Application of Many-Objective Arithmetic Optimization Algorithm and TOPSIS for Optimal Planning of DGS in Distribution Systems

Srikant Ganji, J. N. Manohar, G. Yesuratnam
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Abstract

The traditional planning of distribution networks is changing because of the accelerated expansion of distributed generation (DG) technologies in various capacities and forms. However, the improper integration of DGs in current distribution networks can give rise to several technical difficulties despite the advantages provided by distributed generation technologies. This paper presents the optimal DG planning in the distribution system using a Pareto-based many-objective arithmetic optimization algorithm (MOAOA) for optimal DG planning problems in the distribution system. This work focuses on improving four technical metrics related to distribution systems: mitigation of electrical energy not served (EENS), total voltage deviation (TVD) minimization, voltage stability index (VSI) maximization, and energy loss mitigation. Two scenarios are considered: the first scenario primarily focuses on optimal planning of DGs supporting active power only (e.g. Micro-Turbines DGs), and the second scenario focuses on optimal planning of DGs supporting both active and reactive power support (e.g. BIOMASS DGs). The optimal Pareto fronts between the competing objectives are generated using the Pareto-based MOAOA algorithm. The TOPSIS (a technique for order performance by similarity to ideal solution) multi-criteria decision-making technique is utilized for selecting the best trade-off solution from the optimal Pareto front. The posited method is examined on two standard IEEE-69 bus distribution systems. The efficacy of the MOAOA is compared with the outcomes of MOPSO, MOGWO and NSGA-II.
多目标算术优化算法和 TOPSIS 在配电系统 DGS 优化规划中的应用
由于各种容量和形式的分布式发电(DG)技术的加速发展,传统的配电网络规划正在发生变化。然而,尽管分布式发电技术具有诸多优势,但在当前的配电网络中不适当地集成 DG 会带来一些技术难题。本文针对配电系统中的分布式发电优化规划问题,采用基于帕累托的多目标算术优化算法(MOAOA),提出了配电系统中的分布式发电优化规划。这项工作的重点是改进与配电系统相关的四个技术指标:减少未服务电能(EENS)、总电压偏差(TVD)最小化、电压稳定指数(VSI)最大化和减少能量损失。我们考虑了两种方案:第一种方案主要侧重于仅支持有功功率的 DGs(如微型涡轮机 DGs)的优化规划,第二种方案侧重于同时支持有功功率和无功功率的 DGs(如生物质能发电机组 DGs)的优化规划。使用基于帕累托的 MOAOA 算法生成竞争目标之间的最佳帕累托前沿。利用 TOPSIS(一种通过与理想解决方案的相似性对性能进行排序的技术)多标准决策技术,从最优帕累托前沿中选择最佳权衡解决方案。所提出的方法在两个标准 IEEE-69 总线配电系统上进行了检验。将 MOAOA 的功效与 MOPSO、MOGWO 和 NSGA-II 的结果进行了比较。
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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