Significance of a short-term test drive for prospective plug-in electric vehicle consumers: An exploratory study

The test drive is a well-known step in car buying. In the emerging plug-in electric vehicle (PEV) market, however, the influence of a pre-purchase test drive on a consumer's inclination to purchase is unknown. Policy makers and industry participants both are eager to understand what factors motivate vehicle consumers at the point-of-sale. A number of researchers have used choice models to shed light on consumer perceptions of PEVs, and others have investigated consumer change in disposition toward a PEV over the course of a trial, wherein test driving a PEV may take place over a number of consecutive days, weeks or months. However, there is little written on the impact of a short-term test drive - a typical experience at dealerships or public "ride-and-drive" events. The impact of a typical test drive, often measured in minutes of driving, is not well understood. This paper first presents a synthesis of the literature on the effect of PEV test drives as they relate to consumer disposition toward PEVs. An analysis of data obtained from an Australian case study whereby attitudinal and stated preference data were collected pre- and post- test drive at public "ride-and-drive" event held Brisbane, Queensland in March 2014 using a custom-designed iPad application. Motorists' perceptions and choice preferences around PEVs were captured, revealing the relative importance of their experience behind the wheel. Using the Australian context as a case-study, this paper presents an exploratory study of consumers' stated preferences toward PEVs both before and after a short test drive.

Non-convex dynamic economic/environmental dispatch with plug-in electric vehicle loads

Electric vehicles are a key prospect for future transportation. A large penetration of electric vehicles has the potential to reduce the global fossil fuel consumption and hence the greenhouse gas emissions and air pollution. However, the additional stochastic loads imposed by plug-in electric vehicles will possibly introduce significant changes to existing load profiles. In his paper, electric vehicles loads are integrated into an 5-unit system using a non-convex dynamic dispatch model. The actual infrastructure characteristics including valve-point effects, load balance constrains and transmission loss have been included in the model. Multiple load profiles are comparatively studied and compared in terms of economic and environmental impacts in order o identify patterns to charge properly. The study as expected shows ha off-peak charging is the best scenario with respect to using less fuels and producing less emissions.

A self-learning teaching-learning based optimization for dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

An MILP model for the plug-in electric vehicle charging coordination problem in electrical distribution systems

A new mixed-integer linear programming (MILP) model is proposed to represent the plug-in electric vehicles (PEVs) charging coordination problem in electrical distribution systems. The proposed model defines the optimal charging schedule for each division of the considered period of time that minimizes the total energy costs. Moreover, priority charging criteria is taken into account. The steady-state operation of the electrical distribution system, as well as the PEV batteries charging is mathematically represented; furthermore, constraints related to limits of voltage, current and power generation are included. The proposed mathematical model was applied in an electrical distribution system used in the specialized literature and the results show that the model can be used in the solution of the PEVs charging problem.

Optimal sitting and sizing of distributed generators considering plug-in electric vehicles

Some uncertainties such as the stochastic input/output power of a plug-in electric vehicle due to its stochastic charging and discharging schedule, that of a wind unit and that of a photovoltaic generation source, volatile fuel prices and future uncertain load growth, all together could lead to some risks in determining the optimal siting and sizing of distributed generators (DGs) in distributed systems. Given this background, under the chance constrained programming (CCP) framework, a new method is presented to handle these uncertainties in the optimal sitting and sizing problem of DGs. First, a mathematical model of CCP is developed with the minimization of DGs investment cost, operational cost and maintenance cost as well as the network loss cost as the objective, security limitations as constraints, the sitting and sizing of DGs as optimization variables. Then, a Monte Carolo simulation embedded genetic algorithm approach is developed to solve the developed CCP model. Finally, the IEEE 37-node test feeder is employed to verify the feasibility and effectiveness of the developed model and method. This work is supported by an Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO) Project on Intelligent Grids Under the Energy Transformed Flagship, and Project from Jiangxi Power Company.

An intelligent network control system for plug-in electric vehicles and the associate communication mechanism

The development of an intelligent plug-in electric vehicle (PEV) network is an important research topic in the smart grid environment. An intelligent PEV network enables a flexible control of PEV charging and discharging activities and hence PEVs can be utilized as ancillary service providers in the power system concerned. Given this background, an intelligent PEV network architecture is first developed, and followed by detailed designs of its application layers, including the charging and discharging controlling system, mobility and roaming management, as well as communication mechanisms associated. The presented architecture leverages the philosophy in mobile communication network buildup

Impacts of plug-in electric vehicles on distribution networks

As a good solution to the shortage and environmental unfriendliness of fossil fuels, plug-in electric vehicles (PEVs) attract much interests of the public. To investigate the problems caused by the integration of numerous PEVs, a lot of research work has been done on the grid impacts of PEVs in aspects including thermal loading, voltage regulation, transformer loss of life, unbalance, losses, and harmonic distortion levels. This paper surveys the-state-of-the-art of the research in this area and outline three possible measures for a power grid company to make full use of PEVs.

Predicting voltage unbalance impacts of plug-in electric vehicles penetration in residential low-voltage distribution networks

Plug-in electric vehicles will soon be connected to residential distribution networks in high quantities and will add to already overburdened residential feeders. However, as battery technology improves, plug-in electric vehicles will also be able to support networks as small distributed generation units by transferring the energy stored in their battery into the grid. Even though the increase in the plug-in electric vehicle connection is gradual, their connection points and charging/discharging levels are random. Therefore, such single-phase bidirectional power flows can have an adverse effect on the voltage unbalance of a three-phase distribution network. In this article, a voltage unbalance sensitivity analysis based on charging/discharging levels and the connection point of plug-in electric vehicles in a residential low-voltage distribution network is presented. Due to the many uncertainties in plug-in electric vehicle ratings and connection points and the network load, a Monte Carlo-based stochastic analysis is developed to predict voltage unbalance in the network in the presence of plug-in electric vehicles. A failure index is introduced to demonstrate the probability of non-standard voltage unbalance in the network due to plug-in electric vehicles.

A multi-agent system with a percolation approach to simulate the driving pattern of Plug-In ELectric Vehicles

A multi-agent system with a percolation approach to simulate the driving pattern of Plug-In Electric Vehicle (PEV), especially suited to simulate the PEVs behavior on any distribution systems, is presented. This tool intends to complement information about the driving patterns database on systems where that kind of information is not available. So, this paper aims to provide a framework that is able to work with any kind of technology and load generated of PEVs. The service zone is divided into several sub-zones, each subzone is considered as an independent agent identified with corresponding load level, and their relationships with the neighboring zones are represented as network probabilities. A percolation approach is used to characterize the autonomy of the battery of the PVEs to move through the city. The methodology is tested with data from a mid-size city real distribution system. The result shows the sub-area where the battery of PEVs will need to be recharge and gives the planners of distribution systems the necessary input for a medium to long term network planning in a smart grid environment. © 2012 IEEE.

IMPACT OF PLUG-IN ELECTRIC VEHICLES AND WIND GENERATORS ON HARMONIC DISTORTION OF ELECTRIC DISTRIBUTION SYSTEMS

Harmonic distortion on voltages and currents increases with the increased penetration of Plug-in Electric Vehicle (PEV) loads in distribution systems. Wind Generators (WGs), which are source of harmonic currents, have some common harmonic profiles with PEVs. Thus, WGs can be utilized in careful ways to subside the effect of PEVs on harmonic distortion. This work studies the impact of PEVs on harmonic distortions and integration of WGs to reduce it. A decoupled harmonic three-phase unbalanced distribution system model is developed in OpenDSS, where PEVs and WGs are represented by harmonic current loads and sources respectively. The developed model is first used to solve harmonic power flow on IEEE 34-bus distribution system with low, moderate, and high penetration of PEVs, and its impact on current/voltage Total Harmonic Distortions (THDs) is studied. This study shows that the voltage and current THDs could be increased upto 9.5% and 50% respectively, in case of distribution systems with high PEV penetration and these THD values are significantly larger than the limits prescribed by the IEEE standards. Next, carefully sized WGs are selected at different locations in the 34-bus distribution system to demonstrate reduction in the current/voltage THDs. In this work, a framework is also developed to find optimal size of WGs to reduce THDs below prescribed operational limits in distribution circuits with PEV loads. The optimization framework is implemented in MATLAB using Genetic Algorithm, which is interfaced with the harmonic power flow model developed in OpenDSS. The developed framework is used to find optimal size of WGs on the 34-bus distribution system with low, moderate, and high penetration of PEVs, with an objective to reduce voltage/current THD deviations throughout the distribution circuits. With the optimal size of WGs in distribution systems with PEV loads, the current and voltage THDs are reduced below 5% and 7% respectively, which are within the limits prescribed by IEEE.

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.

Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review

Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.

AGENT BASED, DISTRIBUTED CONTROL STRATEGIES AND OPTIMIZATION OF PLUG-IN ELECTRIC VEHICLES ON SMART/MICROGRID ARCHITECTURES

This thesis will present strategies for the use of plug-in electric vehicles on smart and microgrids. MATLAB is used as the design tool for all models and simulations. First, a scenario will be explored using the dispatchable loads of electric vehicles to stabilize a microgrid with a high penetration of renewable power generation. Grid components for a microgrid with 50% photovoltaic solar production will be sized through an optimization routine to maintain storage system, load, and vehicle states over a 24-hour period. The findings of this portion are that the dispatchable loads can be used to guard against unpredictable losses in renewable generation output. Second, the use of distributed control strategies for the charging of electric vehicles utilizing an agent-based approach on a smart grid will be studied. The vehicles are regarded as additional loads to a primary forecasted load and use information transfer with the grid to make their charging decisions. Three lightweight control strategies and their effects on the power grid will be presented. The findings are that the charging behavior and peak loads on the grid can be reduced through the use of distributed control strategies.