A Deep Reinforcement Learning Control Strategy to Improve the Operating Flexibility of Chp Units Under Variable Load Conditions-Reference-Cited by-同舟云学术

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A Deep Reinforcement Learning Control Strategy to Improve the Operating Flexibility of Chp Units Under Variable Load Conditions

Published:2023 Issue: Volume: Page:
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Author:

Wang Xin,Cui Chenggang,Pan Chunjian,Zhang Chuanlin,Ren Hongbo,Ghias Amer M. Y. M.

Publisher

Elsevier BV

Reference34 articles.

1. Analysis of energy storage demand for peak shaving and frequency regulation of power systems with high penetration of renewable energy;S Wang;Energy,2023

2. Modeling condensate throttling to improve the load change performance of cogeneration units;Z Han;Energy,2020

3. Local sharing of cogeneration energy through individually prioritized controls for increased on-site energy utilization;J Hirvonen;Applied Energy,2014

4. Offline reinforcement learning control for electricity and heat coordination in a supercritical chp unit;G Zhang;Energy,2023

5. Effect of considering demand response in optimal configuration of combined heat and power (chp);H Hosseinnia;International Journal of Ambient Energy,2018

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