Abstract
The Optimal Power Flow (OPF) problem has been widely used in power system operation and planning for determining electricity prices and amount of emission. This paper presents a Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithm to solve the highly constrained multi-objective OPF involving conflicting objectives, considering fuel cost and emission level functions. The proposed technique has been carried out on IEEE 30-bus test system. The results demonstrate the capability of the proposed CLPSO approach to generate well-distributed Pareto optimal non-dominated solutions of multi-objective OPF problem. The results show that the approaches developed are feasible and efficient.
Highlights
One of the major problems in the operation of power system is optimal power flow
This paper proposes the application of Particle Swarm Optimization (PSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) to solve the multi-objective Optimal Power Flow (OPF) problem
Simulation results demonstrate that CLPSO algorithm is superior in compression the other algorithms
Summary
One of the major problems in the operation of power system is optimal power flow. The aim of this problem is minimizing the fuel cost per unit of production to produce a certain amount of power equal to the demand. In this case, the given objective function of pollution is considered in OPF problem. There are several techniques that have been considered in articles to solve the multiobjective problem These methods have received much interest due to the development of multi-objective evolutionary search strategies. This paper proposes the application of PSO and CLPSO to solve the multi-objective OPF problem. Standard test systems of IEEE 30-bus system have been employed to carry out the simulation study Both the PSO and CLPSO methods perform well in such systems for OPF and give satisfactory results. Simulation results demonstrate that CLPSO algorithm is superior in compression the other algorithms
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