Plug-in Electric Vehicles Fleet Research Articles

An empirically-validated methodology to simulate electricity demand for electric vehicle charging

With recent changes in the availability and diversity of plug-in electric vehicles (PEVs) in the United States, there is increasing research interest in the interaction between PEVs and the electric grid. Extensive work in the literature examines these interactions with the assumption that the timing of PEV charging will be scheduled, and that charging loads can be adjusted dynamically at the behest of the utility and the system operator. While it might be technically feasible to aggregate the data on driver schedules and historical PEV use and charging decisions, it is unclear whether PEV owners will readily share these data and accept partial third-party control of their vehicle’s charging. Given the uncertainty in the future relationships between electric utilities and PEV owners, this study examines the region-level effects of PEV charging in the absence of the additional data utilities would need to realize these idealized charging scenarios. In particular, this study focuses on temporally-resolved prediction of electricity demand needed to serve PEV charging loads if charge scheduling or control is not widespread.Vehicle trip data from the National Household Travel Survey (NHTS) were converted into individual vehicle charging profiles. Monte Carlo methods were then used with these profiles to simulate electricity demand for PEV charging. These simulations include accounting for the potential demographic characteristics of PEV drivers and the estimated charging behavior of those drivers. The simulation results were validated using empirical vehicle charging data collected by the Pecan Street Research Consortium from households in Austin, Texas. The simulation results compared favorably with the empirical data, estimating charging behavior to within 7% throughout most of the day. Two different simulation approaches were considered to show that a reduced-order simulation approach yields similar results. Finally, having demonstrated the stability of the simulation to assumptions about PEV owner demographics and PEV type-dependent charging patterns, the simulation results were used to determine the effect of unscheduled PEV charging on peak load in three different regions, Texas, New York, and New England, with three PEV fleet growth projections. These results indicate that for the moderate growth scenario considered, unscheduled charging will increase peak load by less than 1% by 2025 in each of the three regions.

An Integrated Tool to Monitor Renewable Energy Flows and Optimize the Recharge of a Fleet of Plug-in Electric Vehicles in the Campus of the University of Salento: Preliminary Results

A tool has been developed to integrate electric vehicles into a general systems for the energy management and optimization of energy from renewable sources in the Campus of the University of Salento. The tool is designed to monitor the status of plug-in vehicles and recharging station and manage the recharging on the basis of the prediction of power from the photovoltaic roofs and usage of electricity in three buildings used by the Department of engineering. The tool will allow the surplus of electricity from photovoltaic to be used for the recharge of the plug-in vehicles. In the present investigation, the benefits in terms of CO2 and costs of the scheduled recharge with respect to free recharge are evaluated on the basis of the preliminary data acquired in the first stage of the experimental campaign.

A Simple Decentralized Charging Control Scheme of Plug-in Electric Vehicles for Alleviating Wind Farm Intermittency

Variable power output from large-scale wind farms present new challenge of balancing power system load with generation. To alleviate this problem, this paper proposes a decentralized charging control scheme for plug-in electric vehicles (PEVs) to neutralize wind power fluctuations. In the proposed scheme, each PEV autonomously adjusts its power in response to a real-time directing signal and based on its own urgency level of charging. No intelligent central control entity is needed. Simulation results demonstrate the effectiveness of the proposed charging control in directing PEV power to counteract wind power fluctuations. Also, proportionally fair distribution of counteracting duties among PEVs can be achieved so as to meet heterogeneous charging requirements of PEV users, and the total utility of the PEV fleet is proven to be maximized.

Exponential weighted method and a compromise programming method for multi-objective operation of plug-in vehicle aggregators in microgrids

Distribution networks are undergoing radical changes due to the high level of penetration of dispersed generation and storage systems. This trend is strongly modifying the structure as well as the management of distribution networks, which are progressively approaching the new concept of microgrids (MGs). Also, the level of penetration of storage systems for plug-in electric vehicles (PEVs) is increasing significantly due to the significant potential that PEVs have for reducing both emission levels and transportation costs. The inclusion of these vehicles in MGs leads to a series of challenges in grid operation, especially ensuring the provision of services that can improve the operation of distribution networks. This paper deals with MGs, including renewable generation plants and aggregators of PEV fleets connected to the grid through power electronic devices. A multi-objective optimization model is presented for obtaining optimal, coordinated operation of MGs. A multi-objective model was solved using two different methods, i.e., the exponential weighted criterion method and a compromise programming method. Both of these methods appeared to be particularly suitable when computational time is an important issue, as it is in the case of optimal control. The effectiveness of the multi-objective approach was demonstrated with numerical applications to a low-voltage microgrid; other multi-objective model-solving algorithms also were assessed in order to compare their programming complexity and the computational efforts required.

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.

Spinning reserve supply with presence of electric vehicles aggregator considering compromise between cost and reliability

By increasing the spinning reserve capacity, reliability of the power system is improved, however, total cost is increased. Therefore to supply the spinning reserve of power system, a compromise between reliability and cost should be considered. In this study, by considering the value of lost load for each important customer, the spinning reserve capacity is determined based on minimisation of total cost of problem. In addition, to supply the spinning reserve of power system, a new market player called plug-in electric vehicles (PEVs) aggregator is proposed. In this base, the aggregator's self-scheduling problem for participating in spinning reserve market is modelled using an agent-based model and behaviour of the PEVs fleet is modelled from the aggregator's point of view. To improve the accuracy of results, the market players are considered as price makers and their offers to the market are modelled using a dynamic game theory simulation. The numerical results show that the presence of PEVs aggregator in spinning reserve market has desired impacts on power system reliability and total cost of system.

PEVs modeling and impacts mitigation in distribution networks

This paper proposes a novel model to estimate the electric energy consumption of light duty fleet of plug-in electric vehicles (PEVs). This model can be used to evaluate the impacts of plugging such loads in distribution networks. Both vehicles users' habits and diversity of usage are considered in the presented model, as well as different electric ranges and ambient temperature effect. Moreover, the paper proposes a method to optimally allocate distributed generation (DG) units in the distribution network to mitigate the impacts of high penetration of PEVs. The proposed model shall help the local distribution companies (LDC) to better assess the expected effects of PEVs on their networks and evaluate the required upgrades. Furthermore, the proposed DG allocation methodology helps to identify the optimal buses on which to connect these DG units in the presence of high PEVs penetration. A genetic based approach is utilized for the planning problem of determining the optimal locations and sizes of DG units, which is defined as a multi-objective mixed integer programming.

On the business value of ICT-controlled plug-in electric vehicle charging in California

The increasing penetration of variable renewable energy, such as wind and solar, requires the deployment of large scale energy storage or dynamic demand side management. Leveraging the intrinsic energy storage potential of certain electric loads could be the key for an efficient transition to green power generation.Plug-in electric vehicles (PEVs) are about to be introduced on a large scale. In this paper, we investigate the savings potential of electricity retailers resulting from the ability to control the charging behavior of a fleet of PEVs using Information and Communication Technology (ICT). This savings potential is important as it could jumpstart the development of advanced control infrastructures for dynamic demand side management.The paper makes three major contributions: first, it applies a novel car usage model based on data from the National Household Travel Survey (NHTS). Second, it develops and evaluates several charging scheduling algorithms with low computational requirements. Third, it identifies several key parameters influencing the relative and absolute savings potential of ICT-controlled PEV charging.We obtain a relative savings potential of up to 45%. The absolute yearly savings per PEV, however, are rather small, which could limit the economic incentives of electricity retailers to deploy the required infrastructure.

Load Scheduling and Dispatch for Aggregators of Plug-In Electric Vehicles

This paper proposes an operating framework for aggregators of plug-in electric vehicles (PEVs). First, a minimum-cost load scheduling algorithm is designed, which determines the purchase of energy in the day-ahead market based on the forecast electricity price and PEV power demands. The same algorithm is applicable for negotiating bilateral contracts. Second, a dynamic dispatch algorithm is developed, used for distributing the purchased energy to PEVs on the operating day. Simulation results are used to evaluate the proposed algorithms, and to demonstrate the potential impact of an aggregated PEV fleet on the power system.

Electric vehicles in imperfect electricity markets: The case of Germany

We use a game-theoretic model to analyze the impacts of a hypothetical fleet of plug-in electric vehicles on the imperfectly competitive German electricity market. Electric vehicles bring both additional demand and additional storage capacity to the market. We determine the effects on prices, welfare, and electricity generation for various cases with different players in charge of vehicle operations. Vehicle loading increases generator profits, but decreases consumer surplus in the power market. If excess vehicle batteries can be used for storage, welfare results are reversed: generating firms suffer from the price-smoothing effect of additional storage, whereas power consumers benefit despite increasing overall demand. Strategic players tend to under-utilize the storage capacity of the vehicle fleet, which may have negative welfare implications. In contrast, we find a market power-mitigating effect of electric vehicle recharging on oligopolistic generators. Overall, electric vehicles are unlikely to be a relevant source of market power in Germany in the foreseeable future.

Electric Vehicles in Imperfect Electricity Markets: A German Case Study

We analyze the impacts of a hypothetical fleet of plug-in electric vehicles on the imperfectly competitive German electricity market with a game-theoretic model. Electric vehicles bring both additional demand and additional storage capacity to the market. We determine their effects on prices, welfare, and electricity generation for various cases with different players being in charge of vehicle operations. We find that vehicle loading increases generator profits, but decreases consumer surplus. If excess vehicle batteries can be used for storage, welfare results are reversed: generating firms suffer from the price-smoothing effect of additional storage, whereas consumers benefit despite increasing overall demand. Results however depend on the player being in charge of storage operations, and on battery degradation costs. Strategic players tend to underutilize the storage capacity of the vehicle fleet, which may have negative welfare implications. In contrast, we find a small market power mitigating effect of electric vehicle recharging on oligopolistic generators. Overall, electric vehicles are unlikely to be a relevant source of market power in Germany.