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An advanced real-time dispatching strategy for a distributed energy system based on the reinforcement learning algorithm.
- Source :
-
Renewable Energy: An International Journal . Nov2021, Vol. 178, p13-24. 12p. - Publication Year :
- 2021
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09601481
- Volume :
- 178
- Database :
- Academic Search Index
- Journal :
- Renewable Energy: An International Journal
- Publication Type :
- Academic Journal
- Accession number :
- 152062797
- Full Text :
- https://doi.org/10.1016/j.renene.2021.06.032