Sizing a stand-alone solar-wind-hydrogen energy system using weather forecasting and a hybrid search optimization algorithm
Sizing a stand-alone solar-wind-hydrogen energy system using weather forecasting and a hybrid search optimization algorithm
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DOI：
10.1016/j.enconman.2018.08.102
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发表时间：
2019-01
影响因子：
10.4
通讯作者：
Weiping Zhang;A. Maleki;M. Rosen;Jing-qing Liu
中科院分区：
文献类型：
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作者：
Weiping Zhang;A. Maleki;M. Rosen;Jing-qing Liu
由于岛屿和偏远地区的能源需求和化石燃料成本不断增加，可再生能源正变得越来越有吸引力。这些资源的杂交可以克服它们的可变性和间断性，提高效率。许多独立的混合可再生能源系统用于偏远和岛屿地区，这些地区通常无法获得天气数据。为了提高此类系统尺寸优化的准确性，需要更准确的天气数据，而使用天气预报数据是有帮助的。本文提出了一种基于混沌搜索、和谐搜索和模拟退火三种算法的独立太阳能和风能混合系统最优规模的混合优化算法。为了提高尺寸优化算法结果的准确性，将天气预报与人工神经网络相结合，对太阳辐射、环境温度和风速进行预报。在考虑系统可靠性的情况下，以总寿命周期成本最小为主要目标函数来评估混合可再生能源系统的可行性。采用失电概率参数来评估系统的可靠性。新方法在伊朗呼罗珊市的电力负荷中进行了测试。结果与所提算法（和谐搜索和基于模拟退火的人工神经网络）的结果进行了比较。仿真结果表明，将混合优化算法与天气预报数据结合应用于单机混合可再生能源系统具有一定的优越性。
Due to increasing energy demand and fossil fuel costs in island and remote areas, renewable energy resources are becoming increasingly attractive. The hybridization of these resources can help overcome their variability and intermittency and improve efficiency. Many independent hybrid renewable energy systems are used in remote and island areas for which weather data often is unavailable. To increase the accuracy of size optimization of such systems, more accurate weather data is needed and the use of weather forecasting data is helpful. In this article, a new hybrid optimization algorithm is proposed for the optimal sizing of a stand-alone hybrid solar and wind energy system based on three algorithms: chaotic search, harmony search and simulated annealing. To improve the accuracy of the size optimization algorithm results, weather forecasting is used along with artificial neural networks for solar radiation, ambient temperature, and wind speed forecasting. The main objective function of minimizing the total life cycle cost is used to assess the feasibility of the hybrid renewable energy system accounting for system reliability. The reliability of the system is assessed by the loss of power supply probability parameter. The new method is tested for the electrical load of the city of Khorasan, Iran. The results are compared with those obtained by the proposed algorithm (harmony search and simulated annealing-based artificial neural networks). The simulation results demonstrate the advantages of utilizing the hybrid optimization algorithm with weather forecasting data for a stand-alone hybrid renewable energy system.