Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models
Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models
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DOI：
10.3390/en16041636
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发表时间：
2023-02
期刊：
影响因子：
3.2
通讯作者：
Dhaval Dalal;Muhammad Bilal;Hritik Shah;Anwarul Islam Sifat;A. Pal;Philip Augustin
中科院分区：
文献类型：
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作者：
Dhaval Dalal;Muhammad Bilal;Hritik Shah;Anwarul Islam Sifat;A. Pal;Philip Augustin
生成真实场景是分析富可再生能源电力系统可靠性的重要前提。本文满足了这一需求，提出了一个端到端的无模型的方法来创建有代表性的电力系统场景的季节性的基础上。条件递归生成对抗网络作为场景生成的主要引擎。与独立处理变量或专注于短期预测的先前情景生成模型相比，所提出的隐式生成模型有效地捕获了考虑长期规划的变量之间存在的互相关性。使用所提出的方法产生的场景的有效性证明通过广泛的统计评估和调查的最终应用结果。结果表明，分析异常的情况下，这是更关键的电力系统资源规划，受益最多的交叉相关的场景生成。
Generation of realistic scenarios is an important prerequisite for analyzing the reliability of renewable-rich power systems. This paper satisfies this need by presenting an end-to-end model-free approach for creating representative power system scenarios on a seasonal basis. A conditional recurrent generative adversarial network serves as the main engine for scenario generation. Compared to prior scenario generation models that treated the variables independently or focused on short-term forecasting, the proposed implicit generative model effectively captures the cross-correlations that exist between the variables considering long-term planning. The validity of the scenarios generated using the proposed approach is demonstrated through extensive statistical evaluation and investigation of end-application results. It is shown that analysis of abnormal scenarios, which is more critical for power system resource planning, benefits the most from cross-correlated scenario generation.