Optimal sizing of a hybrid microgrid system using solar, wind, diesel, and battery energy storage to alleviate energy poverty in a rural area of Biskra, Algeria

被引:55
作者
Bacha, Badis [1 ,3 ]
Ghodbane, Hatem [1 ]
Dahmani, Habiba [2 ,4 ]
Betka, Abir [5 ,6 ]
Toumi, Abida [1 ]
Chouder, Aissa [2 ]
机构
[1] Univ Biskra, Dept Elect Engn, Biskra, Algeria
[2] Univ Msila, Genie Elect Dept, Msila, Algeria
[3] Univ Biskra LI3CUB, Identificat Command Control & Commun Lab, Biskra, Algeria
[4] Univ Biskra, Energy Syst Modeling Lab LMSE, Biskra, Algeria
[5] Univ Biskra, Lab Vis & Commun Syst LVCS, Biskra, Algeria
[6] Univ El Oued, Dept Elect Engn, El Oued, Algeria
基金
美国国家科学基金会;
关键词
Renewable energy; Hybrid system; Microgrid; Optimization; Stochastic fractal search algorithm; Symbiotic organisms search; The levelized cost of energy (LCOE); PARTICLE SWARM OPTIMIZATION; RENEWABLE ENERGY; OPTIMAL OPERATION; OPTIMAL-DESIGN; POWER-SYSTEM; REMOTE AREA; PV; ALGORITHM; PSO; GENERATION;
D O I
10.1016/j.est.2024.110651
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
This paper presents a model for designing a stand-alone hybrid system consisting of photovoltaic sources, wind turbines, a storage system, and a diesel generator. The aim is to determine the optimal size to reduce the cost of electricity and ensure the provision of electricity at lower and more reliable prices for isolated rural areas. Three scenarios for five, fifteen, and twenty rural homes were sized and analyzed using actual weather data in the rural and pastoral area of Biskra, Algeria. The SFS and SOS algorithms were used for the first time, in addition to the commonly used PSO algorithm, to compare the performance of each algorithm and achieve the goal of meeting all energy load requirements at the lowest cost. Three factors have been taken into consideration within the proposed objective function, which are the levelized cost of energy (LCOE), loss of power supply probability LPSP, and the amount of energy consumed by the dummy load. The results of the scenarios showed that the SFS algorithm outperformed the other algorithms, with the lowest cost of electricity for each of the mentioned scenarios. The levelized cost of energy (LCOE), respectively, was 0.09138 $/kWh, 0.16588 $/kWh, and 0.24862 $/kWh.
引用
收藏
页数:19
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