scholarly journals Determining Equipment Capacity of Electric Vehicle Charging Station Operator for Profit Maximization

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2301 ◽  
Author(s):  
Se Baik ◽  
Young Jin ◽  
Yong Yoon
Keyword(s):  
Capital Investment ◽  
Storage System ◽  
Profit Maximization ◽  
Energy Storage System ◽  
Auxiliary Equipment ◽  
Charging Infrastructure ◽  
Physical Constraints ◽  
Charging Station ◽  
Study Results ◽  
Ev Charging

Related to global efforts to reduce greenhouse gases, numerous electric vehicles (EVs) are expected to be integrated to the power grid. However, the introduction of EVs, particularly in Korea, is still marginal due to the lack of EV charging infrastructure, even though various supportive policies exist. To address this shortage of EV charging stations, the EV charging business needs to be profitable. As with any business, the profitability of the EV charging business is significantly affected by the initial capital investment related to EV chargers and auxiliary equipment such as power conditioning system (PCS), battery energy storage system (BESS), and on-site photovoltaic (PV) generation system. Thus, we propose a formulation to determine the number of EV chargers and the capacity of auxiliary equipment with the objective of a charging station operator (CSO) maximizing profit under regulatory, economic, and physical constraints. The effectiveness of the proposed method is verified with simulations considering various EV charging patterns. The study results will help improve the EV charging infrastructure by encouraging individuals and companies to participate in EV charging business.

scholarly journals Decentralized V2G/G2V Scheduling of EV Charging Stations by Considering the Conversion Efficiency of Bidirectional Chargers

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 962
Author(s):  
Jian-Tang Liao ◽  
Hao-Wei Huang ◽  
Hong-Tzer Yang ◽  
Desheng Li
Keyword(s):  
Storage System ◽  
Energy Storage System ◽  
Large Power ◽  
Carbon Reduction ◽  
Charging Station ◽  
Service Companies ◽  
Battery Energy ◽  
Charging Stations ◽  
Conversion Efficiencies ◽  
Ev Charging

With a rapid increase in the awareness of carbon reduction worldwide, the industry of electric vehicles (EVs) has started to flourish. However, the large number of EVs connected to a power grid with a large power demand and uncertainty may result in significant challenges for a power system. In this study, the optimal charging and discharging scheduling strategies of G2V/V2G and battery energy storage system (BESS) were proposed for EV charging stations. A distributed computation architecture was employed to streamline the complexity of an optimization problem. By considering EV charging/discharging conversion efficiencies for different load conditions, the proposed method was used to maximize the operational profits of each EV and BESS based on the related electricity tariff and demand response programs. Moreover, the behavior model of drivers and cost of BESS degradation caused by charging and discharging cycles were considered to improve the overall practical applicability. An EV charging station with 100 charging piles was simulated as an example to verify the feasibility of the proposed method. The developed algorithms can be used for EV charging stations, load aggregators, and service companies integrated with distributed energy resources in a smart grid.

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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