Nodal Electricity Price Based Optimal Size and Location of DGs in Electrical Distribution Networks Using ANT LION Optimization Algorithm

Md Irfan Ahmed, Ramesh Kumar
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引用次数: 1

Abstract

Distribution system has been the weakest link in the entire power system supply chain. It is also one of the most vital parts of the power system. However, a lot of methods have been developed to improve the condition of the distribution system. The use of distributed generations (DGs) is one such method where the generated power is closer to the load center, and the DG is also providing ancillary services to the grid. The nodal electricity price for DGs location is determined based on the Locational Marginal Price (LMP). LMP implies the price to buy and sell power at each node within electrical distribution markets. In the nodal electricity market (EM), the cost of energy is determined by the location of DG to which it is provided. This paper presents a novel approach that utilizes nodal electricity price for optimal sizing and location (OSL) of DGs. A multi-objective ANTLION optimization (MOALO) has been utilized as an optimization approach to compute the OSL of DGs units. ANTLION optimization (ALO) is based on the unique hunting behaviour of antlions. Optimization has been done for social welfare maximization, loss minimization, and voltage profile improvement in distribution networks (DNs). The results of the proposed technique have been evaluated for IEEE 33 bus DNs.
基于节点电价的配电网dg最优规模和位置的ANT - LION优化算法
配电系统一直是整个电力系统供应链中最薄弱的环节。它也是电力系统中最重要的部分之一。然而,人们已经开发了许多方法来改善配电系统的状况。分布式发电(DG)的使用就是这样一种方法,其中产生的电力更靠近负荷中心,DG还为电网提供辅助服务。dg节点电价是根据区位边际电价(LMP)确定的。LMP指的是在电力分配市场中每个节点买卖电力的价格。在节点电力市场(EM)中,能源的成本取决于向其提供的DG的位置。本文提出了一种利用节点电价来优化分布式发电机组规模和位置的新方法。采用多目标蚁群优化(MOALO)方法计算了DGs单元的OSL。蚁狮优化(ALO)是基于蚁狮独特的狩猎行为。在配电网(DNs)中,社会福利最大化、损耗最小化和电压分布改善已经完成了优化。所提出的技术的结果已经在IEEE 33总线DNs中进行了评估。
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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