| Issue |
Sci. Tech. Energ. Transition
Volume 79, 2024
Emerging Advances in Hybrid Renewable Energy Systems and Integration
|
|
|---|---|---|
| Article Number | 80 | |
| Number of page(s) | 13 | |
| DOI | https://doi.org/10.2516/stet/2024045 | |
| Published online | 10 October 2024 | |
Regular Article
The optimal dispatching strategy of cogeneration based on Deep Q-Network (DQN) algorithm
1
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510000, Guangdong, China
2
Guangdong DangTec Industrial Technology Co., Ltd., Guangzhou 510000, Guangdong, China
* Corresponding author: 1122201051@mail2.gdut.edu.cn; pei_zhang67@outlook.com
Received:
13
April
2024
Accepted:
11
June
2024
This work expands on previous research to offer a state-of-the-art approach for optimizing the dispatching of cogeneration systems, given the limitations faced by conventional coal-fired cogeneration units and the increasing environmental standards. Acknowledging the constraints of flexibility in winter heating, the study aims to improve unit coal use optimization and lower emissions. The paper presents a novel optimization approach for distributing electricity and heat in cogeneration units, utilizing the Deep Q-Network (DQN) algorithm. The suggested approach reduces operating expenses and improves system dependability using a sixth-order function fitting and fuzzy set space division. The study’s results indicate a significant 8.96% increase in performance, demonstrating the effectiveness of the DQN-based strategy in enabling cost-effective scheduling in cogeneration systems. This research offers a road towards sustainable and effective energy use and contributes to the development of cogeneration technology. It also has potential applications in natural energy systems.
Key words: DQN algorithm optimization / Cogeneration / Optimize scheduling / Backbone particle swarm / CHP system / Intelligent sports environment system
Publisher note: This article type has been corrected as Regular Article on 10 January 2025.
© The Author(s), published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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