Energies

40 pages, 4879 KB

Open AccessArticle

Design and Optimization of Solar-Powered Cooling/Heating System with Heat Pump Integration for Natatoriums in Hot–Arid Climates

by Fadi Ghaith, Zaid Al Rayes and Asma’u Umar

Energies 2026, 19(10), 2359; https://doi.org/10.3390/en19102359 (registering DOI) - 14 May 2026

Abstract

Decarbonizing HVAC in hot–arid regions is challenging for natatoriums because year-round cooling must be delivered alongside stringent dehumidification and occasional heating under high ambient temperatures. In this paper, a fully renewable system has been developed and evaluated for an indoor swimming pool located [...] Read more.

Decarbonizing HVAC in hot–arid regions is challenging for natatoriums because year-round cooling must be delivered alongside stringent dehumidification and occasional heating under high ambient temperatures. In this paper, a fully renewable system has been developed and evaluated for an indoor swimming pool located in Abu Dhabi with a 679 m² swimming pool hall designed to accommodate 200 pool users. The hybrid system includes a high-temperature linear Fresnel reflector (LFR) solar field, stratified thermal energy storage (TES), a single-effect LiBr–H₂O absorption chiller for cooling, a water-to-water heat pump as a backup system for the stability of cooling and heating rates, and a photovoltaic (PV) system to offset the ancillary equipment power input of the hybrid system. The system performance was simulated and validated by using hourly data from Abu Dhabi. Optimization of design/operation parameters was carried out by a multi-objective genetic algorithm to achieve the maximum coefficient of performance (COP) and the minimum levelized cost of cooling (LCOE). The initial COP and LCOE were 0.701 and 0.037 $/kWh, respectively. They were optimized to 0.825 and 0.0254 $/kWh, respectively. The annual energy balance revealed a synergistic operation of the solar field, TES, and heat pump. The lifecycle assessment was utilized to compare the proposed hybrid system with the conventional vapor-compression systems in terms of energy, cost, and CO₂ emissions, in which the proposed system proved superior over conventional systems with a positive net present value (NPV) and net zero carbon emissions. Full article

(This article belongs to the Special Issue The Development and Utilization of Solar Energy in Space Cooling)

27 pages, 1671 KB

Open AccessArticle

Adequacy Costs of CCGTs in Portugal: Calculating CONE and CORP to Inform Capacity Mechanisms

by Miguel Gaspar and Rui Castro

Energies 2026, 19(10), 2358; https://doi.org/10.3390/en19102358 (registering DOI) - 14 May 2026

Abstract

The European energy transition is rapidly reducing the role of fossil-based generation, raising concerns about system adequacy and the financial sustainability of dispatchable plants. Combined Cycle Gas Turbines (CCGTs) remain essential to ensure grid flexibility and security of supply, particularly in markets with [...] Read more.

The European energy transition is rapidly reducing the role of fossil-based generation, raising concerns about system adequacy and the financial sustainability of dispatchable plants. Combined Cycle Gas Turbines (CCGTs) remain essential to ensure grid flexibility and security of supply, particularly in markets with high renewable penetration such as Portugal. This paper applies the European Union’s adequacy assessment methodology to compute the Cost of New Entry (CONE) and the Cost of Renewal or Prolongation (CORP) for CCGTs, establishing the minimum annual revenues required for new investments and for extending the lifetime of existing units. A simplified market closure algorithm is developed using 2023 MIBEL data to estimate CCGT revenues under current conditions. Results show that annual market revenues (about 20 k€/MW) are far below the calculated thresholds of 123 k€/MW for CONE and 39 k€/MW for CORP, revealing a substantial investment gap. These results suggest the need for capacity remuneration mechanisms (CRMs) to maintain existing plants and attract new capacity. Indicative values of about 103 k€/MW/year for new entrants and about 20 k€/MW/year for renewals are obtained, which should be interpreted as order-of-magnitude estimates rather than direct payment levels for market implementation. The findings highlight the critical role of CRMs in guaranteeing energy security during the decarbonization process and provide policy-relevant benchmarks for Portugal and other EU countries facing similar challenges. Full article

(This article belongs to the Special Issue Energy Economics, Finance and Policy Towards Sustainable Energy—3rd Edition)

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19 pages, 1816 KB

Open AccessArticle

A Data-Driven Parameter Inversion Method for Converter Valve Thyristor Levels Based on Time-Frequency-Domain Features

by Yingfeng Zhu, Donglin Xu, Ming Li, Chenhao Li, Jie Ren, Junqi Ding, Boyang Xia and Lei Pang

Energies 2026, 19(10), 2357; https://doi.org/10.3390/en19102357 (registering DOI) - 14 May 2026

Abstract

The thyristor level is the basic unit of ultra-high-voltage and extra-high-voltage direct current (DC) converter valves, and its main-circuit parameters are important indicators for characterizing the health status of converter valves. To meet the demand for efficient detection of converter valve thyristor levels, [...] Read more.

The thyristor level is the basic unit of ultra-high-voltage and extra-high-voltage direct current (DC) converter valves, and its main-circuit parameters are important indicators for characterizing the health status of converter valves. To meet the demand for efficient detection of converter valve thyristor levels, this paper proposes a parameter inversion method for converter valve thyristor levels by combining the time-frequency-domain features of valve voltage and current, temporal characteristics of feedback signals from the thyristor-level monitoring unit, and a Grey Wolf Optimizer–Backpropagation Neural Network (GWO-BPNN). First, a six-pulse converter valve circuit simulation model is established. Based on this model, the original dataset is generated using the Latin hypercube sampling (LHS) method. Wavelet packet decomposition is then used to extract time-frequency-domain features, and dimensionality reduction is carried out by comparing the coefficient of variation and explained variance ratio so as to obtain input data suitable for neural network training. A BP neural network is then trained, and the network parameters are optimized using the Grey Wolf Optimizer to improve the accuracy and convergence speed of parameter inversion. Simulation comparison results show that the GWO-BP method is more efficient than the state equation method and is suitable for efficient inversion of damping parameters in multi-level thyristor systems. After GWO optimization, the maximum inversion errors of both parameters are reduced to below 5%. Compared with BP, GA-BP, and PSO-BP, the proposed GWO-BP model provides the best overall balance between resistance-inversion accuracy and training efficiency. By further incorporating feedback feature signals, the inversion error can be reduced to 1%. The proposed method provides a new technical route for efficient detection of thyristor converter valves and has broad application prospects. Full article

(This article belongs to the Special Issue Digital and Intelligent Operations, Maintenance, and Inspection of Dielectrics and High-Voltage Equipment)

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20 pages, 3916 KB

Open AccessArticle

Wave Energy Potential Assessment Along the Coast of Oman

by Abdullah Al-Badi, Jamal AlHinai, Abdulmajeed Al Wahaibi and Sultan Al-Yahyai

Energies 2026, 19(10), 2356; https://doi.org/10.3390/en19102356 (registering DOI) - 14 May 2026

Abstract

The primary aim of this research is to assess the wave energy potential along the coast of Oman especially coasts facing Arabian Sea and Indian ocean by analyzing the wave energy distribution and time series of wave heights, obtained through numerical modeling over [...] Read more.

The primary aim of this research is to assess the wave energy potential along the coast of Oman especially coasts facing Arabian Sea and Indian ocean by analyzing the wave energy distribution and time series of wave heights, obtained through numerical modeling over a three-years period. The study focuses on evaluating the spatial, seasonal, monthly, and directional variability of wave power and energy at multiple coastal locations. The spatial analysis reveals a clear trend of increasing wave power in the southeastern coast, toward the open Indian Ocean, where stronger wind conditions prevail. The monthly analysis indicates that mean wave power peaks during the summer months (June to August), coinciding with the southwest Indian monsoon season, which significantly enhances wave activity along the southern coastline. To simulate and analyze wave characteristics, wave data were obtained from the Global Ocean Waves Analysis and Forecast product provided by Copernicus Marine, which is based on the MFWAM (a third-generation wave model) developed by Météo-France. This dataset enabled the generation of high-resolution data on wave height, period, and direction, providing a comprehensive understanding of wave energy dynamics across the study area. Results indicate that the majority of the annual wave energy is contributed by significant wave heights ranging from 1 to 4 m, suggesting that waves in this range contribute most of the annual wave energy resource in the study area. These findings provide a characterization of the wave energy resource along the coast of Oman and identify the locations and seasons with relatively higher wave energy potential. The results can support future device-specific feasibility studies and technology selection for wave energy development in the region. Full article

(This article belongs to the Section A3: Wind, Wave and Tidal Energy)

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17 pages, 3051 KB

Open AccessArticle

Energy-Oriented Multi-Robot Collaborative Exploration and Mapping for Nuclear Power Plant Operation and Maintenance Based on I-WFD-Gmapping-DT

by Tong Wu, Meihao Zhu, Zhansheng Liu, Xiaofeng Zhang, Fengjuan Chen, Xiaoqing Zhu, Haowen Sun, Chuan Zhang and Jiahao Wu

Energies 2026, 19(10), 2355; https://doi.org/10.3390/en19102355 (registering DOI) - 14 May 2026

Abstract

During the transition of global energy systems toward low-carbon and high-reliability operation, nuclear power plant (NPP) operation and maintenance require environmental perception methods that are safe, energy-efficient, and sufficiently accurate for confined and radiation-risk areas. To address these requirements, this paper proposes an [...] Read more.

During the transition of global energy systems toward low-carbon and high-reliability operation, nuclear power plant (NPP) operation and maintenance require environmental perception methods that are safe, energy-efficient, and sufficiently accurate for confined and radiation-risk areas. To address these requirements, this paper proposes an energy-oriented multi-robot collaborative exploration and mapping framework, termed I-WFD-Gmapping-DT. The framework integrates a digital twin (DT) 5+3 model, improved wavefront frontier detection (I-WFD), energy- and risk-aware task allocation, EKF-AMCL-based initial relative pose estimation, and multi-scale Gmapping map fusion. Unlike conventional frontier-based or single-objective exploration methods, the proposed utility function jointly considers discounted information gain, obstacle-sensitive path cost, estimated battery energy, angular dispersion, and safety constraints. A ROS-Gazebo simulation of an NPP-like environment was used for 30 independent runs with randomized seeds and starting perturbations. Compared with WFD-Gmapping, the proposed method increased the three-robot coverage area percentage from 35.6 ± 2.1% to 40.5 ± 1.9%, reduced exploration time by 13.35%, reduced total and used frontier target points by 38.9% and 23.24%, respectively, and reduced estimated energy consumption by 13.9%. Map accuracy was also improved, with AE decreasing from 12.45% to 11.52%, RMSE from 7.85% to 7.18%, and SSIM increasing from 0.78 to 0.83. Additional sensitivity, ablation, runtime, and initial-pose experiments confirm the robustness of the parameter selection and the contribution of the DT-enabled feedback mechanism. The results show that I-WFD-Gmapping-DT can enhance collaborative inspection efficiency, reduce redundant motion and energy consumption, and provide reliable mapping support for intelligent NPP operation and maintenance. Full article

(This article belongs to the Special Issue Artificial Intelligence for Nuclear Engineering: Enhancing Smart Energy Systems)

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25 pages, 2734 KB

Open AccessArticle

Coordinated Frequency Regulation Control Strategy for Wind-Storage Systems Based on Dynamic Weighting Coefficients and Model Predictive Control

by Dingran Wang and Tingting Cai

Energies 2026, 19(10), 2354; https://doi.org/10.3390/en19102354 (registering DOI) - 14 May 2026

Abstract

Wind-storage coordinated frequency regulation enhances the frequency stability of large-scale wind power systems. However, existing methods often rely on fixed parameters, limiting adaptability and accelerating energy storage depletion. To address these limitations, a coordinated control strategy based on dynamic weighting coefficients and model [...] Read more.

Wind-storage coordinated frequency regulation enhances the frequency stability of large-scale wind power systems. However, existing methods often rely on fixed parameters, limiting adaptability and accelerating energy storage depletion. To address these limitations, a coordinated control strategy based on dynamic weighting coefficients and model predictive control (MPC) is proposed. First, a dynamic weighting mechanism is designed to adaptively adjust the contributions of virtual inertia and droop control based on the system frequency state and the energy storage system’s (ESS) state of charge (SOC), thereby avoiding abrupt power variations and maintaining the SOC within safe limits. Second, an MPC-based rolling optimization model is established to continuously allocate the active power outputs between the doubly fed induction generator (DFIG) and the ESS, aiming to minimize both frequency deviations and regulation costs. Simulation results demonstrate the superiority of the proposed strategy. Under a step load disturbance, the maximum frequency deviation is reduced by 11.3%, and the peak time is shortened by 13% compared to conventional droop control. Furthermore, under continuous load fluctuations, the proposed approach significantly mitigates SOC depletion and minimizes system frequency fluctuations, proving its effectiveness in enhancing the frequency resilience of wind-storage combined systems. Full article

(This article belongs to the Special Issue Advanced Control Technology of Integrated Wind and Wave Energy Conversion Systems: 2nd Edition)

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22 pages, 9075 KB

Open AccessReview

Comparative Analysis of Electricity Generation by Stationary and Tracking Photovoltaic Installations

by Paweł Czaja and Ewa Korzeniewska

Energies 2026, 19(10), 2353; https://doi.org/10.3390/en19102353 (registering DOI) - 14 May 2026

Abstract

The photovoltaic (PV) sector is at present one of the crucial components of renewable power engineering and one of the key pillars in the global power system transformation. This article compares the annual energy yields from real-life PV installations built in Częstochowa (Poland)—three [...] Read more.

The photovoltaic (PV) sector is at present one of the crucial components of renewable power engineering and one of the key pillars in the global power system transformation. This article compares the annual energy yields from real-life PV installations built in Częstochowa (Poland)—three stationary PV installations and one tracker PV installation. The PV installations are located within a 2 km radius, and except for very early morning and late evening hours, there is no shading, thus identical solar exposure conditions can be assumed for all analyzed PV installations. In the case of stationary PV installations, maximum energy production may be achieved if the PV modules are southward oriented and related to their tilt angles. In the case of installations on buildings, PV modules are rarely installed in their optimal orientation. Most often, the orientation of PV modules is directly related to the location of the building and the geometric structure of the roof. A tracking system, which involves mounting PV modules on platforms that track the sun’s path, increases energy yield per module power. Limitations for tracking PV systems include the requirement for adequate, shade-free space for their construction as well as high costs of the structure itself and its maintenance. During the period analyzed (2022–2025), no PV system outages resulting from exceeding the permissible voltage in the distribution network were recorded. The energy produced by individual PV systems was also compared with the values calculated in a simulation program used to estimate annual energy yields during the system design phase. Full article

(This article belongs to the Special Issue Photovoltaic Modules and Systems)

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18 pages, 8946 KB

Open AccessArticle

Joint Scheduling and Coordinating Operation of a Mega Hydropower System Based on Gaussian Radial Basis Functions and the Borg Algorithm in the Upper Yangtze River, China

by Shenglian Guo, Chenglong Li, Bokai Sun, Xiaoya Wang, Peng Li and Le Guo

Energies 2026, 19(10), 2352; https://doi.org/10.3390/en19102352 (registering DOI) - 14 May 2026

Abstract

A large number of reservoirs (or hydropower plants) have been constructed for flood control and energy production in the past several decades in the Yangtze River basin in China. The conventional scheduling rule curves (Scheme A) were designed in the reservoir construction period [...] Read more.

A large number of reservoirs (or hydropower plants) have been constructed for flood control and energy production in the past several decades in the Yangtze River basin in China. The conventional scheduling rule curves (Scheme A) were designed in the reservoir construction period and did not consider river flow alternation, which needs to be modified to increase comprehensive benefits in the reservoir operation period. In this study, six large-scale cascade reservoirs or mega hydropower systems constructed and operated by the China Yangtze Three Gorges Cooperation were selected for this case study. The current joint scheduling plans of cascade reservoirs (Scheme B) were introduced, and a joint scheduling and multi-objective coordinating operation model (Scheme C) was proposed for this mega hydropower system. The Gaussian radial basis functions (GRBFs) were used to fit operation policies of each reservoir, and the Borg multi-objective evolutionary algorithm was selected to optimize three-objective functions for Scheme C. The observed daily flow data series at main hydrometric stations from 2003 to 2025 were used to simulate and compare different operation scheduling schemes. The results show that the performance of joint scheduling of cascade reservoirs (both Schemes B and C) is much better than the single-reservoir scheduling (Schemes A) with overall benefit; Scheme C-best achieves a comprehensive target of decreasing average annual spillway wastewater by 12.82 billion m³ (or a decrease of 28.5%), increasing average annual power generation by 31.02 billion kWh (or an increase of 10.7%), and improving average annual impoundment efficiency rate by 5.0%. The GRBF can fit reservoir operation policies well, while the Borg multi-objective evolutionary algorithm can quickly converge with high-precision non-dominated solution sets. The proposed joint scheduling and multi-objective coordinating operation model will provide a scientific basis for achieving maximum benefits in flood protection and hydropower generation for the mega hydropower system. Full article

(This article belongs to the Special Issue Flexibility Solutions and Innovations for Sustainable Hydropower)

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20 pages, 3334 KB

Open AccessArticle

Two-Stage Robust Optimization Approach Considering Energy Storage Degradation Under High Renewable Penetration

by Ruiqin Duan, Xinchun Zhu, Yan Jiang, Xiaolong Song, Yantao Sun and Youwei Jia

Energies 2026, 19(10), 2351; https://doi.org/10.3390/en19102351 - 14 May 2026

Abstract

The rising penetration of renewable energy introduces greater volatility and uncertainty into energy systems. Energy storage systems (ESS) play a vital role in enhancing system flexibility and stability. However, frequent charge–discharge cycles lead to significant degradation of storage devices, reducing their economic efficiency [...] Read more.

The rising penetration of renewable energy introduces greater volatility and uncertainty into energy systems. Energy storage systems (ESS) play a vital role in enhancing system flexibility and stability. However, frequent charge–discharge cycles lead to significant degradation of storage devices, reducing their economic efficiency and lifespan. This paper proposes a two-stage robust optimization framework under high renewable penetration, explicitly considering battery degradation. The first stage determines the optimal capacity configuration of distributed energy resources, including PV, wind, gas turbines, and ESSs. The second stage optimizes operational strategies under worst-case uncertainty in renewable generation and load, while accounting for the degradation cost and cycle life of the ESS. A linearized degradation model is developed based on depth-of-discharge (DoD), and the overall problem is solved using a Column-and-Constraint Generation (C&CG) algorithm. Simulation results demonstrate that the proposed approach effectively balances investment and operation, reduces degradation-related costs, and ensures reliable performance under uncertainty. Full article

(This article belongs to the Section D: Energy Storage and Application)

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21 pages, 4937 KB

Open AccessArticle

Photovoltaic Expansion Perception Method Based on GWO-PSO-Optimized Robust Extreme Learning Machine

by Houyu He and Yifa Sheng

Energies 2026, 19(10), 2350; https://doi.org/10.3390/en19102350 - 13 May 2026

Abstract

Addressing the safety risks to the distribution network caused by the unauthorized capacity expansion behaviors of distributed photovoltaic (PV) users, this paper proposes a PV capacity expansion detection model based on the gray wolf–particle swarm optimization hybrid optimization robust extreme learning machine (GWO-PSO-MELM). [...] Read more.

Addressing the safety risks to the distribution network caused by the unauthorized capacity expansion behaviors of distributed photovoltaic (PV) users, this paper proposes a PV capacity expansion detection model based on the gray wolf–particle swarm optimization hybrid optimization robust extreme learning machine (GWO-PSO-MELM). Firstly, the PV power generation data is preprocessed using cosine similarity and dynamic time warping (DTW) to reduce the impact of regional meteorological differences. Secondly, by combining the global search capability of the Gray Wolf Algorithm (GWO) with the fast convergence characteristics of the particle swarm optimization (PSO) algorithm, the hidden layer weights and biases of the robust extreme learning machine (MELM) are optimized to enhance the model’s robustness to outliers. Finally, the dynamic diagnosis of capacity expansion intensity and time nodes is achieved by calculating the illegal capacity expansion coefficient K. Experiments based on actual PV data from Changsha show that the probability density analysis of the illegal capacity expansion coefficient can identify capacity expansion behaviors as low as 10%, with a positioning error of capacity expansion time nodes of ≤4%. In actual cases, three illegal capacity expansion users were successfully detected, and the detection deviation remained small under different capacity expansion ratios, verifying the effectiveness of the proposed method in PV capacity expansion detection. Full article

(This article belongs to the Special Issue AI-Enhanced Operation and Management of Renewable Energy-Integrated Power Systems—2nd Edition)

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26 pages, 2519 KB

Open AccessArticle

Nine-Switch-Converter-Based Integrated On-Board Charger for Construction Machinery Adopting Recursive Least Squares Algorithm

by Binqing Lin, Guiping Du, Zhuofeng Deng, Tiansheng Zhu and Yanxiong Lei

Energies 2026, 19(10), 2349; https://doi.org/10.3390/en19102349 - 13 May 2026

Abstract

Pure electric construction machinery (PECM) is gradually becoming the mainstream choice for industrial construction. This paper presents a new configuration of an integrated charger for PECM. The proposed configuration employs a nine-switch-converter (NSC) that can achieve charging and traction functions for the target [...] Read more.

Pure electric construction machinery (PECM) is gradually becoming the mainstream choice for industrial construction. This paper presents a new configuration of an integrated charger for PECM. The proposed configuration employs a nine-switch-converter (NSC) that can achieve charging and traction functions for the target application. In charging mode, the motor is reused as a filter inductor and the NSC is reused as a conventional three-phase PWM rectifier. Data-driven adaptive predictive control (DAPC) based on recursive least squares (RLS) is proposed to cope with the motor’s saturation problem in charging mode. This control has the advantages of excellent robustness and fast dynamic response. Although the initial parameters are derived from the system model in the first sampling cycle, the controller subsequently relies entirely on online identification, which significantly reduces the sensitivity to parameter accuracy and eliminates the need for manual tuning of controller gains. In propulsion mode, the NSC enables independent operation of the two motors. The proposed configuration improves the utilization of devices and motors, which greatly reduces the weight, volume, and cost of the charger. Finally, an experimental platform was built to verify the feasibility and validity of the proposed topology and control algorithm. Full article

(This article belongs to the Special Issue Control of Power Electronics Converters and Electric Motor Drives—2nd Edition)

21 pages, 3175 KB

Open AccessArticle

Study of Long-Term Thermal Performance of Solar Pool Heating Systems at Selected Locations in Europe

by Sebastian Pater and Krzysztof Kupiec

Energies 2026, 19(10), 2348; https://doi.org/10.3390/en19102348 - 13 May 2026

Abstract

Heating water in outdoor pools is common, particularly in regions with cool or temperate climates. Several factors, including solar radiation, ambient temperature, wind speed, and humidity, influence the pool water temperature. A key design challenge is to determine the collector surface area required [...] Read more.

Heating water in outdoor pools is common, particularly in regions with cool or temperate climates. Several factors, including solar radiation, ambient temperature, wind speed, and humidity, influence the pool water temperature. A key design challenge is to determine the collector surface area required to achieve the desired pool water temperature. In this study, a mathematical model was developed that accounts for the aforementioned factors. Under various operating conditions, thermal performance calculations were carried out. Climatic conditions at three locations across Europe, representing different climate regimes, were analyzed. The model was compared with results from the POLYSUN simulation software. Most of the calculations were performed for a pool surface area of 24 m². The calculations showed that wind speed above the pool water surface has a significant impact on heat losses. Locating the pool in a sheltered area results in a consistent reduction in heat losses. It was determined that, under the climatic conditions of Kraków, the installation of solar collectors with a surface area equal to 50% of the pool surface enables the maintenance of daytime water temperatures above 21 °C for approximately 100 days. In the absence of solar collectors, achieving such temperatures is not feasible. Full article

(This article belongs to the Special Issue Recent Advances in Solar Thermal Collectors: Design, Modeling, and Performance Optimization)

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46 pages, 2849 KB

Open AccessSystematic Review

Artificial Intelligence Approaches for Energy Consumption and Generation Forecasting, Anomaly Detection, and Public Decision-Making: A Systematic Review

by David Velasco Ayuso, Jesús Ángel Román Gallego and Carolina Zato Domínguez

Energies 2026, 19(10), 2347; https://doi.org/10.3390/en19102347 - 13 May 2026

Abstract

The large-scale integration of variable renewable energy sources introduces critical challenges of intermittency and uncertainty, yet consumption forecasting, generation forecasting, and anomaly detection are typically addressed in isolation, neglecting the bidirectional feedback between consumption patterns, generation mix, and public decision-making. This PRISMA 2020-compliant [...] Read more.

The large-scale integration of variable renewable energy sources introduces critical challenges of intermittency and uncertainty, yet consumption forecasting, generation forecasting, and anomaly detection are typically addressed in isolation, neglecting the bidirectional feedback between consumption patterns, generation mix, and public decision-making. This PRISMA 2020-compliant systematic review compared statistical, machine learning, and deep learning models for energy forecasting and machine learning and deep learning models for anomaly detection. Searches in Google Scholar and Scopus used seven targeted strings, restricted to peer-reviewed empirical studies (2022–2026; 2023–2026 for anomaly detection), indexed in Q1–Q3 JCR journals, excluding theoretical and non-benchmarked works. A six-item risk of bias questionnaire—with a threshold of four points—guided inclusion, yielding 60 articles. Addressing the first research question (RQ1) on comparative model performance, hybrid deep learning architectures optimized with bio-inspired metaheuristics achieved the highest forecasting accuracy (

R^{2}

up to 0.9984), with metaheuristic optimization acting as a cost-reducing factor; statistical models remained competitive for long-horizon forecasting, while large-language-model-based approaches addressed data scarcity through few-shot learning. Addressing the second research question (RQ2) on smart grid optimization, predictive techniques reduce forecasting errors enabling real-time load adjustment and Demand Response, though a systematic asymmetry constrains their potential: consumption studies integrate socio-economic variables, whereas generation studies rely on meteorological inputs. Addressing the third research question (RQ3) on infrastructure security, supervised and unsupervised approaches detect anomalous operational states and support fault diagnosis, yet remain constrained by scarce labeled fault data and limited cross-regional validation; generative models such as GANs and diffusion models partially address this limitation by enabling Sim2Real strategies and realistic digital twin construction. Evidence is strongest for hybrid forecasting; certainty is lower for anomaly detection given reliance on experimental surrogates. No single paradigm achieves universal superiority. The primary finding is the consistent absence of integrated frameworks jointly modeling consumption, generation, anomaly detection, and public decision-making across the reviewed literature. This result reflects a structural limitation of the current state of the art, rather than a forward-looking research agenda. This study was funded by the ENIA International Chair on Trustworthy Artificial Intelligence European Recovery Plan; the protocol was not pre-registered. Full article

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44 pages, 83798 KB

Open AccessArticle

Neutral Conductor Loss in Residential Photovoltaic Installations: Overvoltage Analysis and Design of a Contactor-Based Automatic Transfer Switch

by Emanuel-Valentin Buică, Andrei Militaru, Dorin Dacian Leț and Horia Leonard Andrei

Energies 2026, 19(10), 2346; https://doi.org/10.3390/en19102346 - 13 May 2026

Abstract

The widespread adoption of photovoltaic systems in residential electrical installations has increased the importance of Automatic Transfer Switches (ATSs) for ensuring power continuity during grid outages. However, many low-cost ATS solutions available on the market prioritize economic efficiency over operational safety, leading to [...] Read more.

The widespread adoption of photovoltaic systems in residential electrical installations has increased the importance of Automatic Transfer Switches (ATSs) for ensuring power continuity during grid outages. However, many low-cost ATS solutions available on the market prioritize economic efficiency over operational safety, leading to significant risks under fault conditions. This paper investigates a real overvoltage incident in a residential three-phase installation equipped with a photovoltaic inverter and an ATS, which resulted in the failure of multiple electronic loads. The study reconstructs the event and demonstrates that the loss of the neutral conductor during backup operation caused severe phase voltage imbalance, generating overvoltage conditions across lightly loaded phases. A simplified electrical model is used to explain current paths and voltage redistribution under asymmetric loads, highlighting the critical role of correct neutral switching in ATS design. Two commercially available ATS architectures, one based on a changeover-contact mechanism and one employing four-pole miniature circuit breakers, are experimentally evaluated. The evaluation reveals major design deficiencies, including the absence of protective elements for control circuits, reliance on mechanical end-position limiters, and the use of switching devices not intended for frequent source transfer. These shortcomings introduce risks such as uncontrolled actuator operation, overheating, mechanical damage, and potential fire hazards. To overcome these limitations, a new ATS architecture was developed using a phase-monitoring relay, interlocked ABB contactors, and dedicated fuse protection for all control circuits. Detailed laboratory measurements were conducted to characterize contactor switching times and internal relay command delays. By optimizing the command sequence, the proposed ATS achieves predictable, fault-tolerant operation with competitive transfer times, representing a meaningful safety improvement over the evaluated commercial alternatives. The proposed solution is scoped to three-phase residential installations equipped with a hybrid photovoltaic inverter providing a dedicated backup output, operating within TN-S or TN-C-S earthing systems with a maximum grid connection capacity of 21 kW. Full article

(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)

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19 pages, 1219 KB

Open AccessReview

Mathematical Modeling of Heat Transfer During the Retrieval of a Downhole Sampler from an Ice Borehole: The Case of Borehole 5G-5, Vostok Station

by Sergey Ignatiev, Mikhail Kuznetsov and Andrey Dmitriev

Energies 2026, 19(10), 2345; https://doi.org/10.3390/en19102345 - 13 May 2026

Abstract

During the drilling of deep ice boreholes in Central Antarctica, one of the key tasks is to collect representative samples of the borehole fluid. The principal challenge is that, during retrieval of the downhole sampler to the surface, the sample is exposed to [...] Read more.

During the drilling of deep ice boreholes in Central Antarctica, one of the key tasks is to collect representative samples of the borehole fluid. The principal challenge is that, during retrieval of the downhole sampler to the surface, the sample is exposed to steep negative temperature gradients, which alter its physical properties and distort the representation of the actual borehole conditions at the sampling depth. For this study, an analytical review of current downhole sampler designs was carried out. For mathematical modeling, the finite difference method was used to solve the two-dimensional axisymmetric heat conduction equation for the "fluid sample–sampler wall" system. The initial temperature distribution was adopted from thermometric data obtained in borehole 5G-5, Vostok Station. The model incorporates actual trip-speed logs recorded during tripping operations. After modeling it was established that the temperature of the near-wall layer of the sample decreases significantly faster than that of the central region and that by the time the sampler reaches the surface, the difference between the sample temperature and the temperature of the surrounding borehole fluid is substantial enough to affect rheological properties of the fluid. The developed model makes it possible to justify the introduction of corrections to the results of direct measurements of fluid properties at the wellhead. Full article

(This article belongs to the Topic Heat and Mass Transfer in Engineering)

17 pages, 7360 KB

Open AccessArticle

Magnetic Levitation Triboelectric Nanogenerator for Vibration Monitoring of Hydroelectric Units

by Yanhui Wang, Xiao Zhang, Song Xu, Futian Geng, Da Che, Guanzheng Xu, Siyu Zhang, Fei Zhong and Jianmei Chen

Energies 2026, 19(10), 2344; https://doi.org/10.3390/en19102344 - 13 May 2026

Abstract

To address dependence on external power and the limited capability of conventional hydroelectric units to detect low-amplitude vibrations, this work introduces a self-contained, highly accurate monitoring device. The design incorporates a magnetically levitated configuration, with triboelectric films placed on both the upper and [...] Read more.

To address dependence on external power and the limited capability of conventional hydroelectric units to detect low-amplitude vibrations, this work introduces a self-contained, highly accurate monitoring device. The design incorporates a magnetically levitated configuration, with triboelectric films placed on both the upper and lower faces of the floating magnet. Under minor oscillations, magnetic repulsion increases the relative displacement between the friction layers, producing a substantial voltage that permits low-level vibration sensing. A surrounding induction coil responds to the levitated pole’s vertical motion; this motion intersects the magnetic flux, generating a current that provides stable energy for wireless data transmission. Experimental outcomes confirm a detection limit of 0.1 mm. At an amplitude of 1 mm and a load of 1000 Ω, the system achieves a maximum output of 9 mW and a power density of 1.587 W/m², ensuring reliable power. This configuration provides a new pathway for monitoring vibrations in hydroelectric turbine generators. Full article

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13 pages, 791 KB

Open AccessArticle

Energy-Efficient Installation for Ventilation Air Methane (VAM) Reduction in Mines

by Artur Dyczko, Andrzej Drwięga, Paweł Kamiński, Krzysztof Skrzypkowski, Adam P. Niewiadomski and Natalia Koch

Energies 2026, 19(10), 2343; https://doi.org/10.3390/en19102343 - 13 May 2026

Abstract

This paper presents a conceptual design for a technological installation aimed at mitigating ventilation air methane (VAM) from coal mine exhaust shafts, offering combined heat and power generation. It addresses the challenge posed by low methane concentrations (below 0.7%), which preclude direct combustion. [...] Read more.

This paper presents a conceptual design for a technological installation aimed at mitigating ventilation air methane (VAM) from coal mine exhaust shafts, offering combined heat and power generation. It addresses the challenge posed by low methane concentrations (below 0.7%), which preclude direct combustion. To overcome this, the proposed concept involves diverting a portion of the VAM to a combustion chamber of the power boiler dedicated to co-combustion with flotation concentrate suspension, which is properly prepared for feeding into the combustion chamber. The heat generated in the power boiler produces steam to drive a turbine generator for electricity production. Back-pressure steam from the turbine can be utilized for district heating or as a thermal energy source for various industrial processes, optimizing the plant’s energy efficiency and reducing its environmental footprint. The feasibility of this technology hinges on its cost-effectiveness and energy efficiency. This aspect of efficiency has been outlined. An energy balance analysis, based on real emission data from a selected mine, is provided to determine power boiler efficiency, fuel consumption, and a VAM reduction rate. The forecast of the amount of energy produced was presented for a single installation with a grate boiler capable of co-firing fuels with a VAM flow participation of 25 m³/s. Such installations can be scaled to meet mine requirements, enabling the neutralization of VAM at a total capacity of up to 300 m³/s, which corresponds to emissions from a large ventilation shaft. Full article

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25 pages, 2298 KB

Open AccessArticle

What Factors Drive the Spatiotemporal Differences in Coal Consumption in the Yangtze River Delta Region of China?

by Rui Cao, Chenjun Zhang and Chengqi Zhang

Energies 2026, 19(10), 2342; https://doi.org/10.3390/en19102342 - 13 May 2026

Abstract

The continuous combustion of coal releases carbon dioxide emissions, which has disrupted the Earth’s climate system and posed severe challenges to sustainable human development. As the world’s largest consumer of coal, China faces a critical challenge in curbing its dependence on this fuel. [...] Read more.

The continuous combustion of coal releases carbon dioxide emissions, which has disrupted the Earth’s climate system and posed severe challenges to sustainable human development. As the world’s largest consumer of coal, China faces a critical challenge in curbing its dependence on this fuel. The Yangtze River Delta region, characterized by its advanced economy and high level of industrialization, accounts for a substantial share of the nation’s coal consumption. Therefore, identifying the driving factors of coal consumption changes in this region is essential for formulating targeted low-carbon transition policies. Based on panel data of the YRD region covering 2000 to 2022, this paper employs the LMDI method to decompose the changes in coal consumption from both production and residential sectors, with four driving factors for the production sector and three for the residential sector. The results show that the total coal consumption in the four provinces of the Yangtze River Delta region follows an inverted U-shaped trend, peaking in 2011, with an average annual growth rate of 4.75% before the peak and an annual decline rate of 4.64% after the peak. Production coal consumption accounts for an average of 96.2% of the region’s total consumption. The effect of production intensity and the effect of economic scale are respectively the main inhibitory and driving factors. Spatially, Shanghai was the only province with negative cumulative coal consumption growth, and its average gap with Anhui was the largest among all pairs. Finally, this paper puts forward targeted policy recommendations, focusing on improving coal utilization efficiency and strengthening inter-regional coordinated emission reduction. Full article

(This article belongs to the Special Issue Factor Analysis and Mathematical Modeling of Coals: 2nd Edition)

30 pages, 2174 KB

Open AccessArticle

Operation Optimization of Electric Vehicle Battery Swapping Stations via Virtual Power Plant and Carbon Trading

by Xieyu Hu, Yuping Huang, Yilin Huang, Yongjian Zhao, Zhouchun Huang and Yu Liang

Energies 2026, 19(10), 2341; https://doi.org/10.3390/en19102341 - 13 May 2026

Abstract

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 [...] Read more.

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

(This article belongs to the Special Issue Carbon Emissions in the Energy Sector: Trends, Challenges, and Solutions)

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