Fully-Decentralized Optimal Power Flow of Multi-Area Power Systems Based on Parallel Dual Dynamic Programming
Jianquan Zhu, Xiemin Mo, Yunrui Xia, Ye Guo, Jiajun Chen, Mingbo Liu
Abstract
In this paper, we propose a parallel dual dynamic programming (PDDP)-based decentralized algorithm for the multi-area optimal power flow (MAOPF), which can preserve the information privacy and operational independence of each area. The MAOPF problem is decomposed into a series of subproblems for individual areas by the dual dynamic programming (DDP) algorithm, and the Benders cut-based value functions are used to reflect the impacts of one area’s decisions to the subsequent areas. The optimal solution of MAOPF can be obtained in a decentralized fashion, requiring only a limited amount of information exchange among neighbor areas. Moreover, a parallel processing technique is designed to avoid the waiting process of the basic DDP algorithm, thus accelerating the computing speed of the proposed decentralized algorithm. Compared with the existing decentralized algorithms, the proposed algorithm has better performance in terms of convergence and computational efficiency. In addition, there is no need for parameter tuning. Case studies on several IEEE test systems and a real 2298-bus system demonstrate the effectiveness of the proposed algorithm.