Search Results (816)

With the increasing adoption of electric vehicles (EV), congestion at charging stations during peak hours has become a prominent issue, imposing significant pressure on station scheduling. Furthermore, the large-scale integration of photovoltaics (PV) introduces dual uncertainties in both generation and load, negatively impacting grid voltage. To tackle the above problems, a strategy for demand response balancing and congestion alleviation of charging stations under grid–road network partition mapping is proposed in this paper. Firstly, a user demand response capability assessment method based on the Fogg Behavior Model is proposed to evaluate the demand response potential of individual users in each zone. The results are aggregated to obtain the demand response participation capability of each zone, thereby realizing capability-based allocation and achieving demand response balancing. Secondly, the road network is divided into several zones and mapped to the power grid, and a two-layer cross-zone collaborative autonomy model is established. The upper layer aims to alleviate inter-zone congestion and balance inter-station power, taking into account the grid voltage level. A tripartite benefit model involving the power grid, charging stations and users is constructed, and an inter-zone mutual-aid model for the upper layer is established and solved optimally. The lower layer establishes an intra-zone self-consistency model, which subdivides different functional zone types within the road network zone, allocates and accommodates the cross-zone power from the upper-layer output inside the zone, and synchronously performs intra-zone cross-zone judgment to avoid congestion at charging stations. Simulation verification is carried out on the IEEE 33-bus system. The results show that the proposed method can effectively alleviate the congestion of charging stations, the balance degree among all zones is increased by 43.58%, and the power grid voltage quality is improved by about 38%. This study offers feasible guidance for exploring large-scale planned participation of electric vehicles in power system demand response. Full article

The global transition towards sustainable and resilient energy systems has emphasized the need for efficient utilization of renewable energy sources (RESs) and rapid electrification of transportation. However, smart grids must address the intermittency of solar and wind power while accommodating the growing demand from electric vehicles (EVs). Hence, in this paper, a data-driven energy management system (EMS) is proposed that combines multivariable forecasting, generation scheduling, and EV charging coordination in a dual-level decentralized framework to increase the efficiency, reliability, and scalability of modern power grids. First, short-term forecasts of solar irradiance, wind speed, and load demand are addressed via five machine learning models ranging from nonlinear to ensemble models. Accordingly, a unified CatBoost-based platform for forecasting these three variables is selected because of its better performance and accuracy. These forecasts are subsequently utilized in a mixed-integer linear programming (MILP) framework for optimal generation scheduling in the considered network, fulfilling load demand at reduced electricity and emission costs while maintaining grid stability. Finally, a priority-based scheme is proposed for charging/discharging coordination of the aggregated EVs, minimizing demand variability while fulfilling vehicles’ charging needs and maintaining their batteries’ lifetime. The superiority of the proposed method lies in integrating a multivariable forecasting pipeline, linear MILP generation scheduling, and battery-health-aware V2G coordination in a unified decoupled framework, unlike many recent frontier works that treat these capabilities independently. Simulation results, under different scenarios, confirm that the proposed intelligent EMS can significantly reduce operational fluctuations, satisfy load and EV demands, optimize RES utilization, and support system cost-effectiveness, sustainability, and resilience. Full article

Approximately one quarter of the European Union’s (EU’s) CO₂ emissions originate from the transport sector, of which road transport, such as cars and heavy-duty vehicles, contributes roughly 72%. Moreover, according to the European Automobile Manufacturers’ Association, 92% of cars in the EU are internal combustion engine vehicles powered by fossil fuels. Therefore, boosting the adoption of Electric Vehicles (EVs) is considered one of the most prominent solutions for reducing GHG emissions and achieving the EU’s climate targets. To increase EV adoption and fulfil the demand of EV users, adequate EV Charging Stations (EVCSs) are required. Nevertheless, since most EVCSs are supplied by electricity grids that remain predominantly fossil fuel-based, their operation entails substantial indirect GHG emissions. A prominent approach to reducing grid-related emissions is integrating renewable energy sources (RESs) with EVCSs, thereby lowering emissions and alleviating grid stress. Although promising, the energy, economic, and environmental (3E) benefits of this integration remain insufficiently explored. Therefore, this study develops and applies a 3E optimisation framework to assess the feasibility and performance of RES-powered EVCS at NOVA University Lisbon (UNL). Data was collected from the UNL parking area, such as time of arrival, and time of departure. Also, a rule-based algorithm was developed to curate data and estimate the EVCS load profile. Furthermore, HOMER optimisation software was employed to evaluate four scenarios, including (i) an EVCS based on PV, Wind Turbine (WT), and the grid, (ii) an EVCS based on PV and the grid, (iii) an EVCS based on WT and the grid, and (iv) an EVCS based only on energy withdrawal from the grid (base scenario). Under the adopted techno-economic assumptions, in the most optimised scenario, economic and environmental analyses illustrate significant improvements over the base scenario: CO₂ emissions are five times lower, and cost of energy is significantly lower, resulting in significantly lower EV charging costs for users. The results demonstrate that, through developed feasibility studies, researchers, decision-makers, and stakeholders can reach better conclusions about EVCS planning and management. Full article

The adoption of electric vehicles (EVs) is a promising strategy for reducing emissions and promoting sustainable mobility. The increasing adoption of EVs has created a demand for efficient and sustainable charging infrastructure. The integration of rooftop solar-powered EV charging stations into distribution networks is a promising solution for reducing carbon emissions and improving grid efficiency. This integration also introduces challenges, such as power quality issues, grid instability, and the impact of environmental factors on solar generation. This study proposes a novel system that integrates a smart control algorithm for a central battery management system (CBMS) with advanced bidirectional DC-DC converters for optimised power distribution. Unlike existing systems that focus on individual components, this study combines real-time environmental monitoring with adaptive power management algorithms to handle variations in generation owing to solar irradiance, temperature, and shading, and ensure maximum power harvesting. This study also presents the role of the DC-to-DC converter integrated with a smart charging control and CBMS in smart grid-enabled EV charging station. The proposed system was validated using MATLAB 2025b Simulink simulations. This study demonstrates an improvement in overall grid stability and highlights the potential of DC-DC converter technologies for smart grid applications and decarbonisation efforts. Full article

The rapid proliferation of electric vehicles (EVs) has introduced significant challenges to the efficient operation of hydrogen-containing integrated energy systems (H-IESs). To cope with these challenges, this paper develops a bi-level optimal scheduling strategy for H-IESs that simultaneously incorporates a ladder-type carbon emission trading mechanism, demand response, and the operational characteristics of EVs. A demand response model is formulated by considering the coupling characteristics of electric and thermal loads. Price-based incentive signals are further designed to coordinate the interactions between the H-IES operator and EV users, enabling flexible resources to actively participate in system scheduling. In the proposed bi-level framework, the upper-level problem aims to minimize the total operating cost of the H-IES, while the lower-level problem seeks to reduce the charging cost of EV users. The resulting bi-level optimization problem is reformulated and solved using the Karush–Kuhn–Tucker (KKT) conditions. Case study results demonstrate that, compared with the single-level benchmark, the proposed bi-level strategy reduces the total operating cost by 34.79% and lowers the EV charging cost by 4.50%. Full article

As the number of electric vehicles (EV) increases, the demand for recommending the best charging location when using a large-scale charge network to charge is also increasing. A successful recommendation will utilize the user’s preference and the operational constraints of the charging network to make sure that it also takes into account the real-time operational requirements of the network. Most current papers focus on optimizing individual algorithmic components in isolation; consequently, many of these papers neglect to provide a holistic view of an integrated system. In addition, there are many operational requirements that current research does not consider, such as cold-start personalization for new users and enforcing real-time operational constraints like station availability, power capacity, maintenance windows, etc. This paper describes a deployable multi-stage recommendation system that creates a candidate list based on location and ranks preferences based on user, station and context features. The recommendation system also adds a configurable rule-based re-ranking layer to ensure that both hard constraints (i.e., charger availability and power-cap limits) and soft objectives (i.e., load balancing and operator priority) are enforced. A method for enabling mixed use between stable Bayesian and adaptive Bayesian methods was developed to provide users starting with cold-start performance that do not have adequate histories. Evaluation of this method using 100k+ real charging sessions showed that the fraction of sessions where the ground-truth station appears in the top-two recommendations (Hit@2) for the recommendation system was 0.82, representing a 37% increase in performance compared to proximity-based recommendation methods. The online deployed recommendation system has a 99th-percentile serving latency (P99) of less than 200 ms. The findings of this paper provide a framework for the implementation of operationally-relevant user-centric recommendation systems for EV services at scale. Full article

To reduce the carbon footprint, electric vehicles (EVs) are considered an alternative transportation choice. However, increased use of EVs could lead to overloading the existing power network when accounting for all installed chargers. With the increasing deployment of EV chargers, universities are potential locations for the oversized power network issue. This paper applies reinforcement learning (RL) to optimize for EV charging infrastructure at the university scale using real-world data, directly contributing to sustainable energy management by reducing grid burden and increasing renewable energy utilization. The RL-based charger aims to reduce the burden on the grid while increasing renewable energy utilization. This study investigated practical relevance in real-world systems, considering three demand scenarios: random, stochastic historical demand from Qatar University, and actual online data from Caltech University. Three RL algorithms—Deep Q-Network (DQN), Advantage Actor–Critic (A2C), and Proximal Policy Optimization (PPO)—are applied. While training, the historical stochastic data requires more tuning of the RL framework than the random demand, emphasizing the importance of realistic demand profiles. The performance of the RL approach depends on the type of demand. The results show that the proposed RL approach can efficiently mitigate the peak charging currents. For the Qatar University historical demand scenario, the PPO algorithm minimized the peak charging currents by 50% relative to uncontrolled charging (160 A to 80 A) and Model Predictive Control maintained the energy transfer capability at 99.710%. For the random demand type, the peak charging currents are minimized by 38.3% as compared to uncontrolled charging (128 A to 79 A), with a nominal reduction in energy transfer capability to 95.89%. Scalability is tested by integrating the model into the IEEE-33 bus network. Without solar integration, the proposed RL-based EV charging management model improves the voltage drop by 0.05 p.u., leading to reduction in the line losses by 17% as compared to the MPC benchmark method and by 32% as compared to the uncontrolled charging scheme. Further, the proposed RL approach leads to a 9% reduction in line current during peak hours in the IEEE-33 bus system. With solar integration into the IEEE-bus system, the proposed framework of the RL approach improved the sustainability of the charging infrastructures by enhancing solar energy utilization by 42.5%. These findings validate the applicability of the proposed model used for optimizing the sustainable EV charging infrastructure while managing the charging coordination problem. Full article

This paper investigates the deployment of four electric vehicle (EV) fast-charging stations (FCSs) in a commercial facility’s parking area, where multiple service centers operate on varying schedules. The commercial load demand is modeled using Monte Carlo Simulation (MCS), introducing realistic stochastic variability and overlapping power patterns with FCS operations. A single-point sensing strategy at the point of common coupling (PCC) is adopted for load disaggregation. Continuous Wavelet Transform (CWT) is employed for feature extraction, and multiclass classification is performed using Error-Correcting Output Codes (ECOC). Under commercial load interference, conventional machine-learning classifiers achieve a macro classification accuracy of 89.53%, with the lowest class accuracy dropping to 76.74%. To address this limitation, a deep learning (DL)-based framework is implemented. Simulation results demonstrate that the proposed DL approach improves overall classification accuracy from 89.53% to 100%, corresponding to a 10.47 percentage-point absolute improvement, an 11.7% relative gain, and complete elimination of misclassification errors. Notably, the most affected charging station class (FCS₂) accuracy increases from 76.74% to 100%. These results demonstrate that the proposed deep learning framework reliably detects FCS activations even under overlapping, variable, and high-power commercial load conditions, enabling more efficient energy management and optimal utilization of electrical resources, reduced energy waste, and enhanced sustainability of EV charging infrastructure within commercial facilities. Full article

To address the challenges of facility–demand mismatch, aggravated congestion, and imbalanced benefit distribution caused by the interdependence between dynamic wireless charging systems (DWCS) and transportation networks, this study proposes an optimization scheme that coordinates DWCS planning, travel flow guidance for electric vehicle (EV) owners, and transportation network operations. We develop a bi-level dynamic collaborative optimization model. The upper-level model aims to maximize the annual net profit of DWCS operators and determines DWCS planning by optimizing the traffic flow distribution. The lower-level model, based on the user equilibrium principle, guides EV route choices via a traffic flow guidance mechanism to mitigate peak-hour congestion and minimize vehicle owners’ travel costs. We validate the model using a test network comprising 9 nodes and 13 links. Results indicate that, compared with a full-coverage planning scenario, the proposed bi-level optimization scheme significantly reduces operational losses by accounting for owners’ optimal travel flow distribution. Introducing a traffic flow guidance mechanism further improves traffic flow distribution, enhances operator revenue, and effectively reduces owners’ travel time costs. Sensitivity analysis reveals that increased battery capacity decreases construction and maintenance costs, thereby improving annual net profit, while lower energy consumption reduces charging demand and weakens dependence on charging infrastructure. These factors are interrelated; specifically, lower energy consumption implies reduced battery capacity requirements for the same driving range. Additionally, the effectiveness of the traffic flow guidance mechanism becomes more pronounced as traffic flow increases. Overall, the proposed framework integrates DWCS planning and traffic flow guidance to achieve a win–win outcome for both operators and owners. These findings demonstrate the practicality and economic feasibility of interactive optimization between DWCS and transportation networks. Full article

The integration of electric vehicles into microgrids demands advanced energy management to coordinate charging with renewable generation and storage resources. This study presents a cohesive and comprehensive evaluation of four distinct optimization strategies—genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and mixed-integer linear programming (MILP)—in coordinating EV charging and energy dispatch within a 55 MW grid-connected microgrid that includes photovoltaic, wind, battery energy storage (BESS), and bidirectional EV systems. Beyond numerical outcomes, this work emphasizes the behavioral and methodological characteristics of each optimization approach, assessing their structural advantages and resource utilization dynamics. A novel MILP solution algorithm is introduced, based on a hybrid branch-and-cut technique integrated with time decomposition, enabling the solver to capture long-horizon optimization dynamics with high precision. All four methods are applied over a year-long simulation with hourly resolution. While each strategy maintains operational feasibility and power balance, the MILP approach consistently achieves the highest economic benefit, delivering approximately $2.43 million in annual cost savings, representing roughly a 72.3% improvement over the best-performing heuristic strategy under the same deterministic operating conditions. GA, PSO, and ACO each capture moderate benefits but show limitations in foresight and storage cycling. The findings not only benchmark algorithmic performance but also provide insight into the internal logic and structural behavior of optimization techniques applied to dynamic energy systems, offering guidance for algorithm selection and design in microgrid EMS. Full article

To address the lagging construction and irrational spatial distribution of current electric vehicle (EV) charging infrastructure, scientific location and capacity planning has emerged as a critical research focus in transportation electrification. Through a systematic review of domestic and international literature, this paper analyzes the evolution of charging station planning models from single economic indicators to multi-objective frameworks incorporating grid constraints, carbon emission benefits, and user behavior. Research indicates that while geometric spatial partitioning and swarm intelligence algorithms are widely utilized, existing methods face limitations in handling iterative spatial service area matching and overcoming the premature convergence of standard Particle Swarm Optimization (PSO). Consequently, this study proposes an integrated technical route utilizing Voronoi diagrams to adaptively partition service areas based on demand density, and constructing a comprehensive model encompassing construction and maintenance costs, environmental costs, and generalized user costs. To solve this highly complex spatial allocation problem, a Catfish Particle Swarm Optimization (CPSO) algorithm is employed as an efficient computational tool. Ultimately, this approach aims to provide practical, quantitative decision support for urban EV charging network planning by balancing the conflicting interests of operators, users, and the power grid within a comprehensive ‘Total Social Cost’ framework. Full article

To address the challenges of insufficient frequency regulation capability of individual stations, poor collaborative economic performance, and unfair benefit allocation caused by fluctuations in photovoltaic (PV) output and variations in electric vehicle (EV) connectivity during vehicle-to-grid (V2G) interactions under high-penetration PV integration, this paper proposes a coordinated optimal operation strategy for peer-to-peer (P2P) energy sharing and joint frequency regulation among multiple electric vehicle charging stations (EVCSs). First, a collaborative framework for P2P energy sharing and joint frequency regulation among EVCSs is constructed to describe the operational mechanism of inter-station energy mutual support and coordinated response to frequency regulation signals. Subsequently, an aggregate model of the dispatchable potential for EV clusters within each station is established based on Minkowski Summation (M-sum), characterizing the charging and discharging power boundaries and frequency regulation potential of the EV clusters. Meanwhile, distributionally robust chance constraints (DRCC) based on the Kullback–Leibler (KL) divergence are introduced to handle the uncertainty of PV power generation within the EVCS. On this basis, a dynamic frequency regulation output model for EV clusters and a multi-station P2P energy sharing model are designed, with the optimization objective of minimizing the total operating cost. Finally, to quantify the differential contributions of each EVCS in the collaborative operation, an asymmetric Nash bargaining benefit allocation mechanism is proposed, which incorporates a comprehensive contribution index considering both energy sharing and joint frequency regulation, The model is solved in a distributed manner using the alternating direction method of multipliers (ADMM). Simulation results demonstrate that, compared to non-cooperative operation, the frequency regulation completeness rates of the EVCSs after cooperation increase by 5.7%, 5.2%, and 4.4%, respectively; meanwhile, the total operating cost drops from CNY 16,187.61 under non-cooperative operation to CNY 15,997.47, achieving a reduction of 1.18%. The proposed strategy not only meets grid frequency regulation demands but also enhances the economic efficiency of multi-station collaborative operation and the fairness of benefit distribution. Full article

As a promising alternative for cleaner vehicles, the growth of Battery Electric Vehicle (BEV) adoption should be supported by a reliable charging infrastructure. Therefore, projecting the charging load is required to ensure that the electricity supply is adequate as BEV adoption increases. This study proposes a multistage approach for projecting BEV charging load demand, linking a One-dimensional Convolutional Neural Network (1D-CNN) forecasting model with BEV users’ travel behavior analysis to perform spatiotemporal agent-based trip and charging simulations, which model various types of BEVs traveling across multiple regions. The 1D-CNN model achieves high performance with an RMSE of 0.073 and an R² of 0.881, providing a 10-year BEV adoption outlook. The empirical study in nine regions of Greater Jakarta, Indonesia, shows the one-week temporal charging load demand for three milestone periods—2025, 2030, and 2035—exploring weekday and weekend demand, as well as home and public charging demand at points of interest (POIs). This study identifies a difference between aggregate charging load demand and per-vehicle load intensity: the aggregate demand concentration occurs in South Jakarta (21% for public charging and 22% for home charging), while the highest per-vehicle spatial concentration ratio occurs in Depok (36% for public charging and 16% for home charging) due to long-distance travel patterns. The distribution of charging demand at the subdistrict level provides a basis for charging infrastructure placement, transformer sizing, and charging tariff design. Full article

In this paper, we propose a data-driven distributionally robust optimization (DRO) framework that ensures the economical and robust operation of solar photovoltaic (PV)-integrated battery charging swapping stations (BCSSs) for electric vehicles (EVs) under uncertainties in active distribution systems with stand-alone PV systems. In the proposed framework, multiple inventory batteries in each BCSS are used through their charging and discharging real and/or reactive power scheduling to perform Volt/VAR control (VVC) along with stand-alone PV systems, and to reduce the BCSS operational cost via battery-to-battery (B2B)-based real power exchange and demand response (DR) while satisfying the desired EV battery swapping load. To handle the uncertainties in both PV generation outputs and DR-induced maximum demand reduction capability, the proposed framework is formulated as a data-driven DRO problem based on the Wasserstein metric using historical samples of the probability distributions of the uncertainties. Using a duality theory, the original Wasserstein-based DRO problem is reformulated into a tractable optimization problem that calculates the distributionally robust bounds of uncertainties using their support information. The effectiveness of the proposed framework was assessed on an IEEE 33-node power distribution system in terms of real power loss reduction via VVC and BCSS operational cost savings via B2B/DR capability. Full article

This paper presents a novel optimization algorithm for electric vehicle (EV) aggregators aiming to maximize net revenue in demand response markets. Aggregated EV charging stations are modeled as a battery with time-varying capacity, enabling participation in these markets. Due to uncertainties in EV plug-in duration and energy demand, it is challenging for aggregators to fulfill bid capacities in real-time (RT). To address this, EV users specify minimum acceptable service levels, allowing aggregators to optimize both charging timing and energy demand in RT. The model is composed of two layers: (1) a Day-Ahead (DA) optimizer that determines optimal EV scheduling and DA demand response market bidding, and (2) a two-stage RT optimizer that fine-tunes the charging schedule using real-time flexibility to mitigate forecast errors. The RT optimizer leverages Model Predictive Control (MPC) in a two-stage structure to address the problem’s non-convexity, which arises from two coupled unknowns: the charging time and the charging energy demand. In the first stage, it determines a cost-optimal charging schedule that ensures full service levels. In the second stage, it optimizes the charging energy demand within a feasible range, bounded above by the first-stage trajectory and below by user-defined minimum service levels, to maximize demand response market revenue. A realistic baseline and a penalty term are integrated into the demand response market revenue term of the cost function to more accurately reflect real-world conditions. Simulation results demonstrate that the proposed method yields a net economic profit at least five times higher than that of immediate (or ‘dumb’) charging. During one month of simulations, the aggregator achieves revenue equivalent to $0.21 per kWh of demand reduction under forecast uncertainty, totaling $3441. Full article