Pareto optimal approach in Multi-Objective Chaotic Mutation Immune Evolutionary Programming (MOCMIEP) for optimal Distributed Generation Photovoltaic (DGPV) integration in power system

Abstract

Abstract The latest advancement in the technology, including the integration of the renewable energy resources, has become a recent trend in the power system infrastructure. Although, this can bring many benefits, excessive integration without proper planning may lead to unwanted circumstances such as voltage instability and higher power losses. This paper proposes a new Pareto optimality based technique namely: Multi-objective Chaotic Mutation Immune Evolutionary Programming. The technique was developed to determines the optimal location and sizing of Distributed Generated Photovoltaic (DGPV) and minimizing the multiple objective functions, namely, the active power losses and Fast Voltage Stability Index (FVSI), simultaneously. The method was tested on the IEEE test system. The results revealed that the proposed technique had the ability to acquire a set of Pareto solutions. Furthermore, this paper also confirmed that Multi-objective Chaotic Mutation Immune Evolutionary Programming (MOCMIEP) outperformed the Multi-objective Evolutionary Programming and Multi-objective Artificial Immune System in most cases.