Probabilistic modeling of EV charging and its impact on distribution transformer loss of life

This paper proposes a two-step methodology for scheduling electric vehicle (EV) charging, which limits the burden on distribution and transmission assets while ensuring all the vehicles are charged. In the first step, the number of vehicles to be charged during each hour is optimized based on day-ahead requests for charging. The second step determines the maximum number of vehicles that can be charged based on operating conditions during the next hour to ensure distribution reliability requirements are met. A numerical example is given to illustrate the EV charging management scheme. This technique will limit the effects of EV charging on distribution assets.Index Terms-Distribution system, electric vehicles (EVs) charging, greenhouse gas (GHG) emission, loss of life (LOL) of components reliability.

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“…Based on atmospheric conditions, the LOL of the transformer could be heavily affected by these conditions [16].…”

Section: A Loading Effects: Transformer Lolmentioning

confidence: 99%

“…Based on the loading in a given system, probability distribution is formed [16], [19]. For example, consider a residential area on a weekday.…”

Section: Problem Formulationmentioning

confidence: 99%

Controlled Electric Vehicle Charging for Mitigating Impacts on Distribution Assets

Aravinthan

Jewell

2015

IEEE Trans. Smart Grid

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“…For the case studies in this work, we consider commercial buildings and use Gaussian distribution to model the arrivals and departures of EV within one day [16]. The arrival time is generated following distribution N (9, 4/1.96) and the departure time is generated following N (18, 4/1.96) (i.e., assuming most people come around 9am and leave around 6pm).…”

Section: B Electric Vehicle Charging Modelmentioning

confidence: 99%

“…Constraint (16) derives from the linear system description in equation (4). U lower and U upper bound the lower and upper air flow limit of the HVAC system, respectively.…”

Section: Hvac Ev and Battery Co-schedulingmentioning

confidence: 99%

Co-scheduling of HVAC control, EV charging and battery usage for building energy efficiency

Wei¹,

Zhu²,

Maasoumy³

2014

2014 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

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Building stock consumes 40% of primary energy consumption in the United States. Among various types of energy loads in buildings, HVAC (heating, ventilation, and air conditioning) and EV (electric vehicle) charging are two of the most important ones and have distinct characteristics. HVAC system accounts for 50% of the building energy consumption and typically operates throughout the day, while EV charging is an emerging major energy load that is hard to predict and may cause spikes in energy demand. To maximize building energy efficiency and grid stability, it is important to address both types of energy loads in a holistic framework. Furthermore, on the supply side, the utilization of multiple energy sources such as grid electricity, solar, wind, and battery storage provides more opportunities for energy efficiency, and should be considered together with the scheduling of energy loads. In this paper, we present a novel model predictive control (MPC) based algorithm to co-schedule HVAC control, EV scheduling and battery usage for reducing the total building energy consumption and the peak energy demand, while maintaining the temperature within the comfort zone for building occupants and meeting the deadlines for EV charging. Experiment results demonstrate the effectiveness of our approach under a variety of demand, supply and environment constraints.

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“…The distributions of T AH and d HH are shown in Figure 1. voltage deviation [5,6,11], and transformer aging [9,10]. The residential distribution network used in this study contains 120 houses and is modeled in GridLAB-D (U.S. Department of Energy (DOE) at Pacific Northwest National Laboratory (PNNL), Richland, WA, USA) [16], a power distribution system simulation and analysis tool.…”

Section: Stochastic Modeling Of Plug-in Electric Vehicle Fleet Chargingmentioning

confidence: 99%

Mitigation of the Impact of High Plug-in Electric Vehicle Penetration on Residential Distribution Grid Using Smart Charging Strategies

2016

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Vehicle electrification presents a great opportunity to reduce transportation greenhouse gas emissions. The greater use of plug-in electric vehicles (PEVs), however, puts stress on local distribution networks. This paper presents an optimal PEV charging control method integrated with utility demand response (DR) signals to mitigate the impact of PEV charging to several aspects of a grid, including load surge, distribution accumulative voltage deviation, and transformer aging. To build a realistic PEV charging load model, the results of National Household Travel Survey (NHTS) have been analyzed and a stochastic PEV charging model has been defined based on survey results. The residential distribution grid contains 120 houses and is modeled in GridLAB-D. Co-simulation is performed using Matlab and GridLAB-D to enable the optimal control algorithm in Matlab to control PEV charging loads in the residential grid modeled in GridLAB-D. Simulation results demonstrate the effectiveness of the proposed optimal charging control method in mitigating the negative impacts of PEV charging on the residential grid.

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Probabilistic modeling of EV charging and its impact on distribution transformer loss of life

Cited by 37 publications

References 5 publications

Controlled Electric Vehicle Charging for Mitigating Impacts on Distribution Assets

Controlled Electric Vehicle Charging for Mitigating Impacts on Distribution Assets

Co-scheduling of HVAC control, EV charging and battery usage for building energy efficiency

Mitigation of the Impact of High Plug-in Electric Vehicle Penetration on Residential Distribution Grid Using Smart Charging Strategies

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