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.

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

2019 ◽  
Vol 142 (5) ◽  
Author(s):  
Yang Chen ◽  
Fadwa Dababneh ◽  
Bei Zhang ◽  
Saiid Kassaee ◽  
Brennan T. Smith ◽  
...  
Keyword(s):  
Energy Storage ◽  
Electric Vehicles ◽  
Optimization Model ◽  
Capacity Planning ◽  
Capital Investment ◽  
Storage System ◽  
Surrogate Modeling ◽  
Energy Storage System ◽  
Planning Problem ◽  
Charging Station

Abstract Due to the promising potential for environmental sustainability, 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 charging stations is developed and aims to balance the current capital investment costs and future operational revenue. The charging station is assumed to be equipped with the solar photovoltaic (PV) panel and an energy storage system, which could be electric battery or recently invented hydropneumatic energy storage (ground-level integrated diverse energy storage (GLIDES)) system. A co-optimization model that minimizes investment and operation cost is established to determine optimal solution while considering capacity planning and following operations. EV mobility is modeled as an Erlang-loss system. Meanwhile, stochastic programming is adopted to capture uncertainties from solar radiation and charging demand of EV fleet. To provide a more general and computationally efficient model, main configuration parameters are sampled in design space and then fixed in solving the co-optimization model. Sampled parameters include EV charging slots number, PV area, capacity of energy storage system, and daily mean EV arrival number. Based on the sampled parameter combinations and its responses, black-box mappings are then constructed using surrogate models, which could provide insights for charging station placement in different practical situations. The effectiveness of the proposed surrogate modeling approach is demonstrated in numerical experiments. The results indicate better profit advantage of GLIDES over battery system with the increased power capacity

scholarly journals Methodology for the Optimal Design of a Hybrid Charging Station of Electric and Fuel Cell Vehicles Supplied by Renewable Energies and an Energy Storage System

2019 ◽  
Vol 11 (20) ◽  
pp. 5743 ◽  
Author(s):  
Higinio Sánchez-Sáinz ◽  
Carlos-Andrés García-Vázquez ◽  
Francisco Llorens Iborra ◽  
Luis M. Fernández-Ramírez
Keyword(s):  
Energy Storage ◽  
Fuel Cell ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Energy System ◽  
Energy Storage System ◽  
Design System ◽  
Charging Station ◽  
Electrical Generation

The global energy system is changing, mainly to achieve sustainable transport technologies and clean electrical generation based on renewable sources. Thus, as fuels, electricity and hydrogen are the most promising transport technologies in order to reduce greenhouse emissions. On the other hand, photovoltaic and wind energies, including energy storage, have become the main sources of distributed generation. This study proposes a new optimal-technical sizing method based on the Simulink Design Optimization of a stand-alone microgrid with renewable energy sources and energy storage to provide energy to a wireless power transfer system to charge electric vehicles along a motorway and to a hydrogen charging station for fuel cell-powered buses. The results show that the design system can provide energy for the charging of electric vehicles along the motorway and produce the hydrogen consumed by the fuel cell-buses plus a certain tank reserve. The flexibility of the study allows the analysis of other scenarios, design requirements, configurations or types of microgrids.

Sign in / Sign up

Export Citation Format

Share Document