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首页> 外文期刊>Journal of Energy Storage >Optimal sizing and allocation of renewable based distribution generation with gravity energy storage considering stochastic nature using particle swarm optimization in radial distribution network
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Optimal sizing and allocation of renewable based distribution generation with gravity energy storage considering stochastic nature using particle swarm optimization in radial distribution network

机译：考虑随机性质在径向分布网络中考虑随机性质考虑随机性质的重心基于可再生能量存储的最佳尺寸和分配

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摘要

This paper presents an optimal sizing and allocation of a renewable energy resource (RES) based distribution generation (DG) units with gravity energy storage (GES) in the radial distribution network (DN). The optimization technique Constriction Coefficient Particle Swarm Optimization (CPSO) is utilized to reduce the total energy loss, which is subjected to equality and inequality constraints. Different DG parameters are considered and evaluated to reduce energy losses in electricity DN. To reduce search space and computational burden, a sensitivity analysis is performed to determine the candidate buses for the placement of DGs. The stochastic nature of RES (solar and wind), load, and storage unit has been handle using the probabilistic technique. The suitable penetration level is so adjusted as to restrict RES output on a certain fraction of the system load for stability consideration. The load flow analysis is performed using a backward-forward sweep algorithm embedded in the probability framework. The proposed approach has been examined on four different cases on DN consisting of 33 buses and it has been found that a notable reduction in losses with improved voltage profile is obtained by optimal sizing and placing DG units at an appropriate location. Results obtained using the CPSO technique has been validated by comparing it with the Simple Genetic Algorithm (SGA) technique. Further, the results obtained in case 3 using GES technology have compared with the battery storage system.
机译：本文介绍了径向分布网络（DN）中具有重力能量存储（GES）的可再生能源（RES）的可再生能源资源（RE）分配（DG）单元的最佳尺寸和分配。优化技术收缩系数粒子群群优化（CPSO）用于降低总能量损失，这受到平等和不等式约束。考虑和评估不同的DG参数以减少电力DN中的能量损失。为了减少搜索空间和计算负担，执行灵敏度分析以确定用于放置DG的候选总线。 Res（太阳和风），负载和存储单元的随机性质使用概率技术处理。因此，适当的穿透水平以限制在系统负载的一定部分上限制RES输出以进行稳定考虑。使用嵌入在概率框架中的后退前进扫描算法进行负载流分析。已经在由33个总线组成的DN的四个不同案例上检查了所提出的方法，并且已经发现通过在适当位置的最佳施胶和放置DG单元，获得具有改进的电压曲线的损耗的显着降低。通过将其与简单的遗传算法（SGA）技术进行比较，通过了使用CPSO技术获得的结果。此外，与电池存储系统相比，在使用GES技术的情况下获得的结果。

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