scholarly journals Optimal Operation Strategy of Energy Storage System in PV-integrated EV Charging Station Based on improved NSGA-II

2021 ◽  
Vol 1754 (1) ◽  
pp. 012035
Author(s):  
Shanshan Shi ◽  
Yu Zhang ◽  
Zhangjie Fu ◽  
Qin Xu ◽  
Qixing Yang ◽  
...  
Keyword(s):  
Energy Storage ◽  
Storage System ◽  
Energy Storage System ◽  
Optimal Operation ◽  
Operation Strategy ◽  
Nsga Ii ◽  
Charging Station ◽  
Ev Charging

scholarly journals Surrogate Modeling for Capacity Planning of Charging Station Equipped With PV and Hydropneumatic Energy Storage

2019 ◽  
Author(s):  
Yang Chen ◽  
Fadwa Dababneh ◽  
Bei Zhang ◽  
Saiid Kassaee ◽  
Brennan T. Smith ◽  
...  
Keyword(s):  
Energy Storage ◽  
Electric Vehicles ◽  
Capacity Planning ◽  
Storage System ◽  
Energy Storage System ◽  
Loss System ◽  
Charging Station ◽  
Operational Decision Making ◽  
Erlang Loss ◽  
Ev Charging

Abstract Due to the promising potential for environmental sustain-ability, there has been a significant increase of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEV) in the market. To support this increasing demand for EVs and PHEVs, challenges related to capacity planning and investment costs of public charging infrastructure must be addressed. Hence, in this paper, a capacity planning problem for EV charging stations is developed and aims to balance current capital investment costs and future operational revenue. The charging station considered in this work is assumed to be equipped with solar photovoltaic panel (PV) and an energy storage system which could be electric battery or the recently invented hydro-pneumatic energy storage (GLIDES, Ground-Level Integrated Diverse Energy Storage) system. A co-optimization model that minimizes investment and operation cost is established to determine the global optimal solution while combining the capacity and operational decision making. The operational decision making considers EV mobility which is modeled as an Erlang-loss system. Meanwhile, stochastic programming is adopted to capture uncertainties from solar radiation and charging demand of the EV fleet. To provide a more general and computationally efficient model, main configuration parameters are sampled in the design space and then fixed in solving the co-optimization model. The model can be used to provide insights for charging station placement in different practical situations. The sampled parameters include: the total number of EV charging slots, the PV area, the maximum capacity of the energy storage system, and daily mean EV arrival number in the Erlang-loss system. Based on the sampled parameter combinations and its responses, black-box mappings are then constructed using surrogate models (RBF, Kriging etc). The effectiveness of proposed surrogate modeling approach is demonstrated in the numerical experiments.

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