Energies

15 pages, 1944 KB

Open AccessArticle

Using Digitalization to Reduce Laboratory Testing Time for Lithium-Ion Cells

by Piotr Duda, Mariusz Konieczny and Piotr Bielaczyc

Energies 2026, 19(2), 312; https://doi.org/10.3390/en19020312 - 7 Jan 2026

The development of lithium-ion batteries for electric vehicles and other applications requires numerous complex and time-consuming research efforts. Numerical modeling can significantly reduce both the scope and duration of laboratory testing by enabling rapid prediction of cell behavior under various operating conditions. In [...] Read more.

The development of lithium-ion batteries for electric vehicles and other applications requires numerous complex and time-consuming research efforts. Numerical modeling can significantly reduce both the scope and duration of laboratory testing by enabling rapid prediction of cell behavior under various operating conditions. In this study, it is demonstrated that the parameters of the Newman–Tiedemann–Gu–Kim (NTGK) battery model can be determined using only extreme discharge current values, omitting intermediate currents. This approach increases the average voltage error by 0.23% but reduces the average temperature error by 0.22%. Additionally, the use of limited experimental data leads to extrapolation errors at an 8 A discharge current from 1.20% to 0.65% for voltage and from 7.04% to 5.78% for temperature. Furthermore, the proposed model enables accurate prediction of the state of charge (SoC) and battery temperature evolution without additional measurements under realistic driving conditions, such as the Worldwide Harmonized Light-Duty Vehicle Test Cycle (WLTC). Full article

27 pages, 10842 KB

Open AccessArticle

Deep Multi-Task Forecasting of Net-Load and EV Charging with a Residual-Normalised GRU in IoT-Enabled Microgrids

by Muhammed Cavus, Jing Jiang and Adib Allahham

Energies 2026, 19(2), 311; https://doi.org/10.3390/en19020311 - 7 Jan 2026

Abstract

The increasing penetration of electric vehicles (EVs) and rooftop photovoltaics (PV) is intensifying the variability and uncertainty of residential net demand, thereby challenging real-time operation in smart grids and microgrids. The purpose of this study is to develop and evaluate an accurate and [...] Read more.

The increasing penetration of electric vehicles (EVs) and rooftop photovoltaics (PV) is intensifying the variability and uncertainty of residential net demand, thereby challenging real-time operation in smart grids and microgrids. The purpose of this study is to develop and evaluate an accurate and operationally relevant short-term forecasting framework that jointly models household net demand and EV charging behaviour. To this end, a Residual-Normalised Multi-Task GRU (RN-MTGRU) architecture is proposed, enabling the simultaneous learning of shared temporal patterns across interdependent energy streams while maintaining robustness under highly non-stationary conditions. Using one-minute resolution measurements of household demand, PV generation, EV charging activity, and weather variables, the proposed model consistently outperforms benchmark forecasting approaches across 1–30 min horizons, with the largest performance gains observed during periods of rapid load variation. Beyond predictive accuracy, the relevance of the proposed approach is demonstrated through a demand response case study, where forecast-informed control leads to substantial reductions in daily peak demand on critical days and a measurable annual increase in PV self-consumption. These results highlight the practical significance of the RN-MTGRU as a scalable forecasting solution that enhances local flexibility, supports renewable integration, and strengthens real-time decision-making in residential smart grid environments. Full article

(This article belongs to the Special Issue Developments in IoT and Smart Power Grids)

17 pages, 1113 KB

Open AccessArticle

Comparative Analysis of Electric Light Commercial Vehicles (ELCV) from Different Manufacturers in Terms of Range, Payload and Charging Time on the Polish Market

by Paweł Marzec and Wioletta Cebulska

Energies 2026, 19(2), 310; https://doi.org/10.3390/en19020310 - 7 Jan 2026

Abstract

The dynamic development of electromobility and tightening emissions regulations are making electric light commercial vehicles an increasingly important element of modern urban transport. The purpose of this article is to analyze and compare selected models of electric light commercial vehicles available on the [...] Read more.

The dynamic development of electromobility and tightening emissions regulations are making electric light commercial vehicles an increasingly important element of modern urban transport. The purpose of this article is to analyze and compare selected models of electric light commercial vehicles available on the market in terms of four key operational parameters: range, charging time, payload, and energy consumption. These parameters directly impact the efficiency of vehicle operation in real-world conditions, especially in last-mile transport. The study employed a multi-criteria decision method (MCDM), which evaluated 10 alternatives and objectively assigned criterion weights using the CRITIC method, which takes into account data variability and correlations between criteria. The article presents the interdependencies between these factors, emphasizing the need to find a compromise between maximum range and usable payload, as well as the impact of charging time on vehicle operational availability. The analysis aims to identify design and technological solutions that contribute most to improving the efficiency of electric light commercial vehicles in urban and suburban applications. Full article

(This article belongs to the Special Issue Electric and Hydrogen Vehicles in Urban Transport Systems: The Current State, Plans, and Technical Requirements)

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23 pages, 673 KB

Open AccessArticle

Advanced Energy Collection and Storage Systems: Socio-Economic Benefits and Environmental Effects in the Context of Energy System Transformation

by Alina Yakymchuk, Bogusława Baran-Zgłobicka and Russell Matia Woruba

Energies 2026, 19(2), 309; https://doi.org/10.3390/en19020309 - 7 Jan 2026

Abstract

The rapid advancement of energy collection and storage systems (ECSSs) is fundamentally reshaping global energy markets and accelerating the transition toward low-carbon energy systems. This study provides a comprehensive assessment of the economic benefits and systemic effects of advanced ECSS technologies, including photovoltaic-thermal [...] Read more.

The rapid advancement of energy collection and storage systems (ECSSs) is fundamentally reshaping global energy markets and accelerating the transition toward low-carbon energy systems. This study provides a comprehensive assessment of the economic benefits and systemic effects of advanced ECSS technologies, including photovoltaic-thermal (PV/T) hybrid systems, advanced batteries, hydrogen-based storage, and thermal energy storage (TES). Through a mixed-methods approach combining techno-economic analysis, macroeconomic modeling, and policy review, we evaluate the cost trajectories, performance indicators, and deployment impacts of these technologies across major economies. The paper also introduces a novel economic-mathematical model to quantify the long-term macroeconomic benefits of large-scale ECSS deployment, including GDP growth, job creation, and import substitution effects. Our results indicate significant cost reductions for ECSS by 2050, with battery storage costs projected to fall below USD 50 per kilowatt-hour (kWh) and green hydrogen production reaching as low as USD 1.2 per kilogram. Large-scale ECSS deployment was found to reduce electricity costs by up to 12%, lower fossil fuel imports by up to 25%, and generate substantial GDP growth and job creation, particularly in regions with supportive policy frameworks. Comparative cross-country analysis highlighted regional differences in economic effects, with the European Union, China, and the United States demonstrating the highest economic gains from ECSS adoption. The study also identified key challenges, including high capital costs, material supply risks, and regulatory barriers, emphasizing the need for integrated policies to accelerate ECSS deployment. These findings provide valuable insights for policymakers, industry stakeholders, and researchers aiming to design effective strategies for enhancing energy security, economic resilience, and environmental sustainability through advanced energy storage technologies. Full article

(This article belongs to the Special Issue Energy Economics and Management, Energy Efficiency, Renewable Energy)

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32 pages, 2653 KB

Open AccessArticle

Influence of Blending Model n-Butanol Alcoholysis Derived Advanced Biofuel Blends with Diesel on the Regulated Emissions from a Diesel Hybrid Vehicle

by Scott Wiseman, Karl Ropkins, Hu Li and Alison S. Tomlin

Energies 2026, 19(2), 308; https://doi.org/10.3390/en19020308 - 7 Jan 2026

Abstract

Decarbonisation of the transport sector, whilst reducing pollutant emissions, will likely involve the utilisation of multiple strategies, including hybridisation and the use of alternative fuels such as advanced biofuels as mandated by the EU. Alcoholysis of lignocellulosic feedstocks, using n-butanol as the [...] Read more.

Decarbonisation of the transport sector, whilst reducing pollutant emissions, will likely involve the utilisation of multiple strategies, including hybridisation and the use of alternative fuels such as advanced biofuels as mandated by the EU. Alcoholysis of lignocellulosic feedstocks, using n-butanol as the solvent, can produce such potential advanced biofuel blends. Butyl blends, consisting of n-butyl levulinate (nBL), di-n-butyl ether, and n-butanol, were selected for this study. Three butyl blends with diesel, two at 10 vol% biofuel and one at 25 vol% biofuel, were tested in a Euro 6b-compliant diesel hybrid vehicle to determine the influence of the blends on regulated emissions and fuel economy. Real Driving Emissions (RDE) were measured for three cold start tests with each fuel using a Portable Emissions Measurement System (PEMS) for carbon monoxide (CO), particle number (PN), and nitrogen oxides (NO_X = NO + NO₂). When using the butyl blends, there was no noticeable change in vehicle drivability and only a small fuel economy penalty of up to 5% with the biofuel blends relative to diesel. CO, NO_X, and PN emissions were below or within one standard deviation of the Euro 6 not-to-exceed limits for all fuels tested. The CO and PN emissions reduced relative to diesel by up to 72% and 57%, respectively. NO_X emissions increased relative to diesel by up to 25% and increased with both biofuel fraction and the amount of nBL in that fraction. The CO emitted during the cold start period was reduced by up to 52% for the 10 vol% blends but increased by 25% when using the 25 vol% blend. NO_X and PN cold start emissions reduced relative to diesel for all three biofuel blends by up to 29% and 88%, respectively. It is envisaged that the butyl blends could reduce net carbon emissions without compromising or even improving air pollutant emissions, although optimisation of the after-treatment systems may be necessary to ensure emissions limits are met. Full article

(This article belongs to the Special Issue Performance and Emissions of Vehicles and Internal Combustion Engines)

17 pages, 1039 KB

Open AccessArticle

An Adaptive Multi-Layer Heuristic Framework for Real-Time Energy Optimization in Smart Grids

by Atef Gharbi, Mohamed Ayari, Nasser Albalawi, Ahmad Alshammari, Nadhir Ben Halima and Zeineb Klai

Energies 2026, 19(2), 307; https://doi.org/10.3390/en19020307 - 7 Jan 2026

Abstract

Smart grids face significant challenges in coordinating demand-side management (DSM), dynamic pricing, data aggregation, and network feasibility in real time. To address this, we propose H-EMOS-Lite, an adaptive, multi-layer heuristic framework that integrates these components into a unified, real-time optimization loop. Evaluated on [...] Read more.

Smart grids face significant challenges in coordinating demand-side management (DSM), dynamic pricing, data aggregation, and network feasibility in real time. To address this, we propose H-EMOS-Lite, an adaptive, multi-layer heuristic framework that integrates these components into a unified, real-time optimization loop. Evaluated on fully reproducible generated demand, price, and grid datasets based on realistic residential energy systems, H-EMOS-Lite achieves a 2.1% reduction in peak load and completes a full 24 h (96-interval) optimization for 100 households in under 0.25 s, demonstrating its suitability for near-real-time residential energy systems. The framework outperforms three baselines—Independent DSM, Sequential Optimization, and Particle Swarm Optimization (PSO)—by effectively balancing energy cost, peak load reduction, and temporal smoothness of the aggregate load profile, while avoiding abrupt, unsynchronized load shifts that induce secondary peaks—common in uncoordinated approaches. By embedding physical feasibility and cross-layer feedback directly into the optimization loop, H-EMOS-Lite enables scalable, interpretable, and deployable coordination for smart distribution systems. Full article

(This article belongs to the Special Issue Smart Grid and Energy Storage)

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28 pages, 6116 KB

Open AccessArticle

A Hybrid Energy Storage System and the Contribution to Energy Production Costs and Affordable Backup in the Event of a Supply Interruption—Technical and Financial Analysis

by Carlos Felgueiras, Alexandre Magalhães, Celso Xavier, Filipe Pereira, António Ferreira da Silva, Nídia Caetano, Florinda F. Martins, Paulo Silva, José Machado and Adriano A. Santos

Energies 2026, 19(2), 306; https://doi.org/10.3390/en19020306 - 7 Jan 2026

Abstract

Alternative energies are essential for meeting the global demand for environmentally friendly energy, especially as the use of fossil fuels is being reduced. In recent years, largely due to diminishing fossil fuel reserves, Portugal has been actively promoting investment in renewable energies to [...] Read more.

Alternative energies are essential for meeting the global demand for environmentally friendly energy, especially as the use of fossil fuels is being reduced. In recent years, largely due to diminishing fossil fuel reserves, Portugal has been actively promoting investment in renewable energies to reduce its reliance on energy imports and fossil fuels. However, despite the country’s high daily sunshine hours and utilization of wind and hydropower, energy production remains unstable due to climate variability. Climate instability leads to fluctuations in the energy supplied to the grid and can even partially withstand blackouts such as the one that occurred on 28 April 2025 on the Iberian Peninsula. To address this problem, energy storage systems are crucial to guarantee the stability of the supply during periods of low production or in situations such as the one mentioned above. This paper analyzes the feasibility of implementing an energy storage system to increase the profitability of a wind farm located in Alto Douro, Portugal. The study begins with a demand analysis, followed by simulations of the system’s performance in terms of profitability based on efficiency and power. Based on these assumptions, a modular lithium battery storage system with high efficiency and rapid charge/discharge capabilities was selected. This battery, with less autonomy but high capacity, is more profitable, since a 5% increase in efficiency results in high profits (€84,838) and curtailment (€70,962) using batteries with lower autonomy, i.e., 2 h (power rating of 5 MW combined with 10 MWh energy storage). Therefore, two scenarios (A and B) were considered, with one more optimistic (A) in which the priority is to discharge the batteries whenever possible. In the more realistic scenario (B), it is assumed that the batteries are fully charged before discharge. On the other hand, in the event of a blackout, it enables faster commissioning of the surrounding water installations, because solar and battery energy have no inertia, which facilitates the back start protocol. Full article

(This article belongs to the Special Issue Development and Efficient Utilization of Renewable and Clean Energy)

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23 pages, 942 KB

Open AccessArticle

Who Wins the Energy Race? Artificial Intelligence for Smarter Energy Use in Logistics and Supply Chain Management

by Blanka Tundys and Tomasz Wiśniewski

Energies 2026, 19(2), 305; https://doi.org/10.3390/en19020305 - 7 Jan 2026

Abstract

Artificial intelligence (AI) is increasingly regarded as a transformative enabler of sustainable logistics and supply chain management, particularly in the context of global energy transition and decarbonization efforts. This study provides a comprehensive conceptual and exploratory assessment of the multidimensional role of AI, [...] Read more.

Artificial intelligence (AI) is increasingly regarded as a transformative enabler of sustainable logistics and supply chain management, particularly in the context of global energy transition and decarbonization efforts. This study provides a comprehensive conceptual and exploratory assessment of the multidimensional role of AI, highlighting both its potential to enhance energy efficiency and reduce greenhouse gas emissions, as well as its inherent environmental costs associated with digital infrastructures such as data centers. The findings reveal the dual character of digitalization: while predictive algorithms and digital twin applications facilitate demand forecasting, process optimization, and real-time adaptation to market fluctuations, they simultaneously generate additional energy demand that must be offset through renewable energy integration and intelligent energy balancing. The analysis underscores that the effectiveness of AI deployment cannot be captured solely through economic metrics but requires a holistic evaluation framework that incorporates environmental and social dimensions. Moreover, regional disparities are identified, with advanced economies accelerating AI-driven green transformations under regulatory and societal pressures, while developing economies face constraints linked to infrastructure gaps and investment limitations. The analysis emphasizes that AI-driven predictive models and digital twin applications are not only tools for energy optimization but also mechanisms that enhance systemic resilience by enabling risk anticipation, adaptive resource allocation, and continuity of operations in volatile environment. The contribution of this study lies in situating AI within the digital–green synergy discourse, demonstrating that its role in logistics decarbonization is conditional upon integrated energy–climate strategies, organizational change, and workforce reskilling. By synthesizing emerging evidence, this article provides actionable insights for policymakers, managers, and scholars, and calls for more rigorous empirical research across sectors, regions, and time horizons to verify the long-term sustainability impacts of AI-enabled solutions in supply chains. Full article

(This article belongs to the Special Issue Artificial Intelligence and Systemic Resilience: Energy, Finance, and Logistics in Sustainable Supply Chains)

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15 pages, 4830 KB

Open AccessArticle

Numerical Investigation on Mixture Formation and Injection Strategy Optimization in a Heavy-Duty PFI Methanol Engine

by Zhancheng Dou, Xiaoting Xu, Changhui Zhai, Xiaoxiao Zeng, Kui Shi, Xinbo Wu, Yi Liu, Yunliang Qi and Zhi Wang

Energies 2026, 19(2), 304; https://doi.org/10.3390/en19020304 - 7 Jan 2026

Abstract

Methanol is a liquid fuel with high oxygen content and the potential for a closed-loop carbon-neutral production cycle. To investigate the mixture formation and combustion characteristics of a heavy-duty Port Fuel Injection (PFI) methanol engine, a three-dimensional numerical simulation model was established using [...] Read more.

Methanol is a liquid fuel with high oxygen content and the potential for a closed-loop carbon-neutral production cycle. To investigate the mixture formation and combustion characteristics of a heavy-duty Port Fuel Injection (PFI) methanol engine, a three-dimensional numerical simulation model was established using the CONVERGE 3.0 software. Multi-cycle simulations were performed to analyze the influence of wall film dynamics on engine performance. The results indicate that the “adhesion–evaporation” equilibrium of the intake port wall film determines the in-cylinder mixture concentration. Due to the high latent heat of vaporization of methanol, severe wall-wetting occurs during the initial cycles, causing the actual fuel intake to lag behind the injection and leading to an overly lean mixture and misfire. Regarding injection strategies, the open valve injection (OVI) strategy utilizes high-speed intake airflow to reduce wall adhesion and improve fuel transport efficiency compared to closed valve injection. OVI refers to the fuel injection strategy that injects fuel into the intake port during the intake valve opening phase. The open valve injection strategy (e.g., SOI −500° CA) demonstrates distinct superiority over closed valve strategies (SOI −200°/−100° CA), achieving a 75% reduction in wall film mass. The long injection duration and early phasing allow the high-speed intake airflow to carry fuel directly into the cylinder, significantly minimizing wall film accumulation and avoiding the “fuel starvation” observed in closed-valve strategies. Additionally, OVI fully utilizes methanol’s latent heat to generate an intake cooling effect, which lowers the in-cylinder temperature and helps suppress knock. Furthermore, a dual-injector strategy is proposed to balance spatial atomization and rapid fuel transport, which achieves a 66.7% increase in the fuel amount entering the cylinder compared with the original strategy. This configuration effectively resolves the fuel induction lag, achieving stable combustion starting from the first cycle. Full article

(This article belongs to the Special Issue Combustion and Application of Carbon Neutral Fuel in Internal Combustion Engine)

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18 pages, 2101 KB

Open AccessArticle

Comparative Simulation and Optimization of “Continuous Membrane Column” Cascades for Post-Combustion CO₂ Capture

by Kirill A. Smorodin, Artem A. Atlaskin, Sergey S. Kryuchkov, Maria E. Atlaskina, Nikita S. Tsivkovsky, Alexander A. Sysoev, Vyacheslav V. Zhmakin, Anton N. Petukhov, Sergey S. Suvorov, Andrey V. Vorotyntsev and Ilya V. Vorotyntsev

Energies 2026, 19(2), 303; https://doi.org/10.3390/en19020303 - 7 Jan 2026

Abstract

This study presents a comprehensive evaluation of a modified membrane cascade operating in “Continuous Membrane Column” mode for selective CO₂ capture in combined heat power plants. For the first time, a novel membrane cascade configuration for separating four-component wet flue gases is [...] Read more.

This study presents a comprehensive evaluation of a modified membrane cascade operating in “Continuous Membrane Column” mode for selective CO₂ capture in combined heat power plants. For the first time, a novel membrane cascade configuration for separating four-component wet flue gases is analyzed and compared with existing technologies in terms of the capital and operating costs required to capture one ton of CO₂. The proposed membrane cascade generates two countercurrent recirculating streams: one continuously depleted of the permeate component and the other enriched in it. Because the internal recirculation streams significantly exceed the bypass product streams, the system demonstrates a multiplicative increase in separation efficiency. As a result, the required membrane area and compression energy can be significantly reduced. The analysis demonstrates that the proposed cascade configuration meets all current performance requirements for CO₂ recovery and the target composition of the product and residual streams. Furthermore, due to its balanced material and energy cost ratio, the system can serve as a competitive alternative to previously developed membrane CO₂ capture technologies, offering lower overall capture losses. Full article

(This article belongs to the Special Issue Process Optimization of Carbon Capture Technology)

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25 pages, 3224 KB

Open AccessArticle

Three-Dimensional Surface High-Precision Modeling and Loss Mechanism Analysis of Motor Efficiency Map Based on Driving Cycles

by Jiayue He, Yan Sui, Qiao Liu, Zehui Cai and Nan Xu

Energies 2026, 19(2), 302; https://doi.org/10.3390/en19020302 - 7 Jan 2026

Abstract

Amid fossil-fuel depletion and worsening environmental impacts, battery electric vehicles (BEVs) are pivotal to the energy transition. Energy management in BEVs relies on accurate motor efficiency maps, yet real-time onboard control demands models that balance fidelity with computational cost. To address map inaccuracy [...] Read more.

Amid fossil-fuel depletion and worsening environmental impacts, battery electric vehicles (BEVs) are pivotal to the energy transition. Energy management in BEVs relies on accurate motor efficiency maps, yet real-time onboard control demands models that balance fidelity with computational cost. To address map inaccuracy under real driving and the high runtime cost of 2-D interpolation, we propose a driving-cycle-aware, physically interpretable quadratic polynomial-surface framework. We extract priority operating regions on the speed–torque plane from typical driving cycles and model electrical power

P_{e}

as a function of motor speed

n

and mechanical power

P_{m}

. A nested model family (M3–M6) and three fitting strategies—global, local, and region-weighted—are assessed using

R^{2}

, RMSE, a computational complexity index (CCI), and an Integrated Criterion for accuracy–complexity and stability (ICS). Simulations on the Worldwide Harmonized Light Vehicles Test Cycle, the China Light-Duty Vehicle Test Cycle, and the Urban Dynamometer Driving Schedule show that region-weighted fitting consistently achieves the best or near-best ICS; relative to Global fitting, mean ICS decreases by 49.0%, 46.4%, and 90.6%, with the smallest variance. Regarding model order, the four-term M4

(+ P_{m}^{2})

offers the best accuracy–complexity trade-off. Finally, the region-weighted fitting M4

(+ P_{m}^{2})

polynomial model was integrated into the vehicle-level economic speed planning model based on the dynamic programming algorithm. In simulations covering a 27 km driving distance, this model reduced computational time by approximately 87% compared to a linear interpolation method based on a two-dimensional lookup table, while achieving an energy consumption deviation of about 0.01% relative to the lookup table approach. Results demonstrate that the proposed model significantly alleviates computational burden while maintaining high energy consumption prediction accuracy, thereby providing robust support for real-time in-vehicle applications in whole-vehicle energy management. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Energy Management)

22 pages, 1269 KB

Open AccessArticle

Probabilistic Power Flow Estimation in Power Grids Considering Generator Frequency Regulation Constraints Based on Unscented Transformation

by Jianghong Chen and Yuanyuan Miao

Energies 2026, 19(2), 301; https://doi.org/10.3390/en19020301 - 7 Jan 2026

Abstract

To address active power fluctuations in power grids induced by high renewable energy penetration and overcome the limitations of existing probabilistic power flow (PPF) methods that ignore generator frequency regulation constraints, this paper proposes a segmented stochastic power flow modeling method and an [...] Read more.

To address active power fluctuations in power grids induced by high renewable energy penetration and overcome the limitations of existing probabilistic power flow (PPF) methods that ignore generator frequency regulation constraints, this paper proposes a segmented stochastic power flow modeling method and an efficient analytical framework that incorporates the actions and capacity constraints of regulation units. Firstly, a dual dynamic piecewise linear power injection model is established based on “frequency deviation interval stratification and unit limit-reaching sequence ordering,” clarifying the hierarchical activation sequence of “loads first, followed by conventional units, and finally automatic generation control (AGC) units” along with the coupled adjustment logic upon reaching limits, thereby accurately reflecting the actual frequency regulation process. Subsequently, this model is integrated with the State-Independent Linearized Power Flow (DLPF) model to develop a segmented stochastic power flow framework. For the first time, a deep integration of unscented transformation (UT) and regulation-aware power allocation is achieved, coupled with the Nataf transformation to handle correlations among random variables, forming an analytical framework that balances accuracy and computational efficiency. Case studies on the New England 39-bus system demonstrate that the proposed method yields results highly consistent with those of Monte Carlo simulations while significantly enhancing computational efficiency. The DLPF model is validated to be applicable under scenarios where voltage remains within 0.95–1.05 p.u., and line transmission power does not exceed 85% of rated capacity, exhibiting strong robustness against parameter fluctuations and capacity variations. Furthermore, the method reveals voltage distribution patterns in wind-integrated power systems, providing reliable support for operational risk assessment in grids with high shares of renewable energy. Full article

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28 pages, 981 KB

Open AccessArticle

Impact of Ultra-Fast Electric Vehicle Charging on Steady-State Voltage Compliance in Radial Distribution Feeders: A Monte Carlo V–Q Sensitivity Framework

by Hassan Ortega and Alexander Aguila Téllez

Energies 2026, 19(2), 300; https://doi.org/10.3390/en19020300 - 7 Jan 2026

Abstract

This paper quantifies the steady-state voltage-compliance impact of ultra-fast electric vehicle (EV) charging on the IEEE 33-bus radial distribution feeder. Four practical scenarios are examined by combining two penetration levels (6 and 12 charging points, i.e., ≈20% and ≈40% of PQ buses) with [...] Read more.

This paper quantifies the steady-state voltage-compliance impact of ultra-fast electric vehicle (EV) charging on the IEEE 33-bus radial distribution feeder. Four practical scenarios are examined by combining two penetration levels (6 and 12 charging points, i.e., ≈20% and ≈40% of PQ buses) with two charger ratings (1 MW and 350 kW per point). Candidate buses for EV station integration are selected through a nodal voltage–reactive sensitivity ranking (

\partial V / \partial Q

), prioritizing electrically robust locations. To capture realistic operating uncertainty, a 24-hour quasi-static time-series power-flow assessment is performed using Monte Carlo sampling (

N = 100

), jointly modeling residential-demand variability and stochastic EV charging activation. Across the four cases, the worst-hour minimum voltage (uncompensated) ranges from 0.803 to 0.902 p.u., indicating a persistent under-voltage risk under dense and/or high-power charging. When the expected minimum-hourly voltage violates the 0.95 p.u. limit, a closed-form, sensitivity-guided reactive compensation is computed at the critical bus, and the power flow is re-solved. The proposed mitigation increases the minimum-voltage trajectory by approximately 0.03–0.12 p.u. (about 3.0–12.0% relative to 1 p.u.), substantially reducing the depth and duration of violations. The maximum required reactive support reaches 6.35 Mvar in the most stressed case (12 chargers at 1 MW), whereas limiting the unit charger power to 350 kW lowers both the severity of under-voltage and the compensation requirement. Overall, the Monte Carlo V–Q sensitivity framework provides a lightweight and reproducible tool for probabilistic voltage-compliance assessment and targeted steady-state mitigation in EV-rich radial distribution networks. Full article

(This article belongs to the Section E: Electric Vehicles)

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26 pages, 1367 KB

Open AccessArticle

Dynamic Cost Management Throughout the Entire Process of Power Transmission and Transformation Projects Based on System Dynamics

by Xiaomei Zhang, Wenqin Ning, Xue Wei, Zinan Cao, Yaning Huang and Jian Zhang

Energies 2026, 19(2), 299; https://doi.org/10.3390/en19020299 - 7 Jan 2026

Abstract

With the advancement of the “dual carbon” goals, power transmission and transformation projects face complex challenges arising from the construction of new power systems. Traditional cost management models struggle to meet dynamic management demands, necessitating the establishment of analytical methods that systematically reflect [...] Read more.

With the advancement of the “dual carbon” goals, power transmission and transformation projects face complex challenges arising from the construction of new power systems. Traditional cost management models struggle to meet dynamic management demands, necessitating the establishment of analytical methods that systematically reflect the relationship between cost management levels and cost dynamics. This paper introduces system dynamics theory and methodology to construct a cost management model applicable to all phases of transmission and transformation projects. It aims to deeply analyze the relationship between project cost levels and expenses from the perspectives of system structure, feedback mechanisms, and dynamic behavior. Research indicates that pathways such as controlling cost deviations and optimizing resource allocation significantly impact total project costs. Specifically, enhancing design accuracy can effectively mitigate cost shocks caused by carbon price fluctuations, while timely implementation of cost control measures can significantly improve cost management levels. The system dynamics approach effectively reveals the dynamic interaction mechanism between cost management levels and costs in power transmission and transformation projects, providing theoretical foundations and methodological support for enhancing project cost control efficiency. Full article

(This article belongs to the Special Issue The Future of Energy Management and Economics: Innovation, Technology, Sustainability, and New Business Models)

36 pages, 9869 KB

Open AccessArticle

Conceptual Basis of Adaptation of a Field-Oriented Control System for Traction Induction Motors to the Operating Parameters of a Locomotive

by Vaidas Lukoševičius, Sergey Goolak, Ihor Derehuz, Larysa Neduzha, Artūras Keršys and Vytautas Dzerkelis

Energies 2026, 19(2), 298; https://doi.org/10.3390/en19020298 - 6 Jan 2026

Abstract

Field-oriented control (FOC) of induction motors (IMs) is used in railway rolling stock. In such control systems, a fixed frequency of the pulse-width modulation (PWM) inverter is used, which leads to an increase in power losses in the traction drive. To optimize power [...] Read more.

Field-oriented control (FOC) of induction motors (IMs) is used in railway rolling stock. In such control systems, a fixed frequency of the pulse-width modulation (PWM) inverter is used, which leads to an increase in power losses in the traction drive. To optimize power losses in the locomotive traction drive system, it is proposed to adapt the number of PWM inverter pulses to the frequency of the FOC speed controller, which is proportional to the locomotive speed. To solve this problem, conceptual foundations for adapting FOC to the locomotive speed have been developed, the key aspects of which are algorithms for adapting the PWM inverter frequency, the controller parameters and the parameters of the FOC speed controller frequency filters. The most significant results of the work are the methods for adjusting the maximum of the controllers of the basic FOC IM system, the filter structure and the inverter control scheme, adapted to the locomotive speed. The modeling results have shown the effectiveness of the proposed technical solutions. The proposed approach to developing FOC will allow minimizing the consumption of energy resources by the locomotive in the entire range of changes in its speed. Full article

(This article belongs to the Special Issue Forecasting and Optimization in Transport Energy Management Systems, 2nd Edition)

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16 pages, 3061 KB

Open AccessArticle

Design and Experimental Evaluation of Polyimide Film Heater for Enhanced Output Characteristics Through Temperature Control in All-Solid-State Batteries

by Soo-Man Park, Chae-Min Lim, Soon-Hyung Lee, Kyung-Min Lee and Yong-Sung Choi

Energies 2026, 19(2), 297; https://doi.org/10.3390/en19020297 - 6 Jan 2026

Abstract

This paper presents a practical thermal control strategy to enhance the output performance of oxide-based all-solid-state batteries (ASSBs), which typically exhibit low ionic conductivity at room temperature. A lightweight polyimide (PI) film heater was designed, fabricated, and integrated into the cell stack to [...] Read more.

This paper presents a practical thermal control strategy to enhance the output performance of oxide-based all-solid-state batteries (ASSBs), which typically exhibit low ionic conductivity at room temperature. A lightweight polyimide (PI) film heater was designed, fabricated, and integrated into the cell stack to locally maintain the optimal operating temperature range (≈65–75 °C) for electrolyte activation. Unlike previous studies limited to liquid or sulfide-based batteries, this work demonstrates the direct integration and coupled numerical–experimental validation of a PI film heater within oxide-based ASSBs. The proposed design achieves high heating efficiency (~92%) with minimal thickness (<100 μm) and long-term stability, enabling reliable and scalable thermal management. Finite-element simulations and experimental verification confirmed that the proposed heater achieved rapid and uniform heating with less than a 10 °C temperature deviation between the cell and heater surfaces. These findings provide a foundation for smart battery management systems with distributed temperature sensing and feedback control, supporting the development of high-performance and reliable solid-state battery platforms. Full article

(This article belongs to the Special Issue Advanced Energy Storage Technologies and Applications (AESAs), 2nd Edition)

30 pages, 1635 KB

Open AccessArticle

Modelling the Impact of Solar Power Expansion on Generation Costs in Kenya

by Margaret Ntangenoi Letiyan, Moses Barasa Kabeyi and Oludolapo Olanrewaju

Energies 2026, 19(2), 296; https://doi.org/10.3390/en19020296 - 6 Jan 2026

Abstract

Climate change and increasing greenhouse gas emissions are driving the global transition to clean energy, with solar energy experiencing the fastest growth among renewable sources in 2024. Solar PV for energy generation in Kenya is gaining momentum as the country moves towards achieving [...] Read more.

Climate change and increasing greenhouse gas emissions are driving the global transition to clean energy, with solar energy experiencing the fastest growth among renewable sources in 2024. Solar PV for energy generation in Kenya is gaining momentum as the country moves towards achieving 100% clean energy by 2030. As solar PV penetration in the grid grows, it is necessary to evaluate its impact on system costs to inform policy decisions on capacity expansion options in the Least-Cost Power Development Plan (LCPDP). This study investigates the effect of large-scale solar PV expansion on electricity costs using the Open-Source Energy Modelling System (OSeMOSYS), a modular, bottom-up capacity expansion model. Four scenarios were developed to assess different levels of solar PV penetration: business-as-usual (BAU), moderate-solar-PV expansion (MSPV), high-solar-PV expansion (HSPV), and very-high-solar-PV expansion (VHSPV). The results indicate that, while overall solar PV expansion significantly contributes to decarbonising Kenya’s electricity mix by displacing fossil-based generation, it also increases annual investment obligations and, consequently, total system costs. The system-levelised cost of electricity (LCOE) is shown to rise by 0.2%, 5.7%, and 14.0% under MSPV, HSPV, and VHSPV, respectively, compared to BAU. Analysing the various cost components against sustainability indicators reveals that the least-cost scenario is BAU while the most favourable scenario based on sustainability indicators is VHSPV, which performs best across technical, environmental, and institutional dimensions but less favourably on economic and social aspects, thereby highlighting a trade-off between sustainability and cost minimisation, at least in the short term. Full article

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20 pages, 6259 KB

Open AccessArticle

Design and Performance Analysis of a Tower Solar Energy S-CO₂ Brayton Cycle Tri-Generation System

by Gang Wang, Tao Bai and Zeshao Chen

Energies 2026, 19(2), 295; https://doi.org/10.3390/en19020295 - 6 Jan 2026

Abstract

Against the backdrop of global energy transition and increasingly severe environmental conditions, developing clean and efficient energy systems has become crucial. This study aims to investigate a solar tower receiver tri-generation (STRT) system combining supercritical CO₂ (S-CO₂) Brayton cycle and [...] Read more.

Against the backdrop of global energy transition and increasingly severe environmental conditions, developing clean and efficient energy systems has become crucial. This study aims to investigate a solar tower receiver tri-generation (STRT) system combining supercritical CO₂ (S-CO₂) Brayton cycle and organic Rankine cycle (ORC), with the objective of achieving the production of electricity, hydrogen, and oxygen. The modeling of the STRT system is completed by using Ebsilon, and the performance of the STRT system is analyzed. The results show that the output power and efficiency of the S-CO₂ Brayton cycle are 62.29 MW and 48.3%, respectively. The net power and efficiency of ORC are 8.02 MW and 16.35%. The hydrogen and oxygen production rates of the STRT system are 183.8 kg·h⁻¹ and 1470.4 kg·h⁻¹, respectively. The STRT system shows stable and effective operation performance throughout the year. Through the exergy analysis, the exergy losses and exergy efficiencies of different components of the STRT system are obtained. The solar tower has the largest exergy loss (218.85 MW) and the lowest exergy efficiency (63%). The levelized electricity cost and the levelized hydrogen cost of the STRT system are 0.0788 USD·kWh⁻¹ and 2.97 USD·kg⁻¹ with a recovery period of 8.05 years, which reveal the economic competitiveness of the STRT system. Full article

(This article belongs to the Special Issue Solar Thermal Heat and Power Technology: Developments and Applications)

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15 pages, 2185 KB

Open AccessArticle

Salt Deposit Detection on Offshore Photovoltaic Modules Using an Enhanced YOLOv8 Framework

by Gang Li, Shuqing Wang, Bo Liu, Mingqiang Xu, Zhenhai Liu and Haoge Wang

Energies 2026, 19(2), 294; https://doi.org/10.3390/en19020294 - 6 Jan 2026

Abstract

To address the challenges of low detection efficiency and limited accuracy in identifying contamination on offshore photovoltaic platforms, this study proposes an enhanced YOLOv8-based algorithm for detecting salt deposit on photovoltaic modules. The SimAM parameter-free attention mechanism is integrated at the end of [...] Read more.

To address the challenges of low detection efficiency and limited accuracy in identifying contamination on offshore photovoltaic platforms, this study proposes an enhanced YOLOv8-based algorithm for detecting salt deposit on photovoltaic modules. The SimAM parameter-free attention mechanism is integrated at the end of the backbone network and within the neck layers to improve feature representation of salt deposits under complex environmental conditions, thereby enhancing detection accuracy. In addition, the WIoU loss function is employed in place of the original CIoU loss to alleviate harmful gradients caused by low-quality data and to strengthen the generalization capability of the model. A dedicated dataset of salt accumulation images from offshore photovoltaic panels is constructed to support this targeted detection task. Experimental results demonstrate that the proposed algorithm achieves an mAP50 of 85.8%, a 3% improvement over YOLOv8, while maintaining a detection speed of 67 frames per second. These findings confirm that the proposed approach meets both the accuracy and efficiency requirements for automated detection of salt deposition on offshore photovoltaic modules. Full article

(This article belongs to the Special Issue Advanced Structural Health Monitoring for Energy Performance and Safety in Marine Renewable Systems)

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