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MULTI-OBJECTIVE DISTRIBUTION NETWORK RECONFIGURATION WITH DG INTEGRATION USING IMPROVED FIREWORKS ALGORITHM

机译:使用改进的烟花算法,用DG集成重新配置多目标分销网络重新配置

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The network integration of distributed generation (DG) units such as wind turbines and photovoltaic (PV) energy systems makes distribution system operation and control more complicated while achieving economic and environmental benefits. This paper presents a multi-objective distribution network reconfiguration optimization model incorporating wind and PV components, aiming at minimizing system power loss, voltage nodes deviation, and load balance under a series of specific operational constraints. The scenario-based analysis technique is introduced to handle the uncertainties of wind turbine generation and PV output. Regarding the highly nonlinear nature of the proposed model, an enhanced fireworks algorithm is applied to obtain the set of Pareto front solutions of the multi-objective optimization model. Some significant improvement measures including improved loop coding to avoid infeasible solution, flexible adjustment of optimal radius pertinent to the Pareto front according to the population algebra and the individual dominant strength are discussed elaborately during analysis. Finally, the case studies are carried out on the IEEE 33-bus test system. The corresponding statistical analysis pertinent to different DG output scenarios and the comparative analysis are elaborately designed and conducted to demonstrate the validity and effectiveness of the proposed model and enhanced method.
机译:诸如风力涡轮机和光伏(PV)能量系统之类的分布式发电(DG)单元的网络集成使得分配系统运行和控制更复杂,同时实现经济和环境益处。本文介绍了一种包含风和光伏元件的多目标分销网络重新配置优化模型,旨在最大限度地减少系统功率损耗,电压节点偏差以及在一系列特定操作约束下的负载平衡。引入了基于场景的分析技术,以处理风力涡轮机生成和PV输出的不确定性。关于所提出的模型的高度非线性性质,应用增强的烟花算法来获得多目标优化模型的帕累托前解。一些显着的改进措施,包括改进的环路编码,以避免不可行的解决方案,根据群体代数和各个主导强度讨论了与帕累托前线相关的最佳半径的灵活调整,并且在分析期间讨论了各个主导强度。最后,在IEEE 33总线测试系统上进行案例研究。精心设计和进行与不同DG输出场景相关的相应统计分析和对比分析,以证明所提出的模型和增强方法的有效性和有效性。

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