Electric Vehicle Load Research Articles

Techno-economic and environmental assessment of solar-based electrical vehicles charging stations integrated with hydrogen production

With the growing interest in adopting both commercial and residential electric vehicles (EVs) utilizing green renewable energy, the techno-economic assessment of EV charging stations with solar energy is a critical aspect of the transition to sustainable transportation. However, battery storage capacity for variable solar energy production is becoming increasingly less cost-effective due to the higher Lithium price. Complementing solar energy production and battery storage, the potential of combining green hydrogen fuel energy integration with solar energy for EV charging stations can provide a sustainable alternative to reach carbon-free transportation. This research aims to assess the technical and economic viability of grid-connected Photovoltaic (PV)-based EV charging stations across Kentucky based on existing EV load profiles. The study considers different PV system sizes, locations, orientations, and the impact of net metering policies and electricity tariffs on the profitability of the PV systems. Using excess PV energy, Lithium-Ion batteries will be charged, which will be assessed considering the techno-economic and environmental impacts of green EV charging stations. To further enhance green renewable energy production, we also investigated the feasibility of on-grid PV-based EV charging stations with Lithium-Ion batteries charged during low tariff periods. In comparison with these scenarios, we studied the potential benefits of integrating green hydrogen energy production using excess solar energy with and without governmental incentives. Furthermore, we assess the environmental impact of these green EV charging stations across Kentucky. Our study indicates that EV stations across Kentucky have similar techno-economic feasibility with insignificant deviation in the levelized cost of electricity, payback period, and PV energy fraction with/without Li-Ion batteries. However, with green hydrogen production, the marginal H2 production cost indicates the first scenario (on-grid PV energy charging) is the most significant, roughly 10 USD/Kg, which is ∼1.5 times higher than the second (charging during low-tariff periods) and the third scenarios (power grid to satisfy the demand deficit – approximately 6.5 USD/Kg). From the environmental perspective, the second scenario would be a good option for either hybrid EV/H2 or EV charging stations in Kentucky, factoring in the hydrogen production cost and the carbon footprint.

A Bi-level optimization strategy for electric vehicle retailers based on robust pricing and hybrid demand response

The high penetration of electric vehicles (EVs) poses both opportunities and challenges for power systems. EV retailers, playing a critical role in the demand response mechanism, face market risks caused by uncertainties in electricity consumption and prices. To address the operational issues of large-scale EVs and tap the potential of EV retailers, a temporal and spatial domain-based optimization strategy is proposed, which is implemented on bi-level (referring to transmission and distribution grid networks). First, a physical scheduling model of the grid is established, consisting of a novel hybrid demand response mechanism considering the incentives of EV retailers and the retail electricity price of EV users, and a new robust retail electricity pricing strategy handling the uncertainties of EV behavior and the electric market. Then, a bi-level optimization strategy is presented: at the upper level, in the transmission network, based on the robust pricing strategy, a unit commitment model that coordinates the hybrid demand response with other distributed energy resources is designed to optimize load periods of EVs in the time domain; at the lower level, in the distribution network, an optimal power flow model is proposed to spatially dispatch the location of EV loads. The impacts of retail price profile, EV penetration, hybrid demand response mechanism, and EV load location are analyzed in ten tests using the IEEE 33 distribution network. Simulation results indicate that the robust pricing strategy can effectively handle uncertainties, the integration of the hybrid demand response mechanism into scheduling can ensure the benefits of all participants, and the bi-level optimization strategy can accommodate distributed energy resources temporally and spatially.

A novel hybrid centralised decentralised framework for electric vehicles coordination

AbstractHybrid Centralised‐Decentralised Electric Vehicle (EV) coordination policy in the urgent charging scenario is presented. First, a robust and complex optimisation problem considering several key features affecting EV coordination is formulated. Then, a solution strategy for the formulated problem is proposed by decomposing the formulated problem into an EV coordination and simple optimisation problem. The decentralised rule‐based EV coordination strategy works on the principle of direct load flattening and utilises practical EV aggregator‐customer interaction, customer behaviour, and temporospatial shifting of the EVs to flatten the load duration curve at the charging station. Then, the centralised optimisation problem is solved to minimise the operation cost, decrease the power loss, and decrease congestion in the grid. A comparison between uncoordinated and coordinated charging in the case study conducted on the IEEE 24 bus system shows that the proposed approach reduces the average EV load by 1283.26 kW/min, average power loss by 2.465 kW/min, and operation cost by 61.99 $/min during the peak hours.

Ultra-Short-Term Load Forecast of Charging Stations Considering the Travelling and Charging Behaviours of Electric Vehicles

The traditional electric vehicle (EV) load forecasting methods are mostly used to predict large areas such as cities, which makes it difficult to serve practical applications. The existing charging station load forecasting methods ignore the traveling and charging behaviors of EVs. To this end, a charging station ultra-short-term load prediction method considering the traveling and charging behaviors of EVs is proposed. Firstly, EV types are classified according to charging behaviors. On this basis, a rolling forecast model of the number of arrivals per unit time of each vehicle type is established, and then the impact of charging randomness on load forecast is reduced by repeated Monte Carlo sampling. The total load of charging stations during the prediction period can be obtained by adding up the load of individual vehicles. The results show that the method enables accurate prediction of ultra-short-term load at charging stations, which has higher accuracy and reference value than the traditional load forecasting methods.

Enhancing the performance of radial distribution systems via optimal integration of electric vehicles

<span lang="EN-US">Electric vehicles (EVs) and their associated charging stations (CSs) are a promising avenue for greenhouse gas reduction and energy security. However, improper location and size of the EV charging station (CSs/EVs) negatively affect the power system. It results in some technical challenges such as increased real power-loss, decreased voltage stability and increased voltage deviation. This paper suggests optimum planning of CSs/EVs locations to conserve voltage, improve power-loss and reduce the effect of CSs/</span><span lang="EN-US">EVs in electrical distribution systems. The multi objective genetic algorithm method (MOGA) is utilized to solve the optimization problem and reduce the impact of the random location of CSs/EVs. <span>The simulations are</span> performed on IEEE 33-bus and IEEE 69-bus radial distribution systems (RDS). In addition, the optimal EV allocation is carried out with two fixed levels of loading from 100% to 150% of the candidate EV load. A comparison between the proposed MOGA technique and other optimization techniques is carried out. The results demonstrated the capability of the proposed technique for optimal placement of CSs/EVs in RDS. </span><span lang="EN-US">Moreover, it is providing an objective value to solve a complex multi-objective nonlinear optimization problem to reduce the total power-loss, improve the voltage profile and extend the voltage stability.</span>

Comparative analysis of unidirectional and bidirectional electric vehicle charging stations (EVCS) optimal configuration in an IEEE 37-bus feeder system using Genetic Algorithm

Various power system problems and challenges may arise in the future due to the large scale of deployment of electric vehicles (EVs). Hence, the proper placement of EV charging stations (EVCS) effectively mitigates the impact of high EV loads connected to the grid. The research intends to explore and analyze differences between the regulation effectiveness of unidirectional and bidirectional charging technologies by utilizing different comparison evaluation indices. Moreover, considering their penetration level, this study tackles the impact analysis of EV and EVCS integration through time. Specifically, this paper aims to identify the optimal EVCS sites in an IEEE 37-bus test feeder system to minimize power loss brought by EV integration. Through MATLAB R2022b simulation and OpenDSS power flow analysis, the EVCS are optimally located near the supply bus. The findings show a direct relationship between the EV penetration level and system power loss. Due to the EV technology growth, there is an observed voltage profile degradation of up to 1.7094 p.u. The paper also highlights that although EV bidirectional charging technology (BCT) might reduce the load on the grid in the next few years of low penetration compared to unidirectional charging technology (UCT), it will give no significant difference due to the rapid increase of load connected during its high EV penetration.

Data-driven method for electric vehicle charging demand analysis: Case study in Virginia

Electric vehicle (EV) adoption in the U.S. will be accelerated by the historic $7.5 billion public investments in EV charging infrastructure. Careful analysis of EV charging demands plays a vital role in understanding the energy requirements, power grid impact, and smart charging management opportunities of EVs. To this end, this paper develops a data-driven trip-chaining-based modeling framework including five steps: Trip data acquisition and preprocessing, EV adoption modeling, travel itinerary synthesis, EV charging demand simulation and EV load profile generation. The developed analysis framework was demonstrated using real-world data for one region in Virginia, U.S. The results show that the proposed modeling framework can work effectively. For the study region in 2040, the predicted number of plug-in EVs is 470,114, resulting in a weekly charging demand of 38,078,127 kWh (55 % home, 9 % work, and 36 % public) in September and 45,920,358 kWh (61 % home, 9 % work, and 30 % public) in February.

Optimization of distribution network operating parameters in grid tied microgrid with electric vehicle charging station placement and sizing in the presence of uncertainties

ABSTRACT The power generated by renewable sources is prone to uncertainties which encourages to perform effective modelling to ensure the reliable operation of the grid-connected microgrid (MG) system. This work focuses on the uncertainty modelling of primary sources of solar power, wind power and electric vehicle (EV) load considering three factors battery capacity, state of charge (SoC) and type of EVs charged by employing Monte-Carlo Simulation (MCS) to minimize the distribution system's voltage stability, reliability and power loss (VRP) index. The objective function is tested on the modified IEEE-33 bus distribution system under three diverse scenarios with optimum sizing and placement of RESs and EVCS. To obtain optimal solutions for the proposed problem in a reasonable computation time, modified version of teaching and learning-based optimization (TLBO) and the JAYA algorithm are applied as the rate of convergence is superior to other existing methods in the literature and does not require any precise control parameters. For all the scenarios, it can be seen that the modified JAYA algorithm outperforms TLBO and other existing approaches. The findings of the results reveal the efficacy of uncertainty modelling in a proposed grid-connected DC MG to curtail the VRP index.

Modeling EV Charging Station Loads Considering On-Road Wireless Charging Capabilities

Electric vehicle (EV) customers are expected to charge EV batteries at a rapid EV charging station or via on-road wireless EV charging systems when possible, as per their charging needs to successfully complete any remaining trips and reach their destination. When on-road wireless EV charging systems are considered as an alternative charging method for EVs, this can affect the load of a rapid EV charging station in terms of time and magnitude. Hence, this paper presents a probabilistic framework for estimating the arrival rate of EVs at an EV rapid charging station, considering the availability of on-road wireless charging systems as an alternative charging method. The proposed model incorporates an Electric Vehicle Decision Tree that predicts the times when EVs require rapid charging based on realistic transportation data. A Monte Carlo simulation approach is used to capture uncertainties in EV user decisions regarding charging types. A queuing model is then developed to estimate the charging load for multiple EVs at the charging station, with and without the consideration of on-road EV wireless charging systems. A case study and simulation results considering a 32-bus distribution system and the US National Household Travel Survey (NHTS) data are presented and discussed to demonstrate the impact of on-road wireless EV charging on the loads of an rapid EV charging station. It is observed that having on-road wireless EV charging as complementary charging to EV charging stations helps to significantly reduce the peak load of the charging station, which improves the power system capacity and defers the need for system upgrades.

Electric Vehicle Load Estimation at Home and Workplace in Saudi Arabia for Grid Planners and Policy Makers

Electric vehicles (Evs) offer promising benefits in reducing emissions and enhancing energy security; however, accurately estimating their load presents a challenge in optimizing grid management and sustainable integration. Moreover, EV load estimation is context-specific, and generalized methods are inadequate. To address this, our study introduces a tailored three-step solution, focusing on the Middle East, specifically Saudi Arabia. Firstly, real survey data are employed to estimate driving patterns and commuting behaviors such as daily mileage, arrival/departure time at home and workplace, and trip mileage. Subsequently, per-unit profiles for homes and workplaces are formulated using these data and commercially available EV data, as these locations are preferred for charging by most EV owners. Finally, the developed profiles facilitate EV load estimations under various scenarios with differing charger ratios (L1 and L2) and building types (residential, commercial, mixed). Simulation outcomes reveal that while purely residential or commercial buildings lead to higher peak loads, mixed buildings prove advantageous in reducing the peak load of Evs. Especially, the ratio of commercial to residential usage of around 50% generates the lowest peak load, indicating an optimal balance. Such analysis aids grid operators and policymakers in load estimation and incentivizing EV-related infrastructure. This study, encompassing data from five Saudi Arabian cities, provides valuable insights into EV usage, but it is essential to interpret findings within the context of these specific cities and be cautious of potential limitations and biases.

Distribution network voltage analysis with data-driven electric vehicle load profiles

The number of electric vehicles (EVs) is expected to increase significantly in the near future, which will affect the operation of future power networks. This study investigates the impact of EVs on the distribution network voltage profile by collecting measurements from residential, slow commercial, and fast public EV charging stations throughout the annual cycle. It is revealed from the data that annual utilization factors for residential, slow commercial, and fast public charging stations are 89%, 55%, and 24%, respectively. The optimal power flow solutions have been presented without the EV and with EV (home, commercial, and public) load profiles. Simulation results identify the voltage violation's extent, instant, and duration that may affect the future distribution grid. Most voltage violations happen in the evening, weekday afternoon, and midday for residential, slow commercial, and fast public EV charging stations, respectively. Furthermore, multiple enabling technologies have been demonstrated to reduce voltage violations, such as coordinated charging (reduced voltage violation by 50%), renewable charging (by 50%), and combinations of multiple types of customers (by 30%). This study presents some key insights into future power system operational scenarios with widespread EV charging stations that can be managed by multiple enabling technologies.

Two-Layer Cooperative Optimization of Flexible Interconnected Distribution Networks Considering Electric Vehicle User Satisfaction Degree

The scaled access of electric vehicles (EVs) exacerbates load fluctuations in distribution networks, which is not conducive to the stable and economic operation of the distribution networks. At present, user satisfaction degree is generally low. To avoid this problem, this paper proposed a two-layer cooperative optimization of flexible interconnected distribution networks considering EV user satisfaction degree. First, the EV user satisfaction degree model is established by considering EV users’ charging waiting time, charging power, and other indicators. At the same time, an EV charging mode switching model is constructed based on the number of EVs entering the network and their battery charge state. On this basis, the Monte Carlo algorithm is used to generate the results of the daily distribution of EV loads taking into account the user satisfaction degree, so as to improve the load ratio of the transformer in the distribution network. Further, a two-layer cooperative optimization of flexible interconnected distribution networks considering electric vehicle user satisfaction degree is developed with the daily operating cost of each network as the optimization objective. Finally, a flexible interconnected power distribution network consisting of three power distribution networks is used as an example for validation. The results show that this method is effective in improving EV user satisfaction degree and reducing the peak-to-valley ratio of the system load while taking into account the safe and economic operation of the distribution network, which greatly improves the reliability and economy of the operation of the flexible interconnected power distribution network.

Strategic planning of distribution network integrated with EV charging stations using fuzzy pareto optimality for performance improvement and grid-side emission reduction benefits

The increasing penetration of electric vehicles (EVs) in distribution systems requires efficient charging load management techniques for delivering high-quality power to consumers. In this paper, we proposed a strategic planning approach for distribution systems integrated with EV charging stations, employing fuzzy Pareto optimality to enhance system performance and minimize carbon emissions. Our approach involves a fuzzy Pareto heuristic network reconfiguration to minimize real power loss and improve voltage profiles. Additionally, Time of Use (ToU) pricing-based EV charging load scheduling is developed to minimize the annual energy cost. These objectives aim to improve minimum node voltage, reduce power losses, and maintain branch current constraints. To simulate the impact of EV charging loads, we aggregate residential, commercial, and industrial loads, along with EV charging loads at charging stations based on customer choice time and ToU pricing methods. The proposed reconfiguration approach is tested on 33 and 69-bus distribution systems with integrated EV charging stations, distributed generations (DGs), and shunt capacitors (SCs). The simulation results demonstrate the advantages of the reconfiguration algorithm in enhancing the performance of the distribution network in the presence of EV loads. Furthermore, we compare ToU pricing-based EV charging time zones with customer comfort-based EV charging, showcasing the advantages of the former in reducing emissions through real power loss reduction. Overall, our strategic planning approach not only improves distribution system performance but also offers economic benefits while minimizing carbon dioxide emissions.

Selection of power sharing factor for hybrid battery/ultracapacitor energy storage‐based EV through load emulation technique

AbstractThe hybrid battery/ultracapacitor (UC) energy storage system for electric vehicles (EVs) proved more reliable and cost‐effective. Even with the best possible sizing of these energy storage devices, unforeseen EV loading cycles are causing a single‐source transition. Mainly, the UC exhaustion in acceleration or transient loading shifts the loading to the battery, causing overdischarge and reducing the life span. This paper presents a deterministic current control algorithm to restrict the maximum allowable battery discharge current. The current discharge control in the battery/UC is implemented using symmetrical carrier‐based digital average current mode (DACM). The proposed energy management algorithm (EMA) with variable battery current sharing factor typically controls the current flow between the battery and the UC so that the battery current is always within safe limits. The DC‐DC converter with proposed novel EMA allows the system to regulate the voltage and current flow between the two power sources. By sharing the currents in this way and limiting the battery current to a safe value, the system helps to prolong the battery's life and ensures that the vehicle operates safely and reliably over the long term. The EV load profile emulator provides the current sharing under dynamic operating conditions. The hardware results are supported to validate the single‐source transition under hybrid energy sources.

Evaluation of hosting capacity of the power grid for electric vehicles – A case study in a Swedish residential area

The deployment of electric vehicles (EVs) is growing significantly in recent years. The increasing EV charging loads pose great stress on power grids in Sweden, as many existing power grids are not designed to host such large shares of new electric loads. Hence, studies investigating the impact of EV charging are needed. This study conducts a case study based on an existing Swedish residential power grid using real-life EV charging data to estimate the local grid hosting capacity (HC) for EVs. A combined time-series and stochastic HC assessment method is used with voltage deviation, cable loading and transformer loading as the performance indices. Uncertainty in EV charging locations and individual charging behaviour have been considered via Monte Carlo simulations. The power grid HC is analysed and compared under three charging strategies and four EV penetration levels. Study results show that a charging strategy based on low electricity prices gave lower HC due to simultaneous EV loads compared to the other two strategies: charging directly after plugging in the EV and an even charging load through the plug-in session. This implies the need for coordinated charging controls of EV fleets or diversified power tariffs to balance power on a large scale.

Analysis of Smart Grid Optimal Scheduling considering the Demand Response of Different EV Owners

The escalating growth of electric vehicle (EV) load has emphasized the growing importance of effective scheduling strategies. Due to the discrepancies among EV owners, their responses to scheduling can differ significantly. Therefore, to achieve better scheduling results, it is crucial to consider the impact of these discrepancies on the optimal scheduling. This paper proposes a classified scheduling method for different types of EV owners. According to charging and vehicle-to-grid (V2G) data of EV owners, the K-means clustering algorithm (K-means) is used to classify EV owners, and the demand response (DR) model is established based on the classification results. The DR model is designed to account for the diverse responses of different EV owners, and the price elasticity, time gap elasticity, and preference time elasticity are important factors in the model. This paper adopts the maximization of smart grid’s revenue as the optimization objective through three approaches: (1) modifying the charging and V2G of EV; (2) obtaining V2G prices for all types of EV; and then (3) adjusting the power output of each unit. To evaluate the proposed method, the IEEE 10-unit system is employed for simulation, and the optimization problem is solved using the CPLEX solver. Compared to previous studies, the proposed classified scheduling method exhibits significant improvements in terms of revenue maximization, load distribution among different types of EVs, generation cost savings, and load variance reduction.

Machine learning-based prediction of on-state voltage for real-time health monitoring of IGBT

Real-time temperature monitoring of power semiconductors is a powerful tool for the predictive maintenance and lifetime prediction of power converters, and is typically performed by measuring temperature sensitive electrical parameters (TSEP). In this work, we demonstrate a machine learning-based method to estimate the on-state voltage of a real converter prototype featuring variable load of electric vehicle (EV) and photovoltaic (PV) systems under different ambient temperatures induced in a climate chamber. The method provides for the first-time insights on the uncertainties and feature importance for the predictions, and is aimed to be industrial compatible by applying only methods that are well established in industry. The approach is further flexible to any converter system, independently of its specifications. We have used the Support Vector Machine, K-Nearest Neighbors, and Decision Tree algorithms to predict the on-state voltage as functions of the readily measured parameters of a converter. We show that for the PV case, the K-Nearest Neighbor method yields the lowest mean absolute error of about 0.75 % for prediction, while for EV, the K-Nearest Neighbor algorithm gives the lowest mean absolute error of 5 %.

Optimal planning of integrated energy system considering swapping station and carbon capture power system

An optimal planning method for an integrated energy system (IES) considering electric vehicles (EVs) swapping station (SS) and carbon capture power system (CCPS) is studied in this paper. Firstly, based on the analysis of swapping EV load, the model of SS is established. Then, we built the models of CCPS, and other energy supply and storage equipment in IES. Based on this, the annual comprehensive cost of planning is regarded as the optimization objective, a planning model of IES considering SS and CCPS is developed, and solved by CPLEX. Finally, an actual case was analyzed to verify the feasibility and effectiveness of the planning method proposed in this paper.

Comparison of electric vehicle load forecasting across different spatial levels with incorporated uncertainty estimation

Accurate load forecasting is important to mitigate the negative impact of Electric vehicle integration into the existing grid. Previous studies mostly focus on individual or aggregated levels without specifying the impact of accuracy due to the selection of different spatial levels and lack the integration of uncertainty estimation in the forecasting models. To address these issues, this study compares the predictive performance of a Random Forest and Artificial Neural Networks at different spatial levels with 15-min resolution data across case studies (i) with 2 Electric Vehicles charging poles and 3 users, (ii) with 75 charging poles, 8 charging rails and 70 users. The outcome shows that forecasting the Electric Vehicle load of smaller case studies will require the presence or calendar information of users. Whereas in case studies with more than 10 charging piles, the features “previous week's power”, “hour of the day” and the “number of connections” can achieve similar results. The results also showed that the aggregated forecasting was more accurate than individual charging piles. Moreover, the uncertainty plot generated for a 90% prediction interval showed that the uncertainty estimates were more reliable for the case study with large numbers of Electric Vehicles.

Planning of fast charging infrastructure for electric vehicles in a distribution system and prediction of dynamic price

The increasing number of electric vehicles (EVs) has led to the need for installing public electric vehicle charging stations (EVCS) to facilitate ease of use and to support users who do not have the option of residential charging. The public electric vehicle charging infrastructures (EVCIs) must be equipped with a good number of EVCSs, with fast charging capability, to accommodate the EV traffic demand, which would otherwise lead to congestion at the charging stations. The location of these fast-charging infrastructures significantly impacts the distribution system (DS). We propose the optimal placement of fast-charging EVCIs at different locations in the distribution system, using multi-objective particle swarm optimization (MOPSO), so that the power loss and voltage deviations are kept at a minimum. Time-series analysis of the DS and EV load variations are performed using MATLAB and OpenDSS. We further analyze the cost benefits of the EVCIs under real-time pricing conditions and employ an autoregressive integrated moving average (ARIMA) model to predict the dynamic price. The simulated test system without any EVCI has a power loss of 164.36 kW and squared voltage deviations of 0.0235 p.u. Using the proposed method, the results obtained validate the optimal location of 5 EVCIs (each having 20 EVCSs with a 50kWh charger rating) resulting in a minimum power loss of 201.40 kW and squared voltage deviations of 0.0182 p.u. in the system. Significant cost benefits for the EVCIs are also achieved, and an R-squared value of dynamic price predictions of 0.9999 is obtained. This would allow the charging station operator to make promotional offers for maximizing utilization and increasing profits.