Electric Vehicle Charging Load Research Articles

Distributed Charging and Discharging Strategy for Electric Vehicle Considering Three-Phase Load Balance for Incremental distribution network

The unbalanced three-phase may occur in distributed network with large-scale uncontrolled charging loads of electric vehicles plugged in, at present, most of the researches on the coordinated electric vehicles charging and discharging strategies are based on the assumption of single-phase power networks, the load balancing constraints of three-phase are not considered. In this paper, a centralized charging and discharging optimization model for electric vehicles is established aiming to achieve a valley-filling concept, it can allocate electric vehicles demand during valley hours. Based on this, a distributed charging and discharging optimization method for electric vehicles considering three-phase load balancing constraints is proposed, and then the centralized optimization problem is decomposed into multiple sub-optimization problems, and a penalty term is added to the single electric vehicle charging and discharging optimization objective function. Solve the three-phase load imbalance problem in distribution network operation, and use CPLEX solver to solve the optimization model in MATLAB. Combining with information interaction mechanism, through the method of iteratively correcting the virtual electricity price, the orderly intelligent charging and discharging of electric vehicles can be realized. The IEEE 33-node distribution system is used for simulation analysis to verify the characteristics and effectiveness of the proposed method.

Demand-Aware Provisioning of Electric Vehicles Fast Charging Infrastructure

The concept of smart city strives for greener technology to reduce carbon emission to ameliorate the global warming. Following this footprint, the transportation sector is experiencing a paradigm shift and the transition to electric vehicles (EVs) has prodigious plausibility in reducing carbon emission. However, the anticipated EV penetration is hindered by several challenges, among them are their shorter driving range, slower charging rate and the lack of ubiquitous availability of charging locations, which collectively contribute to range anxieties for EVs' drivers. Meanwhile, the expected immense EV load onto the power distribution network may degrade the voltage stability. To reduce the range anxiety, we present a two-stage solution to provision and dimension a DC fast charging station (CS) network for the anticipated energy demand and that minimizes the deployment cost while ensuring a certain quality of experience for charging e.g., acceptable waiting time and shorter travel distance to charge. This solution also maintains the voltage stability by considering the distribution grid capacity, determining transformers’ rating to support peak demand of EV charging and adding a minimum number of voltage regulators based on the impact over the power distribution network. We propose, evaluate and compare two CS network expansion models to determine a cost-effective and adaptive CSs provisioning solution that can efficiently expand the CS network to accommodate future EV charging and conventional load demands. We also propose two heuristic methods and compare our solution with them. Finally, a custom built Python-based discrete event simulator is developed to test our outcomes.

Spatiotemporal Distribution Model of Charging Demand for Electric Vehicle Charging Stations Based on Markov Chain

Considering the diversity of travel habits of users, spatiotemporal distribution model of charging load for highly random electric vehicles(EV), based on Markov chain, is proposed. According to the residents’ travel habits of the 2017 National Household Travel Survey (NHTS) in the United States, the destinations are divided into homes, work places, and other locations. Based on Markov Chain and Monte Carlo method, a highly random and complex process chain with unlimited processes is constructed. The distribution of start times and end times in one-day journey is fitted by the parameterless distribution with normal distribution as the kernel. The distribution of travel time, travel distance and dwell time is fitted by lognormal distribution. Then, a spatiotemporal distribution model of one-day vehicle travel is established. Considering the influence of EVs, the dual-input fuzzy algorithm of travel time and travel distance is used to calculate the power consumption of the trip. According to the crowd’s travel anxiety and different travel needs, different charging fredquencies and different charging power levels are used for vehicle charging behavior. Finally, the Monte Carlo method is used to calculate and analyse the charging load of EVs in different classification scenarios, such as household income, home address, weekdays and weekends. The one-day electric vehicle load distribution model was successfully established in three locations. The results show that the load of electric vehicle charging stations is not only affected by factors such as holidays, but also by the composition of the urban population.

Fuzzy Logic Weight Based Charging Scheme for Optimal Distribution of Charging Power among Electric Vehicles in a Parking Lot

Electric vehicles (EVs) parking lots are representing significant charging loads for relatively a long period of time. Therefore, the aggregated charging load of EVs may coincide with the peak demand of the distribution power system and can greatly stress the power grid. The stress on the power grid can be characterized by the additional electricity demand and the introduction of a new peak load that may overwhelm both the substations and transmission systems. In order to avoid the stress on the power grid, the parking lot operators are required to limit the penetration level of EVs and optimally distribute the available power among them. This affects the EV owner’s quality of experience (QoE) and thereby reducing the quality of performance (QoP) for the parking lot operators. The QoE is represents the satisfaction level of EV owners; whereas, the QoP is a measurement representing the ratio of EVs with QoE to the total number of EVs. This study proposes a fuzzy logic weight-based charging scheme (FLWCS) to optimally distribute the charging power among the most appropriate EVs in such a way that maximizes the QoP for the parking lot operators under the operational constraints of the power grid. The developed fuzzy inference mechanism resolves the uncertainties and correlates the independent inputs such as state-of-charge, the remaining parking duration and the available power into weighted values for the EVs in each time slot. Once the weight values for all EVs are known, their charging operations are controlled such that the operational constraints of the power grid are respected in each time slot. The proposed FLWCS is applied to a parking lot with different capacities. The simulation results reveal an improved QoP comparing to the conventional first-come-first-served (FCFS) based scheme.

Research on Coordinated Charging Control Strategy Load Optimization of Electric Vehicles in Residential Area

For the power utilization load encountered in the construction of electric vehicles (EVs) charging facilities in residential area, the coordinated charging control strategy for EV charging loads is studied. Based on the utilization electric load model, EV model, and distribution network information model of the residential area during the EV user using, the minimizing the fluctuation of the EV users charging cost, utilization power load and EV charging load is established. It is verified by an EV coordinated charging residential area, the sliding recursive algorithm employed can realize peak-load shifting, which can effectively reduce the effect of EV charging peak load and move the power peak to the second half of the night.

Power forecasting-based coordination dispatch of PV power generation and electric vehicles charging in microgrid

We propose herein an extended power forecasting-based coordination dispatch method for PV power generation microgrid with plug-in EVs (PVEVM) to improve the local consumption of renewable energy in the microgrid by guiding electric vehicle (EV) orderly charging. In this method, we use a clustering algorithm and neural network to build a power forecasting model (PFM) based on real data which can effectively characterise the uncertainty of PV power generation and EV charging load. Based on the interaction between the energy control centre (ECC) of the PVEVM and the EV users, a one-leader multiple-follower Stackelberg game is formulated, and the Stackelberg equilibrium is determined by using a power forecasting-based genetic algorithm (GA). As a main contribution of this paper, the PV power generation and EV charging load output from the PFM are used to generate a better quality initial population of the GA to improve its performance. A case study using real data from the Aifeisheng PV power station in China and EV charging stations in the UK verifies the good performance of the proposed extended coordination dispatch algorithm.

Study on Orderly Charging of Electric Vehicles Based on Time-of-use Electricity Price

The huge power grid load caused by large-scale centralized charging of electric vehicles is the main factor restricting the development of electric vehicles. At present, there is no definite method to reasonably allocate the charging load of electric vehicles to reduce the load of the power grid. Aiming at the problem of electric vehicles charging, a method of electric vehicles orderly charging is proposed. The model of electric vehicle charging is built, and the method of using time-of-use electricity price to guide users to arrange charging time reasonably is put forward, which meets the differentiated needs of users. In order to verify the correctness of the conclusion, the Monte Carlo method is adopted to simulate the charging behavior of users, and the orderly charging mode is compared with the original disordered charging mode. The results show that compared with the traditional disordered charging mode, the orderly charging method can reduce the load of the power grid and achieve the effect of peak load shifting. In addition, it plays a certain role in reducing user charging cost and increasing charging pile utilization efficiency.

Research on Charging Load Modeling and Influence on Distribution Network of Household Electric Vehicle

The impact of large-scale access of electric vehicles on the power distribution network of electric power systems has become increasingly prominent. To solve the problems brought by electric vehicle charging to the distribution system, it is particularly important to predict the charging load of electric vehicles. Based on the travel chain theory, this paper models the charging demand of household electric vehicles. Considering the uncertainty of charging demand in space and time, combined with the Monte Carlo method, taking the typical IEEE33 node power distribution system as an example, this paper simulates the impact of electric vehicle charging load access on the distribution network under different penetration rates and different access modes. The research results show that the charging load of household electric vehicles is usually concentrated in residential areas, and the impact on the overall load characteristics of the distribution network and the node voltage offset is more significant as the penetration rate of electric vehicles increases

Research on Charging Load Characteristics of EVs Based on Actual Charging Power

With the expansion of the scale of electric vehicles (EVs) and the maturity of fast charging technology and the popularization of fast charging piles, the disorderly fast charging of EVs caused a significant damage on distribution network, the actual charging power of EVs is different from the rated power, the traditional method of using the rated power of the charging pile to forecast the charging load will cause a significant error and affect the dispatching operation of power grid. Based on the actual EV charging curve, this paper establishes a variable power fast charging load model for EVs. The Monte Carlo simulation method is used to simulate the charging load of EV groups. The fast charging loads of EVs with different vehicle types and different battery capacities are compared. The actual variable power fast charging mode of the electric vehicles is compared with the constant power fast charging mode and the slow charging mode, and the charging loads of the three charging modes are significantly different. Finally, the impact of charging load of electric vehicles of different scales on distribution network load is analyzed.

Joint Planning of EV Fast Charging Stations and Power Distribution Systems with Balanced Traffic Flow Assignment

To tackle the challenges introduced by the fast-growing charging demand of electric vehicles (EVs), the power distribution systems (PDSs) and fast charging stations (FCSs) of EVs should be planned and operated in a more coordinated fashion. However, existing planning approaches generally aim to minimize investment costs in PDSs while ignoring the risk of worsening traffic conditions. To overcome this research gap, this article integrates the interests of traffic networks into PDS and FCS joint planning model to mitigate negative impacts on traffic conditions caused by installing FCSs. First, a novel microscopic method that is different from traditional assignment methods is proposed to simulate the influences of FCSs on traffic flows and EV charging loads. Then, a multiobjective joint planning model is developed to minimize both the planning costs and unbalanced traffic flows. A new bilayer Benders decomposition algorithm is designed to solve the proposed joint planning model. Numerical results on two practical systems in China validate the feasibility of our microscopic method by comparing the simulated results with real data. Compared with existing approaches, it is also demonstrated that the proposed joint planning approach helps to balance traffic flow assignments and relieve traffic congestion.

Optimal sizing and power schedule in PV household-prosumers for improving PV self-consumption and providing frequency containment reserve

This paper presents a methodology for jointly optimizing the sizing and power management of PV household-prosumers, namely, photovoltaic (PV) power, electric vehicle charging load (EVCL), household consumption load (HCL), battery bank (BB), and power converters. The optimization includes PV self-consumption enhancement and frequency containment reserve (FCR). This innovative model uses an annual techno-economic assessment to calculate the total costs and revenue by means of the teaching-learning-based optimization (TLBO) algorithm. The assessment of BB aging takes into account the charge/discharge power as well as the depth of discharge (DOD). This methodology is applied to Spanish PV household-prosumers. Results are obtained for scenarios involving PV, EV, and BB. Moreover, the PV household-prosumer approximated the smart user concept by providing FCR service. The scenarios envisaged examined potential revenues based on markets (day-ahead and FCR market) and their influence on profitability. The results of this study confirmed that BB is a cost-effective way of enhancing PV self-consumption by decreasing the levelized cost of electricity (LCOE). In fact, when FCR provision was added, there was a significant increase in the total revenue with a relatively low impact on BB aging.

Community electric vehicle load forecasting based on time series distribution

Electric vehicle charging load forecasting helps power system planning management and optimization. This paper proposes a residential private electric vehicle load forecasting model based on positive-reverse valley time charging, which predicts the load while satisfying the orderly charging of large-scale private electric vehicles in residential areas. Firstly, it analyzes the historical travel rules of private electric vehicles in the community and the charging and discharging characteristics of electric vehicles. Secondly, based on the peak-to- valley time-of-use(TOU) electricity price, the owner of the vehicle is fully utilized to make the electric vehicle orderly charging, so as to obtain the total charging load of the electric vehicle in the community; Finally, the actual data is used to predict the change of the load curve of private electric vehicles in the future after large-scale access to the grid. It is found that under the scenario of disorderly charging of private electric vehicles, the larger volume of the grid has the greater difference between the peaks and valleys of the grid. Through further calculations, it is found that the use of valley time charging can alleviate the impact of the electric vehicle charging load on the power grid to a certain extent. That also can effectively reduce the peak-to-valley difference of the grid load and residential area distribution network overload rate.

Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches

Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model.

Decentralized EV-Based Charging Optimization With Building Integrated Wind Energy

Electric vehicles (EVs) have experienced a rapid growth due to the economic and environmental benefits. However, the substantial charging load brings challenging issues to the power grid. Modern technological advances and the huge number of high-rise buildings have promoted the development of distributed energy resources, such as building integrated/mounted wind turbines. The issue to coordinate EV charging with locally generated wind power of buildings can potentially reduce the impacts of EV charging demand on the power grid. As a result, this paper investigates this important problem and three contributions are made. First, the real-time scheduling of EV charging is addressed in a centralized framework based on the ideas of model predictive control, which incorporates the volatile wind power supply of buildings and the random daily driving cycles of EVs among different buildings. Second, an EV-based decentralized charging algorithm (EBDC) is developed to overcome the difficulties due to: 1) the possible lack of global information regarding the charging requirements of all EVs and 2) the computational burden with the increasing number of EVs. Third, we prove that the EBDC method can converge to the optimal solution of the centralized problem over each planning horizon. Moreover, the performance of the EBDC method is assessed through numeric comparisons with an optimal and two heuristic charging strategies (i.e., myopic and greedy). The results demonstrate that the EBDC method can achieve a satisfactory performance in improving the scalability and the balance between the EV charging demand and wind power supply of buildings. Note to Practitioners —This paper is motivated by the challenging problem due to the substantial charging load of electric vehicles (EVs) on the power grid. Nowadays, modern technological advances and the rapid increase of high-rise buildings have promoted the development of building integrated/mounted wind turbines. As the EVs are usually parked in buildings for a large proportion of time every day, the issue to best utilize locally generated wind power of buildings to suffice EV travelling requirements shows vital significance in reducing their dependence on the power grid. However, there exist two main challenges including: 1) the multiple uncertainties regarding the uncertain wind power generation and the random driving behaviors of EVs and 2) the scalability of the solution method. To tackle the first challenge, the idea of model predictive control is introduced to make charging decisions at each stage based on a short-term prediction of the on-site wind power and the current collection of EVs parked there. To consider the scalability and overcome the lack of global charging information of all EVs in practical deployment, an iterative EV-based decentralized charging algorithm (EBDC) is derived, in which each EV can dynamically update its own charging decisions according to a dynamic charging “price” announced by the buildings. Alternatively, the buildings dynamically adjust the charging “price” to motivate the EVs to get charged during the time periods with sufficient wind power supply. Numeric results demonstrate that the EBDC method is scalable and performs well in improving the balance between the EV charging demand and the wind power supply of buildings.

Analysis of Impact of Electric Vehicle Charging Load on City Grid

With the rapid development of electric vehicles, increasing electric vehicle charging load will have extensive impacts on the power grid. This paper innovatively analyzed the impact of charging load on the urban power grid by taking the power grid of a large city as an example. Firstly, the scale of electric vehicles is predicted. Secondly, considering the travelling characteristics of electric vehicles, the charging load characteristics of electric vehicles in different functional areas of the city were analyzed. Finally, the impact of unordered charging of electric vehicles on urban power grid is quantitatively analyzed from four aspects, including load structure, on-peak and off-peak difference of system, distribution network utilization rate, charging facilities and supporting network construction. Electric vehicle charging will change the load structure of the power grid, reduce the utilization rate of distribution network and investment efficiency, increase the on-peak and off-peak difference of system etc.

A Model for Electric Vehicle Charging Load Forecasting based on Simulated Driving Path

With the replacement of power sources of traditional vehicles, the penetration of EVs in the world is increasing year by year. When large-scale EVs are connected to the power grid, it will affect the structure and power flow of the grid. This paper simulates the vehicle route in the planning area, proposes a method to predict the probability distribution method for the users to choose the specific route based on the Markov process theory and the traffic network database. Refer to the habits and charging preferences of EV users, taking private EVs as the research object, Monte Carlo simulation is used to predict the charging demand generated by EVs in the function area and important road nodes in the planned area. In view of the difference of peak price and valley price, considering the low valley-time price, the probability of load transfer is large. Changing the discharge strategy to realize the peak load shifting in V2V mode.

Direct Control of Plug-In Electric Vehicle Charging Load Using an In-House Developed Intermediate Control Unit

Unscheduled charging of a high number of plug-in electric vehicles (PEVs) can raise the peak electric load and affect the power quality (PQ) of the distribution power grids. This necessitates devising novel solutions to support the power grids when PEVs are being charged by controlling the PEVs’ charging load. In this paper, an intermediate control unit for the commercial battery chargers is developed in-house, which can directly control the PEV charging load by modifying the power levels during ac charging. Furthermore, field measurements are conducted to investigate the impacts of mass-produced PEVs on the point of common coupling during charging by the developed unit with different power levels. In addition, a home charging management system that employs the developed unit is designed. The results from the field experiments, together with the model-based case studies, demonstrate that the developed unit can be readily adopted for direct control of the charging loads of mass-produced PEVs, without the modification of the existing recharging equipment. Moreover, in the context of charging management, the electricity cost can be minimized, whilst the preferences of the PEV owners can be realized and the PQ of the distribution networks can be maintained simultaneously.

A Novel Intelligent Charging-Ordered Strategy Based On The Optimization Electric Vehicle Charging Load

This paper proposes a new optimization based on charging load and the conditions of line loss minimum as the objective function of the orderly intelligent charging control strategy, the charging control strategy using the Monte Carlo simulation method to simulate the user demand, permeability at the same time considering the electric car users in a variety of scenarios, from several aspects, such as load, power loss and voltage electric vehicle charging to the influence of the distribution network are analyzed. Based on this, puts forward a based on the optimal charging load and line loss minimum as the objective function of the intelligent orderly charging methods, this method can meet the demand of the electric car charge, according to the trend of short-term load, in each period of rechargeable power optimization, to achieve steady load, reduce the energy loss and improve the voltage quality goals.

Training-Free Non-Intrusive Load Extracting of Residential Electric Vehicle Charging Loads

Extracting the charging loads of residential electric vehicle (EV) clusters and identifying their charging patterns can help grid operators develop effective regulation strategies. The duration of the power consumption event (PCE) and the interval between adjacent events are used to characterize the difference in the stochastic behavior of the load pattern between the EV cluster and the air conditioner (AC) cluster. An event detection method based on skipping power difference is proposed, which can effectively identify changing edges of the PCE. A training-free non-intrusive load extracting (NILE) algorithm based on bounding-box fitting and load signatures is proposed, which can automatically identify the start time, the end time and the power amplitude of the charging event. The validity of the NILE algorithm is verified by multiple performance metrics on the real data set.

Capacity configuration of electric vehicle charging facilities based on user behavior prediction

By analyzing the behavior of electric vehicle users, this paper predicts the driving and charging activities of electric vehicles in the area and simulates the total charging load of electric vehicle charging load in a charging station in a certain area. Finally, taking a city as an example, this method is used to calculate the specific configuration scheme of the city’s public charging network, and further studies the impact of user charging habits and vehicle differences on the configuration results. The results show that the proposed capacity allocation method is effective and feasible.