Multi-Objective Optimal Reactive Power Dispatch of Power Systems by Combining Classification-Based Multi-Objective Evolutionary Algorithm and Integrated Decision Making

Meng Zhang,Yang Li

doi:10.1109/access.2020.2974961

Meng Zhang, Yang Li

Open Access

https://doi.org/10.1109/access.2020.2974961

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Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 84	License type: CC BY 4.0

Affiliation: Northeast Electric Power University

Abstract

For the purpose of addressing the multi-objective optimal reactive power dispatch (MORPD) problem, a two-step approach is proposed in this paper. First of all, to ensure the economy and security of the power system, the MORPD model aiming to minimize active power loss and voltage deviation is formulated. And then the two-step approach integrating decision-making into optimization is proposed to solve the model. Specifically speaking, the first step aims to seek the Pareto optimal solutions (POSs) with good distribution by using a multi-objective optimization (MOO) algorithm named classification and Pareto domination based multi-objective evolutionary algorithm (CPSMOEA). Furthermore, the reference Pareto-optimal front is generated to validate the Pareto front obtained using CPSMOEA; in the second step, integrated decision-making by combining fuzzy c-means algorithm (FCM) with grey relation projection method (GRP) aims to extract the best compromise solutions which reflect the preferences of decision-makers from the POSs. Based on the test results on the IEEE 30-bus and IEEE 118-bus test systems, it is demonstrated that the proposed approach not only manages to address the MORPD issue but also outperforms other commonly-used MOO algorithms including multi-objective particle swarm optimization (MOPSO), preference-inspired coevolutionary algorithm (PICEAg) and the third evolution step of generalized differential evolution (GDE3).

Highlights

Optimal reactive power dispatch (ORPD) of power systems refers to adjusting the parameters of the control equipment in the system to make the whole network at the optimal operation, which is of great significance to the economic and secure operation of the power system [1]
2) OPTIMIZATION RESULTS In order to appropriately estimating the performance of the CPSMOEA, the preference-inspired coevolutionary algorithm (PICEAg) and multi-objective particle swarm optimization (MOPSO) are introduced as the comparison algorithms
The Pareto front (PF) obtained by the CPSMOEA is approaching the reference Pareto-optimal front more closely than that obtained by the PICEAg and MOPSO

Summary

INTRODUCTION

Optimal reactive power dispatch (ORPD) of power systems refers to adjusting the parameters of the control equipment in the system to make the whole network at the optimal operation, which is of great significance to the economic and secure operation of the power system [1]. The traditional optimization approach can find one solution at most in a single simulation run, which makes the computation cost heavier [5] Taking this into account, multiobjective evolutionary algorithms (MOEAs) have been used to solve the MORPD model [6]. Li: MORPD of Power Systems by Combining Classification-Based MOEA and Integrated Decision Making solutions (POSs) can be obtained, it is difficult to determine the best compromise solutions since different decision-makers have different preferences for a given operational condition. The two-step approach combining MOEAs with integrated decision-making analysis is proposed in this paper, where MOEAs have the ability to find the POSs in one run and decision-making analysis can determine the BCSs reflecting decision-makers’ preferences from the POSs. As for the MORPD issue, from the perspective of system security and economy, it is hoped that the active power loss will be minimized to reduce the investment and the voltage stability will be maximized to guaranteed voltage quality [12].

PROBLEM FORMULATION

APPLICATION OF CPSMOEA IN MORPD PROBLEM

NUMERICAL RESULTS

CONCLUSION