Efficient and Stable Learning for Distribution Network Operation: A Model-Based Reinforcement Learning Approach

Abstract

This paper discusses the application of deep reinforcement learning (DRL) to the economic operation of power distribution networks, a complex system involving numerous flexible resources. Despite the improved control flexibility, traditional prediction-plus-optimization models struggle to adapt to rapidly shifting demands. Modern artificial intelligence (AI) methods, particularly DRL methods, promise faster decision-making but face challenges, including inefficient training and real-world application. This study introduces a reward evaluation system to assess the effectiveness of various strategies and proposes an enhanced algorithm based on the Model-based DRL approach. Incorporating a state transition model, the proposed algorithm augments data and enhances dynamic deduction, improving training efficiency. The effectiveness is demonstrated in various operational scenarios, showing notable enhancements in rationality and transfer generalization.