Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning

• Published in: IEEE transaction on neural networks and learning systems Vol. 29; no. 6; pp. 2192 - 2203
• Main Authors: Liu, Weirong, Zhuang, Peng, Liang, Hao, Peng, Jun, Huang, Zhiwu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation algorithms; Batteries; Computer applications; Computer simulation; Cooperative reinforcement learning; diffusion strategy; distributed economic dispatch; Distributed generation; Economics; Electric power grids; Energy storage; energy storage (ES); function approximation; Learning; Learning (artificial intelligence); Load modeling; Machine learning; Microgrids; Power dispatch; Randomness; Reinforcement; Stochastic processes; Stochasticity
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2018.2801880

Microgrids incorporated with distributed generation (DG) units and energy storage (ES) devices are expected to play more and more important roles in the future power systems. Yet, achieving efficient distributed economic dispatch in microgrids is a challenging issue due to the randomness and nonlinear characteristics of DG units and loads. This paper proposes a cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids. Utilizing the learning algorithm can avoid the difficulty of stochastic modeling and high computational complexity. In the cooperative reinforcement learning algorithm, the function approximation is leveraged to deal with the large and continuous state spaces. And a diffusion strategy is incorporated to coordinate the actions of DG units and ES devices. Based on the proposed algorithm, each node in microgrids only needs to communicate with its local neighbors, without relying on any centralized controllers. Algorithm convergence is analyzed, and simulations based on real-world meteorological and load data are conducted to validate the performance of the proposed algorithm.