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Cuckoo Search Algorithm based environmental economic dispatch of microgrid system with distributed generation

机译:基于布谷鸟搜索算法的分布式发电微电网系统环境经济调度

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In present scenario economic dispatch for the microgrid (MG) is better suited to the requirements of a system in actual operation because it not only considers the lowest cost in a scheduling cycle but also coordinates between different distributed generations (DGs) over many periods. Wind energy and solar energy are subjected to random variations and intervals, so there is a great difficulty in solving the economic dispatch problem. In this paper, different distributed generations are employed in MG which includes photovoltaic arrays, wind turbine, fuel cell, diesel engines, gas-turbine and battery. In this work the MG is considered to be operating in islanding mode. The main objective of this work is to minimize the generation cost and emission cost of the MG while satisfying system hourly demand and system constraints. Economic dispatch for MG involving DGs is an optimization problem and in this paper Cuckoo Search Algorithm (CSA) is used to perform the optimization process. The results obtained from CSA is compared with Particle Swarm Optimization (PSO) algorithm and it is inferred that CSA shows better global convergence when compared to PSO and also gives good optimum solution by reducing system generation cost and emission cost.
机译:在当前情况下,微电网(MG)的经济调度更适合实际操作中系统的要求,因为它不仅考虑调度周期中的最低成本,而且还要考虑多个时期的不同分布式发电(DG)之间的协调。风能和太阳能经受随机变化和间隔,因此解决经济调度问题非常困难。在本文中,MG使用了不同的分布式发电,其中包括光伏阵列,风力涡轮机,燃料电池,柴油发动机,燃气轮机和电池。在这项工作中,MG被视为以孤岛模式运行。这项工作的主要目的是在满足系统每小时需求和系统约束的同时,将MG的发电成本和排放成本降至最低。涉及DG的MG的经济调度是一个优化问题,在本文中,使用布谷鸟搜索算法(CSA)来执行优化过程。将CSA获得的结果与粒子群优化(PSO)算法进行比较,可以推断CSA与PSO相比具有更好的全局收敛性,并且通过降低系统生成成本和排放成本提供了最佳的优化解决方案。

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