Day-Ahead Operation Analysis of Wind and Solar Power Generation Coupled with Hydrogen Energy Storage System Based on Adaptive Simulated Annealing Particle Swarm Algorithm

Kang Chen, Huaiwu Peng (), Zhenxin Gao, Junfeng Zhang, Pengfei Chen, Jingxin Ruan, Biao Li and Yueshe Wang ()
Additional contact information
Kang Chen: Institute of Solar Engineering Technology, Northwest Engineering Corporation Limited, PowerChina, Xi’an 710065, China
Huaiwu Peng: Institute of Solar Engineering Technology, Northwest Engineering Corporation Limited, PowerChina, Xi’an 710065, China
Zhenxin Gao: Institute of Solar Engineering Technology, Northwest Engineering Corporation Limited, PowerChina, Xi’an 710065, China
Junfeng Zhang: Institute of Solar Engineering Technology, Northwest Engineering Corporation Limited, PowerChina, Xi’an 710065, China
Pengfei Chen: Institute of Solar Engineering Technology, Northwest Engineering Corporation Limited, PowerChina, Xi’an 710065, China
Jingxin Ruan: State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Biao Li: State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Yueshe Wang: State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Energies, 2022, vol. 15, issue 24, 1-15

Abstract: As the low-carbon economy continues to evolve, the energy structure adjustment of using renewable energies to replace fossil fuel energies has become an inevitable trend. To increase the ratio of renewable energies in the electric power system and improve the economic efficiency of power generation systems based on renewables with hydrogen production, in this paper, an operation optimization model of a wind–solar hybrid hydrogen energy storage system is established based on electrochemical energy storage and hydrogen energy storage technology. The adaptive simulated annealing particle swarm algorithm is used to obtain the solution, and the results are compared with the standard particle swarm algorithm. The results show that the day-ahead operation scheme solved by the improved algorithm can save about 28% of the system operating cost throughout the day. The analytical results of the calculation example revealed that the established model had fully considered the actual operational features of devices in the system and could reduce the waste of wind and solar energy by adjusting the electricity purchased from the power grid and the charge and discharge powers of the storage batteries under the mechanism of time-of-use electricity price. The optimization of the day-ahead scheduling of the system achieved the minimization of daily system operation costs while ensuring that the hydrogen-producing power could meet the hydrogen demand.

Keywords: energy structure; hydrogen energy storage; optimization model of day-ahead scheduling; time-of-use electricity price mechanism; daily operation cost (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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