Plug-in Electric Vehicles Load Research Articles

Reliability-driven time-of-use tariffs for efficient plug-in electric vehicle integration

Demand response (DR) plays a crucial role in balancing electricity supply and demand, but existing studies on price-based DR for plug-in electric vehicle (PEV) load management lack comprehensive considerations. Therefore, this study designs reliability-driven time-of-use (TOU) tariffs for the integration of PEVs, considering various factors such as random component failures, the stochastic characteristics of non-dispatchable components, and fluctuations in load patterns and uncertainties. The proposed framework employs advanced stochastic simulation models to assess generation facility reliability amidst extensive PEV deployment in three steps. First, individual components, including conventional generation capacity, renewable energy sources, and demand, as well as PEV load models, are developed. Then, a system reserve margin assessment is carried out via the generation of seasonal synthetic time series data. During this step, innovative techniques, including box-plot methods and expert rules, are used to simplify the representation of the system reserve margin at each hour. Finally, optimized TOU pricing schedules for PEVs are generated. These pricing hours are initially categorized as off-peak, mid-peak, or on-peak, streamlining scenario analysis and aligning PEV charging profiles with predefined pricing schedules for grid reliability evaluation. Case studies have shown that the proposed method can improve the reliability indices by effectively mitigating the impacts.

Machine learning-based optimal distributed generation and electric vehicle load management

The load profile of radial distribution networks (RDNs) is significantly impacted when plug-in electric vehicles (PEVs) are connected to them in large numbers. The disturbances in the load profile may lead to increased power losses in distribution lines and deterioration of the network voltage profile. The provision of distributed generation at strategic locations in the distribution network can help to compensate for the impact on the electrical network due to PEV loads. This paper proposes the use of machine learning (ML)-based models for determining the optimal location of distributed generators (DGs) in an RDN. The proposed method considered time-varying load in addition to PEV load. The suggested method determines optimal placement of DGs based on the power loss reduction index and voltage deviation index reduction index. Four distinct types of ML models were used in the proposed approach using synthesised data on the Institute of Electrical and Electronics Engineers' 33-bus RDN. The performance of the ML models was evaluated with respect to mean squared error and mean absolute percentage error and, for the ML models considered, the random forest ML model gave the best performance.

Probabilistic optimal power dispatch in a droop controlled islanded microgrid in presence of renewable energy sources and PHEV load demand

This paper proposes a stochastic optimal power allocation strategy for a droop controlled islanded microgrid (DCIMG) with a medium X/R ratio in presence of combined heat and power-based distributed generators (DGs), renewable generators, switched capacitor banks, plug-in hybrid electric vehicle (PHEV) loads, voltage dependent electric loads and heat loads. Optimal values of stochastic power demand due to PHEV charging and discharging load are determined based on the goals of achieving target state-of-charge of PHEV batteries and effective peak shaving during peak load periods, respectively. Active power of dispatchable DGs is optimally allocated depending on the simultaneous fulfillment of an economic objective, an environmental objective along with three network related objectives. Hong's 2m+1 point estimate method (PEM) and Hong's 2m+1 PEM coupled with Nataf transformation (NT) are employed to handle the uncorrelated uncertainties accompanied with PHEV load demand and the correlated uncertainties associated with spatially correlated renewable generation and load demand. Active and reactive power static droop coefficients, and frequency and voltage reference settings of the dispatchable DG units are considered as the control variables for optimal dispatching of active power. A modified hybrid particle swarm and grey wolf optimizer embedded in fuzzy framework is used to solve the constrained multi-objective optimization problem. The efficacy of the proposed framework is validated on a 33-node droop controlled islanded microgrid test system.

Hierarchical High-Resolution Load Forecasting for Electric Vehicle Charging: A Deep Learning Approach

The mass roll-out of electric vehicles substantially contributes to reducing fossil fuel consumption and environmental emissions. Meanwhile, the large fluctuation, strong uncertainty, and multiple coupled impact factors of plug-in electric vehicle charging significantly challenge the existing load forecasting approaches. Unlike the conventional load, the fluctuation of plug-in electric vehicle charging is particularly significant at the very-short-term minute-level time scale given the large charging power and stochastic human behaviors. In this article, a novel historical-data-based high-resolution plug-in electric vehicle load forecasting model structure is established, accommodating the various factors that affect the charging load. Moreover, an enhanced attention-based long short-term memory deep learning approach is proposed for solving the intractable forecasting problem, where a feature upscaling and downscaling algorithm is proposed for the hierarchical high-resolution data processing. Numerical results on real-world data of a charging station in Shenzhen show that the proposed model structure and algorithm demonstrate well performance in short-term and very-short-term hierarchical high-resolution plug-in electric vehicles charging load forecasting problems.

Plug-in electric vehicle load modeling for charging scheduling strategies in microgrids

Utilization of plug-in electric vehicle (PEV) load models can improve the performance of smart charging strategies, which increase the reliability of the grid by harnessing the flexibility of PEV loads. This paper presents a method for utilizing personal PEV load models in real-time stochastic charging control with single and finite system-time horizons. First, the drivers’ load models are found with Kernel Density Estimation (KDE). Second, a single system-time horizon coordinated charging control algorithm is devised to ensure each PEV is charged at least a critical amount given a feasible set of optimization constraints. The coordinated charging algorithm tackles the NP-hardness of single-deadline charging scheduling problems efficiently with a sorting algorithm utilizing the stochastic PEV load models. Third, we extend the single system-time horizon coordinated charging control algorithm to a scheduling algorithm considering a finite system-time horizon. This approach utilizes the stochastic PEV load models in a model predictive control based approach to decrease the complexity of stochastic online charging scheduling problem into a deterministic case. The scheduling algorithm makes assumptions about the future arrivals to the charging station, unlike the classical online EV charging scheduling algorithms, which optimize the load demand revealed at the current time but underestimate the load demand revealed in the future. Our findings suggest the individual load models complement smart charging algorithms’ decision process by improving the fairness of charging time allocation and extending the degree of knowledge of future random data for the scheduling algorithm.

Stochastic multi-objectives optimal scheduling of energy hubs with responsive demands in smart microgrids

Energy hubs (EHs) are progressively being implemented in microgrids for local generation, transfer, and storage of various forms of energy. However, issues have emerged around developing an operating schedule for the different energy resources incorporated in the EHs to ensure the operation at minimum possible costs and emissions. This paper proposes a stochastic-based multi-objectives optimization model for optimal day-ahead scheduling of microgrids based on EHs. The proposed model simultaneously manages the non-dispatchable distributed generator units, such as wind turbines and photovoltaic systems, and energy storage systems, such as compressed air energy storage (CAES) and battery energy storage systems. Moreover, the thermal network is also established by introducing solar heat collectors, heat generated during the discharge of CAES, and thermal energy storage. Furthermore, the proposed model also considers the uncertainties of wind speed, solar radiation, and residential loads. A demand response program (DRP) is also employed to the residential and plug-in electric vehicles load to flatten the load curve and to perform more cost-effectively. A total reduction of 9.87 % and 21.41 % is achieved in the cost and emission amount, respectively, after applying the DRP. Furthermore, a decrease of 13.41 % and 45.04 % is also achieved for the costs of imported power and operation of the CAES, respectively. Different case studies are performed, and a comparative study with other existing research is conducted to demonstrate the effectiveness of the proposed approach. The simulation results show the efficacy of the proposed stochastic approach for optimal day-ahead scheduling.

Benefit maximization and optimal scheduling of renewable energy sources integrated system considering the impact of energy storage device and Plug-in Electric vehicle load demand

The intermittent nature of renewable-based generation may cause the dip or rise in generation and load imbalances. This paperwork obtains optimal generation scheduling, market benefit maximization, and daily energy loss minimization considering the impact of Plug-in Electric vehicles (PEV) and battery energy storage devices using nonlinear programming. The Plug-in EV load demand has been modelled using the Monte Carlo simulation (MCS), and the size of the energy storage device is obtained in the renewable energy source (RES) Microgrid distribution system. The proposed work's main contributions are (i) total cost-benefit maximization, (ii) daily energy loss minimization, (iii) optimal generation scheduling of RES based Microgrid and (iv) probabilistic modelling of PEV load demand and BES sizing. Furthermore, the results were compared to those available in the literature. The voltage variations in the Microgrid are also calculated with the effects of PEV and BES into account. The IEEE-33 bus test system was the subject of the investigation. The multi-objective problem has been solved using iterative Monte Carlo Simulation (MCS) and Nonlinear Programming (NLP). The proposed approach was implemented using MATLAB and GAMS interface. The daily energy loss has been decreased by 30.085 %, and the voltage deviation (VD) has been minimized by1.165 %.

Optimal allocation of renewable energy source and charging station for PHEVs

Renewable energy forms have a comparatively less impact on the environment when compared with non-renewable sources. Renewable energies like wind power, biomass, hydropower, solar power, and geothermal energies are preferred for production as they do not run out quickly. Owing to the stochastic nature of renewable energy sources (RES) and Plug-in hybrid electric vehicles (PHEVs) load demand, large-scale penetration of these resources from the power system affects the network performance like reduce power quality, increase power loss, and voltage deviation. These issues can be addressed by the optimum planning depending upon the variables outcome in RES to satisfy the extra demand affected by PHEV charging. At the same time, designing a proper and effective energy management strategy in PHEV can be considered an optimization problem and can be resolved by the use of metaheuristic algorithms. With this motivation, this paper presents a novel deep learning with metaheuristic optimization based allocation of RES and charging stations for PHEVs. The proposed model depends upon model predictive control (MPC) where the actual battery state of charge (SOC) is concerned with real time energy management. Moreover, the black widow optimization (BWO) algorithm is used for the optimal allocation of RES and charging stations. The BWO algorithm derives objective functions using different parameters of the charging stations. In addition, based on the MPC model, deep stacked autoencoder (DSAE) is applied for the prediction of near-future velocity. The experimental results stated that the presented model has resulted in improved efficiency of the proposed model over the other methods.

Dispatch management of portable charging stations in electric vehicle networks

The global market share of plug-in electric vehicles (PEVs) is on the rise, resulting in a rapid increase in charging demand in both spatial and temporal domains. The network and coverage of public fixed charging stations (FCSs) are currently constrained by infrastructure costs. As a result, FCSs are not as ubiquitous as traditional gas stations. In addition, as PEVs require a reasonably long time to recharge, waiting times at public charging stations can easily become excessive particularly during busy traffic hours. This paper introduces the new idea of allocating and dispatching portable charging stations (PCSs) in hotspot areas of EV network during busy traffic hours to: i) relieve the burden on FCSs, ii) minimize vehicle waiting times at charging stations, and iii) reduce the overlaps between total PEV demand and peak residential load. We formulate the research challenge for the smart management of PCSs as a constrained optimization problem and introduce a heuristic solution. Detailed simulation results for the Washington green highway EV network show that the proposed approach can significantly reduce the average PEV waiting times and decrease the average PEV loads at FCSs during peak hours by up to 64.7% and 67%, respectively. • A smart dispatch solution to manage vehicle charging demands. • Alleviating the stress/demand at FCSs by smart management of PCSs. • Reducing the overlap between PEVs and residential loads during peak hours.

Correction to “Queuing Analysis-Based PEV Load Modeling Considering Battery Charging Behavior and Their Impact on Distribution System Operation” [Jan 18 261-273

This paper presents a queuing analysis-based method for modeling the 24-h charging load profile of a plug-in electric vehicle (PEV) charging station. The queuing model considers the arrival of PEVs as a non-homogeneous Poisson process with different arrival rates over the day. The first PEV charging load profile assumes customer convenience as the factor that influences the hourly arrival rate of vehicles at the station, while the second profile is developed assuming that customers would respond to PEV charging prices and arrival rates are accordingly affected. One of the main contributions of this paper is to model the PEV service time considering different factors such as the state-of-charge of the vehicle battery, as well as the effect of the battery charging behavior. The impact of PEV load models on distribution systems is studied for a deterministic case, and the impact of uncertainties is examined and compared using the stochastic optimal power flow and the model predictive control approaches.

A Multiagent Fuzzy Reinforcement Learning Approach for Economic Power Dispatch Considering Multiple Plug-In Electric Vehicle Loads

Economic/environmental power dispatch goals were to determine the optimal power generation from thermal plants to satisfy the given demand while diminishing operational costs and also minimizing emissions due to burning of fossil fuels. In this paper, economic and environmental scheduling problems are mathematically formulated by considering various system constraints like the valve point loading effect, ramp rate constraints, plug-in electric vehicle charging constraints, and generator’s capacity constraints. In this work, multiagent fuzzy reinforcement learning (MAFRL) is implemented for an effective elucidation for the economic/environmental power dispatch (EEPD) problem with multiple charging scenarios of plug-in electric vehicle and valve point loading of concern thermal power generators. Here, the EEPD is framed as a multiagent fuzzy reinforcement learning (MAFRL) tasks in which specific plug-in electric vehicles and power generating units act as multiple players for minimizing emissions due to fossil fuels and costs of power generation also satisfying charging constraints of plug-in electric vehicles and several thermal unit’s constraints. To prove the superiority of multiagent fuzzy RL, two standard test functions containing five and fifteen thermal generating units integrated with multiple charging scenarios of plug-in electric vehicles have projected. Numerical consequences and assessment with a numerous current solution methodologies show the potentiality of multiagent fuzzy reinforcement learning method in solving the EEPD problem.

A New Framework for Plug-In Electric Vehicle Charging Models Supported by Solar Photovoltaic Energy Resources Un nouveau cadre pour les modèles de recharge du véhicule électrique rechargeable supporté par des ressources d’énergie solaire photovoltaïque

This article proposes a new framework for modeling plug-in electric vehicle (PEV) charging demand supported by solar photovoltaic (PV) energy resources in a distribution system. The proposed work focuses on modeling the stochastic nature of both PEV loads and PV generation while considering the effect of the temporal-spatial characteristics of the driver's behavior, as well as solar irradiation and temperature. A trip chain, based on the Markov Chain Monte Carlo process, is developed to properly model PEV daily driving activities and the PV uncertainty. Charging facilities are assumed available at home, work, and fast-charging stations, having charging levels of 3.7, 6.6, and 50 kW, respectively. The proposed framework is examined, considering the National Household Travel Survey global data, as well as the city of Buffalo and New York state. The impact of varying the penetration levels of PEV and PV resources is also investigated. This work strengthens the proposed models in the literature by integrating the temporal-spatial characteristics of PEV charging demand into PV stochastic models.

Load Profile Modeling of Plug-In Electric Vehicles: Realistic and Ready-to-Use Benchmark Test Data

The penetration of plug-in electric vehicles (PEVs) has increased in the transportation sector in the last few years and it has increased the uncertain load in the power sector. In order to analyze the impact on the power grid and plan infrastructure, modeling of PEV load profiles is required. Determining realistic PEV load profiles is challenging due to the involvement of serval uncertainties and complex interdependencies among different factors and to date, there are no benchmark load profiles of PEVs. In this paper, realistic and ready-to-use load profiles for PEVs are developed by considering vehicle mobility, charging infrastructure, and the market share of PEVs. Firstly, the U.S. National Household Travel Survey (NHTS) data is filtered to remove vehicles with unrealistic, duplicate, and missing data. Secondly, a set of relevant parameters is extracted to estimate different features of PEVs, such as arrival time, departure time, and daily mileage. Then, all the commercially available PEVs are grouped into four clusters using the K-means algorithm. Finally, the per unit (per PEV) load profiles are estimated using the information of the available PEVs in the market, charging levels in the residential sector, and features extracted in the previous step. A large set of scenarios are considered for each PEV cluster in determining the load profiles. The pre-unit profiles estimated in this study are ready-to-use for researchers and planners in the PEV industry and are realistic due to consideration of different relevant factors and a large traveling database of vehicles. The developed per-unit load profiles are used to estimate and analyze the PEV load profiles of the top four countries with the highest penetration percentage of PEVs.

Intelligent approach for residential load scheduling

Residential load scheduling has been introduced in recent years as a result of implementing demand response programmes in the residential sector. Up to now, studies have focussed on developing algorithms for scheduling the residential loads with the objective of reducing the electricity payment while satisfying the users’ comfort level. To aim at this goal, the user should determine the desired scheduling window for each appliance to initiate the scheduling process. Although these algorithms designed to benefit the end-users, determining the scheduling window for each appliance at the beginning of the scheduling process can be challenging. In this study, an intelligent load scheduling model is proposed to perform the scheduling with the least user intervention. The proposed model predicts the preferred scheduling window for each appliance and initiates the scheduling process. Furthermore, the degradation cost of the battery pack in the vehicle-to-grid mode is considered in the cost function. The model is applied to a case study containing plug-in hybrid electric vehicle and flexible loads such as a washing machine and dishwasher under inclining block rates and hourly pricing tariffs. The results indicate that implementing the model in a residential building can decrease the electricity payment while performing the residential load scheduling with the least user intervention.

Machine Learning Based PEVs Load Extraction and Analysis

Transformation of the energy sector due to the appearance of plug-in electric vehicles (PEVs) has faced the researchers with challenges in recent years. The foremost challenge is uncertain behavior of a PEV that hinders operators determining a deterministic load profile. Load forecasting of PEVs is so crucial in both operating and planning of the energy systems. PEV load demand mainly depends on traveling behavior of them. This paper tries to present an accurate model to forecast PEVs’ traveling behavior in order to extract the PEV load profile. The presented model is based on machine-learning techniques; namely, a generalized regression neural network (GRNN) that correlates between PEVs’ arrival/departure times and traveling behavior is considered in the model. The results show the ability of the GRNN to communicate between arrival/departure times of PEVs and the distance traveled by them with a correlation coefficient (R) of 99.49% for training and 98.99% for tests. Therefore, the trained and saved GRNN model is ready to forecast PEVs’ trip length based on training and testing with historical data. Finally, the results indicate the importance of implementing more accurate methods to predict PEVs to gain the significant advantages in the importance of electrical energy in vehicles in the years to come.

Online Centralized Coordination of Charging and Phase Switching of PEVs in Unbalanced LV Networks With High PV Penetrations

The next decade is likely to witness considerable growth in the application of plug-in electric vehicles (PEVs). However, uncoordinated PEV charging in low-voltage distribution networks may cause grid issues, such as transformer overload and voltage unbalance (VU). Therefore, it is essential to consider a more suitable methodology of voltage regulation for the consumers placed at the end of the feeder when the network is predominantly resistive. With this background, this article investigates the capability of dynamic PEV load transfer within the three phases of a distribution network to propose a new hybrid PEV coordination approach. In addition to the online centralized battery charging and var discharging, this approach performs local voltage profiles improvement by switching PEV at selected single-phase residential houses such that a PEV can be switched from a heavily loaded phase to a (relatively) lightly loaded phase. This results in better voltage regulations and VU reductions. The impacts of uncoordinated and proposed genetic algorithm (GA) coordinated PEV charging on VU and node voltage profiles and losses are simulated for a real unbalanced Western Australian distribution network in the Perth solar city over 24 h.

Multi-objective optimal short-term planning of renewable distributed generations and capacitor banks in power system considering different uncertainties including plug-in electric vehicles

The increasing penetration of solar distributed generations (SDGs) and wind distributed generations (WDGs) together with plug-in electric vehicles (PEVs) will lead to a promising amount of reduction in greenhouse gas emissions. Nevertheless, they bring about adversities such as uncertainty in production-load sides, augmented power loss, and voltage instability in the power system, which should be carefully addressed to increase the reliability. In this concern, this paper proposes a multi-objective optimization methodology for sizing and siting of SDGs, WDGs, and capacitor banks (CBs) in the power system considering uncertainties stemmed from PEVs load demand, solar irradiance, wind speed, and the conventional load. The understudy objectives are the voltage stability index, green-house gas emissions, and the total cost. An unconventional point estimate method (PEM) is used to handle the related uncertainties, and chance-constrained programming method is deployed to deal with smooth constraints. The corresponding probability distribution functions of output variables are estimated by the maximum entropy method. Furthermore, robustness analysis is made by Monte Carlo simulation (MCS). The proposed methodology is applied to a typical radial distribution network. The results show that the presence of PEV’s significantly increases the load demand, which results in voltage collapse in the distribution system without the presence of distributed generations. However, the proposed probabilistic method ensures the safe operation of the distribution system with the optimal allocation of renewable distributed generations and CBs. Moreover, the results of deterministic and probabilistic cases are compared under different penetration levels of PEVs. The best tradeoff solution of the Pareto front is selected by the fuzzy satisfying method.

Optimal integration of DGs into radial distribution network in the presence of plug-in electric vehicles to minimize daily active power losses and to improve the voltage profile of the system using bio-inspired optimization algorithms

PurposeThe increase in plug-in electric vehicles (PEVs) is likely to see a noteworthy impact on the distribution system due to high electric power consumption during charging and uncertainty in charging behavior. To address this problem, the present work mainly focuses on optimal integration of distributed generators (DG) into radial distribution systems in the presence of PEV loads with their charging behavior under daily load pattern including load models by considering the daily (24 h) power loss and voltage improvement of the system as objectives for better system performance.Design/methodology/approachTo achieve the desired outcomes, an efficient weighted factor multi-objective function is modeled. Particle Swarm Optimization (PSO) and Butterfly Optimization (BO) algorithms are selected and implemented to minimize the objectives of the system. A repetitive backward-forward sweep-based load flow has been introduced to calculate the daily power loss and bus voltages of the radial distribution system. The simulations are carried out using MATLAB software.FindingsThe simulation outcomes reveal that the proposed approach definitely improved the system performance in all aspects. Among PSO and BO, BO is comparatively successful in achieving the desired objectives.Originality/valueThe main contribution of this paper is the formulation of the multi-objective function that can address daily active power loss and voltage deviation under 24-h load pattern including grouping of residential, industrial and commercial loads. Introduction of repetitive backward-forward sweep-based load flow and the modeling of PEV load with two different charging scenarios.

A Review on Plug-in Electric Vehicles: Introduction, Current Status, and Load Modeling Techniques

Plug-in electric vehicle (PEV) load modeling is very important in the operation and planning studies of modern power system nowadays. Several parameters and considerations should be taken into account in PEV load modeling, making it a complex problem that should be solved using appropriate techniques. Different techniques have been introduced for PEV load modeling and each of them has individual specifications and features. In this paper, the most popular techniques for PEV load modeling are reviewed and their capabilities are evaluated. Both deterministic and probabilistic methods are investigated and some practical and theoretical hints are presented. Moreover, the characteristics of all techniques are compared with each other and suitable methods for unique applications are proposed. Finally, some potential research areas are presented for future works.

전기차 충전 장소를 고려한 그리드 일부하곡선 분석

This paper presents a methodology to calculate the daily load curve per city by taking into consideration a charging station for electric vehicles, which easily determines the effect of the load owing to the charging of electric vehicles in Seoul. In particular, we calculate the charging power of uncontrolled plug-in electric vehicles (PEVs) in terms of the daily running characteristics of vehicle travelling between homes and workplace, with respect to PEV charging stations in the city, according to the market share of PEVs. Furthermore, we calculate the load curve for the simultaneous charging strategy of controlled PEVs in terms of the vehicles’ daily running characteristics between homes and workplaces and a time-of-use (TOU) price plan of the Seoul Metropolitan Government under uncontrolled and controlled charging types. Thus, we are able to analysis the effect of the total existing electricity load and the abovementioned daily load on the grid. Because we can precisely determine the effect of the hourly charging load of PEVs on a power grid in Seoul using these study results, we can establish a PEV charging load management plan to prevent power grid reinforcement and expansion while satisfying the overload constraint of the power grid in Seoul using an appropriate TOU price plan.