Welcome to the ADAPT Project
Adaptive AI-supported Simulation Toolbox for Energy Market Design
Background
The accelerating energy transition and the growing complexity of electricity markets pose challenges for market players, grid operators, and regulators. Understanding how stakeholders interact and assessing the effects of market or regulatory changes require innovative methods, because traditional models often fail to capture these intricate relationships. Current market‑design developments, such as reforms to grid‑tariff structures and the creation of capacity markets, must ensure reliable supply, incentivize flexible, grid‑supportive behavior and spur investment in the needed amount of new capacity, optimally distributed geographically.
The ADAPT project – Adaptive AI‑supported Simulation Toolbox for Energy Markets Design – addresses exactly this need. It is developing a user‑friendly, adaptive simulation platform built on deep reinforcement learning (DRL). The project builds on the existing ASSUME framework. ASSUME already provides a comprehensive technical foundation that can be used for market‑design studies, and will be further developed in ADAPT.
With ADAPT a team of researchers aim to develop three major extensions:
Adaptive market‑agent – The platform introduces an AI‑driven market‑agent that represents the regulator or market‑design authority. It can autonomously tweak market rules and parameters, observe how participants react, and continuously learn how to improve the design. This creates flexible, self‑optimising market‑designs that stay effective in a rapidly changing environment.
Low‑code, user‑friendly RL toolbox with explainable results – A low‑code reinforcement‑learning interface and an intuitive front‑end make the advanced DRL engine accessible to researchers without deep programming skills. Integrated AI assistants built on large‑language models guide users through configuration and operation, while built‑in explainability tools shed light on the multi‑agent simulation outcomes, fostering trust in the insights generated.
Inclusion of local grids and sector coupling – The tool is extended to model distribution networks, local energy systems and cross‑sector interactions. This enables distribution‑system operators and other downstream actors to simulate congestion‑management measures, test regulatory options (e.g., extensions of § 14a EnWG), and design incentive schemes for flexible loads such as electrolyzers, steel plants and other industrial consumers that can respond to dynamic price and grid signals, thereby supporting overall system optimisation and stability.
The ASSUME Toolbox
Within the ASSUME project (2021-2025), an open-source, agent-based simulation toolbox for electricity markets using deep reinforcement learning (DRL) algorithms was developed. Within ADAPT (2025-2028), we extend the ASSUME toolbox.
- We are very happy about constructive feedback and contributions to the Git-Repository.
- For a deeper dive into the software, check out the readthedocs page.
- To help you get started, we also provide multiple online tutorials using the Jupyter Collab interface.
Publications
Nick Harder, Ramiz Qussous, and Anke Weidlich
Fit for purpose: Modeling wholesale electricity markets realistically with multi-agent deep reinforcement learning
Energy and AI, Volume 14, 2023
https://doi.org/10.1016/j.egyai.2023.100295
Nick Harder, Anke Weidlich, and Philipp Staudt
Finding individual strategies for storage units in electricity market models using deep reinforcement learning
Energy Inform 6 (Suppl 1), 41, 2023
https://doi.org/10.1186/s42162-023-00293-0
Florian Maurer, Kim K. Miskiw, Rebeca Ramirez Acosta, Nick Harder, Volker Sander & Sebastian Lehnhoff
Market Abstraction of Energy Markets and Policies – Application in an Agent-Based Modeling Toolbox
Jørgensen, B.N., da Silva, L.C.P., Ma, Z. (eds) Energy Informatics. EI.A 2023. Lecture Notes in Computer Science, vol 14468.
http://dx.doi.org/10.1007/978-3-031-48652-4_10
Kim K. Miskiw, Nick Harder and Philipp Staudt
Multi Power-Market Bidding: Stochastic Programming and Reinforcement Learning
Proceedings of the 57th Hawaii International Conference on System Sciences, 2024.
https://scholarspace.manoa.hawaii.edu/bitstreams/ab278af7-2dfe-4c36-a538-eaccb8be1262/download
Nick Harder, Anke Weidlich, and Philipp Staudt
Modeling Participation of Storage Units in Electricity Markets using Multi-Agent Deep Reinforcement Learning.
In Proceedings of the 14th ACM International Conference on Future Energy Systems (e-Energy ’23). Association for Computing Machinery, New York, NY, USA, 439–445
https://doi.org/10.1145/3575813.3597351
Manish Khanra, Parag Patil, Marian Klobasa, and Daniel Scholz
Economic Evaluation of Electricity and Hydrogen-Based Steel Production Pathways: Leveraging Market Dynamics and Grid Congestion Mitigation through Demand Side Flexibility.
In Proceedings of the 20th International Conference on European Energy Market (EEM24). IEEE, Istanbul, Turkey, 2024
https://doi.org/10.1109/EEM60825.2024.10608890
Florian Maurer, Felix Nitsch, Johannes Kochems, Christoph Schimeczek, Volker Sander, and Sebastian Lehnhoff
Know Your Tools – A Comparison of Open-Source Energy Market Simulation Models.
In Proceedings of the 20th International Conference on European Energy Market (EEM24). IEEE, Istanbul, Turkey, 2024
https://doi.org/10.1109/EEM60825.2024.10609021
Johanna Adams, Nick Harder, and Anke Weidlich
Do Block Orders Matter? Impact of Regular Block and Linked Orders on Electricity Market Simulation Outcomes.
In Proceedings of the 20th International Conference on European Energy Market (EEM24). IEEE, Istanbul, Turkey, 2024
https://doi.org/10.1109/EEM60825.2024.10608956
Kim K. Miskiw and Philipp Staudt
Explainable Deep Reinforcement Learning for Multi-Agent Electricity Market Simulations.
In Proceedings of the 20th International Conference on European Energy Market (EEM24). IEEE, Istanbul, Turkey, 2024
https://doi.org/10.1109/EEM60825.2024.10608907
People behind the project
Gunter Grimm is a research associate and a doctoral student at the Institute for Sustainable Systems Engineering (INATECH) at the University of Freiburg. He completed his master’s studies in “Renewable Energy Systems” at TU Berlin. His research primarily focuses on the interaction of market participants with regulation and market design in the long vs. the short term. Additionally, he holds the role of project coordinator for the ADAPT project
Kim K. Miskiw is a research associate and a doctoral student at the Chair for Information and Market Engineering (IISM) within the Faculty of Economics and Business Engineering at the Karlsruhe Institute of Technology (KIT). Her research interests revolve around deep reinforcement learning in electricity market simulations, agent-based electricity market modeling, energy market engineering, and stochastic optimization. Previously, she held the position of Junior-Project Associate at the Institute for Industrial Production, Chair of Energy Economics (KIT). Kim completed her Master’s degree in Industrial Engineering at KIT, focusing her master’s thesis on stochastically optimized bidding strategies in sequential electricity markets and examining their benefits in relation to risk preferences and portfolio setups.
Manish Khanra is a research associate at the Competence Center, Energy Technologies and Energy Systems (CC-E) at Fraunhofer ISI. He is also a doctoral student at the Institute for Industrial Production (IIP) at Karlsruhe Institute of Technology (KIT). With specialisation in integrating hydrogen and efuels for decarbonising Hard-to-Abate sectors, Manish investigates their impact on the power system.
Holding an MSc in Renewable Energy and Energy Efficiency for the Middle East and North Africa, his research has focused on the transformation paths in the heat sector in Germany. Currently, his work encompasses developing electricity market and technology diffusion models, analysing policy aspects for sectors such as steel, cement, chemicals, aviation, and maritime, and conducting applied research in the hydrogen economy.
Florian Maurer is a research associate and doctoral student at the University of Applied Sciences Aachen in cooperation with the University of Oldenburg. After completing a dual study program in software development, he obtained his Master’s degree in “Applied Mathematics and Computer Science” at FH Aachen, where he developed charging solutions for e-mobility. Florian is involved in research projects related to energy measurements and prosumer market integration. His research interests include open-source development, wireless communication and energy market design. Currently, he is researching agent-based modeling of energy markets to provide a simulation framework that covers the comparison of different market designs and policies.