Enhanced Genetic Algorithm based computation technique for multi-objective Optimal Power Flow solution

Abstract

Optimal Power Flow (OPF) is used for developing corrective strategies and to perform least cost dispatches. In order to guide the decision making of power system operators a more robust and faster OPF algorithm is needed. OPF can be solved for minimum generation cost, that satisfies the power balance equations and system constraints. But, cost based OPF solutions usually result in unattractive system losses and voltage profiles. In the present paper the OPF problem is formulated as a multi-objective optimization problem, where optimal control settings for simultaneous minimization of fuel cost and loss, loss and voltage stability index, fuel cost and voltage stability index and finally fuel cost, loss and voltage stability index are obtained. The present paper combines a new Decoupled Quadratic Load Flow (DQLF) solution with Enhanced Genetic Algorithm (EGA) to solve the OPF problem. A Strength Pareto Evolutionary Algorithm (SPEA) based approach with strongly dominated set of solutions is used to form the pareto-optimal set. A hierarchical clustering technique is employed to limit the set of trade-off solutions. Finally a fuzzy based approach is used to obtain the optimal solution from the tradeoff curve. The proposed multi-objective evolutionary algorithm with EGA–DQLF model for OPF solution determines diverse pareto optimal front in just 50 generations. IEEE 30 bus system is used to demonstrate the behavior of the proposed approach. The obtained final optimal solution is compared with that obtained using Particle Swarm Optimization (PSO) and Fuzzy satisfaction maximization approach. The results using EGA–DQLF with SPEA approach show their superiority over PSO–Fuzzy approach.