article · IET Generation Transmission & Distribution
This research presents a multi-objective differential evolution algorithm designed to solve optimal power flow problems in electrical grids. The approach models power system operations against various technical and economic objectives, handling single, double, triple, and quadruple objective formulations. To solve these problems efficiently, the algorithm modifies the standard differential evolution technique by incorporating Pareto ranking into the selection process and applying a fuzzy-based compromise mechanism to guide mutations across generations. These adjustments improve search capabilities and achieve faster convergence by continually examining areas surrounding the best compromise solution. The performance of the methodology was validated through simulations on standard IEEE 57-bus and large-scale IEEE 118-bus test power systems, demonstrating effective and balanced technical and economic outcomes in comparison with alternative evolutionary optimisation methods.
Managing modern electricity networks requires balancing competing technical and economic constraints, such as keeping operating costs low while maintaining grid stability. By providing a computational method that quickly resolves multiple competing grid requirements simultaneously, this approach helps ensure power systems run efficiently and reliably under complex operational conditions.
This methodology could support software development for electrical grid operators and power system engineers seeking improved tools for network optimisation. Tested exclusively on standard benchmark networks (the IEEE 57-bus and 118-bus systems), the technology represents early-stage, algorithm-level research that requires integration into commercial energy management software and validation against real-world operational power networks before practical deployment.
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This study investigates a novel multi‐objective differential evolution (MDE) solution methodology for multi‐objective optimal power flow (MOPF) problem. The MOPF problem is modelled with various technical and economical objective functions. These objectives are handled as mono, bi, tri, and quad‐objective MOPF problems. For solving these MOPF formulations, a novel MDE algorithm is proposed. The novel MDE algorithm modifies the DE variant (DE/best/1) with Pareto ranking in the selection operator and develops a fuzzy‐based best compromise solution for each generation to feed the mutation operator. This modification guarantees high convergence speed and enhances the search capability via exploring the neighbourhood of the best compromise solution in successive generations. The standard IEEE 57‐bus power system is emulated to prove the effectiveness and competence solutions of the mono, bi, tri, and quad‐objective MOPF at acceptable techno‐economic benefits compared with other evolutionary methods. Similarly, the standard IEEE 118‐bus test system is used to show the effectiveness of the proposed algorithm for solving the OPF problem in a large‐scale power system.
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DOI: 10.1049/iet-gtd.2016.1379
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