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. The model can be used to provide insights for charging station placement in different practical situations. 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. Meanwhile, stochastic programming is adopted to capture uncertainties from solar radiation and charging demand of the EV fleet.

The operational decision making considers EV mobility which is modeled as an Erlang-loss 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 charging station considered in this work is assumed to be equipped with solar photovoltaic panel (PV) and an energy storage system which more » could be electric battery or the recently invented hydro-pneumatic energy storage (GLIDES, Ground-Level Integrated Diverse Energy Storage) system. 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. To support this increasing demand for EVs and PHEVs, challenges related to capacity planning and investment costs of public charging infrastructure must be addressed. Here, the results indicate better profit advantage of GLIDES over battery system with the increased power capacity « lessĭue 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. The effectiveness of the proposed surrogate modeling approach is demonstrated in numerical experiments. 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. Sampled parameters include EV charging slots number, PV area, capacity of energy storage system, and daily mean EV arrival number. 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. Meanwhile, stochastic programming is adopted to capture uncertainties from solar radiation and charging demand of EV fleet. EV mobility is modeled as an Erlang-loss system.

A co-optimization model that minimizes investment and operation cost is established to determine optimal solution while considering capacity planning and following operations. The charging station is assumed to be equipped with the solar photovoltaic (PV) panel and an energy storage system, which could be electric more » battery or recently invented hydropneumatic energy storage (ground-level integrated diverse energy storage (GLIDES)) system.

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. = ,ĭue 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.

Gridable with place value