Work

Safe-AutoSAC: AutoML-enhanced safe deep reinforcement learning for integrated energy system scheduling with multi-channel informer forecasting and electric vehicle demand response

Year: 2025

Type: article

Source: Applied Energy

DOI: 10.1016/j.apenergy.2025.126468

Authors: Yang Li, Bingsong Zhao, Yuanzheng Li, Chao Long, Sen Li, Zhaoyang Dong, Mohammad Shahidehpour

Institutions: Northeast Electric Power University, Hong Kong University of Science and Technology, Huazhong University of Science and Technology, University of Liverpool, City University of Hong Kong, Illinois Institute of Technology

Language: English

Topic: Integrated Energy Systems Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: closed

Funders: Royal Society, National Natural Science Foundation of China

Awards: G148096710, G4879990372

OpenAlex ID: W4412596705

FWCI: 7.359

Cites: 49

Cited by: 33

Related to: 10