Smart Charging Strategies Research Articles

Managing grid impacts from increased electric vehicle adoption in African cities

Electric vehicles are pivotal for global climate solutions, particularly in emerging markets like Africa. Despite the continent’s clean energy potential, electric vehicle adoption faces unique challenges due to inefficiencies and reliability issues of distribution power grids. Here, we analyze the impacts of expanding electric vehicle fleets—private, commercial, and paratransit—on Nairobi’s power grid. We simulate traffic patterns, charging behaviors, and transformer utilization using local mobility data. Our results show that while electric commercial and paratransit fleets may improve power system efficiency, widespread private EV adoption could significantly strain the grid, increasing peak loads and transformer aging. Smart charging strategies could mitigate these issues, reducing potential transformer replacement costs by up to 40%. Our study highlights the importance of tailored demand management and infrastructure planning to support EV growth in African cities, providing critical insights for policymakers, utilities, and transport planners to facilitate sustainable electric mobility transitions.

Commercial EV fleet smart charging for cost reduction and renewables integration: A case study in Germany

The transition to electric mobility reduces transportation carbon emissions but presents challenges in integrating growing electricity demand with renewable electricity generation and power systems. Smart charging emerges as a key technology to minimize operational costs and support higher utilization of green electricity for electric vehicles (EVs). Market analysis indicates a trend towards dynamic electricity tariffs, incentivizing EV charging at times favourable to the electricity grid, yet existing literature fails to address smart charging technology in sync with times of lower electricity prices and higher renewables integration. This work examines different smart charging strategies for a commercial EV fleet in Germany, considering a Real-Time Pricing (RTP) tariff indexed to day-ahead market prices and renewable electricity integrations. HOMER Grid is used to model these smart charging strategies and generate new EV load profiles. Direct and indirect CO2 emissions and renewable electricity usage ratios are calculated based on detailed electricity grid and self consumption data. The resulting Levelized Cost of Energy (LCOE) is minimized, by adjusting solar PV and battery capacities. The adoption of an RTP tariff decreases the LCOE from €0.530 to €0.314 per kWh. A smart charging strategy designed to reduce peak demand and schedule charging at lower price times further reduces the LCOE to €0.283 per kWh. The lowest LCOE of €0.281 per kWh is achieved with 34.4 kWp of solar PV capacity and no batteries. The carbon impact is minimized with larger solar PV and battery capacities. The performed work indicates a favourable framework for EV charging integrators to maximize cost savings and renewable energy integration for an EV fleet charging use case.

A Comprehensive Review of Smart Charging Strategies for Electric Vehicles and Way Forward

A Comprehensive Review of Smart Charging Strategies for Electric Vehicles and Way Forward

SoC Profile Reconstruction in Residential EV Charging: Assessing the Degradation of V1G and V2G in Denmark

The development of Smart Charging (SC) strategies for Electric Vehicles (EVs) is on the rise, driven by the potential benefits for both customers and grid operators. While several studies have explored the impact of these strategies on battery degradation, there is a lack of studies based on real-world data. To overcome the limited information provided by the current EV-EVSE (EV Supply Equipment) communication protocols, a methodology to reconstruct State of Charge (SoC) profiles based on the charging power is proposed and used to reconstruct the profiles and analyse the battery degradation for three residential EVSEs in Denmark, comparing degradation trends in under Uncontrolled Charging (UC) mode, with the SC ones. Results show that the economic profits from unidirectional (V1G) SC strategies based on price, offer significant cost savings (10-22% annually) and reduce calendar ageing by up to 4% over a decade. V2G strategies entail modest increases in cost savings at the cost of a minimal additional degradation.

A multi-stage framework for coordinated scheduling of networked microgrids in active distribution systems with hydrogen refueling and charging stations

Due to the increase in electric energy consumption and the significant growth in the number of electric vehicles (EV) at the level of the distribution network, new networks have started using new fuels such as hydrogen to improve environmental indicators and at the same time better efficiency from the excess capacity of renewable resources. In this article, the services that can be provided by hydrogen refueling stations and charging electric vehicles in the optimal performance of microgrids have been investigated. The model proposed in this paper includes a two-stage stochastic framework for scheduling resources in microgrids, especially hydrogen refueling stations and electric vehicle charging. In this model, two main goals of cost minimization and greenhouse gas emissions are considered. In the proposed framework and in the first stage, the service range of microgrids is determined precisely according to the electrical limitations of distribution systems in emergency situations. Then, in the second stage, the problem of energy management in each microgrid will be solved centrally. In this situation, various indicators including the output energy of renewable sources, smart charging of hydrogen and electric vehicle charging stations (EV/FCV) and flexible loads (FL) are evaluated. The final mathematical model is implemented as a multivariate integer multiple linear problem (MILP) using the GUROBI solver in GAMS software. The simulation results on the modified IEEE 118-Bus network show the positive effect of the presence of flexible loads and smart charging strategies by charging stations. Also, the numerical derivation shows that the operating costs of the entire system can be reduced by 4.77% and the use of smart charging strategies can reduce greenhouse gas emissions by 49.13%.

Overview of Opportunities and Challenges to Vehicle-to-Grid Integration and Bahrain Perspective

Transportation sector is among the largest consumers of fossil fuels, which also makes it a significant contributor to the greenhouse gas emissions. A great deal of effort has been made to reduce this reliance of transportation on fossil fuel for its operation. These efforts have resulted in innovations in vehicle technology which include vehicles operating on electric and hybrid energy. The advent of these technologies has showed the way towards new possibilities to further increase the efficiency of energy systems and their utilization. One such system utilizes the integration of electric vehicles with the national grid system, it is referred to as vehicle-to-grid (V2G) technology. This technology provides a cost-effective and efficient solution to address the problems associated with the high demand and variable supply of renewable energy systems. This study provides a brief overview of the research conducted in this area, consequently identifying the opportunities and challenges related to this technology. Research shows that V2G integration has environmental benefits in the form of reducing CO2 emissions and reducing reliance on traditional energy sources. In spite of this, the economic feasibility of this technology requires efforts for standardization, infrastructure expansion and smart charging strategies. This paper is expected to provide future direction of work for all stakeholders, including academia, industry, and governmental bodies. There are collaborative efforts required to standardize the vehicle and grid technology and infrastructure expansion, while the research needs to focus on optimization strategies. In the end, discussion on the implementation of this technology in Bahrain is also presented. In this context, it was found that the major challenges are due to the current reliance on fossil fuel and the trend of increase in car ownership which must be addressed before taking steps towards V2G systems.

Decentralized restoration of distribution systems with coupling neighboring microgrids

In this study, a multi-agent system (MAS) is incorporated in a decentralized strategy to restore distribution systems while taking into account coupling neighboring microgrids (CNMGs). This provides modeling for renewable energy sources (RESs), electric vehicles (EVs), battery storage systems (BSS) and load. The desired and most favorable restoration path is found by the MAS, in which zone agents are dispersed across the distribution system. The MAS can also manage microgrids (MGs) overloaded as the unbalance operation of RESs, BSS, EVs, and load. This is realized by making a bridge between MGs and neighboring non-overloaded MGs. The suggested method adheres to voltage and power flow restrictions while operating according to expert system standards. The recommended approach is put to the test using a 33-bus radial distribution system. MATLAB calculations on agents and power flow are carried out in order to verify the validity of the choices made by agents. The proposed restoration plan is able to obtain the best power supply path with a low number of switching in the event of a fault so that the voltage magnitude is higher than 0.9 p.u. and free capacity is available for the distribution lines. The smart charging strategy of EVs reduces 93% of their turn off compared to the non-smart charging strategy. However, if the CNMG plan is established, all vehicles can be powered.

Smart and Sustainable Wireless Electric Vehicle Charging Strategy with Renewable Energy and Internet of Things Integration

This study addresses the challenges associated with electric vehicle (EV) charging in office environments. These challenges include (1) reliance on manual cable connections, (2) constrained charging options, (3) safety concerns with cable management, and (4) the lack of dynamic charging capabilities. This research focuses on an innovative wireless power transfer (WPT) system specifically designed for use in office parking areas. This system incorporates renewable energy resources (RERs) and uses the transformative power of the Internet of Things (IoT). It employs a mix of solar energy systems and battery storage solutions to facilitate a sustainable and efficient energy supply to EVs. The integration of IoT technology allows for the automatic initiation of charging as soon as an EV is parked. Additionally, the implementation of the Blynk application offers users real-time access to information regarding the operational status of the photovoltaic system and the battery levels of their EVs. The system is further enhanced with IoT and RFID technologies to provide dynamic updates on the availability of charging slots and to implement strict security protocols for user authentication and protection. The research also includes a case study focusing on the application of this charging system in office settings. The case study achieves a 95.9% IRR, lower NPC of USD 1.52 million, and 56.7% power contribution by RERs, and it reduces annual carbon emissions to 173,956 kg CO2.

Smart electric vehicle charging for reducing photovoltaic energy curtailment

The increased diffusion of Photovoltaic (PV) generation could be limited by the grid capacity to host the power input from PV systems, especially in mid-day hours on sunny days. This paper proposes a framework of analysis to determine the mitigation of the PV energy curtailment in the grid, based on a novel smart charging strategy for electric vehicles (EVs) that operate in the grid-to-vehicle mode. This framework adopts a combination of methods and techniques, with a clustering procedure to group the EV charging stations with some PV systems based on the electrical distance, a novel smart charging strategy for the EVs based on the definition of fuzzy weights, and the exploitation of the blockchain technology with a Proof-of-Authority consensus protocol to reinforce network security and achieve decentralization. The blockchain provides full traceability of EV charging operations and grid status monitoring during time. The proposed framework contains novel indicators of avoided PV power reduction and energy curtailment. To account for the EV-related uncertainty, these indicators are determined from a Monte Carlo-based assessment of the three-phase power flow results. The results are presented on a three-phase unbalanced 123-node system with given locations of PV systems and EV charging stations.

Urban-scale energy matching optimization with smart EV charging and V2G in a net-zero energy city powered by wind and solar energy

Renewable energy and electric vehicles (EVs) are crucial technologies for achieving sustainable cities. However, intermittent power generation from renewable energy sources and increased peak load due to EV charging can pose technical challenges for the power systems. Improved load matching through energy system optimization can minimize these challenges. This paper assesses the optimal urban-scale energy matching potentials in a net-zero energy city powered by wind and solar energy, considering three EV charging scenarios: opportunistic charging, smart charging, and vehicle-to-grid (V2G). A city on the west coast of Sweden is used as a case study. The smart charging and V2G schemes aim to minimize the mismatch between generation and load, and are formulated as quadratic programming problems. The simulation results show that the optimal load matching performance is achieved in a net-zero energy city with the V2G scheme and a wind-PV electricity production share of 70:30. The load matching performance in the optimal net-zero energy city is increased from 68% with opportunistic charging to 73% with smart charging and further to 84% with V2G. It is also shown that a 2.4 GWh EV battery participating in the V2G scheme equals 1.4 GWh stationary energy storage in improving urban-scale load matching performance. The findings indicate that EVs have a high potential to provide flexibility to urban energy systems.

Optimized ANN for LiFePO4 battery charge estimation using principal components based feature generation

Electric vehicles (EVs) have gained prominence in the present energy transition scenario. Widespread adoption of EVs necessitates an accurate State of Charge estimation (SoC) algorithm. Integrating predictive SoC estimations with smart charging strategies not only optimizes charging efficiency and grid reliability but also extends battery lifespan while continuously enhancing the accuracy of SoC predictions, marking a crucial milestone in sustainable electric vehicle technology. In this research study, machine learning methods, particularly Artificial Neural Networks (ANN), are employed for SoC estimation of LiFePO4 batteries, resulting in efficient and accurate estimation algorithms. The investigation first focuses on developing a custom-designed battery pack with 12 ​V, 4 Ah capacity with a facility for real-time data collection through a dedicated hardware setup. The voltage, current and open-circuit voltage of the battery are monitored with computerized battery analyzer. The battery temperature is sensed with a DHT22 temperature sensor interfaced with Raspberry Pi. Principal components are derived for the collected battery data set and analyzed for feature engineering. Three principal components were generated as input parameters for the developed ANN. Early Stopping for the ANN was also implemented to achieve faster convergence of the ANN. While considering eleven combinations for ten different optimizers loss function is minimized. Comparative analysis of hyperparameter tuning and optimizer selection revealed that the Adafactor optimizer with specific settings produced the best results with an RMSE value of 0.4083 and an R2 Score of 0.9998. The proposed algorithm was also implemented for two different types of datasets, a UDDS drive cycle and a standard cell-level dataset. The results obtained were in line with the results obtained with the ANN model developed based on the data collected from the developed experimental setup.

A joint model of infrastructure planning and smart charging strategies for shared electric vehicles

This paper presents a data-driven joint model designed to simultaneously deploy and operate infrastructure for shared electric vehicles (SEVs). The model takes into account two prevalent smart charging strategies: the Time-of-Use (TOU) tariff and Vehicle-to-Grid (V2G) technology. We specifically quantify infrastructural demand and simulate the travel and charging behaviors of SEV users, utilizing spatiotemporal and behavioral data extracted from a SEV trajectory dataset. Our findings indicate that the most cost-effective strategy is to deploy slow chargers exclusively at rental stations. For SEV operators, the use of TOU and V2G strategies could potentially reduce charging costs by 17.93% and 34.97% respectively. In the scenarios with V2G applied, the average discharging demand is 2.15 ​kWh per day per SEV, which accounts for 42.02% of the actual average charging demand of SEVs. These findings are anticipated to provide valuable insights for SEV operators and electricity companies in their infrastructure investment decisions and policy formulation.

PEV’s Smart Charging Strategy Based on Individual State of Charge

Themain contribution of this work is a smart-charging strategy based on the state of charge analyses to avoid the undervoltages caused by plug-in electric vehicles into the distribution system. The work uses power hardware-in-the-loop simulations, where a modified IEEE 34 bus system, with five groups of electric vehicle stations, is modeled in a real-time digital simulator. The number of electric vehicles charging and the initial state of charge (SoC) are generated randomly. The first analyses identify the undervoltage problems due to the increased number of electric vehicles connected during peak hours load. After, a smart-charging solution is necessary to solve this power quality problem. So, if the voltage profile decreases under certain limits, defined by a hysteresis band, the control occurs, and the smart-charging is applied. The proposed strategy based on individual state of charge priority reduces the electric vehicle recharge power at virtual stations, by comparing its value with the mean of state of charge values from each station group. The experimental results presented show that, by varying the recharge current, the voltage profiles do not reach the limit for sags determined by the Brazilian standard, proving the performance and solving the problem even if not reducing the recharge current of all electric vehicles equally.

Capabilities of compressed air energy storage in the economic design of renewable off-grid system to supply electricity and heat costumers and smart charging-based electric vehicles

The study presents the sizing of a hybrid renewable islanded system that can feed both electrical and thermal energy simultaneously. The aforementioned energies are generated through the utilization of wind systems and bio-waste units. The integration of combined heat and power technology in the bio-waste unit enables the concurrent generation of electricity and heat power. Electric vehicles consume electric energy, but function based on a smart charging. The study employs compressed air energy storage as a means to bridge the disparity between the patterns of electric power generation and consumption, with the aim of enhancing energy efficiency and reducing planning expenses. Thermal energy storage serves as an intermediary between renewable power and load profiles within the thermal sector. Objective here is find the minimum annual capital and maintenance costs of sources, storages, and power electronic converters. Problem is constrained to operational model of renewable sources, storages, and electric vehicles. The present model prioritizes the utilization of renewable sources for the purpose of supplying electrical and thermal loads. Electric vehicles regulate their active power to minimize the disparity between load and electric production profiles. Storages perform charging and discharging functions to bridge the disparity between power generation and consumption. Strategy incorporates uncertainty related to load, wind speed, bio-waste-derived gas production, and electric vehicles parameters. The study employs a point estimate method to attain a dependable solution. The numerical results indicate that incorporation of compressed air storage in the hybrid system results in a decrease of 7.7 % (12.9 %) in the planning costs relative to system with batteries (hydrogen storage). The proposed design's concurrent provision of electrical and thermal energy leads to an approximately 57 % increasing in planning costs when compared to a system that solely provides electrical energy. The implementation of a smart charging strategy for electric vehicles drop the hybrid system's overall scheduling cost by 3.9 %.

Overcoming challenges in life cycle assessment of smart energy systems – A map of solution approaches

Current research underlines the benefits of digitalization to integrate renewable energy sources (RES) efficiently and to coordinate generation and supply. This development towards smart energy systems (SES) includes emerging use cases at the distribution grid level, e.g., smart charging strategies for electric vehicles or energy management systems in buildings. An environmental assessment of such, however, is challenging. The standardized Life Cycle Assessment (LCA) approaches seem insufficient since SES are emerging systems at the interface of digitalization and energy with multiple intended and unintended environmental effects. This article aims to derive solution approaches for LCA professionals that investigate use cases enabled by information and communication technology (ICT), specifically in SES. To identify challenges during the first two phases of an LCA and solution approaches, we examine seven recent research projects and conduct a real-time Delphi workshop with LCA experts in the field of SES. As a result, the article provides approaches on setting up an LCA, filling data gaps, and methods to draw upon the long-term impacts of use cases in SES. Key findings include a modified taxonomy of potential environmental effects. Results show that systemic effects in SES have potentially the highest impact on the LCA, followed by effects caused by user behavior. Assessing these effects of ‘higher-order’ poses an increased complexity. Determined solution approaches include combining the standardized LCA with energy system modeling or surveys on user behavior. Overall, this article reveals orientation strategies and recommendations to guide LCA professionals toward a holistic environmental assessment of use cases in SES.

Energy Management of Microgrids with a Smart Charging Strategy for Electric Vehicles Using an Improved RUN Optimizer

Electric vehicles (EVs) and renewable energy resources (RERs) are widely integrated into electrical systems to reduce dependency on fossil fuels and emissions. The energy management of microgrids (MGs) is a challenging task due to uncertainty about EVs and RERs. In this regard, an improved version of the RUNge Kutta optimizer (RUN) was developed to solve the energy management of MGs and assign the optimal charging powers of the EVs for reducing the operating cost. The improved RUN optimizer is based on two improved strategies: Weibull flight distribution (WFD) and a fitness–distance balance selection (FDB) strategy, which are applied to the conventional RUN optimizer to improve its performance and searching ability. In this paper, the energy management of MGs is solved both at a deterministic level (i.e., without considering the uncertainties of the system) and while considering the uncertainties of the system, with and without a smart charging strategy for EVs. The studied MG consists of two diesel generators, two wind turbines (WTs), three fuel cells (FCs), an electrical vehicle charging station and interconnected loads. The obtained results reveal that the proposed algorithm is efficient for solving the EM of the MG compared to the other algorithms. In addition, the operating cost is reduced with the optimal charging strategy.

Smart home charging of electric vehicles using a digital platform

As the penetration of electric vehicles (EVs) grows, it is important to understand the implications of home charging technologies for grid operations and for the budget of users. We conduct an empirical study analyzing data on 438 EVs over a period of 3,687 consecutive hours, collected by an energy aggregator which operates a digital platform. We first develop an optimization model to compute an optimal schedule of charging for all EVs in the dataset at minimum cost. Then, we compare the realizations against this optimal solution, distinguishing householders who use a smart charging functionality of the digital platform from those who do not use it. Our findings indicate that the smart charging behaviour conduces to better results, and close to the optimal solution. The non-users tend to start charging as soon as they plug-in their EVs, often at peak consumption times. In contrast, the smart charging strategy usually shifts the charging schedules towards times where the consumption is cheaper and the grid is less congested, facilitating a higher load factor and lower power losses. These results highlight the positive role of energy aggregators and digital platforms in coordinating users to lower the cost and enhance efficiency of energy consumption.

Assessing the value of information for electric vehicle charging strategies at office buildings

Smart charging strategies for electric vehicles (EVs) require as input information such as energy requirement and dwell time. In practice, that information is often not available. However, estimations may be obtained from historical charging behavior. This paper investigates the added value of historical information for EV charging strategies based on real-world EV charging data collected in an office building parking lot with 125 chargers. Furthermore, it provides valuable insights into EV charging behavior at office building parking lots, based on a statistical analysis of the data. The added value of data availability in EV charging strategies for day-ahead planning and real-time operation of office building parking lots is assessed via a set of quality metrics that measure user satisfaction and impact on the local grid. Offline charging strategies under various degrees of available information are validated by comparing their performance with the real-time operation of the parking lot. Results show a power peak reduction of more than 50% using historical data and simple estimations of arrival times, dwell times, and energy requirement. A trade-off between power peaks and service quality (on average 4.4kWh energy not served) is observed. It was found that knowledge of individual average energy provides higher added value compared to knowing individual average dwell time in both offline planning and real-time operation of the parking lot.

Techno-economic analysis for the electric vehicle battery aging management of charge point operator

Temperature is a major factor that affects battery aging. Therefore, charging stations exposed to the external environment and charge point operator (CPO) operation strategy can significantly affect battery aging. In particular, when providing power services that may require a high C-rate, electric vehicle (EV) battery aging can be aggravated in certain environments. Therefore, battery aging management according to the provided power service needs to be dealt with from the perspective of the CPO, which establishes the operation strategy, in addition to the EV. This study focuses on EV battery aging management considering the charging station operating environment from the point of view of a CPO. Battery aging model considers the mutual influence between charging station's ambient temperature, EV cabin temperature, battery temperature, and battery temperature management system (BTMS). Based on this, the CPO establishes an optimal smart charging strategy considering the BTMS operating conditions and the power consumption of EVs working in an independent environment. Based on this, the technical and economic effects of battery aging management according to participation in power services were analyzed. Case study results show that indoor stations can reduce management costs for battery aging by up to 13.31% compared to that in outdoor stations.

Smart charging strategy for electric vehicles based on marginal carbon emission factors and time-of-use price

As the penetration of electric vehicles (EVs) has grown rapidly in recent years, the stability and safety of electric grids are inevitably affected. Uncontrolled EV charging can reduce electric grid efficiency and increase greenhouse gas emissions (GHG). This occurs when EV charging aligns with peak demand, requiring the activation of fossil fuel-powered plants to meet the electricity demand. The time-of-use (TOU) price is a traditional management strategy for smoothing the demand power curve, but it does not consider carbon emissions. This study proposes a smart charging strategy based on an improved local search genetic algorithm (LS-GA) that considers both the TOU price and marginal emission factors (MEF). Over 47,000 charging events from 45 charging stations in Tianjin, a major city in North China, were used to simulate the charging pattern and demand. Four different charging strategies are considered in the study. The results indicate that the proposed smart charging strategy can reduce the cost and carbon emissions by up to 27% and 16% compared with uncontrolled charging, respectively. This study demonstrates the significant potential of using optimization methods in the EV smart charging problem.