Integrating plug-in electric vehicles into the electric power system

As more battery electric vehicles and plug-in hybrid electric vehicles are connected to the microgrid, plug-in electric vehicles have a major impact on the microgrid. This paper proposes a multi-objective optimization energy management model including plug-in electric vehicles and other distributed generations. By analyzing the powertrain structure of different kinds of plug-in electric vehicles, the engine fuel consumption model, the charging model, the discharge model, and the battery state of charge model of plug-in electric vehicle in microgrids are given. The proposed model considers the plug-in electric vehicle battery state of charge constraints to prevent the battery from overcharging and over-discharging and gives the state of charge curve in microgrids. Simultaneously, an improved gray wolf algorithm, introducing optimization control factors and greedy strategies to better balance the mining and exploration capabilities of the gray wolf algorithm, is proposed to solve this multi-objective optimization energy management model. Compared with particle swarm optimization and traditional gray wolf algorithm, the improved algorithm further improves the accuracy and convergence speed. Besides, the improved algorithm is applied to three scheduling schemes, and the results show that plug-in hybrid electric vehicles have more advantages in energy economy in some special cases.

Plug-in electric vehicles (PEVs) are becoming increasingly popular. In the next several years, all major vehicle manufacturers will introduce PEVs, either as battery electric vehicles (BEVs), such as the Nissan Leaf, or plug-in hybrid electric vehicles (PHEVs) with a dual powertrain, such as the General Motors Volt and the Toyota Prius. This new generation of vehicles relies on electricity as a power source and a battery as an energy storage medium. Naturally, for PEVs, refueling occurs when the battery connects to an electrical outlet linked to a distribution network. The primary objective of this paper is to study optimal locations for building facilities (i.e., smart garages) that will maximize integrated benefits from the transportation system (i.e., parking fee) and the electric power system [i.e., revenue from vehicle-to-building (V2B) services]. A deterministic smart garage development problem (SGDP) is formulated and modeled as a bilevel program and solved by using a genetic algorithm. The results of a simple numerical example show the sensitivity of the model results with respect to parameters, including PEV penetration rate and battery capacity.

Plug-in hybrid electric vehicles and smart grids: Investigations based on a microsimulation

Author(s): Muehlegger, Erich; Rapson, David | Abstract: This research project explores the plug-in electric vehicle (PEV) market, including both Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs), and the sociodemographic characteristics of purchasing households. The authors use detailed micro-level data on PEV purchase records to answer two primary research questions. Their results confirm that low-income households exhibit a lower share of PEV purchases than they do for conventional, internal combustion engine (ICE) vehicles. Households with annual income less than $50,000 comprise 33 percent of ICE purchases and only 14 percent of PEVS. By comparison, high-income households earning more than $150,000 annually comprise only 12 percent of ICE purchases and 35 percent of PEV purchases over their sample period. Similarly, unsurprising patterns can be seen across ethnicities. For example, non-Hispanic Whites represent 41 percent of ICE purchases but 55 percent of PEV purchases, as compared to Hispanics (38 percent of ICE and 10 percent of PEVs) and African Americans (3 percent of ICEs and 2 percent of PEVS). These differences naturally raise questions about barriers to PEV adoption among low-income and minority ethnic populations. By comparing outcomes in the ICE, hybrid, and PEV markets across income and ethnic groups, the authors are able to test whether price discrimination and barriers to market access are higher in PEV markets for low-income and minority ethnic groups. The authors find that, overall, they are not, although there are mixed results for the used PEV market. In general, non-white, low-income populations face higher prices in the used PEV market, relative to a baseline, than they do in the new PEV market. While some people travel farther to buy used PEVs than they do to buy used ICE vehicles, there is not a pattern that would indicate systematic discrimination (e.g. Hispanics travel farther to buy used PHEVs but less far to buy used BEVs). While the authors admit that their empirical approach cannot control for all potential vehicle composition effects, the authors view their results as being most consistent with a market that provides access to all ethnicities and income groups.View the NCST Project Webpage

Optimal coordination of plug-in hybrid electric vehicles: A stochastic market-based approach

Evaluation of charging infrastructure requirements and operating costs for plug-in electric vehicles

This study compares the carbon footprint of clean versus fossil fuels using the ”Well-To-Wheels”(WTW) methodology, which considers all stages from production to consumption in vehicles. We evaluate fossil fuels like diesel, gasoline, LPG, and electricity. The results show that electric cars emit less CO2 during use, but it is essential to consider emissions from electricity production for a full assessment. Transitioning to clean fuels reduces emissions, but improving technologies is critical to maximizing environmental benefits. The study shows that battery and fuel cell electric vehicles are the most respectful to the environment when generating electricity in renewable energy plants. The analysis shows that fossil fuel engine cars and hybrid and plug-in hybrid electric vehicles have similar carbon footprints because of the high fossil fuel dependence, the high fraction of the driving range, and carbon dioxide emissions in thermal power plants during electricity generation for battery charge in plug-in electric vehicles. This situation reverses producing electricity in renewable energy systems. The study applies to any condition, world area, or country development despite the system development for specific driving conditions and world geographical area since the study develops a comparative analysis between engine types, regardless of driving mode or reference consumption rate.

This report addresses the fundamental question of how much plug-in electric vehicle (PEV) charging infrastructure—also known as electric vehicle supply equipment (EVSE)—is needed in the United States to support both plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs).

This report focuses on the Sleeping Bear Dunes National Lakeshore (SLBE) fleet to identify daily operational characteristics of select vehicles and report findings on vehicle and mission characterizations to support the successful introduction of plug-in electric vehicles (PEVs) into the agencies’ fleets. Individual observations of these selected vehicles provide the basis for recommendations related to electric vehicle adoption and whether a battery electric vehicle (BEV) or plug-in hybrid electric vehicle (PHEV) (collectively plug-in electric vehicles, or PEVs) can fulfill the mission requirements.

In this paper, the economic analysis of a regional coordinated Microgrids system (RCMS) considering the allocation model of plug-in electric vehicles (PEVs) is proposed. Such a system is made up of a Residential Microgrid (RMG) and an Industrial Microgrid (IMG), and the two microgrids are already configured with optimized wind turbines and photovoltaic arrays. In this model, the PEVs are served as a mobile energy storage system, purchasing energy from RMG at off-peak times when electricity prices are low, and selling to the IMG when electricity prices are high. To minimize the total cost of RCMS, the optimal number of PEVs is figured out by means of improved particle swarm optimization (IPSO) algorithm. To research the factors affecting optimal PEVs number and cost of the system, the relationship of PEVs charging and discharging price is deeply analyzed. The proposed smart optimal allocation model is tested with case one and case two. The simulation results show that the total cost of the system is effectively reduced adopting the proposed model.

The recent increase in plug in electric vehicles (PEVs) load has posed severe accommodation issues for the power grids that were not primarily designed to serve PEVs’ charging load. Therefore, the unplanned PEVs’ integration into the distribution networks will deteriorate the system performance by increasing the power loss and voltage deviation at nodes. In this context, this study proposes a methodology to enhance the distribution networks’ PEVs hosting capacity, without degrading the system performance, by optimally allocating the distributed generation (DG). Initially, aiming to minimize the power losses in the distribution network, the optimal siting, sizing and number of the DG units were determined using the particle swarm optimization (PSO) algorithm. Later, the best network bus for PEV accommodation was selected on the criteria of maximum hosting capacity and loss reduction in the distribution network. Then, considering the accessible power output from the grid and the mounted DGs, the PEVs accommodation in the network is evaluated based on different battery capacities (BCs) and state-of-charge (SoC) as decisive factors. The test results on the IEEE 33-bus demonstrate that in the presence of DGs, the distribution network carries a PEV load of 4.418 MW with 48.82% reduction is losses (compared to case without optimal DG allocation) and voltage improvement of 6.98%. It was also examined that the PEVs accommodation in the distribution network varied from 2492 units to 61,446 units, depending on the size of battery pack and its SoC. The proposed research will enable the distribution companies to handle the large number of PEVs in the long-term planning paradigm.KeywordsPlug in electric vehicles accommodationDistributed generationParticle swarm optimizationPower lossesState-of-chargeBattery capacities

The government policies and cost benefits led to the surge in penetration of Plug-in Hybrid Electric Vehicles (PHEVs) and Battery Electric Vehicles (BEVs) into both public and private sectors, challenging the electric grid's potential to maintain its stability and efficiently serve the purpose. The penetration of PHEVs brings technological advancements and environmental benefits to the traditional transportation and electrical power systems. Designing of appropriate charging methods to satisfy PHEV owners helps to improve system performance and avoid transmission and distribution system expansion.;The primary objective of this thesis is to model a coordinated charging algorithm to charge maximum number of PHEVs with the existing infrastructure at a Charging Station (CS) located in residential locality. The stochastic driving patterns of PHEVs are studied and different charging strategies are developed. The Optimized charging scheme is obtained for American Electric Power utility distribution feeder modeled in OpenDSS. Further, PHEV customers are modeled into price responsive consumers capable of participating in Demand Response programs to economically charge their vehicles and render support to the stabilization of power system. Consumer behavior modeling has been done by developing extensive demand-price elasticity matrices (PEMs) which are utilized to calculate the level of demand response.;This thesis also proposes a concept of converting the existing Personal Rapid Transit (PRT) system from guideway power rail propelling vehicles to Battery Electric Vehicles (BEVs) by developing a novel routing algorithm based on the data provided by West Virginia University's Department of Transportation and Parking. This turns into a feasibility analysis of implementing new EV technologies in electrification of public transportation. The routing algorithm involves Mixed Integer Linear Programming (MILP) Optimization resulting in hourly schedule of all the vehicles satisfying the passenger demand. The effect of charging load of BEVs on the electrical distribution system is analyzed by performing a time series simulation on AEP distribution feeder.;Additionally, the impact of PHEV charging on power market prices i.e. Locational Marginal Prices (LMP) in a distribution microgid scenario is presented. Locational marginal pricing is a market based wholesale pricing approach adopted by six out of ten national electricity markets. The uncoordinated charging of EVs results in unplanned peak demand causing system changes and fluctuation of LMP. The stabilizing effect of coordinated charging scheme is demonstrated on IEEE 13 Node test system.

Photovoltaic modules have become a viable renewable energy source for energy systems in communications, commercial, and residential applications. Plug-in electric or hybrid vehicles appear in the market as an emerging technology to reduce carbon emissions and improve energy efficiency. PV modules and plug-in hybrid vehicles interact with the power grid as energy source and energy storage elements, respectively, however little was reported on energy conversion systems featuring three-way energy flow among the power grid, PV modules, and plug-in hybrid vehicles. This paper proposes a plug-in hybrid electric vehicle (PHEV) solar carport charging station concept featuring a multi-port power electronic interface among photovoltaic modules, PHEVs, and the power grid. A unique control strategy is implemented, allowing efficient energy transfer while reducing the conversion stages between the source and load. The system is designed to be modular to improve flexibility and allow for ease of expansion. In the proposed system, a single modular system will provide charging for two parking spaces.

This paper describes “smart charging” systems for plug-in hybrid electric vehicles (PHEVs). The principal design feature is that the system uses gathered information to adaptively control PHEV charging, and does so in a way that allows customer PHEVs to still be charged at a preferred rate (cost). This paper reviews the drivers for smart charging, including electric grid readiness for large adoption rates of PHEVs, and considers national, regional and local distribution level issues. At the distribution level, the effect of increased PHEV charging loads on transformers is considered. The current state of standardization is reviewed with emphasis on communication messages and use cases that reflect smart charging attributes. Centralized system approaches are described, such as integrating electric vehicle supply equipment (EVSE), i.e. chargers, into Advanced Metering Infrastructure (AMI) networks, and treating EVSEs as controllable loads for Demand Response programs. Metering and monitoring the transformers that feed EVSEs can drive a control scheme that is either centralized or distributed. Alternatives to AMI-integration for centralized networks are also reviewed, including commercially available systems. Additionally, smart charging is considered from the billing perspective, where system approaches are described that allow for identification and association between connected PHEVs, EVSEs, premise meters and other smart devices.

Plug-in Electric Vehicles (PEVs) play a pivotal role in transportation electrification. The flexible nature of PEVs' charging demand can be utilized for reducing charging cost as well as optimizing the operating cost of power and transportation networks. Utilizing charging flexibility of geographically spread PEVs requires design and implementation of efficient optimization algorithms. To this end, we propose a fully distributed algorithm to solve the PEVs' Cooperative Charging with Power constraints (PEV-CCP). Our solution considers the electric power limits that originate from physical characteristics of charging station, such as on-site transformer capacity limit, and allows for containing charging burden of PEVs on the electric distribution network. Our approach also enables plug-and-play and valley-filling capabilities of PEV charge scheduling. It distributes computation among agents (PEVs) to solve the PEV-CCP problem in a distributed fashion through an iterative interaction between neighboring agents. The structure of each agent's update functions ensures an agreement on a price signal while enforcing individual PEV constraints. In addition to converging towards the globally-optimum solution, our algorithm ensures the feasibility of each PEV's decision at each iteration. We have tested performance of the proposed approach using a fleet of PEVs. Simulation results verify the plug-and-play capability of the proposed approach by efficiently tracking the changes in terms of arrival/depart of PEVs.