Optimal energy dispatch based on zero bus load flow in microgrid having multiple sources using Fuzzy-Particle Swarm Optimization approach

Hemanth Chaduvula, D. Das
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引用次数: 1

Abstract

The optimal economic-emission dispatch is acquired through optimal energy management of sources in the microgrid. The dispatch from distributed energy resources (DERs) and power exchange with the grid are managed for achieving the optimal operation in the microgrid. The optimal scheduling of microgrid varies according to its mode of connection to the grid. In this paper, the concept of zero bus load flow (ZBLF) is performed in a microgrid by known power injection from the grid. The particle swarm optimization (PSO) technique is employed for attaining the optimum output values of sources with distinct characteristics. In this work, the PSO embedded Fuzzy multi-objective approach is implemented for optimal energy management in the microgrid. The objectives such as operation cost, emission, and cost of energy loss are considered in a 24-hour time horizon. The degree of satisfaction of each objective is attained by representing in the fuzzy domain due to its imprecise nature. The results of Fuzzy-PSO method are validated with nondominated sorting genetic algorithm II (NSGA-II). The proposed technique has been applied to a 33-bus grid connected microgrid system.
基于模糊粒子群算法的多源微电网零负荷流优化能源调度
通过对微电网中能源的最优能量管理,实现最优经济排放调度。为了实现微电网的最优运行,对分布式能源的调度和与电网的电力交换进行了管理。微网的最优调度根据其接入电网的方式不同而不同。在本文中,零母线负荷流(ZBLF)的概念是通过已知的电网功率注入在微电网中实现的。采用粒子群优化(PSO)技术求解具有不同特征的源的最优输出值。本文将粒子群嵌入模糊多目标方法应用于微电网的最优能量管理。在24小时的时间范围内考虑运行成本、排放和能源损失成本等目标。由于目标的不精确性,每个目标的满足程度通过模糊域表示来实现。用非支配排序遗传算法II (NSGA-II)验证了Fuzzy-PSO方法的结果。该技术已应用于33总线并网微电网系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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