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ANN-based scenario generation methodology for stochastic variables of electric power systems

机译：基于ANN的电力系统随机变量情景生成方法

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摘要

In this paper a novel scenario generation methodology based on artificial neural networks (ANNs) is proposed. The methodology is flexible and able to generate scenarios for various stochastic variables that are used as input parameters in the stochastic short-term scheduling models. Appropriate techniques for modeling the cross-correlation of the involved stochastic processes and scenario reduction techniques are also incorporated into the proposed approach. The applicability of the methodology is investigated through the creation of electric load, photovoltaic (PV) and wind production scenarios and the performance of the proposed ANN-based methodology is compared to time series-based scenario generation models. Test results on the real-world insular power system of Crete and mainland Greece present the effectiveness of the proposed ANN-based scenario generation methodology. (C) 2015 Elsevier B.V. All rights reserved.
机译：本文提出了一种新的基于人工神经网络的场景生成方法。该方法是灵活的并且能够生成用于各种随机变量的场景，这些场景用作随机短期调度模型中的输入参数。用于建模所涉及的随机过程的互相关性的适当技术和场景减少技术也被合并到所提出的方法中。通过创建电力负荷，光伏（PV）和风力发电情景来研究该方法的适用性，并将所提出的基于ANN的方法的性能与基于时间序列的情景生成模型进行比较。在克里特岛和希腊大陆的真实绝缘电力系统上的测试结果证明了所提出的基于人工神经网络的情景生成方法的有效性。 （C）2015 Elsevier B.V.保留所有权利。

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