What’s the problem?
To support energy policy-making, mathematical models are developed and deployed. Up to now, mainly optimization models have been applied to develop cost-effective low carbon pathways for the energy system. These models address normative questions (e.g., to what extent should different energy sectors decarbonize to cost-effectively achieve certain emission reduction targets?) with the goal of determining a long-term vision (where do we want to go?). However, many of the current policy questions relate to translating this vision into specific policy and market design choices (how will we get there?). For this purpose, other types of models, such as equilibrium and agent-based models are required.
Energy modeling toolbox
This project therefore aims to develop an energy modeling toolbox comprising complementary model types. New equilibrium and agent-based models will be developed to better simulate decision-making of the agents active in the energy system, like generators, system operators and consumers. In this we can better represent risk-averse behavior and agent-learning. Existing optimization models are aligned to better link such insights to optimization model runs. The results of optimization, equilibrium and agent-based models will be compared for different cases, and methodologies to combine different model types are developed. In addition, improved ways to account for long-term uncertainty and to derive robust policies will be set up. Finally, the different models will be integrated into an openly accessible, transparent and user-friendly modeling platform.
This project targets all stakeholders that participate in the debate regarding energy policy. These include amongst others government agencies, energy companies, consumers and civil society organizations. Stakeholders are involved in different project stages. In the initial project stage this is mainly about understanding what they see as the major energy policy questions the toolbox could address, thus to ensure policy relevance. In later stages, they will be involved in setting up scenario analyses, choosing appropriate scenario assumptions and reflecting on the way data can be visualized in the most accessible, understandable, and usable way.