Search Results (475)

The integration of photovoltaic (PV) generation with bidirectional electric vehicle (EV) fast-charging systems offers a promising pathway toward sustainable transportation and grid support. However, the dynamic coupling between maximum power point tracking (MPPT) perturbations and grid-side power quality presents a fundamental challenge in such multi-converter architectures. This paper addresses this challenge through a coordinated design and optimization framework for a grid-connected, PV-assisted bidirectional off-board EV fast charger. The system integrates a 184.695 kW PV array via a DC-DC boost converter, a common DC link, a three-phase bidirectional active front-end rectifier with an LCL filter, and a four-phase interleaved bidirectional DC-DC converter for the EV battery interface. A comparative evaluation of three MPPT algorithms establishes the Fuzzy Logic Variable Step-Size Perturb & Observe (Fuzzy VSS-P&O) as the optimal strategy, achieving 99.7% tracking efficiency with 46$\mathrm{s}$ settling time. However, initial integration of this high-performance MPPT reveals system-level harmonic distortion, with grid current total harmonic distortion (THD) reaching 4.02% during charging. To resolve this coupling, an Artificial Bee Colony (ABC) metaheuristic algorithm performs coordinated optimization of all critical PI controller gains. The optimized system reduces grid current THD to 1.40% during charging, improves DC-link transient response by 43%, and enhances Phase-Locked Loop (PLL) synchronization accuracy. Comprehensive validation confirms robust bidirectional operation with seamless mode transitions and compliant power quality. The results demonstrate that system-wide intelligent optimization is essential for reconciling advanced energy harvesting with stringent grid requirements in next-generation EV fast-charging infrastructure. Full article

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 transportation sector’s decarbonization represents one of the most critical challenges in achieving global climate targets. This study presents a comprehensive analysis of an integrated renewable energy system that produces green hydrogen through a hybrid solar photovoltaic (PV) and wind power configuration. The proposed system combines a 1.2 MWp solar array with 800 kW wind turbines, feeding a 1 MW proton exchange membrane (PEM) electrolyzer for hydrogen production. The hydrogen is subsequently compressed, stored at 350 (for trucks and buses) and 700 bar (for cars), and then utilized either directly for fuel cell electric vehicles (FCEVs) or reconverted to electricity via a 250 kW stationary PEM fuel cell to support electric vehicle (EV) charging infrastructure. Through detailed techno-economic simulation using HOMER Pro and MATLAB/Simulink 2022a, we demonstrate that the hybrid configuration achieves a 71% electrolyzer capacity factor, producing 55.8 tonnes of hydrogen annually with a levelized cost of 5.82 €/kg. The system ensures over 60 h of grid-independent operation while reducing CO₂ emissions by 1656 tones annually compared to conventional grid-powered alternatives. Results indicate that hybrid renewable hydrogen systems can provide economically viable solutions for sustainable mobility infrastructure, with projected cost reductions making them competitive with fossil fuel alternatives by 2030. Full article

Virtual power plants (VPPs) aggregate flexible resources, such as distributed photovoltaics (PV), energy storage, and flexible loads, to provide substantial reserve capacity for grid operation. However, the combined effects of renewable energy output uncertainty, load forecast errors, and user-bounded rationality responses lead to significant errors in traditional deterministic VPP reserve assessment methods, severely affecting the balance between system supply and demand. To address this challenge, this paper proposes a credible reserve assessment method that accounts for user-bounded rationality. First, thermodynamic models with on–off constraints for air conditioning loads, energy feasible region, and power constraint models for electric vehicles (EVs) and energy storage systems (ESSs), as well as PV forecast error models are established to characterize physical reserve boundaries. Second, prospect theory is introduced to describe user-bounded rationality and a logit-based response probability model is developed. Monte Carlo sampling and kernel density estimation are employed to derive credible reserve sets under different confidence levels, achieving a probabilistic quantification of VPP reserve capacity distribution. Case studies demonstrate that the proposed method accurately characterizes the probabilistic distribution characteristics of VPP reserve provision under multiple uncertainties, providing comprehensive and reliable assessment information for power dispatching agencies. Full article

With the widespread integration of smart buildings equipped with distributed photovoltaics (PV) and electric vehicles (EVs), distribution networks face significant challenges arising from source-load fluctuations. Conventional centralized dispatch approaches are constrained by communication bottlenecks and data privacy requirements. These limitations make it difficult to achieve global coordination while preserving the autonomy of individual entities. This paper proposes a hierarchical coordination framework for the coordinated operation of distribution networks and smart buildings. The distribution management system (DMS) and building energy management systems (BEMSs) perform independent optimization within their respective domains. Only aggregated boundary power information is exchanged to protect data privacy, enabling cross-entity coordination under information boundary constraints. Building-side models incorporating thermal dynamics, EV charging and discharging, and PV generation are developed, along with a distribution network power flow model. To solve the coordinated optimization problem, an Anderson-accelerated alternating direction method of multipliers (AA-ADMM) is introduced. A safeguarding mechanism based on combined residuals is incorporated to enhance convergence efficiency and stability. Case studies on the IEEE 33-bus test system demonstrate that compared with the uncoordinated baseline, the proposed method reduces network loss by 12.1% and lowers PV curtailment from 9.20% to 0.52%, while improving voltage profiles without significantly compromising occupant comfort or EV travel requirements. In addition, AA-ADMM achieves convergence with up to 66% fewer iterations than standard ADMM. 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

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

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

The fast growth of electric vehicles (EVs) and renewable energy motivates reliable charging infrastructure with balanced energy management and good power quality. However, conventional converter controllers like proportional and integral (PI) and fuzzy logic controllers (FLCs) exhibit slow dynamic response, poor adaptability to varying solar conditions, unbalanced energy management, low power quality, and higher total harmonic distortion (THD). To overcome these limitations, this work proposes an adaptive neuro-fuzzy inference system (ANFIS) controller for balanced energy management and improved power quality in EV charging stations. The ANFIS controller is a combination of a fuzzy inference system (FIS) and a neural network (NN). The FIS provides the best maximum power point tracking and robust control during changing solar PV conditions. The NN optimally controls the flow of power between the solar PV system, energy storage battery (ESB), EV, and utility grid. The entire system is simulated in MATLAB/Simulink. It consists of a PV system with a capacity of 2 kW, an ESB with a capacity of 10 kWh and an EV battery with a capacity of 4 kWh, which are linked by bidirectional DC/DC converters. A 30 kVA bidirectional inverter, along with an LCL filter, is connected between the 500 V DC bus and 440 V utility grid, allowing for both directions. The results validate the effectiveness of the proposed ANFIS controller in terms of DC bus voltage stability, faster dynamic response, enhanced renewable energy utilization, improved efficiency to 98.86%, reduced voltage and current THD to 4.65% and 2.15% respectively, reduced utility grid stress, and enhanced energy management compared to conventional PI and FLCs. Full article

This study examines the potential contribution of Solar Electric Vehicles (SEVs) and Vehicle-Integrated Photovoltaics (VIPV) to disaster-related energy resilience through a probabilistic modeling framework. While previous research has highlighted the technical feasibility of EV-based support for microgrids and emergency facilities, it has paid limited attention to the behavioral uncertainty surrounding voluntary energy sharing by EV owners. To address this gap, we develop a Monte Carlo simulation model that integrates technical constraints, solar-generation variability, and heterogeneous participation probabilities to evaluate whether SEVs can sustain essential loads during prolonged outages. The analysis focuses on a worst-case scenario in which external lifelines are disrupted for seven days. Results indicate that approximately 450–1000 SEVs within a 5 km radius are required to maintain a continuous power supply, with BEVs requiring roughly twice as many units due to the absence of onboard PV generation. The findings highlight the sensitivity of resilience outcomes to user behavior and spatial vehicle distribution, underscoring the need for incentive mechanisms to encourage participation. Key limitations include simplified behavioral assumptions, region-specific irradiance conditions, and the exclusion of mobility constraints. Overall, the study provides a quantitative foundation for integrating SEVs into resilience planning while emphasizing the importance of social dynamics in determining real-world feasibility. 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

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

To enhance access to efficient and low-carbon public transportation, the city of Alexandria, Egypt, has introduced a fleet of electric buses. Additionally, an ongoing project aims to upgrade and electrify the existing urban railway system, which is expected to alleviate traffic congestion in this densely populated city. The implementation of electric vehicle (EV) parking facilities is also under consideration. This paper investigates the integration of photovoltaic (PV) systems and green hydrogen-powered gas turbines as components of the integrated energy system (IES). An optimal energy management strategy is proposed to maximize the benefits of incorporating renewable energy sources into the urban transportation system (UTS). The proposed energy management algorithm incorporates demand-side management (DSM) for UTS loads and EVs, increasing the complexity of the decision-making process due to the high uncertainty of decision variables. To address this challenge, a modified multi-agent reinforcement learning (MRL) approach is employed, in which uncertainty is incorporated through stochastic environment sampling. Simulation results demonstrate the economic potential of integrating renewable and sustainable energy resources into the IES of the electrified urban transportation system, achieving a 40.2% reduction in the average daily energy consumption cost. Full article

The large-scale adoption of electric vehicles (EVs) improves transport sustainability but creates severe peak-time stress on distribution grids. In PV-assisted charging networks, station operators must jointly decide retail charging prices and energy-storage dispatch under uncertain demand and generation conditions. This paper develops a distributed federated deep reinforcement learning framework for multi-station scheduling, where each station trains a local soft actor–critic (SAC) policy and only model parameters are exchanged with a global aggregator. To better adapt prices to local supply–demand conditions, we introduce a sales-factor-based correction mechanism that links the announced price to demand pressure and storage status. The objective combines station revenue, operating expenses, and user-discomfort-related penalties under operational constraints. Simulation results on a five-station setting show stable convergence and consistent gains over benchmark methods, with profit improvements of 3.90–39.00%. The framework keeps raw operational data local and supports collaborative optimization across stations. Full article

As the oil price has skyrocketed, the attraction towards electric vehicles has gone up. This scenario has also increased the demand for charging infrastructure. This paper proposes a novel charging infrastructure for electric vehicles which is energized by a solar photovoltaic unit, integrated with a distribution static compensator. The output of the photovoltaic array is regulated by a DC–DC converter, which uses maximum power point tracking to support optimal solar energy conversion. The compensator is integrated into the grid through a zigzag-star transformer, which helps with neutral current compensation, promoting balanced and distortion-free operation. The control algorithm is designed to ensure superior power quality during grid synchronization and sustainable energy management. This novel architecture ensures bidirectional power flow, enabling the charge–discharge dynamics of the electric vehicles, which can be termed Grid-to-Vehicle and Vehicle-to-Grid modes. Better grid flexibility and resilience are ensured by this dynamic power exchange. The control strategy based on the Linear Kalman Filter provides reactive power balance and maintains steady voltage at the point of common coupling, and it ensures enhanced power quality during power flow, resulting in efficient and reliable grid operations. The effectiveness of the control algorithm is tested and validated under Grid-to-Vehicle, Vehicle-to-Grid, nonlinear, unbalanced, and isolated solar conditions. Analytical tuning of the gains in the controller, by using the conventional methods, is not efficient under dynamic conditions and nonlinear loads. An optimization technique is used to estimate the proportional–integral control gains, which avoids the difficulty of tuning the controllers. Simulation of the system is carried out using MATLAB 2022b/SIMULINK. Simulation results under diverse operating scenarios confirm the system’s capability to sustain superior power quality, maintain grid stability, and support a robust and reliable charging infrastructure. By enabling regulated bidirectional energy exchange and autonomous operation during grid disturbances, the charger operates as a resilient grid-support asset rather than as a passive electrical load. Full article