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An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation

机译:考虑分布式发电的最优潮流的多目标模糊线性规划方法

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摘要

This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality.
机译:本文为考虑分布式发电的网状电网多目标最优潮流(MOOPF)问题提出了一种新的公式。提出了一种有效的多目标模糊线性规划优化(MFLP)算法,以解决上述问题,并且不考虑分布式发电(DG)的影响。考虑将目标的变体组合用于同时优化,包括功耗,电压稳定性和MVAR备用电容器。针对这些目标的模糊隶属度函数设计有极端目标,而将不等式约束视为硬约束。在连续线性规划(SLP)框架中将多目标模糊最优潮流(OPF)公式转换为清晰的OPF,并使用有效的内点法(IPM)对其进行求解。为了测试所提出方法的有效性,在IEEE 30总线和IEEE 118总线测试系统上进行了仿真。针对几种优化情况解决了MFLP优化。将获得的结果与文献中提供的结果进行比较。获得了对指定目标高度满意的独特解决方案。结果证明了所提出的MFLP技术在解决方案最优性和快速收敛性方面的有效性。此外,结果表明,将最佳DG位置与​​MFLP算法结合使用可提供最高质量的解决方案。

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