Smart Charging Of Electric Vehicles Research Articles

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.

Multi‐sectoral flexibility measures to facilitate wind and solar power integration

As power systems evolve towards integrating higher shares of renewables, the demand for additional levels of flexibility is increased. Meanwhile, o-ther energy consuming sectors, such as transport and heating, could provide flexibility when they move from fossil fuels to electricity. In this paper, the impact of a range of flexibility measures is assessed for the island system of Ireland, with a high share of renewable energy, particularly wind and solar. Flexibility measures studied include hybrid heating in domestic and industrial processes, smart charging of electric vehicles, renewable hydrogen, power to ammonia, peak shaving demand response and batteries. The novelty of this paper lies in directly quantifying the interactions and dependencies between different flexibility measures, with the objective of increasing the operational flexibility of an increasingly renewable energy-dominated power system. Four different scenarios are modeled to explore this interplay between the different flexibility measures. The costs and benefits of several sector-coupling measures. The scenarios have also been compared in terms of their influence on system inertia, renewable energy curtailment and non-synchronous penetration levels. The results indicate the potential importance of electricity-based heating in the industrial sector, smart charging of electric vehicles, batteries and power-to-ammonia, as part of achieving future targets

Grid congestion mitigation in the era of shared electric vehicles

Rapid integration of photovoltaic systems and electric vehicles in low-voltage grids increasingly causes grid congestion. To avoid costly grid reinforcements, distribution system operators are looking into applying electric vehicle smart charging algorithms to shift part of the load to off-peak hours. However, multiple barriers hamper the large-scale implementation of smart charging. These barriers can be alleviated when using electric vehicles in car sharing schemes for smart charging. Shared electric vehicles make up an increasing share of the car fleet and using these vehicles for smart charging exhibits different advantages over using private vehicles, including better predictable departure times and higher acceptance for smart charging. This study proposes a system for grid congestion mitigation using only shared electric vehicles and assesses this system’s techno-economic potential. Results affirm that grid congestion problems can be fully mitigated in most grids using shared electric vehicles at relatively low car sharing adoption rates. Also, the costs increase of using this system is negligible compared to a system with combined grid congestion mitigation of privately-owned and shared EVs.

Comparative environmental impact assessment of ICT for smart charging of electric vehicles in Germany

This study examines the environmental first-order effects of information and communication technologies (ICT) required for private smart charging of electric vehicles (EV) in Germany. With the focus on CO2-optimized charging, the environmental assessment compares bidirectional (V2G) and unidirectional (V1G) smart charging infrastructure to direct (uncontrolled) charging on a household level. Specifically, the applied life cycle assessment (LCA) investigates the production, transportation, operation and end-of-life phases of intelligent metering systems (iMSys) as well as private wallboxes operating with direct current (DC) and alternating current (AC). First, the technical prerequisites for smart and direct charging are outlined, with differences for direct charging depending on the household’s total electricity consumption. Secondly, the LCA shows an impact of 145.4 kg CO2-eq. per vehicle and year for V2G infrastructure by 2020, being 84 % higher than V1G (79 kg). The impact of direct charging infrastructure is significantly lower with 45.2 – 57.5 kg CO2-eq. per year. Due to the power consumption during the operation phase, the AC and DC wallboxes contribute most with 77% (V2G) and 57% (V1G) of the impact, respectively. Assuming ongoing decarbonization of the annual average German emission factor of electricity, the total impact of private charging infrastructure can be reduced by up to 56 % (V2G) and 67 % (V1G) by 2040. Next to the high energy efficiency of components, manufacturers should focus on a sustainable design of components including longevity. Overall, the environmental impact of the ICT infrastructure for smart charging is highly dependent on the charging strategy as it determines the annual duration of charging and discharging. Suggested further research involves investigations on first-order effects associated with other smart charging strategies (e.g. peak shaving), suitable allocation methods for multifunctional ICT components (e.g. iMSys), along with an assessment of higher-order effects such as energy system-wide environmental consequences.

A systematic comparison of various electric vehicle charging approaches

The use of electric vehicles (EVs) has been on the rise. Most of the existing EV smart charging controls can be categorized into three approaches according to their optimization principles: individual, bottom-up and top-down. Until now, systematic comparison and analysis of the different approaches are still lacking. It is still unknown whether a control approach performs better than others and, if yes, why is it so. This study aims to fill in such knowledge gaps by conducting a systematic comparison of these three different control approaches and analyzing their performances in depth. A representative control algorithm will be selected from each control approach, then the selected algorithms will be applied for optimizing EV charging loads in a building community in Sweden. Their power regulation performances will be comparatively investigated. This study will help pave the way for the developments of more sophisticated control algorithms for EV smart charging.

Smart Charging and Discharging of Electric Vehicles Based on Multi-Objective Robust Optimization in Smart Cities

Smart Charging and Discharging of Electric Vehicles Based on Multi-Objective Robust Optimization in Smart Cities

Energy Data Sharing and the Case of Electric Vehicles Smart Charging

Energy Data Sharing and the Case of Electric Vehicles Smart Charging

Modeling electric vehicle charging patterns: A review

Electric vehicles (EVs) offer an opportunity to move towards greenhouse gas emission reduction targets by decarbonizing the transport sector as well as reduce local air pollution. However, uncontrolled and simultaneous charging of a significant number of EVs could pose a challenge to electricity grids and generation-load adequacy. Studying these impacts requires a predictive model of EV fleet recharging. Here we review techniques for EV charging pattern modeling and the types of studies they are used for. The paper also introduces the wide range of parameters (vehicle types, charging points, plug-in behavior, etc.) that modeling studies can factor in, and the EV smart charging simulation approaches available. We conclude by proposing a framework for future research on EV load prediction models.JEL classification: C02, C65, L62, L94, Q40.

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.

Smart Charging of Electric Vehicles Considering SOC-Dependent Maximum Charging Powers

The aim of this work is to schedule the charging of electric vehicles (EVs) at a single charging station such that the temporal availability of each EV as well as the maximum available power at the station are considered. The total costs for charging the vehicles should be minimized w.r.t. time-dependent electricity costs. A particular challenge investigated in this work is that the maximum power at which a vehicle can be charged is dependent on the current state of charge (SOC) of the vehicle. Such a consideration is particularly relevant in the case of fast charging. Considering this aspect for a discretized time horizon is not trivial, as the maximum charging power of an EV may also change in between time steps. To deal with this issue, we instead consider the energy by which an EV can be charged within a time step. For this purpose, we show how to derive the maximum charging energy in an exact as well as an approximate way. Moreover, we propose two methods for solving the scheduling problem. The first is a cutting plane method utilizing a convex hull of the, in general, nonconcave SOC–power curves. The second method is based on a piecewise linearization of the SOC–energy curve and is effectively solved by branch-and-cut. The proposed approaches are evaluated on benchmark instances, which are partly based on real-world data. To deal with EVs arriving at different times as well as charging costs changing over time, a model-based predictive control strategy is usually applied in such cases. Hence, we also experimentally evaluate the performance of our approaches for such a strategy. The results show that optimally solving problems with general piecewise linear maximum power functions requires high computation times. However, problems with concave, piecewise linear maximum charging power functions can efficiently be dealt with by means of linear programming. Approximating an EV’s maximum charging power with a concave function may result in practically infeasible solutions, due to vehicles potentially not reaching their specified target SOC. However, our results show that this error is negligible in practice.

Smart Charging of Electric Vehicles: An Innovative Business Model for Utility Firms

Problem definition: By providing an environmentally friendly alternative to traditional vehicles, electric vehicles will transform urban mobility, particularly in smart cities. In practice, after an electric vehicle is plugged in, the charging station completes charging as soon as possible. Given that the procurement cost of electricity and associated emissions vary significantly during a day, substantial savings can be achieved by smart charging—delaying charging until the cost is lower. In this paper, we study smart charging as an innovative business model for utility firms. Academic/practical relevance: Utility firms are already investing in charging stations, and they can achieve significant cost savings through smart charging. Methodology: We consider a mechanism design problem in which a utility firm first announces pairs of charging price and completion time. Then, each customer selects the pair that maximizes their utility. Given the selected completion times, the utility firm solves the optimal control problem of determining the charging schedule that minimizes the cost of charging under endogenous, time-varying electricity procurement cost. We assume that there are ample parking spots with chargers at the charging station. Results: We devise an intuitive and practically implementable policy for scheduling charging of electric vehicles under given completion times. We prove that this policy is optimal if all customers arrive at the station simultaneously. We also characterize the optimal pairs of charging price and completion time. By using real electricity demand and generation data from the largest electricity market in the United States, we find that cost and emissions savings from smart charging are approximately 20% and 15%, respectively, during a typical summer month. Managerial implications: In contrast to the current practice of charging vehicles without delay, we show that it is economically and environmentally beneficial to delay charging some vehicles and to set charging prices based on customers’ inconvenience cost of delays. We also find that most of the savings from implementing smart charging can be achieved during peak-demand days, highlighting the effectiveness of smart charging.

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.

Impact of distributed energy resources and electric vehicle smart charging on low voltage grid stability

Environmental policies are pushing cross-sector electrification, including the transport and heating sectors. However, the large integration of distributed energy resources, such as electric vehicles (EV), heat pumps (HP), and photovoltaic (PV) systems, has a strong influence on the stability of low voltage (LV) distribution grids and can lead to high reinforcement costs. This work develops a methodology to quantify the impacts of a joint integration of EVs, PV, and HPs into LV distribution grids and the ability of a decentralized EV smart charging strategy to reduce the constraints. A central occupancy model generates demand and generation profiles for the studied technologies, which are then used to carry out Monte Carlo simulations for unbalanced power flow analysis of LV grids. A case study of a typical Belgian rural grid shows that the main impacts arise from HP integration, creating voltage deviations and transformer overloading. EV integration impacts are less significant, creating grid issues only at high-penetration levels and to a lesser extent than HPs. The proposed smart charging strategy allows reducing the impacts of EV integration; however, it does not allow increased penetration of the other technologies. Other smart charging and Vehicle-to-Grid (V2G) strategies should be analyzed to further increase DER penetration.