Guo Fanghong, Xu Bowen, Zhang Wen-An, Wen Changyun, Zhang Dan, Yu Li
IEEE Trans Neural Netw Learn Syst. 2022 May;33(5):2057-2069. doi: 10.1109/TNNLS.2021.3054778. Epub 2022 May 2.
Currently, numerical optimization methods are used to solve distributed optimal power allocation (OPA) problems for islanded microgrid (MG) systems. Most of them are developed based on rigorous mathematical derivation. However, the complexity of such optimization algorithms inevitably creates a gap between theoretical analysis and real-time implementation. In order to bridge such a gap, in this article we provide a new distributed learning-based framework to solve the real-time OPA problem. Specifically, inspired by the human-thinking scheme, distributed deep neural networks (DNNs) together with a dynamic average consensus algorithm are first employed to obtain an approximate OPA solution in a distributed manner. Then a distributed balance generation and demand algorithm is designed to fine-tune it to obtain the final optimal feasible solution. In addition, it is theoretically proved that the proposed DNN can well approximate one existing OPA algorithm (Guo et al. 2018), where quantitative numbers of at most how many hidden layers and neurons are provided. Several experimental case studies show that our proposed distributed learning framework can achieve similar optimal results to those obtained by using typical existing distributed numerical optimization methods while it is superior in terms of simplicity and real-time capability.
目前，数值优化方法被用于解决孤岛微电网（MG）系统的分布式最优功率分配（OPA）问题。其中大多数方法是基于严格的数学推导开发的。然而，此类优化算法的复杂性不可避免地在理论分析与实时实现之间造成了差距。为了弥合这一差距，在本文中，我们提供了一种基于分布式学习的新框架来解决实时OPA问题。具体而言，受人类思维模式的启发，首先采用分布式深度神经网络（DNN）和动态平均一致性算法以分布式方式获得近似的OPA解决方案。然后设计一种分布式平衡发电与需求算法对其进行微调，以获得最终的最优可行解。此外，从理论上证明了所提出的DNN能够很好地逼近一种现有的OPA算法（Guo等人，2018年），并给出了最多需要多少隐藏层和神经元的定量数据。几个实验案例研究表明，我们提出的分布式学习框架能够实现与使用典型现有分布式数值优化方法所获得的结果相似的最优结果，同时在简单性和实时能力方面更具优势。
