A high-performance democratic political algorithm for solving multi-objective optimal power flow problem

• Published in: Expert Systems with Applications
• Main Author: Ahmadipour M.; Ali Z.; Othman M.M.; Bo R.; Javadi M.S.; Ridha H.M.; Alrifaey M.
• Format: Article
• Language: English
• Published: Elsevier Ltd 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176091164&doi=10.1016%2fj.eswa.2023.122367&partnerID=40&md5=a1a1f48c79971f93b7b1b97f8304fc18

The optimal power flow (OPF) is one of the most noticeable and integral tools in the power system operation and control and aims to obtain the most economical combination of power plants to exactly serve the total demand of the system without any load shedding or islanding through adjusting control variables to meet operational, economic, and environmental constraints. To achieve this goal, the successful implementation of an expeditious and reliable optimization algorithm is crucial. To solve this issue, this research proposes an enhanced democratic political algorithm (DPA), which can effectively solve multi-objective optimum power flow problems. The proposed method is a version of the democratic political optimization algorithm in which the search capability of this method to cover the borders of the Pareto frontier is enhanced. For the sake of practicality, the objectives with innate differences such as total emission, active power loss, and fuel cost are selected. Due to the practical limitations in real power systems, additional restrictions including valve-point effect, multi-fuel characteristics, and forbidden operational zones, are also considered. The proposed approach is tested and validated on IEEE 57-bus and IEEE 118-bus systems with different case studies. Simulation results are analyzed and compared with two popular and commonly used multi-objective-evolutionary algorithms namely, non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) on the problem. The study results illustrate the effectiveness of the proposed method in handling different scales, non-convex, and multi-objective optimization problems. © 2023 Elsevier Ltd