High Penetration Of Electric Vehicles Research Articles

Optimal Charging Strategy of Electric Vehicles in Unbalanced Three-Phase Distribution Network

Background: The increasing penetration of Electric Vehicles (EV) could lead to significant impact on the power systems, particularly for existing distribution networks. Methods: These impacts contain phase unbalance, excessive voltage deviations and overloading of equipment, which arise when a large numbers of Electric Vehicles are simultaneously charged. Results: In the current research, an optimal charging strategy is designed to control charging rates of Electric Vehicles in unbalanced three-phase distribution networks. A technique according to the Particle Swarm Optimization (PSO) is designed in order to reduce the charging cost of vehicles, with considering certain constraints. The constraint set involves transformer and line restrictions, unbalance of phase and voltage range. Conclusion: The proposed charging strategy is investigated on a real distribution network. Findings shows that high penetration of Electric Vehicles can be sustained in the existing distribution networks, demonstrating the effectiveness of the proposed method.

ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation

Large-scale utilization of electric vehicles (EVs) affects the total electricity demand considerably. Demand forecast is usually designed for the seasonally changing load patterns. However, with the high penetration of EVs, daily charging demand makes traditional forecasting methods less accurate. This paper presents an autoregressive integrated moving average (ARIMA) method for demand forecasting of conventional electrical load (CEL) and charging demand of EV (CDE) parking lots simultaneously. Our EV charging demand prediction model takes daily driving patterns and distances as an input to determine the expected charging load profiles. The parameters of the ARIMA model are tuned so that the mean square error (MSE) of the forecaster is minimized. We improve the accuracy of ARIMA forecaster by optimizing the integrated and auto-regressive order parameters. Furthermore, due to the different seasonal and daily pattern of CEL and CDE, the proposed decoupled demand forecasting method provides significant improvement in terms of error reduction. The impact of EV charging demand on the accuracy of the proposed load forecaster is also analyzed in two approaches: (1) integrated forecaster for CEL+CDE, and (2) decoupled forecaster that targets CEL and CDE independently. The forecaster outputs are used to formulate a chance-constrained day-ahead scheduling problem. The numerical results show the effectiveness of the proposed forecaster and its influence on the stochastic power system operation.

Optimal Reconfiguration-Based Dynamic Tariff for Congestion Management and Line Loss Reduction in Distribution Networks

This paper presents an optimal reconfiguration-based dynamic tariff (DT) method for congestion management and line loss reduction in distribution networks with high penetration of electric vehicles. In the proposed DT concept, feeder reconfiguration (FR) is employed through mixed integer programming when calculating the DT, leading to minimized energy cost and reduced DT as compared with the DT concept without FR. This paper further demonstrates that the line losses can be taken into account during the calculation of DT. As a result, the line loss reduction can be realized in a decentralized manner through the DT framework. Three case studies were conducted to validate the optimal reconfiguration-based DT method for congestion management and line loss reduction in distribution networks.

Probabilistic congestion management using EVs in a smart grid with intermittent renewable generation

This paper presents a probabilistic model to reduce the probability of line congestions and voltage violations in a smart grid located in a radial distribution network. Renewable distributed resources and a high penetration of Electric Vehicles (EVs) are considered. The uncertain parameters taken into account are: power demand, power generated by wind and solar photovoltaic generators, and the behavior of the EVs that do not participate in the congestion procedure. A probabilistic power flow based on the point estimate method is firstly used to compute the distribution functions of the line flows and node voltages. Next, a congestion management strategy is proposed in order to keep the line flows and node voltages within the appropriate range at a given confidence level. The strategy is based on the sensitivity distribution factors: Power Transfer Distribution Factors (PTDFs) and Voltage Distribution Factors (VDFs). The control variables to carry out the corrective actions are the active and reactive power injections from a subset of EVs or their charging points. It is assumed that these controlled EVs are able to inject power into the network using Vehicle to Grid (V2G) capabilities. To illustrate the method, a distribution grid based on a modified version of the IEEE-37 Node Test Feeder is tested.

Fair Energy Scheduling for Vehicle-to-Grid Networks Using Adaptive Dynamic Programming.

Research on the smart grid is being given enormous supports worldwide due to its great significance in solving environmental and energy crises. Electric vehicles (EVs), which are powered by clean energy, are adopted increasingly year by year. It is predictable that the huge charge load caused by high EV penetration will have a considerable impact on the reliability of the smart grid. Therefore, fair energy scheduling for EV charge and discharge is proposed in this paper. By using the vehicle-to-grid technology, the scheduler controls the electricity loads of EVs considering fairness in the residential distribution network. We propose contribution-based fairness, in which EVs with high contributions have high priorities to obtain charge energy. The contribution value is defined by both the charge/discharge energy and the timing of the action. EVs can achieve higher contribution values when discharging during the load peak hours. However, charging during this time will decrease the contribution values seriously. We formulate the fair energy scheduling problem as an infinite-horizon Markov decision process. The methodology of adaptive dynamic programming is employed to maximize the long-term fairness by processing online network training. The numerical results illustrate that the proposed EV energy scheduling is able to mitigate and flatten the peak load in the distribution network. Furthermore, contribution-based fairness achieves a fast recovery of EV batteries that have deeply discharged and guarantee fairness in the full charge time of all EVs.

Optimal distribution reconfiguration considering high penetration of electric vehicles

The appearance of Plug-in Electric Vehicles (PEVs) in the electric grids is providing new opportunities when some new challenges are also created. Technically, PEVs are movable loads that can benefit to both owners and utilities in case of using Vehicle-to-Grid (V2G) technology. Therefore, this art icle aims to investigate the Distribution Feeder Reconfiguration (DFR) effect to optimally manage PEV performance in a probabilistic framework. The proposed stochastic framework will capture the uncertainties of location of PEVs as well as driving pattern and battery State-of-Charge (SOC). In addition, a new self-adaptive evolutionary swarm algorithm based on Social Spider Optimization (SSO) algorithm is proposed that will search the problem space globally. The simulation results on the IEEE standard test system shows the high performance of the proposed method.

풍력발전과 전기자동차가 전력계통의 신뢰도에 미치는 영향 평가

An increasing number of electric vehicles (EVs) in power system affects its reliability in various aspects. Especially under high EV penetration level, new generating units are required to satisfy system’s adequacy criterion. Wind power generation is expected to take the major portion of the new units due to environmental and economic issues. In this paper, the system reliability is analyzed using Loss of Load Expectation (LOLE) and Expected Energy Not Served (EENS) under each and both cases of increasing wind power generation and EVs. A probabilistic multi-state modeling method of wind turbine generator under various power output for adequate reliability evaluation is presented as well. EVs are modeled as loads under charging algorithm with Time-Of-Use (TOU) rates in order to incorporate EVs into hour-to-hour yearly load curve. With the expected load curve, the impact of EVs on the system adequacy is analyzed. Simulations show the reliability evaluation of increasing wind power capacity and number of EVs. With this method, system operator becomes capable of measuring appropriate wind power capacity to meet system reliability standard.

Optimal management of plug-in electric vehicles in smart distribution systems

This paper investigates optimal operation management of plug-in electric vehicles (PEVs) in distribution systems. In this regard, the high penetration of electric vehicles (EVs) especially in the form of PEVs is another source of energy that can help the grid if optimally managed. In order to make the existence of PEVs to a suitable opportunity for the grid, vehicle-to-grid (V2G) technology is employed to change the PEVs from moving loads into moving sources. We also suggest a novel smart strategy to first manage the operation of PV and PEVs for providing the operator targets and second model the uncertainties generated by both PV and PEV in the system. The proposed method uses particle swarm optimization algorithm (PSO) along with the point estimate method (PEM) to deal with these issues. The 69-bus IEEE test system is employed as the case study to examine the high performance and ability of the proposed algorithm.

Smart automated distribution system considering plug-in hybrid electric vehicle

By rapid growth in the technology of the electric vehicles (EVs), the new smart power grids will include millions of these devices in the near future. The high penetration of EVs especially in the form of plug-in hybrid EVs (PHEV) can result in new challenges in the optimal operation and management of the systems. The charging behavior of PHEVs in a typical system is affected by a number of uncertain parameters which makes the overall charging behavior of PHEVs uncertain. Therefore, this paper makes use of a newly introduced smart model for charging demand of PHEVs to assess their effect on the reconfiguration issue as a precious and significant strategy in the smart automated distribution systems (ADS). In this regard, two different charging modes including charging at the public station and in a local residential community are investigated. Also, a sufficient scenario- based stochastic framework is proposed to model the uncertainties associated with the PHEVs on the reconfiguration problem. The proposed stochastic framework employs the roulette wheel mechanism in conjunction with a modified evolutionary-based optimization technique to deal with the targets. The feasibility and effectiveness of the proposed method is investigated on the 69 bus IEEE distribution system.

Distribution Locational Marginal Pricing Through Quadratic Programming for Congestion Management in Distribution Networks

This paper presents the distribution locational marginal pricing (DLMP) method through quadratic programming (QP) designed to alleviate the congestion that might occur in a distribution network with high penetration of flexible demands. In the DLMP method, the distribution system operator (DSO) calculates dynamic tariffs and publishes them to the aggregators, who make the optimal energy plans for the flexible demands. The DLMP through QP instead of linear programing as studied in previous literatures solves the multiple solution issue of the aggregator optimization which may cause the decentralized congestion management by DLMP to fail. It is proven in this paper, using convex optimization theory, the aggregator's optimization problem through QP is strictly convex and has a unique solution. The Karush-Kuhn-Tucker (KKT) conditions and the unique solution of the aggregator optimization ensure that the centralized DSO optimization and the decentralized aggregator optimization converge. Case studies using a distribution network with high penetration of electric vehicles (EVs) and heat pumps (HPs) validate the equivalence of the two optimization setups, and the efficacy of the proposed DLMP through QP for congestion management.

A preventive control strategy for static voltage stability based on an efficient power plant model of electric vehicles

With the increasing integration of wind farms and electric vehicles (EVs) in power systems, voltage stability is becoming more and more serious. Based on vehicle-to-grid (V2G), an efficient power plant model of EVs (E-EPP) was developed to estimate EV charging load with available corresponding response capacity under different charging strategies. A preventive control strategy based on E-EPP was proposed to maintain the static voltage stability margin (VSM) of power system above a predefined security level. Two control modes were used including the disconnection of EV charging load (‘V1G’ mode) and the discharge of stored battery energy back to power grid (‘V2G’ mode). A modified IEEE 14-bus system with high penetration of wind power and EVs was used to verify the effectiveness of preventive control strategy. Simulation results showed that the proposed strategy can not only improve the static voltage stability of power system with considerable wind generation, but also guarantee the travelling comfort for EV owners.

Probabilistic Modeling of Nodal Charging Demand Based on Spatial-Temporal Dynamics of Moving Electric Vehicles

High penetration of electric vehicles (EVs) as moving loads in power system have drawn increasing concerns about their negative impacts. Due to the spatial-temporal random dynamics of EVs, it is a challenge for identification and positioning of the space and time varying impacts. Most previous studies investigated system-wide EV charging demand based on data analysis with deterministic charging location and time. In this circumstance, this paper proposes a probabilistic model for nodal charging demand based on the spatial-temporal dynamics of moving EVs. Following the introduction to the integrated system with graph theory, a spatial-temporal model of moving EV loads is established based on random trip chain and Markov decision process (MDP). The nodal EV charging demands are derived from the charging probabilities of single and multiple EVs. The system studies show that this model is capable to assess the nodal charging demand due to the spatial-temporal distribution of moving EVs.

Research on Charge and Discharge Strategy for Centralized EVs Charging Stations

High penetration of electric vehicles has brought huge impact on the safe and stable operation of power grid. How to effectively improve the comprehensive utilization rate of electric vehicle charging and discharging has become the most important problem for the grid operation. In this paper, we establish the centralized station model which takes the minimal real power loss as its objective and bring variance optimization function. Using IEEE 30-bus system as simulation system, this paper uses genetic algorithm to find the best solution of the above model. The result shows that reasonable arrangement of charge and discharge plans not only smoothes the load curve, but reduces real power loss.

Real‐time scheduling of electric vehicles charging in low‐voltage residential distribution systems to minimise power losses and improve voltage profile

Unscheduled charging of plenty of electric vehicles (EVs) might exert an adverse effect on the existing power grid, especially when the charging coincides with daily peak load at distribution level. In this study, a scalable real-time scheduling scheme for EV charging in low-voltage residential distribution system is proposed. Since most often, voltage drop would become a binding constraint when a distribution feeder is subject to a high EV penetration level, a scheduling method is first developed to increase the voltage safety margin. Then, a novel factor is derived to allow the scheduling to shift from voltage-safety-oriented to loss-minimisation-oriented, or vice versa, on demand of the EV penetration level. A number of charging scenarios were simulated to evaluate the proposed scheduling scheme. Simulation results verified that the proposed scheduling scheme is fast and efficient with circuit losses close to optimal at a low EV penetration level and voltage drops maintained within the tolerable limit at a high EV penetration level. The high scalability and effectiveness of the proposed scheme has made it suitable for coordinating large number of EV charging activities in real-time.

V2G strategies for congestion management in microgrids with high penetration of electric vehicles

Modern electric power systems will face new operational challenges due to the influence of a high penetration of electric vehicles (EVs). In this context, power system operators may take advantage of EVs equipped with vehicle-to-grid (V2G) technologies to deal with network congestion management issues.The working framework is an electric power system where energy trading is organized through an auction where the agents can exchange energy within it or alternatively between neighbouring distribution networks.An approach is proposed to address V2G strategies for congestion management issues. Power distribution factors (DFs) are used to determine the amount of energy that a specific EV should contribute to alleviate the congestion in a line. It is assumed that EVs can decrease or increase their battery energy level, stop their charging or even inject energy to the grid aiming for the secure state of the system. This approach is tested for a reference microgrid (MG) example containing several EVs and it is shown to be suitable to solve this kind of technical problems.

Strategic charging method for plugged in hybrid electric vehicles in smart grids; a game theoretic approach

Implementation of various incentive-based and time-based load management strategies has great potential to decrease peak load growth and customer electricity bill cost. In recent years, developments in Plug in Hybrid Electric Vehicles (PHEVs) have provided various environmental and economic advantages. However, high penetration of electric vehicles in to the grid may cause high peak loads at different times of the days. Using advanced metering and automatic chargers makes it possible to optimize the charging cost, and release generation capacities to provide sustainable electricity supply. Using an appropriate encouraging program is a simple way for vehicle owners to manage their energy consumption and shift the time of charging to proper time of the day; and therefore, to reduce their electricity bill. With these objectives, this paper proposes a new practical PHEVs’ charging scheduling programs aiming at optimizing customers charging cost by considering the generation capacity limitation and dynamic electricity price in different time slots of a day. Using a stochastic model for start time of charging and the duration of it in the proposed optimization algorithm, make this method a practical tool for modeling the vehicle owners’ charging behavior with the purpose of peak load shaving.

Impact of Electric Vehicles on High-Voltage Grids: A Swiss Case Study

An increasing number of electric vehicles (EV) will have an impact on required capacity of transmission and distribution grids. For target grid planning, it is essential to identify local impact in terms of infrastructure overloads. We study the impact of EV charging loads on the Swiss high-voltage grid under diff erent EV penetration and pricing scenarios. The simulation model uses real driving pro files, substation load data from a Swiss grid operator and Swisselectricity prices.The results indicate that under flat electricity tariff s and EV penetration levels around 16 % the current substation capacity will be sufficient to cover additional EV charging load. However, at higher penetration levels a number of substations will be overloaded. More significantly, the introduction of market-based variable electricity rates for EV charging may induce substation overloads already at 16 % penetration levels. These results signify that EV charging coordination is required at high EV penetration levels and that exogenous price incentives give rise to strong over-coordination e ffects.

아파트에서의 전기자동차 충전인프라 설계를 위한 요소 고찰

Electric vehicles (EVs) are the most promising alternative substitute to a significant amount of gasoline vehicles for climate friendly transportation. This paper conducted an EV consumer survey through the telephone for 200 residents living in apartments to predict the EV penetration level in the year 2020. The results of EV consumer survey revealed that EV will be penetrated to 10% of the total vehicles in rental apartments and to 23.6% of the total vehicles in apartments for sale(condominium) until 2020.<BR> High EV penetration level, however, will lead to an increase of power consumption and over loading of electrical installations. Integrating more electric vehicles, the capacity of transformer related in apartments should be checked to prevent the over loading of the transformer. This paper checked the possibility of over loading at transformer and suggested the load control system as one of the solutions to reduce the over loading of transformers in apartments. Also this paper reviewed other components that should be checked in the designing of EV recharging infrastructure in apartments.

Implementation of Vehicle to Grid Infrastructure Using Fuzzy Logic Controller

With high penetration of electric vehicles (EVs), stability of the electric grid becomes a challenging task. A greater penetration level would demand a proper coordination amongst the various EVs as they charge or discharge to the grid. Coordination here refers to controlling the charging and discharging patterns of different EVs depending on their individual battery states and the present grid condition. Therefore, a good coordination between EVs is required for making the grid stable. With high penetration of EVs, the vehicle to grid (V2G) concept can be explored where excess energy of the battery can be supplied back to the grid in controlled fashion. Discharging EVs' battery energy to the grid in coordination can make V2G utilization as distributed energy storage. Charging EVs in coordination can flatten the voltage profile of a distribution node. In this work, a typical distribution system of a city is modeled to demonstrate V2G capabilities such as meeting peak demand and voltage sag reduction. The simulation of the distribution system with V2G capabilities are tested using fuzzy logic controller (FLC). Two controllers have been developed, namely the charging station controller and the V2G controller. Together they decide the proper energy flow between the EVs and the grid. Energy discharge to the grid from EVs or energy required for charging EVs is controlled and tested for the real time scenario.