Plug-in Electric Vehicles Charging Research Articles

A Comprehensive Study of the Impacts of PHEVs on Residential Distribution Networks

This paper presents the development of a probabilistic benchmark for assessing the impacts of the uncontrolled charging of plug-in hybrid electric vehicles (PHEVs) on residential distribution networks. Unlike the previous research, which adopted several assumptions and approximations, this paper analyzes the available load and transportation data to extract probability distribution functions describing different uncertainties characterizing the charging process. Monte Carlo simulation is utilized to handle these uncertainties and to predict the anticipated impacts of PHEVs on a representative test network. Finally, conclusions are drawn to assist utilities in integrating PHEVs into their networks.

Apartment Building Electricity System Impact of Operational Electric Vehicle Charging Strategies

This paper discusses the charging of multiple plug-in hybrid electric vehicles (PHEVs) in an apartment building, equipped with a photovoltaic (PV) system. Different charging strategies and charging power ratings are examined, which are assessed in terms of their grid impact, the self-consumption of local electricity generation, and the electric driving range. The impact of a residential building, which incorporates electric vehicle (EV) charging, on the distribution grid can be significantly reduced by using simple EV charging strategies. These strategies include complementing night-time with day-time charging, peak shaving at vehicle level, and charging the surplus of local generation. Effective results are obtained using only the knowledge of the present battery state of charge, next departure time, and the instantaneous local generation surplus. The simultaneity of the EV charging and the PV production increases. The increase in electric driving range is negligible for three-phase charging.

Incorporating Customer Reliability Cost in PEV Charge Scheduling Schemes Considering Vehicle to Home Capability

As the number of plug-in electric vehicles (PEVs) increases, so might their issues and impacts on the power system performance. Toward eliminating the negative impacts of PEVs on the power system, installation of a charging controller at customers' homes, which addresses such issues and brings convenience for customers, is fundamentally required in the smart grid era. This paper develops a novel in-home PEV charge/discharge scheduling method that employs vehicle-to-home (V2H) capability to schedule level of charging/discharging at each time slot. In doing so, a household controller minimizes customer payment cost and reliability cost. The proposed charging algorithm not only responds to time-varying tariffs but can also manage the home outage by supplying household appliances during the home load interruption. The proposed method is verified and tested with different case studies. Results show that applying the proposed method leads to significant decrement in home peak load and customer costs, i.e., payment and interruption costs.

A Dynamic Algorithm for Facilitated Charging of Plug-In Electric Vehicles

Plug-in electric vehicles (PEVs) are a rapidly developing technology that can reduce greenhouse gas emissions and change the way vehicles obtain power. PEV charging stations will most likely be available at home and at work, offering flexible charging options. Ideally, each vehicle will charge when electricity prices are relatively low, to minimize the cost to the consumer and maximize societal benefits. A demand response (DR) service for a fleet of PEVs could yield such charging schedules by regulating consumer electricity use during certain time periods, in order to meet an obligation to the market. We construct an automated DR mechanism for a fleet of PEVs that facilitates vehicle charging to meet the needs of the vehicles and satisfy a load scheduling obligation. Our dynamic algorithm depends only on the knowledge of driving behaviors from a previous similar day, and uses a simple adjusted pricing scheme to instantly assign feasible and satisfactory charging schedules to thousands of vehicles in a fleet as they plug-in. The charging schedules generated using our adjusted pricing scheme can ensure that a new demand peak is not created and can reduce the consumer cost by over 30% when compared to standard charging, which may also increase peak demand by 3.5%. In this paper, we present our formulation, algorithm, and results.

Plug-In Electric Vehicle and Voltage Support for Distributed Solar: Theory and Application

This paper examines plug-in electric vehicle (PEV) grid integration, which is a specific, yet significant, component of the overall innovation being adopted by the electric power system. We propose a PEV charging policy that considers transmission and distribution integration issues and reacts to market signals across time scales and systems. More specifically, we propose that the PEV should make economic charging decisions every 5 min based on a real-time market energy price signal. On the time scale of seconds, the PEV provides voltage support for the distribution network, which may allow increased penetrations of distributed photovoltaic (PV) solar arrays. Simulation results using Electric Reliability Council of Texas wholesale power market data suggest that this voltage support service may be provided at a low cost to the individual PEV owner ($5-$50 per year). Therefore, this may prove a more attractive option for supporting distributed PV arrays than distribution network upgrades such as tap-changer-equipped transformers. Finally, we demonstrate the feasibility of our control algorithm through a test system located at the National Renewable Energy Laboratory.

Estimating the potential of controlled plug-in hybrid electric vehicle charging to reduce operational and capacity expansion costs for electric power systems with high wind penetration

Electric power systems with substantial wind capacity require additional flexibility to react to rapid changes in wind farm output and mismatches in the timing of generation and demand. Controlled variable-rate charging of plug-in electric vehicles allows demand to be rapidly modulated, providing an alternative to using fast-responding natural gas plants for balancing supply with demand and potentially reducing costs of operation and new plant construction. We investigate the cost savings from controlled charging of electric vehicles, the extent to which these benefits increase in high wind penetration scenarios, and the trade-off between establishing a controlled charging program vs. increasing the capacity of generators in the power system. We construct a mixed integer linear programming model for capacity expansion, plant dispatch, and plug-in hybrid electric vehicle (PHEV) charging based on the NYISO system. We find that controlled charging cuts the cost of integrating PHEVs in half. The magnitude of these savings is ∼5% to 15% higher in a system with 20% wind penetration compared to a system with no wind power, and the savings are 50–60% higher in a system that requires capacity expansion.

Optimal Coordination of Plug-In Electric Vehicles in Power Grids With Cost-Benefit Analysis—Part I: Enabling Techniques

Plug-in electric vehicles (PEVs) appear to offer a promising option for mitigating greenhouse emission. However, uncoordinated PEV charging can weaken the reliability of power systems. The proper accommodation of PEVs in a power grid imposes many challenges on system planning and operations. This work aims to investigate optimal PEV coordination strategies with cost-benefit analysis. In Part I, we first present a new method to calculate the charging load of PEVs with a modified Latin hypercube sampling (LHS) method for handling the stochastic property of PEVs. We then propose a new two-stage optimization model to discover the optimal charging states of PEVs in a given day. Using this model, the peak load with charging load of PEVs is minimized in the first stage and the load fluctuation is minimized in the second-stage with peak load being fixed as the value obtained in the first stage. An algorithm based on linear mixed-integer programming is provided as a suitable solution method with fast computation. Finally, we present a new method to calculate the benefit and cost for a PEV charging and discharging coordination strategy from a social welfare approach. These methods are useful for developing PEV coordination strategies in power system planning and supporting PEV-related policy making.

Stochastic Modeling of Plug-in Electric Vehicle Distribution in Power Systems

This paper proposes a stochastic modeling of plug-in electric vehicles (PEVs) distribution in power systems, and analyzes the corresponding clustering characteristic. It is essential for power utilities to estimate the PEV charging demand as the penetration level of PEV is expected to increase rapidly in the near future. Although the distribution of PEVs in power systems is the primary factor for estimating the PEV charging demand, the data currently available are statistics related to fuel-driven vehicles and to existing electric demands in power systems. In this paper, we calculate the number of households using electricity at individual ending buses of a power system based on the electric demands. Then, we estimate the number of PEVs per household using the probability density function of PEVs derived from the given statistics about fuel-driven vehicles. Finally, we present the clustering characteristic of the PEV distribution via case studies employing the test systems.

An online mechanism for multi-unit demand and its application to plug-in hybrid electric vehicle charging

We develop an online mechanism for the allocation of an expiring resource to a dynamic agent population. Each agent has a non-increasing marginal valuation function for the resource, and an upper l...

Model predictive control-based power dispatch for distribution system considering plug-in electric vehicle uncertainty

As an important component of Smart Grid, advanced plug-in electric vehicles (PEVs) are drawing much more attention because of their high energy efficiency, low carbon and noise pollution, and low operational cost. Unlike other controllable loads, PEVs can be connected with the distribution system anytime and anywhere according to the customers’ preference. The uncertain parameters (e.g., charging time, initial battery state-of-charge, start/end time) associated with PEV charging make it difficult to predict the charging load. Therefore, the inherent uncertainty and variability of the PEV charging load have complicated the operations of distribution systems. To address these challenges, this paper proposes a model predictive control (MPC)-based power dispatch approach. The proposed objective functions minimize the operational cost while accommodating the PEV charging uncertainty. Case studies are performed on a modified IEEE 37-bus test feeder. The numerical simulation results demonstrate the effectiveness and accuracy of the proposed MPC-based power dispatch scheme.

Coordination of Generation Scheduling with PEVs Charging in Industrial Microgrids

Conventional industrial microgrids (IMGs) consist of factories with distributed energy resources (DERs) and electric loads that rely on combined heat and power (CHP) systems while the developing IMGs are expected to also include renewable DERs and plug-in electric vehicles (PEVs) with different vehicle ratings and charging characteristics. This paper presents an electricity and heat generation scheduling method coordinated with PEV charging in an IMG considering photovoltaic (PV) generation systems coupled with PV storages. The proposed method is based on dynamic optimal power flow (DOPF) over a 24-hour period and includes security-constrained optimal power flow (SCOPF), IMG's factories constraints, PV storage constraints and PEVs dynamic charging constraints. It will utilize the generators waste heat to fulfill thermal requirements while considering the status of renewable DERs to decrease the overall cost of IMGs. To demonstrate the effectiveness of the proposed method, detailed simulation results are presented and analyzed for an 18-bus IMG consisting of 12 factories and 6 types of PEVs without/with PV generation systems operating in grid-connected and stand-alone modes. The main contribution is including PEVs with dynamic constraints that have changed the nature of scheduling formulation from a simple hourly OPF to a dynamic OPF.

Optimal Power Market Participation of Plug-In Electric Vehicles Pooled by Distribution Feeder

Electric-vehicle grid integration has the potential to stress distribution network equipment and increase peak consumption, unless properly managed. In this paper, we use dynamic programming to develop a decision support algorithm and market participation policy for a load aggregator (LA) managing the charging of plug-in electric vehicles (PEVs) connecting at the same distribution network feeder. The LA submits inflexible and flexible bids to a liberalized hour-ahead power market, while monitoring localized feeder and PEV rate constraints. Flexible bids, which include a bid price or utility, can be cleared as regulation service, cleared as energy, or rejected by the market operator. These market events are probabilistically included within the modeling framework. A case study, based on New York independent system operator data, found that the market participation policy may reduce daily electricity costs for PEVs significantly more than is expected through forecasted electricity-price-based scheduling.

A Hierarchical Decomposition Approach for Coordinated Dispatch of Plug-in Electric Vehicles

Plug-in electric vehicles (PEVs) are increasingly popular in the global trend of energy saving and environmental protection. However, the uncoordinated charging of numerous PEVs can produce significant negative impacts on the secure and economic operation of the power system concerned. In this context, a hierarchical decomposition approach is presented to coordinate the charging/discharging behaviors of PEVs. The major objective of the upper-level model is to minimize the total cost of system operation by jointly dispatching generators and electric vehicle aggregators (EVAs). On the other hand, the lower-level model aims at strictly following the dispatching instructions from the upper-level decision-maker by designing appropriate charging/discharging strategies for each individual PEV in a specified dispatching period. Two highly efficient commercial solvers, namely AMPL/IPOPT and AMPL/CPLEX, respectively, are used to solve the developed hierarchical decomposition model. Finally, a modified IEEE 118-bus testing system including 6 EVAs is employed to demonstrate the performance of the developed model and method.

Do You Mind if I Plug-in My Car? How etiquette shapes PEV drivers’ vehicle charging behavior

Plug-in electric vehicles (PEVs) engage drivers in an essential new behavior—plugging the car into the electrical grid to charge the vehicles’ batteries. Broadly, it has been assumed that (1) away-from-home charging is necessary to grow the PEV market and (2) if away-from-home charging infrastructure is in place and PEV drivers know of it, they will perceive opportunities to charge. The experiences of early PEV drivers cause us to rethink at least the second of these assumptions. Drivers report a lack of what they call “etiquette,” i.e., rules to guide their behavior and their expectations of how they and other PEV drivers ought to behave in these new social interactions. PEV drivers want widely shared, understood, and practiced guidelines to feel comfortable and confident in charging their vehicles away from home. This study uses inductive thematic analysis of transcripts of interviews of 28 PEV driving households conducted in San Diego County, California in spring, 2012. Themes about etiquette emerged within two types of away from home charging. First, public chargers (available to any PEV driver) were the sites of situations in which drivers’ perceived a lack of rules or conflicts between different systems of rules; both were described as inhibiting use of public chargers. Second, workplace charging (typically available only to employees of the entity where the charger is located) adds an additional layer of rules and possibly resources that may either inhibit or encourage PEV charging by employees. As PEV markets and charger networks grow, charging will be shaped by additional systems of rules and regulations, e.g., those governing financial transactions. Our results suggest that absent efforts to help PEV drivers develop, learn, and practice the new rules, they may create as much uncertainty as guidance.

Reducing CO2 emissions on the electric grid through a carbon disincentive policy

This paper studies the operation of an electric grid with renewable wind generation and plug-in electric vehicles (PEVs). In particular, PEVs will be the controllable demand that can mitigate the intermittency in wind generation and improve the capacity factors of the non-renewable generation assets on the grid. Optimization problems are formulated to minimize the costs of electricity generation, and two approaches are proposed to address the grid CO2 emission in the optimization. The first approach directly penalizes CO2 in the objective function, and the second approach adopts a carbon disincentive policy to alter the dispatch order of power plants, so that expensive low-CO2 plants can replace cheap high-CO2 plants. These two approaches result in very different outcomes: the first approach affects only the PEV charging demand on the grid and does not result in significant CO2 reduction, whereas the second approach controls both the generation and load, and CO2 can be reduced substantially. In addition, the carbon disincentive policy, unlike a carbon tax, does not collect any revenue; therefore, the increase in electricity cost is minimal. The effect of the proposed algorithms on the grid electricity cost and carbon emission is analyzed in details and reported.

A framework for allocating greenhouse gas emissions from electricity generation to plug-in electric vehicle charging

Abstract This paper describes a number of different allocation methods for assigning greenhouse gas emissions from electricity generation to charging plug-in electric vehicles. These methods for calculating the carbon intensity of electricity are discussed in terms of merits and drawbacks and are placed into a framework to aid in understanding the relation with other allocation methods. Three independent decisions are used to define these methods (average vs. marginal, aggregate vs. temporally-explicit, and retrospective vs. prospective). This framework is important because the use of different methods can lead to very different carbon intensities and studies or analyses that do not properly identify the methods used can confuse policymakers and stakeholders, especially when compared to other studies using different methods.

Estimating the Impact of Electric Vehicle Smart Charging on Distribution Transformer Aging

This paper describes a method for estimating the impact of plug-in electric vehicle (PEV) charging on overhead distribution transformers, based on detailed travel demand data and under several different schemes for mitigating overloads by shifting PEV charging times (smart charging). The paper also presents a new smart charging algorithm that manages PEV charging based on estimated transformer temperatures. We simulated the varied behavior of drivers from the 2009 National Household Transportation Survey, and transformer temperatures based an IEEE standard dynamic thermal model. Results are shown for Monte Carlo simulation of a 25 kVA overhead distribution transformer, with ambient temperature data from hot and cold climate locations, for uncontrolled and several smart-charging scenarios. These results illustrate the substantial impact of ambient temperatures on distribution transformer aging, and indicate that temperature-based smart charging can dramatically reduce both the mean and variance in transformer aging without substantially reducing the frequency with which PEVs obtain a full charge. Finally, the results indicate that simple smart charging schemes, such as delaying charging until after midnight can actually increase, rather than decrease, transformer aging.

Optimal Design of Grid-Connected PEV Charging Systems With Integrated Distributed Resources

The penetration of plug-in electric vehicles and renewable distributed generation is expected to increase over the next few decades. Large scale unregulated deployment of either technology can have a detrimental impact on the electric grid. However, appropriate pairing of these technologies along with some storage could mitigate their individual negative impacts. This paper presents a framework and an optimization methodology for designing grid-connected systems that integrate plug-in electric vehicle chargers, distributed generation and storage. To demonstrate its usefulness, this methodology is applied to the design of optimal architectures for a residential charging case. It is shown that, given current costs, maximizing grid power usage minimizes system lifecycle cost. However, depending upon the location's solar irradiance patterns, architectures with solar photovoltaic generation can be more cost effective than architectures without. Additionally, Li-ion storage technology and micro wind turbines are not yet cost effective when compared to alternative solutions.

A Heuristic Approach for Coordination of Plug-In Electric Vehicles Charging in Smart Grid

In this paper, a heuristic load management algorithm (H-LMA) is proposed for Plug-in Electric Vehicles (PEVs) charging coordination. The proposed approach is aimed to minimize system losses over a period T (e.g., 24 hours) through re-optimizing the system at time intervals (e.g., 15 minutes) while regulating bus voltages through future smart grid communication system by exchanging signals with individual PEV chargers. Scheduling is performed based on the allowable substation transformer loading level and taking into account PEV owner preference/priority within three designated charging time zones. Starting with the highest priority consumers, H-LMA will distribute charging of PEVs within the selected priority time zones to minimize total system losses over a period T while maintaining network operation criteria such as power generation and bus voltages within their permissible limits. Simulation results are presented for different charging scenarios and are compared to demonstrate the performance of H-LMA for the modified IEEE 23 kV distribution system connected to several low voltage residential networks populated with PEVs. The main contribution of this paper lies in the detailed simulations / analyses of the smart grid under study and highlighting the impacts of and T values on the performance of the proposed coordination approach in terms of accuracy and coordination execution time.

Synergistic control of plug-in vehicle charging and wind power scheduling

Significant synergy exists between plug-in electric vehicles (PEVs) and wind energy: PEVs can be the demand response to mitigate the intermittent wind power outputs, and wind energy can provide low-carbon electricity to PEVs. This paper presents a hierarchical control algorithm to realize this synergy by integrating the PEV charging and wind power scheduling. The control algorithm consists of three levels: the top-level controller optimizes the scheduling for the conventional power plants and wind power; the middle-level controller plans PEV charging to achieve load following based on the battery state of charge and plug-off time of each vehicle; the bottom-level controller uses grid electricity frequency as the feedback cue to control PEV charging and serves as the ancillary service to the grid. Numerical simulations show that the integrated controller can improve the grid frequency regulation and overall electricity generation cost without sacrificing the PEVs charging completion.