Research on Electricity Market Pricing Strategy for Virtual Power Plants Based on Q-Learning Algorithm

With the rapid development of energy and information technologies, the Virtual Power Plant (VPP) has emerged as an innovative form of energy management, significantly impacting the operational models of electricity markets. This paper investigates the electricity market pricing strategy based on the Q-learning algorithm, aiming to provide a dynamic pricing mechanism for VPPs to optimize resource allocation in electricity markets, enhance energy efficiency, and increase economic benefits. Firstly, the paper introduces the concept of VPPs and their role in modern power systems, emphasizing the importance of pricing strategies in balancing supply and demand in electricity markets. It then elaborates on the fundamental principles of the Q-learning algorithm and, tailored to the characteristics of VPPs, designs a reinforcement learning-based electricity market pricing optimization model. This model incorporates three pricing strategies: Real- Time Pricing (RTP) using Q-learning, Time-of-Use (TOU), and Critical Peak Pricing (CPP), enabling dynamic adjustment of electricity prices within VPPs. Simulating electricity demand data for winter and summer seasons, the paper demonstrates the application effectiveness of the algorithm across different periods and compares the impacts of various pricing strategies on the electricity market through graphical analysis. Experimental results indicate that the Q-learning-based pricing strategy effectively responds to changes in electricity market demands, achieving preliminary supply-demand balance while considering economic benefits and environmental impacts of electricity prices. Finally, the paper discusses potential challenges in practical implementation of these pricing strategies and proposes corresponding improvements. Overall, this research contributes to advancing the understanding and implementation of efficient pricing mechanisms within VPPs, leveraging Q- learning algorithms to optimize electricity market operations in response to evolving energy landscapes.