A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study
A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study
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电力市场的基于物理的机器学习：NYISO 案例研究
DOI：
10.48550/arxiv.2304.00062
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
M. Chertkov
中科院分区：
文献类型：
--
作者：
Robert Ferrando;Laurent Pagnier;R. Mieth;Zhirui Liang;Y. Dvorkin;D. Bienstock;M. Chertkov
本文研究了实时电力市场中如何有效求解最优潮流问题。所提出的解决方案被命名为物理知情市场感知主动集学习OPF (PIMA-AS-OPF)，利用物理约束和市场特性来确保市场出清结果的物理和经济可行性。具体来说，PIMA-AS-OPF采用了主动集学习技术，并扩展了其能力，以考虑负荷或可再生发电中的缩减，这是现实世界电力系统中常见的挑战。PIMA-AS-OPF的核心是一个以网络负载和系统拓扑为输入的全连接神经网络。该神经网络的输出包括主动约束，如饱和发电机和输电线路，以及非零负荷削减和弃风。这些输出允许将原始的市场出清优化简化为线性方程系统，可以有效地求解并产生调度决策和位置边际价格（LMPs）。然后，根据有效市场出清结果的要求，测试调度决策和lmp的可行性。在具有当前和未来可再生能源渗透水平的现实1814总线NYISO系统上测试了所提出方法的准确性和可扩展性。
This paper addresses the challenge of efficiently solving the optimal power flow problem in real-time electricity markets. The proposed solution, named Physics-Informed Market-Aware Active Set learning OPF (PIMA-AS-OPF), leverages physical constraints and market properties to ensure physical and economic feasibility of market-clearing outcomes. Specifically, PIMA-AS-OPF employs the active set learning technique and expands its capabilities to account for curtailment in load or renewable power generation, which is a common challenge in real-world power systems. The core of PIMA-AS-OPF is a fully-connected neural network that takes the net load and the system topology as input. The outputs of this neural network include active constraints such as saturated generators and transmission lines, as well as non-zero load shedding and wind curtailments. These outputs allow for reducing the original market-clearing optimization to a system of linear equations, which can be solved efficiently and yield both the dispatch decisions and the locational marginal prices (LMPs). The dispatch decisions and LMPs are then tested for their feasibility with respect to the requirements for efficient market-clearing results. The accuracy and scalability of the proposed method is tested on a realistic 1814-bus NYISO system with current and future renewable energy penetration levels.
学习解决 DCOPF：二元性方法
影响因子：
3.9
作者：
Chen, Yize;Zhang, Ling;Zhang, Baosen
通讯作者：
Zhang, Baosen
DOI：
10.1016/j.epsr.2022.108566
发表时间：
2021-12
期刊：
ArXiv
影响因子：
--
作者：
Wenbo Chen;Seonho Park;Mathieu Tanneau;P. V. Hentenryck
通讯作者：
Wenbo Chen;Seonho Park;Mathieu Tanneau;P. V. Hentenryck
用于学习电力市场实时价格的图神经网络
DOI：
--
发表时间：
2021
期刊：
International Conference on Machine Learning Workshop on Climate Change AI
影响因子：
--
作者：
Liu, Shaohui;Wu, Chengyang;Zhu, Hao
通讯作者：
Zhu, Hao