Designing the future electricity spot market with high renewables via reliable simulations

The global transition to renewable energy is crucial for mitigating climate change, but the increasing penetration of renewable sources introduces challenges such as uncertainty and intermittency. The electricity market plays a vital role in encouraging renewable generation while ensuring operational security and grid stability. This Review examines the optimization of market design for power systems with high renewable penetration. We explore recent innovations in renewable-dominated electricity market designs, summarizing key research questions and strategies. Special focus is given to multi-agent reinforcement learning (MARL) for market simulations, its performance and real-world applicability. We also review performance evaluation metrics and present a case study from the Horizon 2020 TradeRES project, exploring European electricity market design under 100% renewable penetration. Finally, we discuss unresolved issues and future research directions. This Review examines the optimization of electricity market design to support high renewable penetration, focusing on multi-agent reinforcement learning (MARL) for market simulations, performance evaluation and future research directions, with a case study on European market design under 100% renewable penetration.