Optimal Scheduling of the Wind-Photovoltaic-Energy Storage Multi-Energy Complementary System Considering Battery Service Life

Yanpin Li (), Huiliang Wang, Zichao Zhang, Huawei Li, Xiaoli Wang, Qifan Zhang, Tong Zhou, Peng Zhang and Fengxiang Chang
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Yanpin Li: College of Energy and Power Engineering, North China University of Water Resources and Electronic Power, Zhengzhou 450045, China
Huiliang Wang: College of Energy and Power Engineering, North China University of Water Resources and Electronic Power, Zhengzhou 450045, China
Zichao Zhang: College of Energy and Power Engineering, North China University of Water Resources and Electronic Power, Zhengzhou 450045, China
Huawei Li: College of Energy and Power Engineering, North China University of Water Resources and Electronic Power, Zhengzhou 450045, China
Xiaoli Wang: College of Energy and Power Engineering, North China University of Water Resources and Electronic Power, Zhengzhou 450045, China
Qifan Zhang: College of Energy and Power Engineering, North China University of Water Resources and Electronic Power, Zhengzhou 450045, China
Tong Zhou: College of Energy and Power Engineering, North China University of Water Resources and Electronic Power, Zhengzhou 450045, China
Peng Zhang: College of Energy and Power Engineering, North China University of Water Resources and Electronic Power, Zhengzhou 450045, China
Fengxiang Chang: College of Energy and Power Engineering, North China University of Water Resources and Electronic Power, Zhengzhou 450045, China

Energies, 2023, vol. 16, issue 13, 1-17

Abstract: Under the background of “peak carbon dioxide emissions by 2030 and carbon neutrality by 2060 strategies” and grid-connected large-scale renewables, the grid usually adopts a method of optimal scheduling to improve its ability to cope with the stochastic and volatile nature of renewable energy and to increase economic efficiency. This article proposes a short-term optimal scheduling model for wind–solar storage combined-power generation systems in high-penetration renewable energy areas. After the comprehensive consideration of battery life, energy storage units, and load characteristics, a hybrid energy storage operation strategy was developed. The model uses the remaining energy in the system after deducting wind PV and energy storage output as the “generalized load”. An improved particle swarm optimization (PSO) is used to solve the scheduling schemes of different running strategies under different objectives. The optimization strategy optimizes the battery life-loss coefficient from 0.073% to 0.055% under the target of minimizing the mean squared deviation of “generalized load”, which was optimized from 0.088% to 0.053% under the minimized fluctuation of combined system output and optimized from 0.092% to 0.081% under the minimized generation costs of the combined system. The results show that the model can ensure a stable operation of the combined system, and the operation strategy proposed in this article effectively reduces battery life loss while reducing the total power generation cost of the system. Finally, the superiority of the improved PSO algorithm was verified.

Keywords: renewable energy; hybrid energy storage; IPSO algorithm; optimal scheduling; multi-energy complementary (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: 2023
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