Other

State Grid Jiangsu Electric Power Company

Global Energy Interconnection Research Institute North America

State Grid Corporation of China Co., Ltd

State Grid Shandong Electric Power Company

State Grid Jiangsu Electric Power Co., Ltd., Information and Communication Branch

State Grid Smart Research Institute Co., Ltd.

Siqi Wang

Di Shi

Ruisheng Diao

Desong Bian

Haifeng Li

Zhiwei Wang

Bei Zhang

Jing Cao

Chunlei Xu

Yishen Wang

Xiaohu Zhang

Haiwei Wu

Jiajun Duan

You Lin

Zhengyang Ding

Bo Guo

Zheming Liang

Xinan Wang

Systems and methods for generating a load model having composite load components for electric power systems are disclosed which include acquiring state information at a bus of the electric power system, acquiring a training event record, and generating the load model by sequentially executing steps of determining a final load component composition for a predetermined composite load model structure referencing to both the state information and the training event record, and determining a plurality of final load parameters corresponding to the final load component composition and the predetermined load model structure also referencing to both the state information and the training event record.

Systems and methods of composite load modeling for electric power systems

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

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Siqi Wang