A Novel Hybrid Multi-Objective Evolutionary Algorithm for Optimal Power ‎Flow in Wind, PV, and PEV Systems ‎

Abstract

In this paper, a new hybrid decomposition-based multi-objective evolutionary algorithm (MOEA) is proposed for the optimal power flow (OPF) problem including Wind, PV, and PEVs uncertainty with four conflicting objectives. The proposed multi-objective OPF (MOOPF) problem includes minimization of the total cost (TC), total emission (TE), active power loss (APL), and voltage magnitude deviation (VMD) as objectives and a novel constraint handling method, which adaptively adds the penalty function and eliminates the parameter dependence on penalty function evaluation is deployed to handle several constraints in the MOOPF problem. In addition, summation-based sorting and improved diversified selection methods are utilized to enhance the diversity of MOEA. Further, a fuzzy min-max method is utilized to get the best-compromised values from Pareto-optimal solutions. The impact of intermittence of Wind, PV, and PEVs integration is considered for optimal cost analysis. The uncertainty associated with Wind, PV, and PEV systems are represented using probability distribution functions (PDFs) and its uncertainty cost is calculated using the Monte-Carlo simulations (MCSs). A commonly used statistical method called the ANOVA test is used for the comparative examination of several methods. To test the proposed algorithm, standard IEEE 30, 57, and 118-bus test systems were considered with different cases and the acquired results were compared with NSGA-II and MOPSO to validate the suggested algorithm's effectiveness

Keywords

• wind energy
• solar energy
• multi-objective optimization
• electric vehicle
• optimal power flow.‎