Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning

IEEE Transactions on Neural Networks and Learning Systems
Weirong LiuZhiwu Huang

Abstract

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.

References

Feb 27, 2015·IEEE Transactions on Neural Networks and Learning Systems·Yunwen LeiWensheng Zhang
Mar 31, 2015·IEEE Transactions on Neural Networks and Learning Systems·Huanqing WangKefu Liu
Jun 26, 2015·IEEE Transactions on Neural Networks and Learning Systems·Hamidreza ModaresZhong-Ping Jiang
Jan 23, 2016·IEEE Transactions on Neural Networks and Learning Systems·Ganesh Kumar VenayagamoorthyAfshin Ahmadi
Apr 22, 2016·IEEE Transactions on Cybernetics·Zhen ZhangYujie Dai

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