Multi-objective real-time power flow control method using soft actor-critic

US11336092B2 · kind B2 · utility

Inventors

• Ruisheng Diao · San Jose, US
• Di Shi · Taizhou City, CN
• Bei Zhang · Mountain View, US
• Siqi Wang · McLean, US
• Haifeng Li · Zhejiang, CN
• Chunlei Xu · Nanjing, CN
• Desong Bian · San Jose, US
• Jiajun Duan · Hubei, CN
• Haiwei Wu · San Jose, US

Key dates

• Filing date: Nov 9, 2020
• Grant date: May 17, 2022
• Expiry date: Nov 9, 2040

Classification

• Technology area (CPC Y): Emerging Cross-Sectional Technologies
• CPC primary: Y04S40/20
• WIPO field: Control
• WIPO sector: Instruments

Abstract

Systems and methods are disclosed for control voltage profiles, line flows and transmission losses of a power grid by forming an autonomous multi-objective control model with one or more neural networks as a Deep Reinforcement Learning (DRL) agent; training the DRL agent to provide data-driven, real-time and autonomous grid control strategies; and coordinating and optimizing power controllers to regulate voltage profiles, line flows and transmission losses in the power grid with a Markov decision process (MDP) operating with reinforcement learning to control problems in dynamic and stochastic environments.