Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning
Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning
复制标题
基于协作强化学习的微电网分布式经济调度
DOI：
10.1109/tnnls.2018.2801880
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发表时间：
2018-03
影响因子：
10.4
通讯作者：
Weirong Liu;Zhu Peng;Hao Liang;Jun Peng;Zhiwu Huang
中科院分区：
文献类型：
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作者：
Weirong Liu;Zhu Peng;Hao Liang;Jun Peng;Zhiwu Huang
由分布式发电(DG)机组和储能(ES)装置组成的微电网将在未来的电力系统中发挥越来越重要的作用。然而，由于分散发电机组和负荷的随机性和非线性特性，在微电网中实现高效的分布式经济调度是一个具有挑战性的问题。提出了一种微网分布式经济调度的协同强化学习算法。利用该学习算法可以避免随机建模的难度和较高的计算复杂度。在协同强化学习算法中，利用函数逼近来处理大的、连续的状态空间。并采用扩散策略来协调DG单元和ES设备的动作。基于该算法，微电网中的每个节点只需要与其本地邻居进行通信，而不依赖于任何集中控制器。对算法的收敛进行了分析，并基于真实气象数据和负荷数据进行了仿真，验证了算法的性能。
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.