Plug-in Electric Vehicles Charging Research Articles

Decentralized Charging of Plug-In Electric Vehicles and Impact on Transmission System Dynamics

This article focuses on the impact of the charge of Plug-in Electric Vehicles (PEVs) on the dynamic response of power systems and proposes an efficient solution to control electric vehicle chargers, by dynamically allocating the available power in an optimized way. The proposed approach is based on an Additive-Increase-Multiplicative-Decrease (AIMD) stochastic decentralized control strategy to efficiently and seamlessly manage the charge of a high number of PEVs with little communication efforts. A modified version of the New England network is utilized to validate the proposed control through a variety of scenarios and control setups.

Public Plug-in Electric Vehicles + Grid Data: Is a New Cyberattack Vector Viable?

High-wattage demand-side appliances such as Plug-in Electric Vehicles (PEVs) are proliferating. As a result, information on the charging patterns of PEVs is becoming accessible via smartphone applications, which aggregate real-time availability and historical usage of public PEV charging stations. Moreover, information on the power grid infrastructure and operations has become increasingly available in technical documents and real-time dashboards of the utilities, affiliates, and the power grid operators. The research question that this study explores is: Can one combine high-wattage demand-side appliances with public information to launch cyberattacks on the power grid? To answer this question and report a proof of concept demonstration, the study scrapes data from public sources for Manhattan, NY, USA using the electric vehicle charging station smartphone application and the power grid data circulated by the U.S. Energy Information Administration, New York Independent System Operator, and the local utility in New York. It then designs a novel data-driven cyberattack strategy using state-feedback based partial eigenvalue relocation, which targets frequency stability of the power grid. The study establishes that while such an attack is not possible at the current penetration level of PEVs, it will be practical once the number of PEVs increases.

Modeling the Future California Electricity Grid and Renewable Energy Integration with Electric Vehicles

This study focuses on determining the impacts and potential value of unmanaged and managed uni-directional and bi-directional charging of plug-in electric vehicles (PEVs) to integrate intermittent renewable resources in California in the year 2030. The research methodology incorporates the utilization of multiple simulation tools including V2G-SIM, SWITCH, and GridSim. SWITCH is used to predict a cost-effective generation portfolio to meet the renewable electricity goals of 60% in California by 2030. PEV charging demand is predicted by incorporating mobility behavior studies and assumptions charging infrastructure and vehicle technology improvements. Finally, the production cost model GridSim is used to quantify the impacts of managed and unmanaged vehicle-charging demand to electricity grid operations. The temporal optimization of charging sessions shows that PEVs can mitigate renewable oversupply and ramping needs substantially. The results show that 3.3 million PEVs can mitigate over-generation by ~4 terawatt hours in California—potentially saving the state up to about USD 20 billion of capital investment costs in stationary storage technologies.

The potential for community financed electric vehicle charging infrastructure

Charging infrastructure is one of the key ingredients necessary to further increase the market uptake of plug-in electric vehicles (PEVs). Most public charging infrastructure in Europe is not profitable, and 42% of European households reside in dwellings with no possibility to install a private charger, which are key limitations to the build-up of charging infrastructure. This work presents and analyzes an alternative business model for ‘community-owned charging points’ that combines many of the convenience advantages of private charging with the cost savings of a joint purchase. Survey data from 3,131 households in Austria is used to identify barriers to PEV charging and charging infrastructure usage, and investigate consumers’ preferences with respect to community-owned charging points. Four key target groups of customers for such a business model are identified based on their socio-economic characteristics, purchase history, and expressed level of environmental concerns, including current PEV owners, high income households, photovoltaic (PV) owners, and environmentalists. Identified distinct preferences for the configuration of a collective charging station amongst these groups include lower importance attached to flexibility to charge the vehicles at any time by current PEV owners, higher sensitivity to cost per charge of PV owners, and strong preferences for comfort and flexibility demonstrated by high income households.

Dynamic Pricing for Differentiated PEV Charging Services Using Deep Reinforcement Learning

With the increasing popularity of plug-in electric vehicles (PEV), charging infrastructure becomes widely available and offers multiple services to PEV users. Each charging service has a distinct quality of service (QoS) level that matches user expectations. The charging service demand is interdependent, i.e., the demand for one service is often affected by the prices of others. Dynamic pricing of charging services is a coordination mechanism for QoS satisfaction of service classes. In this article, we propose a differentiated pricing mechanism for a multiservice PEV charging infrastructure (EVCI). The proposed framework motivates PEV users to avoid over-utilization of particular service classes. Currently, most of dynamic pricing schemes require full knowledge of the customer-side information; however, such information is stochastic, non-stationary, and expensive to collect at scale. Our proposed pricing mechanism utilizes model-free deep reinforcement learning (RL) to learn and improve automatically without an explicit model of the environment. We formulate our framework to adopt the twin delayed deep deterministic policy gradient (TD3) algorithm. The simulation results demonstrate that the proposed RL-based differentiated pricing scheme can adaptively adjust service pricing for a multiservice EVCI to maximize charging facility utilization while ensuring service quality satisfaction.

A Novel SiC-Based Multifunctional Onboard Battery Charger for Plug-In Electric Vehicles

This article proposes a simple and effective method to reduce the bulky capacitor in onboard battery charger (OBC) where low-voltage (LV) battery charging circuit is utilized as an active power decoupling (APD) circuit to filter out the second-order ripple power. When the OBC is connected to the grid to charge the high-voltage (HV) battery, the switches on the primary side of the LV charging circuit are operated as those of an ac-dc converter with APD function. Consequently, small dc-link capacitors can be used instead of large capacitor banks. In the proposed OBC, the switching devices are shared for the ac-dc converter, APD circuit, and primary-side of the LV charger. For a simultaneous charging of the LV and HV batteries, a dc-dc converter is added, which shares the same transformer core and secondary side with the LV charging circuit. So the functions of the OBC are maintained in the proposed topology whereas the volume and cost are decreased by 52.3% and 46.9% compared with the conventional nonisolated OBC, respectively. A 2-kW SiC-based prototype has been designed and tested to verify the validity of the proposed system. The peak efficiencies of the OBC and LV charger are 96.1% and 95.3%, respectively.

Autonomous PEV Charging Scheduling Using Dyna-Q Reinforcement Learning

This paper proposes a demand response method to reduce the long-term charging cost of single plug-in electric vehicles (PEV) while overcoming obstacles such as the stochastic nature of the user's driving behaviour, traffic condition, energy usage, and energy price. The problem is formulated as a Markov Decision Process (MDP) with an unknown transition probability matrix and solved using deep reinforcement learning (RL) techniques. The proposed method does not require any initial data on the PEV driver's behaviour and shows improvement on learning speed when compared to a pure model-free reinforcement learning method. A combination of model-based and model-free learning methods called Dyna-Q reinforcement learning is utilized in our strategy. Every time a real experience is obtained, the model is updated, and the RL agent will learn from both the real experience and “imagined” experiences from the model. Due to the vast amount of state space, a table-lookup method is impractical, and a value approximation method using deep neural networks is employed for estimating the long-term expected reward of all state-action pairs. An average of historical price and a long short-term memory (LSTM) network are used to predict future price. Simulation results demonstrate the effectiveness of this approach and its ability to reach an optimal policy quicker while avoiding state of charge (SOC) depletion during trips when compared to existing PEV charging schemes.

Coordinated optimisation of PEV charging with the support of reactive discharging and phase switching in unbalanced active distribution networks

Plug-in electric vehicles (PEVs) are being widely deployed, especially in the residential premises. Random PEV charging causes negative impacts on the power grid, a coordinated PEV charging is essential. Currently, PEV charging studies mainly focus on the active power control of PEVs while there is a risk of worse operation, due to the transferring of charging load instead of eliminating. Besides, existing PEV charging studies are commonly based on a three-phase balanced network model, but practical distribution networks, especially on the low-voltage level, are unbalanced. This study proposes a novel coordination strategy of PEV charging in unbalanced distribution networks, with the support of reactive discharging and phase switching. The optimisation model of PEV charging is firstly proposed to minimise the network loss and charging cost while maximising users’ benefit from ancillary reactive service. The optimal power flow problem defined above is solved by a combined solver of discrete particle swarm optimisation and direct load flow. Then, a coordination strategy of PEV charging, reactive discharging and phase switching is presented to apply the proposed optimisation over 24 h. Finally, detailed simulations are conducted on a real Australian distribution network, to test the feasibility and effectiveness of the proposed optimisation and coordination strategy of PEV charging in unbalanced distribution networks.

Refueling infrastructure planning in intercity networks considering route choice and travel time delay for mixed fleet of electric and conventional vehicles

The range anxiety has been a major factor that affects the market acceptance of electric vehicles. Even with the recent development of battery technologies, a lack of charging stations and range anxiety are still significant concerns, specifically for intercity trips. This calls for more investments in building charging stations and advancing battery technologies to increase the market share of electric vehicles and improve sustainability. This study suggests a configuration for plug-in electric vehicle charging infrastructure to support long-distance intercity trips of electric vehicles at the network level. A model is proposed to minimize the total system cost including infrastructure investment (building charging stations/spots) and travel time delays (charging time, waiting time in the queue, and detour time to access charging stations). This study fills existing gaps in the literature by capturing realistic patterns of travel demand and considering flow-dependent charging delays at charging stations. Furthermore, the proposed model, which is formulated as a mixed-integer program with nonlinear constraints, solves the optimization problem at the network level. At the network level, impacts of charging station locations on the traffic assignment problem with a mixed fleet of electric and conventional vehicles need to be considered. To this end, a traffic assignment module is integrated with a simulated annealing algorithm. The numerical experiments show a satisfactory application of the model for a full-scale case study (intercity network in Michigan). The solution quality and efficiency of the proposed solution algorithm are evaluated against those of an enumeration approach for a small case study. The results suggest that even for the current market share and charging stations’ setting, a significant investment is needed to support intercity trips without range anxiety issues and with acceptable delays. Furthermore, through sensitivity analyses, the required infrastructure and battery investments to support intercity trips with acceptable delays are established for hypothetical increased market shares and battery size in the future.

Techno-economic feasibility of a photovoltaic-equipped plug-in electric vehicle public parking lot with coordinated charging

Electrification of transport and the deployment of plug-in electric vehicles (PEVs) shift emissions from tail-pipes to bulk power systems (BPSs). Coordinated distributed energy resources and PEV charging can mitigate the impact of this shift. This study presents an analysis of photovoltaic (PV) solar parking lots that address this benefit. Real-world charging data, solar data, and electricity tariffs are used to determine the microgrid system that minimises the cost of retrofitting an existing parking lot with PV and PEV infrastructure coupled. The result is a load scheduling algorithm that takes into account tariffs and insolation to reduce costs while ensuring customer satisfaction. The techno-economic feasibility of PV infrastructure in the microgrid is determined by minimising the net present cost (NPC) in two case studies: Victoria, BC, and Los Angeles, CA. Relatively low solar irradiation and electricity prices make it economically infeasible to install solar panels in Victoria even though the operational costs are reduced by 11%. In Los Angeles, high time-of-use prices, together with abundant solar radiation, make PV retrofitting economically feasible with any array capacity. At the current solar infrastructure price, coordinated charging in this region yields 8–16% savings on NPC and smaller feeder size requirements with greater load growth opportunities.

Adaptive Distributionally Robust Optimization for Electricity and Electrified Transportation Planning

An increased penetration of plug-in electric vehicles (PEVs) in the transportation sector will raise electricity demand inevitably, while dramatically changing its temporal and spatial patterns. This will result in more reliability stress and security constraints on existing and planned electricity generation and transmission assets, at a time utilities are facing pressure to do more with less. This paper proposes to address grid limitations, which may constrain large-scale PEV integration in future grids, using a novel planning model for transmission and generation expansions under uncertainties, based on adaptive robust optimization via scenario-based ambiguity sets. The new model can achieve decarbonization of both the electricity and transportation sectors economically, while meeting the electric grid reliability requirements. It also incorporates PEV charging controls over the planning horizon aiming at facilitating the maximum PEV penetration in the used test cases, more economically and realistically, considering current technology trends. Successful applications of the proposed model on a 118 bus test system and the simplified Ontario’s transmission grid demonstrate, in a satisfactory manner, its capability and effectiveness for planning deep decarbonization technology paths.

Stochastic Modeling Method of Plug-in Electric Vehicle Charging Demand for Korean Transmission System Planning

The number of plug-in electric vehicles (PEVs) has rapidly increased owing to the government’s active promotion policy worldwide. Consequently, in the near future, their charging demand is expected to grow enough for consideration in the planning process of the transmission system. This study proposes a stochastic method for modeling the PEV charging demand, of which the time and amount are uncertain. In the proposed method, the distribution of PEVs is estimated by the substations based on the number of electricity customers, PEV expansion target, and statistics of existing vehicles. An individual PEV charging profile is modeled using the statistics of internal combustion engine (ICE) vehicles driving and by aggregating the PEV charging profiles per 154 kV substation, the charging demand of PEVs is determined for consideration as part of the total electricity demand in the planning process of transmission systems. The effectiveness of the proposed method is verified through case studies in the Korean power system. It was found that the PEV charging demand has considerable potential as the additional peak demand in the transmission system planning because the charging time could be concentrated in the evening peak time.

Internet of smart charging points with photovoltaic Integration: A high-efficiency scheme enabling optimal dispatching between electric vehicles and power grids

Plug-in electric vehicles (PEVs) and renewable energy sources (RESs) can relief the stress on air pollution. Particularly, using RES for PEV energy requirement can integrate more RESs on the grid. In this paper, a vehicle-to-grid (V2G) scheme concerning on RES and edge computing, i.e. the internet of smart charging points with photovoltaics integration, is presented. Within the architecture of the scheme, each charging point equips computing and storage units, so as to store PEV sensitive information locally and conduct “burn after scheduling”. Besides, this architecture can transform the traditional large-scale V2G problem into several sub-problems, which are small enough to optimize. Based on the architecture of the scheme, an associated high-efficiency algorithm is designed. Six typical scenarios of PEV charging are elaborated and two indexes are presented to facilitate 1) the self-consumption of photovoltaics energy by PEV charging and 2) the peak-shaving and valley-filling of net load. Additionally, voltage regulation and real-time control are applied to ensure the security of the distribution grid and mitigate the uncertain conditions. Finally, compared with uncoordinated charging, the short-time scale simulation realizes the peak-shaving and valley-filling by 17.54% and 12.42%, respectively; and the amount of self-consumption of photovoltaics energy increases by 258.74%. Furthermore, the long-time scale simulations also present a satisfying performance for the grid energy saving and the load factor. Particularly, the proposed scheme offers high computational efficiency compared with different architecture and algorithm, and the execution time for scheduling one PEV at one-time interval shows a microsecond basis.

Charging Coordination of Plug-In Electric Vehicle for Congestion Management in Distribution System Integrated With Renewable Energy Sources

This article presents a novel strategy for congestion management in a distribution system, which occurs due to the uncoordinated charging of plug-in electric vehicles (PEVs). The large proliferation of PEVs has been significantly creating impact on the transportation and power sector in recent times. However, uncoordinated charging of PEVs leads to congestion in distribution feeders when the penetration of PEVs is reasonably high. A charging coordination strategy of grid-to-vehicle (G2V) and vehicle-to-grid (V2G) is formulated considering the large PEVs penetration in the distribution system. In this article, the popular ensemble method, i.e., gradient boosting regression tree method is employed to predict the state-of-charge for PEVs batteries. The charging coordination of G2V-V2G mode is developed in order to study the maximum PEVs penetration in the distribution system with an objective function of minimizing overall cost. The strategy is implemented in workplace car park powered by grid and grid-connected photovoltaic generation located in industrial areas of the IEEE 38 bus radial distribution system. An analytical study is presented to evaluate the maximum possible PEV penetration that the distribution network can accommodate without violating the grid constraints. Moreover, the performance of proposed strategy with or without the presence of solar powered car park is analyzed to assess the maximum possible PEVs penetration in the network so that no congestion occurs in the distribution lines.

Optimal Scheduling and Real-Time Operation of Distribution Networks With High Penetration of Plug-In Electric Vehicles

In this article, a method for real-time control and optimal operation scheduling of distribution networks with high penetration of plug-in electric vehicles (PEVs) is proposed. The main targets of the method are to minimize PEV charging cost at aggregator level, satisfy all distribution network and PEV constraints using accurate models of low computational requirements. An innovative definition of the flexibility of a PEV to change its active power is used to achieve efficient optimal active power dispatch to each PEV of a cluster. Real-time optimal dispatch of the reactive power to each PEV is also achieved. A hierarchical multiagent system is designed in order to deal with the increased complexity of the controlled system and collect the necessary information from the network. It is shown that the proposed method requires very small computation and communication times that do not depend on the number of PEVs. The convergence of the method is ensured with only one round of agent negotiations. The efficiency of the method is proved by detailed simulation results of a realistic distribution network and based on processed real data sets of the activity of the drivers.

Location Planning of PEV Fast Charging Station: An Integrated Approach Under Traffic and Power Grid Requirements

Plug-in Electric Vehicles (PEV) are characterized as a type of unconventional electric load, and their Fast Charging Stations (FCS) are capital-intensive transportation service infrastructure. Therefore, FCS location planning must consider the requirements of electricity and transportation infrastructures simultaneously. This paper, for the first time, proposes a graph-computing based integrated location planning model, which maximizes PEV charging convenience while ensuring the power grid’s reliability. In addition, the proposed model captures the long-term costs of critical grid assets induced by uncertainty and impulsiveness of charging demand. Finally, the model can be easily scaled to various coupling configurations and temporal resolutions through a graph-based scheme. The proposed model is cast as a multi-objective mixed-integer problem and is solved by the cross-entropy (CE) optimization algorithm, in which the computational efficiency is significantly improved with graph parallel computing techniques. The proposed planning model and graph implementation method are validated on a synthetic power-transportation coupled network.

Online Centralized Coordination of Charging and Phase Switching of PEVs in Unbalanced LV Networks With High PV Penetrations

The next decade is likely to witness considerable growth in the application of plug-in electric vehicles (PEVs). However, uncoordinated PEV charging in low-voltage distribution networks may cause grid issues, such as transformer overload and voltage unbalance (VU). Therefore, it is essential to consider a more suitable methodology of voltage regulation for the consumers placed at the end of the feeder when the network is predominantly resistive. With this background, this article investigates the capability of dynamic PEV load transfer within the three phases of a distribution network to propose a new hybrid PEV coordination approach. In addition to the online centralized battery charging and var discharging, this approach performs local voltage profiles improvement by switching PEV at selected single-phase residential houses such that a PEV can be switched from a heavily loaded phase to a (relatively) lightly loaded phase. This results in better voltage regulations and VU reductions. The impacts of uncoordinated and proposed genetic algorithm (GA) coordinated PEV charging on VU and node voltage profiles and losses are simulated for a real unbalanced Western Australian distribution network in the Perth solar city over 24 h.

Resilient Distributed Coordination of Plug-In Electric Vehicles Charging under Cyber-Attack

The coordinated scheduling of plug-in electric vehicle (PEV) charging should be constructed in distributed architecture due to the growing population of PEVs. Since the information and communication technology makes the adversary more permeable, the distributed PEV charging coordination is vulnerable to cyber-attack which may degrade the performance of scheduling and even cause the failure of scheduler task. Considering the tradeoff between system-wide economic efficiency, distribution level limitations and PEV battery degration, this paper investigates the resilient distributed coordination of PEV charging to resist cyber-attack, where the steps of detection, isolation, updating and recovery are designed synthetically. Under the proposed scheduling scheme, the misbehaving PEVs suffering from cyber-attack are gradually marginalized and finally isolated, and the remaining well-behaving PEVs obtain their own optimal charging strategy to minimize the total system cost in distributed architecture. The simulation results verify the effectiveness of theoretical method.

Novel standalone plug-in hybrid electric vehicle charging station fed by solar energy in presence of a fuel cell system used as supporting power source

A novel standalone charging station (CS) fed by solar energy and a supporting fuel cell (FC) stack has been built to charge electric vehicles (EVs). In addition to this, a novel maximum power point tracking (MPPT) technique has been utilized to maximally extract electric power from photovoltaic (PV) array used in the constructed CS. Battery bank is the important unit of a conventional standalone CS that absorbs/produces extra electric power available/required in the CS by charging/discharging itself. This is the main shortcoming of conventional standalone CSs because battery bank is an expensive device that provides a lifetime shorter than normal when it is utilized in a CS. This is because of the huge number of charge/discharge cycle that should be conducted by it. In this work, to overcome this issue, the battery bank has been replaced with a proposed FC system comprising a FC stack, an electrolyzer, two hydrogen tanks and a FC controller. At any time when charge demand is greater than PV output power, the rest of demand is supplied by the FC system. Vice versa, the electrolyzer absorbs extra PV output power and produces hydrogen by extracting it from water when PV output power is greater than charge demand. Experimental results and data recorded under normal operation of the built CS are given. Compared to conventional standalone CSs, the contributions of the built CS are as follows. First, the proposed FC system with permanent lifetime and a price of $39800 has been used as substitute for battery banks with limited lifetime and being significantly more expensive ($140000 over 30 years). Second, a novel MPPT technique with adaptive step-size has been used in the CS to extract maximum PV power. Third, the CS built in this research work not only is standalone but also is only fed by solar energy, so it can be utilized in absence of electric grids and negative effects such as air pollution.

A data decomposition approach to design a dynamic pricing mechanism for residence-based plug-in electric vehicles in wind energy-rich grids

Abstract Grid-level energy storage presents a potential mechanism for storing excess wind energy in wind energy-rich grids. However, the current economic infeasibility of grid-level storage hinders widespread practical application. Alternatively, plug-in electric vehicles (PEVs) present a potentially adaptable load that can facilitate managing excess wind energy. Our previous research justifies the need for a pricing mechanism that can coordinate wind energy and PEVs charging. In this paper, we employ a data decomposition approach (i.e., the dynamic mode decomposition) to design a dedicated pricing mechanism for PEVs. We use data from a city with relatively high current and projected PEV adoption rates (Austin, Texas) located in a wind-rich electricity grid (ERCOT). Our results indicate that a dedicated seasonal time of use pricing for PEVs can reduce wind energy curtailments, reduce emissions from electricity generation and PEV charging, and potentially avoid wind energy output curtailments.