Energies doi: 10.3390/en19184445

Authors: Sabelo N. Nhambe Peter M. Mashinini Bonginkosi A. Thango

Optimising grid-connected photovoltaic (PV)-battery renewable energy systems involves balancing economic performance, renewable energy use, and grid reliance. While HOMER Grid offers dependable optimisation, running multiple simulations for benchmarking algorithms is computationally costly. Surrogate-assisted optimisation presents a more efficient alternative for testing several algorithms under consistent conditions. This research evaluated six classic metaheuristics, Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimiser (GWO), Whale Optimisation Algorithm (WOA), and Stochastic Fractal Search Algorithm (SFSA), to find the most cost-effective techno-economic sizing of a PV battery system. Using a dataset of 272 core HOMER Grid cases, with 258 cases used for extended economic indicators, a distance-weighted k-nearest neighbour surrogate model was developed. All algorithms ran with the same population sizes, iteration limits, and independent repetitions. Their performance was assessed through solution quality, convergence behaviour, runtime, Friedman ranking, and Holm-adjusted Wilcoxon tests. The best design included a 7.799 kW PV array, 23 batteries, and a 2.560 kW converter, with a levelised cost of energy (LCOE) of R0.6149/kWh, a net present cost (NPC) of R52,401.45, a renewable fraction of 97.25%, and annual grid energy purchases of 216.26 kWh. Differential Evolution (DE) delivered the top average performance; SFSA was statistically comparable to DE (Holm-adjusted p = 0.999945), and PSO showed the fastest convergence with consistently near-optimal results. Overall, DE, SFSA, and PSO proved highly robust and effective, establishing a useful reference point for evaluating alternative surrogate-assisted PV battery optimisation approaches.

Energies doi: 10.3390/en19184446

Authors: Yalei Yuan Xiang Wang Shiwang Gu Xiaobin Mu Honghao Li

The increasing replacement of synchronous generators (SGs) by grid-following (GFL) and grid-forming (GFM) inverters is reshaping the dynamic bus characteristics of modern power systems. This transition makes a fixed ideal-voltage-source representation of the upstream main grid increasingly inadequate, especially when the interaction between inverter controls and network dynamics becomes significant. This paper investigates how the upstream grid bus admittance evolves with changes in source composition and observation location. An existing whole-system closed-loop impedance modeling framework is adopted. First, the parameter-dependent port characteristics of individual GFL and GFM inverters are analyzed to identify the frequency ranges dominated by external grid strength, synchronization mechanisms, and inner control loop dynamics. All frequencies reported herein are expressed in the synchronous dq frame. Five representative scenarios are then constructed on the IEEE 16-machine 68-bus system to describe the transition from SG-dominated operation to GFL-rich and SG/GFL/GFM hybrid operation. The results show that increasing GFL penetration together with SG decommissioning does not cause a uniform change in bus admittance, but redistributes the low-frequency resonance characteristics among different locations. At 40% GFL penetration, disconnecting the corresponding SGs changes the dominant peak at electrically remote buses from 56.2 dB at 1.32 Hz to 66.0 dB at 2.44 Hz. When the GFL share is kept at 40% and 15% GFM capacity is introduced, the dominant low-frequency peaks decrease by 8.9 dB and 14.0 dB for buses close to generation sources and electrically remote buses, respectively, while the corresponding average low-frequency magnitude variations decrease by 20.6 dB and 14.7 dB. However, the dependence of bus admittance on network location remains evident. These results indicate that future transmission and distribution interface equivalents should account for source composition, observation location, and frequency dependence rather than relying solely on a fixed grid equivalent.

Energies doi: 10.3390/en19184444

Authors: Iacovos Ioannou Saher Javaid

Power mini-grids are being transformed from locally automated electrical systems into cyber-physical ecosystems in which heterogeneous distributed energy resources, storage, converters, flexible demand and uncertain external conditions must be coordinated. Existing surveys commonly treat microgrid control, artificial intelligence (AI), multi-agent systems, communications, digital twins and cybersecurity as separate research streams. In this survey, the cognitive power mini-grid is introduced as a unifying paradigm in which physical and social contexts are perceived by distributed agents, task-relevant semantic information is exchanged, auditable coordination readiness is checked and proposed actions are subject to independent safety checks. A corpus of 247 scholarly publications, standards and technical sources is synthesized across microgrid engineering, distributed AI, semantic communication, language models, federated learning, neuro-symbolic AI, digital twins, causal reasoning, runtime assurance, cybersecurity and community energy markets. A six-layer architecture is proposed in which fast deterministic control is separated from semantic coordination, distributed learning, agentic deliberation and assurance. Representative studies are compared by problem, method, control horizon, information assumptions, validation environment, hardware-in-the-loop (HIL) status, reported outcome, limitations and architectural relevance. Applications are organized into balancing, resilience, demand-response, maintenance and inter-mini-grid markets. Cross-layer challenges are identified in stability under asynchronous interaction, semantic interoperability, hallucination, common-knowledge failure, edge resources, privacy and cyber-physical security constraints. A research agenda is developed around proof-carrying actions, causal digital twins, edge small language models, federated multimodal foundation models, neuromorphic semantic control and human-agent governance. Cognition is therefore positioned as a safety-gated coordination capability above verified physical control loops rather than as a replacement for established control.

Energies doi: 10.3390/en19184443

Authors: Maha Saleh Al Munidi Layan Fahad Al Tmimi Hawra Ibrahim Al Saihati Abdelkrim Zitouni

The major challenge in photovoltaic (PV) maximum power point tracking (MPPT) systems is finding a balance between the high performance of artificial intelligence techniques and the interpretability and reliability of physics-based approaches. This paper proposes a new MPPT controller based on a combination of Lagrangian and artificial intelligence techniques. In the proposed method, power maximization is modeled as a Lagrangian system, and the duty cycle is determined by physics equations. An artificial neural network is utilized to adaptively adjust the parameters of inertia and damping in real-time based on an eight-dimensional feature vector. Simulation results for step changes, ramp changes, and partial shading conditions confirm the effectiveness of the approach. The controller has 99.7% tracking efficiency in 18.2 ms, which is superior to P&O (55 ms), INC (45 ms), PSO (28.3 ms), and conventional ANN (22.5 ms). Under partial shading conditions, the controller correctly identifies the global maximum power point. The steady-state ripple is very low (±0.1 W), and the transient energy losses are significantly reduced compared to the benchmark algorithms. The results confirm that the integration of Lagrangian dynamics with adaptive neural tuning provides a systematic and efficient approach for designing reliable PV energy systems, effectively bridging the gap between data-driven and physics-based methods.

Energies doi: 10.3390/en19184442

Authors: Zhongjian Liu Ruixin Qian Xianing Jin Bingliang Shan Qingxi Li Yapeng Dai Xin Zou

When a virtual power plant (VPP) participates in energy and reserve markets, day-ahead bids, reserve commitments, and real-time delivery share the same internal resource constraints, while aggregate models cannot readily trace internal responsibility allocation or identify local shortfalls. This study develops a fixed-responsibility partitioning framework for day-ahead bidding and real-time delivery. Fixed membership mappings are screened under a unified budget; resource-type-specific reserve certificates and sustained-delivery constraints are embedded in a finite-support Wasserstein distributionally robust optimization (WDRO) model; and hierarchical model predictive control executes the market commitments. The 365-day evaluation retains three non-dominated candidates, K∈{6,7,8}, whose mean operational-fitness objective values are reduced by 25.5%–43.1% relative to the corresponding independent spectral-clustering baselines. In a common 30-day ablation study, detailed partition responsibilities increase mean daily adjusted operating profit by CNY 6442–8162 and reduce settlement deviation by 1.19–1.23 MW relative to two equivalent-responsibility rules. Here, adjusted operating profit is profit net of balancing-correction cost, settlement deviation is the schedule–settlement difference, and partition tracking error is the deviation between partition targets and aggregated resource response. The directions of these effects remain consistent across two independent 10-day windows, and hierarchical real-time coordination further reduces settlement deviation and partition tracking error. Public-aggregate-data replays show that the profit and deviation effects of WDRO relative to sample average approximation (SAA) vary with market settings and node-downscaled inputs. As an engineering extension, bounded contracted-capability factors, defined as multipliers on market-committable capability, increase mean daily adjusted operating profit by 6.00% in the 2024 holdout set, and attribution analysis indicates that most of this gain is associated with the average contracted-capability factor and released fast-response headroom. Under the tested settings, fixed-responsibility partitioning enables the continuous transfer of resource-type-specific day-ahead responsibilities into real-time resource control and makes the trade-offs among profit, delivery quality, and execution cost explicit.

Energies doi: 10.3390/en19184441

Authors: Lin Huang Lin Liu Pengpeng Zhang

Accurate detection of early minor faults in electric vehicle traction batteries is important for preventing thermal runaway under complex operating conditions. Aging-related capacity degradation and measurement noise can mask the weak voltage distortions caused by early faults, leading to false alarms in data-driven diagnostic models. To improve robustness to these disturbances, this paper develops an early multi-fault decoupling and diagnosis framework based on PatchTSSA, a lightweight Transformer architecture that adapts time-series patching and Token Statistics Self-Attention (TSSA) to battery diagnostic sequences. The framework combines static–dynamic feature fusion with time-series patching to capture both global voltage drift and local morphological gradients. Within this adapted framework, TSSA replaces quadratic dot-product attention with second-order moment pooling, giving linear complexity with respect to the number of tokens and supporting future investigation of embedded Battery Management System (BMS) implementation. A physics-informed fault-injection strategy is used to construct a five-class dataset comprising the healthy state (E00), minor internal short circuit (E01), severe internal short circuit (E02), penetration fault (E03), and sensor drift (E04) from public Center for Advanced Life Cycle Engineering (CALCE) and National Aeronautics and Space Administration (NASA) battery-aging data. Across five raw-cycle-grouped splits and training seeds under 5 mV Gaussian white noise, the complete PatchTSSA configuration achieves 91.5±1.9% overall accuracy, 94.1±1.2% macro recall, and 85.4±5.3% E01 recall for the simulated fault patterns. The direct E01–E04 confusion rate is 0.09±0.20%, whereas the E00-to-E01 false-alarm rate is 15.1±6.9%. CALCE–NASA protocol differences are used only to describe cross-dataset domain shift; no transfer-performance claim is made without a matched capacity-free evaluation. The results indicate the potential of the framework for online fault-pattern discrimination, while validation using real fault data and embedded hardware remains necessary.

Energies doi: 10.3390/en19184440

Authors: Weibo Li Jiatao Tao Zixin He Cenhai Wang Chengying Yang Chiyu Peng Tike Wu

In cascaded H-bridge (CHB) high-voltage drives using unipolar frequency-doubling carrier phase-shifted sinusoidal pulse width modulation (CPS-SPWM), unequal carrier phase offsets weaken switching-harmonic cancelation. This paper proposes a carrier-synchronization method that improves timing accuracy without adding dedicated synchronization fibers. The start edge of the downlink data frame is multiplexed as the common timing reference, hardware timer input capture removes interrupt-response latency from edge acquisition, and a dual-stage strategy combines standstill hard alignment with rate-limited on-line soft alignment. A phasor model links synchronization error to residual carrier-group amplitude and establishes a conservative accuracy target below 5 μs. On a complete 36-cell prototype, the measured spatial root-mean-square phase-offset deviation across the 11 Phase-A cell pairs referenced to A1 is 0.22° (1.22 μs), and the maximum absolute inter-cell phase-offset deviation is 0.36° (2.00 μs). A 380 V cascaded-output test confirms the expected 12 kHz dominant ripple and strong suppression of the 500 Hz and 1 kHz sidebands. Compared with a dedicated synchronization-link architecture, the proposed scheme halves the fiber and transceiver counts, requires only three pin-to-pin connections per-cell controller, and preserves address-free unified firmware. The method therefore provides a practical accuracy-cost compromise for large CHB systems, while full-voltage electromagnetic-compatibility validation remains future work.

Energies doi: 10.3390/en19184439

Authors: Shifu Gu Chunyu Zhou Tao Wang

To improve post-fault service restoration and network reconfiguration in distribution networks with distributed generation, this paper proposes a fault recovery and reconfiguration method based on a membrane computing multi-objective optimization algorithm. The proposed method formulates the restoration problem as a multi-objective optimization model that simultaneously considers load-restoration maximization, switching-operation minimization, network-loss reduction, and voltage-deviation minimization, while prioritizing the restoration of critical loads. Within the membrane-computing multi-objective optimization algorithm framework, the hierarchical parallel structure and evolutionary mechanisms of membrane systems are employed to enhance global search capability. Non-dominated sorting and crowding-distance calculation are incorporated to generate a well-distributed Pareto solution set, providing decision-makers with multiple candidate schemes for island partitioning and network reconfiguration. A weighted decision-making strategy is then used to select the optimal restoration scheme from the Pareto solution set. The proposed method is validated on the IEEE 33-bus distribution system. Simulation results show that, under distributed generation integration, the proposed method can effectively partition electrical islands, improve critical-load restoration, reduce network losses and voltage deviations, and support efficient post-fault restoration and reconfiguration of distribution networks. Compared with BWO, the best-performing benchmark method, the proposed method increases the total load-restoration rate from 92.1% to 95.3%, representing an improvement of 3.2 percentage points. It also reduces active power losses from 112.6 kW to 89.4 kW, a reduction of 20.6%, and decreases the maximum voltage deviation from 0.036 p.u. to 0.023 p.u., a reduction of 36.1%.

Energies doi: 10.3390/en19184438

Authors: Duc-Thien Huynh Cong-Thanh Pham Huynh-Quang-Duc Lam Van-Trung Trieu Minh-Tam Nguyen

This paper develops distributed Backstepping Integral Terminal Sliding Mode (BITSM) control for consensus speed tracking of networked Permanent Magnet Synchronous Motor (PMSM) drives under bounded matched uncertainty. A leader–follower speed loop supplies the q-axis current reference, and local integral terminal sliding surfaces regulate the dq currents. Under explicit disturbance and estimator-error bounds, unsaturated continuous-time analysis gives finite-time surface reaching; an input-to-state-stability argument then bounds the physical speed error, with topology dependence exposed through the pinned graph matrix. Sampling, saturation, delay, packet loss, and unmodelled implementation effects are treated as practical limitations rather than covered by the exact theorem. Four-agent simulations show about 83% lower peak transient speed spread and more than 90% lower steady-state inter-agent spread for the reported cases. Archived two-drive traces also show a 91.5% lower selected loaded-agent steady-state consensus RMSE than the reported PID comparator, while the unloaded agent does not improve in every region. Because source code, raw samples, matched-bandwidth tuning, repeated trials, and an independent rerun of the corrected law are unavailable, this last arithmetic contrast is descriptive and is not claimed as causal superiority or experimental certification of the corrected controller.

Energies doi: 10.3390/en19184437

Authors: Barbara Uliasz-Misiak Radosław Tarkowski

Underground hydrogen storage (UHS) is poised to emerge as a pivotal technology for large-scale and seasonal energy storage in future low-carbon energy systems. Nevertheless, ensuring its safe deployment necessitates implementation of monitoring strategies that account for the distinct physicochemical and microbiological characteristics of hydrogen. These characteristics cannot be directly transposed from underground gas storage (UGS) or carbon capture and storage (CCS) applications. In this study, we propose an integrated multi-domain monitoring framework for UHS developed through a thorough evaluation of European regulations; ISO and DNV standards; monitoring practices; and representative CCS, UGS, and UHS case studies. The framework includes a conceptual digital twin architecture for integrating heterogeneous monitoring data and supporting model updating, anomaly screening, and operational interpretation. It comprises five complementary monitoring domains: subsurface, near-surface, surface, remote, and digital. The integration of geophysical, geochemical, microbiological, atmospheric, and digital monitoring techniques, including 4D seismic surveys, fiber-optic sensing, hydrogen leakage detection systems, and satellite- and UAV-based observations, is a key aspect of this study. The analysis demonstrates that monitoring approaches developed for CCS and UGS provide a solid foundation for UHS but require adaptation to account for hydrogen’s high diffusivity, low viscosity, limited geochemical footprint, flammability, material compatibility issues, and potential for microbial conversion. The digital domain is proposed as an integration layer for monitoring data, model updating, anomaly screening, and decision support. Predictive and real-time capabilities require future field-scale implementation and validation. The proposed framework may provide a structured conceptual basis for the development of future UHS monitoring guidance, pilot applications, and regulatory requirements.

Energies doi: 10.3390/en19184436

Authors: Bashar B. Alzuwayer Saad F. Almokmesh

Cellular substrates placed beneath the piezoceramic are increasingly used to raise the output of vibration energy harvesters, yet the enhancement is commonly attributed to auxeticity, and the durability penalty of perforation is seldom quantified. This paper presents a perforated-root piezoelectric cantilever in which a doubly periodic array of bow-tie (double-arrowhead) through-holes occupies the high-curvature root beneath the electrode while the distal span remains solid. Using a cell of positive effective Poisson’s ratio, we show that power gain is a substrate-compliance and strain-relocation effect set by the position of the perforation relative to the electrode rather than by a negative Poisson’s ratio. A segmented distributed-parameter model in which the perforated root is homogenised and enters the beam through its longitudinal effective modulus E1*, with the piezoelectric coupling reduced to its plane-stress value, is derived and tested against three independent finite-element measurements on the explicit hole geometry in ANSYS Parametric Design Language (APDL). Two agree closely: a direct axial-tension test returns E1*/Es=0.270 against 0.268 predicted (0.7%), and the substrate-only fundamental is 21.7 Hz against 21.5 Hz predicted (1.0%). The third does not: with the piezoceramic present, the reduction predicts 47.7 Hz against 36.4 Hz, so the absolute frequency of the complete device is not yet established, and only the trends are relied upon here. At equal overall dimensions and piezoceramic, the baseline cell raises the peak power by about 14% over the solid beam, from 38.6 to 44.0 μW, and lowers the resonance from 57.6 to 50.7 Hz; both effects grow with hole size, reaching 23% and 46.5 Hz at the largest cell examined. A fatigue-constrained formulation, in which the net-section ligament stress bounds the usable fill factor, caps the fill at f≈0.59 under a 1 g excitation and yields a fill factor–load design map for durable operation.

Energies doi: 10.3390/en19184435

Authors: Andrey Zhuikov Tatyana Pyanykh Egor Hramtsov Galina Grishina Yana Zhuikova Dmitrii Glushkov Andrey Pleshko Kirill Gulkin Petr Kuznetsov

In the present study, using advanced methodologies together with analytical and experimental equipment, the ignition and combustion characteristics were determined for coal-based fuels, sorbent derived from this coal, and oil-impregnated sorbent (33.3 wt.% oil), as well as blended compositions of coal with oil-impregnated sorbent, where the mass fraction of the latter in the mixture was varied from 10 to 30%. By means of simultaneous thermal analysis, high-speed video recording under radiant-convective (500–800 °C) and radiant-conductive (600–1000 °C) heating conditions, and flue gas composition analysis, the principal combustion patterns and characteristics of the fuel set were established. The following parameters were determined: ignition temperature Ti, burnout temperature Tb, maximum Rmax and mean Rmean mass loss rates, integral combustion index S, ignition delay time td, as well as CO, CO2, and NOx concentrations in the flue gases. The experimental results indicate that the addition of 20% oil-impregnated sorbent to coal yields the maximum combustion index (3.18 min−2·°C−3) and the lowest burnout temperature (537 °C), whereas at a 10% addition, the highest burnout efficiency (98.4%) and the shortest ignition delay time in a heated air flow are achieved. Co-combustion of coal with 10–20% oil-impregnated sorbent is a promising approach for spent sorbent utilization while maintaining high energy performance of solid fuel combustion.

Energies doi: 10.3390/en19184434

Authors: Tingyan Lyu Bojin Tang Youhan Deng Xiaojun Hua Rong Kang Weiwei Yao Yaru Hao Chunyang Li Huilin Yuan

Low-switching-frequency operation is essential for high-power modular multilevel converters (MMCs), but it makes the simultaneous achievement of low harmonics, fast transient response, and reliable internal control more difficult. Conventional lookup-table selective harmonic elimination PWM (SHEPWM) preserves steady-state harmonic optimization, yet its fundamental-period pattern update limits transient flexibility; predictive pulse-pattern methods improve current or flux tracking, but do not directly coordinate the MMC execution chain from arm-level pulse displacement to physical submodule gating. This paper proposes a coordinated switching-event control scheme for SHEPWM-based MMCs. The offline harmonic-optimized pattern is used as the steady-state backbone, while selected time-stamped events are corrected online through a virtual-flux formulation. The same events are then processed by causal pulse-edge compensation, paired upper/lower-arm displacement for circulating-current suppression, and threshold-based asynchronous submodule scheduling. Simulations of startup, active-power steps, and power-flow reversal show that more than 90% of the virtual-flux error is compensated within approximately 5 ms. Device-level co-simulation reduces current THD from 1.5881% to 0.702%, and paired-event control reduces arm-current THD from 21.600% to 4.9266%. At rated steady state, the method achieves 1.25% grid-current THD with a 50 Hz average submodule switching frequency, supporting low-loss MMC operation without abandoning SHEPWM harmonic optimization.

Energies doi: 10.3390/en19184433

Authors: Mario R. Arrieta Paternina Gilberto Lopez Rios Pablo Moreno Villalobos Alejandro Zamora-Mendez Amrit Parajuli Camila Castrillón-Franco Gabriel E. Mejia-Ruiz Alfredo Velazquez-Ibañez Felix Rafael Segundo Sevilla Petr Korba

This paper focuses on deriving power-system linear models directly from collected PMU data with the aim of feeding a wide-area damping controller (WADC) architecture. To this end, a data-assisted dominant modal realization in state space is derived from frequency-response data, impulse-response data, and the retained impulse dynamic subspace, enabling WADC synthesis without requiring a full phenomenological network model. Then, a discrete-time linear quadratic Gaussian (LQG) structure is synthesized on the identified dominant modal realization, combining damping action with reconstruction of reduced states from WAMS measurements. Finally, the symbiosis between the proposed realization and the controller is validated on the Kundur and New England–New York power networks by using modal displacement and nonlinear post-fault simulations, demonstrating coordinated damping through selected AVR supplementary inputs.

Energies doi: 10.3390/en19184432

Authors: Hongmei Wang Hong Chen Hongtao Wang Yu Sun Yiqiang Li Yafan Xing Zhaoyang Lu

Due to varying development histories of reservoirs with different physical properties in Block X, Huabei Oilfield, the remaining oil distribution and microscopic mobilization limits after waterflooding remain unclear, and the subsequent CO2 transition timing urgently requires clarification. In this study, high-temperature and high-pressure core displacement experiments combined with online nuclear magnetic resonance (NMR) were conducted to simulate CO2 injection after waterflooding to water cuts of 50%, 80%, and 100% and compared with continuous gas injection (CGI) to reveal the effect of different switching timings on the oil recovery and pore-throat mobilization for the three reservoir classes tested in this study. Results indicate that waterflooding primarily mobilizes pores larger than 0.1 μm, while crude oil in small pores (<0.1 μm) is difficult to effectively displace. After switching to CO2 flooding, the mobilization efficiency across all pore-throat classes increases significantly, with Class IV reservoirs exhibiting the largest enhancement in micropore recovery—from 5.8% after waterflooding to 34.5% after CO2 flooding, representing an increment of approximately 28.70 percentage points—demonstrating that CO2 can significantly expand the effective mobilization range relative to waterflooding. The underlying microscopic mechanism is that the degree of waterflooding alters the oil–water distribution within the core: at low-water-cut stages, remaining oil is continuously distributed, allowing injected CO2 to contact crude oil through sufficient diffusion and dissolution, which is interpreted as providing a mobility-control effect that helps suppress gas channeling; at high-water-cut stages, remaining oil is segmented and trapped in micropores, limiting CO2–crude oil contact, while long-term waterflooding establishes preferential water pathways that promote localized gas channeling and restrict mass transfer. These findings provide experimental evidence for the class-specific design of post-waterflooding CO2 injection strategies.

Energies doi: 10.3390/en19184431

Authors: Yanna Gao Hong Dong Fanhong Zeng Yuqun Gao Liujun Hu Shangjun Ke

Import-dependent megacities must decarbonize while accommodating distributed photovoltaics (PV) under constrained local-generation and interchange boundaries. This study develops a full-year, hourly, 48 h rolling mixed-integer linear programming model for coordinated city-level scheduling in Guangzhou, China. The VPP-controlled portfolio includes distributed PV, grid- and user-side battery energy storage systems, demand response, and aggregated electric vehicles, whereas local generators and inter-regional imports are represented as system-operator coordination variables. After correcting the energy balances of flexible resources and recalibrating the hourly interchange boundary, the baseline scheduled-import share is 68.68%, only 0.64 percentage points above the measured schedule. Across four policy-defined PV anchors, combined annual operating-and-carbon cost decreases from CNY 75.01 to 65.50 billion, carbon intensity declines from 467.9 to 412.2 g CO2/kWh, and import share falls from 68.68% to 63.92%. A dense 0–12 GW sweep at 0.5 GW intervals reveals a continuous response: curtailment first exceeds 1 MWh at 4.0 GW but remains only 0.006%, providing no evidence of a unique breakpoint. A conventional time-of-use tariff reduces import share by 0.58 percentage points while increasing carbon intensity by 1.25 g CO2/kWh. These results are conditional on the deterministic supply construction, calibrated interchange boundary, adopted emission factors, and operating-cost scope.

Energies doi: 10.3390/en19184430

Authors: Jing Wang Meng Chen Mengfei Peng Xue Cui Huangyi Yang Xuehan Dang

Residential load profiling can support the design of differentiated demand-side management, but clustering methods are sensitive to representation quality and initialization. We developed a profiling workflow that combines a convolutional attention variational autoencoder (CA-VAE), adaptive gray wolf optimizer K-Means (GWO-K-Means) search over candidate cluster numbers and centers, and a clustering-loss refinement stage. On 6390 prepared residential average-day load profiles from the Commission for Energy Regulation dataset, ten independent runs yielded a final silhouette coefficient of 0.5002 (SD 0.0314), a Davies–Bouldin index of 0.8532 (SD 0.0383), and a Calinski–Harabasz index of 3694.3 (SD 260.5). Relative to raw K-Means, the final workflow improved all three internal validity measures (paired Wilcoxon signed-rank test, p = 0.001953 for each metric). GWO selected K = 5 in 6 of 10 runs; this modal solution produced five interpretable load profiles. The profiles provide load-shape-derived candidate groups for future demand-response trials, rather than validated estimates of operational flexibility.

Energies doi: 10.3390/en19184425

Authors: Qing Chen Qin Liu Xiaofei Chang Feng Huo Jing Nan Yeming Ma

The ±500 kV valve halls of wind farm offshore platforms are integrated through a high-voltage direct current (HVDC) transmission system and constructed in deep-sea conditions, making the design of net air clearance distances particularly critical. This paper investigates key factors influencing air clearance distances associated with the impulse overvoltage characteristics of the main equipment in the valve hall. Based on the preliminary design of a ±500 kV offshore converter valve hall, critical air gaps were determined. The full-scale samples were employed to conduct standard switching impulse overvoltage tests. In this way, the overvoltage discharge characteristics of air gaps in the offshore converter valve hall were obtained. Finally, based on the air gap configuration method and the obtained discharge characteristic data, a recommended net air clearance distance range is proposed for the ±500 kV valve hall of the offshore platform, providing a vital reference for the compact design of offshore wind farm valve halls.

Energies doi: 10.3390/en19184428

Authors: Yixuan Zhu Wenhao Yao Tianhui Fang

This study examines whether a structure-preserving representation of the joint Open-High-Low-Close (OHLC) vector provides coherent forecasts for China’s INE crude-oil futures and how seven benchmark models compare within that representation. The sample contains 1547 trading days from the contract launch to 8 January 2025. Under the originally reported 80:20 setting, GRU has the smallest aggregate MAPE (0.7825%), MAE (4.6774), and RMSE (7.2957), together with the largest interval-overlap Success Ratio (0.5629). These figures establish a numerical ranking only: the archived materials do not verify the exact temporal split, training-only preprocessing, component-specific errors, or price-limit-event robustness. The economic section therefore contains only illustrative Close-to-Close and Open-to-Close mappings and does not claim executable profitability. The defensible contribution is the structure-preserving OHLC framework and a bounded numerical comparison.

Energies doi: 10.3390/en19184429

Authors: Muhammad Zeeshan Tariq Asma Aziz Barun K. Das

The current study combines solar photovoltaic (PV) power, energy storage batteries and wind power to propose a novel and effective method for energy management and optimisation of hybrid renewable energy systems. Optimising the management and coordination of many energy sources is becoming essential, as the world moves towards sustainable energy choices. This study seeks to improve system performance, reliability and operating costs, using a unique hybrid control and power management paradigm, the Improved Human Evolutionary Optimisation (IHEO) algorithm, presented as a novel optimisation method that balances energy flow, generation, storage and consumption. This study shows that the proposed model greatly improves the efficiency of the operation of hybrid systems. The optimisation’s main objective is to reduce the overall cost of energy production while maintaining a smart energy management system. In this context, the cost function accounts for energy losses during electricity distribution as well as the generation costs of solar, wind, and battery storage. By minimising energy losses and optimising power flow between energy sources (wind, solar), storage (battery) and load demand, this can be used to assess system performance. The applied approach ensures system stability, optimises the use of renewable energy sources and reduces the power imbalance. The improved effectiveness of the IHEO algorithm in this study in minimising energy losses, lowering operating costs and enhancing overall system efficiency is demonstrated by thorough comparison with conventional particle swarm optimisation (PSO). Further to this, the integration of MPPT with PV systems and the IHEO algorithm enhances energy extraction efficiency by dynamically optimising power flow, ensuring maximum output from renewable sources under varying environmental conditions. Additionally, the BESS charging current ripple is also reduced from ±15 A to ±3 A using the model applied in this study, confirming smoother and safer battery charging operation. The key novelty lies in using IHEO for global exploration to find the best solution and PSO for local refinement to improve battery coordination with renewables and smooth DC-link regulation, which is then compared with conventional WOA.

Energies doi: 10.3390/en19184427

Authors: Tianqiong Chen Huijuan Huo Xiaofang Zhao Cheng Xin Ruochen Zhang Ye Ke Shuo Wang Weiwei Li

Reliable review of preliminary design estimates is important for cost control in power grid substation projects, yet fixed thresholds have limited ability to reflect project-specific engineering conditions. This study develops a multidimensional cost-deviation screening framework integrating CatBoost, Conformalized Quantile Regression (CQR), Cost Structure family-wise calibration, and SHapley Additive exPlanations (SHAP)feature attribution. Total Cost, Unit Cost, and Cost Structure are evaluated jointly, with CQR providing project-specific marginal prediction intervals and an economic-exposure-weighted calibration controlling simultaneous screening across the five Cost Structure components. SHAP is subsequently used to provide feature-level interpretation of screened projects. The framework was evaluated using 906 substation projects from 27 provincial grid companies in China, including 761 projects for development and 145 for held-out evaluation. CatBoost achieved R2 values of 0.9363 for Total Cost and 0.8899 for Unit Cost. After Cost Structure family-wise calibration, 22 held-out projects (15.17%) received review flags. Incorporating Cost Structure increased the number of flagged projects from 15 to 22, identifying seven additional projects not flaggedby Total Cost or Unit Cost alone. The results demonstrate competitive predictive performance and the incremental screening value of Cost Structure, although prediction-interval efficiency remained target dependent. The proposed framework provides an uncertainty-aware and interpretable approach for prioritizing preliminary-design cost review.

Energies doi: 10.3390/en19184426

Authors: Piotr Szeląg Tomasz Popławski Michał Adamusiński

This study assessed how the selected seasonal test period, training-window strategy, and hyperparameter selection were associated with differences in XGBoost day-ahead forecast accuracy and interpretation for approximately 300 G11-tariff households in Poland. Sixteen configurations combined four 31-day periods, sliding or expanding training windows, and shared (H1) or window-specific (H2) hyperparameters. The model used 19 temporal, meteorological, and calendar features; meteorological predictors for training, validation, and testing were archived numerical weather prediction (NWP) forecasts from the same operational forecasting system, available before the forecasted day. Accuracy was evaluated using mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE); paired comparisons used the Diebold–Mariano test with the Harvey–Leybourne–Newbold correction and Holm adjustment. Global SHAP (SHapley Additive exPlanations) rankings were compared using Spearman’s coefficient. MAE ranged from 7.79 to 14.98 kWh, and all configurations had lower MAE, RMSE, and MAPE than both persistence benchmarks. After Holm correction, no training-window strategy showed a statistically supported advantage; H2 was supported for the autumn expanding-window comparison, whereas the summer result depended on the variance estimator. The 24 h consumption lag ranked first in every configuration, and mean rank agreement across 120 pairs was 0.897. Accuracy varied across periods and configurations, whereas feature hierarchy remained highly consistent.

Energies doi: 10.3390/en19184423

Authors: Yunzhao Wu Guanglin Sha Tao Zhou Haijun Yu Jianfang Chen Yuanchao Li Jinjin Ding Guansen Wang Jianing Wang

Existing photovoltaic (PV) power forecasting methods face challenges in simultaneously capturing multi-scale temporal characteristics, spatial dependencies among PV plants, long-term temporal correlations, and output uncertainty. To address these issues, this paper proposes a spatiotemporal probabilistic forecasting framework for PV plant clusters that integrates Variational Mode Decomposition (VMD), Graph Convolutional Networks (GCN), Informer, and Quantile Regression (QR). VMD decomposes non-stationary PV power series into components with different frequency characteristics, while GCN captures spatial dependencies among PV plants. Informer efficiently models long-term temporal dependencies, and QR generates probabilistic forecasts to quantify output uncertainty. The proposed VMD-GCN-Informer-QR model is evaluated using data from the Australian DKASC PV system. At the 5-min forecasting horizon, the proposed model obtains an MAE of 42.834 kW and the lowest RMSE of 72.182 kW. For probabilistic forecasting, the proposed model achieves a PICP of 89.682%, with an MPIW of 201.818 kW. Multi-step forecasting further shows that the proposed model outperforms Persistence from 15 to 60 min, with its relative advantage increasing as the forecasting horizon extends. Seasonal analysis also confirms the adaptability of the proposed model under different seasonal conditions. These results demonstrate the effectiveness of the proposed framework in limiting large forecasting errors, quantifying forecasting uncertainty, and maintaining robust performance over extended forecasting horizons.

Energies doi: 10.3390/en19184424

Authors: Rui Wang Xiuying Liang Xue Bai Jiajia Shao Jianhong Cheng

Energy-efficiency labeling may yield lower savings than engineering estimates suggest because market uptake is constrained by consumers and enterprises. This study aims to quantify this gap for dishwasher energy-efficiency labeling in China, identify the binding constraint, and assess its recoverable share. A logistic stock-flow model estimates theoretical savings under GB 38383-2019, while surveys of 199 consumers and 191 enterprises parameterize a dual-sided indicator system. Assuming that consumer and enterprise capacities are non-substitutable, a weakest-link model and scenario simulations estimate realized and recoverable savings. Theoretical savings reach 20.26 TWh by 2030, but only 55.9% (11.33 TWh) is realizable, leaving an 8.94 TWh gap. Consumer-side capacity (0.559) is lower than enterprise-side capacity (0.657), mainly because of experience-related barriers. The highest-intensity bilateral scenario recovers 33.0% of the gap and raises the realization rate to 70.4%. Thus, stricter standards alone cannot ensure realized savings; policies should prioritize consumer experience and label accessibility alongside enterprise compliance.

Energies doi: 10.3390/en19184420

Authors: Adrián Geovanny Urgilés Rojas Nicolás Dueñas Vargas Julio César Zambrano Abad

Modern wind turbines require effective control strategies to maximize energy capture under partial-load conditions while maintaining generator-speed and power regulation above the rated wind speed. This study proposes and applies a controlled and reproducible benchmarking framework for evaluating classical Differential Evolution (DE) variants in the tuning of Proportional–Integral–Derivative (PID) and Proportional–Integral–Derivative–Accelerative (PIDA) controllers for a nonlinear model of the National Renewable Energy Laboratory 5-MW reference wind turbine. Twenty classical DE variants were assessed using a fixed-seed initialization strategy, unified simulation procedures, consistent objective-function definitions, and region-specific optimization settings applied uniformly to all variants within each operating region. The controllers were evaluated in Region 2, where maximum power point tracking is required, and Region 3, where generator speed and electrical power must be regulated under above-rated wind conditions. Their generalization performance was subsequently evaluated in simulation using a measured wind-speed profile obtained from a Supervisory Control and Data Acquisition system. In Region 2, under the considered fixed-seed configuration, the DE-tuned controller achieving the lowest objective-function value reduced the objective function by approximately 2.93% compared with the baseline PID controller, indicating a moderate improvement. In Region 3, the PIDA controller tuned with the DE variant yielding the lowest objective-function value achieved a reduction of up to 92% relative to the baseline controller, demonstrating a substantially greater benefit under above-rated operation. However, validation using the measured wind profile indicated that some controllers with favorable tuning-stage results showed signs of overfitting and reduced generalization performance. Overall, the results indicate that the suitability of the controller structure and DE configuration depends on the wind turbine operating region and highlight the importance of controlled, reproducible optimization procedures and validation beyond the tuning scenario in wind turbine controller design.

Energies doi: 10.3390/en19184422

Authors: Nikolay Markov Evgeny Yudin Svetlana Kraeva Nikita Trubnikov Sofia Permyakova Roman Bondorov

Operational management of hard-to-recover (HTR) reservoirs requires modelling tools that can be repeatedly updated and recalculated as new field data become available. This study presents a reduced-order framework that combines a source-based boundary element formulation for pressure redistribution with streamline-based oil–water transport and sequential history matching. The framework is evaluated using a synthetic benchmark and five field cases with different geological and operational characteristics. Three field cases are additionally compared with existing three-dimensional hydrodynamic models. Under matched simplified physical conditions, the synthetic benchmark reproduced pressure and saturation behaviour with a saturation field MSE below 0.2% and production rate MPE below 3%. In the field cases, retrospective liquid rate forecast MAPE ranged from 3.03% to 8.28%, while oil production accuracy showed greater sensitivity to unresolved displacement complexity. A large-scale test with 2483 wells demonstrated the computational feasibility of the combined workflow. The calibrated model was further applied to injection redistribution and new-well forecasting under uncertainty, with the optimized injection scenario yielding approximately 10% higher predicted cumulative oil production over the first forecast year. Optional components for historical data reconstruction and physics-informed pressure refinement were also considered but were not used in the main field case calculations. The results demonstrate the potential of the framework for repeated operational forecasting and scenario evaluation within the applicability limits of its reduced-order assumptions.

Energies doi: 10.3390/en19184421

Authors: Jacek Misiak Barbara Uliasz-Misiak

Large-scale underground hydrogen storage (UHS) in saline aquifers requires preliminary screening methods that consider both subsurface suitability and the spatial context of the energy system. This study develops an integrated geographic information system with multi-criteria decision analysis (GIS–MCDA) framework for the regional assessment of candidate UHS sites under conditions of limited data availability. Eleven geological and operational criteria were grouped into reservoir quality (RQ), seal integrity (SI), and operational conditions (OC) and weighted using the Best–Worst Method (BWM), resulting in component weights of 0.520, 0.296, and 0.184, respectively. The normalised criteria were aggregated into a Geological Hydrogen Suitability Index (GHSI), which was subsequently combined with the spatial distribution of installed renewable energy (RE) capacity and proximity to high-voltage power infrastructure (DI) to derive a Total Suitability Index (TSI). Three weighting scenarios representing geological, balanced, and energy–infrastructure priorities were analysed. The framework was applied to five candidate structures in north-western Poland: structure S4 achieved the highest GHSI (0.757), followed by structure S1 (0.699); structures S2 and S5 obtained very similar intermediate values (0.579 and 0.578, respectively); and structure S3 received the lowest score (0.505). The contrasting component profiles demonstrate that high performance in a single criterion group does not necessarily result in high overall geological suitability. The scenario analysis also showed that increasing the weights of the components relating to installed renewable energy (RE) capacity and distance from power transmission infrastructure (DI) can significantly alter the assessment, which is based primarily on geological suitability; these changes are particularly pronounced in the case of structures S4 and S5. The proposed framework provides a transparent spatial decision-support approach for preliminary regional UHS screening. Its results should be interpreted as relative suitability assessments rather than direct estimates of storage safety, capacity, injectivity, or commercial feasibility.

Energies doi: 10.3390/en19184419

Authors: Yuncai Tan Leping Sun Xianlin Chen Xiaogui Chen Tingzhe Pan Yulin Gong Zhongwei Sun

Traditional ports adopt separate scheduling modes for logistics and multi-energy subsystems without deep bidirectional coupling, which leads to low demand response (DR) participation, severe mismatch between the logistics power demand and multi-energy supply, and failure to fully exploit the thermal flexibility of port facilities. Single-link logistics optimization cannot generate complete time-varying load curves, making it impossible to coordinate vessel operation efficiency with port economic operation. To tackle the above limitations, this paper proposes HTGA-AFADMM, namely a hybrid topology genetic algorithm associated asynchronous fuzzy alternating direction method of multipliers (ADMM), as an integrated two-layer collaborative solver. HTGA-AFADMM presents four progressive core innovations. First, it establishes a DR-driven coupled architecture with shore power (SPS) as the core hub, building bidirectional information interaction channels to guide proactive logistics load shifting via thermal flexibility. This breaks passive energy matching under separate scheduling. Second, it embeds a customized hybrid topology genetic solver to solve the NP-hard five-stage flexible flow shop scheduling problem, coordinating vessels, SPS, and all handling equipment to strengthen constraint adaptability and convergence performance. Third, it constructs a heterogeneous-unit oriented distributed robust framework, eliminating idle waiting via asynchronous iteration and quantifying uncertainties with fuzzy membership factors. Fourth, it realizes nested closed-loop iteration between two layers, which takes logistics power curves as coupling signals and iteratively updates consensus variables to obtain optimal scheduling schemes. The proposed HTGA-AFADMM collaborative framework effectively coordinates port logistics scheduling and multi-energy optimal dispatch. It achieves satisfactory economic performance while suppressing power-balance deviations under communication delay and load uncertainty and exhibits good convergence and adaptability for practical port cross-domain scheduling scenarios.

Energies doi: 10.3390/en19184418

Authors: Daniel Sanin-Villa Vanessa Botero-Gómez Adrián Felipe Martínez Pérez

This paper evaluates the operation of battery energy storage systems under grid-following and grid-forming representations in a photovoltaic-rich 33-bus radial distribution network. The study combines a 24-h scheduling model, an AC radial power flow, converter apparent power limits, battery state-of-charge constraints, and a voltage-dependent reactive power model for the grid-forming mode. Photovoltaic units are installed at buses 13, 25, and 30, while battery energy storage systems are installed at buses 6, 14, and 31. Four operating cases are assessed: the base feeder without distributed energy resources, photovoltaic generation under grid-following operation, photovoltaic generation with a grid-following battery system, and photovoltaic generation with a grid-forming battery system. The grid-forming representation reduces daily losses by 48.58%, raises the minimum voltage from 0.8955 p.u. in the grid-following BESS case to 0.9042 p.u., and lowers daily grid imports to 64.026 MWh. Its lower-loss and lower-cost ordering relative to the grid-following BESS is preserved under high-load/low-PV and low-load/high-PV conditions, although it produces higher maximum branch loading. Islanding screening at hours 12, 18, 19, and 20 shows that the available grid-forming reserve is insufficient in every case; both BESS modes cross the 57-Hz threshold. The proposed formulation shows that battery energy storage systems should not be represented solely as active-power scheduling devices when their inverter control mode can modify voltage support, feeder loading, and islanded operation.

Energies doi: 10.3390/en19184417

Authors: Michał Igielski

Renewable energy transition management increasingly depends on the integration of digital technologies, organizational capabilities, and innovative forms of value creation. This study examines how digital transformation, organizational learning, and Sustainable Business Model Innovation jointly support renewable energy transition management. A convergent mixed-methods design was adopted, integrating comparative quantitative analysis of secondary data with qualitative evidence derived from strategic documents and complementary evidence obtained through structured expert assessments conducted in Poland, Germany, Denmark, and Spain. The comparative evidence suggests that higher levels of digital maturity are generally associated with stronger renewable energy performance, greater innovation intensity, and more advanced forms of Sustainable Business Model Innovation. Digitally advanced energy systems appear to exhibit stronger capacities for decentralized coordination, participatory governance, and adaptive learning processes, while less mature systems rely more heavily on localized experimentation and fragmented institutional arrangements. The comparative analysis also highlights differences in renewable energy transition pathways across the analyzed European countries, emphasizing the importance of organizational and institutional conditions in shaping transition processes. The study contributes to the literature by proposing an integrated analytical framework integrating digital transformation, organizational learning, and Sustainable Business Model Innovation within renewable energy transition research. The findings underline the importance of digitally enabled coordination and learning-based adaptation for supporting resilient renewable energy transition pathways and provide practical implications for policymakers, energy companies, and other stakeholders involved in sustainability-oriented energy transitions.

Energies doi: 10.3390/en19184416

Authors: Michael Short Sean Williams

Cities worldwide are confronting major challenges associated with rapid urbanization, climate change, resource constraints, aging infrastructure, and the transition toward decarbonized, sustainable economies [...]

Energies doi: 10.3390/en19184415

Authors: Jianan Zhang Maanasa Bhat Yan Zhao

Ammonia is a promising carbon-free energy carrier for decarbonizing existing fossil fuel power plants. However, its economic viability and life-cycle environmental performance remain uncertain across different power generation technologies. This study develops an integrated techno-economic analysis (TEA) and life-cycle analysis (LCA) framework to evaluate 50% ammonia co-firing in four power generation systems: subcritical pulverized coal (SubC-CP), supercritical pulverized coal (SC-CP), integrated gasification combined cycle (IGCC), and natural gas combined cycle (NGCC). Process models modified from National Energy Technology Laboratory (NETL) simulations were used to determine mass and energy balances, while TEA quantified total plant cost, levelized cost of electricity (LCOE), breakeven ammonia price, and CO2 avoidance cost (CAC). LCA evaluated well-to-gate (WTG) CO2 emissions considering both blue and green ammonia. Results show that under the current blue ammonia price of $318/tonne, all ammonia co-firing cases exhibit higher LCOEs than the reference plants, and the breakeven ammonia prices remain below current market values. Blue ammonia reduces WTG CO2 emissions by approximately 30% for coal-fired plants and 19% for NGCC where upstream ammonia production is the major emission source. Replacing blue ammonia with green ammonia further reduces WTG CO2 emissions by 27–36% and decreases the CAC by 38–60%. These results demonstrate that low-carbon, low-cost ammonia production is essential for realizing economically competitive and environmentally sustainable ammonia co-firing in future low-carbon power systems.

Energies doi: 10.3390/en19184413

Authors: José Magano Teresa Nogueira

Grid-impact studies treat battery electric vehicles as loads, and ask when network capacity will be exhausted. This paper reverses the question: how much of the fleet must operate bidirectionally, and with what probability will an achievable participation rate suffice, for the network to remain within its limits? A conceptual framework adds a vehicle-to-grid and vehicle-to-building flexibility term to the balance between available and required power, nests the authors’ earlier deterministic model for twenty municipalities in Northern Portugal as its zero-flexibility special case, derives a closed-form break-even participation rate per municipality and year, and keeps the simultaneity assumption of that model explicit as a coincidence factor. Participation, location, plug-in and export parameters follow beta-PERT distributions calibrated on published trials and surveys, propagated by Monte Carlo simulation without new field data. The framework is an apparent-power balance per municipality, so its outputs are an upper bound on usable flexibility, not a feeder-level feasibility check. An enrolled vehicle provides about 11 kVA of peak relief, over nine tenths from not charging rather than exporting. Under worst-case simultaneity, observed participation rates, if in place from the outset, halve the 2028 shortfall probability but cannot prevent shortfall by 2030; under realistic coincidence the regional network is not constrained and only eight of twenty municipalities remain critical. The network balance is replicable wherever municipal substation data exist; behavioural parameters require local calibration.

Energies doi: 10.3390/en19184414

Authors: Temitope Adefarati Gulshan Sharma Pitshou N. Bokoro Rajesh Kumar

Accurate forecasting of energy consumption, renewable power output and utility expenditure is essential for sustainable planning of residential buildings and improving smart grid integration. This study presents several techniques such as random forest, gradient boosting regression, extreme gradient boosting, deep belief networks, random vector functional link, multi-layer perceptron and hybrid ensemble for forecasting of residential load demand, electricity bills, solar energy generation and solar irradiance. Electricity bills under Time-of-Use tariffs are introduced in the paper to accomplish realistic evaluation of economic implications and facilitation of optimized energy usage and cost savings using real-time residential energy data collected from Durban, South Africa. The performance of the forecasting model is assessed by root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), coefficient of determination (R2) and mean absolute scaled error (MASE). The outcomes of the study show that the hybrid ensemble model accomplished the highest forecasting accuracy of the electricity bill with MAE, RMSE, MSE, MASE and R2 of 0.018126, 0.022961, 0.00052719, 0.30006 and 0.97978 when compared to other models. The findings of the research can be used as potential benchmarks for intelligent tariff forecasting, demand response planning, smart energy management and renewable energy integration in residential buildings.

Energies doi: 10.3390/en19184406

Authors: Yunxiang Li Runsheng Yang Yuefeng Guo Xingkang Su Youpeng Zhang

Spacer wires are widely employed in lead-cooled fast reactor fuel assemblies to maintain rod positioning. The helical spacer structure induces rotational flow and enhances transverse mixing between subchannels, thereby significantly influencing thermo-hydraulic performance. To further regulate coolant mixing intensity and reconstruct internal flow structures, a double-wire configuration with variable radial phase differences is proposed. Three-dimensional steady RANS simulations of liquid lead–bismuth flow in a 19-pin double-wire fuel assembly are conducted using a four-equation turbulent heat transfer model. Results indicate that pressure and velocity fields exhibit periodic distributions along the helical direction, with a clear inverse correlation between high-pressure and high-velocity regions. Transverse secondary flow intensity shows pronounced axial periodicity and attains a maximum value of 0.32, with stronger mixing observed in the vicinity of the spacer wires. Peripheral and corner subchannels maintain lower average coolant temperatures, whereas peak temperatures are concentrated within internal subchannels. The overall convective heat transfer coefficient decreases gradually along the axial direction and presents a localized enhancement in the mid-axial region. The axially averaged convective heat transfer coefficient of DP60 is approximately 4.58% higher than that of DP90. This difference is a thermal comparison and does not establish overall thermo-hydraulic superiority. Peak modeled coolant temperature fluctuations are observed at the interface between peripheral and outer internal subchannels, while maximum turbulent Prandtl numbers are concentrated within internal subchannels.

Energies doi: 10.3390/en19184412

Authors: Jiawei Zhu Yalou Li Wenjia Xu Ping Ding Kaiyao Wan

Virtual synchronous generator control offers a practical grid-forming (GFM) solution for converter-rich power networks by enabling power-electronic units to reproduce selected electromechanical responses of conventional synchronous generation. Nevertheless, severe fault currents may arise when grid disturbances occur. A tractable analytical description is therefore required to reveal the temporal evolution of the converter current, identify the principal control-dependent variables, and support the design of effective overcurrent mitigation measures. A full-order model that retains every control loop, however, leads to a highly coupled nonlinear system and is not well suited to closed-form analysis. This study develops a reduced analytical framework for a grid-connected VSG equipped with virtual impedance-based current restriction under a balanced three-phase fault. A fault-specific circuit representation is first established to characterize the electrical interaction between the converter and the external network. The reactive power relation obtained from this representation is then introduced into the voltage-regulation dynamics, yielding a time-domain expression for the internal voltage magnitude. This result is subsequently embedded in the network current equation to obtain an explicit formulation of the phase current without assuming a constant internal voltage. The derived current response is separated into a sustained fundamental term, an exponentially attenuating DC offset, and an exponentially attenuating fundamental-frequency term. The effects of virtual impedance, retained grid voltage, reactive power–voltage integral gain, and transient decay constant are further examined. Comparisons with electromagnetic transient simulations under multiple disturbance levels and parameter configurations show that the proposed formulation captures the principal fault current characteristics and the corresponding parameter-dependent trends over the investigated operating conditions. The present formulation is restricted to balanced three-phase voltage-magnitude disturbances in an infinite-bus system with unity-power-factor pre-fault operation (Qref = 0) and fixed virtual impedance voltage-source operation. Its applicability has not been demonstrated for unbalanced faults, nonzero fault impedance, substantial grid-voltage phase jumps, nonzero pre-fault reactive power operation, active current limiting, or broader weak-grid conditions.

Energies doi: 10.3390/en19184411

Authors: Bartosz Owczarczuk Bogdan Dziadak Jacek Starzyński

Online partial discharge monitoring is increasingly complementing periodic offline testing in medium-voltage switchgear, particularly through the use of embedded and edge-computing platforms. This review critically examines systems based on artificial intelligence and machine learning for partial discharge detection and classification, considering the complete diagnostic chain from sensing to field deployment. The analyzed literature is organized into five interdependent layers: sensors and analog front-ends, data acquisition and triggering architectures, phase-synchronized signal representations, machine learning models, and target embedded hardware. Sensing techniques based on high-frequency current transformers, transient earth voltage, and ultra-high-frequency sensors are compared in terms of bandwidth, sensitivity, installation requirements, and immunity to interference. Particular attention is given to phase-resolved partial discharge patterns, time–frequency representations, event-driven acquisition, hardware-assisted data reduction, and synchronization mechanisms. The analysis demonstrates that high classification accuracy obtained under offline laboratory conditions does not, by itself, indicate deployment readiness. Practical implementations must also satisfy constraints related to analog-to-digital converter bandwidth, buffering, memory usage, inference latency, energy consumption, quantization, thermal performance, and field noise. Lightweight neural networks, optimized object detectors, input dimensionality reduction, quantized inference, and multimodal data fusion are identified as promising development directions. However, current research remains limited by laboratory-scale validation, incomplete hardware reporting, and insufficient long-term field datasets.

Energies doi: 10.3390/en19184410

Authors: Mariola Michałowska

Electromobility is a key direction in the development of sustainable transport. The aim of the study was to determine its level of development and to identify and assess factors associated with the perception of electromobility among residents of the Lubusz Voivodeship. Additionally, an attempt was made to identify user segments with differing perceptions of this phenomenon. The analysis covered the years 2010–June 2026, with a survey conducted at the turn of 2025/2026. The level of electromobility development was assessed based on quantitative indicators, while resident opinions were collected via a survey. Linear regression and cluster segmentation were used to analyze the data. Relationships between variables were examined, focusing on factors associated with the price acceptance of electric vehicles. The regression results indicated that, of the eight predictors included in the model, only four—infrastructure quality, charging time, vehicle range, and environmental awareness—exhibited statistically significant associations with price acceptance. Cluster analysis allowed for the identification of diverse user segments with different preferences, motivations, and barriers related to electromobility. The results can form the basis for designing public policies, marketing strategies, and investment activities in the area of sustainable transport.

Energies doi: 10.3390/en19184409

Authors: Lian Wang Jiecheng Zhang Zizheng An Zheng Zeng Chongzhi Lu Yang Xiang

Gas hydrate-bearing formations are characterized by low temperature and high pressure, weak cementation, strong phase sensitivity, and pronounced heterogeneity. During drilling in such formations, the drilling fluid is required to maintain wellbore stability, transport cuttings, and control bottom-hole pressure; however, it may also alter near-wellbore hydrate stability through heat transfer, pressure-driven invasion, and component migration. Previous studies have shown that drilling-induced disturbances may trigger or intensify the dissociation of hydrates, sediment strength degradation, pore-pressure redistribution, seepage-channel development, and secondary hydrate formation in the wellbore. Under certain conditions, these processes may manifest as wellbore instability, gas invasion, changes in drilling-fluid properties, and wellbore blockage. This review summarizes recent progress in four aspects: risk manifestations, formation-response mechanisms, control strategies, and numerical modeling. The reviewed studies indicate that thermal disturbance, pressure disturbance, and component migration jointly control the phase, mechanical, and seepage responses of hydrate-bearing formations; temperature–pressure window management, inhibitors, plugging particles, functional materials, and dynamic load control act at different stages of risk evolution; and numerical models mainly include thermal–chemical, thermal–hydraulic–chemical, thermal–hydraulic–mechanical–chemical, wellbore multiphase flow and phase transition models and wellbore–formation coupled models. Current research still faces challenges in representing realistic formation heterogeneity, characterizing dynamic mud-cake evolution, integrating multiscale experiments with field data, and reducing the computational cost and improving the numerical robustness of fully coupled models. Future work should strengthen field-constrained multiscale experiments, dynamic parameter calibration, and integrated wellbore–formation control models, thereby providing a more reliable theoretical and technical basis for safe drilling in hydrate-bearing formations.

Energies doi: 10.3390/en19184408

Authors: Katarzyna Ignatowicz Jacek Piekarski Dariusz Kozłowski

The study investigated whether a dose of a commercial iron-based reagent determined under controlled laboratory conditions could be used as a starting point for full-scale hydrogen sulfide control in biogas. Laboratory tests were conducted in a reactor with a working volume of 30 dm3, and technical validation was performed in an operating anaerobic digester with a process volume of approximately 7200 m3. The common parameter used for scale transfer was the mass dose of reagent relative to the sulfur load supplied with the sludge (DSF, kg reagent/kg S). An operational reference range of 300 ppm H2S ±10% (270–330 ppm) was adopted for biogas supplied to combined heat and power units. In the final phase of the laboratory experiment, at a volumetric dose of 2.0 dm3/m3, the H2S concentration stabilized at 312–318 ppm, corresponding to DSF = 4.91 kg reagent/kg S. Full-scale validation was initiated at a similar dose of 4.75 kg/kg S, after which dosing was gradually reduced on successive days. The lowest tested dose was 1.26 kg/kg S, and the H2S concentration on the final day of observation was 268 ppm. The CH4 fraction at full scale remained stable at 62.3–64.0%. Reducing DSF from 4.75 to 1.26 kg/kg S decreased the unit reagent cost from approximately 2.21 to 0.59 EUR/kg S. Within the 8-day full-scale validation, the laboratory-derived dose functioned as a suitable starting point for scale transfer, but the results do not establish a long-term optimum and do not support using the laboratory value directly as a fixed operating dose.

Energies doi: 10.3390/en19184407

Authors: Miaoyi Liu Yongliang Liang Wei Cong Zhexuan Shuai Fangyuan Wang

Against the backdrop of the global low-carbon transition, power-to-ammonia (PtA) has emerged as a pivotal direction for large-scale energy storage. Distributionally robust optimization can effectively address uncertainties in energy systems; however, traditional distributionally robust dispatch models generally suffer from over-conservatism that leads to increased operational costs, and existing PtA studies mostly focus on scenario-based adaptations of established optimization tools, lacking mechanistic and methodological innovations tailored to the electricity–hydrogen–ammonia coupling characteristics. To address these issues, this paper proposes a distributionally robust chance-constrained economic dispatch model (WMDRCC) based on the Wasserstein metric and first-order moment information, and introduces a logical mapping relationship that links hydrogen storage capacity with the operating modes of ammonia synthesis. This mechanism enables real-time optimization of the H2/N2 feed ratio, mitigates hydrogen source fluctuations, and avoids reactor instability and cost-ineffective shutdowns. Furthermore, by integrating Conditional Value at Risk (CVaR), duality theory, and big-M linearization, the complex robust chance-constrained problem is reformulated into a computationally tractable mixed-integer linear programming (MILP) model. Numerical results on the IEEE 33-bus system demonstrate that, compared with a distributionally robust model based solely on the Wasserstein distance, the proposed method effectively reduces system operating costs under the tested 24 h daily scenarios, while simultaneously improving renewable energy accommodation and reducing network losses, thereby providing a novel dispatch scheme for PtA systems that balances both economic efficiency and robustness.

Energies doi: 10.3390/en19184405

Authors: Songkai Liu Yuhan Chen Pan Hu Shunkang Ye Lei Liu

To address the dependence of uncertainty analysis methods on assumed probability density functions of wind power output and the computational burden of time-domain simulations in transient stability-constrained optimal power flow (TSCOPF) problems, this paper proposes a chance-constrained transient stability-constrained optimal power flow (CCTSCOPF) solution method based on data-driven polynomial chaos expansion (DDPCE) and the maximum entropy method (MEM). The method eliminates the need for predefined distribution assumptions for wind power variables. Specifically, using N = 5000 historical wind power forecast error samples, raw statistical moments up to order 2p = 8 are extracted, and orthogonal polynomial basis functions up to order p = 4 are derived by solving a 5 × 5 linear algebraic equation system constructed from these moments. Based on the constructed polynomials, Gaussian quadrature collocation points of wind power output are obtained, and time-domain simulations are performed at these points to solve the expansion coefficients, establishing a surrogate model that maps wind power fluctuations to transient responses. The surrogate model then computes the statistical moments of transient stability indices. MEM is subsequently used to reconstruct the probability density function of the transient stability index, and the transient stability chance constraint is converted into an algebraic boundary condition. Finally, an optimization model incorporating power system operating constraints is formulated and solved. Case studies conducted on the modified IEEE 39-bus test system with two 100 MW wind farms demonstrate that the proposed surrogate model achieves high accuracy with R2 = 0.987, significantly improving uncertainty quantification accuracy over the standard Wiener–Askey polynomial chaos expansion (PCE) method. Furthermore, the total computational runtime is reduced from 66,280.00 s under full-scale Monte Carlo simulations to 47.92 s, achieving a 1383× computational speedup while strictly satisfying transient stability chance constraints.

Energies doi: 10.3390/en19184404

Authors: Lucas Ribeiro Alves Costa Bruno Pinto Braga Guimaraes Ronny Francis Ribeiro Junior Matheus Varella Costa Danilo Amaral Dantas Julian David Hunt Frederico de Oliveira Assuncao Erik Leandro Bonaldi Luiz Eduardo Borges-da-Silva

The increasing penetration of renewable energy sources in isolated microgrids has intensified the need for Energy Management Systems (EMS) capable of reducing diesel dependence while ensuring reliable operation. This study compares three EMS strategies for a photovoltaic–diesel–battery microgrid representative of remote communities in the Brazilian Amazon: a Myopic strategy (M1), a Predictive strategy (M2), and a PPO-RL strategy (M3), evaluated under identical physical and operational constraints using real irradiance and demand data over the complete year of 2025 at a 15 min resolution. The three strategies achieved very similar diesel consumption, with a difference of less than 0.7% between the best and worst cases; M2 achieved the lowest consumption (218,863.19 L), the highest renewable penetration (52.14%), and the lowest curtailment (1.06%), with M3 performing comparably but with more frequent generator starts. An illustrative, qualitative analysis of the highest- and lowest-PV days suggested that differences among strategies become more pronounced under high photovoltaic availability, while low renewable availability drives all strategies toward similar diesel-dominated operation. These findings indicate that the main benefit of advanced EMS strategies lies not in large diesel savings, but in improved coordination and utilization of renewable and storage resources. Additionally, a simplified economic analysis of the photovoltaic–battery hybridization, based on M2’s diesel savings, showed an internal rate of return of approximately 30.7% p.a. and a payback period of approximately 3.2 years, well above the 14% p.a. benchmark, confirming the hybridization as an economically attractive investment regardless of which EMS strategy is adopted.

Energies doi: 10.3390/en19184403

Authors: Mohamed Djelbane Mohamed Elbar Naas Charrak Ahmed Elottri Mario Versaci Matilde Pietrafesa Ievgen Zaitsev Vladislav Kuchansky

In order to improve power quality in low-voltage distribution networks with nonlinear, distorted, and unbalanced loads, this research suggests an innovative design for Shunt Active Power Filters (SAPFs). Compared to conventional single-inverter SAPF structures, the improved method uses a combination of two T-Type three-level inverters operating in a parallel configuration to improve compensator performance, leading to a higher current-carrying capacity as well as better harmonic reduction and system scalability. The Synchronous Reference Frame (SRF) algorithm is used to extract reference currents in order to achieve the required accuracy of harmonic cancellation. In order to ensure both a quick response and a suitable switch state selection for compensatory current references, the Model Predictive Current Control (MPCC) technique is employed. To maintain the DC-link voltage at a steady level and guarantee its correct operation under rapidly fluctuating loading conditions, a Proportional Integral (PI) controller-based DC–DC converter is also utilized. Four real-time operational circumstances are used to verify the performance of the proposed method using MATLAB/Simulink R2023a (i) SAPF activation under nonlinear loading conditions, (ii) dynamic load variation, (iii) distorted and unbalanced operation, and (iv) grid voltage disturbances including sag and swell conditions. The simulation study’s results show that, in all of the previously indicated scenarios, the source current Total Harmonic Distortion (THD) is reduced and an almost unity power factor is maintained while maintaining a constant DC-link voltage. Furthermore, the obtained performance meets IEEE-519-2022 requirements, demonstrating the feasibility of the suggested SAPF with two inverters under high-load circumstances.

Energies doi: 10.3390/en19184401

Authors: Zaid Salah Al-Haydri Konstantin V. Osintsev Sergei V. Aliukov Pavel A. Drogovoz Alexander N. Shishkov Nikita A. Pshenisnov

This study numerically investigates unsteady natural convection and Soret–Dufour double-diffusion in a two-dimensional sinusoidally wavy porous cavity saturated with an Al2O3–Cu/Water hybrid nanofluid (φ = 0.02), using a Darcy–Brinkman formulation with temperature-dependent viscosity and Rosseland thermal radiation. The governing equations are solved on a boundary-fitted grid; an independent Method of Manufactured Solutions verification confirms second-order accuracy, with a finest-grid (120 × 120) L2 error of 2.6807 × 10−5 and a fine-grid GCI of 0.013225%. Within the investigated dimensionless range, Nuavg increases with the Rayleigh number, wall-wave amplitude, radiation parameter, and viscosity-thinning magnitude, while the hybrid nanofluid yields a 2.8–3.2% enhancement over pure water. Because no independent experimental measurements or complete dimensional operating envelope are available, the reported engineering implications are presented as model-based guidance rather than validated design prescriptions.

Energies doi: 10.3390/en19184402

Authors: Yan Ren Guangdong Wang Wenjing Huang Zhan Yin Ziwei Bai Huanran Wang Yufei Zhang Lixiao Zhou Yao Wang Bo Wang

Growing wind and photovoltaic generation increases the demand for large-scale, long-duration energy storage. Pumped hydro compressed-air energy storage (PHCAES) stores and releases energy through pressure transfer between water and compressed air, using air pressure to provide an equivalent hydraulic head and thereby reducing dependence on natural elevation while retaining hydraulic energy conversion and the potential for near-isothermal operation. This review establishes a taxonomy of hydraulically coupled compressed-air storage comprising PHCAES, liquid-piston systems, hydraulically compensated constant-pressure CAES, and hydraulic–pneumatic cascade or hybrid systems. Variable- and constant-pressure PHCAES are compared with pumped hydro energy storage (PHES) and compressed-air energy storage (CAES) in terms of efficiency, economics and environmental implications; this is followed by a critical analysis of the mechanisms governing PHCAES performance. The results show that improved siting flexibility is the principal conditional advantage of PHCAES, rather than inherently higher efficiency or lower cost. Its net performance depends on pressure–volume matching, gas–liquid heat transfer, hydraulic-machine operation, auxiliary consumption and storage infrastructure. Pressure regulation and thermal enhancement are beneficial only when their gains exceed the associated compression, throttling and auxiliary losses. The principal research gap is the lack of engineering-scale, full-cycle validation using consistent electrical, economic and lifecycle assessment boundaries, which currently prevents robust comparison with PHES and CAES.

Energies doi: 10.3390/en19184400

Authors: Shihao Yin Xiaodong Xing Bin Zhang Shixian Hui Penglin Wang Guangtao Feng Wei Liu

Multi-terminal HVDC transmission lines contain a T-zone, where no explicit line boundary element exists. Conventional traveling-wave (TW) protection schemes mainly identify faults by utilizing the attenuation characteristics of TWs caused by transmission lines and boundary elements. Therefore, their performance is sensitive to transition resistance and sampling frequency, and they cannot be directly applied to fault identification in the T-zone. To address this issue, this paper proposes a single-ended directional protection scheme based on the normalized covariance of line-mode backward TWs. The proposed method constructs the protection criterion according to the overall waveform variation in line-mode backward TWs. It does not rely on boundary-effect-induced wave attenuation or the extraction of specific high- or low-frequency components. Fault regions are identified using only local measurements collected by the protection devices installed on both sides of the T-zone, and the fault pole is determined by the bipolar voltage ratio. A ±500 kV three-terminal MMC-based multi-terminal DC system is established in PSCAD to evaluate the proposed scheme. Simulation results show that the proposed method identifies the fault region within a 0.5 ms data window. It can tolerate transition resistances up to 300 Ω and 40 dB Gaussian white noise. The proposed scheme features fast operation, low computational complexity, and high robustness.

Energies doi: 10.3390/en19184399

Authors: Qingmei Lu Xin Shi Xiuhao Zhao Mingdong Li Dongxi Li Liu Liu Xiaohan Ren

This study investigates the effects of secondary air supply on the combustion characteristics of briquettes with different moisture contents using a coupled FLIC–Fluent framework. FLIC was employed to simulate drying, pyrolysis, volatile release, and char oxidation in the fixed bed, and the resulting bed outlet temperature, velocity, and gas composition were transferred to Fluent as inlet boundary conditions for three-dimensional furnace simulations. At a constant total air supply, all-primary-air operation was compared with staged primary–secondary air supply for briquettes with moisture contents of 10%, 20%, and 30%. Increasing moisture content prolonged drying and preheating, reduced the bed outlet temperature and gas velocity, and weakened fixed-bed combustion. Without secondary air, the furnace’s high-temperature region progressively decreased, while relatively low-temperature regions expanded, and the mean furnace temperature declined from 1125 to 960 K as moisture content increased from 10% to 30%. Although CO release from the fuel bed decreased at higher moisture content, lower furnace temperatures and insufficient gas mixing suppressed subsequent CO oxidation, increasing furnace outlet CO from 820 to 1780 ppm. Redistributing 23.6% of the total combustion air as secondary air improved oxygen–fuel mixing and gas-phase burnout, with a stronger effect at higher moisture content. At 30% moisture, secondary air increased the mean furnace temperature from 960 to 1045 K, reduced outlet CO from 1780 to 980 ppm by 44.9%, and increased the volume fraction above 900 K by approximately 21%. Model validation showed relative errors below 1.0% for mean furnace temperature and below 5.0% for outlet CO and O2, confirming the reliability of the coupled model. These results demonstrate that the investigated air-staging configuration can effectively mitigate the deterioration of furnace combustion caused by high briquette moisture content.

Energies doi: 10.3390/en19184398

Authors: Yumin Peng Changhong Deng Rufei He Fanqi Huang Yikai Li Qiuling Yang

Variable speed pumped storage units (VSPSUs) offer flexible active-power regulation capability; however, during large power variations, it remains challenging to simultaneously achieve fast power tracking and effective rotor-speed regulation. To address this issue, this paper proposes a power–speed hybrid control strategy for VSPSUs in generating mode. Within a unified control framework, a weighting coefficient λ is introduced to coordinate the allocation of power and speed control objectives between the electromagnetic and hydraulic–mechanical control channels. A sensitivity analysis is further conducted for λ = 0.1–0.9 based on the dynamic performance indices of active power and rotor speed. The results show that, under the operating condition considered, the composite performance index is minimized at λ = 0.5. For an active power step from 0.4 p.u. to 0.8 p.u., compared with power-priority control, the proposed strategy effectively suppresses power oscillations, reducing the active-power overshoot from 29.88% to 0.85% and shortening the ±5% settling time from 2.829 s to 0.230 s. In addition, the proposed strategy maintains good dynamic adaptability when the water inertia time constant Tw varies by ±20%. The proposed method provides a low-complexity control solution for coordinating active-power response and rotor-speed regulation in VSPSUs.

Energies doi: 10.3390/en19184396

Authors: Arkadiusz Frącz Stanislaw Czapp

Low-voltage networks are designed as solidly grounded neutral networks (TN, TT) or isolated neutral networks (IT). The latter type is used when continuity of supply and effective protection against electric shock are required despite a single ground fault. A characteristic feature of the IT network is the application of insulation monitoring systems, currently officially named Insulation Monitoring Device (IMD). The aim of this device is to signal the first ground fault, and the network can still be powered. This article shows a comprehensive overview of IMD solutions, from historical to contemporary. IMD structures and characteristic features, as well as critical evaluation, are presented, highlighting their advantages and disadvantages. The desired directions for the development of IMDs are indicated to ensure their proper functioning in modern power networks.

Energies doi: 10.3390/en19184397

Authors: Dervis Kirikkaleli Seyed Alireza Athari Mohamed Djafar Henni Emmanuel Oluwatosin Adewusi Anar Eminov Ruth Oluyemi Bamidele

Artificial intelligence is increasingly viewed as both a catalyst for clean-energy development and a potential constraint on green innovation. This study examines the quantile-specific relationships between artificial intelligence, clean-energy market performance, and green innovation using daily market data from 15 June 2018 to 29 June 2026. The analysis employs Quantile Kernel Regularized Least Squares (QKRLS), which captures nonlinear and heterogeneous associations across different market conditions. The findings reveal a positive and statistically significant association between artificial intelligence and clean-energy-market performance across all examined quantiles, with the strongest estimated marginal associations occurring in the lower quantiles. Conversely, artificial intelligence is negatively and significantly associated with green innovation throughout the distribution, with the strongest negative associations observed at the 0.10 and 0.90 quantiles. The study therefore highlights the need for policies that align artificial-intelligence growth with the sustained development of green innovation.

Energies doi: 10.3390/en19184395

Authors: Lukasz Apiecionek

The growing scale of photovoltaic (PV) installations creates an urgent need for automated fault detection systems that operate in real time on resource-constrained monitoring hardware. Although deep learning methods achieve high classification accuracy on PV fault benchmarks, their computational requirements make them impractical for deployment on embedded devices such as smart inverters and industrial IoT controllers. This paper proposes a lightweight classification approach based on the fuzzy k-Nearest Neighbor (Fuzzy kNN) algorithm, in which every electrical measurement is represented as an Ordered Fuzzy Number (OFN) whose spread is automatically calibrated from the local standard deviation of the measurement window. This adaptive fuzzification encodes the inherent sensor noise and environmental variability of SCADA measurements without any manual parameter tuning. Six fuzzy distance metrics, obtained by combining three defuzzification operators (FOM, LOM, MOM) with the Euclidean and Manhattan distance functions, were evaluated on the public GPVS-Faults benchmark containing approximately 1.8 million samples describing seven fault types in a grid-connected PV system operating under MPPT and IPPT control. In binary anomaly detection, the proposed method achieved an accuracy of 92.97% ± 1.17% (k = 3, MOM defuzzification with Manhattan distance), which is statistically comparable to the Random Forest baseline (92.42% ± 0.65%) while offering approximately one hundred times faster inference (1.2 ms versus 120 ms per sample) and a model footprint of only 2 MB. In multiclass fault type classification, the method reached 96.18% ± 1.80% accuracy against 97.88% ± 0.69% for Random Forest. A consistent and previously unreported observation is that the Manhattan distance systematically outperforms the Euclidean distance on three-phase electrical measurements, improving accuracy by approximately 0.90 percentage points across all tested configurations. The complete source code and the experimental pipeline are publicly released to ensure full reproducibility of the reported results. To assess generalization rigorously, a stratified group cross-validation was additionally performed in which all windows from a given experimental recording are confined to a single fold; under this leakage-free protocol every evaluated method, including Random Forest, degrades to the 50–62% range, which shows that cross-recording transfer is an intrinsic difficulty of the single-run GPVS-Faults benchmark rather than a weakness specific to the proposed classifier.

Energies doi: 10.3390/en19184394

Authors: Assem Yerzhankyzy Utegenova Aman Tulegenovich Shakenov Ivan Nikitovich Stolpovskikh Ainura Berikbolovna Orumbassarova Boris V. Malozyomov Nikita V. Martyushev

Haul-road condition can affect traction demand and diesel-to-LNG substitution in mining trucks. We evaluated a 140-t truck at the Ekibastuz coal mine using 180 registered cycles (88 diesel-only [DOM], 92 dual-fuel [DGB]), 24 road segments, 36 defect events and 30 matched fuel-mode pairs. Mean ECM-reported substitution was 30.61% across DGB cycles. In matched pairs, diesel use declined from 34.00 to 23.76 L/cycle, a mean saving of 10.24 L/cycle (95% CI 9.53–10.95); calculated combustion-stage CO2 declined by 7.10%. Total fuel energy, specific fuel-energy consumption and cycle time did not differ significantly (p > 0.30). Across Good-to-Poor road classes, engine load increased from 66.11% to 74.40% and substitution from 28.97% to 32.31%, while specific fuel-energy consumption increased from 5.91 to 6.72 MJ/(t·km). The load association persisted after temperature and wind adjustment and shift-clustered inference. Reference-state haul-road energy penalty was associated with rolling resistance and roughness, but remains dependent on a supplied reference input. The results distinguish diesel displacement from improved transport energy performance and are conditional on the cycle register. They do not establish causal road effects or a life-cycle climate benefit.

Energies doi: 10.3390/en19184393

Authors: Qin Gao Yingjie Chen Yong Liu Jianxin Zhang Nan Zhang Yong Mei

As renewable penetration increases, the inertia, damping, and primary frequency regulation characteristics of renewable-rich power systems become strongly time-varying, making fixed offline frequency response models increasingly difficult to maintain for emergency-control calculations. This paper proposes a physics–data fusion-driven framework for frequency response parameter identification, model validation, and emergency load shedding based on post-disturbance multi-source measurements. First, a quality-aware dual-stage LSTM-PINN fuses multi-source measurements to provide event-specific initial estimates of the disturbance magnitude and physical system frequency response (SFR) parameters. A bounded event-level local constrained refinement then aligns the dynamic parameters with the measured frequency trajectory, while differentiable SFR constraints, key response losses, and identifiability regularization improve physical consistency and parameter distinguishability. Second, local identifiability, physical parameter plausibility, and trajectory consistency are jointly evaluated to characterize the credibility of the identified SFR model. Finally, the measurement-updated controlled-SFR model determines the minimum emergency load-shedding amount satisfying the frequency nadir constraint and allocates the action to candidate buses according to electrical distance and available controllable capacity. Case studies on a modified New England 39-bus system and the renewable-rich CSEE-FS benchmark evaluate parameter identification accuracy, model credibility, robustness, generalization, control security, spatial allocation, and computational efficiency.

Energies doi: 10.3390/en19184392

Authors: Jiahao Zhao Muriel Iten Yongcai Li Zixiong Qin Wuyan Li Shiquan Chu

Against the backdrop of the global energy transition and carbon reduction in the building sector, this study proposes a photovoltaic solar chimney coupled with an earth–air heat exchanger (PVSC-EAHE) system to integrate passive ventilation, thermal pre-treatment, and photovoltaic power generation in cold and arid regions. A coupled numerical model is presented using MATLAB 7.2 and TRNSYS 16.0, with user-defined models for the earth–air heat exchanger, solar chimney, and photovoltaic system implemented as TRNSYS-callable modules. The novelty of the study lies in the systematic year-round investigation of five inclination angles of photovoltaic (PV) panel (20°, 30°, 40°, 50°, and 60°) and their effects on the coupled thermal, ventilation, and electricity-generation performance of the system under different seasonal conditions. An 8760 h annual dynamic simulation was conducted using typical meteorological data for Hami, Xinjiang, China, to evaluate system performance and the effect of PV inclination angles ranging from 20° to 60°. The results show that, compared with a conventional building without the coupled system, the PVSC-EAHE system reduces the average indoor summer temperature by approximately 4 °C, maintaining temperatures below 28 °C, while increasing winter indoor temperatures by up to 8 °C. The system also provides an average fresh-air supply of 132 m3/h and generates 2180.1 kWh of electricity annually. The inclination analysis reveals a season-dependent trade-off: higher inclination angles are more favorable for solar radiation capture and ventilation during winter, whereas lower angles are comparatively more favorable during summer. The inclination angle has a relatively limited effect on indoor thermal conditions, while increasing it from 20° to 60° raises the maximum instantaneous PV output from 198 to 220 W/m2. Overall, the results demonstrate the potential of the PVSC–EAHE system to improve indoor thermal conditions, enhance natural ventilation, and generate renewable electricity in cold and arid climates. As annual heating and cooling consumption was not explicitly calculated, the observed performance improvements indicate the potential to reduce building energy demand rather than quantitatively demonstrating specific annual energy savings.

Energies doi: 10.3390/en19184391

Authors: Shuo Cao Si Gao Jianbo Qi Xihan Mu Donghui Xie Wenjing Liu Guangjian Yan

In operational environments, photovoltaic (PV) forecasting models progressively degrade due to drift events induced by changing environmental conditions, system aging, and surface soiling. Mainstream forecasting models are typically trained under static assumptions and rarely account for such non-stationary effects, resulting in biased predictions, reduced reliability, and suboptimal dispatch decisions. To address this limitation, we propose a drift-aware forecasting strategy that identifies drift points by jointly monitoring variations in data distributions and prediction errors, and dynamically adjusts model update timing to maintain forecasting accuracy. First, an integrated STL-EDDM strategy is developed to decompose input features, detect anomalous fluctuations, and track performance variations for robust drift identification. Subsequently, Neuralprophet is employed as the forecasting backbone to generate ultra-short-term PV power predictions, and the model is adaptively retrained when drift events are detected. Validation results from 10 PV power stations in China show that the proposed STL-EDDM strategy reduces the average MAE from 2.480 MW to 1.287 MW, corresponding to a 48.1% improvement over the non-adaptive baseline. Furthermore, comparative experiments on the PVOD dataset showed that the proposed framework outperformed the evaluated baseline models overall, reducing average MAE and RMSE by 15.2% and 16.5%, respectively, relative to the best-performing baseline. These findings demonstrate that the proposed drift-aware strategy enhances both interpretability and robustness in non-stationary PV forecasting scenarios. The framework provides operational insights into model degradation mechanisms and practical guidance for real-time model management, with potential applicability to a broader class of real-time forecasting tasks.

Energies doi: 10.3390/en19184389

Authors: Feiwei Li Dexing Sun Junwei Zhang Pei Liu Xiaoshun Zhang Haoxia Jiang

As global climate change intensifies, decarbonizing the power system is key to achieving carbon-reduction targets. Inspired by the multi-objective ant-lion optimization (MALO) method and deep neural networks (DNNs), this study proposes an innovative approach to address the complex carbon reduction challenges faced by energy-consuming enterprises. The proposed fully connected deep neural networks with multi-head self-attention mechanisms based on the multi-objective ant-lion optimization algorithm (FCDNN-MHSAM-MALO, FMM) integrate the advantages of MALO and DNN. MALO plays a key role in optimizing the decision variables in economic scheduling. By contrast, DNN predicts the search direction of the optimal solution by learning historical optimization results. This reduces the number of iterations, improves computational efficiency, and speeds up the solution process. The multi-head self-attention mechanism calculates the importance of various input features, enabling the model to focus on factors that significantly impact the scheduling solution. This attention-driven approach improves prediction accuracy and enables MALO to optimize from an earlier starting point, thus achieving global convergence more efficiently. Compared with several state-of-the-art algorithms on IEEE 118- and IEEE 300-bus systems, the simulation results show that (1) both carbon dioxide emissions and costs can be reduced: carbon emissions are reduced by at least 1.02% and the cost is lowered by at least 0.64% when the FMM algorithm is adopted; (2) better real-time performance: an at least 17.11% reduction in computation time is achieved using FMM; and (3) better stability performance: the curves obtained by FMM for the two cases are closer to the Pareto frontier.

Energies doi: 10.3390/en19184390

Authors: Rui Jing Xinqiang Yi Yapeng Jiang Zhifang Yuan Wenzhong Yu

Hybrid excitation generators offer high power density and adjustable magnetic field, making them attractive for power generation applications with strict volume and weight constraints. However, under strong field excitation conditions, deep saturation of the iron core leads to a strongly nonlinear relationship between the resultant air-gap flux linkage and the field current, causing traditional linear models to exhibit large errors in the saturation region. To address this issue, this paper proposes a piecewise nonlinear function-based method for fitting magnetic saturation characteristics. The function’s nonlinear trend is exploited to construct an analytical model that describes the flux–current relationship in both the linear and deep saturation regions. The unknown model parameters are then determined by solving an optimization problem that minimizes the sum of squared output voltage errors. Particle swarm optimization (PSO) is employed for global search, overcoming the challenges of initial-value dependence and local optima in such multimodal parameter spaces. Experimental data from a hybrid excitation generator are used as samples for validation. The results show that the nonlinear model optimized by PSO accurately fits the flux linkage variation over the full current range, reducing the error from 7.78% to approximately 1%. The proposed model is concise in form, requires low computational effort, and can be directly used as an accurate analytical method for performance analysis of hybrid excitation generators, demonstrating good engineering application value.

Energies doi: 10.3390/en19184388

Authors: Ziyao Zhou Yujue Wang Yanan Zhang Gan Zhou Yanjun Feng

Within the same low-voltage distribution network (LVDN), the short electrical distances between customers lead to highly similar steady-state voltage curves. This reduces the discriminative capability of traditional similarity metrics and limits the accuracy of customer–meter box relationship identification. To address this problem, this paper proposes a two-stage framework for identifying customer–meter box relationships based on dynamic responses to load switching. In the first stage, the consistency of the dynamic voltage responses of same-phase customers within the same meter box is used to calculate the similarity between customers voltage event sequences. The customers are then divided into single-phase clusters by phase. In the second stage, using the current events generated by load switching as the driving quantity and the cross-phase voltage events as the response quantity, a cross-phase cluster matching model is constructed. Combined with a voting mechanism and an optimized allocation strategy, this approach enables accurate matching of single-phase clusters across different phases and complete reconstruction of the meter box topology. A case study using high-frequency measurement data from an actual LVDN in Nanjing shows that the proposed method achieves an identification accuracy of 100%, demonstrating its effectiveness and superiority in identifying the meter boxes assignments of customers that are electrically close to one another.

Energies doi: 10.3390/en19184387

Authors: Zhifeng Wang Guangwei Deng Tao Wang Yi Zhang

This paper proposes a Priority–Urgency Index (PUI) mechanism to address the multi-objective conflict problem in electric vehicle charging scheduling at public fast-charging stations. The mechanism dynamically quantifies the charging urgency of each vehicle based on remaining dwell time, current state of charge (SoC), and target SoC, enabling differentiated service prioritization under resource scarcity. The PUI is systematically integrated into four multi-agent reinforcement learning (MARL) algorithms. To handle the time-varying number of vehicle entities caused by random arrivals and departures, the chargers are modeled as fixed agents; a partially observable Markov decision process (POMDP) is formulated, and a centralized training with decentralized execution (CTDE) architecture is adopted. On this basis, a state-aware dynamic threshold mechanism is introduced to distinguish urgency levels of charging tasks, and an adaptive reward function is designed to accommodate complex operating conditions. Empirical comparisons show that PUI-MAPPO (multi-agent proximal policy optimization) achieves the best performance among all PUI-enhanced variants. Under extreme supply–demand conditions—such as resource-scarce and heavy-traffic scenarios—PUI-MAPPO improves the target-SoC fulfillment rate and net revenue by up to 42.7% and 23.2%, respectively, and reduces the cumulative grid-limit exceedance by 22.8% to 47.3%, relative to the first-come, first-served (FCFS) baseline. Ablation studies further validate the individual effectiveness of the PUI urgency mechanism, the dynamic threshold framework, and the adaptive reward function.

Energies doi: 10.3390/en19184386

Authors: Wei Chen Liwei Chen Liangliang Wang Yipeng Zhang Xiaoming Su

Post-well conformance assessment requires identifying the realized locations where an as-drilled trajectory changes between its principal inclination regimes; planned breakpoints are targets, not observations. We formulate this task as sparse event localization using domain-informed multiscale inclination features, planned-trajectory deviations, event-specific LightGBM models, a well-level Drop-presence model, and exact Build–Hold or Build–Hold–Drop decoding. The operational scope is restricted to completed, conventional single-cycle trajectories containing exactly one Build, exactly one Hold, and at most one Drop; build-only, repeated-transition, seven-section, and online trajectories are excluded. We evaluated 63 field wells, including 25 with Drop, by fixed five-fold well-level cross-validation. At the prespecified 60 m tolerance, the detector achieved a macro-F1 of 0.806 (95% confidence interval, 0.737–0.869), versus 0.616 for a derivative rule, 0.567 for change-point dynamic programming, and 0.615 for segmented regression. Its paired advantage over the strongest classical comparator was 0.190 (0.111–0.268). Build, Hold, and Drop F1 values were 0.984, 0.857, and 0.577. Drop-presence ranking was strong (average precision, 0.944), but only 15 of 22 emitted true Drops were localized within 60 m. All seven localization failures were late and spanned 115.9–347.5 m; they were associated with lower ranks of the labeled station score and weaker early inclination decline. On 47 matched wells, planned anchors achieved 0.281, versus 0.809 for the detector; sensitivity analysis over 324 anchor-rule configurations did not close this gap. Exact decoding guaranteed valid event order but produced the same predictions as greedy selection; thus, its demonstrated contribution on this dataset is output validity rather than localization-accuracy improvement. Validation was limited to 63 wells from a single competition-supplied field dataset; external multi-field generalization remains untested.

Energies doi: 10.3390/en19184385

Authors: Xiaoyan Zhao Wencheng Wu Yimin Wu Kaiwen Hou Hairong Yi Renjie Chen Tao Wu

Renewable Power to Ammonia (RePtA) systems provide an important pathway for the accommodation of renewable energy and promoting the low-carbon transition of the conventional ammonia industry. With the increasing penetration of renewable energy in power systems, output fluctuations and forecast errors are posing growing challenges for RePtA system operations, making multi-timescale optimal scheduling increasingly important. This study proposes a multi-timescale optimal scheduling model for a RePtA system with a variable medium-term horizon. First, a key component model is established to characterize the system’s operations under a dynamic time interval regulation strategy. Then, stage-wise scheduling models are developed, where the medium-term stage adaptively generates a variable horizon based on the long-term scheduling results to better match renewable output variations. Finally, a multi-timescale coordinated operation strategy is proposed to ensure effective coordination among scheduling decisions across different timescales. Through a case study, it is demonstrated that the proposed method improves the total system profit by 5.12% and reduces the purchasing cost of electricity by 76.85% when compared with conventional scheduling strategies.

Energies doi: 10.3390/en19184384

Authors: Tarun Varshney Vinay Pratap Singh Jagadish Kumar Bokam

This article develops an analytic hierarchy process (AHP)-assisted model-reduction approach for a higher-order airport-centric microgrid (ACM). The full-order ACM model is approximated by a computationally simpler lower-order transfer function while retaining its essential steady-state and transient characteristics. The model approximation helps in analysis and controller design. For the same, a multi-objective fitness function is formulated from selected time moments and Markov parameters of the original and reduced models. AHP is used to determine the relative importance of the individual matching objectives through pairwise comparisons and normalized priority weights. The resulting weighted optimization problem is solved using the brown-bear optimization algorithm (BBOA) to estimate coefficients of the reduced model. During optimization, exact matching of steady-state gain is imposed to eliminate steady-state mismatch, while Hurwitz stability conditions ensure that the reduced model remains stable. Effectiveness of the proposed approach is evaluated using step and impulse responses, frequency-domain characteristics, time-domain specifications, and integral error indices. The results show that the AHP–BBOA-based reduced model closely reproduces the dominant dynamics of the higher-order ACM while offering a compact representation suitable for controller design, simulation, and real-time frequency-response studies.

Energies doi: 10.3390/en19184383

Authors: Saeed Ullah Xiaoyan Fan Jingming Liu Uneeb Ur Rehman Ali Badshah Hussain

The low-carbon transition is increasingly mineral-intensive, raising the question of whether industrial-metal market conditions coincide with different carbon-intensity adjustments across inherited mineral structures. We examine 45 Asian and Pacific economies over 2005–2022 (756 economy-year observations), combining a real aluminium–copper–nickel–tin–zinc price index with predetermined mineral dependence measured from 1995–2004 non-fuel mineral rents. Economy and year fixed-effects estimates indicate a positive exposure gradient: across the interquartile exposure difference, a one-standard-deviation larger annual metal-price movement is associated with a 0.00577-log-point (0.58%) differential in annual carbon-intensity change. Design-aware inference is weaker than conventional economy-clustered inference (economy-CR2/Satterthwaite p = 0.0571; exact circular-shift p = 0.0556). The accounting decomposition yields point estimates of 0.0097 for primary-energy intensity and 0.0012 for CO2 per unit of primary energy, although their difference is not statistically distinguished under CR2 inference. Allowing world industrial activity and oil-price movements to separate mineral dependence slopes reduces the focal coefficient to 0.00448 (p = 0.474), and identifying support is concentrated among materially mineral-dependent economies. The evidence documents a bounded exposure-conditioned association embedded in the wider industrial and commodity cycle. For planning, the findings support assessing energy demand, efficiency, and the carbon characteristics of supporting energy supply alongside mineral development decisions.

Energies doi: 10.3390/en19184381

Authors: Mihail Senyuk Ismoil Odinaev Murodbek Safaraliev

The operation of protection algorithms and emergency control of power system modes is based on processing information about voltage levels and currents flowing through network elements. To measure these quantities electromagnetic voltage and current transformers with a closed magnetic core are widely used. Operating experience shows that under fault conditions with a large proportion of DC component, the core of the current transformers is subject to the risk of saturation. This leads to a violation of the correct operation of protection and emergency control algorithms. As a result, a loss of stability of the power system elements may occur, which, in turn, may lead to a disruption of the power system. In this paper a method for detecting saturation and restoring current of a current transformer, as well as a saturation-robust algorithm for emergency control are proposed. Computational experiments were conducted based on the mathematical model of the IEEE39 power system. Computational experiments with current transformer saturation showed that the proposed method’s average error was 4%. After integrating the method into the emergency control algorithm, the error in estimating the generator and turbine rotor acceleration energy decreased from 47.6% to 0.6%, and the error in analyzing the extreme point of the distorted measured current waveform decreased from 4 ms to 0.11 ms. This allowed the studied synchronous generator №7 to maintain stability after a fault in the power system.

Energies doi: 10.3390/en19184382

Authors: Barbara Klemczak Jacek Gołaszewski Małgorzata Gołaszewska

Cementitious materials are the most widely used construction materials worldwide. Beyond their structural function, they are increasingly recognized as multifunctional materials capable of contributing to thermal energy management and improving the energy performance of buildings. This review provides a comprehensive assessment of cement-based materials for thermal energy storage (TES), covering the fundamental mechanisms of heat transfer, the thermophysical properties governing thermal performance, and the principal TES technologies applicable to cementitious composites. Particular attention is given to sensible heat storage in conventional cement-based materials and latent heat storage achieved through the incorporation of phase change materials (PCMs), including PCM classification, encapsulation techniques, incorporation methods, thermal performance, and current limitations. The review also examines recent advances in multifunctional cementitious composites incorporating lightweight and porous aggregates, recycled materials, nanomaterials, carbon-based additives, fibers, and other functional constituents that enable simultaneous enhancement of thermal energy storage, heat transfer, mechanical performance, durability, and sustainability. The interactions and trade-offs between thermal, mechanical, and durability-related properties are critically discussed to identify the most promising material design strategies for practical applications. Finally, the review highlights the major scientific and technological challenges and outlines future research directions toward intelligent, low-carbon, and energy-efficient multifunctional cementitious materials for next-generation buildings.

Energies doi: 10.3390/en19184380

Authors: Tomasz Sliwa Remigiusz Kunasz Mohsen Assadi

The increasing use of low-temperature geothermal systems for heating and cooling requires reliable determination of borehole heat exchanger (BHE) thermal parameters, which directly influence heat pump performance and system design. The thermal response test (TRT) is commonly used for this purpose, but its results may depend on the selected interpretation time interval. This study investigates the influence of the TRT interpretation time range on calculated effective thermal conductivity and borehole thermal resistance, and compares these parameters for different BHE configurations. Field tests were conducted at the AGH University of Krakow, Laboratory of Geoenergetics, on three BHEs: single U-tube, double U-tube and triple U-tube. Measurements were performed under identical flow rate and heating power conditions. The temperature data were interpreted using the classical infinite line source method, the point method and the constant thermal resistance method for selected characteristic time intervals. The results show that both effective thermal conductivity and borehole thermal resistance depend on the interpretation interval and exchanger construction. Across the three investigated configurations, average thermal resistance was lower for BHEs with a larger internal pipe surface area; however, the installations also differed in pipe geometry and construction, so this trend cannot be attributed to surface area alone. These findings emphasize the importance of selecting an appropriate TRT interpretation method and time range for accurate geothermal system design.

Energies doi: 10.3390/en19184379

Authors: Zuohan Yu Fenglin Tian Boping Tian

Supplier concentration and dependence on Hormuz-associated origins describe different dimensions of China’s crude-oil import security. Using 195 monthly observations for January 2010–March 2026, we estimate separate vector error-correction models (VECMs) driven by a geopolitical news-risk proxy combining Israel, Saudi Arabia and Türkiye. In the six-variable reference, the Herfindahl–Hirschman index (HHI) for 35 reported supplier categories excluding Iran rises by 2.383% after twelve months (95% interval, 0.728% to 3.677%), while the adjusted five-country origin share declines by 0.453 percentage points after one month (−0.916 to −0.069). The initial exposure decline remains supported in models with improved residual diagnostics. Persistent HHI growth is not established across dynamic specifications or under the original adjusted-HHI scenario, and adding Saudi Arabia limits the generalization of the exposure result. These estimates document a baseline contrast between two import-security indicators rather than an identified procurement-substitution mechanism. Connectedness, local projections and spatial statistics provide supporting evidence; post-2024 associations remain exploratory. The results support monitoring supplier balance and source-group dependence separately, with country-origin shares interpreted as proxies rather than observed strait traffic.

Energies doi: 10.3390/en19184378

Authors: Vasiliki Rokani Stavros D. Kaminaris C. S. Psomopoulos Petros Karaisas Anthoula Menti

Power transformers are critical assets in complex power grid systems, yet On-Load Tap Changers (OLTCs) account for over 30% of documented outages. This study introduces a Health Index (HI) model for OLTCs that employs a Fuzzy-Logic system to enhance condition-based maintenance (CBM) techniques. This research presents a component-wise, Fuzzy-Logic–based HI evaluation using the Scoring Methodology. The OLTC component is subdivided into six smaller components or contributors. Every contributor is a crucial subsystem whose efficacy is vital to the transformer’s overall reliability. Each contributor is divided into three sub-contributors/values and assessed using a three-tier classification scale (A, B, or C). These values could indicate condition measurements, operational observations, and diagnostic data. Each value is evaluated against reference ranges or boundary values derived from a synthesis of international standards, statistical population studies, and expert knowledge. To verify the precision of the proposed methodology, several defective OLTC cases were evaluated under diverse operational settings. This Fuzzy-Logic (FL) approach seeks to deliver a more accurate and interpretable assessment of OLTC condition than traditional crisp-value methods by integrating expert knowledge, diagnostic metrics, and standards within a structured Fuzzy-Inference framework. The implicit FL model is inherently more attuned to early indicators of deterioration and hidden risk factors that may be underestimated in conventional expert evaluations. Implementation of this intelligent monitoring approach enables early fault diagnosis, extends the transformer’s operational life, and reduces the risk of catastrophic failures and unplanned outages in the electrical power network.

Energies doi: 10.3390/en19184377

Authors: Şirin Alibaş Songmin Yu Stella Oberle Anna Billerbeck Hans-Martin Henning

Decarbonizing the German building sector by 2045 requires a rapid transformation of the heating technology stock, in which decentralized heat pumps (HPs) compete with district heating (DH) and green-gas-based solutions. Yet, the existing literature has not sufficiently resolved which segments of the building stock are most appropriately served by which low-carbon technology. This paper addresses this gap through a bottom-up, dynamic analysis using the agent-based building stock model RENDER-Building, which combines building-specific environmental heat-source potentials with DH and gas distribution infrastructure availability across Germany. Three explorative techno-economic scenarios are evaluated, differing in their electricity network charge development, DH expansion, and gas infrastructure trajectories. The modeling results show that HPs are cost-competitive over their lifetime in most building stock segments, delivering unit heat at an average cost of 11–15 ct/kWh. Between 11 and 15 million units are projected to be heated by HPs by 2045, covering 25 to 30% of building heating demand. Settlement type and local heat-source availability are found to be the primary determinants of feasibility and adoption. The findings underline the importance of ensuring the availability of energy carriers and stable long-term policies for a cost-effective and climate-friendly transformation of heating in buildings.

Energies doi: 10.3390/en19184376

Authors: Abdul Rauf Abdul-Sattar Nizami Muhammad Waqas Anjum Muhammad Ibrahim Mohammad Rehan

The generation of biomass residues and biowaste is both a challenge to the environment and to resource management worldwide but is also a significant renewable resource for energy and material recovery. Biomass residues are utilised along multiple pathways such as direct use for energy production, anaerobic digestion and biogas production, biofuel generation, composting, soil amendment, and conversion into value-added products at a global scale. On the other hand, many lignocellulosic and other biomass residues are characterized by low bulk density, heterogeneous characteristics, and seasonal availability, thus complicating collection, handling, storage, and transportation. Densification into solid fuel pellets is a viable solution for transforming these low-density residues into a compact, handleable, and transportable form with increased energy density. Although biomass residues have global potential, past pelletisation studies in Punjab, Pakistan, have generally considered only a few of two to three seasonal feedstocks that do not cover the uncertainty of potential feedstock availability in several seasons of the year, alongside the judgment of suitable biomass input. This study was, therefore, designed to explore nine agro-residues with different seasons in Punjab, e.g., tree residues, grass clippings, animal waste, crop residues, and herb biomass, for robust conversion into solid fuel pellets. Gate-to-gate Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) methods were used to evaluate the production system of the pellets from biomass residues. A functional unit of 1 tonne of biomass solid fuel pellets was defined, and the resulting environmental impacts were modelled using GaBi software and the ReCiPe 2016 methodology. Process performance and production hotspots were assessed by attributing the environmental impacts that occurred at individual pellet-production stages to the overall impacts, while LCC was used to evaluate economic costs, both internal and external, from pellet production. The results of hotspot analysis indicated that flash/pneumatic drying represented the most important contributor for all investigated impact categories, followed by the pellet-milling and packaging stages. The total estimated cost of the pellets was USD 72.6 t−1, with an internal cost of USD 51.7 t−1 and an environmental cost of USD 20.9 t−1. Two other energy scenarios provided evidence through which to understand the potential for reductions in GHG emissions linked to the production of pellets. These findings suggest that differing and seasonally accessible biomass residues in Punjab can be converted into solid biofuels to concurrently aid sustainable waste management, energy security, climate-change amelioration, and rural economic growth. This study is novel because it compares nine heterogeneous biomass residues in a seasonal common-pellet production framework, using an integrated environmental and economic approach to provide stakeholders with practical evidence they can use to select the biomass feedstock that is most environmentally and economically preferable. These results help achieve the Sustainable Development Goals 7, 11, 12, and 13.

Energies doi: 10.3390/en19184375

Authors: Bincheng Li Fei Tang Jinxiu Ding Tingyu Zhou Yixin Yu Shihan Wang Ying Wang

The increasing penetration of renewable generation and the rapid growth of data-center demand require coordinated use of heterogeneous flexibility while respecting network operating limits. This study proposes a three-objective electricity–computing–carbon dispatch method that minimizes physical system cost, direct carbon dioxide emissions, and unutilized renewable energy. Data centers are represented through task arrivals, deadlines, server capacity, and facility efficiency, whereas pumped-storage plants retain independent power limits, reservoir states, terminal conditions, and connection locations. A diversity-archive disturbance–multi-objective particle swarm optimizer (DAD-MOPSO) couples archive diversity and stagnation feedback with resource-group temporal-block disturbance. In 30 paired runs, DAD-MOPSO produced feasible final solutions in all runs and reduced mean spacing by 51.8% relative to conventional MOPSO, with a Holm-adjusted p-value of 0.0071. Repeated evaluation of 12 workload-resource cases showed that pumped storage provided the dominant improvement in renewable-energy utilization, while workload rescheduling provided a smaller marginal contribution. Factorial analysis indicated partially overlapping rather than universally superadditive flexibility. Fixed-total-capacity tests further revealed pronounced siting dependence: the same 1200 MW/7200 MWh storage capacity was feasible in 30/30 runs at Bus16 but only 16/30 runs at Bus27 because of undervoltage. The proposed framework therefore provides a unified basis for coordinating heterogeneous flexibility and distinguishing marginal, interaction, siting, and network-limited effects.

Energies doi: 10.3390/en19184374

Authors: Shanjing Huang Ruiqi Wang Yaodong Wang

Decarbonising hard-to-abate transport sectors, including maritime shipping and heavy-duty land transport, requires sustainable and energy-dense alternatives to fossil fuels. Green methanol synthesised from biogas offers a promising low-carbon fuel option, providing a more sustainable methanol production pathway, establishing a closed-loop carbon cycle, and mitigating greenhouse gas emissions from organic waste. This study proposes and evaluates a comprehensive system for producing 10 tonnes of green methanol per day in Shanghai through biogas bi-reforming, which integrates steam and dry methane reforming to generate syngas with a suitable composition for methanol synthesis. The proposed system integrates bi-reforming process with wind-powered electricity and hydrogen generation to enhance its sustainability. A detailed techno-economic assessment was conducted to evaluate system performance, resource utilisation, and economic viability of this green methanol production pathway. The results demonstrate that a methane conversion of 99.7% can be achieved, with a levelised methanol production cost of 3880 RMB/t. These findings present the technical feasibility and economic potential of biogas bi-reforming for green methanol production, also helping bridge the gap between theoretical research and industrial implementation while supporting China’s ‘Dual Carbon’ goals and the global transition towards sustainable energy.

Energies doi: 10.3390/en19184373

Authors: Fahad Radhi Alharbi

Dust accumulation has a substantial impact on photovoltaic (PV) power generation in desert environments, and soiling losses also cause significant operational difficulty for large-scale PV systems in desert countries like Saudi Arabia. This study describes a simulation-based approach for evaluating seasonal PV soiling losses and identifying the most economical cleaning interval for PV systems operating in desert conditions. The analysis was conducted as a literature-constrained, climate-informed scenario study representing Arar-like desert conditions using PVsyst and externally applied soiling assumptions. PV system performance was assessed under various cleaning scenarios, and seasonal soiling profiles were included as monthly loss factors based on desert environmental conditions. A nonlinear climate-informed scenario model incorporating wind speed and relative humidity was used to illustrate how these environmental drivers can be incorporated into the representation of dust adhesion and accumulation, while a Monte Carlo uncertainty analysis was employed to quantify the variability associated with the adopted seasonal soiling severity ranges and its effect on annual energy production. Under the base case economic assumptions, the 60-day interval produced the minimum total annual cost. Using a predefined near-optimality threshold of 1% above the minimum cost, the 90-day interval was also classified as a near-optimal operational alternative, resulting in a conditional base case range of 60–90 days. However, the sensitivity analysis reveals that the optimum is economically sensitive to the tariff and cleaning cost assumptions: under a high-power tariff, it moves to 45 days as the value of recovered energy grows; when the cleaning cost doubles, it moves to 90 days, as reducing maintenance frequency becomes more economically advantageous.

Energies doi: 10.3390/en19184372

Authors: Volodymyr Shpylov Olexander Sudak Stanislav Boldyryev

Nitric acid production is among the most significant industrial point sources of nitrous oxide, which is a greenhouse gas with a global warming potential approximately 298 times that of carbon dioxide, yet the large installed base of legacy dual-pressure plants continues to operate with non-selective catalytic tail gas treatment systems that offer limited greenhouse gas abatement and impose rigid thermal constraints on the gas turbine cycle. A validated steady-state process model of an industrial dual-pressure nitric acid plant producing 52.1 t per hour of nitric acid on a 100% HNO3 basis, corresponding to 88.1 t per hour of 59.2 wt.% product acid, is developed, validated against measured plant data, and used to evaluate two selective catalytic retrofit configurations. The first heats raw tail gas to catalyst ignition temperature using the existing process heater, then raises the purified gas to turbine inlet conditions by mixing with flue gas from a newly installed combustion chamber. The second achieves the required temperature rise internally through catalytic fuel gas oxidation within an additional catalyst shelf in a two-bed reactor, eliminating supplementary combustion equipment entirely. The first configuration reduces total greenhouse gas emissions by 42% in carbon dioxide equivalent terms, lowers ammonia slip below 5 ppmv, and enables a 5% production capacity increase worth 5.75 million EUR per year, at a capital cost of 5.25 million EUR and a discounted payback period of 12 months when European Union Emissions Trading System allowance savings are credited; excluding those savings the investment is not recovered within the five-year evaluation horizon. The second achieves a 44% emissions reduction at a capital cost of 1.98 million EUR, reduces annual utility costs by 1.75 million EUR, and recovers its investment within 4 months with allowance savings and 17 months without them, in both cases without increasing electricity demand. The results demonstrate that selective catalytic tail gas treatment retrofit is a value-generating investment rather than a compliance cost. Projected across the global fleet of unabated dual-pressure plants, equivalent adoption could reduce sectoral nitrous oxide emissions by up to 21.6 Mt of carbon dioxide equivalent per year, or 6.5 Mt per year at a 30% adoption rate.

Energies doi: 10.3390/en19184371

Authors: Jiaxu Cao Haiwen Chen Liyuan Zhao Yuzhen Wang Shaoying Wang Yanyan Lu Jingzhi Wang Haonan Lu

Accurate estimation of Behind-the-meter Photovoltaic (BTM PV) generation is essential for load forecasting and grid planning. Most distributed PV systems are installed behind customer meters, making their output unobservable. Disaggregating PV output from net load is therefore critical for improving distribution network observability. However, existing deep-learning-based disaggregation methods require large labeled datasets, and obtaining such data is costly. Under limited budgets, only a few users can be labeled, which constrains model performance. This paper proposes a user-level BTM PV disaggregation method based on active learning with adaptive weight updates, aiming to maximize model performance with minimal labeling cost. We design a multi-dimensional user value assessment system incorporating epistemic uncertainty, aleatoric uncertainty, and representativeness. In each iteration, the most informative users are selected for sub-meter installation. To dynamically optimize the selection strategy, we propose an adaptive weight update mechanism that adjusts the weights for the next round based on performance improvement gradients across dimensions. This closed-loop feedback enables the strategy to capture evolving model needs and prioritize users that yield the greatest performance gains. The proposed method is validated on the public Ausgrid dataset, and experimental results demonstrate its effectiveness under limited budgets.

Energies doi: 10.3390/en19184370

Authors: Lifang Wu Jiajia Wei Qingren Jin Biyun Zhang Yidan Lu Xiaoxuan Guo

Modern distribution networks increasingly face reverse power flow, heavy loading or overloading, and voltage violations as distributed generation and flexible demand introduce large spatiotemporal variations in active-power injections and withdrawals. This paper proposes a planning-operation consistent DC-AC time-series optimal power flow (OPF) method for flexible-resource planning and operation optimization to mitigate these problems. The DC module optimizes investment decisions with embedded DG curtailment and flexible-load regulation to improve the operational relevance. The AC module further considers resource reactive-power flexibility and optimizes their operation under voltage constraints. The consistent design of the two modules in objective structure, operating constraints, and flexible-resource representation allows the planning results to be parsed as the initial schedule for AC operation refinement, improving operation-optimization efficiency. Furthermore, the model introduces discrete type-and-number BESS planning, endogenous initial state of charge (SOC) optimization, and a unified flexible-load model to improve operability and economic relevance. The method is implemented in a CloudPSS-based DSLab environment and tested on a real distribution feeder and the IEEE 123-node benchmark. The real-feeder case demonstrates coordinated mitigation of reverse-power export, branch overloads, and voltage violations. In the IEEE 123-node benchmark, the 8760 h AC operation case converges in 376.31 s, confirming tractability for long-horizon time-series optimization.

Energies doi: 10.3390/en19184369

Authors: Zhaolong Liu Qun Zhao Honglin Liu Feng Liang Hailong Li Zhiliang Zhao Zhongyun Chen Hualin Liu Wenhua Bai Jin Wu Wen Lin Qifeng Wang

Shale gas development in China is dominated by high-pressure gas reservoirs occurring in deep horizons in Sichuan Basin and its peripheral areas. With the progress of exploration and development technologies, medium-shallow normal-pressure shale gas represented by Anchang Syncline in Guizhou has also realized commercial operation. To address the problems of overall low production and high gas breakthrough flowback ratio of medium-shallow normal-pressure shale gas, this paper systematically analyzes its development performance laws based on the complex tectonic setting and the reservoir characteristics of “four lows and one high” of the Anchang Syncline. A series of key technologies were developed, including the static–dynamic iterative identification technology for faults and micro-amplitude structures in complex tectonic areas, the coupled iterative fine modeling technology of geology-development dual chain, the iterative optimization technology of fractures and simulation parameters, as well as low-cost drilling-completion and drainage-production process technology. The application of the above technical suite achieved commercial development of normal-pressure shale gas. By the end of 2024, 66 wells had produced 6.5 × 108 m3 cumulatively, and the well Estimated Ultimate Recovery (EUR) is 2000–4000 × 104 m3. It was clarified that geological conditions constitute the intrinsic basis of gas well production capacity, and engineering factors such as horizontal section length, well type selection, fracturing matching degree, and drainage-production timing are the key to production enhancement. The established development technical system and practical experience for normal-pressure shale gas provide an important reference for the efficient development of analogous gas reservoirs.

Energies doi: 10.3390/en19184368

Authors: Ayrton L. L. do Nascimento Jonathan Muñoz Tabora Carminda Célia Moura de Moura Carvalho Vitor Almeida Bernardes Bruno Santana de Albuquerque Maria Emília de Lima Tostes

This paper evaluates the multi-year, multi-scale hydro–photovoltaic (PV) complementarity of the Belo Monte–Pimental run-of-river hydropower complex in the Brazilian Amazon and its implications for renewable-resource diversification and electricity planning. Official hourly hydropower-generation records from the Brazilian National System Operator were combined with physically modeled PV generation over 2021–2025, comprising 43,824 synchronized hourly observations. The raw-hourly hydro–PV association was weak (Pearson r=−0.119; Spearman ρ=−0.100), whereas substantially stronger inverse associations were observed for the mean intraday (r=−0.541; ρ=−0.560) and seasonal profiles (r=−0.580; ρ=−0.650). Seasonal correlations remained negative in all five analyzed years, while intraday association varied substantially among years. Capacity-equivalent scenarios from 1.1 to 1000 MWp showed progressively greater portfolio effects. At 1000 MWp, PV generation corresponded to 18.68% of the five-year mean dry-season hydropower energy, reduced the hourly portfolio coefficient of variation by 4.71%, and increased the daytime fifth-percentile generation by 191.2 MW. Under the sample-relative classification adopted for 2021–2025, 2024 was identified as the low-generation year. Its June–November hydropower generation was 1679.3 GWh below the five-year dry-season mean. Under the adopted PV model, this deficit corresponds to a seasonal energy-equivalent capacity of approximately 2327 MWp, while the 1000 MWp scenario would provide energy equivalent to about 43% of the observed deficit. The results characterize persistent seasonal diversification within the 2021–2025 observation window and capacity-dependent portfolio effects rather than hour-by-hour balancing, firm capacity, or coordinated hydro–PV dispatch.

Energies doi: 10.3390/en19184367

Authors: Mohamed Shaik Honnurvali Badar Ali Al Washahi Mazhar Baloch Touqeer Ahmed Jumani Abdul Manan Sheikh Sohaib Tahir Chauhdary Muhammad Bux Alvi Mansoor Soomro Muhammad I. Masud Syed Abdul Moiz

The increasing use of rooftop photovoltaic (PV) systems in distribution networks can lead to operational challenges, including feeder overloading, reverse power flow, fluctuating net-load characteristics, and future hosting-capacity constraints. This research presents an integrated data-driven approach for the holistic evaluation of the impacts of rooftop PV on the Sohar Grid Station (GSS) distribution network in Oman. The proposed framework integrates spatial feeder-loading analysis, PV adoption assessment, net-load and duck-curve evaluation, reverse-power-flow detection, PV performance analysis under dust conditions, machine learning-based net-load forecasting, hosting-capacity screening, and SHAP-based explainability. The framework is based on operational feeder data, Sahim PV installation and export data, meteorological factors, irradiance, and dust-related metrics. The findings demonstrate that the present network is already under operational stress, with 10 out of 35 feeders exceeding the 100% loading reference limit, including two feeders that reached loading levels of 192.8% and 183.3%, respectively. The present PV penetration of the Sahim system causes a modest decrease in net daytime demand and localised reverse-power-flow effects on feeders with higher PV-to-load ratios. The hosting-capacity study identified a PV accommodation potential of 27.11 MWp DC, compared with an existing installed capacity of 2.037 MWp DC, indicating substantial screening-level potential for additional PV deployment, although the available capacity varies across feeders and should not be interpreted as a definitive interconnection limit. Among the evaluated forecasting methods, the combined convolutional neural network and long short-term memory (CNN-LSTM) model achieved the lowest root mean square error (0.063 MW) and the highest coefficient of determination (0.984). In addition, SHAP analysis of the Random Forest model showed that recent and weekly historical net-load values were the dominant predictors. The proposed framework provides a useful decision-support tool for reliable rooftop PV integration and distribution network planning.

Energies doi: 10.3390/en19184365

Authors: Milica Mladenović Miroslav Ferenčak Peđa Milosavljević Dušan Dobromirov

The growth in electricity demand driven by artificial intelligence and digital infrastructure is directing attention toward nuclear energy as a stable source. The sector has been transformed by the emergence of SMRs and the growing number of publicly listed developers. The aim of this paper is to investigate whether the stock returns of publicly listed SMR companies are more closely associated with the AI-related equity benchmark or with the energy commodity benchmark. Due to the absence of a unified SMRs index, a market-capitalization-weighted synthetic NUC index was constructed from Oklo Inc., NuScale Power Corporation, and NANO Nuclear Energy Inc. for the period from 1 July 2024 to 1 July 2026. The Global X Artificial Intelligence & Technology ETF (AIQ) and The S&P GSCI Energy Spot Index represented the AI-related equity and energy commodity benchmarks, respectively. The relationships were examined using correlation and regression analyses with Newey–West HAC standard errors, based on 501 daily logarithmic returns. NUC increased by 166.80%, versus 77.79% for AIQ and −7.68% for the energy benchmark. Correlations were 0.510 (p < 0.001) with AIQ and −0.059 (p = 0.186) with the energy benchmark; standardized coefficients were 0.509 and −0.046, respectively, differing significantly (one-sided p < 0.001).

Energies doi: 10.3390/en19184366

Authors: Menaouar Berrehil El Kattel Ildenor David Sales Junior Robson Mayer Cristina do Carmo Lucio Berrehil El Kattel Paulo Peixoto Praça Luiz Henrique Silva Colado Barreto

Power electronic converters play an important role in proton exchange membrane (PEM) electrolyzers, where current ripple has been associated with electrode potential fluctuations, electrocatalyst degradation, and long-term system durability. This paper presents a unified analytical framework for multicell interleaved Buck DC-DC converters with parallel switching arms intended for low-ripple operation in electrochemical energy conversion systems. Existing steady-state analytical models are generally topology-specific and require new derivations for different converter configurations, limiting scalability and design flexibility. The proposed formulation considers a converter composed of k interleaved cells and M parallel switching arms per cell, resulting in a generalized converter architecture where the total number of switching devices is n = kM. Closed-form expressions are derived for the DC voltage gain, inductor current ripple, electrolyzer voltage ripple, and capacitor RMS current. The analysis shows that ripple cancellation and ripple-frequency multiplication are governed by the operating-region distribution and the total number of switching devices. In addition, normalized closed-form expressions are developed to establish scalable ripple-oriented design charts applicable to arbitrary converter configurations. By enabling the identification of operating conditions that minimize current ripple, the proposed methodology provides practical guidelines for the design of power converters supplying PEM electrolyzers and other catalyst-based electrochemical energy conversion systems. Consequently, the proposed model contributes to the development of power electronic interfaces that mitigate electrical stress on electrocatalysts, supporting improved durability and reliability of electrochemical energy conversion systems.

Energies doi: 10.3390/en19184364

Authors: Wenli Ma Haoyong Wang Xiulun Zhang Yakui Li Penghui Jia Zening Cheng Junyao Jiang Kai Zhao Ming Liu

Selective catalytic reduction (SCR) systems in coal-fired power plants must maintain low NOx emissions during increasingly frequent load changes. Variations in flue gas temperature and flow complicate ammonia-injection control and can cause NOx overshoot or excessive NH3 slip. This study evaluates an implicit generalized predictive self-tuning controller using a coupled dynamic model of a 660 MW ultra-supercritical coal-fired power plant and its SCR system. The controller combines recursive least-squares identification with generalized predictive control (GPC) and is compared with proportional–integral–derivative (PID) control between 50% and 75% turbine heat acceptance (THA), at load-cycling rates of 0.5–2.0% Pe0 min−1. GPC improves NOx set-point tracking and reduces NH3 slip over the conditions examined. During loading-down, the maximum outlet NOx concentrations are 48.43 mg m−3 with GPC and 65.78 mg m−3 with PID. During loading-up at 1.0% and 2.0% Pe0 min−1, GPC reduces the cumulative NH3-slip index by 46.52% and 75.56%, respectively. The identified model coefficients vary more strongly at higher ramp rates, while the loading-down response also depends on the transient SCR inlet temperature. These results indicate that online model adaptation can improve ammonia-injection control during load-cycling.

Energies doi: 10.3390/en19184363

Authors: Ningbo Zhang Hongqiang Li Xutao Li Lei Zhou Yangjun Zeng Yiwei Qiu

To mitigate capital investment costs, the multi-to-one configuration, wherein multiple alkaline electrolyzers share a single balance of plant (BoP) system, is widely adopted in projects. However, compared with the one-to-one configuration, the strong coupling inherent in the shared BoP imposes complex mutual constraints on the on-off switching and load allocation of multiple electrolyzers, thereby compromising the operational flexibility of the hydrogen plant. To leverage the complementary strengths of multi-to-one and one-to-one configurations regarding investment costs and operational flexibility, this paper proposes a mixed configuration method for multi-to-one electrolyzers in solar power-to-hydrogen systems. First, the hydrogen production characteristics, on-off switching, and power allocation of multiple alkaline electrolyzers are modeled. Furthermore, the coupling constraints specific to multi-to-one clusters are characterized based on practical engineering experience. Subsequently, a mixed configuration model targeting the minimization of the levelized cost of hydrogen (LCOH) is proposed. This problem is formulated as a mixed-integer fractional programming (MIFP) model with second-order cone constraints. Meanwhile, the information gap decision theory (IGDT) is applied to address photovoltaic power output uncertainty, and a bounded Dinkelbach algorithm is employed to reduce computational complexity. Case studies based on a real-world project in northern China demonstrate that the proposed mixed configuration model reduces the LCOH by 0.6%, 0.7%, and 1.3%, respectively, compared to uniform four-to-one, two-to-one, and one-to-one configurations. Additionally, the robustness analysis based on the IGDT demonstrates that the mixed configuration achieves the largest tolerable PV uncertainty radius under the prescribed LCOH limits.

Energies doi: 10.3390/en19184362

Authors: Zhenxing Li Dawei Cui Jiaqi Qin Xinghua Fu Guang Yang

The hybrid operation of grid-following (GFL) and grid-forming (GFM) renewable energy units can lead to phase-reference inconsistencies and distorted transient impedance trajectories, which in turn cause maloperation or failure-to-operate of conventional distance protection. To address this issue, this paper proposes a novel distance protection method based on transient impedance fitting. First, a dynamic phase transformation is applied to map the currents of GFL units into a unified reference frame, enabling consistent representation of heterogeneous currents from the hybrid renewable energy station. Second, a transient equivalent impedance model is established based on the transient voltage-current relationship of the transmission line, revealing the influence mechanisms of the rates of change in current amplitude and phase angle on the transient additional impedance. Finally, the magnitude of the transient impedance within a short post-fault data window is selected as the fitting object. The least-squares method is employed to extract the linear fitting slope and intercept, which characterize the evolution trend and initial position of the transient impedance trajectory, respectively, thereby forming the criteria for distinguishing internal and external faults. Simulation results demonstrate that the proposed method correctly identifies fault sections under various fault locations, transition resistances, and renewable power output conditions, with the protection decision completed within 15 ms. The proposed method effectively overcomes the susceptibility of conventional distance protection to maloperation and failure-to-operate in scenarios with high renewable energy penetration.

Energies doi: 10.3390/en19184361

Authors: Angelina K. Stoyanova-Ivanova Todor E. Vlakhov Petar A. Lilov Galia D. Ivanova Ognyan S. Dimitrov Antonia E. Stoyanova Yordan G. Marinov

The multifunctional bismuth-containing cuprate ceramic—Bi1.7Pb0.3Sr2CaCu2Ox (B(Pb)SCCO 2212)—has been employed as a functional additive at 5 wt.% for the zinc electrode in alkaline Ni-Zn batteries in order to improve electrochemical performance and cycling stability. Our study focuses on the electrochemical role of this additive in a 7 M KOH electrolyte, simulating the battery environment. Cyclic Voltammetry (CV) and Chronopotentiometry (CP) were used to investigate the electrochemical processes occurring during electrode operation. The obtained results reveal that an additional cathodic response associated with the B(Pb)SCCO 2212-containing electrode occurs at potentials preceding the main ZnO/Zn reduction process. The CV curves have shown broad cathodic a broad cathodic response consistent with the electrochemical reduction of Bi- and Cu-containing species originating from the ceramic additive. Since these processes occur before the main ZnO/Zn reduction, the formation of reduced Bi/Cu-containing phases may contribute to charge transport within the active mass and to the reduced polarization observed for the modified electrode. These findings provide a possible electrochemical explanation for the improved behavior of zinc electrodes containing 5 wt.% B(Pb)SCCO 2212.

Energies doi: 10.3390/en19184360

Authors: Andrea Di Mario Antonella Accardo Carlo Beatrice Ezio Spessa

This study presents a Life Cycle Assessment (LCA) conducted to evaluate the environmental performance of a Free-Piston Linear Generator (FPLG) compared to conventional internal combustion engines in two applications: automotive range extenders and stationary generator sets. A cradle-to-grave approach was adopted, covering production, use, and end-of-life phases, with consistent modelling of engine materials, fuel supply chains (gasoline, diesel, hydrogen), and mission profiles. The FPLG, modelled based on key components (e.g., stator, magnets, coils), was evaluated under multiple hydrogen-production pathways, including steam methane reforming, wind- and photovoltaic-powered electrolysis, nuclear-based hydrogen production, and coal gasification. Conventional engine baselines were assessed using gasoline, diesel, and hydrogen pathways. Results highlight that environmental impacts are strongly driven by the fuel supply chain, particularly the Well-to-Tank phase. When hydrogen is produced through wind-powered electrolysis, the FPLG achieves over 90% reduction in fossil fuel consumption and more than 80% reduction in greenhouse gas emissions compared to gasoline. With hydrogen produced through steam methane reforming, benefits are limited in automotive applications but remain significant for stationary systems (>20% reduction in greenhouse gas emissions). However, hydrogen produced through wind-powered electrolysis increases impacts in categories such as resource use and toxicity due to materials and infrastructure required for wind farms and electrolyzers. The extended pathway analysis further showed that nuclear-based hydrogen achieved the lowest global warming potential among the investigated hydrogen scenarios, whereas coal gasification produced the highest climate-change impacts. These results confirm that the environmental performance of hydrogen-fuelled FPLG systems depends strongly on the upstream hydrogen-production pathway.

Energies doi: 10.3390/en19184359

Authors: Zhaowang Zhu Zichuan Wang Zhichang Yuan Chenxiao Hu Pengju Qin

Geothermal energy piles (GEPs) synergistically integrate structural load-bearing with subsurface heat exchange, yet their deployment in marine soft clay prevalent along China’s eastern coast introduces complex thermo-hydro-mechanical (THM) challenges. Although the Modified Cam-Clay (MCC) model is extensively employed for such soils, regional parameter variability and quantitative sensitivity to energy pile performance remain insufficiently resolved. This study establishes a three-dimensional finite element model using COMSOL Multiphysics and conducts systematic sensitivity analysis by perturbing key MCC parameters (M, λ, and κ) within ±20% and ±40% around benchmark values for Shanghai marine soft clay. Results demonstrate that the compression index λ dominates pile head settlement control, with a ±40% perturbation inducing variations up to 0.7 mm—over five times more sensitive than M or κ—whereas all parameters exhibit negligible influence (<0.5%) on thermal exchange performance. Comparative analysis further reveals significant regional disparities, with Shanghai soft clay showing notably higher λ (0.29) and lower M (0.721) than Fuzhou, Shenzhen, and other coastal regions. Accordingly, for settlement-critical designs, λ should preferentially be determined from high-quality undisturbed sampling. Conservative λ values are to be used where uncertainties remain, laying a rigorous foundation for parameter-testing procedures and safety-factor calibration within GEP design.

Energies doi: 10.3390/en19184357

Authors: Justyna Swolkień Nikodem Szlązak

Coal self-heating in longwall goaf areas results from strongly coupled gas flow, heat transfer, mass transport, and chemical reactions occurring within a porous medium containing residual coal. This study presents a mathematical and numerical model for analysing these transient and non-isothermal processes with spatially variable permeability based on in-situ mining data. The model accounts for gas filtration through the porous goaf, heat and mass transfer between the gas and solid phases, heterogeneous coal oxidation, homogeneous gas-phase reactions, continuous methane emission, and the possibility of nitrogen inertisation. The governing equations form a strongly coupled non-linear system and are solved using the finite volume method. Numerical simulations were performed for U-type and Y-type ventilation layouts. The results provide spatial distributions of methane, oxygen, and carbon monoxide concentrations, gas temperature, solid-phase temperature, pressure, and gas velocity. The simulations demonstrate that ventilation configuration affects oxygen penetration, gas composition, and temperature development within the goaf. In particular, the Y-type ventilation system promotes deeper oxygen ingress into the porous zone, which may increase the extent of regions susceptible to coal self-heating. The proposed approach provides a framework for analysing coupled thermal and transport phenomena associated with spontaneous coal combustion and for assessing the influence of ventilation conditions on the development of thermal hazards in longwall goaf areas.

Energies doi: 10.3390/en19184358

Authors: Peihua Hao Mengjia Fu Weiwei Wang Lei Mei

Conventional five-degree-of-freedom (5-DOF) magnetic bearing supports often require a large axial span, while highly integrated magnetic circuits can introduce axial–radial flux coupling through shared return paths. This study proposes a compact permanent-magnet-biased 5-DOF magnetic bearing for flywheel energy storage systems, integrating two radial support sections and one axial support unit within a common stator. A series-opposed compensation winding is introduced to suppress axial-control-induced leakage into the radial branch. An electromagnetic design procedure considering leakage, ampere-turns, and magnetic-saturation constraints is evaluated using a three-dimensional magnetostatic finite-element model. At 6 A, the modeled axial and radial forces reach approximately 1.50 and 0.75 kN, respectively, while the maximum ferromagnetic flux density remains below 1.25 T. Under permanent-magnet-biased operation, the compensation winding reduces the peak radial-field deviation from approximately 161.5 to 1.7 mT, corresponding to approximately 99.0% suppression; the full-path RMS-deviation metric indicates approximately 93.1% suppression. Among the investigated configurations, the one with 50 turns provides the closest restoration to the bias-only radial field. Prototype tests demonstrate stable five-channel closed-loop static suspension, supporting physical realizability, but do not directly validate the predicted decoupling ratios.

Energies doi: 10.3390/en19184356

Authors: Long Wang Zizhen Zhao Chengfeng Li Xilai Li Meilin Wang Tongguang Wang

With the increasing size of wind turbines and the trend toward longer and more flexible blades, the aeroelastic response of blades has become significant. To achieve efficient and high-fidelity modeling, a new approach is required. In this study, a parametric two-dimensional cross-sectional model is developed based on the Variational Asymptotic Method, retaining Saint-Venant free warping. Vlasov theory is incorporated to add the warping-rigidity term associated with longitudinal variation in the torsion rate to the energy functional, thereby representing non-uniform-warping energy. To address the taper effect in variable-section blade structures, dimensionally consistent offset- and gradient-dependent correction terms are introduced through Tapered Beam Modification (TBM) to establish an expression for tapered torsional stiffness. Numerical results are presented for a tapered beam, a uniform composite beam, and the NH1500 blade; the method is then applied to the IEA 15-MW blade. The results show that, for the tapered beam, TBM reduces the deviation in the global frequency-equivalent stiffness from 4.2% to 2.1%, while for the IEA 15-MW application the peak sectional correction is 3.88% at x=0.320 and the first torsional frequency differs by 5.21% between the two reduced-order models. The reported time-domain and AEP differences quantify sensitivity to the structural model.

Energies doi: 10.3390/en19184355

Authors: Fan Li Yahan Dong Jishuo Qin Rui Shi Hanqing Liang Haoyang Yu

The multi-infeed short-circuit ratio (MSCR) is widely used to characterize the steady-state strength of AC receiving systems with multiple line-commutated converter high-voltage direct-current (LCC-HVDC) infeeds. Its rated-power denominator, however, does not describe the disturbance actually imposed during converter blocking, commutation failure, AC-fault clearing, or concurrent disturbances at neighboring infeeds. This paper introduces a unified disturbance-driven transient multi-infeed short-circuit ratio (UDTMSCR). Six quantities obtained from the fault trajectory—reactive-power deviation, active-power reduction, reactive-power ramp, disturbance energy, recovery duration, and a fault-class correction—are normalized and combined into an infeed disturbance term. Substitution of this term for rated power retains the original impedance-coupling structure of MSCR, weights each neighboring disturbance by a bounded participation coefficient, and thereby accounts for the severity and timing of local and neighboring disturbances. The denominator is further separated into local and mutual contributions, and percentile thresholds may be used for severity classification. Numerical evaluation comprises eight representative two-infeed cases and a 48-case parametric study of mutual-path impedance, neighboring-event delay, and neighboring-disturbance amplitude. For the eight cases, the Pearson and Spearman correlations between inverse UDTMSCR and the fault-side voltage peak are 0.979 and 0.976, compared with 0.193 and 0.246 for inverse MSCR. The corresponding values in the parametric study are 0.973 and 0.983 for inverse UDTMSCR and −0.294 and −0.310 for inverse MSCR. A peak-only transient index and a transient voltage severity index are included as additional references, and the sensitivity of the results to the disturbance weights, coupling weights, grading thresholds, event window, and record imperfections is quantified. These results show that the proposed index retains the engineering interpretation of MSCR while better reflecting fault severity, inter-infeed coupling, and recovery. Because the trajectories are generated by a reduced-order model, verification with detailed electromagnetic-transient models and field records remains necessary.

Energies doi: 10.3390/en19184354

Authors: Dervis Kirikkaleli Seyed Alireza Athari Danielle Khalife Eric Tieku Agyemang

Climate policy in Italy has come to treat energy and resource productivity as central levers, reflecting a broader recognition that how efficiently an economy uses energy and materials shapes its environmental outcomes. This study aims to investigate the dynamic nexus between energy productivity, resource productivity, and carbon dioxide (CO2) emissions in Italy. To do this, this study uses quarterly time series data from 2000 to 2022 and employs wavelet quantile methods, specifically wavelet quantile regression and wavelet quantile correlation. The wavelet quantile correlation reveals that a euro per kilogram unit change in resource productivity negatively correlates with CO2 emissions by approximately −0.9 metric tonnes, and a euro per kilogram of oil equivalent unit change in energy productivity negatively correlates with CO2 emissions by approximately −0.9 metric tonnes across all periods and quantiles, particularly in the long term. Moreover, the findings of wavelet quantile regression reveal that, in the long run, a euro per kilogram unit change in resource productivity and a euro per kilogram of oil equivalent unit change in energy productivity reduce CO2 emissions across all periods and quantiles by approximately −0.40 and −1.0 metric tonne, respectively. The study recommends that stakeholders in Italy should invest in both short- and long-term energy productivity programs, such as investing in smart appliances that adjust energy consumption and a Combined Heat and Power (CHP) System, as all will be effective. Beyond its methodological novelty, Italy’s dualistic industrial base and EU-driven decarbonisation commitments make it a substantively useful case for a distributional, multi-horizon analysis of this kind.

Energies doi: 10.3390/en19184353

Authors: Paul Andrei Sorin Deleanu Marilena Stănculescu Emil Cazacu Emil Diaconu Dan Micu Horia Andrei

Non-sinusoidal operating conditions are frequently present in modern power systems and can adversely affect the normal operation and performance of industrial equipment connected to the electrical grid. This paper presents a method for evaluating the relationship between the harmonic weights of voltage and current and the sensitivities of reactive and apparent power under these conditions. These conditions primarily arise from non-linear loads and circuit components, particularly power electronic converters and other power electronic devices. In practical industrial environments, the harmonic composition of voltage and current may vary considerably, leading to significant changes in their RMS values and consequently affecting power transfer and overall power quality. The proposed approach first establishes the mathematical dependencies between active, reactive, and apparent power and the harmonic components of voltage and current. Based on these relationships, the sensitivities of the power quantities can be determined when one or more system parameters undergo variations. To validate the proposed methodology, we developed and implemented a numerical algorithm in MATLAB/Simulink for a practical industrial case. We compare the sensitivities obtained using the proposed approach with those calculated directly from measured data. The small differences between the two sets of results confirm the method’s accuracy and show that it is suitable for assessing harmonic variations on power quantities in non-sinusoidal power systems.

Energies doi: 10.3390/en19184352

Authors: Patrycja Walichnowska Robert Kasner Anna Lewandowska Weronika Kruszelnicka Andrzej Tomporowski Zbigniew Kłos

Renewable energy sources and circular economy principles play a significant role in reducing the environmental impact of the food industry. Implementing low-emission energy sources, using recycled materials, and properly managing packaging at the end of its life can contribute to reducing the environmental impact of products throughout their life cycle. The aim of this study was to assess the impact of changes in energy sources, the share of recycled materials in packaging materials, and packaging waste management methods on the potential environmental impact of a beverage packaging system throughout its life cycle. The study conducted a life cycle analysis of three packaging system scenarios consisting of a 1.5 L PET bottle with a cap and label, a secondary packaging unit, and a shipping container. The functional unit involved the packaging, transportation, and storage of 12,000 L of beverage. Under the conditions assumed in the study, the future scenario resulted in a 70.8% reduction in the total environmental score compared to the baseline scenario, including a 54.3% reduction in climate change and a 63.8% reduction in fossil resource use. This improvement was due to the combined effect of a higher share of renewable energy, a higher share of recycled materials, and changes in waste management. At the same time, the transition towards renewable energy was accompanied by a slight increase in the use of minerals and metals compared to the actual scenario, indicating a possible environmental trade-off associated with decarbonizing the energy system.

Energies doi: 10.3390/en19184351

Authors: Shujia Guo Yifan Tong Xin Tong Yiqiu Cheng Cheng Li Mingchen Wang

With the increasing penetration of renewable energy and inverter-based resources, power systems exhibit stronger uncertainty and nonlinear dynamic characteristics, which increases the need for accurate short-term prediction of dynamic states. This study proposes a hybrid prediction method combining Long Short-Term Memory (LSTM) networks and XGBoost to improve the forecasting accuracy of key dynamic variables. The LSTM module is used to extract temporal dependencies from historical time-series data, and the extracted deep features are fused with the raw input features to construct an augmented feature vector. An XGBoost regressor is then employed to capture nonlinear feature interactions and generate the final prediction results. The proposed method is evaluated using rotor speed, active power, and power angle as representative dynamic variables. Test-set results in physical units show that the proposed model achieves RMSE values of 1.0 × 10−6 p.u., 2.5808 MW, and 0.0001 deg, and MAE values of 1.0 × 10−6, 1.0269 MW, and 0.0001 deg, respectively. Compared with the reference model, the proposed method reduces both RMSE and MAE for all three variables, indicating that the LSTM-XGBoost framework can improve dynamic-state prediction accuracy in power systems.

Energies doi: 10.3390/en19184350

Authors: Rabab El Attar Hanane Karmouss Mouad Bahij Abdellah Bah Saïd Kardellass Mustapha Malha

Thermal management in deep-space environments remains a critical bottleneck for miniaturized energy systems due to the inherent absence of convective heat transfer. This study investigates the thermo-electrochemical coupling of a miniaturized Proton Exchange Membrane Fuel Cell (PEMFC) thermally integrated with a 10 W power electronic Printed Circuit Board (PCB) under high-vacuum conditions. Beyond conventional parametric studies, this work explores the synergistic interaction within six passive thermal control architectures, utilizing paraffin-based Phase Change Materials (PCMs) and high-conductivity graphite Thermal Interface Materials (TIMs). Transient numerical simulations reveal that unmanaged configurations lead to thermal runaway (>128 °C), critically threatening membrane hydration and electrochemical stability. In contrast, the development of a hybridized PCM-TIM architecture—comprising a 5 mm PCM and 10 mm TIM layer coupled with a radiative finned sink—established a high-efficiency thermal shunt. This optimized configuration stabilized the PEMFC at 60 °C and achieved superior temperature homogeneity (<67 °C) across the PCB surface. Furthermore, polarization curve analysis demonstrates that this passive strategy significantly mitigates voltage degradation and concentration losses at high current densities. These findings establish design scaling laws for convection-independent energy subsystems, providing a robust, lightweight, and scalable framework for future microsatellite power architectures in extraterrestrial environments.

Energies doi: 10.3390/en19184349

Authors: Małgorzata Zysińska Jolanta Żak Ewa Dębicka Agnieszka Misztal

This article summarizes the results of a demand-and-supply analysis of hydrogen road transport development in Poland, conducted as part of a research project at the Motor Transport Institute in 2025, with a time horizon extending to 2040. The central aim of the study is to identify optimal hydrogen refueling station locations and develop a phased deployment plan. It also assesses the potential for using hydrogen as an alternative transport fuel in Poland compared with other European countries. The article presents a multi-criteria decision-making model developed to support the planning of hydrogen infrastructure for the Polish transport system. The model’s assumptions and the research results concerning the selection of hydrogen refueling station locations in large cities are discussed. Technical, ecological, logistical, legal, and social aspects related to the implementation of hydrogen infrastructure are analyzed. The article also outlines methods for parameterizing and assessing the potential for hydrogen adoption in transport. Forecasts indicate that the development of a hydrogen vehicle fleet could contribute to improved public health, sustainable development, and a reduction in the negative effects of high emissions. The objective of the research is to determine the environmental and energy-transition impacts of shifting from combustion-powered transport to hydrogen over the coming decades. The study introduces an innovative modeling approach, as previous research has not examined city rankings and urban mobility plans with comparable depth to identify, structure, and weight the criteria used for selecting optimal hydrogen refueling station locations. Although independent validation remains unattainable due to the absence of certain operational data, the study’s findings can support transport, energy, and climate strategies, with robustness defined strictly as internal stability and cross-method consistency.

Energies doi: 10.3390/en19184348

Authors: Kazuhiro Kamata Ryosuke Shikuma Yu Fujimoto Yasuhiro Hayashi

As inverter-based renewable generation increases, load modeling from ambient measurements is important for power-system stability assessment. Under ambient conditions, voltage and frequency variations are small, whereas time-varying nominal power can substantially affect load-power variation. If nominal-power variation is not considered, it may be misattributed to voltage- and frequency-dependent load responses, resulting in biased parameter estimates. Because this misattribution depends on the variations within each time window, single-window estimation may suffer reduced accuracy. This study proposes a method that models time-varying nominal power using an autoregressive moving-average model to separate nominal-power variation from voltage- and frequency-dependent load responses and aggregates validation errors across multiple time windows to reduce dependence on any specific window. Numerical simulations under two nominal-power variation levels were compared with single-window estimation and a previously proposed sensitivity-based window-selection method. When the maximum 100 s peak-to-peak nominal-power variation was 1.0% of the mean nominal power, the proposed method achieved an overall error of 0.100, 86% lower than the mean single-window error and 79% lower than the best sensitivity-based result. Additional analyses under modified identification and data-generation conditions showed that the proposed method generally maintained comparatively low identification errors, although accuracy varied with the conditions.

Energies doi: 10.3390/en19184347

Authors: Stephen Youngjun Park Yasemin Boztug Namjun Cha HyungBin Moon

The rapid diffusion of electric vehicles (EVs) is weakening the conventional fuel tax base, creating new challenges for transport-energy taxation. Although South Korea does not currently tax EV charging electricity, such taxation is becoming increasingly relevant as EV adoption expands. This study examines consumer preferences for EV fuel-tax design by focusing on revenue recycling and tax salience, operationalized as the visibility of tax information on EV charging receipts. Using stated-preference data from a discrete choice experiment with South Korean adults aged 20–59, we estimate a mixed-mixed multinomial logit model capturing both discrete preference segments and continuous within-segment heterogeneity. A post-estimation multinomial logit analysis of individual-level salience coefficients identifies characteristics associated with relatively strong positive or negative preferences for tax salience. The results reveal two distinct segments. The majority shows positive preferences for tax salience and the revenue-use outcomes, whereas the minority is highly cost-sensitive and favors direct EV performance improvements. Stronger preferences for tax salience are associated with greater knowledge of the tax system, while negative preferences are associated with lower education and not currently driving. The results therefore represent conditional preferences among alternative EV fuel-tax designs, rather than acceptance of an EV fuel tax relative to no tax. The study provides policy-relevant insights into consumer preferences for EV fuel-tax design during the electric mobility transition.