Pareto-based multi-objective whale optimization algorithm for economic-environmental optimal power flow

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Chinese explanation / 中文解读

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Original abstract

This paper presents a Multi-Objective Whale Optimization Algorithm (MOWOA) for solving the Optimal Power Flow (OPF) problem in the IEEE 30-bus test system. The proposed approach simultaneously optimizes four conflicting objectives: fuel cost minimization ($/h), active power loss minimization (MW), voltage deviation minimization (p.u.), and emission reduction (ton/h). A Pareto-based archive mechanism combined with a crowding distance strategy is employed to maintain a well-distributed set of non-dominated solutions. The MOWOA effectively mimics the bubble-net hunting behavior of humpback whales to explore the multi-dimensional search space. Simulation results on the IEEE 30-bus system demonstrate that the proposed algorithm successfully generates a rich Pareto-optimal front comprising 50 non-dominated solutions. The best cost solution achieves 798.34 $/h, which is competitive with state-of-the-art single-objective methods. Comprehensive trade-off analysis reveals that a 62.7% reduction in emissions can be achieved at only a 22.7% increase in fuel cost, offering decision-makers flexible and insightful options for sustainable power system operation.

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