Electric Vehicles Owners Research Articles

Reaping the Benefits of Smart Electric Vehicle Charging and Vehicle-to-Grid Technologies: Regulatory, Policy and Technical Aspects

Electric vehicles (EVs) are penetrating rapidly into the transport sector while making profound implications on the electricity and energy sectors. Although EVs have many benefits, it poses several challenges on power grid operators. Uncoordinated EV charging is one of the critical issues that need to be addressed to mitigate the potential adverse effects on power grids. Smart charging and vehicle-to-grid (V2G) technologies could alleviate these adverse effects but requires policies and regulatory frameworks to increase the uptake of these technologies. This paper presents a critical review on the effects of uncoordinated charging of EVs, and the benefits of smart charging and V2G considering the published research studies and real-world field trials. Simulation case studies also demonstrate the adverse effects of uncoordinated charging and the benefits of smart charging. According to this study, uncoordinated charging increases the peak load, which in turn causes high power losses, voltage violations, voltage unbalance, reduction of transformer lifespan and harmonic distortion. This study has established that smart charging alleviates these network issues and brings a wide range of economic, social, and environmental benefits. In particular, role of smart charging as a mandatory requirement for attaining the net-zero decarbonization target of the transport sector is highlighted. Finally, the paper sheds light on the policy, standards and regulatory frameworks that need to be implemented to promote smart charging and V2G technologies among EV owners and charging infrastructure developers.

Distributed Vehicle to Grid Integration Over Communication and Physical Networks With Uncertainty Effects

This paper proposes a distributed framework for vehicle grid integration taking into account the communication and physical networks. To this end, we model the electric vehicle behavior that includes times of departure and arrival, state of charge, required energy, and its objectives, e.g., avoid battery degradation and willingness to pay. Next, we formulate the centralised day ahead distribution market which explicitly represents the physical system, supports unbalanced three phase networks with delta and wye connections, and incorporates the charging needs of electric vehicles under a uni- and a bi-directional charging scheme. The solution of the centralised market requires knowledge of electric vehicle information in terms of desired energy, departure and arrival times that electric vehicle owners are reluctant in providing. Moreover, the computational effort required to solve the day ahead distribution market in cases of numerous electric vehicles is very intensive. As such, we propose a distributed solution of the day ahead distribution market clearing mechanism over a time-varying communication network where no private information is exchanged. We also incorporate uncertainty of electric vehicle charging needs and provide guarantees on the probability of constraint satisfaction. We illustrate the proposed vehicle grid integration framework through the 13-bus, 33-bus, and 141-bus distribution feeders.

Distribution system resilience enhancement through resilience-oriented optimal scheduling of multi-microgrids considering normal and emergency conditions interlink utilizing multi-objective programming

One of the major concerns of power systems is dealing with extreme power outages during severe events and consequently, its repercussions on the lives of people in society and social aspects. Therefore, the optimal application of microgrids that can be identified as a smart city which is equipped with renewable energies and distributed energy generation is considered with the aim of distribution system resilience improvement. In this study, an innovative modeling approach is formulated as resilience-oriented multi-objective two-stage scenario-based stochastic modeling based on mixed-integer-linear-programming. Meanwhile, innovative modeling is executed based on disaster effect on human society response (HSR) according to the presence of plug-in-electric-vehicles as clean transportation systems. Using a sensitivity analysis on the maximum allowable energy that can be stored by electric-vehicle owners, according to a conservative decision, it is possible to charge electric vehicles up to 40% in a specific emergency situation, without significantly jeopardizing the resilience of microgrids and distribution systems. In this regard, on average, the resilience metric of MG1, MG2, and MG3 improves by 3.94%, 11.3%, and 4.4%, respectively, compared to the situation where electric vehicles are allowed to charge up to 100% of their battery capacity. More comprehensive studies are also investigated in results.

Electric Vehicle Charging Management Based on Deep Reinforcement Learning

A time-variable time-of-use electricity price can be used to reduce the charging costs for electric vehicle (EV) owners. Considering the uncertainty of price fluctuation and the randomness of EV owner's commuting behavior, we propose a deep reinforcement learning based method for the minimization of individual EV charging cost. The charging problem is first formulated as a Markov decision process (MDP), which has unknown transition probability. A modified long short-term memory (LSTM) neural network is used as the representation layer to extract temporal features from the electricity price signal. The deep deterministic policy gradient (DDPG) algorithm, which has continuous action spaces, is used to solve the MDP. The proposed method can automatically adjust the charging strategy according to electricity price to reduce the charging cost of the EV owner. Several other methods to solve the charging problem are also implemented and quantitatively compared with the proposed method which can reduce the charging cost up to 70.2% compared with other benchmark methods.

Dynamic Energy/Mobility Allocation with EV Consumer Behavior Coupling Transmission Power and Traffic Systems

Electric vehicles (EVs) are the key driver to coordinate the sector-coupled system for energy and mobility. Meanwhile, the mobility operation of EVs depends on the consumer behavior of the EV owner. This paper considers a dynamic energy and mobility allocation with the EV consumer behavior in the dynamical sector-coupled system to stabilize a transmission power network and optimize a traffic flow management via EVs. After formulating the dynamical systems coupling the transmission power systems and the traffic systems, we propose a dynamic optimal energy/mobility allocation problem to minimize the physical cost and the behavior cost during the planned period. To analyze the sharing of EVs in the aspect of both energy and mobility, we illustrate and discuss the effectiveness and the implementation of our proposed allocation through simulation imitating the Tokyo area.

Trilevel Mixed Integer Optimization for Day-Ahead Spinning Reserve Management of Electric Vehicle Aggregator With Uncertainty

This paper studies a trilevel profit maximization problem of electric vehicle (EV) aggregator participating in the day-ahead reserve market, considering the uncertain EV connectivity to the grid. At the upper level (UL), the aggregator purchases reserve from individual EVs and trades it in the reserve market. It determines the optimal reserve purchasing price and reserve trading amount to maximize profit. Responding to the reserve purchasing price, at the middle level (ML), each EV owner maximizes his/her own utility by scheduling the battery usage for charging/discharging, reserve or transportation. As the connectivity of EV to the grid is uncertain due to transportation randomness, we characterize the worst-case connectivity at the lower level (LL) such that energy consumption for transportation tasks can be guaranteed. The proposed trilevel optimization problem is challenging because of its multi-level structure and binary variables at ML and LL. Firstly, total unimodularity property, primal-dual and value-function methods are used to convert this problem into a single-level mixed integer nonlinear program (MINLP). Then, a sample-based algorithm is developed to solve the single-level MINLP and the convergence is proved. In addition, an acceleration strategy is proposed to facilitate the computation. Case studies validate the effectiveness of our proposed solution method.

Research on Orderly Charge and Discharge Strategy of EV Based on QPSO Algorithm

The change of electric vehicle (EV) cluster schedulable capacity in each period under multi-time scales has strong randomness and volatility. To ensure the trip demand and the charge and discharge cost of users, and increase the reliability of load demand curve prediction, this paper proposes a dynamic charge and discharge optimization strategy that takes the charge and discharge control coefficient in each response period as the control object. According to the trip chain, the proposed method models the trip time-space distribution of EV users, sorts each response period from small to large according to the starting time, comprehensively considers the interests of users and the peak shaving demand of the power grid, and uses Quantum Particle Swarm Optimization (QPSO) to solve the multi-objective optimization of charge and discharge control coefficient for the sorted response period. The charge and discharge control coefficient is modified by introducing virtual charge time and the virtual state of charge. Compared with the traditional method, the proposed method considers the mismatch between the expected parking time of users and the actual parking time of users and can update the load demand curve in real-time due to the dynamic changes in users’ trip behavior, which is more practical. To verify the effectiveness of the method proposed in this paper, according to the simulation results of the time-space distribution of electric private car users, the load demand curves under different charge strategies, different optimization weights, and different vehicle-to-grid (V2G) responsiveness are simulated and analyzed. The results show that the proposed method can effectively reduce the peak valley difference and variance of the load demand curve under the condition of ensuring the trip demand and economic benefits for EV owners.

Distributed Electric Vehicle Charging Scheduling with Transactive Energy Management

A distributed electric vehicle (EV) charging scheduling strategy with transactive energy (TE) management is presented in this paper to deal with technical issues in distribution network operation and discuss the economic benefits of EV charging. At an individual EV level, EV owners propose bids to actively participate in the distribution system operation. At the node level, an electric vehicle aggregator (EVA) optimally allocates the available charging power to meet EV charging requirements and cost benefits. At the distribution network level, a distribution system operator (DSO) integrates an electricity price market clearing mechanism with the optimal power flow (OPF) technique to ensure the reliability of the distribution network. Moreover, a distributed algorithm is discussed for solving the EV charging problem with transactive energy management (TEM). The clearing electricity price is achieved through a negotiation process between the DSO and EVAs using the alternating direction method of multipliers (ADMM). The presented EV charging scheduling with TEM is tested on a modified IEEE 33-bus distribution network scenario with 230 EV charging loads. The simulation results demonstrate the effectiveness of the TE-based EV charging scheduling system.

Collaborative Optimization of Electric Vehicles Based on MultiAgent Variant Roth–Erev Algorithm

With the implementation of the carbon neutral policy, the number of electric vehicles (EVs) is increasing. Thus, it is urgently needed to manage the charging and discharging behavior of EVs scientifically. In this paper, EVs are regarded as agents, and a multiagent cooperative optimization scheduling model based on Roth–Erev (RE) algorithm is proposed. The charging and discharging behaviors of EVs will influence each other. The charging and discharging strategy of one EV owner will affect the choice of others. Therefore, the RE algorithm is selected to obtain the optimal charging and discharging strategy of the EV group, with the utility function of the prospect theory proposed to describe EV owners’ different risk preferences. The utility function of the prospect theory has superior effectiveness in describing consumers’ utility. Finally, in the case of residential electricity, the effectiveness of the proposed method is verified. Compared with that of random charging, this method reduces the total EV group cost of EVs by 52.4%, with the load variance reduced by 26.4%.

Integrating Electric Vehicles into Power System Operation Production Cost Models

The electrification of the transportation sector will increase the demand for electric power, potentially impacting the peak load and power system operations. A change such as this will be multifaceted. A power system production cost model (PCM) is a useful tool with which to analyze one of these facets, the operation of the power system. A PCM is a computer simulation that mimics power system operation, i.e., unit commitment, economic dispatch, reserves, etc. To understand how electric vehicles (EVs) will affect power system operation, it is necessary to create models that describe how EVs interact with power system operations that are suitable for use in a PCM. In this work, EV charging data from the EV Project, reported by the Idaho National Laboratory, were used to create scalable, statistical models of EV charging load profiles suitable for incorporation into a PCM. Models of EV loads were created for uncoordinated and coordinated charging. Uncoordinated charging load represents the load resulting from EV owners that charge at times of their choosing. To create an uncoordinated charging load profile, the parameters of importance are the number of vehicles, charger type, battery capacity, availability for charging, and battery beginning and ending states of charge. Coordinated charging is where EVs are charged via an “aggregator” that interacts with a power system operator to schedule EV charging at times that either minimize system operating costs, decrease EV charging costs, or both, while meeting the daily EV charging requirements subject to the EV owners’ charging constraints. Beta distributions were found to be the most appropriate distribution for statistically modeling the initial and final state of charge (SoC) of vehicles in an EV fleet. A Monte Carlo technique was implemented by sampling the charging parameters of importance to create an uncoordinated charging load time series. Coordinated charging was modeled as a controllable load within the PCM to represent the influence of the EV fleet on the system’s electricity price. The charging models were integrated as EV loads in a simple 5-bus system to demonstrate their usefulness. Polaris Systems Optimization’s PCM power system optimizer (PSO) was employed to show the effect of the EVs on one day of operation in the 5-bus power system, yielding interesting and valid results and showing the effectiveness of the charging models.

Analysis of total cost of ownership and cost competitiveness of privately-owned electric vehicles in Nepal

TThe fossil fuel has been the dominant power source of the transportation all over the world in the past centuries. With the development of electric powertrain technology and growing concern over the detrimental effects of fossil fuel like greenhouse gas emission, the transport sector is transforming to electric globally. The Government of Nepal has also announced different policy measures as well as national plans to adapt to this growing trend of electric vehicle (EV) adoption. Since EVs are considered to have higher capital cost compared to the internal combustion engine vehicles (ICEV), the operational cost differs significantly due to the different maintenance cost, electricity prices and separate tax policies. In this study, a model to calculate the total ownership cost of EVs and their ICEV counterparts is developed. The different parameters like purchase price, annual kilometer travel (AKT), annual tax and insurances, fuel and electricity prices, annual maintenance cost etc. are used to estimate the total cost of ownership per km (TCO/km) for the two wheeler EVs and ICEVs (E2W, ICE2W) and four wheeler EVs and ICEVs (E4W, ICE4W) along with different policies imposed by the government for such vehicles in two different scenario (i.e., before and after finance act 2020/2021). The sensitivity analysis of input parameters and the breakeven distance that makes EVs economical than ICEVs is also calculated.

Coordinating energy management systems in smart cities with electric vehicles

The rapid proliferation of Electric Vehicles (EVs) creates an inherent link between the previously independent transport and power sectors. This is especially relevant in the smart cities paradigm, which focuses on optimizing resource management using modern software tools and communication infrastructures. The optimal management of energy resources is of key importance, and with mobile EVs playing a pivotal role in smart city power flows, the coordination of energy management systems (EMSs) at their parking locations can bear global benefits. In this study the coordination between home energy management systems (HEMSs) and EV parking lot management systems (PLEMS) is proposed, modeled, and simulated, as a new contribution to earlier studies. The EMSs coordinate through partially sharing individual EV schedules and without sharing private information. Missing information is completed through public cloud repositories and services. The HEMS and PLEMS are implemented using mixed-integer linear programming (MILP). The proposed coordination framework is computationally implemented and simulated based on a real-life case study. The results show that the proposed EMS coordination framework is both technically beneficial for power grids and economically beneficial for EV owners.

THE IMPORTANCE OF PUBLIC-FUNDED CHARGING STATIONS TO INCREASE THE RATE OF ELECTRIC VEHICLE (EV) OWNERSHIP IN INDONESIA

The current surplus of electricity across Indonesia has further underlined many opportunities to optimize the usage of electricity in many sectors; including on the issue of Electric Vehicle (EV) ownership within the country. According to the government’s projection, the state-owned enterprise (SOE) of PLN would construct 254.181 units of charging stations by 2030. However, there exists the problem of ‘chicken and egg’; in which more EV charging stations would be required to spur EV sales and vice versa. In addition to that, the lack of charging stations has also led to the disinterest from the public to purchase EVs due to fear of range anxiety. Hence, this paper is written to address the importance of publicly funded charging stations in Indonesia to help cultivate EV development within the country. Not only that, since Indonesia is the largest member country of ASEAN, it could be the ‘trendsetter’ of this issue in the region and would have the upper hand position as an early adopter. Our hypotheses suggest that not only publicly funded the development of charging stations would be beneficial to the future-buyer of EV, but also for the government itself. 
 
 Keywords: Electric Vehicle, Charging Station, Public-Funded, Range Anxiety 

EV Prioritization and Power Allocation During Outages: A Lexicographic Method-Based Multiobjective Optimization Approach

The growth in number of electric vehicles (EVs) has resulted in increased dependence of transportation on the power sector. During power outages, especially for elongated time spans, the locally available energy may not be sufficient to fulfill the energy needs of all the EVs. Therefore, a multicriteria EV prioritization scheme is proposed in this study to fairly allocate available energy among EVs during power outages. The five major factors considered for EV prioritization are trip purpose, EV occupants, energy gap, departure time, and customer behavior. With different combinations of the five prioritization factors, three indices are formulated, one each for social welfare, community wellbeing, and individual satisfaction of the EV owners. To this end, a multiobjective optimization problem is formulated, based on the three indices, to allocate the available power among EVs with higher index values. The formulated multiobjective optimization problem is solved using the lexicographic method, which has superior performance over the conventionally used weighted-sum method and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -constraint (EC) method. The proposed method is not sensitive to the weights of individual functions and has the ability to handle multiple priority levels. In order to quantify the results, percent served and unserved indices are formulated for each of the three parameters (social welfare, community wellbeing, and individual satisfaction) and the results of the proposed method are compared with those of the weighted-sum method and the EC method. Sensitivity analysis of different uncertain factors, such as number of EVs, uncertainty in EV demand, uncertainty in renewable power, and error in battery state-of-charge estimation, is also carried out. Simulation results have shown the superiority of the proposed method in allocating power to EVs during outages.

A Particle-Swarm-Optimization-Based Approach for the State-of-Charge Estimation of an Electric Vehicle When Driven Under Real Conditions

Recent developments in lithium-ion (Li-ion) storage technology have enabled a revolution in the automotive industry. Fully electric vehicles (EVs) operate under the most diverse combination of driving and environmental conditions affecting the autonomy range. In other words, an equal state-of-charge (SOC) on two same model EV does not mean the same traveling distance since the conditions such as the state-of-health (SOH) of the battery, type of driver and even the type of route will influence the EV performance. Typically, SOC estimation algorithms are proposed and validated under controlled laboratory conditions. However, when real conditions are present, it is necessary to incorporate new tools capable of handling the diverse variability present in all the conditions. For instance, the topography of the route influences the current that the battery pack delivers, and the performance on the same route can be affected by the SOH. One of the main concerns for EV owners is that once a battery pack is installed, it becomes almost impossible to perform laboratory tests under controlled conditions. This paper proposes a novel approach to estimate the SOC by extending an existing SOC model (obtained in laboratory conditions) with the novelty of the assistance of Particle-Swarm-Optimization (PSO) to estimate the model parameters using real EV driving data. The data was obtained by a real-driving experiment, which consists on driving the EV in a complete discharge cycle on a highway. During this experiment, the initial SOC was at 100\%, and the idea was to discharge the battery pack driving through a highway where the driving conditions are almost uniform making it possible to characterize the SOC curve. Then, PSO is used to estimate the model parameters, and afterwards the model is validated in different types of routes. The obtained results show that the proposed approach can estimate the SOC satisfactorily. In this regard, this type of real-driving experiment can be performed by any driver, and by combining the particular results with the proposed approach, the users can personalize the SOC estimation model to their vehicles, and even more, create their own knowledge base of their EV performance through time. Therefore, the real-driving experiment can be replicated when needed to update the model parameters, thus allowing a better understanding of the actual SOH of the battery pack. Furthermore, by combining the obtained model with the elevation profile of a given route, the user can assess where to stop in case that a recharge is necessary.

Economic feasibility of electric vehicles: A case study

AbstractPersonal transportation is prevalent in Brunei Darussalam because of the lack of adequate public transportation. However, Brunei is not immune to the effects of climate change and as such, has launched a national policy to mitigate the impacts. As electric vehicles presently are very rare to Brunei, this article seeks to determine the total cost of ownership of internationally popular electric vehicles (EVs) and internal combustion engine vehicles (ICEVs) to put things in perspective for the Brunei market. The methodology investigated how the total cost of ownership (TCO) is affected by depreciation, interest repayment, maintenance and repair, fuel costs, taxation, and insurance premiums. We have determined that without any subsidies, predictably TCOs of EVs are higher than popular ICEVs; however, they are comparable to TCOs of European premium brands. The Tesla Model 3 has a TCO of USD 760 per month whereas a Mercedes Benz C200 has a TCO USD800 per month. The authors have suggested that a 30% subsidy on the showroom price of EVs be introduced and to improve charging infrastructure as this would be vital to governmental policy planning as Brunei seeks to increase the ownership of electric vehicles to 60% by 2035.

A smart discrete charging method for optimum electric vehicles integration in the distribution system in presence of demand response program

Electric vehicle charging is one of the common techniques for techno-economic energy management in the distribution system, which, if implemented properly, brings several benefits such as the peak load shaving, total costs reduction, losses minimization, etc. Unlike traditional charging methods, it is not necessary to completely charge electric vehicles. Instead, each vehicle can be charged smartly just based on its demand for its next trips. In this way, charging takes less time and the total consumption is balanced without any discomfort for the drivers. To implement this smart method, electric vehicle owners must fill in an information sheet that contains the number and lengths of the their next. Then, its required energy is calculated this info, and the initial battery state of charge. Finally, the charging management system starts the planning according to its upstream distribution system operator. Several parameters are needed for the planning such as time of use tariff of the electricity, the distribution transformer or substation physical limits, the rate of the charge (normal or fast), etc. The aim of the planning is to minimize the charging cost considering the technical and economic constraints. Here, the YALMIP and MOSEK software are used as the solver of the proposed mixed-integer linear programming model.

Aggregate Electric Vehicles in Demand Side for Ancillary Service

This paper investigates the win-win commercialization mode of aggregating electric vehicles (EVs) in demand side for ancillary service. We have conducted a half-year-long incentive verification experiment covering 10,066 electric vehicle owners in Beijing. Based on the experimental results, we develop an incentive-based mechanism that enables electric vehicles to participate the wholesale capacity market through an aggregator. The aggregator, which is held by charging service operators can make a profit by designing a smart pricing policy. In this process, not only the electric vehicle owners but also the utility can gain benefits.

Reinforcement Learning-Based Multiple Constraint Electric Vehicle Charging Service Scheduling

The popularization of electric vehicles faces problems such as difficulty in charging, difficulty in selecting fast charging locations, and comprehensive consideration of multiple factors and vehicle interactions. With the increasingly mature application of navigation technology in vehicle-road coordination and other aspects, the proposal of an optimal dynamic charging method for electric fleets based on adaptive learning makes it possible for edge computing to process electric fleets to effectively execute the optimal route charging plan. We propose a method of electric vehicle charging service scheduling based on reinforcement learning. First, an intelligent transportation system is proposed, and on this basis a framework for the interaction between fast charging stations and electric vehicles is established. Subsequently, a dynamic travel time model for traffic sections was established. Based on the habits of electric vehicle owners, an electric vehicle charging navigation model and a reinforcement learning reward model were proposed. Finally, an electric vehicle charging navigation scheduling method is proposed to optimize the service resources of the fast charging stations in the area. The simulation results show that the method balances the charging load between stations, can effectively improve the charging efficiency of electric vehicles, and increases user satisfaction.

Electric vehicle charging and end-user motivation for flexibility: a case study from Norway

BackgroundNorway is currently in the process of replacing internal combustion engine (ICE) vehicles with electric vehicles (EVs). A steadily increasing number of EVs being charged in the evening when the demand peaks are already high has added to the strain on local electricity grids. Smart charging is proposed as a solution to peak-load challenges of this kind, as it can delay charging until evenings and nights when demand peaks are generally low. However, current knowledge of the preferences and motivations of EV owners regarding the installation and use of smart chargers is lacking. Accordingly, the study aims to contribute an improved understanding in this respect.MethodsThe authors conducted a qualitative study based on semi-structured interviews, in which they gained information on householder’s experiences following their participation in a pilot project to test smart charging of EVs. The interviews explored the interviewees’ motivation for participating in the project, their experiences of using and charging EVs, and their thoughts on flexibility in the timing of EV charging. Interviews were recorded and transcribed verbatim, after which a domestication theory approach was employed to perform an in-depth analysis of the interview data.ResultsThe analysis of the interviews revealed that the study participants had four motivations for participating in the smart charging demonstration project and engaging in the further use of smart charging: (1) the fire-safety aspect and speed of charging devices for EVs was better than charging from a socket; (2) they derived a broader interest in and more joy from using smart home technology; (3) attractive practical and economic benefits; and (4) the flexibility offered by smart charging had the potential to have a positive impact on physical comfort.ConclusionsThe study revealed key domestication processes unfolding in the Norwegian energy transition with the increasing numbers of EVs and the corresponding grid peaks. The findings clearly indicate that users’ motivations go much further than simple economic rationalization, which should be of interest to policymakers, smart home developers, and all others that work with end-user flexibility in the grid.