Combined optimal power flow and profit maximization in the power sector using an enhanced multi-objective grey wolf optimizer

Abstract Modern power systems require advanced optimization tools to balance efficiency, profitability and sustainability. This is because traditional methods struggle with scalability, diversity and convergence in increasingly complex power systems. This study introduces an optimization framework for multi-objective optimal power flow and profit maximization. It uses a variant of Multi-Objective Grey Wolf Optimizer, which is based on Adaptive Diversity Tuning and Levy Flights theories (EMOGWO-ADTLF). The algorithm aimed to optimize power system decision-making by minimizing power losses, reducing operational costs, lowering emissions and maximizing profit. The approach also addresses both equality constraints and inequality constraints. The proposed optimization framework was developed using the EMOGWO-ADTLF and implemented in MATLAB software. The framework was tested on standard IEEE 30-, 57-, and 118-bus systems to evaluate its performance. The algorithm generated a set of non-dominated solutions, known as the Pareto-optimal set, from which the best compromise solution was selected using a fuzzy logic decision-making technique. Comparative analysis using identical test systems and data under five test cases demonstrated that the proposed optimization framework consistently outperforms other optimisation tools developed from Multi-Objective Mayfly Algorithm (MMA), Multi-Objective Dragonfly Algorithm (MODA), and Multi-Objective Antlion Algorithm (MOALO). It achieved superior results, securing at least two optimal values across the four-objective optimization tasks. For the large and complex power system (118-bus), the proposed algorithm outperformed all other algorithms by average improvements of 1.56%, 20.18%, and 24.76% in cost, emission and power loss, respectively, in the four-objective optimisation. This improvement is attributed to the EMOGWO-ADTLF’s capability to effectively explore wide solution spaces through Lévy flights while preserving solution diversity via adaptive diversity tuning. The results confirm the framework’s effectiveness in improving efficiency, profitability, and sustainability in power systems.