Deep reinforcement learning based model-free optimization for unit commitment against wind power uncertainty-Reference-Cited by-同舟云学术

Deep reinforcement learning based model-free optimization for unit commitment against wind power uncertainty

Published:2024-01 Issue: Volume:155 Page:109526
ISSN:0142-0615
Container-title:International Journal of Electrical Power & Energy Systems
language:en
Short-container-title:International Journal of Electrical Power & Energy Systems

Author:

Xu Guilei^ORCID,Lin Zhenjia,Wu Qiuwei^ORCID,Chan Wai Kin Victor^ORCID,Zhang Xiao-Ping

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Reference37 articles.

1. Unit commitment-a bibliographical survey;Padhy;IEEE Trans Power Syst,2004

2. On the complexity of the unit commitment problem;Bendotti;Ann Oper Res,2019

3. A systematic review on power systems planning and operations management with grid integration of transportation electrification at scale;Zhang;Adv Appl Energy,2023

4. Power system planning with increasing variable renewable energy: A review of optimization models;Deng;J Clean Prod,2020

5. Robust unit commitment with wind power and pumped storage hydro;Jiang;IEEE Trans Power Syst,2011

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An objective feasibility pump method for optimal power flow with unit commitment variables;Electric Power Systems Research;2024-11

2. Feasible-enabled integer variable warm start strategy for security-constrained unit commitment;International Journal of Electrical Power & Energy Systems;2024-09

3. The Rise and Advancement;Advances in Logistics, Operations, and Management Science;2024-08-23

4. Chance Constrained Day Ahead Stochastic Unit Commitment with Multiple Uncertainties;Journal of Electrical Engineering & Technology;2024-08-02

5. Data Driven Solution to Market Equilibrium via Deep Reinforcement Learning;2024 IEEE 2nd International Conference on Power Science and Technology (ICPST);2024-05-09