Abstract The optimal power flow (OPF) is a key tool in the planning and operation of power systems, and aims to optimize the operational costs involved in the production and transport of energy by adjusting control variables to meet operational, economic, and environmental constraints. To achieve this goal, a successful implementation of an expeditious and reliable optimization algorithm is crucial. To this end, this paper proposes and scrutinizes a novel fuzzy adaptive hybrid configuration oriented to a joint self-adaptive particle swarm optimization (SPSO) and differential evolution algorithms, namely FAHSPSO-DE, to address the multi-objective OPF (MOOPF) problem. For the sake of practicality, the objectives with innate differences such as total fuel cost, active power losses, and the emission are selected. Due to the practical limitations in real power systems, additional restrictions, including valve-point effect, multi-fuel characteristic, and prohibited operating zones, are also taken into account. In order to validate the performance of the proposed approach, ten various benchmark functions are examined, while three IEEE standard systems such as IEEE 30-, 57-, and 118-bus test systems are employed to demonstrate the performance and suitability of the proposed approach in solving the OPF problem expeditiously. Results have been compared with those in the literature and show the effectiveness of our proposal in handling different scales, multi-objective, and non-convex optimization problems.

中文翻译：

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一种处理单目标和多目标最优潮流问题的新型混合自适应启发式算法

摘要 最优潮流（OPF）是电力系统规划和运行的关键工具，旨在通过调整控制变量以满足运行、经济和环境约束来优化能源生产和运输中涉及的运行成本。为了实现这一目标，快速可靠的优化算法的成功实施至关重要。为此，本文提出并研究了一种新的模糊自适应混合配置，面向联合自适应粒子群优化 (SPSO) 和差分进化算法，即 FAHSPSO-DE，以解决多目标 OPF (MOOPF) 问题。出于实用性考虑，选择了燃料总成本、有功功率损耗、排放等具有先天差异的目标。由于实际电力系统的实际限制，还考虑了额外的限制，包括阀点效应、多燃料特性和禁止运行区域。为了验证所提出方法的性能，我们检查了 10 个不同的基准函数，同时采用了三个 IEEE 标准系统，例如 IEEE 30、57 和 118 总线测试系统来证明所提出方法的性能和适用性快速解决OPF问题。结果与文献中的结果进行了比较，并显示了我们的建议在处理不同规模、多目标和非凸优化问题方面的有效性。为了验证所提出方法的性能，我们检查了 10 个不同的基准函数，同时采用了三个 IEEE 标准系统，例如 IEEE 30、57 和 118 总线测试系统来证明所提出方法的性能和适用性快速解决OPF问题。结果与文献中的结果进行了比较，并显示了我们的建议在处理不同规模、多目标和非凸优化问题方面的有效性。为了验证所提出方法的性能，我们检查了 10 个不同的基准函数，同时采用了三个 IEEE 标准系统，例如 IEEE 30、57 和 118 总线测试系统来证明所提出方法的性能和适用性快速解决OPF问题。结果与文献中的结果进行了比较，并显示了我们的建议在处理不同规模、多目标和非凸优化问题方面的有效性。