Optimizing Joint Bidding and Incentivizing Strategy for Price-Maker Load Aggregators Based on Multi-Task Multi-Agent Deep Reinforcement Learning

The increasing penetration of renewable energy sources poses significant challenges for modern power systems, particularly in supply-demand balance and peak regulation. Load aggregators (LAs) play a crucial role by integrating small to medium-sized loads and coordinating demand response (DR). However, previous research works ignored the inherent coupling between price-maker LAs’ decision-making of bidding price and quantity in the ancillary service market and decision-making of incentive price in DR. This study introduces a joint bidding and incentivizing model for a price-maker LA participating in a peak-regulation ancillary service market (PRM) and developing an incentive-based demand response (IBDR), where the LA’s objective is to maximize its long-term cumulative payoff. In order to solve this complex joint decision-making optimization problem more effectively and efficiently, a model-free multi-task multi-agent deep reinforcement learning-based (MTMA-DRL-based) method incorporating a shared, centralized prioritized experience replay buffer (PERB) is proposed. Case studies in real-world settings confirm that the proposed model effectively captures the interdependence between bidding price, bidding quantity, and incentive price decisions. The proposed MTMA-DRL-based method is also proven to outperform existing methods.