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Large-scale electric-vehicle (EV) uptake is challenging the power grid due to a lack of sufficient hosting capacity. Most smart-charging studies still treat EVs as stationary loads in one location and ignore their mobility. This work closes that gap by evaluating an EV-based virtual electricity network (EVEN) that lets vehicles charge at one feeder and discharge at another. We formulate a comprehensive framework that combines inter-network energy-delivery optimization, stochastic time-series hosting capacity analysis, and battery-degradation assessment. The approach is tested on two real-world distribution systems: a voltage-constrained 50-bus rural residential network and a capacity-rich 76-bus industrial network. Simulation results reveal that shifting only 10% of the evening demand from the rural to industrial network cuts the rural undervoltage index by roughly 80% and weekly violation counts by 65%, while adding no more than two minor violations upstream. At this level, average yearly battery-cycling degradation rises modestly from 0.4% to 0.8%. The study thus demonstrates EVEN as a cost-effective, scalable alternative to physical reinforcement and provides the first integrated assessment linking network-level benefits with battery-health impacts. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

The integration of electric vehicles (EVs) into power systems offers a promising pathway to enhance energy resilience during grid outages. Most of the existing studies treat EV outage support as a short-term, technical problem, with limited attention to economics, business models (e.g., ownership and stakeholders), or long-term outages. To bridge the gap, this study presents a comprehensive framework to evaluate the techno-economic feasibility of using EV mobility as a decentralized electricity delivery solution during both short-term and long-term outages. A rule-based control strategy is developed to operate EVs under realistic operational constraints, with the primary objective of maximizing energy delivery and minimizing unmet demand during outages. The framework incorporates real-world data, including household and critical load profiles, EV battery capacities, ownership types, outage durations, travel distances, and seasonal variations. Scenario-based simulations are performed to compare various technical and economical key performance indicators. Results show that EVs with personal ownership type and large capacity (100 kWh) can effectively meet residential energy needs during short-term outages. In long-term outages, expanding the EV fleet effectively reduce unmet demand, and higher solar generation in summer and spring significantly improves energy availability compared to winter, where unmet load is more than twice as high under identical conditions. The findings offer practical insights for stakeholders and policymakers in developing EV-based resilience strategies for future energy systems. Furthermore, the study highlights the importance of designing appropriate business models to support the integration of mobile energy services into resilience planning. © 2025 The Authors.

The rapid growth of electric vehicles (EVs) is placing new demands on residential distribution networks. Smart charging and Vehicle-to-Grid (V2G) technologies offer potential solutions for mitigating the large peak load and enhancing the grid hosting capacity (HC), yet their effectiveness across varying EV penetration levels remains underexplored. Therefore, this study evaluates how EV penetration levels affect the effectiveness of these two technologies. Furthermore, to help improve the grid performances, this study also pioneeringly proposes two hypothetical pricing settings—diverse electricity buying prices and reduced electricity selling prices—and evaluates their performances by comparing with existing price settings. Using real-world network and EV charging data from Sweden, we assess peak load and HC under different scenarios of power flow direction, charging controls, and electricity prices strategies across eight EV penetration levels. Results reveal that coordinated charging, especially with V2G, more effectively reduces peak loads compared to individual controls. However, V2G, if not regulated well, can increase peak loads at high EV penetration levels. Diversified electricity buying prices help lower aggregated peak loads but are less effective in enhancing local HC due to peak load shifting rather than peak load reduction. Additionally, high electricity selling prices benefit at low EV penetration but become less effective as penetration grows. The findings suggest that electricity pricing strategies and charging controls should adapt dynamically to the level of EV penetration. These insights provide critical guidance to policymakers, distribution system operators, and aggregators in designing adaptive pricing and control strategies to integrate EVs without overburdening the grid.

Accurate electric vehicle (EV) load forecasting is crucial for efficient grid operations and demand-side management, yet it is challenging in data-scarce scenarios. Transfer learning (TL) offers a solution by transferring knowledge from data-rich to data-limited scenarios. However, when the knowledge domain exhibits highly diverse behaviors, applying TL alone could introduce large biases, reducing accuracy and limiting its effectiveness. To address this problem, this study proposes a hybrid deep learning-based framework that integrates TL and K-means clustering. The proposed approach consists of two phases. In the source domain phase, a deep-learning-based model is trained using the full dataset and then fine-tuned using clustered user behaviors. In the target domain phase with limited data, TL is applied to transfer knowledge from the source-domain finetuned cluster models. For validation, the developed prediction method has been tested using real-world datasets and compared with two other cases: one with applying TL from the source-domain base model trained from full dataset, and one without applying TL. Results show the hybrid method improves forecasting accuracy, reducing the normalized root mean squared error by 3.99 % and 8.22 %, respectively. This study establishes a structured approach for targeted knowledge transfer, enhancing prediction accuracy in data-scarce settings. The framework is scalable and adaptable to other energy forecasting applications, supporting sustainable and resilient energy management.

This study proposes an integer linear program model for ride-sharing, electric, autonomous mobility on demand (RE-AMoD) system operations and develops a model predictive control (MPC) algorithm to optimize the decisions of ride matching, vehicle routing, rebalancing, and charging. The system ensures that electric autonomous vehicles provide transportation services for up to two customers to share a ride and that they can be charged automatically during the operating period. The RE-AMoD problem is formulated as a network flow optimization problem considering ride-sharing and charging control. The objective is to minimize the customers' waiting time while minimizing the system's energy consumption. An iterative MPC is developed to compute the optimal control policy for real-time control. The case study uses real-world data from San Francisco to validate the model performance by comparing benchmark models in an RE-AMoD simulation platform and investigating the impact of ride-sharing and smart charging strategies on system performance by comparing models with no ride-sharing and heuristic charging strategies. The results show that the smart charging policy is critical for realizing ride-sharing's full advantages in RE-AMoD systems. Allowing the sharing of trips significantly improves system performance in terms of reducing fleet sizes and energy consumption while improving the customer level of service.

With substantial heat dissipation capacity and high energy efficiency, pool boiling represents a promising thermal management solution for high-power-density computing technologies. To address the increasing demand for improved heat dissipation, pool boiling heat transfer must be enhanced to attain a lower initial boiling temperature, increased heat transfer coefficient, and improved critical heat flux. Modification of surface structures is effective to achieve these enhancements, and recent studies have focused on multiscale hybrid surface structure modifications for synergistic effects. Compared with single-scale surface structure modifications, multiscale hybrid strategies are more complex in terms of enhancement mechanisms, influencing factors, and numerical modeling. However, timely reviews that explore and summarize these achievements are still lacking. To bridge this gap, this study presents a state-of-the-art review on multiscale hybrid surface structure modifications aimed at enhancing pool boiling heat transfers. First, This research introduces three typical scaled surface structure modifications, including macroscale, microscale, and nanoscale strategies. Subsequently, their hybrid use, enhancement mechanisms, and major influencing factors are systematically explored, reviewed, and summarized. Specifically, this research focus on macro/micro hybrid structures, micro/micro hybrid structures, micro/nano hybrid structures, and nano-amphiphilic structures. For each hybrid structure, different formats and combinations are presented and analyzed. Furthermore, the associated numerical modeling techniques are summarized and comparatively analyzed. Lastly, the major findings are outlined, and recommendations for future studies are highlighted. This review can serve as a timely contribution to advancing our understanding of multiscale hybrid surface structure modifications for enhanced pool boiling and provide guidance for advanced surface structure modification techniques.

District heating (DH) is a key component of Sweden’s heating infrastructure, more than half of which is currently reliant on biofuels. However, recent energy scenarios from the Swedish energy agency (March 2025) project up to an 80 % reduction in biomass use by 2035, creating an urgent need for alternative heat sources for district heating. Solar thermal collectors and heat pumps combined with short- and long-term storage represent a promising solution, yet their optimal integration and performance remain largely underexplored in the Swedish DH context. This study presents a techno-economic analysis of integrating solar thermal collectors, pit thermal energy storage, and a heat pump in the Härnösand DH network. A multi-stage simulation approach combining simplified (in Python) and detailed (in TRNSYS) models was used to optimize system design. Geological assessment and hydraulic constraints identified a DN400 feed-in pipe as optimal. Results show that a 35 % solar fraction minimizes the levelized cost of heat (LCOH), with an optimized system comprising 102 000 m² of collectors, 325 000 m³ PTES, and a 5 MW HP. The LCOH of the evaluated system is 70 €/MWh at 5% discount rate for 20 years. The study highlights the importance of accounting for practical constraints including feed-in pipe size, geological conditions, PTES location, and integration strategy for achieving cost-effective solar district heating integration in Swedish networks.

A significant challenge is to determine the specific services Battery Energy Storage System (BESS) should provide to maximize profits. This study investigates the most profitable markets and sizes of BESS with utility-scale solar Photovoltaics (PV) power plants using techno-economic analysis frameworks. The objective is to maximize profitability in energy and frequency markets, focusing on primary regulation and day-ahead markets for Sweden and Germany. The inputs are historical market prices and frequency data, as well as real measurement PV power data. The results show that adding a BESS to an existing PV park does not result in a lower payback period than if implementing a stand-alone BESS. However, the payback period differs between Sweden and Germany during 2023, i.e., being 1.8 and 6.8 years, respectively. This is explained by the lower frequency market prices for Germany compared to Sweden. The technical results indicate that the BESS energy capacity after 10 years of operation is approximately 83% for Germany, whereas, for Sweden, it is around 87%. Also, combining the operating of BESS on primary regulation and day-ahead markets showed a 6-year payback period with a slight increase in loss of energy capacity (from 83 to 80%) for Germany. Moreover, combining various PV-BESS sizes showed a discrepancy in economic and technical metrics for the BESS in Germany, resulting in a best-case of a 6-year payback period. A sensitivity analysis, which examines a drop in the frequency control prices in the future relative to 2023 (by 20% and 50% for Germany and Sweden, respectively), reveals an increase in the payback period for both countries by approximately 1 year.

The growing frequency of power grid disruptions demands innovative solutions to enhance supply resilience. Electric vehicle (EV) fleets, as mobile energy storage units, offer a sustainable response to prolonged outages by forming an EV-based virtual electricity network (EVEN), which transfers electricity from functioning to affected areas. Unlike existing studies focusing on individual EVs as backup, this paper addresses this gap by developing a generic model for the EVEN solution and a method for coordinating electricity delivery via EVs. The model features a central emergency hub which functions during main grid failures and EV-equipped households. Using a real-world system in Sweden, the EVEN model is evaluated for resilience metrics, including days without energy deficits, electricity delivery, and battery degradation. Additionally, seven key parameters-energy supply, demand, EV configuration, and renewable energy systems-are analyzed to identify optimal conditions. Results indicate that the EVEN solution significantly enhances power supply resilience, particularly for small energy users, with small battery degradation, and is most effective when households are near the central hub. This study advances understanding of EVs in strengthening grid resilience, offering a resource-efficient, scalable and sustainable solution for future energy security.