Novel PEV Charging Approaches for Extending Transformer Life

Smith, Theron; Garcia, Joseph P.; Washington, Gregory

doi:10.3390/en15124454

Cited by 6 publications

(6 citation statements)

References 33 publications

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“…They adopt vehicles of 16 kWh and 24 kWh charging at 3.3 kW and 6.6 kW. Reference [9] proposes a smart charging algorithm with variable-rate to mitigate transformer overloading and loss-of-life. The method is compared with other strategies, such as uncontrolled charging and fixed-rate controlled charging, with promising results.…”

Section: Introductionmentioning

confidence: 99%

Impact on Distribution Transformer Life Using Electric Vehicles with Long-Range Battery Capacity

2023

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This paper presents a comparative analysis of the effects of short-range and long-range electric vehicles charging on transformer life. Long-range vehicles are expected to become more common in the future. They have higher battery capacity and charge at higher power levels, modifying demand profile. A probabilistic analysis is performed using the Monte Carlo Simulation, evaluating the transformer hottest-spot temperature and the aging acceleration factor. Residential demand is modeled based on real electricity measurements, and EVs’ demand is modeled based on real data collected from a trial project developed in the United Kingdom. Simulations are conducted considering the influence of ambient temperature analyzing summer and winter seasons and several EV penetration levels. Results show the impacts caused by long-range vehicles are more severe because they charge at higher power levels, especially during winter, when residential demand is higher. For penetration level of 50% during summer, the use of long-range EVs brings a minimum equivalent aging factor of 5.2, which means the transformer aged 124.8 h in a cycle of only 24 h, decreasing its lifetime.

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Section: Introductionmentioning

confidence: 99%

Impact on Distribution Transformer Life Using Electric Vehicles with Long-Range Battery Capacity

2023

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“…It has been concluded that with a high penetration of EVs, and no support from photovoltaic (PV) and ES, the transformers will dramatically age. Furthermore, researchers have dealt with the influence of the charging method on transformers' aging [8][9][10][11][12][13]. The main idea is to find an optimal charging scheme to minimize the impact of EVs' charging demand on distribution transformers.…”

Section: Introductionmentioning

confidence: 99%

“…In one paper [10], a simple method is used to coordinate the charging process of PEVs to avoid the overloading of transformers by using the fact that idle time represents a significant percentage of EV parking time. In the paper, [11] reducing the impact on transformer aging by implementing the capability of delivering a variable charging rate has been proposed. Two charging algorithms have been used as strategic ways for reducing the overload of distribution transformers.…”

Section: Introductionmentioning

confidence: 99%

Impact of Reactive Power from Public Electric Vehicle Stations on Transformer Aging and Active Energy Losses

Pavlićević

Mujović

2022

Energies

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Climate change at the global level has accelerated the energy transition around the world. With the aim of reducing CO2 emissions, the paradigm of using electric vehicles (EVs) has been globally accepted. The impact of EVs and their integration into the energy system is vital for accepting the increasing number of EVs. Considering the way the modern energy system functions, the role of EVs in the system may vary. A methodology for analyzing the impact of reactive power from public electric vehicle charging stations (EVCSs) on two main indicators of the distribution system is proposed as follows: globally, referring to active power losses, and locally, referring to transformer aging. This paper indicates that there is an optimal value of reactive power coming from EV chargers at EVCSs by which active energy losses and transformer aging are reduced. The proposed methodology is based on relevant models for calculating power flows and transformer aging and appropriately takes into consideration the stochastic nature of EV charging demand.

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“…Recently, the charging problem of PEVs has been extensively studied [5][6][7][8][9][10]. In these studies, the electrical-energy-related aspects of the PEV charging problem are managed through different aspects.…”

Section: Introductionmentioning

confidence: 99%

Solving PEV Charging Strategies with an Asynchronous Distributed Generalized Nash Game Algorithm in Energy Management System

et al. 2022

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As plug-in electric vehicles (PEVs) become more and more popular, there is a growing interest in the management of their charging power. Many models exist nowadays to manage the charging of plug-in electric vehicles, and it is important that these models are implemented in a better way. This paper investigates a price-driven charging management model in which all plug-in electric vehicles are informed of the charging strategies of neighboring plug-in electric vehicles and adjust their own strategies to minimize the cost, while an aggregator determines the unit price based on overall electricity consumption to coordinate the charging strategies of the plug-in electric vehicles. In this article, we used an asynchronous distributed generalized Nash game algorithm to investigate a charging management model for plug-in electric vehicles in a smart charging station (SCS). In a charging management model, we need to consider constraints on the charge and discharge rates of plug-in electric vehicles, the battery capacity, the amount of charge per plug-in electric vehicle, and the maximum electrical load that the whole system can allow. Meeting the constraints of plug-in electric vehicles and smart charging stations, the model coordinates the charging strategy of each plug-in electric vehicle to ultimately reduce the cost of smart charging stations, which is the cost that the smart charging station should pay to the higher-level power supply facility. To the best of our knowledge, this algorithm used in this paper has not been used to solve this model, and it has better performance than the generalized Nash equilibria (GNE) seeking algorithm originally used for this model, which is called a fast alternating direction multiplier method (Fast-ADMM). In the simulation results, the asynchronous algorithm we used showed a correlation error of 0.0076 at the 713th iteration, compared to 0.0087 for the synchronous algorithm used for comparison, and the cost of the smart charging station was reduced to USD 4800.951 after coordination using the asynchronous algorithm, which was also satisfactory. We used an asynchronous algorithm to better implement a plug-in electric vehicle charging management model; this also demonstrates the potential advantages of using an asynchronous algorithm for solving the charging management model for plug-in electric vehicles.

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Novel PEV Charging Approaches for Extending Transformer Life

Cited by 6 publications

References 33 publications

Impact on Distribution Transformer Life Using Electric Vehicles with Long-Range Battery Capacity

Impact on Distribution Transformer Life Using Electric Vehicles with Long-Range Battery Capacity

Impact of Reactive Power from Public Electric Vehicle Stations on Transformer Aging and Active Energy Losses

Solving PEV Charging Strategies with an Asynchronous Distributed Generalized Nash Game Algorithm in Energy Management System

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