Abstract Renewable energy sources (RESs) are considered to be reliable and green electric power generation sources. Photovoltaics (PVs) and wind turbines (WTs) are used to provide electricity in remote areas. Optimal sizing of hybrid RESs is a vital challenge in a stand-alone environment. The meta-heuristic algorithms proposed in the past are dependent on algorithm-specific parameters for achieving an optimal solution. This paper proposes a hybrid algorithm of Jaya and a teaching–learning-based optimization (TLBO) named the JLBO algorithm for the optimal unit sizing of a PV–WT–battery hybrid system to satisfy the consumer’s load at minimal total annual cost (TAC). The reliability of the system is considered by a maximum allowable loss of power supply probability (LPSPmax) concept. The results obtained from the JLBO algorithm are compared with the original Jaya, TLBO, and genetic algorithms. The JLBO results show superior performance in terms of TAC, and the PV–WT–battery hybrid system is found to be the most economical scenario. This system provides a cost-effective solution for all proposed LPSPmax values as compared with PV–battery and WT–battery systems.

中文翻译：

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基于 Jaya 学习的优化，用于优化独立光伏、风力涡轮机和电池系统的尺寸

摘要 可再生能源（RES）被认为是可靠的绿色发电来源。光伏 (PV) 和风力涡轮机 (WT) 用于为偏远地区提供电力。混合 RES 的最佳尺寸是独立环境中的一项重要挑战。过去提出的元启发式算法依赖于算法特定的参数来实现最优解。本文提出了一种 Jaya 混合算法和一种名为 JLBO 算法的基于教学的优化 (TLBO) 算法，用于优化 PV-WT-电池混合系统的单元尺寸，以最小的年总成本 (TAC) 满足消费者的负载. 系统的可靠性是通过最大允许的电源损耗概率 (LPSPmax) 概念来考虑的。将 JLBO 算法获得的结果与原始 Jaya、TLBO 和遗传算法进行了比较。JLBO 结果显示出在 TAC 方面的优越性能，并且发现 PV-WT-电池混合系统是最经济的方案。与 PV 电池和 WT 电池系统相比，该系统为所有提议的 LPSPmax 值提供了具有成本效益的解决方案。