Electricity markets need to continuously evolve to address the growing complexity of a predominantly renewable energy-driven,highly interconnected,and sector-integrated energy system.Simulation models allow testing ma...Electricity markets need to continuously evolve to address the growing complexity of a predominantly renewable energy-driven,highly interconnected,and sector-integrated energy system.Simulation models allow testing market designs before implementation,which offers advantages for market robustness and efficiency.This work presents a novel approach to simulate the electricity market by using multi-agent deep reinforcement learning for representing revenue-maximizing market participants.The learning capability makes the agents highly adaptive,thereby facilitating a rigorous performance evaluation of market mechanisms under challenging yet practical conditions.Through distinct test cases that vary the number and size of learning agents in an energy-only market,we demonstrate the ability of the proposed method to diagnose market manipulation and reflect market liquidity.Our method is highly scalable,as demonstrated by a case study of the German wholesale energy market with 145 learning agents.This makes the model well-suited for analyzing large and complex electricity markets.The capability of the presented simulation approach facilitates market design analysis,thereby contributing to the establishment future-proof electricity markets to support the energy transition.展开更多
文摘Electricity markets need to continuously evolve to address the growing complexity of a predominantly renewable energy-driven,highly interconnected,and sector-integrated energy system.Simulation models allow testing market designs before implementation,which offers advantages for market robustness and efficiency.This work presents a novel approach to simulate the electricity market by using multi-agent deep reinforcement learning for representing revenue-maximizing market participants.The learning capability makes the agents highly adaptive,thereby facilitating a rigorous performance evaluation of market mechanisms under challenging yet practical conditions.Through distinct test cases that vary the number and size of learning agents in an energy-only market,we demonstrate the ability of the proposed method to diagnose market manipulation and reflect market liquidity.Our method is highly scalable,as demonstrated by a case study of the German wholesale energy market with 145 learning agents.This makes the model well-suited for analyzing large and complex electricity markets.The capability of the presented simulation approach facilitates market design analysis,thereby contributing to the establishment future-proof electricity markets to support the energy transition.
