Probabilistic reliability evaluation of distribution systems considering the spatial and temporal distribution of electric vehicles (original) (raw)
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Probabilistic modeling of electric vehicle charging pattern in a residential distribution network
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It has been recognized that an increased penetration of electric vehicles (EVs) may potentially alter load profile in a distribution network. Charging pattern of EVs and its corresponding electrical load pattern may be assessed and quantified by using either a deterministic method or stochastic approach. However, deterministic method does not account for stochastic nature of EV users which affects the load pattern and of stochastic nature of grid condition. Thus, a stochastic method is applied to develop a probabilistic model of EVs charging pattern that takes into account various factors such as vehicle class, battery capacity, state of charge (SOC), driving habit/need, i.e. involving trip type and purpose, plug-in time, mileage, recharging frequency per day, charging power rate and dynamic EV charging price under controlled and uncontrolled charging schemes. The probabilistic model gives EV charging pattern over a period of day for different months to represent the load pattern during different seasons of a year. The presented model gives a rigorous estimation of EV charging load pattern in a distribution network which is considered important for network operators.
Stochastic Analyses of Electric Vehicle Charging Impacts on Distribution Network
IEEE Transactions on Power Systems, 2014
A stochastic modeling and simulation technique for analyzing impacts of electric vehicles charging demands on distribution network is proposed in this paper. Different from the previous deterministic approaches, the feeder daily load models, electric vehicle start charging time, and battery state of charge used in the impact study are derived from actual measurements and survey data. Distribution operation security risk information, such as over-current and under-voltage, is obtained from three-phase distribution load flow studies that use stochastic parameters drawn from Roulette wheel selection. Voltage and congestion impact indicators are defined and a comparison of the deterministic and stochastic analytical approaches in providing information required in distribution network reinforcement planning is presented. Numerical results illustrate the capability of the proposed stochastic models in reflecting system losses and security impacts due to electric vehicle integrations. The effectiveness of a controlled charging algorithm aimed at relieving the system operation problem is also presented.
Electrical Engineering, 2020
Usage of electrical vehicles (EV) is increasing at high rate due to their great benefits to the community well-being. However, EVs have considerable impacts to electrical power networks and especially to the low voltage side of the distribution network. In order to determine the impacts of EVs accurately, uncertain behaviors of drivers were modeled using Monte Carlo simulations. This method is proven to be a robust tool for the evaluation of stochastic processes and getting deterministic results out of it. Furthermore, real-world traffic pattern data were used to model drivers' behaviors. Return home time of EVs was used as a charging start time, and average commute distance of drivers was used to determine the charging duration. Also, residential area was taken as a pilot network. Hourly basis transformer loading data were obtained and used to realistically reflect the base load of the pilot network. Load flow analysis was performed for non-EV and with-EVs scenarios. The results of the analysis were represented in a probabilistic approach. Violations of results were investigated according to power quality limits. Consequently, impacts of the EV charging load to the low voltage side of distribution network were analyzed in terms of voltage drops, transformers' loadings, power losses and voltage unbalance. This study showed that with a 50% penetration rate of EVs, the probability of voltage violation increases by approximately 25%.
C I Stochastic Analysis of the Impact of Electric Vehicles on Distribution Networks
Advances in the development of electric vehicles, along with policy incentives, will see a wider uptake of this technology in the transport sector in future years. However, large penetrations of EVs could lead to adverse effects on power system networks, especially at the residential distribution network level. These effects could include excessive voltage drop and thermal loading of network components. A stochastic method is developed to take account of the uncertainties associated with EV charging and the technique is implemented on a residential test network using power system simulation software. The results show how voltage levels, component loading network losses are impacted from EV charging, taking into account the probabilistic behaviour of the EV owners.
Travel Activity Based Stochastic Modelling of Load and Charging State of Electric Vehicles
Sustainability
The uptake of electric vehicles (EV) is increasing every year and will eventually replace the traditional transport system in the near future. This imminent increase is urging stakeholders to plan up-gradation in the electric power system infrastructure. However, for efficient planning to support an additional load, an accurate assessment of the electric vehicle load and power quality indices is required. Although several EV models to estimate the charging profile and additional electrical load are available, but they are not capable of providing a high-resolution evaluation of charging current, especially at a higher frequency. This paper presents a probabilistic approach capable of estimating the time-dependent charging and harmonic currents for the future EV load. The model is based on the detailed travel activities of the existing car owners reported in the travel survey. The probability distribution functions of departure time, distance, arrival time, and time span are calculat...
Energies
It has been recognized that an increased penetration of electric vehicles (EVs) may potentially alter load profile in a distribution network. As EVs are regarded as a diversely distributed load so a deterministic method, to predict EV charging load, may not account for all possible factors that could affect the power system. Thus, a stochastic approach is applied that takes into account various realistic factors such as EV battery capacity, state of charge (SOC), driving habit/need, i.e., involving type and purpose of trip, plug-in time, mileage, recharging frequency per day, charging power rate and dynamic EV charging price under controlled and uncontrolled charging schemes. A probabilistic model of EVs charging pattern associated with residential load profile is developed. The probabilistic model gives an activity based residential load profile and EV charging pattern over a period of 24 h. Then, the model output is used to assess the power quality index such as voltage unbalance factor under different electric vehicle penetration levels at different nodes of the system. An uneven EV charging scenario is identified that could cause the voltage unbalance to exceed its permissible limit.
System Sciences (HICSS), …, 2010
Since private passenger cars drive on average less than 2 hours per day, each electric vehicle could potentially provide capacities for grid services during more than 22 hours per day. The presented stochastic model, which bases on more than 167,000 data points from a mobility survey, simulates the driving behavior of private passenger cars. The article estimates the availability of electric vehicles for grid balancing services using the generated profiles from a stochastic model. Three basic ("zero-intelligence") charging strategies were applied.
Stochastic analysis of the impact of plug-in hybrid electric vehicles on the distribution grid
IET Conference Publications, 2009
Alternative vehicles, such as plug-in hybrid electric vehicles (PHEVs), become more popular. The batteries of these PHEVs are designed to be charged at home, from a standard outlet in the garage, or on a corporate car park. These extra electrical loads have an impact on the distribution grid. The uncoordinated power consumption on a local scale can lead to grid problems. Therefore coordinated charging is proposed. The exact forecasting of household loads is not possible, so stochastic programming is introduced. The stochastic approach represents an error in the forecasting of the daily load profiles. Two main program techniques are analyzed: quadratic and dynamic programming. The coordination of the PHEVs reduces the power losses and improves the power quality. The estimation of the costs of grid reinforcement must be compared with the cost of the implementation of a smart metering system for the coordination of the charging.
Impacts of Stochastic Residential Plug-In Electric Vehicle Charging on Distribution Grid
In this paper, the impacts of residential Plug-In Electric Vehicles (PEVs) charging on a distribution grid are investigated. A stochastic charging model is developed and used to study the impacts on distribution transformer loading, hotspot temperature variation and Accelerated Aging Factor (AAF) of the transformer. Different penetration levels of PEVs are considered in a typical distribution system. Furthermore, distribution of State of Charge (SOC) is discussed which can be used to optimize battery capacity and required charging infrastructure. Distribution of parking time interval is also discussed which can be used to evaluate availability of PEVs for overnight charging. The merit of stochastic approach compared with deterministic approach is also illustrated. The main contribution of this paper is the stochastic approach to evaluate the impact of residential PEV charging on the distribution grid.
A Monte Carlo method to evaluate electric vehicles impacts in distribution networks
2010 IEEE Conference on Innovative Technologies for an Efficient and Reliable Electricity Supply, CITRES 2010, 2010
This paper describes a statistical approach developed for assessing the impacts resulting from EV presence in a given electricity network was developed. The algorithm, developed for this purpose, is based on a Monte Carlo method and can be seen as a planning tool that allows obtaining average values for several system indexes, like buses voltages, branches loading and energy losses. Additionally, it also allows identifying the most critical operation scenarios and the network components that are subjected to more demanding conditions and that might need to be upgraded. The example of a small grid from one of the Azores islands, Flores Island, was used for illustration purposes and two scenarios of EV integration were considered: 25% and 50% of the current light vehicles fleet replaced by EV.