Smart Charging Of Electric Vehicles Research Articles

Integration of electric vehicles into transmission grids: A case study on generation adequacy in Europe in 2040

Electric vehicles (EVs) are expected to grow massively in the coming years, and grid integration of a large number of them could challenge electricity-system infrastructure. This paper aims at describing a methodology to study the technical and economic impacts on power systems of mass EV charging for several EV-owner connection behavior profiles (systematic, when necessary, when convenient) and the range of recharge modes available (uncontrolled, time-of-use tariff, smart unidirectional charging, and vehicle-to-grid). This framework is applied to a case study at hourly resolution of high penetration of electric vehicles and renewable energy sources in Europe at the 2040 time-horizon, in line with the ‘National Trends Scenario’ grid mix under the pan-EU ENTSO-E Ten-Year Network Development Plan. Results show that the European electricity system can accommodate large EV growth and that widespread adoption of smart charging in France can significantly reduce operational electricity system costs by up to 1.1 G€ and reduce carbon emissions by up to 3.2 MtCO2 per year. Multiple EV smart charging modes are also compared and the parameters have the largest impact on EV flexibility are identified, including gas prices, smart charging adoption, weekly flexibility, and mid-day charging.

Synergy of Unidirectional and Bidirectional Smart Charging of Electric Vehicles for Frequency Containment Reserve Power Provision

Besides the integration of renewable energies, electric vehicles pose an additional challenge to modern power grids. However, electric vehicles can also be a flexibility source and contribute to the power system stability. Today, the power system still heavily relies on conventional technologies to stay stable. In order to operate a future power system based on renewable energies only, we need to understand the flexibility potential of assets such as electric vehicles and become able to use their flexibility. In this paper, we analyzed how vast amounts of coordinated charging processes can be used to provide frequency containment reserve power, one of the most important ancillary services for system stability. Therefore, we used an extensive simulation model of a virtual power plant of millions of electric vehicles. The model considers not only technical components but also the stochastic behavior of electric vehicle drivers based on real data. Our results show that, in 2030, electric vehicles have the potential to serve the whole frequency containment reserve power market in Germany. We differentiate between using unidirectional and bidirectional chargers. Bidirectional chargers have a larger potential but also result in unwanted battery degradation. Unidirectional chargers are more constrained in terms of flexibility, but do not lead to additional battery degradation. We conclude that using a mix of both can combine the advantages of both worlds. Thereby, average private cars can provide the service without any notable additional battery degradation and achieve yearly earnings between EUR 200 and EUR 500, depending on the volatile market prices. Commercial vehicles have an even higher potential, as the results increase with vehicle utilization and consumption.

Optimized power dispatch for smart building(s) and electric vehicles with V2X operation

Smart buildings are being built as a synergetic deployment of electric vehicles (EVs) and renewable energy sources. Smart charging of EVs and vehicle-to-everything (V2X) technologies are seen as way forward in this context in terms of achieving economic, technological, and environmental advantages. This paper proposes a framework for multi-objective techno-economic optimization for profit maximization of multiple inter-connected buildings (with bilateral contracts) and scheduling of EVs. The optimization problem is modeled as mixed integer linear programming problem, which is solved using CPLEX solver in ILOG optimization studio. The primary building owns the photovoltaic system coupled with storage and charging infrastructure for the fleet of EVs. The optimized charging of EVs at affordable rates using local resources at primary building assists the grid in managing the EVs’ load during peak hours. Results indicate that the primary building gains up to 62% daily profit after factoring in solar, storage, and charging station deployment costs. Additionally, secondary buildings (without solar, storage and charging facilities) earn up to 20% cost savings depending upon the nature of bilateral contracts with primary building. The results further suggest that fleet of EVs gains 35%–65% savings in charging cost based on lower charging rates and V2X operations with primary and secondary buildings.

Potential of Load Shifting in a Parking Garage with Electric Vehicle Chargers, Local Energy Production and Storage

The electrification of the transport sector is of crucial importance for a successful transition to a fossil-free society. However, the electricity grid constitutes a bottleneck. This article provides a case study based on a real-world parking garage with a smart grid infrastructure, called Dansmästaren. The analysis shows how renewable energy sources, energy storage technologies, and smart charging of electric vehicles can smooth out the load curve of the parking garage and relieve the electric grid during peak hours. Dansmästaren is located in Uppsala, Sweden, and equipped with 60 charging points for electric vehicles, a PV system, and a battery storage system. The study utilizes an energy flow model to show the potential of a realistically dimensioned smart energy system, that can benefit the parking facility in itself and the local distribution grid in a city, Uppsala, with grid capacity challenges. The results suggest that the parking garage demand on the local grid can be significantly lowered by smarter control of its relatively small battery energy storage. Moreover, further smart control strategies can decrease demand up to 60% during high load hours while still guaranteeing fully charged vehicles at departure in near future scenarios. The study also shows that peak shaving strategies can lower the maximum peaks by up to 79%. A better understanding of the potential of public infrastructures for electric vehicle charging helps to increase knowledge on how they can contribute to more sustainable cities and a fossil-free society.

Hierarchical Energy Management System for Home-Energy-Hubs Considering Plug-In Electric Vehicles

The escalating demand on electric vehicles (EVs) has enhanced the necessity of adequate charging infrastructure, especially in residential areas. This article proposes a smart charging approach for off-board EVs chargers in home-energy-hub (HEH) applications along with dc sources such as photovoltaic and battery storage (BS). The proposed method facilitates smart charging and discharging of EVs to obtain both vehicle-to-x and x-to-vehicle operations focusing on the domestic applications integrated with renewable and storage elements. Furthermore, the optimal state-of-charge (SOC) profiles for BS and EV in the HEHs system is defined by the extended Bellman-Ford-Moor algorithm (BFMA). This modified BFMA utilizes the forecasted data such as solar irradiation, electricity tariff, and power consumption to gain economic benefits in HEHs with respect to the user and EV requirements. Moreover, the plugging time, duration, and initial/final SOC are fluctuating at each connection due to the stochastic nature of EV conditions and user settings. This study presents a laboratory implementation of two-level hierarchical energy management system for HEHs with plug-in electric vehicles. In fact, the primary level includes power converters controller, while the proposed algorithm is implemented in the secondary level. Finally, the simulation and experimental results confirm the effectiveness of the proposed analysis regarding the interaction of HEHs and power grid with EVs behavior.

Multiobjective Optimized Smart Charge Controller for Electric Vehicle Applications

The continuous deployment of distributed energy sources and the increase in the adoption of electric vehicles (EVs) require smart charging algorithms. The existing EV chargers offer limited flexibility and controllability and do not fully consider factors (such as EV user waiting time and the length of next trip) as well as the potential opportunities and financial benefits from using EVs to support the grid, charge from renewable energy, and deal with the negative impacts of intermittent renewable generation. The lack of adequate smart EV charging may result in high battery degradation, violation of grid control statutory limits, high greenhouse emissions, and high charging cost. In this article, a neuro-fuzzy particle swarm optimization (PSO)-based novel and advanced smart charge controller is proposed, which considers user requirements, energy tariff, grid condition (e.g., voltage or frequency), renewable (photovoltaic) output, and battery state of health. A rule-based fuzzy controller becomes complex as the number of inputs to the controller increases. In addition, it becomes difficult to achieve an optimum operation due to the conflicting nature of control requirements. To optimize the controller response, the PSO technique is proposed to provide a global optimum solution based on a predefined cost function, and to address the implementation complexity, PSO is combined with a neural network. The proposed neuro-fuzzy PSO control algorithm meets EV user requirements, works within technical constraints, and is simple to implement in real time (and requires less processing time). Simulation using MATLAB and experimental results using a dSPACE digital real-time emulator are presented to demonstrate the effectiveness of the proposed controller.

Requesting control and flexibility: Exploring Swedish user perspectives of electric vehicle smart charging

The electrification of several sectors and the increasing role of volatile energy production mean that future energy systems will have to balance supply and demand to a higher degree. The increasing use of electric vehicles in particular will pose both challenges and opportunities for energy systems. Smart electric vehicle charging and user flexibility have been proposed as potential remedies for future imbalances in energy consumption, but the success of such remedies requires close collaboration between users and system operators. In smart charging schemes, users are expected to relinquish their control over charging while also being more flexible in their energy use. This study constitutes a mixed method case study, in which supplier-managed charging was tested at public charging stations and followed by three surveys (N = 1428, N = 24, N = 31) and semi-structured interviews (N = 27). The data was analyzed through statistical tests and by applying the framework of knowledge and perceptions. The data sets were then compared by using the concept of flexibility capital, in order to explore the flexibility capacity of users. The results show that while the participants were positive toward smart charging as a concept, the technology was also associated with uncertainty and anxiety. Furthermore, while the participants were willing to relinquish some control, their capacity for flexible energy use was dependent on factors beyond their direct control, including working patterns, financial resources, and access to charging stations. The conclusion is that the implementation and optimization of smart charging schemes are reliant not only on user attitudes but also on the extent to which these factors can be compensated for to facilitate user flexibility and avoid unfair flexibility markets.

Electric Vehicle Traction Drives and Charging Station Power Electronics: Current Status and Challenges

With the need for more environmentally friendly transportation and the wide deployment of electric and plug-in hybrid vehicles, electric vehicle (EV) charging stations have become a major issue for car manufacturers and a real challenge for researchers all over the world. Indeed, the high cost of battery energy storage, the limited EV autonomy and battery lifespan, the battery charging time, the deployment cost of a fast charging infrastructure, and the significant impact on the power grid are the origin of several research projects focused on advanced power electronics topologies and the optimization of the EV charging stations in terms of power transfer and geographical location. Three charging levels can be distinguished, which differ in terms of output power and charging time. The higher the level of charging, the faster the charging process, as more power is delivered to the vehicle at the expense of power quality issues and disturbances. Moreover, three types of charging systems can be distinguished, which are inductive recharging (contactless power transfer), conductive charging systems, and battery swapping. Additionally, EVs encompass fuel cell (FC) EVs, which uses hydrogen as primary energy resources, which is nowadays under extensive research activities in academia and industry. This review paper aims at presenting a state of the art review of major advances in power electronics architectures for EVs traction drives, and battery-based EVs charging stations. Specifically, the focus is made on light-duty electric vehicles drivetrain power electronics and charging stations specifications, the proposed power electronics solutions, the advantages and drawbacks of all these technologies, and perspectives for future research works in terms of smart EV charging and up-to-date solutions for power system disturbances mitigation.

Profit maximization for large-scale energy storage systems to enable fast EV charging infrastructure in distribution networks

Large-scale integration of battery energy storage systems (BESS) in distribution networks has the potential to enhance the utilization of photovoltaic (PV) power generation and mitigate the negative effects caused by electric vehicles (EV) fast charging behavior. This paper presents a novel deep reinforcement learning-based power scheduling strategy for BESS which is installed in an active distribution network. The network includes fast EV charging demand, PV power generation, and electricity arbitrage from main grid. The aim is to maximize the profit of BESS operator whilst maintaining voltage limits. The novel strategy adopts a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and requires forecasted PV power generation and EV smart charging demand. The proposed strategy is compared with Deep Deterministic Policy Gradient (DDPG), Particle Swarm Optimization and Simulated Annealing algorithms to verify its effectiveness. Case studies are conducted with smart EV charging dataset from Project Shift (UK Power Networks Innovation) and the UK photovoltaic dataset. The Internal Rate of Return results with TD3 and DDPG algorithms are 9.46% and 8.69%, respectively, which show that the proposed strategy can enhance power scheduling and outperforms the mainstream methods in terms of reduced levelized cost of storage and increased net present value.

The role of asymmetric prediction losses in smart charging of electric vehicles

Climate change prompts humanity to look for decarbonisation opportunities, and a viable option is to supply electric vehicles with renewable energy. The stochastic nature of charging demand and renewable generation requires intelligent charging driven by predictions of charging behaviour. The conventional prediction models of charging behaviour usually minimise the quadratic loss function. Moreover, the adequacy of predictions is almost solely evaluated by accuracy measures, disregarding the consequences of prediction losses in an application context. Here, we study the role of asymmetric prediction losses which enable balancing the over- and under-predictions and adjust predictions to smart charging algorithms. Using the main classes of machine learning methods, we trained prediction models of the connection duration and compared their performance for various asymmetries of the loss function. In addition, we proposed a methodological approach to quantify the consequences of prediction losses on the performance of selected archetypal smart charging schemes. In concrete situations, we demonstrated that an appropriately selected degree of the loss function asymmetry is crucial as it almost doubles the price range where the smart charging is beneficial, and increases the extent to which the charging demand is satisfied up to 40%. Additionally, the proposed methods improve charging fairness since the distribution of unmet charging demand across vehicles becomes more homogeneous.

Reinforcement learning-based demand-side management by smart charging of electric vehicles

Reinforcement learning-based demand-side management by smart charging of electric vehicles

Subsidizing Residential Low Priority Smart Charging: A Power Management Strategy for Electric Vehicle in Thailand

Government policies are crucial factors for supporting the growth of the electric vehicle (EV) industry—a growth that can be encouraged, for example, by subsidization designed to reduce the considerable anxiety stemming from the inconvenience of refueling at public charging stations. Subsidizing low priority charging for residential enables cost-effective load management for example controlling of EV charging power for grid reliability at the off-peak rate for 24 h. This solution provides the convenient recharging of EVs at home and prevents an expensive grid upgradation. To advance our understanding of the EV situation, this research used a regression model to forecast the growth rate of the EV market alongside the EV expansion policies in Thailand. The agreement between a policy and forecasting urges the government to prepare power system adequacy for EV loading. The analysis showed that power demand and voltage reduction in a typical low-voltage distribution system that assumes maximum EV loading constitute voltage violations. To address this limitation, this study proposed a rule-based strategy wherein low priority smart EV charging is regulated. The numerical validation of the strategy indicated that the strategy reduced power demand by 25% and 39% compared with that achieved under uncontrolled and time of use (TOU) charging, respectively. The strategy also limited voltage reduction and prolonged battery life. The study presents implications for policymakers and electricity companies with respect to possible technical approaches to stimulating EV penetration.

A Backwards Induction Framework for Quantifying the Option Value of Smart Charging of Electric Vehicles and the Risk of Stranded Assets under Uncertainty

The anticipated electrification of the transport sector may lead to significant increase in the future peak electricity demand, resulting in potential violations of network constraints. As a result, a considerable amount of network reinforcement may be required in order to ensure that the expected additional demand from electric vehicles that are to be connected will be safely accommodated. In this paper we present the Backwards Induction Framework (BIF), which we use for identifying the optimal investment decisions, for calculating the option value of smart charging of EV and the cost of stranded assets; these concepts are crystallized through illustrative case studies. Sensitivity analyses depict how the option value of smart charging and the optimal solution are affected by key factors such as the social cost associated with not accommodating the full EV capacity, the flexibility of smart charging, and the scenario probabilities. Moreover, the BIF is compared with the Stochastic Optimization Framework and key insights are drawn.

Smart EV Charging Strategies Based on Charging Behavior

Coming years, the number of electric vehicles (EVs) shall increase significantly, so the demand for electricity for charging EVs will proportionately increase as well. Thus, the growing energy requirements for charging these EVs might put huge burden on the electricity generation and supply infrastructure. Such a huge load growth opportunity for utilities if integrated successfully, or if not, a significant challenge to operate and balance grid loads in the future. Customarily, the increase in adoption of EVs in recent years has yielded challenges to the utilities as the electricity demand of EVs occurs mostly during peak hours. In some cases, a sojourn time may be longer than a charging time, that means, EVs will be connected to the charging station without charging. However, the load shifting potential of EVs may be consequential and might subsequently be used to alleviate challenges to the electric grid system. Considering charging behaviors for EV scheduling is crucial, as they depend on uncertainties of EV availabilities (i.e., sojourn time and energy required). Such uncertainties would impact substantially on the deployment of feasible EV charging scheduling. To address above-mentioned issues, firstly, we define an idle time ratio, which is basically load shifting potential. Consequently, we develop a heuristic EV charging scheduling scheme with an emphasis on inevitable charging behaviors of the EV users. Such a scheduling incorporates priority determination using the idle time ratio and TOU period as well as priority-based time slot allocation. Moreover, accurate prioritization of EVs is realized by predicting the energy demand and idle time ratio. Minimization of charging cost is perhaps the most perceptive objective, such that, the EV charging scheduling is done when TOU tariff is low. Performance evaluation shows that the proposed flexible smart charging scheduling outperforms the baseline scheduling in terms of the charging power and charging cost.

A multi-vector community energy system integrating a heating network, electricity grid and PV production to manage an electrified community

Electrification in energy supply–demand plays a critical role in domestic heating and road transport, delivering an electrified community to reduce carbon emissions. This solution, however, places a significant power demand increase on the distribution networks. To ensure the security of electricity supply, an efficient energy system and energy demand reduction are essential. In this paper, a multi-vector community energy system, applying an electrified heating network, electric vehicle smart charging, community-scale peak shaving and photovoltaic (PV) generation, is demonstrated in three models to manage an electrified community. Firstly, a heating network model, comprising a central ground source heat pump, low-temperature district heating system, electric heaters and thermal storage, is established to measure the optimum distribution temperature. Next, an electrified community model illustrates hourly electricity demands and performances of a community energy system, which is then used to identify the required degree of housing thermal efficiency improvement (i.e., heating demand reduction). The third model evaluates decentralised PV/storage units to maintain the power demand below a targeted power. Modelling results show that the demand ratio of domestic hot water to space heating determines the distribution temperature, which indicates the temperature is increasing with growing housing thermal efficiency. Moreover, the electrification of a community could increase the peak power demand on the highest demand day by over five times, converting heating demands into electricity directly. This significant peak demand can be possibly reduced to only a 33% increase by employing a community energy system. The model of PV/storage units is validated through a 12-week assessment. Ultimately, a modelling tool is developed by assembling the mentioned models, providing four pathways to attain electrification. Users can adjust specific parameters and database to align with the local conditions. The results indicate the requirements of building a community energy system and electricity demands in the highest consumption period.

Electric Vehicle Smart Charging Reservation Algorithm.

The widespread adoption of electromobility constitutes one of the measures designed to reduce air pollution caused by traditional fossil fuels. However, several factors are currently impeding this process, ranging from insufficient charging infrastructure, battery capacity, and long queueing and charging times, to psychological factors. On top of range anxiety, the frustration of the EV drivers is further fuelled by the uncertainty of finding an available charging point on their route. To address this issue, we propose a solution that bypasses the limitations of the “reserve now” function of the OCPP standard, enabling drivers to make charging reservations for the upcoming days, especially when planning a longer trip. We created an algorithm that generates reservation intervals based on the charging station’s reservation and transaction history. Subsequently, we ran a series of test cases that yielded promising results, with no overlapping reservations and the occupation of several stations without queues, assuring, thus, a proper distribution of the available energy resources, while increasing end-user satisfaction. Our solution is independent from the OCPP reservation method; therefore, the authentication and reservation processes performed by the proposed algorithm run only through the central system, authorizing only the creator of the reservation to start the charging transaction.

Smart Electric Vehicle Charging in the Era of Internet of Vehicles, Emerging Trends, and Open Issues

The Internet of Vehicles (IoV), where people, fleets of electric vehicles (EVs), utility, power grids, distributed renewable energy, and communications and computing infrastructures are connected, has emerged as the next big leap in smart grids and city sectors for a sustainable society. Meanwhile, decentralized and complex grid edge faces many challenges for planning, operation, and management of power systems. Therefore, providing a reliable communications infrastructure is vital. The fourth industrial revolution, that is, a cyber-physical system in conjunction with the Internet of Things (IoT) and coexistence of edge (fog) and cloud computing brings new ways of dealing with such challenges and helps maximize the benefits of power grids. From this perspective, as a use case of IoV, we present a cloud-based EV charging framework to tackle issues of high demand in charging stations during peak hours. A price incentive scheme and another scheme, electricity supply expansion, are presented and compared with the baseline. The results demonstrate that the proposed hierarchical models improve the system performance and the quality of service (QoS) for EV customers. The proposed methods can efficiently assist system operators in managing the system design and grid stability. Further, to shed light on emerging technologies for smart and connected EVs, we elaborate on seven major trends: decentralized energy trading based on blockchain and distributed ledger technology, behavioral science and behavioral economics, artificial and computational intelligence and its applications, digital twins of IoV, software-defined IoVs, and intelligent EV charging with information-centric networking, and parking lot microgrids and EV-based virtual storage. We have also discussed some of the potential research issues in IoV to further study IoV. The integration of communications, modern power system management, EV control management, and computing technologies for IoV are crucial for grid stability and large-scale EV charging networks.

Smart charging of electric vehicles to minimize the cost of chargingand the rate of transformer aging in a residential distribution network

Electric vehicles (EVs) exhibit several benefits over combustion engine vehicles, making them an attractive mode of mobility for the future. However, supplying the electrical energy required to recharge their batteries could adversely affect the power system infrastructure. The most severe impact of EV integration is expected to be on the distribution transformers, which are among the costliest equipment in the distribution network. Sustained overloads on the transformer could lead to accelerated aging and early retirement. As the rate of EV deployment rises, so does the probability of transformer overloads and the subsequent loss of life. There is a need for smart charging schemes in which the distribution system operator can schedule the charging of EVs in an optimal manner that prevents overloads and extends the transformer's life. However, EV owners might hesitate to surrender the charging control of their vehicles for the utility's benefit alone. Charging-cost minimization has been identified as an effective motivation for EV users to participate in charge management schemes. This paper presents a smart charging scheme, which minimizes the cost of charging EVs by optimizing their charging powers with respect to a real-time pricing tariff. As the electricity price changes dynamically with the system demand, cost minimization is equivalent to network decongestion. A decongested network is less susceptible to overloads and equipment damage. Simulation results show that with smart charging, the charging cost incurred by EV owners as well as the rate of aging undergone by the transformer can be significantly reduced.

Energy-cost minimization with dynamic smart charging of electric vehicles and the analysis of its impact on distribution-system operation

Energy-cost minimization with dynamic smart charging of electric vehicles and the analysis of its impact on distribution-system operation

Do I need to charge right now? Tailored choice architecture design can increase preferences for electric vehicle smart charging

The increasing diffusion of electric vehicles (EVs) can challenge the stability of distribution grids. Smart charging systems can reduce the stress of EV charging on the grid, but the potential of the technology depends on EV drivers' participation in smart charging schemes. To investigate this potential, we conducted an online randomised-controlled experiment with two waves (baseline and experimental phase, N = 222), in which we examined drivers' preferences for smart charging and tested a behavioral intervention to increase the number of smart charging choices. We translated state-of-charge (SoC) information from percentage of battery level into miles corresponding to the battery level and tailored information, i.e., the number of driving days covered by the actual SoC based on participants’ personal driving profiles. Participants preferred to use smart charging systems to decrease costs and to increase renewable energy use. However, they tended to overestimate the importance of the battery SoC when setting charging preferences. This overestimation was especially evident for participants who only drive short distances and may be lead to inefficient use of smart charging technology. Translating battery SoC into tailored information corrected for this bias and increased the number of smart charging choices. Our findings illustrate how behavioral interventions can be leveraged to attain energy transition goals.