scholarly journals Stochastic optimization framework for online scheduling of an EV charging station in a residential place with photovoltaics and energy storage system

Author(s):  
Gustavo Aragon ◽  
Otilia Werner-Kytola ◽  
Erdem Gumrukcu
Author(s):  
Yang Chen ◽  
Fadwa Dababneh ◽  
Bei Zhang ◽  
Saiid Kassaee ◽  
Brennan T. Smith ◽  
...  

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.


Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3152
Author(s):  
Leon Fidele Nishimwe H. ◽  
Sung-Guk Yoon

Sufficient and convenient fast-charging facilities are crucial for the effective integration of electric vehicles. To construct enough fast electric vehicle-charging stations, station owners need to earn a reasonable profit. This paper proposed an optimization framework for profit maximization, which determined the combined planning and operation of the charging station considering the vehicle arrival pattern, intermittent solar photovoltaic generation, and energy storage system management. In a planning horizon, the proposed optimization framework finds an optimal configuration of a grid-connected charging station. Besides, during the operation horizon, it determines an optimal power scheduling in the charging station. We formulated an optimization framework to maximize the expected profit of the station. Four types of costs were considered during the planning period: the investment cost, operational cost, maintenance cost, and penalties. The penalties arose from vehicle customers’ dissatisfaction associated with waiting time in queues and rejection by the station. The simulation results showed the optimal investment configuration and daily power scheduling in the charging station in various environments such as the downtown, highway, and public stations. Furthermore, it was shown that the optimal configuration was different according to the environments. In addition, the effectiveness of solar photovoltaic, energy storage system, and queue management was demonstrated in terms of the optimal solution through a sensitivity analysis.


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