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<p>This paper investigates the application of the recently developed Barber Optimization Algorithm (BaOA) to demand-side management (DSM), with a focus on load shifting and peak clipping. BaOA is a human-inspired metaheuristic that models barber–customer interactions to balance exploration and exploitation during the optimization process. In this study, the algorithm is adapted to reduce peak demand through controlled clipping while shifting flexible loads to off-peak periods in order to minimise electricity costs under time-varying tariff structures. The proposed approach is evaluated using a heterogeneous case study that integrates residential, commercial, and industrial load profiles, representing realistic distribution system conditions. Simulation results indicate that the proposed BaOA-based DSM strategy achieves noticeable peak demand reduction and electricity cost savings, with observed improvements of up to approximately 11,8–22,4% in peak reduction and 8–13% in cost savings, depending on the load composition and tariff scenario. A comparative analysis with Particle Swarm Optimization (PSO) demonstrates that BaOA provides comparable and, in several scenarios, improved performance in terms of convergence stability, constraint handling, and solution robustness. The results suggest that BaOA is a viable and effective optimization approach for DSM applications, with potential relevance for smart grid and energy management systems.</p>

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Barber optimization algorithm for efficient load shifting and peak clipping in demand side management

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月19日

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  • Abuyile Mpaka,
  • Senthil Krishnamurthy

摘要

This paper investigates the application of the recently developed Barber Optimization Algorithm (BaOA) to demand-side management (DSM), with a focus on load shifting and peak clipping. BaOA is a human-inspired metaheuristic that models barber–customer interactions to balance exploration and exploitation during the optimization process. In this study, the algorithm is adapted to reduce peak demand through controlled clipping while shifting flexible loads to off-peak periods in order to minimise electricity costs under time-varying tariff structures. The proposed approach is evaluated using a heterogeneous case study that integrates residential, commercial, and industrial load profiles, representing realistic distribution system conditions. Simulation results indicate that the proposed BaOA-based DSM strategy achieves noticeable peak demand reduction and electricity cost savings, with observed improvements of up to approximately 11,8–22,4% in peak reduction and 8–13% in cost savings, depending on the load composition and tariff scenario. A comparative analysis with Particle Swarm Optimization (PSO) demonstrates that BaOA provides comparable and, in several scenarios, improved performance in terms of convergence stability, constraint handling, and solution robustness. The results suggest that BaOA is a viable and effective optimization approach for DSM applications, with potential relevance for smart grid and energy management systems.