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首页> 外文期刊>Fuzzy sets and systems >Multi-objective particle swarm optimization based on fuzzy-Pareto-dominance for possibilistic planning of electrical distribution systems incorporating distributed generation
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Multi-objective particle swarm optimization based on fuzzy-Pareto-dominance for possibilistic planning of electrical distribution systems incorporating distributed generation

机译：基于模糊-Pareto支配的多目标粒子群算法用于分布式发电系统的可能规划

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This paper presents a multi-objective planning approach for electrical distribution systems under uncertainty in load demand incorporating distributed generation (DG). Both radial and meshed systems are considered. The overall influence of load demand uncertainty on planned networks is investigated in detail. Uncertainty in load demand is possibilistically modeled using a fuzzy triangular number. The two objectives in system planning are: (i) minimization of total installation and operational costs, and (ii) minimization of the risk factor. The risk factor is a function of the contingency load-loss index (CLLI), which measures load loss under contingencies, and the degree of network constraints violations. CLLI minimization improves network reliability. The network variables optimized are: (i) the network structure type (radial or meshed), (ii) the number of feeders and their routes, and (iii) the number and location of sectionalizing switches. The optimization tool is a multi-objective particle swarm optimization (MOPSO) variant that uses heuristic selection and assignment of leaders or guides for efficient identification of non-dominated solutions. The optimal number, location, and size of the DG units are determined in another planning stage. Performance comparisons between the planning approaches with possibilistic and deterministic load models highlight the relative merits and demerits. The advantages of networks obtained using the proposed planning approach in the context of DG integration are described. The proposed planning approach is validated using three typical distribution systems.
机译：本文提出了在负荷需求不确定的情况下并入分布式发电（DG）的配电系统的多目标规划方法。径向系统和网格系统均被考虑。详细研究了负载需求不确定性对计划网络的总体影响。使用模糊三角数可以对负荷需求的不确定性进行建模。系统规划的两个目标是：（i）最小化总安装和运营成本，以及（ii）最小化风险因素。风险因素是意外负载损失指数（CLLI）的函数，该指标衡量突发情况下的负载损失以及违反网络约束的程度。最小化CLLI可提高网络可靠性。优化的网络变量是：（i）网络结构类型（径向或网状），（ii）馈线及其路线的数量，以及（iii）分段交换机的数量和位置。该优化工具是一种多目标粒子群优化（MOPSO）变体，它使用启发式选择和领导者或指南的分配来有效识别非支配解。 DG单位的最佳数量，位置和大小在另一个计划阶段中确定。在具有可能性和确定性负荷模型的计划方法之间的性能比较突出了相对的优点和缺点。描述了使用建议的规划方法在DG集成中获得的网络的优势。所提出的规划方法已使用三种典型的分销系统进行了验证。

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