzerland: 50% increase in regional equity when allocating DREG to various Swiss regions on the basis of population or electricity demand leads to 18% higher electricity generation costs. Least-cost allocation implies concentrating DREG and associated investments to few most productive locations only. Solar PV is the key technology for increasing regional equity. We conclude that in countries with spatially-uneven DREG resources like Switzerland, any policies that focus on cost efficiency should anticipate regional equity implications in advance and, if desired, minimize them by promoting solar PV. |
| https://doi.org/10.1016/j.apenergy.2022.119906 | Accuracy indicators for evaluating retrospective performance of energy system models | Retrospective evaluation of energy system models and scenarios is essential for ensuring their robustness for prospective policy support. However, quantitative evaluations currently lack systematic methods to be more holistic and informative. This paper reviews existing accuracy indicators used for retrospective evaluations of energy models and scenarios with the aim to find a small suite of complementary indicators. We quantify and compare 24 indicators to assess the retrospective performance of D-EXPANSE electricity sector modeling framework, used to model 31 European countries in parallel from 1990–2019. We find that symmetric mean percentage error, symmetric mean absolute percentage error, symmetric median absolute percentage error, root-mean-squared logarithmic error, and growth error together form the most informative suite of indicators. This study is the first step towards developing a model accuracy testbench to assess energy models and scenarios in multiple dimensions retrospectively. |
References
Sasse, J.-P., & Trutnevyte, E. (2020). Regional impacts of electricity system transition in Central Europe until 2035. Nature Communications, 11(1), 4972. https://doi.org/10.1038/s41467-020-18812-y
Sasse, J.-P. & Trutnevyte, E. Distributional trade-offs between regionally equitable and cost-efficient allocation of renewable electricity generation. Appl. Energy 254, (2019).
Wen, X., Jaxa-Rozen, M., & Trutnevyte, E. (2022). Accuracy indicators for evaluating retrospective performance of energy system models. Applied Energy, 325, 119906. https://doi.org/10.1016/j.apenergy.2022.119906
