An advanced real-time dispatching strategy for a distributed energy system based on the reinforcement learning algorithm

• Published in: Renewable energy Vol. 178; pp. 13 - 24
• Main Authors: Meng, Fanyi, Bai, Yang, Jin, Jingliang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2021
• Subjects: algorithms; Coordinated dispatching strategy; Distribution system; Economic dispatch; energy; Markov decision process; Monte Carlo method; Real-time control; Reinforcement learning; wind; wind power; Real-time control; Markov decision process; Economic dispatch; Distribution system; Coordinated dispatching strategy; Reinforcement learning
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2021.06.032

A desirable dispatching strategy is essentially important for securely and economically operating of wind-thermal hybrid distribution systems. Existing dispatch strategies usually assume that wind power has priority of injection. For real-time control, such strategies are simple and easy to realize, but they lack flexibility and incur higher operation and maintenance (O&M) costs. This study analyzed the power dispatching process as a dynamic sequential control problem and established a Markov decision process model to explore the optimal coordinated dispatch strategy for coping with wind and demand disturbance. As a salient feature, the improved dispatch strategy minimizes the long-run expected operation and maintenance costs. To evaluate the model efficiently, a Monte Carlo method and the Q-learning algorithm were employed to the growing computational cost over the state space. Through a specified numerical case, we demonstrated the properties of the coordinated dispatch strategy and used it to address a 24-h real-time dispatching problem. The proposed algorithm shows high efficiency in solving real-time dispatching problems. ●An advanced real-time dispatching strategy was proposed for distribution systems.●The strategy minimizes long-run expected cost instead of myopic immediate cost.●The model considers the volatilities of wind speed and demand load.●Monte Carlo and Q-learning algorithm are employed to solve the model efficiently.