Stationary Energy Storage System for Fast EV Charging Stations: Simultaneous Sizing of Battery and Converter

Abstract

Optimal sizing of stationary energy storage systems (ESS) is required to reduce the peak load and increase the profit of fast charging stations. Sequential sizing of battery and converter or fixed-size converters are considered in most of the existing studies. However, sequential sizing or fixed-converter sizes may result in under or oversizing of ESS and thus fail to achieve the set targets, such as peak shaving and cost reduction. In order to address these issues, simultaneous sizing of battery and converter is proposed in this study. The proposed method has the ability to avoid the under or oversizing of ESS by considering the converter capacity and battery size as two independence decision variables. A mathematical problem is formulated by considering the stochastic return time of electrical vehicles (EVs), worst-case state of charge at return time, number of registered EVs, charging level of EVs, and other related parameters. The annualized cost of ESS is computed by considering the lifetime of ESS equipment and annual interest rates. The performance of the proposed method is compared with the existing sizing methods for ESS in fast-charging stations. In addition, sensitivity analysis is carried out to analyze the impact of different parameters on the size of the battery and the converter. Simulation results have proved that the proposed method is outperforming the existing sizing methods in terms of the total annual cost of the charging station and the amount of power buying during peak load intervals.

Consensus Algorithm-Based Distributed Operation of Microgrids During Grid-Connected and Islanded Modes

“…The parameters related to BESS are tabulated in Table II. The forecasted values of the market price are taken from [29], for the summer season and are scaled, as shown in Fig.6. The forecasted values of load and renewable power are taken from [30] and are given in Fig.7a and 7b, respectively.…”

Impact of Energy Storage Useful Life on Intelligent Microgrid Scheduling

“…For the analysis of the costs associated with electric vehicles, this study considers a certain number of Plug-in Electric Vehicles (PEVs) that are grouped in aggregators [23,33,34]. These aggregators are defined as a place that groups an amount of electric vehicles, which supplies PEVs charging when is more appropriate in economic terms, as reference [23,33,34] presents. Each one of the aggregators is associated with the power values and the amount of kilometers that the vehicles, which depend on the aggregator, can travel.…”

“…All this information regarding the function that models the operation cost and the vehicles charging, beside their limits and approach, was adapted from [23,33,34]. The first process that the aggregators do is to calculate the maximum energy dispatch for an electric vehicle to see if its operation and charge are feasible.…”

“…According to [21], the uncertainty cost can be represented as in Equation 23, where C PV is the photovoltaic generator cost, and C w is the wind power generation cost. The development of these terms (C PV and C w ) are in Equations (24) to (33), and they are coming from [23].…”

An Optimization Model for Energy Community Costs Minimization Considering a Local Electricity Market between Prosumers and Electric Vehicles

“…The popularization of EVs has been possible not only because they allow the reduction of GHGs but also because the cost of batteries has drastically reduced [10], thus causing a reduction in the costs of acquiring EVs. Electric storage units (including EVs) will need to be intelligently installed and distributed in the system in order to ensure maximum utilization and capital savings [11].…”