Search Results (174)

With the increasing variety of electric vehicles (EVs) joining virtual power plants (VPPs), VPP operators increasingly require precise and tailored models for schedulable EV energy. Based on a publicly available anonymous EV charging power dataset, EV users are clustered through a weighted K-means++ algorithm. Secondly, based on the results of clustering, we analyzed the daily traveling patterns of various types of EVs, including commuting EVs, electric light-duty trucks (ELDTs) and electric tractors (ETs), and then customized the all-day schedulable energy domain model (SEDM) for each category. Subsequently, an optimal bidding strategy for a VPP consisting of diversified-member EVs, air conditionings (ACs), energy storage (ES) and distributed energy resources (DERs) is constructed. By modifying the levels of participation in supplementation and absorption of DERs among VPP members, while integrating considerations such as user comfort, EV defying rate, and seasonal variability, diverse VPP operational frameworks are established. Finally, using the Gurobi solver, the optimal bidding strategies and profit results under different scenarios are derived. The results indicate that (1) increasing the VPP members’ participation in the supplementation and absorption of DERs will bring higher benefits to both the VPP and its members; (2) with the increased sensitivity of users to room temperature and range anxiety, the demand response capacity of AC clusters decreases, reducing EV clusters’ market participation and VPP profits; and (3) among various types of EVs, ELDTs and ETs have a larger battery energy adjustment range, which can fully supplement the output shortfalls of DERs. Therefore, these EVs prove to be a good supplement for the improvement of VPP’s schedule capability and profitability. Full article

The growing demand for lithium-ion batteries, driven by the electrification of transportation and the expansion of renewable energy systems, is accelerating the deployment of large-scale manufacturing facilities worldwide. This rapid industrial growth increases the need to ensure high product quality and process reliability, given the complexity and strong interdependence of battery manufacturing stages. In this context, traceability becomes a key requirement for monitoring production processes, detecting deviations, and ensuring consistent performance. Digital twins, defined as virtual representations of physical systems integrating data, models, and simulation tools, are emerging as a promising approach to address these challenges. By enabling enhanced process visibility, predictive capabilities, and decision support, digital twins can contribute to improved control and optimization of battery manufacturing processes. This paper presents a review of current developments in digital twin applications for lithium-ion battery cell production and highlights the potential benefits that these tools can offer to the battery industry, particularly in supporting traceability, process optimization, and quality assurance in next-generation gigafactories. The outcomes of this review provide actionable insights for both academia and industry by identifying research gaps, technological limitations, and opportunities to advance the development and industrial adoption of digital twins in battery cell manufacturing. Full article

The increasing penetration of distributed renewable energy sources and electric vehicles (EVs) introduces significant operational challenges for residential energy management systems (HEMS), including stochastic renewable generation, uncertain load demand, device coupling, and physical safety constraints. This paper proposes a digital-twin-aided safe multi-agent reinforcement learning framework for coordinated energy management in renewable-integrated residential systems. The proposed approach models the battery energy storage system and the EV as independent agents and employs a multi-agent soft actor–critic (MASAC) algorithm with a centralised critic to capture the interactions among distributed energy resources. To improve decision quality under uncertainty, a digital twin module is developed to maintain a virtual representation of the residential energy system, synchronise operational states, update degradation-sensitive parameters, and generate short-term predictive information on photovoltaic (PV) generation and household load. The updated digital twin states and forecasts are incorporated into the observations of the reinforcement learning agents. In addition, a safety projection layer is incorporated to improve operational feasibility during both training and deployment. The environment considers realistic residential characteristics, including time-of-use electricity prices, battery degradation, EV mobility patterns, and grid energy trading. Simulation results show that the proposed framework reduces daily energy costs compared with rule-based baselines while maintaining EV charging reliability and operational feasibility. These results highlight the potential of combining predictive information, safety-constrained action execution, and multi-agent reinforcement learning for intelligent residential energy management. Full article

The development of electric bus superstructures requires an integrated engineering approach combining structural design, numerical simulation, experimental validation and durability assessment. This need is particularly important for electric buses, where heavy roof-mounted battery systems and auxiliary components influence structural load paths, fatigue durability and rollover crashworthiness. This paper presents a measurement-supported workflow for the development of a virtual electric bus superstructure model, including finite element analysis, multibody dynamics simulations, operational load assessment, fatigue-oriented evaluation and rollover crashworthiness analysis. The finite element model is used to assess static load cases, modal properties and structural response under selected design conditions. A multibody vehicle model with nonlinear suspension characteristics is applied to simulate representative operating scenarios and to support the definition of dynamic load cases. Operational measurement data from previous work are used as a basis for realistic load characterization. Experimental torsional stiffness and modal tests are used to validate the numerical model. The main contribution of the study is the integration of these numerical, experimental and operational-data-based activities into a consistent early-stage verification process. The proposed workflow supports early identification of critical structural regions, assessment of design modifications and reduction in prototype-based design iterations. Full article

Controller comparisons for grid-forming battery inverters are often confounded by simultaneous changes in the plant model, saturation law, measurement filtering, and disturbance envelope. This paper addresses that problem through a reproducible weak-grid benchmark and a switching-averaged EMT validation layer for a battery-backed PV microgrid. Droop, virtual synchronous machine (VSM), and power-synchronization control (PSC) are compared under identical plant data, load disturbance, grid-strength reduction, voltage sag, current limit, and metric-extraction rules. The benchmark reveals a consistent trade-off: VSM provides the best frequency moderation, droop provides the fastest post-fault restoration and the lowest implementation burden, and PSC provides the most balanced compromise across recovery, stability, EMT, and implementation metrics. The averaged EMT layer preserves the low-order restoration ordering and sharpens the waveform trade-off during the fault window. Additional analyses quantify the converter-angle excursions during the sag, clarify the reduced lag tolerance of VSM as the grid becomes weaker, and test the local robustness of the reported ranking against representative tuning perturbations. The resulting message is benchmark-specific but operationally useful: controller selection should follow the dominant project objective—frequency quality, restorative efficiency, or balanced performance—before controller-specific switching EMT, hardware-in-the-loop, and plant-level studies are launched. Full article

The commercialization of lithium metal batteries, a key technology for high-density energy storage, is hindered by issues with coulombic efficiency, which dictates battery stability and life. In this paper, we propose a machine learning framework to forecast liquid electrolyte efficiency, where two experimental data sources were combined to create a curated dataset of 283 records. In addition, to assess several ensemble learning algorithms, thirteen chemical descriptors were used, as well as interpretability analysis and Bayesian optimization to guarantee physicochemical consistency. We found that the optimized CatBoost model achieved a coefficient of determination (R2) of 0.61 on the test set and a mean squared error (MSE) of 0.0924, representing a significant improvement in predictive accuracy compared to previous standards. Furthermore, these results demonstrate that regulating oxygen levels in solvent environments is a key component of high-density energy storage. These results can serve as a virtual screening tool in order to discover high-performance electrolytes with the minimum experimental costs. Full article

This paper presents a probabilistic sensitivity analysis of a nonlinear electrochemical model for lithium-ion batteries. The model is treated as a reduced virtual replica for uncertainty-aware analysis rather than as a full digital twin. A reduced electrochemical formulation is combined with constrained inverse parameter identification using experimental current–voltage data to relate observable battery behavior to effective model parameters. Predictive variability is assessed through Monte Carlo uncertainty propagation and global sensitivity analysis under both charging and discharging conditions. The results indicate that the particle radius of the positive active material and the effective electrodes area are the dominant contributors to terminal-voltage uncertainty, whereas the electrode thickness parameter and negative electrode active material particle radius have a moderate influence within the studied ranges. Rank-based and variance-based sensitivity measures are more informative than linear indices for this reduced nonlinear system. From a mathematical perspective, the work integrates reduced-order modeling, inverse problem formulation, numerical simulation, and uncertainty quantification in one computational framework for battery analysis. The results support uncertainty-aware parameter prioritization, calibration of reduced electrochemical models, and provide a basis for future work on battery design, control, and digital-twin-oriented extensions under uncertainty. Full article

This study presents a forecast-driven Advanced Forecasting Model (AFM) and Virtual Power Plant (VPP) framework for a hybrid renewable energy system comprising utility-scale solar PV, wind generation, and a Battery Energy Storage System. Long Short-Term Memory neural networks provide real-time short-term forecasts to dynamically schedule power flows based on battery state-of-charge, grid import limits, and system constraints. Solar irradiance forecasting achieved MAE = 10.674 W/m², RMSE = 16.348 W/m², and MAPE = 14.18%, while wind speed forecasting achieved MAE = 0.880 m/s, RMSE = 1.115 m/s, and MAPE = 22.01%. Two dispatch scenarios were evaluated over a 72 h window: a reactive baseline and the proposed AFM/VPP strategy. The AFM reduced total grid imports by 57.48% (1466.34 MWh to 623.47 MWh), increased renewable utilization, and minimized curtailment. Financial analysis indicates an accelerated break-even (Year 6 vs. Year 9), a higher net present value, and cumulative 20-year profits exceeding R26.01 billion despite marginally higher capital expenditure. Emissions analysis shows annual CO₂ reductions from 123,680 t to 61,841 t, yielding 1.236 million tons of avoided emissions over 20 years. These results confirm that forecast-driven dispatch enhances operational efficiency, economic performance, and environmental sustainability, establishing a scalable approach for VPP operation in renewable-rich energy systems. Full article

Real-time energy management for large electric vehicle (EV) clusters requires both fast aggregate flexibility estimation and executable per-vehicle dispatch. Classical LP/MILP/MPC formulations provide strong feasibility and optimality guarantees when the model is fully specified, but their online solve time increases rapidly with cluster size; learning-based methods are fast but often rely on soft constraint penalties or external feasibility repair. We propose the Physics-Constrained Mamba-3 MIMO Aggregator (PC-M3), an amortized, constraint-aware sequence model that integrates a MIMO Mamba backbone, a history-dependent differentiable projection, a sparse routing layer, and an aggregation–disaggregation consistency loop, scaling AI-EMS from a single battery to ten-thousand-vehicle clusters in one forward pass. PC-M3 assigns every EV to one channel of a multi-input multi-output (MIMO) state-space recurrence and embeds the per-vehicle state-of-charge, power and energy constraints as a differentiable in-loop projection, jointly producing the cluster-level flexibility envelope and the per-vehicle charging trajectory. A sparse Routing-Mamba mixture-of-experts layer adaptively allocates capacity to behaviourally distinct sub-populations without supervised labels, and a consistency-trained aggregation–disaggregation loop binds the predicted envelope to the executed dispatch, forming a digital-twin-style predictive EMS pipeline that couples cluster dispatch with per-vehicle SoC evolution. On a single NVIDIA A100, PC-M3 sustains 0.34 s inference for 10,000 EVs over a 24-h horizon, about 18× faster than an Informer baseline and 2.4× faster than PowerMamba. Evaluated on the open ACN-Data and ElaadNL workplace and public charging corpora and on a 10,000-vehicle NREL dsgrid-TEMPO 2030 stress test, PC-M3 reduces the normalised envelope Hausdorff distance from 9.7% (PowerMamba) to 3.4%, cuts closed-loop cluster tracking RMSE from 1.45 MW (model predictive control) to 0.82 MW, and maintains zero observed feasibility violations with respect to the specified or imputed per-vehicle polytopes on every evaluated session. The framework provides a scalable, predictive, constraint-aware AI-EMS for V2G/G2V virtual-power-plant operation of large EV fleets. Full article

Distributed Electric Propulsion aircraft have gained significant attention for advancing green aviation. However, their application is constrained by the limited energy density of batteries, resulting in weight compensation and flight range limitation. Current research on DEP energy management predominantly focuses on thrust allocation during the cruise phase while largely neglecting the energy regeneration potential during the descent phase. Conventional all-motors active energy recovery strategies force the multi-motor array to operate within a low-efficiency region, since the required drag torque is small under low aerodynamic drag conditions. To solve this issue, this paper proposes an energy recovery strategy that dynamically adjusts the number of activated motors during the descent phase of aircraft. The proposed N-Active strategy can adaptively regulate the number of operating motors, shifting motor operating points from the low-efficiency region to the high-efficiency region, which effectively decouples energy regulation within the longitudinal symmetry plane and maximizes energy recovery benefits. In this study, a high-fidelity simulation platform is established, including nonlinear aerodynamic characteristics and propeller windmilling motor efficiency models. Moreover, the optimal performance of the N-Active multi-motor cooperative energy recovery optimization strategy is verified based on the constructed platform. Simulation results demonstrate that compared with the traditional all motors active strategy, the proposed method improves battery state of charge by 11.96% and reduces virtual weight of battery. This method can effectively alleviate the weight compensation effect of distributed electric propulsion aircraft without additional physical weight increment, thereby enhancing the loading capacity of aircraft. Full article

Battery swapping stations (BSSs) serve as critical nodes for electric vehicle energy supply and power load regulation, representing important regulatory resources in modern power systems and making their operational optimization essential for reducing carbon emissions and improving energy efficiency. To address the lack of carbon emission management and low battery utilization efficiency in existing BSS operations, this study proposes a collaborative optimization method that integrates virtual power plants (VPPs) and carbon trading mechanisms. The proposed approach dynamically adjusts charging and discharging schedules to achieve coordinated optimization of energy costs and carbon emissions. A comprehensive BSS operational model considering VPP participation and carbon trading is established, comparing the performance between conventional operation modes and collaborative mechanisms, followed by optimization analysis of four strategic approaches. The simulation results demonstrate that the proposed method effectively promotes collaborative optimization of BSS in both VPP and carbon trading markets. Through flexible strategy combinations, the approach significantly reduces overall carbon emissions while maximizing both the economic and environmental benefits of BSS operations, providing important support for the sustainable development of modern power systems. Full article

Rapid load growth and increasing operational uncertainty pose significant challenges to conventional distribution network planning. To address these challenges, this paper proposes a robust dynamic co-planning framework for distribution feeders and virtual distribution feeders (VDFs). Firstly, a dynamic co-planning model is developed to jointly optimize feeder reinforcement and battery energy storage system (BESS)-based non-wire alternatives over a multi-year horizon. Then, a robustness index (RI) is proposed to quantitatively evaluate the robustness of planning solutions under operational uncertainties without relying on assumed probability distributions. Four objectives are optimized simultaneously: (1) net present value cost (NPV), (2) cumulative overload quantity (COQ), (3) incremental capacity idle rate (ICIR), and (4) robustness index (RI). In addition, feeder emergency rating and battery degradation are incorporated to capture feeder-side thermal flexibility and storage aging characteristics in the planning process. Finally, the proposed method is tested on a modified IEEE 33-bus system. The results show that the proposed framework achieves a superior trade-off between cost, utilization, and robustness than conventional approaches, while reducing unnecessary BESS deployment and improving the physical consistency of the planning results. Full article

This study presents a techno-economic analysis of deploying distributed energy resources (DERs), specifically photovoltaic (PV), battery energy storage systems (BESSs) and electric vehicles (EVs), in apartment buildings configured as Virtual Power Plants (VPPs). Utilizing cooperative game theory, the research models strategic collaboration between apartment residents (demand side) and utility operators (plant side) to maximize energy efficiency and economic returns. The VPP structure is analyzed over a 15-year life cycle, incorporating net present value (NPV), payback period (PBP), and government subsidy impacts. A cooperative game framework is applied using the Shapley value to ensure fair profit allocation based on each party’s contribution. Results indicate improved self-sufficiency, peak load reduction, and mutual financial benefits. Scenario analyses show that government subsidies to the plant side significantly increase the likelihood of successful cooperation, while declining DER costs enhance the VPP’s economic viability. The findings demonstrate that apartments configured as VPPs achieve strong economic viability (39% ROI, 10.5-year payback) and operational performance (70% self-sufficiency, 40% peak reduction) when grid arbitrage is enabled and moderate government subsidies (35% PV, 45% BESS) are provided. This research provides a replicable model for urban energy planning and policy development, promoting sustainable energy transitions through shared DER infrastructure and cooperative stakeholder engagement. Full article

Weak-grid operation, with a low short-circuit ratio (SCR), degrades voltage and frequency regulation and impacts the power control performance of inverter-based resources, triggering oscillations. This paper proposes a community-scale battery energy storage system (BESS)-supported grid-forming control scheme, where the grid-forming inverter acts a virtual synchronous generator (VSG). A grid-connected BESS-powered VSG model with cascaded voltage-current dual-loop control is developed to assess the impacts of line impedance and P-Q coupling on weak-grid connection and stability. In addition to the conventional VSG, dq-axis decoupling, virtual impedance, and adaptive inertia-damping (J-D) are incorporated and evaluated through multi-scenario MATLAB/Simulink simulations. The results indicate that virtual impedance effectively suppresses coupled oscillations, and the coordinated J-D adaptation yields the most pronounced peak mitigation during edge disturbances (e.g., fault clearance and load shedding). In particular, under a 50% three-phase voltage sag, the coordinated strategy reduces the post-clearance peaks of $v_{pcc,rms}$ and $i_{pcc,rms}$ by approximately 79.9% and 93.5%, respectively, and decreases the intensity of frequency fluctuations by approximately 97.6%. Overall, the proposed community-scale BESS-VSG scheme enhances the dynamic stability of voltage and frequency under weak-grid conditions and provides a practical control framework for engineering-oriented weak-grid support studies. Full article

The accelerated low-carbon transition of power systems and the widespread integration of Electric Vehicles (EVs) present both severe operational challenges and substantial flexible regulation potential for Active Distribution Networks (ADNs). This paper provides an integrative review of the coordinated control and multi-market bidding mechanisms for EV-centric Technical Virtual Power Plants (TVPPs). Moving beyond descriptive surveys, this review systematically synthesizes the fragmented literature across three critical dimensions: (1) the physical-economic bidirectional mapping, which considers nonlinear power flow constraints and node voltage limits within the TVPP framework; (2) multi-market coupling mechanisms, evolving from unilateral energy bidding to coordinated participation in carbon trading and ancillary services; and (3) real-time control strategies, critically evaluating the trade-offs between optimization techniques (e.g., Model Predictive Control) and cutting-edge artificial intelligence approaches (e.g., Deep Reinforcement Learning) in mitigating battery degradation. Furthermore, a transparent review methodology is adopted to ensure literature rigor. By explicitly outlining the boundaries between TVPPs, Commercial VPPs (CVPPs), and EV aggregators, this paper identifies core unresolved trade-offs among aggregation fidelity, market complexity, and communication latency, providing evidence-backed pathways for future engineering demonstrations and V2G applications. Full article