A Stochastic Resource-Sharing Network for Electric Vehicle Charging

We consider a distribution grid used to charge electric vehicles such that voltage drops stay bounded. We model this as a class of resource-sharing networks, known as bandwidth-sharing networks in the communication network literature. We focus on resource-sharing networks that are driven by a class of greedy control rules that can be implemented in a decentralized fashion. For a large number of such control rules, we can characterize the performance of the system by a fluid approximation. This leads to a set of dynamic equations that take into account the stochastic behavior of EVs. We show that the invariant point of these equations is unique and can be computed by solving a specific ACOPF problem, which admits an exact convex relaxation. We illustrate our findings with a case study using the SCE 47-bus network and several special cases that allow for explicit computations.Comment: 13 pages, 8 figure

Probabilistic agent-based model of electric vehicle charging demand to analyse the impact on distribution networks

Electric Vehicles (EVs) have seen significant growth in sales recently and it is not clear how power systems will support the charging of a great number of vehicles. This paper proposes a methodology which allows the aggregated EV charging demand to be determined. The methodology applied to obtain the model is based on an agent-based approach to calculate the EV charging demand in a certain area. This model simulates each EV driver to consider its EV model characteristics, mobility needs, and charging processes required to reach its destination. This methodology also permits to consider social and economic variables. Furthermore, the model is stochastic, in order to consider the random pattern of some variables. The model is applied to Barcelona’s (Spain) mobility pattern and uses the 37-node IEEE test feeder adapted to common distribution grid characteristics from Barcelona. The corresponding grid impact is analyzed in terms of voltage drop and four charging strategies are compared. The case study indicates that the variability in scenarios without control is relevant, but not in scenarios with control. Moreover, the voltages do not reach the minimum voltage allowed, but the MV/LV substations could exceed their capacities. Finally, it is determined that all EVs can charge during the valley without any negative effect on the distribution grid. In conclusion, it is determined that the methodology presented allows the EV charging demand to be calculated, considering different variables, to obtain better accuracy in the results.Peer ReviewedPostprint (published version

Power System Steady-State Analysis with Large-Scale Electric Vehicle Integration

It is projected that the electric vehicle will become a dominant method of transportation within future road infrastructure. Moreover, the electric vehicle is expected to form an additional role in power systems in terms of electrical storage and load balancing. This paper considers the latter role of the electric vehicle and its impact on the steady-state stability of power systems, particularly in the context of large-scale electric vehicle integration. The paper establishes a model framework which examines four major issues: electric vehicle capacity forecasting; optimization of an object function; electric vehicle station siting and sizing; and steady-state stability. A numerical study has been included which uses projected United Kingdom 2020 power system data with results which indicate that the electric vehicle capacity forecasting model proposed in this paper is effective to describe electric vehicle charging and discharging profiles. The proposed model is used to establish criteria for electric vehicle station siting and sizing and to determine steady-state stability using a real model of a small-scale city power system

Probabilistic Harmonic Calculation in Distribution Networks with Electric Vehicle Charging Stations

Integrating EV charging station into power grid will bring impacts on power system, among which the most significant one is the harmonic pollution on distribution networks. Due to the uncertainty of the EV charging process, the harmonic currents brought by EV charging stations have a random nature. This paper proposed a mathematical simulation method for studying the working status of charging stations, which considers influencing factors including random leaving factor, electricity price, and waiting time. Based on the proposed simulation method, the probability distribution of the harmonic currents of EV charging stations is obtained and used in the calculation of the probability harmonic power flow. Then the impacts of EVs and EV charging stations on distribution networks can be analyzed. In the case study, the proposed simulation and analysis method is implemented on the IEEE-34 distribution network. The influences of EV arrival rates, the penetration rate, and the accessing location of EV charging station are also investigated. Results show that this research has good potential in guiding the planning and construction of charging station

Sensitivity of Electric Vehicles Demand Profile to the Batteries Departure State-of-Charge

This paper focuses on the impacts of considering batteries state-of-charge (SOC) at the departure time on thedemand modeling of plug-in electric vehicles (PEVs). Almost all of the previous researches assumed that PEVs batteries at the departure time are fully charged; however, this assumption is highly questionable because it is probable for a PEV to not be charged every day. The probability density function of a vehicle owners’ willingness to fulfill the daily charging is extracted according to the initial SOC of a PEV and the estimated distance of its next trip. Afterwards, with the aim of considering the uncertainties with the associated random variables as well as properly adjusting vehicles SOC at the departure time, a Monte Carlo based multi loop (MCML) algorithm is developed which is composed of two loops, namely the inner loop and the outer loop. In order to implement the proposed stochastic method, a case study has been conducted employing the gathered datasets related to the ICE vehicles in Tehran. Appropriate Student’s t copula functions have been fitted to the datasets in order to take into account the correlation structure among them as well as to generate the required random samples

Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach

The market penetration of Plug-in Electric Vehicles (PEVs) is escalating due to their energy saving and environmental benefits. In order to address PEVs impact on the electric networks, the aggregators need to accurately predict the PEV Travel Behavior (PEV-TB) since the addition of a great number of PEVs to the current distribution network poses serious challenges to the power system. Forecasting PEV-TB is critical because of the high degree of uncertainties in drivers’ behavior. Existing studies mostly simplified the PEV-TB by mapping travel behavior from conventional vehicles. This could cause bias in power estimation considering the differences in PEV-TB because of charging pattern which consequently could bungle economic analysis of aggregators. In this study, to forecast PEV-TB an artificial intelligence-based method -feedforward and recurrent Artificial Neural Networks (ANN) with Levenberg Marquardt (LM) training method based on Rough structure - is developed. The method is based on historical data including arrival time, departure time and trip length. In this study, the correlation among arrival time, departure time and trip length is also considered. The forecasted PEV-TB is then compared with Monte Carlo Simulation (MCS) which is the main benchmarking method in this field. The results comparison depicted the robustness of the proposed methodology. The proposed method reduces the aggregators’ financial loss approximately by 16 $/PEV per year compared to the conventional methods. The findings underline the importance of applying more accurate methods to forecast PEV-TB to gain the most benefit of vehicle electrification in the years to come.Peer ReviewedPostprint (author's final draft

Assessing and Mitigating Impacts of Electric Vehicles on Active Distribution Systems

The transportation sector is expected to undergo a worldwide shift to zero-carbon emission automobiles. Major research advancements and government policies have been addressing the financial and technical barriers to electric vehicle (EV) use. Battery packs constitute an important component of EV technology. Improvements in battery pack technology are leading to lower battery cost, higher battery density, and increased driving range, making EVs more appealing to the consumers. On the other hand, EV charging loads can cause power quality issues such as harmonic distortion, voltage drop, power unbalance, power losses and transformer aging. EV increased charging load is urging the need of assessing its negative impacts on the grid to protect power system components. A comparison of the impacts of different levels of EV charging on the grid can allow EV users and utilities to understand the risks associated with their choices. Harmonic distortion due to nonlinear devices can be evaluated using harmonic power flow methods. Decoupled harmonic power flow technique is widely used in power systems analysis due to its simplicity and computational efficiency. Mitigation techniques to reduce harmonic impacts on the grid are crucial for power system reliability and maintenance. Incorporating distributed generation (DG) units into the network can achieve harmonic compensation of EV charging. A genetic algorithm is proposed to determine the current harmonic spectrum of each DG unit, accomplishing an optimal harmonic compensation of EV charging. DG integration improves grid power quality and voltage profile. It also helps in reducing voltage and current disturbances produced by EV loads