Learning Warm-Start Points For Ac Optimal Power Flow
Learning Warm-Start Points For Ac Optimal Power Flow
复制标题
学习交流最佳功率流的热启动点
DOI：
10.1109/mlsp.2019.8918690
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
K. Baker
中科院分区：
文献类型：
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作者：
K. Baker
多年来，电网运营商解决交流最优潮流（ACOPF）问题产生了大量数据，我们探索如何利用这些数据来帮助解决未来的ACOPF问题。我们使用这些数据来训练随机森林来预测未来ACOPF问题的解决方案。为了保持预测变量之间的相关性和关系，我们利用多目标方法直接通过仅使用网络负载来学习ACOPF问题的近似电压和发电解，而无需了解其他网络参数或系统拓扑。我们探索了使用学习解作为求解ACOPF的智能热起点的好处，并使用多个IEEE测试网络对所提出的框架进行了数值评估。使用学习的ACOPF解决方案的好处被证明是求解器和网络相关的，但也显示了快速找到ACOPF问题近似解的希望。
A large amount of data has been generated by grid operators solving AC optimal power flow (ACOPF) throughout the years, and we explore how leveraging this data can be used to help solve future ACOPF problems. We use this data to train a Random Forest to predict solutions of future ACOPF problems. To preserve correlations and relationships between predicted variables, we utilize a multi-target approach to learn approximate voltage and generation solutions to ACOPF problems directly by only using network loads, without the knowledge of other network parameters or the system topology. We explore the benefits of using the learned solution as an intelligent warm start point for solving the ACOPF, and the proposed framework is evaluated numerically using multiple IEEE test networks. The benefit of using learned ACOPF solutions is shown to be solver and network dependent, but shows promise for quickly finding approximate solutions to the ACOPF problem.