Grey Wolf Equilibrium Optimisation for Optimal Power Flow

The implementation for a Multi-objective Optimal Power Flow for Distribution Networks Utilizing Grey Wolf Equilibrium Optimizer

This paper presents an approach to address the drawback of stagnation in the local optimum and poor diversity of the Pareto front exhibited in existing heuristic algorithms on optimal power flow problem. We propose an improved grey wolf equilibrium optimizer (GWEO) that integrates the search mechanisms of grey wolf optimization (GWO) and equilibrium optimizer (EO). A disturbance component of EO involves in the search mechanism of GWO to relieve the diversity problem. To validate the algorithm, we implement GWEO to solve the multi-objective optimal power flow (MOOPF) problem in a modified 118-bus distribution system with high distributed photovoltaic (PV) penetration. The objective functions of MOOPF are active power losses, voltage deviation and PV curtailment rates. Besides, we compare GWEO with GWO, salp swarm algorithm (SSA), equilibrium optimizer (EO), and hybrid gray wolf optimizer (HGWO) in terms of convergence rate, diversity, and optimum value. The numerical results reveal that our proposed GWEO shows faster convergence rate and better performance on diversity and optimum value, providing decision-makers with better solutions in multi-objective optimization scenarios.