Optimization, Power Management and Reliability Evaluation of Hybrid Wind-PV-Diesel-Battery System for Rural Electrification

A. Yahiaoui, A. Tlemçani, A. Kouzou
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引用次数: 2

Abstract

Photovoltaic and wind energy are the most promising as a future energy technology and can be classified as a clean sources of electric energy in the world. Size optimization of the hybrid renewable energy system play an important role in minimizing the total cost of the system (TCS) and suitable load supply. The main focus of this research is to develop the efficient approach for the optimization of hybrid renewable energy system composed by photovoltaic area, wind turbine (WT), diesel generator (DG) and battery bank (BB). For this purpose, this paper proposes a new metaheuristic technique called modified grey wolf optimizer (M-GWO) for minimize the TCS of the hybrid system considering power balanced between the components. The study of reliability with loss power supply probability (LPSP), the energy not supplied (ENS) and the reliability of the power supply (RPS) methods are demonstrated. For improving the high exploration and exploitation to find the global optimum and robustness of our new approach the obtained results by M-GWO are compared with Grey Wolf Optimizer (GWO) and particle swarm optimization (PSO) methods.
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农村电气化风电-光伏-柴油-电池混合系统优化、电源管理及可靠性评估
光伏和风能是未来最有前途的能源技术,在世界范围内可归类为清洁的电能来源。混合可再生能源系统的规模优化对于最小化系统总成本和合理的负荷供给具有重要意义。本研究的重点是开发由光伏发电区、风力发电机组(WT)、柴油发电机组(DG)和蓄电池组(BB)组成的混合可再生能源系统的高效优化方法。为此,本文提出了一种新的元启发式方法——改进灰狼优化器(M-GWO),以考虑各部件之间的功率平衡,使混合动力系统的TCS最小。介绍了基于损失供电概率(LPSP)、无供电能量(ENS)和供电可靠性(RPS)方法的可靠性研究。为了提高该方法的全局寻优性和鲁棒性,将该方法的结果与灰狼优化器(GWO)和粒子群优化器(PSO)方法进行了比较。
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