Electricity Market
Electricity markets are complex systems aiming to efficiently match electricity supply and demand, often incorporating renewable energy sources and storage technologies. Current research focuses on improving price forecasting accuracy using diverse machine learning models, including neural networks (like GRUs and transformers), quantile regression, and deep reinforcement learning, to optimize trading strategies and market clearing mechanisms. These advancements are crucial for grid stability, efficient resource allocation, and the integration of renewable energy, impacting both market participants and policymakers through improved decision-making and potentially more sustainable energy systems.
Papers
How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 2: Method and Applications
Ziqing Zhu, Siqi Bu, Ka Wing Chan, Bin Zhou, Shiwei Xia
How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 1: A Paradigmatic Theory
Ziqing Zhu, Siqi Bu, Ka Wing Chan, Bin Zhou, Shiwei Xia