Optimised electric vehicles charging scheme with uncertain user-behaviours in smart grids

The high share of electric vehicles (EVs) in the transportation sector is one of the main pillars of sustainable development. Availability of a suitable charging infrastructure and an affordable electricity cost for battery charging are the main factors affecting the increased adoption of EVs. The installation location of fixed charging stations (FCSs) may not be completely compatible with the changing pattern of EV accumulation. Besides, their power withdrawal location in the network is fixed, and also, the time of receiving the power follows the EVs’ charging demand. The EV charging demand pattern conflicts with the network peak period and causes several technical challenges besides high electricity prices for charging. A mobile battery energy storage (MBES) equipped with charging piles can constitute a mobile charging station (MCS). The MCS has the potential to target the challenges mentioned above through a spatio-temporal transfer in the required energy for EV charging. Accordingly, in this paper, a new method for modeling and optimal management of mobile charging stations in power distribution networks in the presence of fixed stations is presented. The MCS is powered through its internal battery utilizing a self-powered mechanism. Besides, it employs a self-driving mechanism for lowering transportation costs. The MCS battery can receive the required energy at a different time and location regarding EVs accumulation and charging demand pattern. In other words, the mobile station will be charged at the most appropriate location and time by moving between the network buses. The stored energy will then be used to charge the EVs in the fixed stations’ vicinity at peak EV charging periods. In this way, the energy required for EV charging will be stored during off-peak periods, without stress on the network and at the lowest cost. Implementing the proposed method on a test case demonstrates its benefits for both EV owners and network operator.

The transport sector significantly contributes to global greenhouse gas emissions, making electromobility crucial in the race toward the United Nations Sustainable Development Goals. In recent years, the increasing competition among manufacturers, the development of cheaper batteries, the ongoing policy support, and people’s greater environmental awareness have consistently increased electric vehicles (EVs) adoption. Nevertheless, EVs charging needs—highly influenced by EV drivers’ behavior uncertainty—challenge their integration into the power grid on a massive scale, leading to potential issues, such as overloading and grid instability. Smart charging strategies can mitigate these adverse effects by using information and communication technologies to optimize EV charging schedules in terms of power systems’ constraints, electricity prices, and users’ preferences, benefiting stakeholders by minimizing network losses, maximizing aggregators’ profit, and reducing users’ driving range anxiety. To this end, accurately forecasting EV charging demand is paramount. Traditionally used forecasting methods, such as model-driven and statistical ones, often rely on complex mathematical models, simulated data, or simplifying assumptions, failing to accurately represent current real-world EV charging profiles. Machine learning (ML) methods, which leverage real-life historical data to model complex, nonlinear, high-dimensional problems, have demonstrated superiority in this domain, becoming a hot research topic. In a scenario where EV technologies, charging infrastructure, data acquisition, and ML techniques constantly evolve, this paper conducts a systematized literature review (SLR) to understand the current landscape of ML-based EV charging demand forecasting, its emerging trends, and its future perspectives. The proposed SLR provides a well-structured synthesis of a large body of literature, categorizing approaches not only based on their ML-based approach, but also on the EV charging application. In addition, we focus on the most recent technological advances, exploring deep-learning architectures, spatial-temporal challenges, and cross-domain learning strategies. This offers an integrative perspective. On the one hand, it maps the state of the art, identifying a notable shift toward deep-learning approaches and an increasing interest in public EV charging stations. On the other hand, it uncovers underexplored methodological intersections that can be further exploited and research gaps that remain underaddressed, such as real-time data integration, long-term forecasting, and the development of adaptable models to different charging behaviors and locations. In this line, emerging trends combining recurrent and convolutional neural networks, and using relatively new ML techniques, especially transformers, and ML paradigms, such as transfer-, federated-, and meta-learning, have shown promising results for addressing spatial-temporality, time-scalability, and geographical-generalizability issues, paving the path for future research directions.

Transportation vehicles are a large contributor of the carbon dioxide emissions to the atmosphere. Electric Vehicles (EVs) are a promising solution to reduce the CO2 emissions which, however, requires the right electric power production mix for the largest impact. The increase in the electric power consumption caused by the EV charging demand could be matched by the growing share of Renewable Energy Sources (RES) in the power production. EVs are becoming a popular sustainable mean of transportation and the expansion of EV units due to the stochastic nature of charging behavior and increasing share of RES creates additional challenges to the stability in the power systems. Modeling of EV charging fleets allows understanding EV charging capacity and demand response (DR) potential of EV in the power systems. This article focuses on modeling of daily EV charging profiles for buildings with various number of chargers and daily events. The article presents a modeling approach based on the charger occupancy data from the local charging sites. The approach allows one to simulate load profiles and to find how many chargers are necessary to suffice the approximate demand of EV charging from the traffic characteristics, such as arrival time, duration of charging, and maximum charging power. Additionally, to better understand the potential impact of demand response, the modeling approach allows one to compare charging profiles, while adjusting the maximum power consumption of chargers.

Electric vehicles (EVs) have experienced a rapid growth due to the economic and environmental benefits. However, the substantial charging load brings challenging issues to the power grid. Modern technological advances and the huge number of high-rise buildings have promoted the development of distributed energy resources, such as building integrated/mounted wind turbines. The issue to coordinate EV charging with locally generated wind power of buildings can potentially reduce the impacts of EV charging demand on the power grid. As a result, this paper investigates this important problem and three contributions are made. First, the real-time scheduling of EV charging is addressed in a centralized framework based on the ideas of model predictive control, which incorporates the volatile wind power supply of buildings and the random daily driving cycles of EVs among different buildings. Second, an EV-based decentralized charging algorithm (EBDC) is developed to overcome the difficulties due to: 1) the possible lack of global information regarding the charging requirements of all EVs and 2) the computational burden with the increasing number of EVs. Third, we prove that the EBDC method can converge to the optimal solution of the centralized problem over each planning horizon. Moreover, the performance of the EBDC method is assessed through numeric comparisons with an optimal and two heuristic charging strategies (i.e., myopic and greedy). The results demonstrate that the EBDC method can achieve a satisfactory performance in improving the scalability and the balance between the EV charging demand and wind power supply of buildings. Note to Practitioners —This paper is motivated by the challenging problem due to the substantial charging load of electric vehicles (EVs) on the power grid. Nowadays, modern technological advances and the rapid increase of high-rise buildings have promoted the development of building integrated/mounted wind turbines. As the EVs are usually parked in buildings for a large proportion of time every day, the issue to best utilize locally generated wind power of buildings to suffice EV travelling requirements shows vital significance in reducing their dependence on the power grid. However, there exist two main challenges including: 1) the multiple uncertainties regarding the uncertain wind power generation and the random driving behaviors of EVs and 2) the scalability of the solution method. To tackle the first challenge, the idea of model predictive control is introduced to make charging decisions at each stage based on a short-term prediction of the on-site wind power and the current collection of EVs parked there. To consider the scalability and overcome the lack of global charging information of all EVs in practical deployment, an iterative EV-based decentralized charging algorithm (EBDC) is derived, in which each EV can dynamically update its own charging decisions according to a dynamic charging “price” announced by the buildings. Alternatively, the buildings dynamically adjust the charging “price” to motivate the EVs to get charged during the time periods with sufficient wind power supply. Numeric results demonstrate that the EBDC method is scalable and performs well in improving the balance between the EV charging demand and the wind power supply of buildings.

This paper presents the day-ahead energy planning of passenger cars with 100% electric vehicle (EV) penetration in the Nordic region by 2050. EVs will play an important role in the future energy systems which can both reduce the greenhouse gas (GHG) emissions from the transport sector and provide the demand side flexibility required by smart grids. On the other hand, the EVs will increase the electricity consumption. In order to quantify the electricity consumption increase due to the 100% EV penetration in the Nordic region to facilitate the power system planning studies, the day-ahead energy planning of EVs has been investigated with different EV charging scenarios. Five EV charging scenarios have been considered in the energy planning analysis which are: uncontrolled charging all day, uncontrolled charging at home, timed charging, spot price based charging all day and spot price based charging at home. The demand profiles of the five charging analysis show that timed charging is the least favorable charging option and the spot priced based EV charging might induce high peak demands. The EV charging demand will have a considerable share of the energy consumption in the future Nordic power system.

Electric Vehicle (EV) technology is one of the most promising solutions to reduce dependence on fossil fuels and greenhouse gas (GHG) emissions in the transportation sector. However, a large increase of EVs raises concerns about negative impacts on electricity generation, transmission, and distribution systems. This study analyzes the benefits and trade-offs for EV penetration in Thai road transport based on EV penetration scenarios from 2019 to 2036. Two charging strategies are considered to assess the impact of EV charging: free charging and off-peak charging. Uncertainty variables are considered by a stochastic approach based on Monte-Carlo simulation (MCS). The simulation results shown that the adoption of EVs can reduce both energy consumption and GHG emissions. The results also indicate that the increased load due to EV charging demand in all scenarios is still within the buffer level, compared to the installed generation capacity in the Power Development Plan 2018 revision 1 (PDP2018r1), and the off-peak charging strategy is more beneficial than the free-charging strategy. However, the increased load demand caused by all EV charging strategies has a direct impact on the power generating schedule, and also decreases the system reliability level.

Two-phase operation for coordinated charging of electric vehicles in a market environment: From electric vehicle aggregators’ perspective

Evaluating the role of behavior and social class in electric vehicle adoption and charging demands

Non-intrusive load monitoring (NILM) is an important topic in smart-grid and\nsmart-home. Many energy disaggregation algorithms have been proposed to detect\nvarious individual appliances from one aggregated signal observation. However,\nfew works studied the energy disaggregation of plug-in electric vehicle (EV)\ncharging in the residential environment since EVs charging at home has emerged\nonly recently. Recent studies showed that EV charging has a large impact on\nsmart-grid especially in summer. Therefore, EV charging monitoring has become a\nmore important and urgent missing piece in energy disaggregation. In this\npaper, we present a novel method to disaggregate EV charging signals from\naggregated real power signals. The proposed method can effectively mitigate\ninterference coming from air-conditioner (AC), enabling accurate EV charging\ndetection and energy estimation under the presence of AC power signals.\nBesides, the proposed algorithm requires no training, demands a light\ncomputational load, delivers high estimation accuracy, and works well for data\nrecorded at the low sampling rate 1/60 Hz. When the algorithm is tested on\nreal-world data recorded from 11 houses over about a whole year (total 125\nmonths worth of data), the averaged error in estimating energy consumption of\nEV charging is 15.7 kwh/month (while the true averaged energy consumption of EV\ncharging is 208.5 kwh/month), and the averaged normalized mean square error in\ndisaggregating EV charging load signals is 0.19.\n

Uncontrolled charging of electric vehicles can affect the power system operation and stability. In cities with high population density and with short driving distances like Singapore, car parks bear a high proportion of private vehicles most of the day. These car parks can be regarded as aggregators and control the charging process. Demand response could be used by the car park operators as an additional source of revenue and thus help offset the high installation costs. This paper investigates different charging strategies and demand response management possibilities within a specific car park. Data for arrival times and parking durations are obtained using available statistical information, charging of individual electric vehicles is controlled by the car park operator and effects of demand response management on the total charging cost of the system is analysed.

Smart cities require the development of information and communication technology to become a reality (ICT). A “smart city” is built on top of a “smart grid”. The implementation of numerous smart systems that are advantageous to the environment and improve the quality of life for the residents is one of the main goals of the new smart cities. In order to improve the reliability and sustainability of the transportation system, changes are being made to the way electric vehicles (EVs) are used. As EV use has increased, several problems have arisen, including the requirement to build a charging infrastructure, and forecast peak loads. Management must consider how challenging the situation is. There have been many original solutions to these problems. These heavily rely on automata models, machine learning, and the Internet of Things. Over time, there have been more EV drivers. Electric vehicle charging at a large scale negatively impacts the power grid. Transformers may face additional voltage fluctuations, power loss, and heat if already operating at full capacity. Without EV management, these challenges cannot be solved. A machine-learning (ML)-based charge management system considers conventional charging, rapid charging, and vehicle-to-grid (V2G) technologies while guiding electric cars (EVs) to charging stations. This operation reduces the expenses associated with charging, high voltages, load fluctuation, and power loss. The effectiveness of various machine learning (ML) approaches is evaluated and compared. These techniques include Deep Neural Networks (DNN), K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT) (DNN). According to the results, LSTM might be used to give EV control in certain circumstances. The LSTM model’s peak voltage, power losses, and voltage stability may all be improved by compressing the load curve. In addition, we keep our billing costs to a minimum, as well.

Electric vehicles (EV) are an emerging mode of transportation, and big cities in the United States have witnessed an ever-growing demand for EV usage. The primary benefit of EVs is the high fuel efficiency by using only electricity, and hence lowers the dependency on fossil fuels and significantly reduces greenhouse gas emissions. Although the number of EVs has increased, the availability of EV charging stations for public use has been disproportionate to its demand. More recently, populations residing in the Southern California region have been faced with challenges such as range anxiety owing to the uneven spatial distribution of charging stations throughout the region. As the EV population continues to expand, identifying hotspots of EV charging and barriers to the equitable access of charging stations have gained much importance. Our study uses a geospatial data fusion approach with spatial statistics to combine EV charging station data, land use information, and American Community Survey (ACS) data at the census block group level in Orange County, California to discover optimal locations to broaden the EV charging network and identify potential equity issues surrounding charging station placements.

The transition from internal combustion engine vehicles to electric vehicles (EVs) is gaining momentum due to their significant environmental and economic benefits. This study addresses the challenges of integrating renewable energy sources, particularly solar power, into EV charging infrastructures by using deep learning models to predict photovoltaic (PV) power generation and EV charging demand. The study determines the optimal battery energy storage capacity and charging schedule based on the prediction result and actual data. A dataset of a 15 kWp rooftop PV system and simulated EV charging data are used. The results show that simple RNNs are most effective at predicting PV power due to their adept handling of simple patterns, while bidirectional LSTMs excel at predicting EV charging demand by capturing complex dynamics. The study also identifies an optimal battery storage capacity that will balance the use of the grid and surplus solar power through strategic charging scheduling, thereby improving the sustainability and efficiency of solar energy in EV charging infrastructures. This research highlights the potential for integrating renewable energy sources with advanced energy storage solutions to support the growing electric vehicle infrastructure.

While the number of the vehicle actuated with liquid fuels are settled, the count of electric vehicles is increasing. For the present moment most of them are scheduled for daily urban usage. This paper presents an analytical approach for estimation of the impact of electrical vehicle (EV) battery charging on the distribution grid. Based on the EV charge profile, load curve and local distributed generation the grid nodes, the time variation of grid parameters is obtained. A set of typical load profiles of EV charging modes is studied and presented. A software implementation and a 24h case study of low voltage distribution network with EV charging devices is presented in order to illustrate the approach and the impacts of EV charging on the grid. In the current paper an approach using variable nonlinear algebraic equations for dynamic time domain analysis of the charge of the electric vehicles is presented. Based on the results, the challenges due to EV charging in distribution networks including renewable energy sources are discussed. This approach is widely applicable for various EV charging and distributed energy resources studies considering control algorithms, grid stability analysis, smart grid power management and other power system analysis problems.

An intelligent electric vehicle charging system for new energy companies based on consortium blockchain