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ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 |
doi: 10.3389/fenrg.2024.1383376
Optimal scheduling study of green warehousing microgrid based on improved sparrow search algorithm
Provisionally accepted- 1 State Grid Liaoning Electric Power Co., Ltd, Shenyang, Liaoning Province, China
- 2 School of Electrical Engineering., Shenyang University of Technology, Shenyang, Liaoning Province, China
Combining green warehousing with wind-solar-storage systems can improve the current situation of green warehousing with economic power consumption, energy saving, and emission reduction. To realize the efficient and stable operation of the wind-solar-storage microgrid, this paper proposes a microgrid optimal scheduling strategy based on the Improved Sparrow Algorithm (ISSA). Firstly, a comprehensive benefit model is established based on the economic and environmental benefits of microgrid daily operation. Then, an innovative improved sparrow search algorithm is proposed, which aims to improve the global search and local search capability of the microgrid scheduling problem by introducing improvements such as Logistic-Circle chaotic mapping, Bottle Sea Sheath swarm optimization algorithm, dynamic inertia weights, water wave dynamic factor, and Cauchy-Gaussian variational strategy. Finally, the microgrid optimal scheduling model is solved by the improved sparrow search algorithm and compared with other algorithms. In this paper, Matlab 2016b is used for simulation, and the simulation results show that the ISSA algorithm outperforms other algorithms in terms of solution stability and optimization search capability. Under three modes of operation, ISSA improves the microgrid operation revenue by 6.29%, 5.98%, and 6.31% at least. Therefore, the optimal scheduling scheme obtained based on ISSA improves the daily operating total revenue and the system operation stability of the microgrid.
Keywords: Green warehousing, Wind-solar-storage, improved sparrow search algorithm, Optimal scheduling, Economic optimization
Received: 07 Feb 2024; Accepted: 30 Aug 2024.
Copyright: © 2024 Liu, Zhang, Zhang, Zhang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Liyang Liu, State Grid Liaoning Electric Power Co., Ltd, Shenyang, 110004, Liaoning Province, China
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