Optimal Power Dispatch of Multiple DGs Using a Hybrid Algorithm for Mitigating Voltage Deviations and Losses in a Radial Distribution System with Economic Benefits

Ankeshwarapu Sunil, C. Venkaiah, D. Kumar
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Abstract

In this research, a meta-heuristic-based hybrid algorithm was used to optimize the power dispatch of numerous Distributed Generators (DGs) in a Radial Distribution System (RDS) for hourly fluctuating seasonal loads in order to reduce losses and voltage variations while also saving money. With hourly seasonal load changes, renewable DGs like PV, Wind, and Hybrid (PV+Wind) were used. The HA is proposed in this paper as a way to achieve successful outcomes by merging two meta-heuristic algorithms. The findings of the HA are compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Shuffled Frog Leap Algorithm (SFLA), and Jaya Algorithm (JA) when they are applied to a standard IEEE 33 bus RDS and PG&E 69 bus RDS. According to the test findings simulated in the MATLAB environment, Hybrid Algorithm (HA) beat GA, PSO, SFLA, and JA in terms of optimal power dispatch of numerous DGs to minimise losses and voltage variations, as well as the cost-benefit analysis of renewable DGs energy generation.
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基于混合算法的径向配电系统电压偏差和损耗优化调度,具有经济效益
在本研究中,采用基于元启发式的混合算法对径向配电系统(RDS)中多个分布式发电机(dg)的功率调度进行优化,以适应每小时波动的季节负荷,从而减少损耗和电压变化,同时节省资金。随着每小时的季节性负荷变化,可再生dg如光伏、风能和混合(光伏+风能)被使用。本文提出的HA是一种通过合并两种元启发式算法来获得成功结果的方法。将HA算法应用于标准IEEE 33总线RDS和PG&E 69总线RDS,并与遗传算法(GA)、粒子群算法(PSO)、shuffle Frog Leap算法(SFLA)和Jaya算法(JA)进行了比较。根据在MATLAB环境中模拟的测试结果,混合算法(Hybrid Algorithm, HA)在众多dg的最优功率调度以最小化损耗和电压变化以及可再生dg发电的成本效益分析方面优于遗传算法(GA)、粒子群算法(PSO)、SFLA和JA。
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