Deep Reinforcement Learning-Based Scheduling Optimization of a Wind-solar Coupled CHP Unit System Considering Source-load Uncertainty-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning-Based Scheduling Optimization of a Wind-solar Coupled CHP Unit System Considering Source-load Uncertainty

Published:2024-05-10 Issue: Volume:50 Page:879-884
ISSN:
Container-title:2024 3rd Conference on Fully Actuated System Theory and Applications (FASTA)
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Author:

Wang Baohua¹,Gao Xinglong¹,Wang Xianlian²,Wang Zhixiao²,Sun Li²,Hua Shan¹

Affiliation:

1. China Energy Science and Technology Research Institute Co.Ltd.,State Key Laboratory of Low-Carbon Smart Coal-Fired Power Generation and Ultra-Clean Emission,Nanjing,China,210023

2. Southeast University,National Engineering Research Center of Power Generation Control and Safety, School of Energy and Environment,Nanjing,China,210096

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10595059/10595098/10595106.pdf?arnumber=10595106

Reference17 articles.

1. Sustainable conditions for the development of renewable energy systems: A triple bottom line perspective

2. A hybrid framework for forecasting power generation of multiple renewable energy sources

3. Operation optimization for integrated energy system based on hybrid CSP-CHP considering power-to-gas technology and carbon capture system

4. Energy, exergy, economic and environmental (4E) analyses of an integrated system based on CH-CAES and electrical boiler for wind power penetration and CHP unit heat-power decoupling in wind enrichment region

5. Optimal clean heating mode of the integrated electricity and heat energy system considering the comprehensive energy-carbon price

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