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

Chen, Yang; Dababneh, Fadwa; Zhang, Bei; Kassaee, Saiid; Smith, Brennan T.; Liu, Xiaobing; Momen, Ayyoub M.

doi:10.1115/1.4045733

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

Journal Article · 16 January 2019 · Journal of Energy Resources Technology

DOI: https://doi.org/10.1115/1.4045733 · OSTI ID:1606717

^[1]; Dababneh, Fadwa ^[2]; Zhang, Bei ^[3]; Kassaee, Saiid ^[4];

^[1];

^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Georgia Southern Univ., Statesboro, IL (United States)
GEIRI, Global Energy Interconnection, Research Inst., San Jose, CA (United States)
Univ. of Tennessee, Knoxville, TN (United States)

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. Here, the results indicate better profit advantage of GLIDES over battery system with the increased power capacity

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1606717

Journal Information:: Journal of Energy Resources Technology, Journal Name: Journal of Energy Resources Technology Journal Issue: 5 Vol. 142; ISSN 0195-0738

Publisher:: ASMECopyright Statement

Country of Publication:: United States

Language:: English

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battery storage
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Title: Surrogate Modeling for Capacity Planning of Charging Station Equipped With Photovoltaic Panel and Hydropneumatic Energy Storage

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