离散学习优化算法在含分布式电源的配网重构中的应用

灵活的网架重构作为主动配电网的重要特征,利于高效消纳分布式能源。传统的数学规划方法难以求解非凸的含分布式电源的配电网重构问题。为此,提出了一种离散学习优化算法(DLOA),并将其应用于有源配电网重构问题。所提方法主要包括三个模块:学习优化算法、离散策略以及拓扑结构分析技术。其中,学习优化算法作为程序优化的核心,离散策略用于确定配电网线路的开闭状态,拓扑结构分析技术则用于分析配电网的网架结构。通过33节点测试系统验证离散学习优化算法的有效性,算例分析表明,所提方法能够有效求解高度非凸的含分布式电源的配电网重构问题。

Application of discrete learning optimization algorithm to distribution network reconfiguration considering distributed generation

Flexible network reconfiguration is considered as an essential characteristic of active distribution network and it is helpful for accommodating distributed energy resources high-efficiently. However, many conventional mathematical programming methods can not tackle the problem of non-convex distribution network reconfiguration with DG. This paper presents a Discrete Learning Optimization Algorithm (DLOA) to solve this problem, which mainly includes three modules, learning optimization algorithm, discrete strategy, and topology analysis technique. It takes learning optimization algorithm as the core of program optimization, uses discrete strategy to determine the on/off status of distribution line, and applies topology analysis technique to analyze the structure of distribution network. The validity of DLOA is testified through 33-bus test system. Simulation results show that DLOA can effectively solve the highly non-convex distribution network reconfiguration with DG. This work is supported by Technology Project of Guangdong Power Grid Company (No. GDKJQQ20152029).