| Issue |
E3S Web Conf.
Volume 615, 2025
2024 International Conference on Environmental Protection and Pollution Control (EPPC 2024)
|
|
|---|---|---|
| Article Number | 01005 | |
| Number of page(s) | 5 | |
| Section | Research on Environment and Ecosystem Optimisation and Management | |
| DOI | https://doi.org/10.1051/e3sconf/202561501005 | |
| Published online | 14 February 2025 | |
Environmental and Economic Dispatching of Fire-Wind Combined System Based on Improved MOPSO
1 Northeast Electric Power University, School of Computer Science, Jilin, 132011, China
2 China General Nuclear Power Group, Liaoning Hongyanhe Nuclear Power Co, Ltd., Dalian, 116319, China
* Corresponding author: 17604417200@163.com
a duruiqi0325@163.com
b luyi981216@163.com
c js08062013@163.com
The environmental and economic dispatch of fire-wind combined system aims to minimize the cost and pollution gas emissions by coordinating various generator sets while meeting the constraints. Aiming at the characteristics of this problem, this paper puts forward an environmental and economic dispatching method of fire and wind combined system. Firstly, considering the uncertainty of wind power, a combined environmental and economic dispatching model of fire and wind is constructed. Then, according to the characteristics of the model, a constraint processing method is designed to improve the solution efficiency and make full use of wind energy. Then, an improved multi-objective particle swarm optimization algorithm, LRMOPSO, is proposed by combining multi-objective particle swarm optimization algorithm, Levy flight jamming strategy and reverse learning strategy. Finally, on an improved IEEE39 test system, four excellent algorithms are selected and compared with the proposed method, which verifies the effectiveness of the proposed method. The results show that the method proposed in this paper can find the solution with lower cost and emission more effectively, and the designed constraint handling method also significantly improves the efficiency of the algorithm.
© The Authors, published by EDP Sciences, 2025
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