Search Results (515)

To address the limitations of conventional black-start sources associated with geographical constraints and capacity configuration, a coordinated black-start control method was proposed for an islanded auxiliary-power microgrid of a biomass combined heat and power (CHP) plant, in which a diesel generator and a vehicle-to-grid (V2G) cluster operate cooperatively. First, a dynamic available-capacity assessment model was established for the V2G cluster. The diesel-generator capacity was then verified by considering the startup impacts of plant auxiliary motors, and the corresponding black-start restoration scheme was determined. Second, a coordinated control strategy for the diesel generator and V2G was developed. The diesel generator establishes the voltage and frequency references of the islanded microgrid through droop control, while local secondary frequency and voltage correction improves post-disturbance frequency and voltage recovery. The grid-following V2G cluster provides transient active-power support through primary frequency regulation and inertia-based frequency control. Finally, an electromagnetic transient model of the auxiliary-power microgrid of the biomass CHP plant was developed in PSCAD/EMTDC to simulate the sequential startup of mixed auxiliary motors. The results show that the improved diesel-generator control enhances the frequency and voltage recovery characteristics of the islanded microgrid. Fast active-power support from the V2G further suppresses frequency fluctuations caused by successive auxiliary-motor connections. The proposed coordinated strategy effectively improves the dynamic restoration performance of the black-start process. Full article

The rapid adoption of electric vehicles (EVs) has intensified the demand for advanced battery technologies capable of delivering higher energy density, improved safety, faster charging, and longer operational life. This review provides a comprehensive assessment of the evolution of EV battery technologies, covering conventional batteries, lithium-ion batteries, and emerging next-generation chemistries, including solid-state, lithium–sulfur, sodium-ion, and lithium–air batteries. Key battery performance characteristics, such as energy density, power density, efficiency, cycle life, charging and discharging behavior, and degradation mechanisms, are critically discussed to highlight their influence on battery performance and lifespan. The review further examines the application of artificial intelligence (AI) in battery management systems, emphasizing machine learning, deep learning, reinforcement learning, and hybrid AI techniques for improving battery state estimation, fault diagnosis, predictive maintenance, thermal management, and charging optimization. The roles of State of Charge (SOC), State of Health (SOH), and Remaining Useful Life (RUL) estimation in enhancing battery reliability and operational safety are also reviewed. In addition, current research gaps related to battery degradation, fast charging, thermal management, recycling, and explainable AI are found, together with future research trends involving digital twins, smart charging, Vehicle-to-Grid (V2G) integration, and sustainable battery technologies. The review concludes that the integration of next-generation battery chemistries with AI-driven battery management systems offers significant potential to improve battery efficiency, extend service life, enhance safety, and accelerate the widespread adoption of electric vehicles while supporting the global transition toward sustainable and intelligent transportation systems. Full article

Residential photovoltaic–battery energy storage systems (PV-BESSs) and electric vehicles (EVs) expose households to a persistent conflict between electricity cost minimization and multi-dimensional comfort. This paper proposes a day-ahead joint scheduling strategy for a PV-BESS, flexible household loads, and a vehicle-to-grid (V2G) capable EV that explicitly quantifies this trade-off. Thermal comfort and temporal preference satisfaction are formulated as bounded indicators and aggregated into a single electricity-consumption comfort index through the efficacy coefficient method. EV availability is characterized by a probability-distribution-based travel model, in which Monte Carlo sampling of the daily mileage and return time is decoupled offline from the optimization so that the conditional charging and discharging logic reduces to linear constraints, and a two-stage scenario-based extension with a chance constraint on the departure energy requirement co-optimizes the household schedule against travel uncertainty. The economic and comfort objectives, explicitly incorporating a cycle-based battery degradation cost to accurately evaluate V2G arbitrage profitability, are combined by a normalized weighted-sum scalarization, and the resulting mixed-integer program with convex quadratic comfort constraints is solved to proven global optimality by GUROBI through YALMIP. For a typical summer household in Southern China, the proposed strategy reduces the daily electricity cost by approximately 7.3% (from 10.69 CNY to 9.91 CNY) at the price of a moderate decrease in the comfort index from 0.86 to 0.77. A sensitivity study of the peak–valley penalty coefficient further shows that flattening the load profile by 1.5 kW raises the user’s daily cost by 2.4 CNY, quantifying the compensation a distribution utility would need to offer to elicit load smoothing. To account for the inevitable forecasting errors of PV generation and outdoor temperature without incurring prohibitive computational burdens, conservative reserve margins and adaptive thermal bounds are introduced into the formulation. Relative to the deterministic representative-scenario schedule, the stochastic extension reduces the expected out-of-sample cost while raising the probability of meeting the EV departure energy requirement from 91.4% to 98.3%. Furthermore, comprehensive benchmark comparisons against pure cost minimization, pure comfort maximization, and deterministic schedules reveal the superiority of the proposed multi-dimensional index over conventional one-dimensional penalties in balancing household economics and occupant satisfaction. Full article

Accelerating the low-carbon transition of transport is essential to addressing global climate change. Although the rapid growth of the electric vehicle (EV) fleet highlights the environmental benefits of electrified mobility, carbon-intensive upstream electricity generation and energy-intensive battery production continue to constrain the life-cycle emissions reduction potential of EVs. Focusing on China, this structured narrative review examines 121 core references from 2004 to 2026 to synthesize carbon-accounting methods and emissions across the manufacturing, use, and end-of-life stages. A supplementary keyword co-occurrence analysis of 1180 Web of Science records provides an overview of the research field. The reviewed evidence indicates that embodied emissions from manufacturing account for more than 30% of total life-cycle emissions, making this stage important to life-cycle decarbonization. The use stage offers the greatest emissions reduction potential and is closely linked to regional grid carbon intensity and vehicle-to-grid (V2G) scheduling. At the end of life, second-life battery use and closed-loop recycling can reduce the burden associated with initial production and support material circularity. Drawing on these findings and the development of China’s EV industry, this review proposes decarbonization pathways for enterprises and policymakers to support the coordinated development of the transport and energy sectors. Full article

Low-altitude unmanned aerial vehicles (UAVs) serving as aerial base stations for ground vehicles create air-to-vehicle (A2V) links in which both endpoints move, compressing the channel coherence time so that the reported channel quality indicator (CQI) is already stale when applied. Link adaptation calibrated for terrestrial deployments does not account for this dual-mobility aging, and the resulting overestimation of link quality inflates first-transmission errors. This paper proposes a Doppler-aware CQI correction for three-dimensional (3D) A2V Open RAN networks: an offline-calibrated back-off, indexed by the maximum Doppler frequency and the Rician K-factor, is subtracted from the measured signal-to-interference-plus-noise ratio (SINR) before CQI quantization. Because the back-off depends only on parameters the network already derives from geometry and mobility, the correction acts from the first transmission and requires no feedback convergence, unlike outer-loop link adaptation (OLLA). The scheme is implemented in Simu5G within a 3D network model providing aerial cells, dual-mobility fading decorrelation, and an Open Radio Access Network (O-RAN)-based measurement plane, and evaluated across a factorial campaign spanning three schedulers, two deployment topologies, and paired random seeds. Applying the correction to A2V links alone reduces the first-transmission error rate of aerial-served vehicles by 46–54% in an urban grid with no penalty to terrestrial links; applied network-wide, it reduces total network error by 57–62% (urban) and 54–58% (highway) and mean latency by up to 0.38 ms, at a cost of 7.3–8.8 percentage points in resource-block utilization and negligible throughput loss. Against OLLA under identical conditions, it matches or improves the error rate in the urban grid with 2.7–3.8 percentage points less overhead and reduces highway error by a further 1.2–1.3 percentage points. Full article

Electric vehicle (EV) charging stations and vehicle-to-grid (V2G) systems depend on outbound Domain Name System (DNS) resolution for firmware retrieval, backend discovery, and fleet synchronization, making DNS tunneling an attractive covert command-and-control and data-exfiltration channel in charging infrastructure. Machine learning detectors trained on lexical and statistical DNS features achieve excellent in-distribution accuracy, yet they are rarely stress-tested against adaptive adversaries that deliberately reshape query characteristics toward benign traffic. This paper presents a station-independent evaluation framework and an adaptive-augmented, cyber-physical detection architecture for evasive DNS tunneling in EV charging and V2G networks. Using a 200-station synthetic dataset that couples 24 DNS features with 16 EV/Open Charge Point Protocol (OCPP)/V2G telemetry features and 14 cross-modal consistency features, we evaluate every detector over ten repeated grouped station-level splits and across three attack regimes: an adaptive-strength sweep (β = 0.25–0.95) of the interpolation mechanism used in training, a separately held-out constraint-aware adaptive mechanism excluded from all training and model selection, and multiplicative perturbation of the physical-anchor telemetry at relative scales of 5–20%. Under strong interpolation-based evasion at the training strength (β = 0.90), detectors relying on DNS evidence retain almost no detection capability at their original operating point (mean F1 = 0.041 ± 0.023), although part of their threshold-free ranking ability survives, and recalibrating the decision threshold alone does not repair the collapse. We propose a safe EV-anchored fusion detector that treats physical telemetry as a protected anchor, hardens a cross-modal branch with adaptive examples drawn only from training stations, and admits DNS evidence only through a bounded, validation-selected correction. Across the ten splits, the proposed detector sustains F1 = 0.909 ± 0.013 at β = 0.90 and F1 = 0.923 ± 0.016 under the held-out mechanism, retaining approximately 94–96% of its original F1 of 0.964 ± 0.006 at a false-positive rate near 5.5% (about 55 false alarms per 1000 benign windows), and it degrades gracefully (F1 ≥ 0.911) when the anchor telemetry is perturbed at up to 20% relative scale. The results indicate that anchoring detection in physical-side telemetry, with bounded and adaptively hardened cross-modal evidence, provides consistent performance across the evaluated repeated station partitions and is computationally feasible under the evaluated conditions. Full article

The Energy Community (EC) can play an essential role in collective energy sharing and management by maximising on-site energy use among its members (i.e., consumers, producers, and prosumers). ECs can play a pivotal role in maximising emission reductions in the transport sector; thus, they need to expand the deployment of Electric Vehicle (EV) charging facilities (EVCFs) powered by renewable energy sources (RESs). Therefore, the main aim of this study is to optimise energy consumption costs and maximise the RESs utilisation for household loads and EV charging (EVC) loads in a Spanish EC through load scheduling. To improve the performance of the Energy Management System (EMS) and EVC operations, minimise the EC’s energy consumption cost, and maximise the RESs utilisation, the metaheuristic Particle Swarm Optimisation algorithm is employed to solve the multi-objective optimisation problem. Moreover, two strategies are applied and compared in this study to describe the EVC load variability, including machine-learning algorithms and statistical methods. By comparing these strategies, the accuracy and reliability of load estimation and prediction can be improved. This study analyses six scenarios: Scenario 1: grid-estimated load, which can be considered grid-to-vehicle (G2V); Scenario 2: grid-predicted load; Scenario 3: grid-RES-estimated load; Scenario 4: grid-RES-predicted load; Scenario 5: grid-RES-estimated load-scheduling; and Scenario 6: grid-RES-predicted load-scheduling. By developing and examining this integrated, two-step optimisation framework, the study demonstrates the significant economic and environmental benefits of EVCF supported by RESs. Overall, the results demonstrate that centralised EVCF powered by RESs can offer sustainable, cost-effective charging options for EC members, increase EV adoption, and assist in fulfilling the European Union’s greenhouse gas reduction targets. Full article

The rapid expansion of renewable energy sources and the growing adoption of electric passenger vehicles (EVs) necessitate intelligent energy management strategies to ensure grid stability and operational efficiency. Achieving a sustainable energy system requires very high shares of renewable energy, potentially up to 100%. To balance fluctuations in renewable generation and electricity demand, energy storage technologies are essential. Stationary battery storage is an effective solution, but battery production is associated with significant emissions and resource consumption. From a sustainability perspective, minimizing additional battery production is therefore desirable. Vehicle-to-grid (V2G), which enables bidirectional energy exchange between EVs and the power grid, offers a promising alternative by utilizing batteries that are already installed in electric vehicles instead of deploying additional stationary storage systems. This increases resource efficiency and reduces the environmental impact of energy storage deployment. At the same time, automated charging technologies can further improve the sustainability of future mobility and energy systems by increasing the usability of electric vehicles and optimizing charging infrastructure utilization, particularly in long-term parking scenarios such as airports. As vehicle automation advances, the relevance of automated charging solutions will continue to grow. However, it remains unclear whether automation will support or hinder the implementation of vehicle-to-grid (V2G) concepts and their potential contribution to a more sustainable transport and energy sector. This paper therefore provides a comprehensive overview of the recent advancements in automated charging systems and evaluates their suitability for enabling grid-supportive V2G applications. Using a weighted point rating system, different technologies are systematically assessed across relevant EV use cases. The results highlight both synergies and trade-offs between automation and V2G, offering insights into how automated charging technologies can be designed to maximize their contribution to sustainable mobility and energy systems. Full article

The rising uptake of electric vehicles (EVs) is a critical challenge for how power systems function, as it places a strain on the grid, especially during peak times, and as more EVs come into the system, uncoordinated charging demands will massively increase the stress on the grid. In this research, seven metaheuristic algorithms, including the Particle Swarm Optimization (PSO), the Differential Evolution (DE), the Whale Optimization Algorithm (WOA), the Grey Wolf Optimizer (GWO), the Enhanced Whale Optimization Algorithm (EWOA), the Particle Swarm Optimization–Whale Optimization Algorithm (PSO-WOA), and the Adaptive Particle Swarm Optimization (APSO), are used for the optimum scheduling of EV charging and discharging in the vehicle-to-grid (V2G) environment, and they are also tested statistically. The approaches are assessed using a realistic time-of-use (ToU) electricity pricing scheme, with peak, mid-peak, and off-peak time zones. The simulation results clearly demonstrate that APSO achieves the lowest mean value of the composite scheduling objective among the seven tested algorithms, improving on PSO by 8.0% and on the PSO-WOA hybrid by 9.7%, as well as showing a measurable improvement in the peak load demand profile. Moreover, PSO-WOA is not statistically different from PSO, and EWOA has not been shown to be better than WOA after family-wise correction (FWC) for seven algorithms. Thorough statistical validation, including parametric tests (t-test, ANOVA), non-parametric tests (Mann–Whitney U, Wilcoxon Signed-Rank, and Friedman) and post hoc analyses (Holm’s Step-Down, Bonferroni–Dunn, and Nemenyi), is done to distinguish true performance gains from random error. The results indicate that, for this problem class, the effective mechanism is adaptive control of swarm parameters rather than hybridization of search operators. Full article

Vehicle-to-grid (V2G) aggregators must commit energy and power that connected vehicles can actually deliver, yet they often observe only charger-side telemetry, and vehicle-reported state of charge (SOC) is optional or non-authoritative. This paper proposes an observability-aware framework for estimating tradeable V2G capacity: each session is classified by its measurement boundary, channels, sampling, latency, and setpoint control, then mapped to the battery through uncertain conversion paths. The estimator outputs conservative safe energy and safe power rather than absolute SOC, treats vehicle-reported values as noisy hints, and abstains when observability is insufficient. A reproducible synthetic study shows that regularization and a split-conformal margin bring the bound to the 95% target with a finite-sample guarantee under within-regime exchangeability, and that the required capacity haircut grows from about 2.6 through 4.3 to 6.5 SOC points as observability degrades. Coverage alone does not distinguish the method, since any conformalized predictor reaches the target; the observability-aware bound adds tradeable capacity at that coverage, and its advantage grows as telemetry degrades. End-to-end market deliverability is established only in synthesis: a laboratory proof of concept on one bidirectional charger with four production vehicles demonstrates AC-boundary telemetry ingestion and capability assignment, but measures realized throughput after the fact rather than a bound committed before dispatch. Full article

The increasing deployment of Renewable Energy Sources (RESs), particularly wind and solar power, plays a critical role in reducing carbon emissions and supporting sustainable energy transitions. However, the large-scale integration of RESs into smart grids introduces significant technical challenges related to frequency stability, voltage regulation, rotor angle stability, power quality, inertia reduction, harmonic distortion, reverse power flow, Sub-Synchronous Interactions (SSIs), and protection coordination. Although numerous review studies have examined renewable energy integration, most focus on high-level frameworks, bibliometric analyses, optimization techniques, or isolated applications of artificial intelligence (AI) while lacking a comprehensive synthesis that bridges AI-driven solutions with the physical dynamics, control mechanisms, and protection requirements of highly renewable power systems. To address this gap, this review provides a comprehensive technical assessment of wind generator topologies, solar inverter architectures, grid-forming and grid-following control strategies, virtual inertia and virtual Synchronous Generator (SG) technologies, adaptive load-frequency control, energy storage integration, protection coordination, and real-time stability enhancement techniques for high-RES smart grids. Furthermore, the review systematically examines the role of AI in frequency regulation, voltage control, harmonic mitigation, predictive operation, parameter optimization, and system resilience. Unlike previous reviews, this study integrates physical-layer perspectives by connecting AI-driven decision-making with practical grid control mechanisms, inverter dynamics, wide-area monitoring, microgrid operation, High Voltage Direct Current (HVDC) interconnections, EV/Vehicle-to-Grid (V2G) integration, and multi-resource energy management. The review identifies key research priorities, including the development of real-time AI-assisted frequency control, adaptive protection schemes for low-inertia systems, coordinated grid-forming inverter control, resilient autonomous grid operation, and scalable multi-energy management frameworks. The findings provide actionable guidance for researchers, utilities, policymakers, and industry stakeholders seeking to enhance stability, reliability, and operational flexibility in future smart grids with very highly renewable energy penetration. Full article

Grid-impact studies treat battery electric vehicles as loads, and ask when network capacity will be exhausted. This paper reverses the question: how much of the fleet must operate bidirectionally, and with what probability will an achievable participation rate suffice, for the network to remain within its limits? A conceptual framework adds a vehicle-to-grid and vehicle-to-building flexibility term to the balance between available and required power, nests the authors’ earlier deterministic model for twenty municipalities in Northern Portugal as its zero-flexibility special case, derives a closed-form break-even participation rate per municipality and year, and keeps the simultaneity assumption of that model explicit as a coincidence factor. Participation, location, plug-in and export parameters follow beta-PERT distributions calibrated on published trials and surveys, propagated by Monte Carlo simulation without new field data. The framework is an apparent-power balance per municipality, so its outputs are an upper bound on usable flexibility, not a feeder-level feasibility check. An enrolled vehicle provides about 11 kVA of peak relief, over nine tenths from not charging rather than exporting. Under worst-case simultaneity, observed participation rates, if in place from the outset, halve the 2028 shortfall probability but cannot prevent shortfall by 2030; under realistic coincidence the regional network is not constrained and only eight of twenty municipalities remain critical. The network balance is replicable wherever municipal substation data exist; behavioural parameters require local calibration. Full article

This paper presents a simulation-based proof of concept for integrating an existing Variational Quantum Linear Solver (VQLS) with a Newton-type AC power flow procedure for smart grids with vehicle-to-grid (V2G) participation. The novelty of the work lies not in proposing a new VQLS algorithm but in its engineering integration with a reduced Jacobian-based power flow subproblem and V2G operating scenarios. The proposed framework linearizes the nonlinear AC power flow equations and applies a four-qubit Real-Amplitudes VQLS circuit with the COBYLA optimizer to approximate a selected 16 × 16 reduced Jacobian system. The complete voltage profile is then reconstructed through the hybrid quantum–classical procedure. The method is evaluated using a modified IEEE 33-bus radial distribution system with an aggregated V2G unit connected at Bus 18. The main optimization run shows a rapid reduction and subsequent stabilization of the VQLS cost, while the resulting bus-voltage profile follows the overall trend of the Newton–Raphson reference solution. A separate 50-iteration assessment also reduces the cost substantially but does not reach the reference tolerance of 10⁻⁴. The V2G scenario analysis shows that prescribed V2G active-power support can reduce active power losses under both normal and stressed operating conditions, with loss reductions of 26.26%, 27.85%, and 30.52% in the base peak-load, N-1 contingency support, and dynamic railway-peak support cases, respectively. A resource-scaling assessment using a normalized classical computation index and a VQLS circuit-depth index is included only to illustrate resource-growth trends, not to establish computational superiority. The results support the feasibility of the proposed integration under ideal statevector simulation, but no quantum speedup or advantage over established classical solvers is claimed. Full article

High penetration of renewable energy imposes greater demands on the scheduling flexibility of demand-side resources in virtual power plant (VPP) dispatch. Nevertheless, heterogeneous resources exhibit remarkable differences in response reliability, and electric vehicles (EVs) in particular show distinct execution performance between orderly charging and vehicle-to-grid (V2G) modes. To tackle this issue, this paper proposes a two-stage optimal scheduling strategy for multi-agent VPPs incorporating scheduling success probability. A quantitative model for the effective dispatch contribution coefficient is constructed from two dimensions, i.e., relative capacity weight and dispatch execution reliability, with differentiated parameters tailored for EV charging and V2G modes. The two-stage leader–follower game problem is decoupled via backward induction, and the optimal dispatch price is rigorously derived through Karush–Kuhn–Tucker conditions. A 24 h case study covering wind power, photovoltaics, energy storage, EVs, and air-conditioning loads validates the proposed method. Results indicate that the strategy boosts total VPP revenue by 7.43% compared with independent operation, lifts the renewable energy accommodation rate from 88.3% to 94.6%, and reduces the average operating cost by 19 CNY/MWh. Through dual-mode differentiated scheduling, EVs achieve 5.10% revenue growth and serve as a key flexible resource for VPP economic operation. Full article

Electric-vehicle (EV) charging can intensify voltage drops, feeder currents, and technical losses in radial distribution networks, particularly when charging coincides with periods of high residential demand. This paper investigates how the adopted load representation affects reported EV–DER feeder performance when rule-based vehicle-to-grid (V2G) support and renewable distributed energy resources are considered. Particle swarm optimization (PSO) is used for DG siting, sizing, and power-factor selection at the full-load planning condition, after which the selected DG plans are evaluated using a 24 h, 15 min backward/forward sweep (BFS) time-series simulation. Two load representations are compared using the same EV, V2G, and DER models: a constant full-load representation in which the nominal base demand is maintained over all 96 intervals, and a daily load-curve (DLC) representation in which the base demand varies over time. Across the three examined scenarios, the DLC representation reduces daily loss energy by approximately 29.2–36.0% relative to the constant full-load case, while changes in peak real-power loss and peak source current remain comparatively smaller. The results demonstrate that load representation has a substantial influence on energy-based conclusions even when the feeder, DG plan, and operational models are unchanged. The study therefore supports time-series DLC assessment for energy-based reporting and transparent comparison of EV–DER distribution-feeder performance. Full article