Global Energy Interconnection Research Institute Co. Ltd

Other

Battelle Memorial Institute

State Grid Jiangsu Electric Power Company

State Grid Beijing Electric Power Company

Global Energy Interconnection Research Institute North America

State Grid Corporation of China Co., Ltd

State Grid Shanxi Electric Power Company

State Grid Coporation of China Co. Ltd

Ruisheng Diao

Di Shi

Haifeng Li

Chunlei Xu

Siqi Wang

Xiaohu Zhang

Desong Bian

Bei Zhang

Zhiwei Wang

Zhehan Yi

Qibing Zhang

Renke Huang

Jiajun Duan

Xinya Li

Qiuhua Huang

Xiaoying Zhao

Haiwei Wu

Yingzhong Gu

Guanyu Tian

Emily L Barrett

James G O'Brien

Zhangshuan Hou

Zheming Liang

Xiaoyuan Fan

A system with deep reinforcement learning based control determines optimal actions for major components in a commercial building to minimize operation costs while maximizing comprehensive comfort levels of occupants. An unsupervised deep Q-network method is introduced to handle the energy management problem by evaluating the influence of operation costs on comfort levels considering the environment factors at each time slot. An optimum control decision can be derived that targets both immediate and long-term goals, where exploration and exploitation are considered simultaneously.

Deep reinforcement learning based real-time scheduling of Energy Storage System (ESS) in commercial campus

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.

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

State estimation in an electric power system includes acquiring electrical measurements from the electric power system at a reporting rate of the electrical measurements, processing the electrical measurements into sequence data including positive sequence data, processing positive sequence data by an observable state estimator to generate a plurality of estimated states including a plurality of estimated observable states, parameters of the observable state estimator being updated by a first training module in a first time thread, processing the plurality of estimated states by an unobservable state estimator to generate a plurality of estimated unobservable states, parameters of the unobservable state estimator being updated by a second training module in a second time thread independent of the first time thread, and outputting a plurality of final estimated states generated by concatenating the plurality of estimated observable states and the plurality of estimated unobservable states.

Systems and methods of power system state estimation

Techniques and apparatuses are described that enable transformative Remedial Action Scheme (RAS) analyses and methodologies for a bulk electric power system, including methods of designing, reviewing, revising, testing, implementing, verifying, or validating a RAS. An improved RAS improves operation of the power system, including performance, reliability, control, and asset utilization. The example methodologies discussed&#x2014;also referred to as a transformative Remedial Action Scheme tool (TRAST)&#x2014;provide an end-to-end solution for adaptively setting RAS parameters based on realistic and near real-time operation conditions to improve power grid reliability and grid asset utilization, by leveraging utility data analysis and employing dynamic simulations and machine learning to significantly simplify and shorten the entire RAS process.

Transformative remedial action scheme tool (TRAST)

Systems and methods are disclosed to control a power inverter by extracting photovoltaic (PV) maximum power under a plurality of operating conditions as one or more reference variables; predicting the future behaviors of a PV output voltage as one or more controlled variables based on a current operational condition; generating one or more control signals for the H5 inverter that minimizes an error between the reference variable and the controlled variables; and controlling the power inverter with the one or more control signals.

MPC-based PV maximum power point tracker for transformerless H5 inverter with leakage current reduction

Growing community of inventors

Richland, WA, United States of America

Ruisheng Diao