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On recent advances in PV output power forecast

机译:关于光伏输出功率预测的最新进展

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In last decade, the higher penetration of renewable energy resources (RES) in energy market was encouraged by implementing the energy polices in several developed and developing countries due to increasing environmental concerns. Among wide range of RES, Photovoltaic (PV) electricity generation get higher attention by researcher, energy policy makers and power production companies due to its economic and environmental benefits. Therefore, a large PV penetration was observed in energy market with rapid growth in the last decade. The PV output power is highly uncertain due to several meteorological factors such as temperature, wind speed, cloud cover, atmospheric aerosol levels and humidity level. The inherent variability of PV output power creates different issues directly or indirectly for power grid such as power system control and reliability, reserves cost, dispatchable and ancillary generation, grid integration and power planning. Therefore, there is need to accurately forecast the PV output over the spectrum of forecast horizon at different chronological scales. In this paper, a comprehensive and systematic review of PV output power forecast models were provided. This review covers the different factors affecting PV forecast, PV output power profile and performance matrices to evaluate the forecast model. The critical analysis regressive and artificial intelligence based forecast models are also presented. In addition, the potential benefits of hybrid techniques for PV forecast models are also thoroughly discussed. (C) 2016 Elsevier Ltd. All rights reserved.
机译:在过去的十年中,由于对环境的日益关注,在一些发达国家和发展中国家实施了能源政策,这鼓励了可再生能源在能源市场中的更高普及率。在广泛的RES中,由于其经济和环境效益,光伏(PV)发电受到研究人员,能源政策制定者和电力生产公司的更多关注。因此,在过去十年中,在能源市场中观察到了较大的PV渗透,并且增长迅速。由于多种气象因素,例如温度,风速,云量,大气气溶胶水平和湿度水平,光伏输出功率高度不确定。光伏输出功率的固有可变性直接或间接给电网带来了不同的问题,例如电力系统的控制和可靠性,储备成本,可调度和辅助发电,电网集成和电力规划。因此,需要在不同的时间尺度上准确地预测整个预测水平范围内的PV输出。在本文中,对光伏输出功率预测模型进行了全面而系统的审查。这篇综述涵盖了影响光伏预测,光伏输出功率曲线和评估预测模型的性能矩阵的不同因素。还提出了基于临界分析回归和人工智能的预测模型。此外,还充分讨论了混合技术对PV预测模型的潜在好处。 (C)2016 Elsevier Ltd.保留所有权利。

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