Impact of electric vehicle charging on the power demand of retail buildings

Gilleran, Madeline; Bonnema, Eric; Woods, Jason; Mishra, Partha; Doebber, Ian; Hunter, Chad; Mitchell, Matt S.; Mann, Margaret

doi:10.1016/j.adapen.2021.100062

Cited by 80 publications

(28 citation statements)

References 27 publications

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“…Indeed in [18], the author employs a bottom-up approach to assess the EVs charging impact on residential consumption without relying on historical data. Many other studies in literature focus on the impact of EVs charging in commercial [19] and office buildings [20]. Instead, little research can be found on the impact of ultra-fast charging with power rates higher than 50 kW per charger.…”

Section: Load Power Demandmentioning

confidence: 99%

“…The distribution is shown in FIG. 3, it has a peak around SOC equal to 20%, indeed according to [29] and [19] the EVs users are more likely to charge their EVs when their SOC is low and in the range of 15%-25% , instead for lower values EV drivers would worry about running out of electricity on the middle of the way. Then, the probability decreases almost equally on both sides; for SOCs higher than 60%, the probability of stopping at the station to charge is almost null.…”

Section: B Electric Vehicles Arrival State Of Chargementioning

confidence: 99%

“…𝐴𝐶 𝐵𝐸𝑆𝑆 = 𝐴𝐼𝐶 𝐵𝐸𝑆𝑆 + 𝐴𝑅𝐶 𝐵𝐸𝑆𝑆 + 𝐴𝑂&𝑀 𝐵𝐸𝑆𝑆 (18) Finally, the annual electricity cost is computed in (19):…”

Section: ) Economic Functionmentioning

confidence: 99%

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Multi-Objective Optimization of PV and Energy Storage Systems for Ultra-Fast Charging Stations

et al. 2022

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The installation of ultra-fast charging stations (UFCSs) is essential to push the adoption of electric vehicles (EVs). Given the high amount of power required by this charging technology, the integration of renewable energy sources (RESs) and energy storage systems (ESSs) in the design of the station represents a valuable option to decrease its impact on the grid and the environment. Therefore, this paper proposes a multi-objective optimization problem for the optimal sizing of photovoltaic (PV) system and battery ESS (BESS) in a UFCS of EVs. The proposed multi-objective function aims to minimize, on one side, the annualized cost of the station, and on the other side, the produced pollutant emissions. The decision variables are the number of PV panels and the capacity of the ESS to be installed. The optimization problem is reduced to a single-objective problem by applying the linear scalarization method. Then the equivalent singleobjective function is optimized through a genetic algorithm (GA). The proposed optimization framework is applied to a study case and the results prove that PV and ESS could lead to a significant reduction of both the annualized cost and the pollutant emissions. Finally, a sensitivity analysis is also presented to validate the effectiveness of the proposed solution.INDEX TERMS extreme fast charging, integrated charging station, bi-objective optimization, electric vehicles, fast-charging load demand.

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Section: Load Power Demandmentioning

confidence: 99%

Section: B Electric Vehicles Arrival State Of Chargementioning

confidence: 99%

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Multi-Objective Optimization of PV and Energy Storage Systems for Ultra-Fast Charging Stations

et al. 2022

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“…One of the main factors to consider with DCFCs is how peak demand can impact the monthly and annual cost of electricity, and vice versa [133,134]. The results in [133] suggest that the addition of battery storage to the DCFCs not only reduces the operating cost, but also addresses the negative impacts of high peak demand.…”

Section: Annual Electricity Costsmentioning

confidence: 99%

“…The results in [133] suggest that the addition of battery storage to the DCFCs not only reduces the operating cost, but also addresses the negative impacts of high peak demand. In [134], the likely impacts of EV charging were determined through the energy consumption of DCFCs in non-residential and large retail stores in Centennial, Colorado. Monte Carlo simulations were used to determine the EV's arrival time and waiting duration and predicted a significant increase in monthly peak demand but not in monthly electricity usage.…”

Section: Annual Electricity Costsmentioning

confidence: 99%

Review of Fast Charging for Electrified Transport: Demand, Technology, Systems, and Planning

2022

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As the number and range of electric vehicles in use increases, and the size of batteries in those vehicles increases, the demand for fast and ultra-fast charging infrastructure is also expected to increase. The growth in the fast charging infrastructure raises a number of challenges to be addressed; primarily, high peak loads and their impacts on the electricity network. This paper reviews fast and ultra-fast charging technology and systems from a number of perspectives, including the following: current and expected trends in fast charging demand; the particular temporal and spatial characteristics of electricity demand associated with fast charging; the devices and circuit technologies commonly used in fast chargers; the potential system impacts of fast charging on the electricity distribution network and methods for managing those impacts; methods for long-term planning of fast charging facilities; finally, expected future developments in fast charging technology and systems.

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Electric Vehicle Smart‐Charging Control for Parking Lots Based on Individual State of Charge Priority

Haasis,

Solano,

Dias

et al. 2024

Energy Storage

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The integration of electric vehicles (EVs) into the power grid could pose challenges to power quality (PQ) depending on quantity of EVs and when they are connected. To mitigate these impacts without using drastic measures, such as disconnecting EVs, this study investigates centralized control strategies within parking facilities that prioritize EV charging based on individual State of Charge (SoC) levels. The study utilizes the IEEE 34 Bus system and conducts 3888 simulations for different scenarios to assess the impact of the quantity and placement of EVs in parking lots. The study applies the Monte Carlo method to compare the performance of different proposed controls: (i) limiting the charging current to a fixed level and (ii) varying the current based on the voltage droop step. Furthermore, Power Hardware‐in‐the‐Loop (PHIL) simulations were carried out to validate the hierarchical control using the droop step control, demonstrating the best average performance in the previous scenarios. The findings indicated that the control responded within the expected timeframe and successfully addressed voltage sag issues, maintaining PQ in the distribution system in most cases, with its performance being influenced by the placement of parking lots in the network. Additionally, it was confirmed through quartiles that the classification based on SoC leads to a more balanced charging time for different SoC levels.

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Impact of electric vehicle charging on the power demand of retail buildings

Cited by 80 publications

References 27 publications

Multi-Objective Optimization of PV and Energy Storage Systems for Ultra-Fast Charging Stations

Multi-Objective Optimization of PV and Energy Storage Systems for Ultra-Fast Charging Stations

Review of Fast Charging for Electrified Transport: Demand, Technology, Systems, and Planning

Electric Vehicle Smart‐Charging Control for Parking Lots Based on Individual State of Charge Priority

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