论文标题
部分可观测时空混沌系统的无模型预测
Learning Regionally Decentralized AC Optimal Power Flows with ADMM
论文作者
论文摘要
下一代智能电网的一个潜在未来是使用分散的优化算法和有安全的通信来协调可再生能源生成（例如，风/太阳能），可调度设备（例如煤炭/天然气/核代国家），需求响应，电池和存储设施，电池和储存设施和拓扑优化。乘数的交替方向方法（ADMM）已在社区中广泛使用，以解决这种分散的优化问题，尤其是AC最佳功率流（AC-OPF）。本文研究了机器学习如何有助于加快ADMM解决AC-OPF的融合。它提出了一种新型的分散机器学习方法，即ML-ADMM，每个代理都使用深度学习来学习耦合分支上的共识参数。本文还探讨了仅从具有高质量收敛属性的ADMM运行中学习的想法，并提出了过滤机制以选择这些运行。基于法国系统的测试案例的实验结果证明了该方法在加速ADMM的收敛性方面的潜力。
One potential future for the next generation of smart grids is the use of decentralized optimization algorithms and secured communications for coordinating renewable generation (e.g., wind/solar), dispatchable devices (e.g., coal/gas/nuclear generations), demand response, battery & storage facilities, and topology optimization. The Alternating Direction Method of Multipliers (ADMM) has been widely used in the community to address such decentralized optimization problems and, in particular, the AC Optimal Power Flow (AC-OPF). This paper studies how machine learning may help in speeding up the convergence of ADMM for solving AC-OPF. It proposes a novel decentralized machine-learning approach, namely ML-ADMM, where each agent uses deep learning to learn the consensus parameters on the coupling branches. The paper also explores the idea of learning only from ADMM runs that exhibit high-quality convergence properties, and proposes filtering mechanisms to select these runs. Experimental results on test cases based on the French system demonstrate the potential of the approach in speeding up the convergence of ADMM significantly.
