Intelligent load shifting strategy for economic operation of islanded microgrid system

Swarupa Pinninti, Srinivasa Rao Sura
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Abstract

This work employs a robust and efficient teaching learning-based optimization (TLBO) to optimally schedule the DGs of a low voltage (LV) off-grid microgrid (MG) system in order to lower the active power production cost of the systems. The topic test systems' cost-effective fitness functions considers efficiency of the distributed energy resources (DERs) that constitutes the MG system. Photovoltaic (PV) systems and wind turbines were selected to share load demand, and the load profiles of various systems were evaluated. The numerical and graphical findings show that TLBO is beneficial in minimizing the generation cost of test systems, surpassing a large list of techniques available in the literature. Furthermore, a strategic demand side management (DSM) technique was implemented to restructure the load demand shifting the elastic loads to reduce the peak demand without altering the daily load demand. The new restructured load demand furthered reduced the generation cost of the system. Non-parametric statistical analysis, and processing time all testify to TLBO's exceptional efficiency in dealing with any dimensions test system.
孤岛微网系统经济运行的智能负荷转移策略
本文采用一种鲁棒高效的基于教学学习的优化方法(TLBO)对低压离网微电网(MG)的dg进行优化调度,以降低系统的有功发电成本。主题测试系统的成本效益适应度函数考虑了构成MG系统的分布式能源(DERs)的效率。选择光伏发电系统和风力发电系统分担负荷需求,并对不同系统的负荷分布进行了评估。数值和图形结果表明,TLBO在最小化测试系统的生成成本方面是有益的,超过了文献中可用的大量技术。此外,在不改变日负荷需求的前提下,通过调整弹性负荷来降低高峰负荷,实现了电力需求的战略性需求侧管理。新的重构负荷需求进一步降低了系统的发电成本。非参数统计分析和处理时间都证明了TLBO在处理任何维度测试系统时的卓越效率。
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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