Search Results (230)

Electric vehicles (EVs) are essential for sustainable urban mobility, coordinating transportation demands with energy distribution networks. However, uncoordinated EV charging neglects trip chain continuity, inducing spatial–temporal congestion and overloading local charging capacities. Thus, effectively guiding EVs is a key problem in mitigating traffic emissions and preventing power grid-side stress. In this paper, a two-stage dynamic routing framework within a traffic–energy coordination architecture is proposed, integrating an AHP–Entropy–TOPSIS model for station selection and an Improved Ant Colony Optimization algorithm for trajectory execution. Using this framework, a series of macro–micro simulations on the Sioux Falls network was conducted alongside a congestion-driven dynamic pricing mechanism. The results indicate that the pricing strategy facilitates spatial load balancing through peak shaving at core nodes. Compared to conventional standard meta-heuristic baselines, this framework reduces average economic costs by 28.9% while ensuring battery safety and limiting indirect carbon emissions. The proposed framework provides a multi-objective navigation solution that prevents cross-layer decision fragmentation, supporting the sustainable development of smart city infrastructure. Full article

The public deployment of electric vehicle charging stations must simultaneously balance construction economics, user accessibility, queueing pressure, feeder security, tail risk under demand uncertainty, and spatial fairness. These criteria are strongly coupled, yet most existing studies either rely on static optimization with limited behavioral realism or use multi-agent reinforcement learning for short-term charging operation rather than for long-term siting. This paper proposes a preference-conditioned multi-agent deep deterministic policy gradient (PC-MADDPG) framework for the urban charging station siting problem in a coupled traffic–distribution environment. Candidate charging sites are modeled as cooperative agents under centralized training and decentralized execution. Each agent outputs a continuous pile-allocation action, which is repaired into an integer expansion plan under a budget constraint. The environment evaluates each plan through attraction-based demand assignment, queue approximation, LinDistFlow-style feeder analysis, and a six-objective performance vector, including annual net cost, travel burden, service inconvenience, grid penalty, CVaR of unmet charging demand, and equity loss. On a reproducible benchmark with 12 demand zones, 10 candidate sites, an 11-bus radial feeder, and 16 stochastic daily scenarios, the proposed framework generates a non-dominated archive with 42 unique feasible plans. A representative PC-MADDPG solution opens 5 of 10 candidate sites and installs 20 fast-charging piles, achieving 99.88% mean demand coverage with an annual profit of 2.083 M$ and a maximum line utilization of 0.999. Relative to the NoGrid ablation, the selected full model reduces grid penalty by 23.87% and equity Gini by 51.08%, with only a 0.35% profit concession. Relative to the NoRisk ablation, the CVaR of unmet demand is lowered by 69.70%. Compared with a demand-greedy baseline, the proposed method reduces grid penalty by 11.72% and equity Gini by 25.19% while preserving similar demand coverage. These results provide proof-of-concept evidence, on a reproducible coupled benchmark, that preference-conditioned multi-agent learning can serve as a practical many-objective siting engine for charging-infrastructure planning when coupled traffic and feeder constraints are explicitly modeled. Full article

As the electrification of transportation systems accelerates, incentivizing electric vehicle (EV) participation in vehicle-to-grid (V2G) operations is becoming increasingly crucial. This paper introduces a dynamic EV charging and discharging strategy that incorporates integrated energy station (IES) congestion and electricity purchase and sale scenarios. The proposed strategy seeks to facilitate orderly EV charging and discharging within a real-time simulation framework that integrates the transportation network (TN), IES, and the external grid (EG). First, we develop a real-time collaborative simulation framework that combines microscopic traffic flow (MTL) and IES–grid energy interaction models to account for mutual feedback among these components. Second, we propose an EV IES selection strategy aimed at maximizing discharge revenue, which takes into account various factors, including driving distance, time costs, battery degradation, discharge benefits, and government subsidies. Finally, we design a dynamic discharge pricing model based on real-time vehicle arrival patterns at the IES and the status of electricity purchases and sales. Simulation results show that the EV IES selection strategy, optimized for discharge revenue, reduces average user waiting time by 5.36%, decreases network time loss by 3.86%, and increases EV discharge revenue by 6.79%. Furthermore, the introduction of dynamic pricing leads to additional reductions in waiting time and network time loss by 3.46% and 4.80%, respectively. The proposed mechanism and pricing strategy effectively mitigate traffic congestion, enhance user discharge revenue, and provide flexible scheduling options for IES operations. Full article

To address the issues of load aggregation and traffic congestion in faulty electricity–transportation coupled networks (ETCNs), this paper proposes an electric vehicle (EV) charging guidance strategy based on Graph Reinforcement Learning (GRL). First, a graph-structured feature extraction model is developed. The GraphSAGE module is employed to capture the multi-scale spatiotemporal features of the ETCN. The topological changes and energy-information interaction characteristics under fault scenarios are analyzed. Second, a Finite Markov Decision Process (FMDP) framework is established to address the stochastic and dynamic nature of EV charging behavior. The charging station selection and route planning problem is transformed into an agent decision-making process. A reward function is designed by incorporating voltage constraints, traffic flow constraints, and state-of-charge margin penalties. This ensures a balanced consideration of power grid security and traffic efficiency. The FMDP model is then solved using a Deep Q-Network (DQN) to achieve optimal EV charging guidance under fault conditions. Finally, case studies are conducted on a coupled simulation scenario consisting of an IEEE 33-node power distribution system and a 23-node transportation network. Results show that the proposed method reduces the system operation cost to 218,000 CNY, controls the voltage deviation rate of the distribution network at 3.1% in line with the operation standard, and enables the model to achieve stable convergence after only 250 training episodes. It can effectively optimize the charging load distribution and maintain the voltage stability of the power grid under fault conditions. Full article

In recent years, DC-DC partial power converters (PPCs) have become increasingly important in fast-charging architectures for electric vehicles (EVs). Their key feature is that only a fraction of the energy delivered to the battery is processed by the PPC, while the rest is transferred directly, bypassing the conversion stage. This reduces DC-DC conversion losses and improves overall charging efficiency. However, the nonlinear dynamics of these converters can limit performance, especially with model-based controllers such as proportional–integral (PI) controllers. This paper proposes a data-driven control framework for EV fast-charging stations using a DC-DC PPC that is controlled by deep reinforcement learning (DRL). A value-based deep Q-network (DQN) directly selects switching actions and jointly regulates the partial-voltage and output current. The control problem is formulated as a discrete-time Markov decision process, and a two-stage transfer learning scheme ensures safe, efficient deployment. Firstly, the DQN agent is trained in a high-fidelity simulation and then fine-tuned with a small set of experimental data to capture parasitic and modeling errors. The controller is integrated into a constant-current–constant-voltage (CC-CV) charging algorithm and validated over a full charging cycle of a 60 kWh EV battery. The proposed control scheme exhibits a settling time of approximately 2 ms in response to current reference variations while maintaining steady-state errors below 2% in current regulation and below 1% in partial voltage regulation. Simulation results show that the proposed DRL controller has a small steady-state tracking error and improved robustness to reference changes compared with conventional PI and sliding mode controllers. The low computational cost of the trained DQN policy also enables real-time execution on embedded platforms for EV charging. Full article

With the increasing penetration of distributed energy resources, distribution networks face elevated risks of power disruptions, which call for rapid and flexible emergency response mechanisms. There are not enough traditional emergency generator vehicles, and they are not highly adaptable when it comes to operations, which makes it hard to meet changing dispatching needs. Electric vehicles (EVs), on the other hand, can be used as distributed emergency resources that can be dispatched through vehicle-to-grid (V2G) interaction. Electric vehicle charging stations (EVCSs), on the other hand, are integrated energy storage units that use existing charging infrastructure to provide on-site grid support. To address this gap, this study proposes a comprehensive V2G-powered pre- and post-disaster coordination framework for enhancing distribution network resilience, with three core novelties: first, a refined individual EV model considering dual power and energy constraints is developed, and the Minkowski summation method is applied to accurately quantify the real-time aggregate regulation potential of EVCSs for the first time; second, a two-stage robust optimization model is formulated for pre-event strategic planning, which jointly optimizes EVCS participant selection and distribution network topology to address photo-voltaic (PV) power generation uncertainties; third, a multi-source collaborative dynamic scheduling model is constructed for post-disaster recovery, which explicitly incorporates the spatiotemporal dynamics of EVs and coordinates EVCSs, gas turbine generators (GTGs) and other resources for the first time. We carried out simulations on a modified IEEE 33-bus system with a 10 h extreme fault scenario. The results show that the proposed strategy raises the average critical load recovery ratio to 97.7% (2% higher than traditional deterministic optimization), lowers the total load shedding power by 0.2 MW and the load reduction cost by 19,797.63 CNY, and gives a net V2G power output of 3.42 MW (86.9% higher than the comparison strategy). The proposed V2G-enabled coordinated pre- and post-disaster fault recovery strategy significantly improves the resilience of distribution networks compared to traditional methods. This makes it easier and faster to recover from extreme disaster scenarios, with the overall load recovery rate reaching 91.8% and the critical load restoration rate staying above 85% throughout the recovery process. Full article

In the era of renewable dominated grids, integration of dynamic loads such as EV charging stations have increased the operational challenges in multifolds, particularly in DC microgrids (DC MGs). Traditional battery-dominated grid energy management strategies (EMSs) are often not capable of handling fast transients due to the limitations of battery electrochemistry. To overcome this limitation, a hierarchical hybrid energy management strategy is proposed that uses the combination of data-driven and metaheuristic algorithms. The designed optimization framework consists of particle swarm optimization (PSO) and a neural network (NN) implemented in the central controller of a 4-bus ringmain DC MG. An efficient decoupling of fast and slow storage dynamics is performed, where the supercapacitor (SC) is optimized using the NN and the battery is optimized using PSO. This selective optimization reduces the computational overhead on the PSO making it more feasible for real-time implementation. The designed hybrid PSO-Neural EMS framework is initially designed on MATLAB and further validated on a real-time hardware setup. Robustness of the control scheme is verified with various case studies, such as renewable intermittency, dynamic loading and partial shading scenarios. An effective optimization of the SC in both transient and heavy load scenarios are observed. LabVIEW interfacing is used for MODBUS-based interaction with PV emulators and DC-DC converters. Full article

This paper addresses the critical challenges of multi-stakeholder interest coordination and low-carbon operation in modern power systems, specifically focusing on the interaction among an Integrated Energy System (IES), Electric Vehicle Charging Stations (EVCS), and Load Aggregators (LA). To tackle these challenges, we propose a novel data-model driven optimization framework. A bi-level model is established, where the upper-level IES acts as the leader, and the lower-level EVCS and LA serve as followers. At the core of our approach is an integrated dynamic pricing mechanism that synergistically combines EVCS operational schedules, carbon emission signals, and load demand response. This mechanism, enhanced by predictive insights from historical data, effectively guides lower-level entities to participate in the upper-level IES’s optimization, thereby aligning individual benefits with system-wide low-carbon goals. The resulting bi-level problem is solved iteratively using CPLEX, with the optimal equilibrium selected via a joint optimality formula. The proposed methodology is validated on a multi-stakeholder case study. Results demonstrate that our AI-enhanced dynamic pricing and dispatch model not only effectively balances the interests of all parties but also significantly improves the system’s low-carbon economic performance, showcasing the potential of integrating physical models with data-driven insights for future energy system management. Full article

Decarbonizing the transport sector is crucial for achieving global carbon peaking and carbon neutrality goals. Electric taxis (e-taxis), which play a vital role in urban public transportation, are central to this transition. However, their operational performance deteriorates significantly under extremely cold conditions. Existing planning models for charging infrastructure often overlook the impact of low temperatures, creating a critical research gap. To address this issue, we propose a novel planning framework using Urumqi, China (43.8° N, 87.6° E) as a case study. Urumqi is a major cold-region metropolis, where January temperatures regularly drop below $-20$ °C. Our methodology includes two key steps: integrating 412 driver questionnaires and 1.2 million high-resolution GPS trajectories to extract temperature-sensitive charging demand profiles; and incorporating these profiles into an integer linear programming (ILP) model to minimize lifecycle costs, considering climatic constraints, taxi operation patterns, and grid limitations. A key innovation is a temperature-correction coefficient, which dynamically adjusts vehicle energy consumption and driving range based on ambient temperature. Results show superiority over conventional (temperature-ignoring) and random plans: 14-fold lower annualized cost, 23-fold shorter average queuing time, 96.2% high-frequency demand coverage (+16.6%), and 78% charging station utilization (+50.0%). It achieves 29.8–32.3% cost savings at $-5$ °C (over 25.9% even at $-35$ °C) and scales stably for 5–50% e-taxi penetration, offering a transferable framework for cold-region e-taxi charging optimization. Full article

This paper presents a novel edge-computing-based architecture for optimal inverse time overcurrent relays installed to protect mesh microgrids (MGs) with distributed generation. The procedure employs graph theory to automate the detection of network changes, fault locations, and relay pairs in an MG. In addition, an automated process obtains the initial protection settings based on the operating conditions of the MG. Furthermore, the Continuous Genetic Algorithm (CGA), Salp Swarm Algorithm (SSA), and Particle Swarm Optimization (PSO) were implemented to determine the optimal protection settings to obtain better coordination between primary and backup protection relays. These processes were implemented using PowerFactory 2024 Service Pack 5A and Python 3.13.1. The proposal was validated in 68 operating scenarios that considered the islanded and connected operation modes of the MG, charging and discharging cycles of electric vehicle stations, and the presence or absence of photovoltaic generation. The overcurrent protection relays were organized into 100 primary–backup relay pairs to ensure proper coordination and selectivity. The total miscoordination time (TMT) index was used to measure when all pairs of relays were coordinated, with a minimum time close to zero. The results of the graph theory show that all the meshes, fault locations, and relay pairs were identified in the MG. The approach successfully coordinated 100 relay pairs across 68 scenarios, demonstrating its scalability in complex real-world MGs. The automation process obtained an average TMT of 12.2%, while the optimization obtained a TMS of 91.6% with the CGA, and a TMT of 99% was obtained with the SSA and PSO, demonstrating the effectiveness of the optimization process in ensuring selectivity and appropriate fault clearing times. Full article

Accurate forecasting of citywide electric vehicle (EV) charging load is critical for alleviating station-level congestion, improving energy dispatching, and supporting the stability of intelligent transportation systems. However, large-scale EV charging networks exhibit complex and heterogeneous spatio-temporal dependencies, and existing approaches often struggle to scale with increasing station density or long forecasting horizons. To address these challenges, we develop a modular spatio-temporal prediction framework that decouples temporal sequence modeling from spatial dependency learning under an encoder–decoder paradigm. For temporal representation, we introduce a global aggregation mechanism that compresses multi-station time-series signals into a shared latent context, enabling efficient modeling of long-range interactions while mitigating the computational burden of cross-channel correlation learning. For spatial representation, we design a dynamic multi-scale attention module that integrates graph topology with data-driven neighbor selection, allowing the model to adaptively capture both localized charging dynamics and broader regional propagation patterns. In addition, a cross-step transition bridge and a gated fusion unit are incorporated to improve stability in multi-horizon forecasting. The cross-step transition bridge maps historical information to future time steps, reducing error propagation. The gated fusion unit adaptively merges the temporal and spatial features, dynamically adjusting their contributions based on the forecast horizon, ensuring effective balance between the two and enhancing prediction accuracy across multiple time steps. Extensive experiments on a real-world dataset of 18,061 charging piles in Shenzhen demonstrate that the proposed framework achieves superior performance over state-of-the-art baselines in terms of MAE, RMSE, and MAPE. Ablation and sensitivity analyses verify the effectiveness of each module, while efficiency evaluations indicate significantly reduced computational overhead compared with existing attention-based spatio-temporal models. Full article

This study presents a robust framework for optimizing the site selection of Electric Vehicle Charging Stations (EVCS) in Qatar by integrating a Geographic Information System (GIS) with a Multi-Criteria Decision-Making (MCDM) model. The core innovation lies in the enhancement of the conventional Analytic Hierarchy Process (AHP) with a Removal Sensitivity Analysis (RSA). This unique integration moves beyond traditional, subjective expert-based weighting by introducing a transparent, data-driven methodology to quantify the influence of each criterion and generate objective weights. The Analytic Hierarchy Process (AHP) was used to evaluate fourteen criteria related to accessibility, economic and environmental factors that influence EVCS site suitability. To enhance robustness and minimize subjectivity, a Removal Sensitivity Analysis (RSA) was applied to quantify the influence of each criterion and generate objective, data-driven weights. The results reveal that accessibility factors, particularly proximity to road networks and parking areas exert the highest influence, while environmental variables such as slope, CO concentration, and green areas have moderate but spatially significant impacts. The integration of AHP and RSA produced a more balanced and environmentally credible suitability map, reducing overestimation of urban sites and promoting sustainable spatial planning. Environmentally, the proposed framework supports Qatar’s transition toward low-carbon mobility by encouraging the expansion of clean electric transport infrastructure, reducing greenhouse gas emissions, and improving urban air quality. The findings contribute to achieving the objectives of Qatar National Vision 2030 and align with global efforts to mitigate climate change through sustainable transportation development. Full article

Prediction of the Electric Vehicle (EV) charging demand is of great importance to charging stations, especially for newly established charging stations whose demand is difficult to predict due to the absence of past time-series transaction data. This paper develops a deep learning method to fill the literature gap to predict charging demands for the new EV charging stations in the next few days, using a transaction dataset containing over 270 charging stations in Nanjing, eastern China. Specifically, our study introduces the average transactions of neighboring stations as new time-series variables and constructs a Convolutional Neural Network (CNN) model, which is a novel deep learning method. The R-squares of the CNN model achieve an average value of 0.90, which outperforms four time-series prediction models, e.g., the Long Short-Term Memory Network (LSTM) and the Extreme Gradient Boosting (XGBoost). In addition, we visualize the areas with high predicted demand for new charging stations using the trained CNN model and achieve a recommendation accuracy rate of 0.70, providing a reference for EV charging operation companies to find the optimal location of new charging stations. Accurate prediction for new charging stations in this study can provide actionable insights to charging station operators in location selection and create a more favorable EV ecosystem. Full article

This paper quantifies the steady-state voltage-compliance impact of ultra-fast electric vehicle (EV) charging on the IEEE 33-bus radial distribution feeder. Four practical scenarios are examined by combining two penetration levels (6 and 12 charging points, i.e., ≈20% and ≈40% of PQ buses) with two charger ratings (1 MW and 350 kW per point). Candidate buses for EV station integration are selected through a nodal voltage–reactive sensitivity ranking ($\partial V/\partial Q$), prioritizing electrically robust locations. To capture realistic operating uncertainty, a 24-hour quasi-static time-series power-flow assessment is performed using Monte Carlo sampling ($N=100$), jointly modeling residential-demand variability and stochastic EV charging activation. Across the four cases, the worst-hour minimum voltage (uncompensated) ranges from 0.803 to 0.902 p.u., indicating a persistent under-voltage risk under dense and/or high-power charging. When the expected minimum-hourly voltage violates the 0.95 p.u. limit, a closed-form, sensitivity-guided reactive compensation is computed at the critical bus, and the power flow is re-solved. The proposed mitigation increases the minimum-voltage trajectory by approximately 0.03–0.12 p.u. (about 3.0–12.0% relative to 1 p.u.), substantially reducing the depth and duration of violations. The maximum required reactive support reaches 6.35 Mvar in the most stressed case (12 chargers at 1 MW), whereas limiting the unit charger power to 350 kW lowers both the severity of under-voltage and the compensation requirement. Overall, the Monte Carlo V–Q sensitivity framework provides a lightweight and reproducible tool for probabilistic voltage-compliance assessment and targeted steady-state mitigation in EV-rich radial distribution networks. Full article

Deploying public EV charging infrastructure while balancing efficiency, equity, and implementation feasibility remains a key challenge for sustainable urban mobility. This study develops an integrated, grid-based planning framework for Wuhan that combines attention-enhanced ConvLSTM demand forecasting with a trajectory-derived, rank-based accessibility index to support equitable network expansion. Using large-scale charging-platform status observations and citywide ride-hailing mobility traces, we generate grid-level demand surfaces and an accessibility layer that helps reveal structurally connected yet underserved areas, including demand-sparse zones that may be overlooked by utilization-only planning. We screen feasible grid cells to construct a new-station candidate set and formulate expansion as a constrained three-objective optimization problem solved by NSGA-II: maximizing demand-weighted neighborhood service coverage, minimizing the Group Parity Gap between low-accessibility populations and the citywide population, and minimizing grid-connection friction proxied by road-network distance to the nearest power substation. Practical deployment plans for 15 and 30 sites are selected from the Pareto set using TOPSIS under an explicit weighting scheme. Benchmarking against random selection and single-objective greedy baselines under identical candidate pools, constraints, and evaluation metrics demonstrates a persistent coverage–equity–cost tension: coverage-driven heuristics improve demand capture but worsen parity, whereas equity-prioritizing strategies reduce gaps at the expense of coverage and feasibility. Full article