Composite vulnerability index-based mitigation-aware control law for multiple electric vehicle charging system

Introduction: Smart grid technology is a crucial direction for the future development of power systems, with electric vehicles, especially new energy vehicles, serving as important carriers for smart grids. However, the main challenge faced by smart grids is the efficient scheduling of electric vehicle charging and effective energy management within the grid.Methods: To address this issue, we propose a novel approach for intelligent grid electric vehicle charging scheduling and energy management, integrating three powerful technologies: Genetic Algorithm (GA), Gated Recurrent Unit (GRU) neural network, and Reinforcement Learning (RL) algorithm. This integrated approach enables global search, sequence prediction, and intelligent decision-making to optimize electric vehicle charging scheduling and energy management. Firstly, the Genetic Algorithm optimizes electric vehicle charging demands while minimizing peak grid loads. Secondly, the GRU model accurately predicts electric vehicle charging demands and grid load conditions, facilitating the optimization of electric vehicle charging schedules. Lastly, the Reinforcement Learning algorithm focuses on energy management, aiming to minimize grid energy costs while meeting electric vehicle charging demands.Results and discussion: Experimental results demonstrate that the method achieves prediction accuracy and recall rates of 97.56% and 95.17%, respectively, with parameters (M) and triggers (G) at 210.04 M and 115.65G, significantly outperforming traditional models. The approach significantly reduces peak grid loads and energy costs while ensuring the fulfilment of electric vehicle charging demands and promoting the adoption of green energy in smart city environments.

Electric vehicle (EV) smart charging, which regulates the charging rates of EVs in response to the availability of surplus solar photovoltaics (PV) power or the electricity prices, can effectively enhance the local power demand–supply balance and help improve the PV power local utilization. In this regard, researchers have developed two categories of EV charging controls: (i) B2V or G2V ((i.e., building-to-vehicle or grid-to-vehicle) power flow in which the EV can only be charged; and (ii) B2V2B or G2V2G (i.e., building to vehicle to building or grid-vehicle-to-grid) in which the vehicles can be both charged and discharged. The frequent charging/discharging could potentially accelerate the EV battery degradation, which might make such applications not economical. However, systematic investigation has rarely been conducted for the impact of various EV usage and charging factors (including the EV charging strategy, different EV charging forms, EV charging limits, and commuting distance) on the power balancing performances and EV battery cycling degradation. As a result, the EV owners may not be willing to join the smart charging demand response due to the concerns of accelerated battery degradation, and this hinders the applications of EVs in the power regulation in the future energy system. This chapter aims to investigate the effect of different ways of using EVs on the demand response performances and the EV battery degradation. A parametric study considering a set of different scenarios combining various EV charging forms, EV charging limits and commuting distances will be conducted in Sweden. A smart charging control method of the EV will be developed, which can optimize the EV charging and discharging rates to minimize the grid interactions. A degradation model, which can evaluate the EV battery degradation due to charging/discharging cycling, will be constructed to investigate the EV battery degradation under typical scenarios. The performances of each scenario will be analyzed and compared to draw conclusions. The study results can help improve researchers’ understanding of the impacts of smart EV charging in the building community performances. The obtained impacts on the battery degradation can also support decision makers in selecting suitable EV charging and usage strategies.

Reliable electric vehicle (EV) charging depends on both sufficient infrastructure and stable power quality. In real-world distribution networks, single power quality (PQ) disturbances, such as frequency deviation, harmonics, temporary undervoltage/overvoltage, transient events, voltage deviation, interruptions, sags, and swells can significantly influence charging efficiency, equipment safety, and battery longevity. However, existing public resources rarely provide standardized, high-resolution datasets linking specific PQ disturbances to EV charging performance under controlled and replicable conditions. We present a dataset that systematically evaluates the impact of ten representative single PQ disturbances on EV charging. Test cases were designed following IEEE standards, and experiments were conducted on a proprietary full-vehicle charging test platform to capture authentic charging responses. The dataset includes grid-side voltage and current waveforms, charger telemetry, and battery charging profiles at high temporal resolution, covering the most representative AC charging scenarios. Technical validation demonstrates the reliability of data collection, consistency across repeated tests, and alignment with PQ definitions. The dataset provides foundation for: (i) benchmarking diagnostic and classification algorithms for PQ events, (ii) quantifying the impact of specific disturbances on charging current and efficiency, and (iii) supporting the design of robust EV chargers and grid-integration strategies. While the present release focuses on single disturbances, it establishes a reference framework for future studies on more complex or composite PQ scenarios.

This article explores the potential impacts of integrating electric vehicles (EVs) and variable renewable energy (VRE) on power system operation. EVs and VRE are integrated in a production cost model with a 5 min time resolution and multiple planning horizons to deduce the effects of variable generation and EV charging on system operating costs, EV charging costs, dispatch stacks, reserves and VRE curtailment. EV penetration scenarios of the light-duty vehicle fleet of 10%, 20%, and 30% are considered in the RTS-GMLC test system, and VRE penetration is 34% of annual energy consumption. The impacts of EVs are investigated during the annual peak in the summer and during the four weeks of the year in which high VRE and low loads lead to overgeneration. Uncoordinated and coordinated EV charging scenarios are considered. In the uncoordinated scenario, charging is undertaken at the convenience of the EV owners, modeled using data from the Idaho National Laboratory’s EV Project. Coordinated charging uses an “aggregator” model, wherein EV charging is scheduled to minimize operating costs while meeting the daily charging requirements subject to EV availability and charging constraints. The results show that at each EV penetration level, the uncoordinated charging costs were higher than the coordinated charging costs. During a high-VRE, low-load week, with uncoordinated EV charging at 30% penetration (3% energy penetration), the peak load increased by as much as 27%. Using coordinated charging, the EV load shifts to hours with low prices, coincident with either low load, high VRE, or both. Furthermore, coordinated charging substantially reduces the curtailment of PV by as much as nine times during the low-load seasons, and the curtailment of wind generation by more than half during the summer peak season, compared to the scenarios with no EVs and uncoordinated EV charging. Using a production cost model with multiple planning cycles, load and VRE forecasts, and a “look ahead” period during scheduling and dispatching units was crucial in creating and utilizing the flexibility of coordinated EV charging.

The integration of Internet of Things (IoT) technology has significantly simplified the scheduling of electric vehicles (EVs) in everyday life, leading to a transformative impact on the electric load curve. This manuscript presents a hybrid method for optimizing EV charging in smart grids. The proposed hybrid approach is the joint execution of both the Narwhal Optimizer (NO) and Time Aware Recurrent Neural Networks (TARNN). Hence, it is named as TARNN‐NO approach. The primary objective is to develop a radial EV charging system to reduce overall energy costs and maximize self‐consumption. The TARNN is used to predict the EVs' behaviors to make charging decisions. The NO algorithm is used to optimize the power flow of the Electric Vehicle Charging Station (EVCS). The proposed method is assessed utilizing the MATLAB platform and contrasted with other existing techniques, including differential evolution optimization (DEO), reinforcement learning (RL), and deep deterministic policy gradient (DDPG). Experimental outcomes establish that the TARNN‐NO approach achieves the highest self‐consumption rate of 0.926 and a maximum cost reduction of 24.23%, highlighting its effectiveness for intelligent EV charging management in smart grids.

This paper presents a study of controlled charging of electric vehicles (EVs) using a fuzzy logic controller. The controller regulates the charging power according to the state of charge (SoC) of EV and the voltage at the point of connection of EV charger. The objective of the controller is to charge EVs while keeping the voltage of different distribution system points within the acceptable limits. Most of the proposed methods for controlled charging of EVs depend on the availability of the communication infrastructure which enables communication between utility operator and EV chargers which is not available in the current distribution networks and requires a huge investment. So, an autonomous controller which does not need communication infrastructure is developed in this study. The controller is designed to charge EVs without violating the voltage limits which can occur in case of uncontrolled charging of EVs. The effectiveness of the controller is tested on a residential low voltage (LV) distribution network and the simulations are executed with MATLAB/SIMULINK. The network performance in case of controlled charging of EVs is compared with the uncontrolled charging of EVs and the base case when no EVs are connected. The results demonstrate the distinction of the proposed controlled charging method in terms of total power demand, transformer loading, cable loading, and voltage profile over uncontrolled charging.

The integration of PV with the electric vehicle (EV) charging system has been on the rise due to several factors, namely continuous reduction in the price of PV modules, rapid growth in EV and concern over the effects of greenhouse gases. Over the years, numerous papers have been published on EV charging using the standard utility (grid) electrical supply; however, there seems to be an absence of a comprehensive overview using PV as one of the components for the charger. With the growing interest in this topic, it is timely to review, summarize and update all the related works on PV charging, and to present it as a single reference. For the benefit of a wider audience, the paper also includes the background of EV, as well as a brief description of PV systems. Some of the main features of battery management system (BMS) for EV battery are also presented. It is envisaged that the information gathered in this paper will be a valuable one–stop source of information for researchers working in this topic.The integration of PV with the electric vehicle (EV) charging system has been on the rise due to several factors, namely continuous reduction in the price of PV modules, rapid growth in EV and concern over the effects of greenhouse gases. Over the years, numerous papers have been published on EV charging using the standard utility (grid) electrical supply; however, there seems to be an absence of a comprehensive overview using PV as one of the components for the charger. With the growing interest in this topic, it is timely to review, summarize and update all the related works on PV charging, and to present it as a single reference. For the benefit of a wider audience, the paper also includes the background of EV, as well as a brief description of PV systems. Some of the main features of battery management system (BMS) for EV battery are also presented. It is envisaged that the information gathered in this paper will be a valuable one–stop source of information for researchers working in this topic.

Efficient real-time management of electric vehicle (EV) charging in a charging station (CS) is vital to the integration of large-scale EVs in power grids. It faces critical challenges such as frequent changes in the grid's dispatch commands, the complexity of EVs' costs, and the uncertainties in the EVs' charging/traveling behaviors and in the future dispatch commands. To tackle these challenges, this paper proposes a deep reinforcement learning (DRL)-based allocation approach that optimally and efficiently allocates the grid's commands to the EVs and controls their charging in real time. It includes two stages. Stage 1 includes a data-driven EV cost quantification method, which efficiently quantifies the EVs' flexibility contributions with long-term return consideration. Stage 2 proposes a high sample efficiency DRL-based allocation method, which optimizes the EVs' charging and addresses the EV- and grid-related uncertainties. The proposed allocation has a fast computational speed. Finally, to address the security risk due to DRL's stochastic exploratory actions, two safety modules are developed which ensure the EV charging security and the allocation accuracy. The effectiveness and efficiency of the proposed strategy are verified by comparing its performance against multiple benchmark approaches.

The growing popularity of private vehicles’ electrification will have a negative impact on the electric power system, especially on the distribution networks, if electric vehicles (EVs) charging is not managed properly. In this paper, a new technique for smart charging of EVs is proposed and tested with simulation. A fuzzy logic controller is used to control and manage the EV charging process to maximize electric utility and EV owner benefits. The electric utility’s benefit is to mitigate the EV charging impacts on the distribution network by shifting EV charging to the off-peak period, while EV owners’ benefit is to charge the EV at low cost. The controller regulates and controls the EV charging power depending on electricity price signal provided by the electric utility and EV battery state of charge (SoC). This controller needs basic communication with the electric utility to receive the electricity price signal every 1 hour. The objective of the controller is to charge EVs at low cost while keeping the normal operating conditions of the distribution network. MATLAB/SIMULINK is used to perform simulations and test the effectiveness of the proposed smart charging method. The results demonstrated that the proposed smart charging method reduced the impacts of EVs charging on the distribution network compared with uncontrolled charging.

This review article gives a comprehensive review of existing research on renewable solar photovoltaic (PV) nanogrid, which is described from small-scale power system with a single domain for reliability, control, and power quality (PQ) for electric vehicle (EV) charging. A primary feeder on the Microgrid is connected to a nanogrid test bed that includes PV as power source, a battery energy storage system (BESS), smart-inverter multiple and EV charging stations (EVCS). The control algorithms are graded on four metrics: (1) voltage profiles, (2) renewable penetration, (3) PV curtailed and (4) net power flows. To investigate the local power quality, a steady-state power flow model of the nano-grid is created. The control algorithms successfully employ the battery to shift the nano-grid peak load while limiting the nano-grid demand to set level. Furthermore, an increasing emphasis is being placed on commonly used strategies for addressing the characteristics of each renewable system. This review paper characterizes the dynamic operation of 4 distinct BESS control algorithms for solar EV charging nanogrid: (1) peak load shifting, (2) reduce peak period impact, (3) cap demand, and (4) photovoltaic capture. These control modes are executed and analyzed on real-world nano-grid site, and optimal BESS control modes are assessed in terms of (1) solar electric vehicle charging, (2) power quality, (3) grid net demand, (4) photovoltaic curtailment, and (5) solar penetration. Finally, the problems highlight research gaps, and discussions on future trends are critical for enhancing the general technology of the renewable solar photovoltaic nano-grid for EV charging.

The transition to sustainable transportation has fueled the need for innovative electric vehicle (EV) charging solutions. Building Integrated Photovoltaics (BIPV) systems have emerged as a promising technology that combines renewable energy generation with the infra-structure of buildings. This paper comprehensively reviews the BIPV system for EV charging, focusing on its technology, application, and performance. The review identifies the gaps in the existing literature, emphasizing the need for a thorough examination of BIPV systems in the context of EV charging. A detailed review of BIPV technology and its application in EV charging is presented, covering aspects such as the generation of solar cell technology, BIPV system installation, design options and influencing factors. Furthermore, the review examines the performance of BIPV systems for EV charging, focusing on energy, economic, and environmental parameters and their comparison with previous studies. Additionally, the paper explores current trends in energy management for BIPV and EV charging, highlighting the need for effective integration and recommending strategies to optimize energy utilization. Combining BIPV with EV charging provides a promising approach to power EV chargers, enhances building energy efficiency, optimizes the building space, reduces energy losses, and decreases grid dependence. Utilizing BIPV-generated electricity for EV charging provides electricity and fuel savings, offers financial incentives, and increases the market value of the building infrastructure. It significantly lowers greenhouse gas emissions associated with grid and vehicle emissions. It creates a closed-loop circular economic system where energy is produced, consumed, and stored within the building. The paper underscores the importance of effective integration between Building Integrated Photovoltaics (BIPV) and Electric Vehicle (EV) charging, emphasizing the necessity of innovative grid technologies, energy storage solutions, and demand-response energy management strategies to overcome diverse challenges. Overall, the study contributes to the knowledge of BIPV systems for EV charging by presenting practical energy management, effectiveness and sustainability implications. It serves as a valuable resource for researchers, practitioners, and policymakers working towards sustainable transportation and energy systems.

Demand-side energy management increases the unpredictability and ambiguity of forecasting the load profiles of residential energy management. The energy management accuracy seems to be low by employing a traditional residential energy forecasting algorithm. This research work emphasizes on design and development of computer-assisted residential energy management by forecasting employing a deep learning algorithm. Hankel matrix is formed using copula function to process the collected automatic metering infrastructure (AMI) load data in the smart grid. From this data processing, model optimization was obtained by the proposed novel pooling-based deep neural network (PDNN). Moreover, this proposed PDNN avoid overfitting problem in training and testing by increasing AMI data variety and data size. The proposed PDNN is implemented in the TensorFlow platform. Based on real-time AMI southern grid data onto Tamil Nadu Electricity Board, India testing case studies was carried. Compared to traditional residential energy management techniques the proposed deep learning model outperforms support vector machine by 9.5% and 12.7%, deep belief network by 6.5% and 9.5%, and neural network auto aggressive integral moving average by 20.5% and 8.5% in terms of accuracy of energy forecasting and mean absolute error, respectively. Overall, the obtained results proved the effectiveness of the proposed deep learning algorithm for residential short-term load forecasting and management over other traditional methods. Highlights In this research work: Novel pooling-based deep neural network is applied for residential energy management in a smart grid. The copula fusion theory is adopted to improve the accuracy of load management in a smart grid. Day-ahead and week-ahead prediction load on Tamil Nadu Electricity Board dataset in summer and winter season was used to validate the performance of the proposed method with other data-driven methods.

When it comes to realizing a safe, efficient, and dependable power supply. More lately, there has been a rise in the use of data-driven methods for modeling electric vehicle (EV) charging. Since this problem involves a lot of unknowns, researchers are trying to implement model-free solutions. Reinforcement learning (RL) is one of several model-free methods now in use, and has seen extensive use in EV charging control. RL is a method to machine learning that prioritizes optimizing for cumulative reward rather than individual rewards. Solar photovoltaic and wind energy both have the potential to be used in the future to generate electricity. The CDDQN technique is developed, which incorporates an act constraint into the DDQN system in order to address the charging issue with such restrictions. The CDDQN optimizes charging procedures to better predict Q values and reduce charging choosing action errors. Wind and solar energy are great options since they do not harm the environment. The layout of the charging circuit is created and evaluated in MATLAB Simulink, taking into account the changing recharging requirements of EVs. Additionally, this work explores the use of RL in EV coordinating to study and design state-of-the-art optimized EMSs that may be used for EV recharging.

This paper investigates the double randomness matching mechanism between electric vehicle (EV) charging and wind power in a distribution network. The Markov Decision Process is employed to model the uncertainties of both EV and wind power. The optimized dispatching models and strategies for EV charging under multi-time scale are developed to achieve the objective function. The goal of this function is to maximize matching degree between EV charging and wind power as well as minimize the power losses fully taking the physical constraints of distribution network into account. This actualizes dynamic control of EV charging. The simulation results on IEEE 5 node system and IEEE 30 node system demonstrate that the optimal dispatching method can reduce the network power losses and improve the matching degree between the EV charging and wind power.

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.