Distributionally Robust Optimal Power Flow with Uncertain Renewable Energy Output
Distributionally Robust Optimal Power Flow with Uncertain Renewable Energy Output
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发表时间：
2023-06
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通讯作者：
Jia Yang;Jun Song;Chaoyue Zhao
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作者：
Jia Yang;Jun Song;Chaoyue Zhao
最优潮流（OPF）是独立系统运营商（iso）进行发电管理的重要工具。随着可再生能源越来越多地渗透到电网中，由于可再生能源输出的间歇性，在解决OPF问题方面出现了挑战。为了解决这些问题，我们开发了一种多阶段分布式鲁棒方法来解决直流最优潮流（DC-OPF）问题，以最大限度地降低可再生能源不确定性下的总发电成本。在我们的模型中，我们假设可再生能源输出遵循一个模糊的分布，可以用一个置信集来表征。该方法可以在不限制可再生能源输出分配到任何特定分配类别的情况下，通过顺序利用揭示的数据，提供可靠且鲁棒的最优OPF决策。计算结果也验证了该方法在降低保守性的同时保持可靠性的有效性。
Optimal power flow (OPF) is an important tool for Independent System Operators (ISOs) to deal with the power generation management. With the increasing penetration of renewable energy into power grids, challenges arise in tackling the OPF problem due to the intermittent nature of renewable energy output. To address these challenges, we develop a multi-stage distributionally robust approach for the direct-current optimal power flow (DC-OPF) problem to minimize total generation cost under renewable energy uncertainty. In our model, we assume the renewable energy output follows an ambiguous distribution that can be characterized by a confidence set. By utilizing the revealed data sequentially, the proposed approach can provide a reliable and robust optimal OPF decision without restricting the renewable energy output distribution to any particular distribution class. The computational results also verify the effectiveness of our approach to reduce the conservativeness and meanwhile maintain the reliability.