Plug-in Electric Vehicles Charging Research Articles

Coordination of bidirectional charging for plug-in electric vehicles in smart distribution systems

The random and uncoordinated charging of plug-in electric vehicles (PEVs) at the home applications has negative effects on the technical operation indexes such as power loss and voltage stability of smart distribution systems. Hence, this paper provides an optimized approach which coordinates PEVs charging to reduce power losses and improve voltage profile of feeders in both cases grid to vehicle and vehicle to grid in real-time domain. In the proposed approach, there is a load smart management center (LSMC), which the coordination of EVs is its main duty. Moreover, this algorithm manages PEV based on time priorities due to the on-peak and off-peak periods of distribution system. The proposed algorithm uses maximum sensitivity selection for the optimized management of the vehicle charging in order to minimize power losses and minimize variations of average voltage of feeders. In order to show the performance of the proposed algorithm and LSMC, the actual distribution network (voltage levels of 20 and 0.4 kV) has been simulated that belongs to a city of southwest Iran with residential and commercial loads (for exact simulation of network). Total power losses and voltage profile have been calculated to show capability of proposed method.

Fuzzy Load Modeling of Plug-in Electric Vehicles for Optimal Storage and DG Planning in Active Distribution Network

Plug-in electric vehicle (PEV) charge challenges can be addressed by including their effects on the planning of distribution network components. The planning problem becomes more combinatorial when the uncertainty of PEVs is considered as well. In this paper, storage and distributed generation (DG) planning is considered as an option to deal with the problems arising from PEV uncertainty. The optimal location, capacity, and power rating of the stationary batteries, as well as the location and capacity of dispatchable DGs, are determined to minimize the cost objective function under technical constraints. Short-term scheduling and long-term planning, as optimization problems, are solved using Tabu Search and simulated annealing algorithms, respectively. Simulation results show that when connecting PEVs to the distribution network, both of the stationary battery and DG units are needed from technical and economic points of view. Moreover, the optimal penetration of stationary storage units increases if the uncertainty of PEVs is considered.

Distributed Initialization-Free Cost-Optimal Charging Control of Plug-In Electric Vehicles for Demand Management

This paper considers the optimal charging problem of plug-in electric vehicles (PEVs) on demand side management. PEVs provide a promising alternative solution to reduction of environmental pollution and fuel emission. With a large number of PEVs connected to the grid, a well-designed charging coordination approach is needed to release the impacts on the power system. A distributed cooperative control strategy of PEVs is proposed to meet system interests while respecting each PEV's charging constraint. The proposed strategy is distributed, which only needs to be interacted with the neighboring agents. Our analysis shows that the proposed strategy solves the optimal charging problem of PEVs in an initialization-free approach, which avoids any procedure for initialization during PEVs’ charging process. Furthermore, the proposed strategy is robust to the time-varying available charging power and plug-and-play operations. The simulation studies validate the effectiveness of the proposed distributed strategy.

Multi-period Dynamic Optimization Model for Plug-in Hybrid Electric Vehicles and Electric Vehicles Charging Service Station

Multi-period Dynamic Optimization Model for Plug-in Hybrid Electric Vehicles and Electric Vehicles Charging Service Station

Confusion of innovations: Mainstream consumer perceptions and misperceptions of electric-drive vehicles and charging programs in Canada

Consumer demand is an important aspect of a successful transition to low-carbon technology—where consumers must have basic awareness and understanding of a technology in order to purchase and use it. In this study we explore consumer knowledge, confusion and perceptions for two related technology cases: plug-in electric vehicles (PEVs) and a program that allows the electric utility to control the timing of PEV charging to support renewable electricity. We focus on “Mainstream” vehicle buyers, who differ from the first PEVs buyers. We conducted semi-structured interviews with 22 new-vehicle buying households in the greater Vancouver area of British Columbia, Canada. Overall, participant awareness was very low for both technologies; most participants were confused about hybrid and plug-in hybrid technology and did not understand the sources of electricity that PEVs might consume. Once the case technologies were explained, most participants expressed a wide range of positive and negative perceptions of both, which we categorize into a framework of perceived functional (e.g. cost and performance), symbolic (e.g. “strangeness” and loss of control), or societal (e.g. pollution reduction) attributes. We conclude with suggestions of how research and policy can consider and further examine the roles of knowledge and perceptions in markets for low-carbon technologies.

SDN-Based Framework for the PEV Integrated Smart Grid

In this article, we investigate the plug-in electric vehicle (PEV) integrated smart grid for efficient system operation. Due to the stochastic characteristics of PEVs, the intermittent nature of renewable energy sources, and the heterogeneity of the devices, it is a great challenge to achieve system flexibility, reliability, and interoperability. To address these issues, we propose a two-tier SDN-based framework for the PEV integrated smart grid. The upper tier targets the primary feeder level to have a general view of the system, while the lower tier focuses on the secondary feeder level to achieve granular control of data and power operation. The framework is presented hierarchically, followed by a detailed explanation of the system operation in each tier. In particular, the integration of PEVs, which includes both PEV charging and V2G, is well illustrated in the SDNbased framework. Finally, we provide a case study to validate the emerging need for SDN deployment in the smart grid.

Cyber Insurance for Plug-In Electric Vehicle Charging in Vehicle-to-Grid Systems

V2G systems bring many benefits to power systems in stabilizing energy demand and supply fluctuations as well as to PEV users in reducing energy costs. To achieve the maximum efficiency of V2G systems, data communication plays an important role. However, it is subject to cyber attack and failure, which hinder the effectiveness of V2G systems. In this article, we introduce a novel concept of using cyber insurance to transfer cyber risk from a user to a third party in PEV charging. We first introduce V2G systems and briefly discuss the cyber risks. Additionally, the basic concepts of cyber insurance are presented. We then introduce the use of cyber insurance to remove the risk of paying high energy costs of PEV charging due to the unavailability of data communication. We show that the PEV user can achieve the maximum benefit in deciding to charge its PEV and to buy insurance.

Economical staging plan for implementing electric vehicle charging stations

Abstract This paper proposes an economical staging planning method that optimally matches Plug-in Electric Vehicle (PEV) charging demand with the installation of Fast Charging Stations (FCSs) in the distribution system. The proposed plan consists of two stages. The first stage evaluates the capability of distribution systems to supply PEV charging demands with the existence infrastructure. To investigate the influence of using different types of charging (i.e. Level 2, Level 3), PEV demand is distributed between residential and public charging facilities with different shares considering the travel patterns when modeling PEV loads. Optimal Power Flow (OPF) analysis is utilized to obtain the maximum penetration level of PEVs that the existing distribution system can adapt without any technical violations. In the second stage, the growth of public PEV demand is optimally matched by the installed FCS capacity using the economical staging plan model. By including the waiting and the service times of charging service, the proposed planning model considers not only the economic assessment of the FCS plan but also the quality of FCS service. A comprehensive case study for coupled transportation and electrical networks is examined. The results show that no major distribution system upgrades are required to serve public PEV demand, up to a 30% penetration level, during the early stage of adoption. The influence of using FCS to allocate and manage the PEV demand is illustrated, and the current work provides to FCS investors a means to evaluate the profitability of such a business.

A semi-cooperative decentralized scheduling scheme for plug-in electric vehicle charging demand

The paper proposes a decentralized control scheme for scheduling the flexible charging demand of plug-in electric vehicles in residential distribution networks. This control scheme is designed for execution by a multi-agent system at two consecutive stages of static and dynamic scheduling. The distinctive attributes of the developed control scheme are (i) to realistically prioritize both the customers’ and the utility’s objectives, (ii) to incorporate the uncertainty in the forecasted demand, (iii) to account for customers’ flexibility in their charging demand, and (iv) to specify a fair pricing scenario to all customers while protecting their privacy. The paper includes extensive numerical studies using a set of recorded real-world driving data, representing heterogeneous vehicular demand. In order to assess the efficacy of the proposed scheduling scheme, comparative assessments are also presented against an optimization-based charging scheduling scheme.

Optimal Scheduling of Energy Resources and Management of Loads in Isolated/Islanded Microgrids

Plug-in electric vehicles (PEVs) present a promising solution to mitigate greenhouse gas emissions but on the other hand, their increased penetration can impact power system operation, particularly so in an isolated microgrid. Similarly, demand response (DR) has the potential to provide significant flexibility in the operation of an isolated microgrid with limited generation capacity, by altering the demand and introducing an elasticity effect. This paper proposes a new mathematical model for optimal scheduling of energy resources and smart management of loads which includes smart charging of PEVs, DR, and operation of battery energy storage systems (BESSs), for isolated microgrids. Different case studies are developed to examine isolated microgrid operations when the demand increases, and how the energy management model copes with such increase. The proposed model develops energy management strategies considering the network constraints and different objective functions from the perspective of the microgrid operator as well as from the owners of PEVs and BESS.

A SiC-Based High-Efficiency Isolated Onboard PEV Charger With Ultrawide DC-Link Voltage Range

In LLC-based onboard battery charging architectures used in plug-in electric vehicles (PEV), the dc link voltage can be actively regulated to follow the battery pack voltage so that the LLC converter can operate in proximity of resonant frequency and achieve high efficiencies over the wide range of battery pack voltage. However, conventional boost-type power factor correction (PFC) converters are unable to provide ultrawide dc link voltages since their output voltages should always be larger than their input voltages. This paper proposes a Silicon Carbide (SiC)-based onboard PEV charger using single-ended primary-inductor converter (SEPIC) PFC converter followed by an isolated LLC resonant converter. With the proposed charger architecture, the SEPIC PFC converter is able to provide an ultrawide range for dc link voltage, and consequently enhance the efficiency of the LLC stage by ensuring operation in proximity of resonant frequency. A 1-kW SiC-based prototype is designed to validate the proposed idea. The experimental result shows that the SEPIC PFC converter achieves unity power factor, 2.72% total harmonic distortion, and 95.3% peak conversion efficiency. The LLC converter achieves 97.1% peak efficiency and always demonstrates a very high efficiency across the ultrawide dc-link voltage range. The overall efficiency of the charger is 88.5% to 93.5% from 20% of the rated load to full load.

Distribution system planning to accommodate distributed energy resources and PEVs

With deregulation of the power industry, environmental policy changes, advancements in technology, and the transformation to smart grid, the distribution planning paradigm has gone through significant changes in recent years. Concurrently, with increase in gas prices, driven by a foreseeable fossil fuel depletion in the future, developments in the automotive sector, and environmental concerns, penetration of plug-in electric vehicles (PEVs) has been increasing. These changes will continue to drive the distribution planning problem to evolve in the coming years. This paper presents a comprehensive long-term distribution planning framework from the perspective of local distribution companies (LDCs) considering distributed generation (DG), substations, capacitors, and feeders. Apart from considering the usual demand profile, the proposed framework considers uncontrolled and controlled (smart) PEV charging demand, as well as demand response (DR) options. Based on a back-propagation algorithm combined with cost-benefit analysis, a novel approach is proposed to determine the optimal upgrade plan, allocation, and sizing of the selected components in distribution systems, to minimize the total capital and operating cost. A new iterative method is proposed which involves post-processing the plan decisions to guarantee acceptable adequacy levels for each year of the planning horizon. The performance of the proposed framework is examined considering several case studies on the 33-bus and 69-bus test systems. It is noted from the studies that the presence of uncontrolled PEV charging loads results in much higher plan costs as compared to the case without PEVs, impacts the distribution plan significantly, and hence these loads indeed need be considered in the planning process. On the other hand, smart charging of PEVs have a much reduced impact on the plan cost, and helps alleviate some investments which were needed with uncontrolled charging PEVs. Therefore, the LDCs and policy makers need to encourage customers to adapt these options in the long-run.

Threshold-Based Random Charging Scheme for Decentralized PEV Charging Operation in a Smart Grid.

Smart grids have been introduced to replace conventional power distribution systems without real time monitoring for accommodating the future market penetration of plug-in electric vehicles (PEVs). When a large number of PEVs require simultaneous battery charging, charging coordination techniques have become one of the most critical factors to optimize the PEV charging performance and the conventional distribution system. In this case, considerable computational complexity of a central controller and exchange of real time information among PEVs may occur. To alleviate these problems, a novel threshold-based random charging (TBRC) operation for a decentralized charging system is proposed. Using PEV charging thresholds and random access rates, the PEVs themselves can participate in the charging requests. As PEVs with a high battery state do not transmit the charging requests to the central controller, the complexity of the central controller decreases due to the reduction of the charging requests. In addition, both the charging threshold and the random access rate are statistically calculated based on the average of supply power of the PEV charging system that do not require a real time update. By using the proposed TBRC with a tolerable PEV charging degradation, a 51% reduction of the PEV charging requests is achieved.

A flexible control strategy of plug-in electric vehicles operating in seven modes for smoothing load power curves in smart grid

Plug-in electric vehicles (PEVs) seem to be an interesting new electrical load for improving the reliability of smart grid. The purpose of this work is to investigate a supervision strategy based on regulated charging of PEVs in order to guarantee an optimized power management of the system and consequently a flatter power demand curve. The system mainly includes PEVs powered by a Lithium-ion battery ensuring the charging and discharging operations of these PEVs at home and a daily load power demanded by home appliances. The purpose of the considered strategy is to detect the connection status of each PEV and to establish the priority order between these PEVs with certain flexibility which results in managing the PEVs through seven operating modes. The response of the control algorithm enables to ensure the power flow exchange between the PEVs and the electrical grid, especially at rush hours, and to minimize load power variance aiming to achieve the smoothness for the power demand curve and to reduce the stress of the electrical grid. The simulation results are presented in order to illustrate the efficiency of this power control approach.

Mitigation of the Impact of High Plug-in Electric Vehicle Penetration on Residential Distribution Grid Using Smart Charging Strategies

Vehicle electrification presents a great opportunity to reduce transportation greenhouse gas emissions. The greater use of plug-in electric vehicles (PEVs), however, puts stress on local distribution networks. This paper presents an optimal PEV charging control method integrated with utility demand response (DR) signals to mitigate the impact of PEV charging to several aspects of a grid, including load surge, distribution accumulative voltage deviation, and transformer aging. To build a realistic PEV charging load model, the results of National Household Travel Survey (NHTS) have been analyzed and a stochastic PEV charging model has been defined based on survey results. The residential distribution grid contains 120 houses and is modeled in GridLAB-D. Co-simulation is performed using Matlab and GridLAB-D to enable the optimal control algorithm in Matlab to control PEV charging loads in the residential grid modeled in GridLAB-D. Simulation results demonstrate the effectiveness of the proposed optimal charging control method in mitigating the negative impacts of PEV charging on the residential grid.

A comprehensive study of economic unit commitment of power systems integrating various renewable generations and plug-in electric vehicles

Significant penetration of renewable generations (RGs) and mass roll-out of plug-in electric vehicles (PEVs) will pay a vital role in delivering the low carbon energy future and low emissions of greenhouse gas (GHG) that are responsible for the global climate change. However, it is of considerable difficulties to precisely forecast the undispatchable and intermittent wind and solar power generations. The uncoordinated charging of PEVs imposes further challenges on the unit commitment in modern grid operations. In this paper, all these factors are comprehensively investigated for the first time within a novel hybrid unit commitment framework, namely UCsRP, which considers a wide range of scenarios in renewable generations and demand side management of dispatchable PEVs load. UCsRP is however an extremely challenging optimisation problem not only due to the large scale, mixed integer and nonlinearity, but also due to the double uncertainties relating to the renewable generations and PEV charging and discharging. In this paper, a meta-heuristic solving tool is introduced for solving the UCsRP problem. A key to improve the reliability of the unit commitment is to generate a range of scenarios based on multiple distributions of renewable generations under different prediction errors and extreme predicted value conditions. This is achieved by introducing a novel multi-zone sampling method. A comprehensive study considering four different cases of unit commitment problems with various weather and season scenarios using real power system data are conducted and solved, and smart management of charging and discharging of PEVs are incorporated into the problem. Test results confirm the efficacy of the proposed framework and new solving tool for UCsRP problem. The economic effects of various scenarios are comprehensively evaluated and compared based on the average economic cost index, and several important findings are revealed.

Distributed Control for Charging Multiple Electric Vehicles with Overload Limitation

Severe pollution induced by traditional fossil fuels arouses great attention on the usage of plug-in electric vehicles (PEVs) and renewable energy. However, large-scale penetration of PEVs combined with other kinds of appliances tends to cause excessive or even disastrous burden on the power grid, especially during peak hours. This paper focuses on the scheduling of PEVs charging process among different charging stations and each station can be supplied by both renewable energy generators and a distribution network. The distribution network also powers some uncontrollable loads. In order to minimize the on-grid energy cost with local renewable energy and non-ideal storage while avoiding the overload risk of the distribution network, an online algorithm consisting of scheduling the charging of PEVs and energy management of charging stations is developed based on Lyapunov optimization and Lagrange dual decomposition techniques. The algorithm can satisfy the random charging requests from PEVs with provable performance. Simulation results with real data demonstrate that the proposed algorithm can decrease the time-average cost of stations while avoiding overload in the distribution network in the presence of random uncontrollable loads.

Demand management to mitigate impacts of plug-in electric vehicle fast charge in buildings with renewables

Plug-in electric vehicle penetration is increasing due to technical advancements and environmental concerns. Along with residential plug-in electric vehicle charging, the public charging infrastructure is much needed to reduce plug-in electric vehicles' range anxiety and foster their adoption. Renewable energy and demand management programs are considered viable options that can reduce the impacts of widespread plug-in electric vehicle penetration on the electric grid. This research studies the impacts of plug-in electric vehicle direct current fast charging on a simulated standalone retail building's peak demand and energy consumption, and presents the ability of renewable energy and demand management options to reduce their impacts. Additionally, insights into a public charge station usage are presented by monitoring different types of plug-in electric vehicle charge behaviors at a retail site. Research findings indicate that demand management of building end-use loads along with the use of solar photovoltaic can contribute to absorbing plug-in electric vehicle penetration at the building level ranging from the average of 7% for the demand management option alone to an average of 38% for the combination of demand management and solar photovoltaic, and contributing to shifting building peak demand to late evening hours.

On the role of prosumers owning rooftop solar photovoltaic in reducing the impact on transformer’s aging due to plug-in electric vehicles charging

This paper investigates the synergetic effect of rooftop solar photovoltaic (PV) generation owned by residential prosumers (power producers and consumers) on reducing the distribution substation transformer’s aging caused by charging plug-in electric vehicles (PEVs). Unlike previous work considering surveys based on internal combustion engine-based vehicle data (e.g., National Household Travel Survey (NHTS)) are used in estimating the charging demand of PEVs, this work considers actual PEVs charging data based on the 2015 Canadian Plug-in Electric Vehicle Survey (CPEVS). This work further quantifies the resultant aging seen on substation transformers when internal combustion engine-based National Household Travel Survey data as compared to plug-in electric vehicle driving data from the Canadian Plug-in Electric Vehicle Survey. Moreover, a comparison of the substation transformer aging is performed based on varying plug-in electric vehicle charging levels. Results of the scenarios have shown substation transformer loss of life is found to be at least 30% higher when vehicle data is based on plug-in electric vehicle studies versus conventional internal combustion engine vehicles with 0% PV penetration, up to twice as high at 100% PV penetration. Further studies have shown grouped plug-in electric vehicle charging using 3.3kW at 7pm results in twice the transformer aging seen versus plug-in electric vehicles charging beginning upon returning home. Finally, results have shown that the effect of rooftop solar PV owned by residential prosumers was found to reduce substation transformer loss-of-life by 75% in the case of 3.3kW plug-in electric vehicle charging when 100% PV penetration was added to the system. Such reduction is due to a decrease in transformer’s winding hot-spot temperature caused by PV generation despite the non-coincidence between the peak charging demand of plug-in electric vehicles and the peak power generation from rooftop solar photovoltaic.

Decentralized Charging of Plug-in Electric Vehicles With Distribution Feeder Overload Control

As the number of charging Plug-in Electric Vehicles (PEVs) increase, due to the limited power capacity of the distribution feeders and the sensitivity of the mid-way distribution transformers to the excessive load, it is crucial to control the amount of power through each specific distribution feeder to avoid system overloads that may lead to breakdowns. In this technical note we develop, analyze and evaluate charging algorithms for PEVs with feeder overload constraints in the distribution grid. The algorithms we propose jointly minimize the variance of the aggregate load and prevent overloading of the distribution feeders.