Plug-in Electric Vehicles Demand Research Articles

Electric vehicle and supply equipment adoption dynamics in the United States

In the present era of climate change concerns, plug-in electric vehicles (PEV) and their associated infrastructure are strategic investments by many governments. Investment discussions often include the dynamic supply-demand relationship between PEV adoption and charging station deployment, but rarely is it given an empirical treatment. We make use of publicly available charging station installation and PEV vehicle registration data by U.S. County between the years 2012 and 2021 to examine the phasing and causal relationship between adoption trends in these two markets. Non-linear Granger causality tests suggest a feedback relationship between PEV vehicle adoption and charging station installations. It can be further explained that when individuals adopt PEV, the demand for the charging infrastructure increases to meet the demand for PEV. Conversely, when more charging station installations become available, it encourages adoption of this technology making it convenient for individuals to have access to these charging facilities. It is observed in aggregate that PEVs are penetrating across the U.S., but the penetration rate differs across states and counties. We examine this interaction using a generalized propensity score (GPS) identification strategy for the effect of charging station installations on PEV registrations, coupled with a generalized additive model (GAM) to construct a causal dose-response curve. We observe statistically significant effects on per capita charging station rates for income and total population. We suggest a policy package that includes charging infrastructure investments but also provides wider adoption of subsidies for low-income households across all states.

Placement and Capacity of EV Charging Stations by Considering Uncertainties With Energy Management Strategies

At the present context, Plug-in electric vehicles (PEVs) are gaining popularity in the automotive industry due to their low CO2 emissions, simple maintenance, and low operating costs. As the number of PEVs on the road increases, the charging demand of PEVs affects distribution network features, such as power loss, voltage profile, and harmonic distortion. Furthermore, one more problem arises due to the high peak power demand from the grid to charge the PEVs at the charging station (CS). In addition, the location of CS also affects the behavior of EV users and CS investors. Hence, this paper applies CS investor, PEV user, and distribution network operator who could approach to CS's optimal location and capacity. Integrating renewable energy sources (RESs) at the charging station is suggested to lower the energy stress on the grid. Moreover, to keep down the peak power demand from the grid and utilize renewable energy more efficiently, energy management strategies (EMS) have been applied through the control of charging and discharging of the battery storage system (BSS). In addition, vehicle to grid (V2G) strategy is also applied to discharge the EV battery at charging station. Furthermore, the uncertainties related to PEV charging demand and PV power generation are addressed by the Monte Carlo Simulation (MCS) method.

Integration of Distributed Generation and Plug-in Electric Vehicles on Power Distribution System by Using Queuing Theory

Plug-in electric vehicles (PEVs) and distributed generation (DG) can positively and negatively impact the distribution system. Therefore, this paper presents the modeling and analysis of DG and PEVs’ penetration levels of the three-phase unbalanced radial distribution system. The study aims to optimize the distribution system’s DG sizing and PEV charging to minimize total power loss. The test system is the 4th circuit of the Nonsung service station along Thaharn Road, Muang District, Udon Thani, Thailand. According to objective function and constraints, such control variables as installation buses and rated outputs of DG and the penetration levels of PEVs were obtained to evaluate the total power loss. Significantly, the charging demand of PEVs is an uncertain load estimated by queuing theory integration with the minimization tool called the differential evaluation (DE) method. According to the result comparison of a four case simulation, the total power losses of queuing theory and DE application are minimum. Finally, total power losses conform to the regulation of the Provincial Electricity Authority (PEA), Thailand.

Aggregative Game for Distributed Charging Strategy of PEVs in a Smart Charging Station

This paper proposes a charging strategy for plug-in electric vehicles (PEVs) in a smart charging station (SCS) that considers load constraints and time anxieties. Due to the rapidly growing load demand of PEVs and the load capacity investments in infrastructure, PEV charging needs to be subject to overload limits, beyond which failures can occur. The time anxiety is presented to address some of the uncertainties that may arise while charging PEVs. Under an aggregative game framework, this paper constructs a price-driven charging model to minimize costs by choosing the optimal charging strategy. Meanwhile, since the driver information is an aggregated item in the PEV cost function, the drivers’ privacy can be protected. Then, a distributed reflected forward–backward (RFB) splitting method is developed to search for the generalized Nash equilibria (GNE) of the game. The convergence of the proposed algorithm and the effectiveness of the charging strategy are verified by the detailed simulation and results.

Stochastic Optimal Sizing of Plug-in Electric Vehicle Parking Lots in Reconfigurable Power Distribution Systems

Charging demand of plug-in electric vehicles (PEVs) can cause reliability and operational challenges in power distribution systems. The aggregated charging control of PEVs in the parking lots (PLs) may alleviate the challenges, if the place and size of PEV PLs are optimally determined. This paper develops a stochastic framework for finding optimal location and sizing of PLs as well as optimal charging profile of PEV PLs with the vehicle to grid capability in a reconfigurable distribution system. The main aims are to reduce distribution system losses and enhance network reliability, subject to numerous constraints of the power distribution system, PLs, and PEVs. To guarantee the global optimum solutions, the proposed optimization problem is linearized to achieve a mixed-integer linear program model. Furthermore, kernel density estimator (KDE) is presented to model the temporal uncertainties associated with PEV owners’ behavior with small number of iterations and no necessary assumptions. Various scenarios and sensitivity analysis are conducted to show the efficacy of the proposed method.

Assessment of a Stand-alone Hybrid PV-Hydrogen Based Electric Vehicle Charging Station Model Using HOMER

Amongst all introduced renewable resources, hydrogen is one of the most encouraging contenders for meeting energy demands. Hydrogen is the most eco-friendly fuel. Hydrogen is a clean and effective energy carrier, and a hydrogen-based economy is extensively considered as a practical solution for energy security and sustainability in the future. Due to its richness and diverse production resources, hydrogen, is becoming an increasingly possible clean and green alternative for energy storage and transportation. As the existing demand for energy soars, hydrogen has grown into the most important source. This article introduces a techno-economic assessment of a novel stand-alone hybrid PV-hydrogen energy-based charging station in Jamshoro, Sindh, Pakistan, to meet daily charging demand for plug-in Electric Vehicles (EV). The proposed design is modelled and simulated using HOMER software to investigate the conceptual system. The hybrid PV-hydrogen-battery energy system was witnessed to be more cost-effective than the PV-hydrogen energy system, with a unit cost of electricity of 0.379 Rs/kWh versus 0.432 Rs/kWh for the PV-hydrogen energy system. With a daily load demand of 1750 kWh/day, the simulation results showed that total electricity production consists of power generated by solar PV (89.4%) and fuel-cells (10.6%). These outcomes indicate that a renewable-energy-powered EV charging station is cost-effective for the proposed site.

Optimal Allocation of Plug-in Electric Vehicle Parking Lots for Maximum Serviceability and Profit in the coupled distribution and transportation networks

Optimal planning and management of electric vehicle parking lots (EVPLs) can be an effective approach for improving the operation of both the distribution system and traffic networks. However, the limited land areas of cities can be an obstacle for constructing a large number of parking lots (PLs). This paper proposed a model for optimal siting and sizing of EVPLs as well as their charging schedule to maximize the total profit of their owners, while maximum parking demand of plug-in electric vehicles (PEVs) can be satisfied. In the proposed model, the purpose of trips, number of PEVs, plus their arrival and departure time in different urban areas are considered. Distribution network constraints are also taken into account using linearized load flow equations. The proposed model is implemented in a 37-bus distribution system coupled with a 25-node transportation network which includes four different areas in terms of PEV travel type. The simulation results show the effectiveness of the proposed model to cover the parking demand of PEVs with a limited number of PLs.

Demand for plug-in electric vehicles across segments in the future vehicle market

• Preferences for PEVs are estimated for a representative national sample. • Vehicle choice is analysed taking both fuel type and car segment into account. • A mixed logit model with random effects reveals many substitution patterns. • Twenty WTP measures are found, e.g. running costs, emissions and charging aspects. • Elasticities for price and range are derived based on the model after calibration. The electrification of transport systems requires a change in the composition of the vehicle fleet towards higher shares of electric vehicles. A successful transition, however, depends on many factors of which some relate to purchase prices and vehicle features, while others relate to technology and charging infrastructure. This paper analyses the transition towards plug-in electric vehicles. We use data from a large representative Danish stated choice survey. Based on these data, we estimate a mixed logit model that allows for correlated random effects across fuel types and car segments as well as systematic heterogeneity. The results show that correlation and substitution indeed goes across these dimensions. Willingness-to-pay (WTP) measures are estimated for a variety of attributes. These suggest that the WTP for range varies with fuel types, that the possibility for home charging is highly valued, and that CO 2 is a significant concern among individuals in the sample.

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.

Resiliency‐oriented islanding of distribution network in the presence of charging stations for electric vehicles

Background Fast Charging Stations (FCSs) for Plug-in Electric Vehicles (PEVs) can affect the performance of distribution networks due to high charging demand of a large number of PEVs. Aims To reduce negative impacts of FCSs, the interactions between transportation and distribution networks must be taken into account in decision-making models. This paper proposes an optimization model to increase the resiliency of the distribution network after occurring severe events considering the availability of FCSs Materials & Methods A new bi-level optimization model is proposed, the lower level of which determines the dynamic charging demand of the in-service FCSs according to the transportation network. At the upper-level problem, the charging demand of the in-service FCSs are considered as electrical loads for the distribution network, and the resiliency-oriented restoration problem determines the optimal boundaries of the islands with the aim of maximizing the recovered loads Results The proposed model is implemented in the 118-bus distribution network coupled with a 21-node transportation network through FCSs Discussion The simulation results show that in general, the presence of FCSs reduces the resiliency of the distribution network. Conclusion However, the proposed model can reduce the negative effects of the FCSs and improve the load restoration of the distribution network while meeting the charging demand of PEVs

Allocation of RESs and PEV Fast-Charging Station on Coupled Transportation and Distribution Networks

Utilization of plug-in electric vehicles (PEVs) in the world is increasing, causing researchers to pay more attention to issues related to PEV fast-charging stations (FCSs). Since PEV FCSs are used simultaneously by a large number of PEVs, they can cause negative effects on electrical networks, including increased losses and voltage deviations. Therefore, optimal siting and sizing of these stations to decrease their negative effects on electrical networks is one of the main challenges in the extensive use of PEVs. In this paper, optimal location and capacity of FCSs and renewable energy sources (RES) are simultaneously determined, while deviation paths and uncertainties of RESs are considered. For this purpose, first a capacitated deviation flow refueling location model (CDFRLM) is proposed to cover the charging demand for PEVs on the transport network. Then, the desired problem is modeled based on the mixed-integer linear programming (MILP), which can be solved using commercially available softwares. The proposed model is implemented on 14-bus and 33-bus distribution test systems which are coupled with a 25-node transportation network. The results show that considering the volatile RES and deviation paths in the problem will affect the location and capacity of charging stations, investment costs, and coverage rates of vehicles charging.

Stochastic Modeling Method of Plug-in Electric Vehicle Charging Demand for Korean Transmission System Planning

The number of plug-in electric vehicles (PEVs) has rapidly increased owing to the government’s active promotion policy worldwide. Consequently, in the near future, their charging demand is expected to grow enough for consideration in the planning process of the transmission system. This study proposes a stochastic method for modeling the PEV charging demand, of which the time and amount are uncertain. In the proposed method, the distribution of PEVs is estimated by the substations based on the number of electricity customers, PEV expansion target, and statistics of existing vehicles. An individual PEV charging profile is modeled using the statistics of internal combustion engine (ICE) vehicles driving and by aggregating the PEV charging profiles per 154 kV substation, the charging demand of PEVs is determined for consideration as part of the total electricity demand in the planning process of transmission systems. The effectiveness of the proposed method is verified through case studies in the Korean power system. It was found that the PEV charging demand has considerable potential as the additional peak demand in the transmission system planning because the charging time could be concentrated in the evening peak time.

A probabilistic capacity planning methodology for plug-in electric vehicle charging lots with on-site energy storage systems

• Operation of charging station with an on-site storage is modelled with an MMPP. • Outage probability is used as the main performance metric. • Station and storage capacity are calculated by a Matrix-geometric algorithm. • Investigate the relationship between PEV statistics, storage size, and grid power. • Case studies show peak demand and electricity bills can be reduced drastically. Plug-in electric vehicles (PEV) have gained popularity to support environmental sustainability and reach net-zero emission goals. However, accommodating large numbers of PEVs is a complex problem as concurrent PEV demand significantly increases peak demand and stresses supporting network elements. In this paper, we present a large-scale PEV charging lot equipped with an on-site storage. Power drawn from the grid is utilized to meet customer demand and charge the storage unit which, in return, is employed to lower peak load and demand charges. By considering the probabilistic nature of the customer demand, the proposed architecture is modelled by a Markov-modulated Poisson Process and a matrix-geometric based algorithm is developed to solve the associated capacity planning problem. Station outage probability (defined as the probability of not serving PEV demand) is used as the main metric to size station resources. Case studies show that by accounting for the statistical variations in customer demand, the power required for the station is significantly less than the sum of chargers’ rated power. In addition, on-site storage can considerably reduce the stress on the supporting grid components and lower stations’ running cost.

Plug-in Electric Vehicle Behavior Modeling in Energy Market: A Novel Deep Learning-Based Approach With Clustering Technique

Growing penetration of Plug-in Electric Vehicles (PEVs) in the transportation fleet and their subsequent charging demands introduce substantial intermittency to the electric load profile which imposes techno-economic challenges on power distribution networks. To address the uncertainty in demand, a novel deep learning-based approach equipped with a hybrid classification task is developed which can take into account the travel characteristics of the PEV owners. The classification structure helps us scrutinize the PEVs demand by allocating a specific forecasting network to each cluster of travel behavior patterns. In our hybrid classification task, first, an unsupervised classifier discerns hidden travel-behavior patterns between the historical PEVs data by clustering them; then, a supervised classifier directs each new PEV data to its appropriate cluster-specific forecasting network. The deep learning-based forecasting and classification networks are constructed based on the Long Short-Term Memory networks to investigate long- and short term features in PEV behaviors. The data-driven structure of our proposed method enables us to observe and preserve the correlation between PEV travel data parameters (departure time, arrival time and traveled distance) and avoid the generation of unrealistic travel samples found in scenario-based approaches. To verify the effectiveness of the proposed method in practical environments, we have studied the impact of the precise forecasting of the PEVs demand in an aggregator’s financial profit in the energy market of the California Independent System Operator market. The numerical results confirm the outstanding performance of our proposed deep learning-based method in forecasting PEVs demand against benchmark approaches in this field such as Monte Carlo, Quasi-Monte Carlo, and Copula with only a 6.77% error in comparison with real data.

Co-Optimized Parking Lot Placement and Incentive Design for Promoting PEV Integration Considering Decision-Dependent Uncertainties

This article proposes a new planning framework for optimal allocation of parking lot (PL)-based charging infrastructures to facilitate the efficient integration of plug-in electric vehicles (PEVs). Unlike existing works, the present article explicitly considers the uncertain implications of incentive policy on PEV owners’ charging behaviors and its effects in PL planning. For this aim, a regret-matching technique is introduced to model the bounded rationality of PEV owners in deciding the choice to use different charging options for recharging their vehicle, as a dependency with respect to the incentive value and the accessibility of the charging service in long-term horizon. Such endogenous uncertainties are considered simultaneously with the inherent exogenous randomness of PEV demand and captured by the proposed PL planning model using a proper scenario generation method. The resulting model turns out to be a two-stage stochastic programming with decision-dependent uncertainties and it is solved by using the genetic algorithm. Numerical studies based on an illustrative test system verify the effectiveness of the proposed model and the approach.

Decentralized PEV Power Allocation With Power Distribution and Transportation Constraints

Plug-in Electric Vehicles (PEVs) keep on penetrating the automobile market. However, uncoordinated PEV charging can impair the reliability of power grid. In this paper, an interesting problem of PEV charging power allocation is investigated, in which both power distribution and transportation constraints are considered. A novel approach for PEV charging management based on optimal power flow (OPF) analysis is proposed to optimize PEV charging energy in a power distribution system. Firstly, spatial and temporal PEV demand scheduling is introduced to maximize PEV charging service capacity while considering the maximum traveling distance of PEVs. Secondly, to ensure the scalability of the OPF analysis, a distributed optimization technique, i.e., proximal Jacobian alternating direction multiplier method, is applied to attain the optimal power allocation in a decentralized manner. The resulting PEV charging service capacity in the power distribution system is improved without violating power distribution and transportation constraints. Furthermore, kernel density estimation method is adopted to identify the PEV range anxiety constraint without the PEV battery information. Simulation results are presented to validate the effectiveness of our approach with high PEV penetration.

A Comparison Study on Stochastic Modeling Methods for Home Energy Management Systems

Obtaining an appropriate model is very crucial to develop an efficient energy management system for the smart home, including photovoltaic (PV) array, plug-in electric vehicle (PEV), home loads, and heat pump (HP). Stochastic modeling methods of smart homes explain random parameters and uncertainties of the aforementioned components. In this paper, a concise yet comprehensive analysis and comparison are presented for these techniques. First, modeling methods are implemented to find appropriate and precise forecasting models for PV, PEV, HP, and home load demand. Then, the accuracy of each model is validated by the real measured data. Finally, the pros and cons of each method are discussed and reviewed. The obtained results show the conditions under which the methods can provide a reliable and accurate description of smart home dynamics.

A hierarchical charging control of plug-in electric vehicles with simple flexibility model

The demand for plug-in electric vehicles (PEVs) is increasing exponentially, which could add a considerable amount of load onto the power grid, because charging one PEV is nearly equivalent to adding three houses to the power system. On the other hand, their capability to actively interact with the grid presents excellent opportunities for load management, demand response, and various other grid services. This paper proposes a hierarchical charging scheduling and control framework to enable PEVs for grid services while meeting vehicle owners’ travel needs. The proposed control framework consists of coordination and vehicle layers. In the vehicle layer, each set of electric vehicle supply equipment (EVSE) is equipped with a controller that estimates charging power and energy flexibility based on vehicle characteristics, charging equipment power rating, battery energy state, and upcoming trip information. The controller also controls vehicle charging power in real-time. In the coordination layer, with the charging flexibility model received from each EVSE controller, the central coordinator determines the optimal power allocation for a look-ahead time window based on grid services to be provided. The proposed charging coordination approach can help reduce computational complexity and communication requirement compared with existing methods. It is also scalable to the expanding PEV fleet and robust to uncertainties in upcoming vehicle trips and future system condition. The proposed method is studied using a prototypical feeder developed at Pacific Northwest National Laboratory and detailed trip information extracted from National Household Travel Survey.

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

The market penetration of Plug-in Electric Vehicles (PEVs) is escalating due to their energy saving and environmental benefits. In order to address PEVs impact on the electric networks, the aggregators need to accurately predict the PEV Travel Behavior (PEV-TB) since the addition of a great number of PEVs to the current distribution network poses serious challenges to the power system. Forecasting PEV-TB is critical because of the high degree of uncertainties in drivers’ behavior. Existing studies mostly simplified the PEV-TB by mapping travel behavior from conventional vehicles. This could cause bias in power estimation considering the differences in PEV-TB because of charging pattern which consequently could bungle economic analysis of aggregators. In this study, to forecast PEV-TB an artificial intelligence-based method -feedforward and recurrent Artificial Neural Networks (ANN) with Levenberg Marquardt (LM) training method based on Rough structure - is developed. The method is based on historical data including arrival time, departure time and trip length. In this study, the correlation among arrival time, departure time and trip length is also considered. The forecasted PEV-TB is then compared with Monte Carlo Simulation (MCS) which is the main benchmarking method in this field. The results comparison depicted the robustness of the proposed methodology. The proposed method reduces the aggregators’ financial loss approximately by 16 $/PEV per year compared to the conventional methods. The findings underline the importance of applying more accurate methods to forecast PEV-TB to gain the most benefit of vehicle electrification in the years to come. • A novel approach is proposed to forecast travel behavior in Plug-in Electric Vehicle. • The method improves the accuracy of travel behavior forecasts by about 10%. • The aggregators’ financial loss and load profile spikes reduced by 23% and 20%. • The findings help to increase market penetration of EVs and lower emissions.

Optimal Bidding/Offering Strategy for EV Aggregators under a Novel Business Model

Realizing the full potential of plug-in electric vehicle (PEVs) in power systems requires the development of business models for PEV owners and electric vehicle aggregators (EVAs). Most business models neglect the significant economic potential of PEV demand response. This paper addresses this challenge by proposing a novel business model to optimize the charging energy of PEVs for maximizing the owners’ profits. The proposed business model aims to overcome the opportunity cost neglect for PEV owners, whose charging energy and charging profiles are optimized with full consideration of the demand curves and market conditions. Lagrangian relaxation technology is used for the relaxation of the constraint of satisfying the charging demand, and as a result, the optimization potential becomes greater. The bidding/offering strategy is formulated as a two-stage stochastic optimization problem, considering the different market prices and initial and target state of energy (SOE) of the PEVs. By case studies and analyses, we demonstrate that the proposed business model can effectively overcome the opportunity cost neglect and increase the PEV owners’ profits. Furthermore, we demonstrate that the proposed business model is incentive-compatible. The PEV owners will be attracted by the proposed business model.