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首页> 外文期刊>International journal of green energy >A novel framework-based cuckoo search algorithm for sizing and optimization of grid-independent hybrid renewable energy systems
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A novel framework-based cuckoo search algorithm for sizing and optimization of grid-independent hybrid renewable energy systems

机译:基于新型框架的Cuckoo搜索算法,用于对网格无关的混合可再生能源系统的大小和优化

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摘要

Hybrid renewable energy systems (HRES) turned into an appealing choice for supplying loads in remote areas. The application of smart grid principals in HRES provides a communication between the load and generation from the HRES. Using smart grid in the HRES will optimally utilize the generating resources to reschedule the loads depending on its importance. This paper presents a new proposed design and optimization simulation program for techno-economic sizing of grid-independent hybrid PV/wind/diesel/battery energy system using Cuckoo search (CS) optimization algorithm. Using of CS will help to get the global minimum cost condition and prevent the simulation to be stuck around local minimum. A new proposed simulation program (NPSP) is acquainted using CS to determine the optimum size of each component of the HRES for the lowest cost of generated energy and the lowest value of dummy energy, at highest reliability. A detailed economic methodology to obtain the price of the generated energy has been introduced. Results showed that using CS reduced the time required to obtain the optimal size with higher accuracy than other techniques used iterative techniques, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Numerous significant outcomes can be extracted from the proposed program that could help scientists and decision makers.
机译:混合可再生能源系统(HRES)转变为偏远地区供应负荷的吸引力选择。 HRES中的智能电网主体在HRE之间的应用提供了负载与HRE之间的沟通。在HRES中使用智能电网将最佳地利用生成资源来重新安排负载,这取决于其重要性。本文介绍了一种新的建议设计和优化仿真程序,用于使用Cuckoo搜索(CS)优化算法的网格独立式混合PV /柴油/柴油/柴油/电池能量系统的技术经济尺寸。使用CS将有助于获得全局最小成本状况,并防止模拟围绕局部最小。新的建议模拟程序(NPSP)熟悉使用CS以最高可靠性确定产生能量的最低成本和最低值的HRES的每个部件的最佳尺寸。已经介绍了获得产生能量价格的详细经济方法。结果表明,使用CS降低了获得最佳尺寸所需的时间,比其他技术使用迭代技术,遗传算法(GA)和粒子群优化(PSO)。可以从可以帮助科学家和决策者的拟议计划中提取许多显着结果。

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