A Dual-Layer MPC of Coordinated Control of Battery Load Demand and Grid-Side Supply Matching at Electric Vehicle Swapping Stations

Minan Tang (), Chenchen Zhang, Yaqi Zhang, Yaguang Yan, Wenjuan Wang and Bo An
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Minan Tang: College of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Chenchen Zhang: College of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Yaqi Zhang: College of Electrical and Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Yaguang Yan: College of Electrical and Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Wenjuan Wang: College of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Bo An: College of Electrical and Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Energies, 2024, vol. 17, issue 4, 1-26

Abstract: The uncontrolled charging of electric vehicles may cause damage to the electrical system as the number of electric vehicles continues to rise. This paper aims to construct a new model of the power system and investigates the rational regulation and efficient control of electric vehicle battery charging at electric vehicle exchange battery stations in response to the real-time grid-side supply situation. Firstly, a multi-objective optimization strategy is established to meet the day-ahead forecasted swap demand and grid-side supply with the maximization of day-ahead electric vehicle battery swapping station (BSS) revenue in the core. Secondly, considering the variable tariff strategy, a two-layer Model Predictive Control (MPC) coordinated control system under real-time conditions is constructed with the objective function of maximizing the revenue of BSS and smoothing the load fluctuation of the power system. Then, the day-ahead optimization results are adopted as the reference value for in-day rolling optimization, and the reference value for in-day optimization is dynamically adjusted according to the real-time number of electric car changes and power system demand. Finally, verified by experimental simulation, the results show that the day-ahead-intraday optimization model can increase the economic benefits of BSS and reduce the pressure on the grid to a certain extent, and it can ensure the fast, accurate, and reasonable allocation of batteries in BSS, and realize the flexible, efficient, and reasonable distribution of batteries in BSS.

Keywords: electric vehicles; battery swapping station; model predictive control; multi-stage optimization; peak shaving (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: 2024
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