Journal of Energy Storage ( IF 9.4 ) Pub Date : 2021-01-22 , DOI: 10.1016/j.est.2021.102282 Arun Rathore , N.P. Patidar
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.
中文翻译:
径向分布网络中具有随机性的考虑随机性的重力能量存储可再生能源发电的最优规模和分配
本文介绍了在径向配电网(DN)中具有重力能量存储(GES)的基于可再生能源(RES)的分布式发电(DG)单元的最佳尺寸和分配。利用优化技术压缩系数粒子群优化算法(CPSO)来减少总能量损失,该能量损失受到等式和不等式约束。考虑并评估了不同的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,执行敏感性分析以确定用于放置DG的候选总线。RES(太阳能和风能),负载和存储单元的随机性已经使用概率技术进行了处理。出于对稳定性的考虑,调整合适的穿透水平以将RES输出限制在系统负载的特定部分。使用嵌入在概率框架中的后向扫描算法执行潮流分析。在包含33条母线的DN的四种不同情况下,对提出的方法进行了研究,结果发现,通过优化DG尺寸并将DG单元放置在适当的位置,可以显着降低损耗并改善电压。通过将CPSO技术与简单遗传算法(SGA)技术进行比较,已验证了结果。此外,将案例3中使用GES技术获得的结果与电池存储系统进行了比较。使用嵌入在概率框架中的后向扫描算法执行潮流分析。在包含33条母线的DN的四种不同情况下,对提出的方法进行了研究,结果发现,通过优化DG尺寸并将DG单元放置在适当的位置,可以显着降低损耗并改善电压。通过将CPSO技术与简单遗传算法(SGA)技术进行比较,已验证了结果。此外,将案例3中使用GES技术获得的结果与电池存储系统进行了比较。使用嵌入在概率框架中的后向扫描算法执行潮流分析。在包含33条母线的DN的四种不同情况下,对提出的方法进行了研究,结果发现,通过优化DG尺寸并将DG单元放置在适当的位置,可以显着降低损耗并改善电压。通过将CPSO技术与简单遗传算法(SGA)技术进行比较,已验证了结果。此外,将案例3中使用GES技术获得的结果与电池存储系统进行了比较。在包含33条母线的DN的四种不同情况下,对提出的方法进行了研究,结果发现,通过优化DG尺寸并将DG单元放置在适当的位置,可以显着降低损耗并改善电压。通过将CPSO技术与简单遗传算法(SGA)技术进行比较,已验证了结果。此外,将案例3中使用GES技术获得的结果与电池存储系统进行了比较。在包含33条母线的DN的四种不同情况下,对提出的方法进行了研究,结果发现,通过优化DG尺寸并将DG单元放置在适当的位置,可以显着降低损耗并改善电压。通过将CPSO技术与简单遗传算法(SGA)技术进行比较,已验证了结果。此外,将案例3中使用GES技术获得的结果与电池存储系统进行了比较。