Search Results (601)

Driven by the global energy transition, the rapid expansion of photovoltaic (PV) capacity—particularly in China’s “sand-Gobi-desert” mega-bases—demands highly efficient collection technologies. DC collection, offering low losses, compactness, and high reliability, is emerging as a critical solution for large-scale integration. This paper provides a comprehensive review of PV DC step-up collection systems. First, it analyzes typical network architectures, compares AC versus DC schemes, and examines design constraints imposed by DC bus voltage levels. Second, control strategies are summarized across device, equipment, and system levels. Third, based on engineering practices in ultra-large-scale bases, key challenges regarding fault detection, efficiency optimization, economic viability, and grid code compatibility are identified alongside representative solutions. Finally, future trends in high-voltage hardware maturation, protection bottlenecks, real-time artificial intelligence, and specialized standardization are proposed. This study serves as a vital reference for the topology design and engineering standardization of PV DC collection systems. Full article

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, reason about goals, plan multi-step actions, and interact with operators in real time. This review presents the latest advances in agentic AI for power systems, including architectures, multi-agent control strategies, reinforcement learning frameworks, digital twin optimization, and physics-based control approaches. The synthesis is based on new literature sources to provide an aggregate of techniques that fill the gap between theoretical development and practical implementation. The main application areas studied were voltage and frequency control, power quality improvement, fault detection and self-healing, coordination of distributed energy resources, electric vehicle aggregation, demand response, and grid restoration. We examine the most effective agentic AI techniques in each domain for achieving operational goals and enhancing system reliability. A systematic evaluation is proposed based on criteria such as stability, safety, interpretability, certification readiness, and interoperability for grid codes, as well as being ready to deploy in the field. This framework is designed to help researchers and practitioners evaluate agentic AI solutions holistically and identify areas in which more research and development are needed. The analysis identifies important opportunities, such as hierarchical architectures of autonomous control, constraint-aware learning paradigms, and explainable supervisory agents, as well as challenges such as developing methodologies for formal verification, the availability of benchmark data, robustness to uncertainty, and building human operator trust. This study aims to provide a common point of reference for scholars and grid operators alike, giving detailed information on design patterns, system architectures, and potential research directions for pursuing the implementation of agentic AI in modern power systems. Full article

Accurate short-term forecasting of renewable energy sources is essential for stable and efficient microgrid operation. Existing models primarily focus on either solar or wind prediction, often neglecting their combined stochastic behavior within isolated systems. This study presents a comparative evaluation of three machine-learning models—Random Forest, ANN, and LSTM—for short-term solar and wind forecasting in microgrid environments. Historical meteorological data and power generation records are used to train and validate three ML models: Random Forest, Long Short-Term Memory, and Artificial Neural Networks. Each model is optimized to capture nonlinear and rapidly fluctuating weather dynamics. Forecasting performance is quantitatively evaluated using Mean Absolute Error, Root Mean Square Error, and Mean Percentage Error. The predicted values are integrated into a microgrid energy management system to enhance operational decisions such as battery storage scheduling, diesel generator coordination, and load balancing. Among the evaluated models, the ANN achieved the lowest prediction error with an MAE of 64.72 kW on the one-year dataset, outperforming both LSTM and Random Forest. The novelty of this study lies in integrating multi-source data into a unified ML-based predictive framework, enabling improved reliability, reduced fossil fuel usage, and enhanced energy resilience in remote microgrids. This research used Orange 3.40 software and Python 3.12 code for prediction. By enhancing forecasting accuracy, the project seeks to reduce reliance on fossil fuels, lower operational costs, and improve grid stability. Outcomes will provide scalable insights for remote microgrids transitioning to renewables. Full article

Planning in robotics represents an ongoing research challenge, as it requires the integration of sensing, reasoning, and execution. Although large language models (LLMs) provide a high degree of flexibility in planning, they often introduce hallucinated goals and actions and consequently lack the formal reliability of deterministic methods. In this paper, we address this limitation by proposing a hybrid Sense–Plan–Code–Act (SPCA) framework that combines perception, LLM-based reasoning, and symbolic planning. Within the proposed approach, sensory information is first transformed into a symbolic description of the world in Planning Domain Definition Language (PDDL) using an LLM. A heuristic planner is then used to generate a valid plan, which is subsequently converted to code by a second LLM. The generated code is first validated syntactically through compilation and then semantically in simulation. When errors are detected, local corrections can be applied and the process is repeated as necessary. The proposed method is evaluated in the OpenAI Gym MiniGrid reinforcement learning environment and in a Gazebo simulation on a UR5 robotic arm using a curriculum of tasks with increasing complexity. The system successfully completes approximately 71–75% of tasks across environments with a relatively low number of simulation iterations. Full article

To maintain power system stability and supply quality when integrating doubly fed induction generator (DFIG)-based wind energy conversion systems (DFIG-WECSs), regulators regularly update grid codes specifying low voltage ride-through (LVRT) requirements. This paper presents a systematic literature review (SLR) on the use of STATCOMs to enhance LVRT capability in DFIG-WECSs. Objectives included a structured literature search, bibliographic analysis, thematic synthesis, trend identification, and proposing future research directions. A PRISMA-based methodology guided the review, utilising PRISMA 2020 for Abstracts in the development of the abstract. The final search was conducted on Scopus (31 March 2025). Eligible studies were primary research in English (2009–2014) where STATCOM was central to LVRT enhancement; exclusions included non-English studies, duplicates, reviews, and studies without a STATCOM focus. Quality was assessed using an adapted Critical Appraisal Skills Programme (CASP) tool. No automation or machine learning tools were used. Thirty-eight studies met the criteria and were synthesised under four themes: operational contexts, STATCOM-based schemes, control strategies, and optimisation techniques. Unlike prior reviews, this study critically evaluates merits, limitations, and practical challenges. Trend analysis shows evolution from hardware-based fault survival strategies to advanced optimisation and coordinated control schemes, emphasising holistic grid stability and renewable integration. Identified gaps include cyber-physical security, techno-economic assessments, and multi-objective optimisation. Actionable research directions are proposed. By combining technical evaluation with systematic trend analysis, this review clarifies the state of STATCOM-assisted LVRT strategies and outlines pathways for future innovation in DFIG-WECS integration. Full article

With the acceleration of digital transformation, open-source software has become a fundamental component of modern smart grids and other critical infrastructures. However, the complex dependency structures of open-source ecosystems and the continuous emergence of vulnerabilities pose substantial challenges to software supply chain security. In power information networks and cyber–physical control systems, vulnerabilities in open-source components integrated into Supervisory Control and Data Acquisition (SCADA), Energy Management System (EMS), and Distribution Management System (DMS) platforms and distributed energy controllers may propagate along the supply chain, threatening system security and operational stability. In such application scenarios, large language models (LLMs) often suffer from limited semantic accuracy when handling domain-specific security terminology, as well as deployment inefficiencies that hinder their practical adoption in critical infrastructure environments. To address these issues, this paper proposes KD-SecBERT, a domain-specific semantic bidirectional encoder optimized through multi-level knowledge distillation for open-source software supply chain security in smart grid applications. The proposed framework constructs a hierarchical multi-teacher ensemble that integrates general language understanding, cybersecurity-domain knowledge, and code semantic analysis, together with a lightweight student architecture based on depthwise separable convolutions and multi-head self-attention. In addition, a dynamic, multi-dimensional distillation strategy is introduced to jointly perform layer-wise representation alignment, ensemble knowledge fusion, and task-oriented optimization under a progressive curriculum learning scheme. Extensive experiments conducted on a multi-source dataset comprising National Vulnerability Database (NVD) and Common Vulnerabilities and Exposures (CVE) entries, security-related GitHub code, and Open Web Application Security Project (OWASP) test cases show that KD-SecBERT achieves an accuracy of 91.3%, a recall of 90.6%, and an F1-score of 89.2% on vulnerability classification tasks, indicating strong robustness in recognizing both common and low-frequency security semantics. These results demonstrate that KD-SecBERT provides an effective and practical solution for semantic analysis and software supply chain risk assessment in smart grids and other critical-infrastructure environments. Full article

Household adoption of residential photovoltaic systems in community microgrids is shaped by economic, infrastructural, and social factors. Previous studies have shown that agent-based modelling can help analyse adoption, but it often lacks clear mathematical foundations, systematic validation, and reproducibility. This study presents an open-source agent-based model with two advances: (1) a fuzzy-utility method for household decision-making and (2) combined modelling of financial incentives, grid reliability, infrastructure access, and peer effects as adoption drivers. The model explores adoption under diverse policy and technical scenarios, validates results against Bass diffusion and discrete choice models, and applies a Sobol-based sensitivity analysis to identify key parameters. Results clarify how incentives, barriers, and social influence shape adoption trajectories. By demonstrating cost-sharing dynamics and peer network effects and openly sharing model code and data, this study provides a transparent and reproducible benchmark for future community microgrid research. Full article

During voltage dips, wind turbines must remain connected to the electrical grid and contribute to voltage stabilization. This study analyzes the impact of voltage dips arising from grid faults on Doubly Fed Induction Generator (DFIG) based Wind Energy Conversion Systems (WECSs). This paper presents a review of the technical regulations for integrating the Algerian electricity grid with the Low Voltage Ride Through (LVRT) system, along with specific requirements for renewable power generation installations. Additionally, the modeling and control strategy of DFIG based WECS has been outlined. Voltage dips can induce excessive currents that threaten the DFIG rotor and may cause harmful peak oscillations in the DC-link voltage, and can lead to turbine speed increase due to the sudden imbalance between the mechanical input torque and the reduced electromagnetic torque. To counter this, a modified vector control and crowbar protection mechanism were integrated. Its role is to mitigate these risks, thereby ensuring the system remains stable and operational through grid faults. The proposed system successfully meets the stringent Algerian LVRT requirements, with voltage dipping to zero for 0.3 s and recovering gradually. Simulations confirm that rotor and stator currents remain within safe limits (peak rotor current at 0.93 $\mathrm{p}\mathrm{u}$, and peak stator current at 1.36 $\mathrm{p}\mathrm{u}$). The DC-link voltage, despite a transient rise due to the continued power conversion from the rotor-side converter during the grid fault, was effectively stabilized and maintained within safe operating margins (with less than 14% overshoot). This stability was achieved as the crowbar ensured power balance by managing active and reactive power. Notably, the turbine rotor speed demonstrated stability, peaking at 1.28 $\mathrm{p}\mathrm{u}$ within mechanical limits. Full article

With the rapid increase in photovoltaic (PV) micro-installations in Europe, ensuring the stability and safety of the power grid has become a critical challenge. A key aspect in this context is the reliable detection of unintentional islanding by distributed energy resources. This paper presents the results of metrological tests on seven commercially available three-phase and single-phase PV inverters, conducted in accordance with the requirements of the EN 50549-1 and EN 62116 standards. A dedicated test setup was developed to enable measurements following standardized procedures. The tests assessed both the response time and the effectiveness of islanding detection mechanisms under various fault scenarios, including simulations of autonomous operation of multiple inverters. The main findings indicate that while all inverters with active islanding protection successfully detected islanding within the mandated 2-s limit, their individual response times varied significantly. Parallel operation further influenced this behavior: when one inverter operated with its islanding protection intentionally disabled, the remaining units exhibited notably increased detection times, though still within regulatory thresholds. Moreover, the inverter with disabled protection was capable of sustaining stable islanded operation indefinitely under balanced load conditions. Repeated multi-inverter tests also revealed significant variability in detection time within the same scenario, demonstrating that detection dynamics are sensitive to subtle changes in operating conditions. These findings highlight important limitations of existing certification procedures, which focus primarily on single-inverter testing. Real-world interactions between simultaneously operating inverters can substantially affect detection performance. The results therefore support the need to revise and extend test standards to include controlled multi-inverter parallel-operation conditions, ensuring the safe integration of prosumer PV systems into distribution networks. Full article

Transient safety analysis is a critical aspect of ensuring the safe design of Liquid Metal-cooled Fast Reactors (LMRs), relying heavily on advanced system analysis programs. To this end, the China Institute of Atomic Energy (CIAE) independently developed the Fast Reactor Transient Analysis Code (FRTAC) system analysis code for LMRs, which has been applied to the safety analysis of several reactor types. However, long-term use has revealed certain limitations, such as complex control system modeling and numerical dissipation from the first-order numerical scheme. This study analyzes the current limitations of the code and carries out systematic improvements and validation. The main improvements include enhancing the system compilation architecture and refactoring functional modules to improve computational efficiency, scalability, and usability; introducing a second-order accurate numerical scheme based on a limiter to reduce numerical dissipation in the convection term while ensuring computational stability; and optimizing the solution procedure to accommodate the new architecture and algorithms. The improved code’s computational stability and accuracy were validated using the Edwards blowdown experiment and the Energy Technology Engineering Center (ETEC) once-through steam generator steady-state test, respectively. The validation results show that the improved code maintains excellent numerical stability in problems with rapid transient pressure changes. In steady-state convective heat transfer problems, the computational accuracy and grid convergence are significantly improved, with the relative deviation of the water-side outlet temperature reduced from −3.56% to −0.59%. Under the same computational conditions, the computational efficiency was increased by up to 36.1%. The results of this study will provide a more accurate and efficient system analysis code for the transient safety analysis of LMRs. Full article

In complex urban low-altitude (ULA) airspace, unmanned aerial vehicles (UAVs) face several safety challenges, such as building obstacles, airspace restrictions, and environmental uncertainties. In this study, these issues are addressed by adopting a novel quantitative method for evaluating UAV flight safety margins and integrating this method into a ULA airspace grid model. This method comprehensively considers critical factors such as airspace obstacles, environmental conditions, and UAV performance to compute a quantitative safety margin. Once safety buffer grids around restricted and potential conflict grids are introduced, dynamic constraints can be imposed on the trajectory planning process. The proposed model not only achieves a balance between path cost and safety redundancy but also significantly enhances UAV flight safety and the efficiency of airspace resource utilization in complex urban environments. The experimental results validate the effectiveness of this approach for planning multi-UAV trajectories, demonstrating its feasibility and potential for broader application. This research not only extends the safety implications of low-altitude airspace grid modeling but also provides a new technical pathway and theoretical foundation for future ULA airspace safety management, multi-UAV collaborative scheduling, and refined airspace governance. Full article

Photovoltaic (PV) generation, a vital component of renewable energy, is key to supporting energy supply and reducing reliance on traditional energy sources. Given the substantial energy consumption of oilfield well groups, increasing the proportion of PV energy is imperative. Furthermore, as oilfields enter mid-to-late production stages, wells experience reduced oil production with increased energy consumption, necessitating intermittent pumping schedules. This paper addresses the optimized scheduling of pumping unit well groups within a photovoltaic-grid microgrid. The article aims to minimize the difference between the well group system’s total energy consumption and the PV power generation. A nonlinear mixed-integer programming (NMIP) model is constructed, incorporating a PV power forecasting model, a well group energy consumption model, and relevant constraints. An improved Particle Swarm Optimization (PSO) algorithm, integrating a hybrid coding scheme and multiple improvement strategies, is proposed to efficiently solve the NMIP model. The resulting optimal intermittent pumping schedule maximizes on-site PV power consumption, effectively mitigating PV energy wastage and potential grid stability issues associated with direct grid integration. The effectiveness of the proposed optimization algorithm is validated through numerical simulation case studies. Full article

Fine particulate matter (PM_2.5) poses substantial urban health risks that vary across space, time, and population vulnerability. We integrate a spatio-temporal INLA–SPDE PM_2.5 field with an agent-based model (ABM) of 10,000 daily home–work commuters in Indianapolis’s Pleasant Run airshed (50 weeks; 250 m grid). The PM_2.5 surface fuses 23 corrected PurpleAir PA-II-SD sensors with meteorology, land use, road proximity, and MODIS AOD. Validation indicated strong agreement (leave-one-out R² = 0.79, RMSE = 3.5 μg/m³; EPA monitor comparison R² = 0.81, RMSE = 3.1 μg/m³). We model a spatial-equity counterfactual by assigning susceptibility independently of residence and workplace, isolating vulnerability from residential segregation. Under this design, annual PM_2.5 exposure was statistically indistinguishable across groups (16.22–16.29 μg/m³; max difference 0.07 μg/m³, <0.5%), yet VWDI differed by ~10× (High vs. Very Low). Route-level maps reveal recurrent micro-corridors (>20 μg/m³) near industrial zones and arterials that increase within-group variability without creating between-group exposure gaps. These findings quantify a policy-relevant “floor effect” in environmental justice: even with perfect spatial equity, substantial health disparities remain driven by susceptibility. Effective mitigation, therefore, requires dual strategies—place-based emissions and mobility interventions to reduce exposure for all, paired with vulnerability-targeted health supports (screening, access to care, indoor air quality) to address irreducible risk. The data and code framework provides a reproducible baseline against which real-world segregation and mobility constraints can be assessed in future, stratified scenarios. Full article

The inverter is the key element that converts the intermittent DC power of the PV array into a quality AC flow to the grid and simultaneously performs functions such as power factor control, reactive services, and grid code compliance. Therefore, the detailed operating profile of the inverter, how the power, dynamics, power quality, and efficiency evolve over time, is critical for both the scientific understanding of the system and the daily operation (O&M). Monitoring only aggregated energy indicators or single KPIs (e.g., PR) is often insufficient: it does not distinguish weather-related variations from technical limitations (clipping, curtailment), does not show dynamic loads (ramp rate), and does not provide confidence in the quality of the injected energy (PF, P–Q behavior). These deficiencies motivate research that simultaneously covers the physical side of the conversion, the operational dynamics, and the climatic reference of the resource. The analysis covers the window of 25 January–15 April 2025 (winter→spring). Due to the pronounced seasonality of the solar resource and temperature regime, all quantitative results and conclusions regarding efficiency, dynamics, clipping, and degradation are valid only for this window; generalizations to other seasons require additional data. In the next stage, we will add ≥12 months of data and perform a comparable seasonal analysis. Full specifications of the measuring equipment (DC/AC current/voltage, clock synchronization, separate high-frequency $PQ$-logger) and quantitative uncertainty estimates, including distribution to key indicators ($\eta$, $PR$, $THD$, $I_{DC}$), are presented. The PVGIS per-kWp climate reference is anchored to the nameplate DC peak and cross-checked against percentile scaling; $a\pm \epsilon$ scale error shifts $PR$ by $\epsilon$ and changes $\Delta E$ proportionally only on hours with $\widehat{P}>P$. The capacity for the climate reference (PVGIS per-kWp) is calibrated to the tabulated DC peak power $C_{cert}$ and is cross-validated using a percentile scale ($Q_{0.99}$). Full article

This article assesses the use of solar photovoltaic radiation as a renewable resource for a region of the Isthmus of Tehuantepec in Mexico, where a 163.2 kW grid-connected photovoltaic system is located. The study aims to understand the system’s performance under the specific location conditions and to demonstrate the feasibility of installing photovoltaic systems in the Isthmus region. System monitoring was conducted for one year, with monthly and daily averages of normalized performance parameters determined. A three-month study of the power quality was conducted to assess compliance with interconnection and power quality requirements for power plants with a rated power Pn ≤ 500 kW. Results show higher energy production in the spring–summer months (138.946 MWh) than in autumn–winter (136.500 MWh), while the best overall performance occurred in autumn–winter (PR = 85% vs. 79.5% in spring–summer), probably due to cooler photovoltaic module temperatures. The final yield and PR indicate stable and predictable operation, even without maintenance, with PR = 82.3%. This supports the feasibility of photovoltaic installations in the southwestern region of Mexico. The present work is particularly relevant as it advances understanding of photovoltaic performance in understudied regions with substantial solar potential, such as the Isthmus of Tehuantepec, where policy prioritizes wind resource exploitation over solar energy. Full article