Search Results (464)

The integration of electric vehicles with grid vehicles promotes the creation of multi-energy microgrid models. One of the aims of these models is to decrease electricity usage through Vehicle-to-Grid planning. Effective management of microgrids necessitates sophisticated automation and control systems, which can prove challenging to establish and sustain. To tackle these challenges, the author introduces a hybrid model that merges a Linear Regression model and a Feedforward Neural Network, created using Matlab software. This combined algorithm adjusts the quantity of hidden neurons to enhance performance, guided by the evaluation criteria of Mean Squared Error, Root Mean Squared Error, and Mean Absolute Percentage Error based on batteries B0005, B0006, and B0007 from the NASA PCoE Research Center Dataset. The author forecasts the lifespan of the battery that most accurately reflects its degradation, revealing important implications for the future advancement of systems that employ Linear Regression and Feedforward Neural Networks for integrating electric vehicles into Vehicle-to-Grid systems. The comparison among the training, testing, and validation stages of the methodology serves to thoroughly demonstrate its effectiveness. Furthermore, the author indicates that the LR-FFN algorithm provides predictive tools relevant for the management of V2G-compatible EV systems and performs superiorly compared to other methods noted in the existing literature. Additionally, the author aimed to specifically identify the attributes of the LR-FNN model for prospective usages, emphasizing its efficacy in developing effective microgrid management, promoting energy efficiency, and ensuring that microgrids remain secure and resilient against failures or threats. Full article

Vehicle-to-grid (V2G) can provide flexibility and storage for low-carbon power systems while supporting sustainable mobility, yet real-world deployment remains largely confined to pilots despite substantial technical progress. This article presents a PRISMA-guided systematic review of 974 V2G/V2X studies published between 2009 and 2025 to explain why implementation lags and how it can be accelerated. Within this corpus, a total of 162 implementation-critical articles are identified and, within these, 95 studies that primarily address non-technical dimensions such as policy, markets, user behavior, and ecosystem coordination. Drawing on full-text coding, a four-domain socio-technical framework is developed that clusters recurring non-technical barriers and enablers into business–economic, governance–policy, social, and infrastructure and ecosystem domains. The analysis reveals (i) a temporal shift from technical dominance to multidisciplinary acceleration after 2021; (ii) distinct regional priorities in which Europe emphasizes regulation and business models, Asia focuses on infrastructure scaling, and the Americas on frequency services and resilience; and (iii) persistent revenue uncertainty, regulatory gaps, user resistance, and grid unreadiness as cross-cutting obstacles. For each domain, concrete transition levers and indicative deployment key performance indicators (KPIs) are derived, such as multi-actor revenue-sharing mechanisms, aggregator recognition in market rules, privacy-by-design user participation models, and targeted bidirectional charging deployment in constrained grids. Synthesizing these insights, three archetypal V2G transition pathways are proposed—regulation-led, infrastructure-first, and service-driven—that reflect regional conditions and offer alternative routes to large-scale adoption. The framework and roadmap provide researchers, policymakers, system operators, and mobility providers with an integrated basis for designing, monitoring, and evaluating V2G policies, business models, and pilots in line with energy system decarbonization goals. Full article

The exhaustion of fossil fuels, environmental concerns, and difficulties in deploying smart grids have expedited the development of renewable energy distributed generators (REDGs) and electric vehicles (EVs). In recent decades, there has been a notable rise in the production and marketing of EVs. Previous research has proposed reactive power control solutions, including the use of power electronic converters associated with distributed generators (DGs) to alleviate voltage fluctuations. This research presents a strategy for the best integration of electric vehicles through bidirectional charging and renewable energy distributed generators inside power systems, with the objective of efficiently managing voltage, active power, and reactive power flows at interconnection points. Furthermore, it entails determining appropriate locations and dimensions for electric car charging stations through a comparative examination of computing time and iterations between the Hybrid Genetic Algorithm Improved Particle Swarm Optimization (HGAIPSO) and several other optimization methods, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Improved Particle Swarm Optimization (IPSO). This analysis was performed on the IEEE-118 bus system, incorporating Vehicle-to-Grid (V2G), Grid-to-Vehicle (G2V), and REDG allocations. The simulation results indicated that the suggested HGAIPSO approach is more rapid and effective regarding calculation time for complex networks, attaining optimal solutions with greater efficiency. Full article

With the rapid electrification of urban transportation and increasing penetration of renewable energy, maintaining frequency stability in smart-city multi-microgrids (MMG) systems increasingly depends on coordinated vehicle-to-grid (V2G) flexibility. However, existing load frequency control strategies typically treat electric vehicles (EVs) as homogeneous resources and overlook the impacts of charging-infrastructure diversity, user mobility constraints, and extreme weather conditions on regulation availability. To address these challenges, this study proposes a weather-adaptive intelligent load frequency control strategy for smart-city MMG considering heterogeneous charging stations and energy requirements of EV users. Fast and slow charging infrastructures are modeled separately to reflect their distinct regulation characteristics, while time-varying charging and discharging margins are derived from travel demand, parking duration, and state-of-charge preferences and further adjusted under extreme weather scenarios. Based on these dynamic constraints, an enhanced multi-agent soft actor–critic (MA-SAC) controller coordinates micro gas turbines and charging stations for distributed frequency regulation. Simulations demonstrate MA-SAC outperforms PID, Fuzzy, and MA-DDPG methods, achieving a 98.51% frequency excellent rate normally and 91.47% during extreme weather. It reduces maximum deviations by up to 80% versus PID, while preserving user travel requirements. The proposed framework provides a practical pathway for integrating electrified mobility into resilient smart-city MMG frequency regulation. Full article

Electric vehicles (EVs) provide fast charging/discharging flexibility; however, single-layer assessments may overestimate the flexibility that can be physically delivered under downstream distribution-network constraints. This paper proposes a process-oriented hierarchical adjustable-potential assessment framework for transmission–distribution–microgrid coordination. At the microgrid/station layer, a chance-constrained vehicle feasible set is constructed to capture user uncertainty, and probabilistic Minkowski-sum aggregation is used to obtain a station-level theoretical envelope. At the distribution layer, voltage and line-thermal constraints are modeled using LinDistFlow and intersected with the theoretical envelope to derive an effective potential satisfying network security limits. At the transmission layer, the effective feasible region is further packaged into a time-varying generalized-battery parameter set for consistent upward reporting without introducing dispatch optimization. In addition, a bottleneck truncation effect (BTE) metric is defined to quantify how distribution constraints reduce upstream-usable flexibility. Case studies show that hierarchical network constraints compress both peak EV flexibility and the all-day feasible-region area. Specifically, the microgrid-layer theoretical envelope reaches 432 kW on the charging side, 124 kW on the discharging side, and 3799 kWh in feasible-region area. After distribution-layer security clipping, the effective envelope becomes 299 kW, 124 kW, and 2063 kWh, corresponding to reductions of 30.79%, 0.00%, and 45.70%, respectively, relative to the microgrid layer. After transmission-layer packaging, the deliverable envelope is further reduced to 285 kW, 118 kW, and 1946 kWh, i.e., reductions of 34.03%, 4.84%, and 48.78%, respectively, relative to the microgrid baseline. These results demonstrate that the proposed workflow provides verifiable and time-varying deliverable capability boundaries for cross-layer EV flexibility assessment. Full article

The integration of large-scale electric vehicle (EV) fleets into vehicle-to-grid (V2G) systems offers significant potential for enhancing the operation of renewable-based smart grids. However, stochastic uncertainties in photovoltaic (PV) generation, vehicle availability, and load demand—coupled with battery degradation and life-cycle assessment (LCA) carbon emissions—pose major challenges to optimal scheduling. This paper proposes a scenario-based multi-objective MILP framework for a 500-EV fleet aggregator. The model incorporates Monte Carlo simulations for multi-source uncertainty quantification (±25% PV forecast errors, ±40% availability), LCA penalties (45 kgCO₂eq/kWh), and ancillary service revenues (25 USD/MW-h). Long-term state-of-health (SOH) projections, including a 1-year fade to 96.5%, are also integrated. Comparative analysis of V2X scenarios shows that the V2G Hybrid strategy reduces daily costs by 34.6% (from ~11,000 USD in the uncontrolled case to 7741 USD when reserve revenues are included), achieves over 50% peak shaving, and maintains voltage stability within 0.994–1.008 pu. The stochastic Pareto frontier identifies knee-point solutions that lower normalized expected costs to 134.61 while achieving 1–2% lower expected emissions compared to deterministic baselines. These results demonstrate a comprehensive framework, uncertainty-aware framework that balances economic viability, grid resilience, and environmental sustainability, offering actionable insights for fleet aggregators and policymakers working toward net-zero energy systems. Full article

With the increasing penetration of renewable energy, power systems require fast and reliable frequency regulation resources. Vehicle-to-grid (V2G) aggregation can provide fast response capability. However, it relies heavily on communication networks and is vulnerable to communication degradation and false data injection attacks (FDIAs). To address this challenge, this paper proposes a detection-free resilient control method for V2G-based frequency regulation. Rather than relying on explicit attack detection or compensation, the proposed method achieves decision-level adaptation from closed-loop system feedback through dynamic selection and switching of aggregator subsets. In this way, unreliable or compromised aggregators are implicitly avoided, improving system robustness under uncertain communication and cyber conditions. To further enhance robustness, a diffusion-based adversarial reinforcement learning framework is developed. A conditional diffusion model is used to generate diverse capacity scenarios with spatial heterogeneity. Adversarial training formulates the interaction between the attacker and the defender as a zero-sum game. This enables the learning of robust selection–switching policies under worst-case disturbances. Simulation results on the IEEE 39-bus system show that the proposed method improves frequency regulation performance under communication degradation and FDIA. The RMS frequency deviation is reduced from 0.13426 Hz to 0.09174 Hz compared with the no-defense case. Full article

This paper presents a comprehensive methodology for evaluating the flexibility potential of Electric Vehicle (EV) charging infrastructures from the perspective of a Charge Point Operator (CPO). The proposed framework is general and applicable to different types of charging infrastructures, provided that a set of operational assumptions is satisfied. These include unidirectional smart charging (V1G), AC charging sessions, preservation of user energy delivery when providing flexibility, and explicit modeling of rebound effects induced by temporal load shifting, requiring subsequent recovery of the shifted energy. The methodology is then applied to a real-world workplace charging facility to quantify the amount and temporal distribution of flexibility under different baseline charging strategies and levels of on-site photovoltaic integration. The analysis shows that a significant share of daily energy demand (i.e., between 20% and 36%) can be made available for flexibility services within the considered assumptions. Furthermore, the results highlight a strong operating cost trade-off between local optimization strategies and participation in system-level flexibility markets in the considered case study. Full article

To address the power quality deterioration caused by the large-scale integration of grid-following (GFL) electric vehicle charging stations, this paper proposes a comprehensive assessment method based on relative entropy distance fusion weighting and a dynamic ideal solution VIKOR algorithm. First, a multi-dimensional power quality evaluation system is constructed, focusing on key indicators such as voltage deviation, frequency deviation, three-phase imbalance, and harmonic distortion, to accommodate the operational characteristics of vehicle-to-grid (V2G) under grid-following and grid-forming (GFM) interaction scenarios. Building on this, the three-scale analytic hierarchy process (AHP) is employed to determine subjective weights, while the divergence-maximized entropy weight method is used to derive objective weights. The relative entropy distance model is then applied to achieve adaptive fusion of subjective and objective weights, resulting in an optimal combined weighting. Subsequently, a dynamic ideal solution mechanism is introduced into the VIKOR algorithm, where the range of the ideal solution is adjusted based on the indicator weights to enhance the discrimination of key indicators. By comprehensively calculating the group utility value, individual regret value, and compromise evaluation index, accurate ranking and performance assessment of different mitigation schemes are achieved. Using measured data from a vehicle-grid interaction demonstration base for analysis, the results demonstrate that the proposed method can effectively quantify the actual effects of various mitigation schemes, providing decision-making support for power grid safety and stability under high penetration of renewable energy and converter-interfaced generation. Full article

With the increasing penetration of renewable energy, power systems are facing stronger frequency fluctuations, which make fast and flexible frequency support increasingly important. Although vehicle-to-grid (V2G) technology provides a promising source of distributed regulation capacity, many existing studies do not explicitly consider EV owners’ participation, which may lead to a mismatch between theoretical regulation potential and practically available V2G support. To address this issue, this paper proposes a distributed Grid–Aggregator–EV frequency-regulation (FR) framework that incorporates EV participation factor into the control design. A three-layer architecture and a dynamic participation-aware model are established to describe the coordination of distributed V2G resources, and a Hamiltonian-based robust control law is developed under V2G power constraints. An integral reinforcement learning scheme is then adopted to realize the optimal regulation policy online, where the controller does not require explicit online knowledge of the system drift matrix, while preserving the physical control structure. In this way, the proposed method explicitly links the EV participation factor, dispatchable V2G regulation capacity, and coordinated FR, thereby improving robustness, adaptability, and practical relevance. Simulation studies on the IEEE 14-bus and IEEE 39-bus systems, together with an evening-period, time-varying participation case, demonstrate that the proposed method provides more effective frequency-deviation suppression, better overall regulation performance, and stable operation under dynamic EV participation. Full article

This paper comprehensively considers the interest demands of three core stakeholders in V2G scenarios: electric vehicle (EV) users, the power grid, and electric vehicle aggregators (EVAs). EV users prioritize charging waiting time and queuing probability to improve travel experience; the power grid focuses on charging facility utilization and power supply reliability to maximize operational benefits; and the EVA concerns its own load level and charging/discharging pricing strategies to optimize operating income. A tripartite multi-objective optimization model for grid–EV–EVA-coordinated charging and discharging is constructed, and an improved multi-objective cuckoo search algorithm is proposed to solve the model. The algorithm integrates an iterative search process (initialization, Lévy flight search, nest abandonment and update) and a cooperative game process (iteration, convergence conditions, equilibrium implementation). Guided by the dominant strength law, the algorithm’s Pareto-optimal solution set is ranked. Finally, a V2G collaborative optimization strategy that balances the interests of all stakeholders is obtained, which can effectively reduce EV users’ charging waiting time, improve the utilization rate of grid charging facilities, and guarantee the static voltage stability of the distribution network. Full article

The proliferation of vehicle-to-grid (V2G) charging stations in distribution networks introduces both voltage regulation challenges and untapped reactive power resources. This paper proposes a reactive–active power coordination control strategy for grid-forming (GFM) V2G charging stations to achieve voltage regulation in radial distribution networks. First, a voltage–reactive power sensitivity matrix is analytically derived from the linearized DistFlow equations, quantifying the voltage influence of each V2G station. The sensitivity matrix is computed from the network topology and line parameters, and its accuracy under varying operating conditions is validated against nonlinear power flow solutions. Second, a dynamic residual reactive capacity model exploits the inverter apparent power margin without curtailing active power, and a sensitivity-weighted proportional allocation distributes the reactive power references among stations. Third, a two-timescale hierarchical control architecture is designed: the upper layer solves a quadratic programming problem every 60 s to determine optimal set-points, while the lower layer employs GFM droop control with a 1 ms response to track references and provide inertia support. Simulation results on a modified IEEE 33-bus system demonstrate that the proposed method reduces the maximum voltage deviation by 62% compared with active-power-only control, while maintaining a frequency nadir of 49.73 Hz, confirming negligible frequency performance degradation. Extended simulations covering a 24 h period with stochastic EV arrival and departure patterns as well as varying load conditions further confirm the robustness of the proposed strategy. Full article

To further enhance the active participation of electric vehicles in grid interaction and reduce the decision-making costs for electric vehicle aggregators, this paper addresses the challenges in current EV charging and V2G (Vehicle-to-Grid) management. Considering the owners’ willingness to participate, an optimal charging and V2G model for EV charging stations based on a Deep Q-Network is established. The paper analyzes in detail the mutual influence between the level of EV owner participation and the strategies of EV aggregators. Based on the owners’ willingness and the physical constraints of the EVs, an evaluation metric for EV participation in charging scheduling is developed. The Deep Q-Network is employed to make decisions regarding EV participation, thereby enhancing the decision-making capability of the EV aggregator, reducing the instability of its scheduling plans, and improving the reliability of these plans. Simulation results demonstrate that this method can dynamically consider EV owners’ willingness to participate, adaptively optimize the scheduling margin ratio, make global decisions across multiple time periods, and formulate charging and V2G scheduling plans for the EV aggregator. Full article

Vehicle-to-Grid (V2G) is increasingly recognized as a promising source of flexibility for low-carbon energy systems, yet its deployment remains limited in practice. While previous research has largely focused on technical feasibility and market integration, less attention has been paid to V2G as a multi-actor service system. This study addresses that gap by applying a service design perspective to the co-development of a V2G service blueprint in the Swedish context. The research was conducted through an exploratory multi-stakeholder co-design process. The resulting blueprint maps customer actions, frontstage and backstage processes, stakeholder interactions, and communication flows across the V2G service lifecycle. The study identifies several service-level challenges related to onboarding, coordination, pre-qualification, contractual complexity, and user-facing value communication. The findings show how service blueprinting can support the structuring, analysis, and early-stage design of V2G services, while also highlighting the need for further validation in pilot implementation and across different regulatory contexts. Full article

To improve the operational efficiency of the park source-load-storage system and reduce operation costs and the wind-solar curtailment rate, this paper establishes a Park Integrated Energy System (PIES) model with multiple energy storage and vehicle-to-grid (V2G) components and proposes an adaptive comprehensive fitness multi-objective particle swarm optimization algorithm. First, each component of the PIES is modeled. Second, electric vehicle (EV) scheduling boundaries, determined by wind and PV output, as well as a dynamic charging-discharging incentive mechanism, are designed to enhance renewable energy accommodation. Finally, an adaptive comprehensive fitness index is defined, and convergence and particle-update strategies are improved to achieve better scheduling performance. Simulation results verify that the proposed PIES model achieves optimal performance in terms of carbon-emission cost, total operation cost, and wind-solar curtailment rate. Meanwhile, the improved algorithm also outperforms traditional multi-objective methods in PIES scheduling. Full article