Search Results (62)

To address the pressing challenges of low new energy utilization, high power system operating costs, and compromised power supply reliability in regional grids, we propose a multi-time-scale source–storage–load coordinated scheduling strategy that explicitly accounts for the characteristic distribution of grid-connected energy storage stations, including their state-of-charge constraints, round-trip efficiency profiles, and location-specific operational dynamics. A day-ahead scheduling framework is developed by integrating the multi-time-scale behavioral patterns of diverse load-side demand response resources with the dynamic operational characteristics of energy storage stations. By embedding intra-day rolling optimization and real-time corrective adjustments, we mitigate prediction errors and adapt to unforeseen system disturbances, ensuring enhanced operational accuracy. The objective function minimizes a weighted sum of system operation costs encompassing generation, transmission, and auxiliary services; wind power curtailment penalties for unused renewables; and load shedding penalties from unmet demand, balancing economic efficiency with supply quality. A mixed-integer programming model formalizes these tradeoffs, solved via MATLAB 2020b coupled CPLEX to guarantee optimality. Simulation results demonstrate that the strategy significantly cuts wind power curtailment, reduces system costs, and elevates new energy consumption—outperforming conventional single-time-scale methods in harmonizing renewable integration with grid reliability. This work offers a practical solution for enhancing grid flexibility in high-renewable penetration scenarios. Full article

Telecommunication Base Transceiver Stations (BTSs) require a resilient and sustainable power supply to ensure uninterrupted operation, particularly during grid outages. Thus, this paper proposes an Adaptive Model Predictive Control (AMPC)-based Energy Management System (EMS) designed to optimize energy dispatch and demand response for a BTS powered by a renewable-based microgrid. The EMS operates under two distinct scenarios: (a) non-grid outages, where the objective is to minimize grid consumption, and (b) outage management, aiming to maximize BTS operational time during grid failures. The system incorporates a dynamic weighting mechanism in the objective function, which adjusts based on real-time power production, consumption, battery state of charge, grid availability, and load satisfaction. Additionally, a demand response strategy is implemented, allowing the BTS to adapt its power consumption according to energy availability. The proposed EMS is evaluated based on BTS loss of transmitted data under different renewable energy profiles. Under normal operation, the EMS is assessed regarding grid energy consumption. Simulation results demonstrate that the proposed AMPC-based EMS enhances BTS resilience. Full article

The rapidly growing demand of electric vehicle (EV) charging is one of the main challenges to modern electrical distribution systems. Accurate modelling of the EV charging load is crucial for charging load prediction and optimization. However, previous methods based on the charging behaviors of private EVs are hard to collect user’s private data. In this study, charging load data from 962 charging and battery swapping stations (CBSSs), classified into dedicated charging stations, public charging stations, and battery swapping stations, collected during 2021–2022, are analyzed to investigate seasonal variations in the charging coincidence factor. A data-driven probabilistic model of charging load, based on the Gaussian Mixture Model, is developed to address various scenarios, including new station construction, capacity expansions, and optimized charging strategies. This model is applicable to different types of CBSSs. A real-world 10 kV feeder system is employed as a case study to validate the model, and a delayed charging strategy is proposed. The results demonstrate that the proposed model accurately estimates charging load peaks after new construction and expansion in 2023, with an error rate under 3%. Furthermore, the delayed charging strategy achieved a 24.79% reduction in maximum load and a 31.96% decrease in the peak–valley difference. Its implementation in the real-world feeder significantly alleviated nighttime overloading in 2024. Full article

Managing electric vehicle (EV) charging at stations with on-site solar (PV) generation is a complex task, made difficult by volatile electricity prices and the need to guarantee services for drivers. This paper proposes a robust optimization (RO) framework to schedule EV charging, minimizing electricity costs while explicitly hedging against price uncertainty. The model is formulated as a tractable linear program (LP) using the Bertsimas–Sim reformulation and is implemented in an online, adaptive manner through a model predictive control (MPC) scheme. Evaluated on extensive real-world charging data, the proposed controller demonstrates significant cost reductions, outperforming a PV-aware Greedy heuristic by 17.5% and a deep reinforcement learning (DRL) agent by 12.2%. Furthermore, the framework exhibits lower cost volatility and is proven to be computationally efficient, with solving times under five seconds even during peak loads, confirming its feasibility for real-time deployment. The results validate our framework as a practical, reliable, and economically superior solution for the operational management of modern EV charging infrastructure. Full article

The rapid adoption of electric vehicles (EVs) has intensified the demand for intelligent, scalable, and interoperable charging infrastructure. Traditional EV charging networks based on the Open Charge Point Protocol (OCPP) face challenges related to dynamic load management, cybersecurity, and efficient integration with renewable energy sources. This paper presents a novel AI-driven framework that integrates federated learning, predictive analytics, and real-time control within OCPP-compliant networks to enhance performance and sustainability. The proposed system utilizes edge AI modules at charging stations, supported by a central aggregator that employs federated learning to preserve data privacy while enabling network-wide optimization. A case study involving simulated smart charging stations demonstrates significant improvements, including an 18% reduction in peak load demand, a 29% increase in forecasting accuracy (MAPE of 8.5%), a 10% decrease in average charging wait times, and a 12% increase in on-site solar energy utilization. The framework’s compatibility with OCPP and related standards (e.g., IEC 61851, ISO 15118) ensures ease of deployment on existing infrastructure. These results indicate that the proposed AI-OCPP integration provides a scalable and intelligent foundation for next-generation EV charging networks that align with the goals of sustainable transportation and smart grid evolution. Full article

Distributed generation strengthens distribution network reliability by placing generators close to load centers. The integration of electric vehicle charging stations (EVCSs) with PV systems mitigates the effects of EV charging burden. In this research, the objective is to combineEVCSs with distributed generation (DG) units in the Puducherry smart grid system to obtain optimized locations and enhance their reliability. To determine the right nodes for DGs and EVCSs in an uneven distribution network, the modified decision-making (MDM) algorithm and the model predictive control (MPC) approach are used. The Indian utility 29-node distribution network (IN29NDN), which is an unbalanced network, is used for testing. The effects of PV systems and EVCS units are studied in several settings and at various saturation levels. This study validates the correctness of its findings by evaluating the outcomes of proposed methodological approaches. DIgSILENT Power Factory is used to conduct the simulation experiments. The results show that optimizing the location of the DG unit and the size of the PV system can significantly minimize power losses and make a distribution network (DN) more reliable. Full article

This research introduces a comprehensive AI-driven framework for secure and efficient load management in multi-station electric vehicle (EV) charging networks, responding to the increasing demand and operational difficulties associated with widespread EV adoption. The suggested architecture has three main parts: a Smart Load Balancer (SLB), an AI-driven intrusion detection system (AIDS), and a Real-Time Analytics Engine (RAE). These parts use advanced machine learning methods like Support Vector Machines (SVMs), autoencoders, and reinforcement learning (RL) to make the system more flexible, secure, and efficient. The framework uses federated learning (FL) to protect data privacy and make decisions in a decentralized way, which lowers the risks that come with centralizing data. The framework makes load distribution 23.5% more efficient, cuts average wait time by 17.8%, and predicts station-level demand with 94.2% accuracy, according to simulation results. The AI-based intrusion detection component has precision, recall, and F1-scores that are all over 97%, which is better than standard methods. The study also finds important gaps in the current literature and suggests new areas for research, such as using graph neural networks (GNNs) and quantum machine learning to make EV charging infrastructures even more scalable, resilient, and intelligent. Full article

In response to the challenges of imbalanced economic efficiency of charging stations caused by disorderly charging of large-scale electric vehicles (EVs), rising electricity expenditure of users, and increased risk of stable operation of the power grid, this study designs a user-side vehicle pile resource interaction strategy considering source load clustering to enhance the economy and safety of electric vehicle energy management. Firstly, by constructing a dynamic traffic flow distribution network coupling architecture, a bidirectional interaction model between charging facilities and transportation/power systems is established to analyze the dynamic correlation between charging demand and road network status. Next, an EV charging and discharging electricity price response model is established to quantify the load regulation potential under different scenarios. Secondly, by combining urban transportation big data and prediction networks, high-precision inference of the spatiotemporal distribution of charging loads can be achieved. Then, a multidimensional optimization objective function covering operator revenue, user economy, and grid power quality is constructed, and a collaborative decision-making model is established. Finally, the IEEE69 node system is validated through joint simulation with actual urban areas, and the non-dominated sorting genetic algorithm II (NSGA-II) based on reference points is used for the solution. The results show that the optimization strategy proposed by NSGA-II can increase the operating revenue of charging stations by 33.43% while reducing user energy costs and grid voltage deviations by 18.9% and 68.89%, respectively. Full article

The global demand for transportation systems is growing due to the rise in passenger and cargo traffic, predicted to reach twice the current level by 2050. Although this growth signifies social and economic progress, its impact on energy consumption and greenhouse gas emissions cannot be overlooked. Developments in the transportation industry must align with advancements in emerging energy production systems. In this regards, UNSDG 7 advocates for “affordable and clean energy”, leading to a global shift towards the electrification of transport systems, sourcing energy from a mix of renewable and non-renewable resources. This paper proposes an integrated hybrid renewable energy system with grid connectivity to meet the electrical and thermal loads of a tourist complex, including an electric vehicle charging station. The analysis was carried on in nine locations with different weather conditions, with various components such as wind turbines, photovoltaic systems, diesel generators, boilers, converters, thermal load controllers, and battery energy storage systems. The proposed model also considers the effects of seasonal variations on electricity generation and charging connectivity. Sensitivity analysis has been carried on investigating the impact of variables on the techno-economic parameters of the hybrid system. The obtained results led to interesting conclusions. Full article

With the rapid increase in electric vehicle (EV) ownership, the uncertainty of EV charging demand has become a significant concern, especially in distributed photovoltaic (PV) power distribution networks (DNs) with high penetration rates. This growing demand presents challenges in meeting the needs of EV owners and grid charging/discharging stations (GCDSs), jeopardizing the stability, efficiency, reliability, and sustainability of the DNs. To address these challenges, this study introduces innovative models, the anchoring effect, and regret theory for EV demand response (DR) decision-making, focusing on dual-sided demand management for GCDSs and EVs. The proposed model leverages the light spectrum optimizer–convolutional neural network to predict PV output and utilizes Monte Carlo simulation to estimate EV charging load, ensuring precise PV output prediction and effective EV distribution. To optimize DR decisions for EVs, this study employs time-of-use guidance optimization through a logistic–sine hybrid chaotic–hippopotamus optimizer (LSC-HO). By integrating the anchoring effect and regret theory model with LSC-HO, this approach enhances satisfaction levels for GCDSs by balancing DR, enhancing voltage quality within the DNs. Simulations on a modified IEEE-33 system confirm the efficacy of the proposed approach, validating the efficiency of the optimal scheduling methods and enhancing the stable operation, efficiency, reliability, and sustainability of the DNs. Full article

The load prediction of electric vehicle charging stations is the basis of their static safety, which directly affects the safety of operation, the rationality of planning, and the economy of supply. However, various factors lead to drastic changes in short-term power consumption, which makes the data more complicated and difficult to predict. In this paper, the moving average–gated recurrent unit method is proposed to predict the electric load of electric vehicle charging stations. A prediction model is established based on the historical data of electric load of electric vehicle charging stations to realize the accurate prediction of future electric loads. Firstly, considering the problems of noise in the historical data of electric vehicle charging stations, the moving average method is used for smoothing. Secondly, the smoothed data are modeled by the gated recurrent unit, and the future prediction results are obtained. Finally, the validity and practicability of the proposed method are proved by the research and testing of the actual electric vehicle charging station power load dataset. Compared with the classic LSTM prediction model, the proposed MA-GRU method can achieve more accurate prediction performance. Full article

To understand the charging behavior of electric vehicle (EV) users and the sustainable use of the flexibility resources of EV, EV charging behavior analysis and load prediction via order data of charging stations was proposed. The user probability distribution model is established from the characteristic dimensions of EV charging initial time, initial state of charge, power level, and charging time. Under the conditions of specific districts, seasons, multiple EV types, and specific weather, the Monte Carlo simulation method is used to predict the EV load distribution at the physical level. The correlation between users’ willingness to charge and the electricity price is analyzed, and the logistic function is used to establish the charging load prediction model on the economic level. Taking a city in Henan Province, China, as an example, the calculation results show that the EV charging load distribution varies with the district, season, weather, and EV type, and the 24 h time-of-use (TOU) electricity price and EV quantity distribution are analyzed. The proposed method can better reflect EV charging behavior and accurately predict EV charging load. Full article

Electric vehicle (EV) users have the flexibility to fulfill their charging needs using either high-speed charging stations or innovative on-road wireless charging systems, ensuring uninterrupted travel to their destinations. These options present a spectrum of benefits, enhancing convenience and efficiency. The adoption of on-road wireless charging as a complementary method influences both the timing and extent of demand at fast-charging stations. This study introduces a comprehensive probabilistic framework to analyze EV arrival rates at fast-charging facilities, incorporating the impact of on-road wireless charging availability. The proposed model utilizes transportation data, including patterns from the US National Household Travel Survey (NHTS), to predict the specific times when EVs would need fast charging. To account for uncertainties in EV user decisions concerning charging preferences, a Monte Carlo simulation (MCS) approach is employed, ensuring a comprehensive analysis of charging behaviors and their potential impact on charging stations. A queuing model is developed to estimate the charging demand for numerous electric vehicles at a charging station, considering both scenarios: on-road EV wireless charging and relying exclusively on fast-charging stations. This study includes an analysis of a case and its simulation results based on a 32-bus distribution system and data from the US National Household Travel Survey (NHTS). The results indicate that integrating on-road EV wireless charging as complementary to fast charging significantly reduces the peak load at the charging station. Additionally, considering the on-road EV wireless charging system, the peak load of the station no longer aligns with the peak load of the power grid, resulting in improved power system capacity and deferred system upgrades. Full article

The continuous growth of electric vehicle (EV) ownership has increased the proportion of EV charging station load (EVCSL) in the distribution network (DN). The prediction of EVCSL is important for the safe and stable operation of the DN. However, simply predicting the EVCSL based on the characteristics of the DN, ignoring the impact of coupled transportation network (TN) characteristics, will reduce prediction performance. Few studies focus on combining DN and TN data for EVCSL prediction. On the premise of protecting the privacy of TN data, this paper proposes a vertical adaptive attention-based federated prediction method of EVCSL based on an edge aggregation graph attention network combined with a long- and short-term memory network (V2AFedEGAT combined with LSTM) to fully utilize the characteristics of DN and TN. This method introduces a spatio-temporal hybrid attention module to alleviate the characteristic distribution skew of DN and TN. Furthermore, to balance the privacy protection and training efficiency after multiple modules are integrated into the secure federated linear regression framework, the training strategy of the federated framework and the update strategy of the model are optimized. The simulation results show that the proposed federated method improves the prediction performance by about 4% and has a sub-second response speed. Full article

Rapid technology advances have made managing charging loads and optimizing routes for electric vehicle (EV) fleets, especially in cities, increasingly important. IoT sensors in EV charging stations and cars enhance prediction and optimization algorithms with real-time data on charging behaviors, traffic, vehicle locations, and environmental factors. These IoT data enable the GNN-ViGNet hybrid deep learning model to anticipate electric vehicle charging needs. Data from 400,000 IoT sensors at charging stations and vehicles in Texas were analyzed to identify EV charging patterns. These IoT sensors capture crucial parameters, including charging habits, traffic conditions, and other environmental elements. Frequency-Aware Dynamic Range Scaling and advanced preparation methods, such as Categorical Encoding, were employed to improve data quality. The GNN-ViGNet model achieved 98.9% accuracy. The Forecast Accuracy Rate (FAR) and Charging Load Variation Index (CLVI) were introduced alongside Root-Mean-Square Error (RMSE) and Mean Square Error (MSE) to assess the model’s predictive power further. This study presents a prediction model and a hybrid Coati–Northern Goshawk Optimization (Coati–NGO) route optimization method. Routes can be real-time adjusted using IoT data, including traffic, vehicle locations, and battery life. The suggested Coati–NGO approach combines the exploratory capabilities of Coati Optimization (COA) with the benefits of Northern Goshawk Optimization (NGO). It was more efficient than Particle Swarm Optimization (919 km) and the Firefly Algorithm (914 km), reducing the journey distance to 511 km. The hybrid strategy converged more quickly and reached optimal results in 100 rounds. This comprehensive EV fleet management solution enhances charging infrastructure efficiency, reduces operational costs, and improves fleet performance using real-time IoT data, offering a scalable and practical solution for urban EV transportation. Full article