Search Results (58,500)

Hybrid power generation systems are an effective solution for matching energy production with electrical load demand. In this study, we examine the viability of a grid-connected hybrid PV/Wind system for meeting the electricity demand of the Lafarge cement factory in Al-Tafilah, Jordan, using HOMER Pro software. The results indicate that the optimal configuration consists of a $6.1 \mathrm{M}\mathrm{W}$ wind turbine and a $22.8 \mathrm{M}\mathrm{W}$ PV array, producing $71.94 \mathrm{G}\mathrm{W}\mathrm{h}$ annually, with wind and PV contributing $31.3\%$ and $68.7\%,$ respectively. The system achieves a 100% renewable fraction while maintaining a high level of reliability, with unmet load and capacity shortage limited to $0.057\%$ and $0.1\%,$ respectively. The economic evaluation reveals a levelized cost of energy (LCOE) of $0.13 \mathrm{U}\mathrm{S}\mathrm{D}/\mathrm{k}\mathrm{W}\mathrm{h}$ and a net present cost (NPC) of $\mathrm{U}\mathrm{S}\mathrm{D} 25.827 \mathrm{m}\mathrm{i}\mathrm{l}\mathrm{l}\mathrm{i}\mathrm{o}\mathrm{n}$, representing a $27.8\%$ reduction in LCOE compared to the national grid tariff. In this study, we present a novel large-scale PV/Wind system for the cement industry in Jordan, based on real data, with enhanced techno-economic performance. The innovation of this research lies in the development and optimization of a large-scale grid-connected hybrid PV/Wind system for the cement industry in Jordan, utilizing actual industrial load data and site-specific renewable energy resources. Unlike previous PV-dominated studies, the proposed system integrates a significant contribution of wind energy to improve system reliability and renewable energy penetration, reduce dependency on the national grid, and improve the overall techno-economic performance under actual industrial operating conditions. Full article

The increasing penetration of power electronic devices and distributed generation is significantly altering power quality conditions in low-voltage systems. While power quality assessment is commonly based on RMS currents, voltage quality indicators, and overall distortion metrics, these parameters may not fully reveal phase-selective harmonic behaviour in modern converter-dominated installations. This paper presents a measurement-based power quality assessment of a secondary school building equipped with a grid-connected photovoltaic (PV) system. A one-week monitoring campaign was conducted at the point of common coupling (PCC), capturing voltage, current, harmonic distortion, and power flow characteristics under real operating conditions. The results reveal pronounced phase-selective current harmonic distortion, with substantially elevated total harmonic distortion (THD_I) and total demand distortion (TDD) in one phase despite relatively balanced RMS current levels and acceptable voltage quality. The harmonic spectrum is dominated by low-order odd harmonics, whereas voltage distortion remains comparatively low and well balanced across phases. The study demonstrates that significant harmonic asymmetry may remain hidden in apparently balanced three-phase systems when assessment relies primarily on conventional RMS-based indicators. The findings highlight the importance of detailed current harmonic analysis and show that acceptable voltage quality does not necessarily imply acceptable current quality. The presented results provide measurement-based evidence of hidden harmonic asymmetry in modern low-voltage buildings and contribute to a better understanding of power quality challenges associated with nonlinear loads and distributed energy resources. Full article

Industrial processes consume large amounts of thermal energy, while many recoverable heat streams remain unused because heat sources and sinks differ in time, temperature level, power demand, and operating schedule. Thermal energy storage (TES) can decouple heat supply from heat demand and support waste heat recovery, peak-load reduction, process heat electrification, and flexible operation of continuous, batch, and intermittent processes. This narrative review assesses industrial TES from a process integration perspective rather than from a storage-material perspective alone. Sensible, latent, thermochemical, sorption-based, hybrid, and steam-based storage systems are compared with respect to delivery temperature, storage duration, charging and discharging power, response time, heat losses, reliability, integration complexity, and techno-economic feasibility. Sector-specific opportunities are discussed for the iron and steel, cement, ceramics, chemical and petrochemical, pulp and paper, and food and beverage industries. The review shows that deployment is constrained less by the availability of storage concepts than by heat exchanger limitations, inconsistent Key Performance Indicator (KPI) definitions, unclear system boundaries, scarce long-term operating data, and insufficient coupling with pinch analysis, heat exchanger network design, control, and safety requirements. A practical technology-selection workflow and a research roadmap are proposed for scalable, reliable, and economically viable industrial TES deployment. Full article

Large Language Models (LLMs) are rapidly transitioning from research concepts to transformative artificial intelligence components within the power and energy domain. Their ability to fuse diverse data, spanning SCADA logs, real-time sensor readings, and regulatory documentation enables unprecedented capabilities in forecasting, operator decision support, anomaly detection, and wide-area situational awareness for future intelligent grids. However, the integration of LLMs into safety-critical and highly regulated power systems introduces a convergence of novel and severe security risks. Beyond exhibiting model-intrinsic vulnerabilities like hallucination, prompt injection, and data poisoning, these models are susceptible to system-level threats that could compromise grid stability, distort energy market operations, or facilitate the leakage of sensitive operational data. Moreover, integrating LLM workloads into cloud or hybrid architectures necessitates strict compliance with critical standards and emerging governance frameworks like the EU AI Act. While existing surveys address AI security in power systems, general LLM security, and AI in smart grids separately, this paper bridges these threads by providing a unified treatment of LLM-specific risks, power-system deployment constraints, and emerging governance frameworks—a combination not covered in prior surveys. We provide a systematic taxonomy of risks across five dimensions: cybersecurity, privacy, robustness, explainability, and governance. We synthesize technological advances, clarify the complex interplay between LLM failure modes and grid security, and propose a forward-looking research agenda to guide future investigation. This work aims to be an indispensable resource for researchers, utility operators, and policymakers in designing resilient, trustworthy, and compliant AI-enabled energy infrastructures. Full article

This paper addresses non-steady-state scenarios in the human–robot collaborative disassembly process of retired power batteries, including component aging, ambiguous instructions, and sensor drift. In such scenarios, the robot exhibits execution robustness problems. This paper proposes a Planning Domain Definition Language (PDDL) generation framework that integrates long-term and short-term memory. The framework combines large language models with knowledge graphs as a long-term memory module for symbolic task decomposition and domain semantic rule generalization, while using meta-heuristic optimization algorithms as a short-term memory module to adapt and optimize action parameters based on real-time sensor feedback. Through this closed-loop mechanism that combines long-term memory guidance with short-term memory adaptation, the system addresses the limitation of traditional PDDL, which, when facing open, time-varying, and heterogeneous industrial disassembly scenarios, has symbolic action models that have difficulty capturing the uncertainty and unpredictable disturbances in real physical systems, limiting its practicality in complex non-steady-state scenarios. Furthermore, the system establishes a feedback mechanism from short-term memory to long-term memory, enhancing disassembly capabilities in non-steady-state environments by transforming scenario information into supplementary understanding. The research validates this method on a real disassembly platform. Compared with baselines of traditional PDDL, a planning method using only large language models (LLMs), and heuristic algorithms, this method achieved an 88.0% task success rate (significantly superior to the 38.0% of traditional PDDL). Full article

This paper highlights the eco-efficiency of a sustainable digital solution to support decision-making in resolving the problem of sudden production drops and associated energy waste in industrial power plants, especially those operating with a steam turbomachine. The solution involves a multi-interface digital dashboard that generates insightful visual reports and gives proactive alerting to the decision-makers about potential underperformances to ensure resource optimization. For the studied use case, it involves the development of three interfaces of the dashboard, so as to perform the sustainable monitoring of a thermoelectric power plant based on the Rankine–Hirn cycle as follows: the first interface is about real-time monitoring of thirty-two key physical parameters equipped with a notification system. The second interface displays the historical trends of all the plant variables, in order to help in detecting incipient abnormal deviations before they impact environmental efficiency. Lastly, the third platform covers a predictive model using the XGBoost algorithmic method to forecast the future behavior of the electrical power as the target variable of the power plant. The XGBoost method was selected after a comparative assessment which also included the algorithms of Random Forest Regressor (RFR) and Gated Recurrent Unit (GRU). As a final step, this solution was later tested in a simulation environment built under the “Node-Red” platform, through an industrial decision scenario. The concrete findings validate the framework’s sustainability metrics, demonstrating the ability of the solution to help in preserving, for each production cycle of two years, up to 7.6 GWh of electrical energy that would otherwise be wasted, which translates into a potential cost-saving exceeding 633,247.9 USD, as well as an ecological impact by preventing the emission of 4628 tons of CO₂. Full article

Rapid urbanization, climate change, and uneven regional development have increasingly intensified spatial heterogeneity in food security. As one of China’s major commercial grain-producing areas, the Main Grain-Producing Region in the Middle Reaches of the Yangtze River (MGPR-MRYR) plays a critical role in ensuring national food security. However, existing studies have paid limited attention to spatial heterogeneity and driving mechanisms at the urban agglomeration scale. Taking the Wuhan (WUA), Changsha–Zhuzhou–Xiangtan (CZXUA), and Poyang Lake (PYLUA) urban agglomerations as analytical units, this study constructs a multidimensional food security evaluation framework covering supply security, production resource security, and circulation–consumption security. Based on panel data from 2013 to 2023, the entropy weight method, kernel density estimation (KDE), Theil index decomposition, spatial autocorrelation analysis, and the optimal-parameter geographical detector (OPGD) model were employed. Food security levels in the MGPR-MRYR exhibited an overall upward trend, particularly after 2020, although significant spatial heterogeneity persisted among urban agglomerations. A spatial pattern of “higher in the west than east, and inland over lakeside” emerged, with significant positive clustering gradually expanding westward. Intra-agglomeration disparities—especially within the WUA—contributed more to regional inequality than inter-agglomeration differences. Agricultural machinery power and rural population remained the dominant driving factors, while the influence of urbanization and annual precipitation increased over time. All factor interactions showed enhancement effects, indicating that food security is shaped by the synergistic interplay of natural, socioeconomic, and agricultural production factors. This study reveals the transition of driving mechanisms from traditional factor dependence to multi-factor system synergy. These findings suggest that food security governance in rapidly urbanizing grain-producing regions should shift from uniform policies to differentiated, synergy-oriented strategies tailored to each urban agglomeration’s development stage and resource constraints. Full article

With the rapid growth of transportation electrification and smart energy systems, the reliable operation of electric vehicle (EV) charging infrastructure has become an important issue for sustainable transport, since charging faults may interrupt service and shorten equipment lifetime. However, practical charging environments are often characterized by heterogeneous operating conditions and severely imbalanced fault distributions, which limit the effectiveness of conventional fault diagnosis methods. To address these challenges, this study proposes a lightweight Proto-Contrastive Discriminative Learning (PCDL) framework for intelligent fault diagnosis in EV charging systems. The proposed method combines supervised contrastive learning with a prototype-distance discrimination mechanism to improve the identification of rare abnormal states under long-tailed data conditions. Heterogeneous charging features, including discrete control signals and continuous total harmonic distortion (THD) indicators, are projected into a discriminative embedding space, while anomaly detection is performed according to the relative distances between samples and class prototypes. Experimental results on a publicly available EV charging-pile monitoring dataset, containing 122,144 samples with four discrete control/safety features and two THD-based power-quality features, demonstrate that the proposed framework maintains stable detection performance under imbalance ratios of 1:1, 1:10, and 1:100. Under the most challenging 1:100 condition, the proposed method achieves an F1-score of 84.21%, representing a 29.08% improvement over the strongest baseline method. In addition, the framework requires only approximately 11 KB of memory and maintains CPU inference latency below 6.3 ms, demonstrating strong potential for real-time deployment on resource-constrained edge devices. These results suggest that the proposed framework can provide a lightweight diagnostic tool for practical charging stations and support safer and more reliable EV charging operation. Full article

Affine frequency division multiplexing (AFDM) is regarded as a promising waveform for high-mobility wireless systems. However, the public codebook used in AFDM raises security concerns when the link is observed by an eavesdropper, and meanwhile AFDM communication suffers from a high peak-to-average power ratio (PAPR). This paper proposes a security-aware codebook design for low-PAPR AFDM systems. Specifically, the codebook is designed to minimize an eavesdropper-oriented cross-alignment metric while maintaining the legitimate user’s decoding reliability and keeping the PAPR low. Since the resulting design problem is non-convex, we develop a dedicated alternating discrete coordinate descent algorithm to solve it. Simulation results show that the proposed codebook design significantly degrades the eavesdropper’s decoding performance without degrading that of the legitimate receiver while maintaining the low-PAPR. Full article

Transformerless inverters are increasingly becoming essential in renewable energy generation, particularly for grid-connected photovoltaic (PV) and other sustainable and alternative energy resources. The transformerless designs offer higher efficiency, compact size, and reduced cost compared to traditional inverters with bulky transformers. These inverters minimize energy losses and enable direct connection to the grid by removing the low-frequency transformer. This paper investigates a highly reliable single-phase common-ground inverter for solar panels and other alternative energy generation. The proposed PV inverter has the benefits of existing non-isolated common-ground PV inverters, including direct connection of an input source’s negative terminal to the AC neutral terminal, eliminating leakage ground currents. The inverter is an enhancement of the dual-buck inverter, incorporating one additional diode and a flying capacitor. The dual-buck structure with the inductor inserted between the inverter phase leg prevents short-circuiting. This increases the reliability of the entire power electronics system. Moreover, using external diodes to freewheel the current, the configuration has no reverse recovery issues, allowing power MOSFETs to be employed with safe commutation at higher DC-link voltage and achieve higher efficiency. Summarily, this design prevents short-circuit issues, enhancing reliability and efficiency, and relaxing pulse-width-modulation dead times. The derivation of the PV inverter is carefully analyzed. A 700 W prototype of power converter hardware has been built. The comparative study validates the operational performance, and the grid-connected experiment confirms its theoretical analysis. Experimental results of the hardware prototype are discussed to prove the feasibility and effectiveness of the proposed PV inverter. Full article

Convolutional Neural Network (CNN) accelerators are widely deployed in edge Artificial Intelligence (AI), embedded vision, and object detection systems, but their hardware designs often incur significant power consumption due to intensive multiply–accumulate (MAC) operations, frequent register toggling, memory transactions, and persistent signal switching. This study examines Register Transfer Level (RTL)-level power optimization of a CNN accelerator on a Field-Programmable Gate Array (FPGA) using three design approaches: a baseline, a Local Explicit Clock Gating (LECG) + Memory Split scheme, and a sparsity-aware scheme. The LECG + Memory Split approach reduces redundant sequential and memory-switching operations, while the sparsity-aware scheme further minimizes arithmetic operations on zero-valued operands. FPGA power measurements on a Xilinx ZCU102 platform reveal a total power decrease from 3.644 W in the baseline to 2.775 W with LECG + Memory Split and 2.442 W with sparsity-aware optimization. This achieves up to a 32.99% reduction in total power without increasing Digital Signal Processing (DSP) block or Block Random Access Memory (BRAM) usage. The findings confirm that integrating control-based, memory-aware, and data-aware RTL methods enhances the power efficiency of CNN accelerators while maintaining the main compute and memory architectures. Full article

As renewable energy sources become increasingly integrated into interconnected power networks, system operating points (OPs) undergo frequent and unpredictable shifts. However, conventional delays in updating equivalent model parameters during these OP transitions often compromise modeling accuracy. To address this challenge, this study proposes an online dynamic OP clustering method for interconnected grids featuring wind and photovoltaic generation. First, an equivalent model for renewable-integrated interconnected grids is established. Subsequently, a dynamic OP clustering strategy is developed; this strategy combines an offline construction phase utilizing joint probability distributions and data clustering with an online update mechanism that dynamically adjusts cluster boundaries via membership calculations. This approach enables real-time clustering, effectively minimizing equivalence errors and adapting swiftly to ongoing network variations. Simulation results based on the China–Mongolia interconnected power grid demonstrate that the proposed method significantly outperforms traditional static approaches in both equivalence accuracy and computational adaptability. By delivering precise, real-time network equivalents, this approach provides robust support for practical grid operations, including online security assessment, optimal power dispatching, and transient stability analysis, thereby contributing to the long-term stability and sustainability of modern power systems. Full article

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

This study presents an Ant Colony Optimization (ACO)-based methodology for the optimal placement of lithium-ion battery energy storage systems (BESSs) in radial electrical distribution networks. The proposed framework integrates base-case power-flow assessment, critical-bus identification, discrete BESS siting, technical–economic objective evaluation, and post-optimization validation. The methodology is applied to the IEEE 33-bus radial distribution test system, where the initial operating condition is characterized in terms of nodal voltage profile, voltage deviation, voltage-stability index, active-power losses, and annual loss cost. The optimization process identifies buses 13 and 31 as the most suitable locations for two identical BESS units, with the reported validation case evaluating each unit at upper admissible capacity limits of $1000\mathrm{kW}$ and $4000\mathrm{kWh}$. The obtained results show that the optimized BESS allocation increases the minimum voltage profile to values above $0.94\mathrm{p}.\mathrm{u}.$, raises the voltage-stability index to more than 0.88, reduces active-power losses to approximately $0.0166\mathrm{p}.\mathrm{u}.$, and decreases the annual cost associated with active-power losses by more than $66\%$ relative to the base case. Additional validation through sensitivity analysis, repeated stochastic runs, operating-mode evaluation, and comparison against a genetic algorithm confirms the consistency and robustness of the proposed ACO-based methodology. The results demonstrate that the proposed framework provides a technically consistent and computationally accessible solution for improving voltage regulation, reducing feeder losses, and lowering loss-related operating costs in radial distribution systems. Full article

Ultra-short-term electricity load forecasting is crucial for real-time power system operation, but its accuracy is limited by the nonstationary and multiscale characteristics of load data. To address this issue, this study proposes a multi-scale decomposition–clustering and heterogeneous gated fusion framework. The original load sequence is decomposed by ICEEMDAN and then grouped into high-, mid-, and low-frequency components using K-means clustering. MS-gTCN is used to capture high-frequency fluctuations, adaptive DLinear is used to model low-frequency trends, and a gated fusion mechanism is designed for mid-frequency components. A lightweight error correction network is further introduced to reduce residual prediction errors. Experiments on two real-world datasets show that the proposed method achieves the best performance across 1-, 4-, 8-, and 12-step horizons. For the 12-step task, it reduces MAE by 29.3% on Dataset A and 26.2% on Dataset B compared with the second-best baselines. Compared with ICEEMDAN-LSTM on Dataset A, it reduces MAE by 17.7% and improves R² from 0.9127 to 0.9418. Ablation, sensitivity, significance, and complexity analyses further verify the effectiveness, robustness, and real-time feasibility of the proposed framework. Full article