Search Results (14,301)

The large-scale integration of photovoltaic (PV) generation and electric vehicles (EVs) into distribution networks introduces significant operational challenges, including voltage fluctuations, increased energy losses, and feeder congestion. While previous studies have addressed distribution system reconfiguration (DSR), PV scheduling, or EV intelligent parking lot (IPL) management separately, no unified framework exists that simultaneously optimizes all three flexibility tools. This research therefore aims to develop a coordinated scheduling framework that minimizes both energy losses and voltage deviations over a 24 h horizon. For solving the mathematical formulation, an Enhanced Grey Wolf Optimizer (EGWO) is developed using the concepts of dynamic neighborhood influence and self-adaptive convergence factor to prevent the issue of premature convergence and dynamic balancing of the algorithm during the search process. Simulation results on the IEEE 33-bus system across five scenarios quantify the benefits of each control layer. DSR alone reduces daily energy loss by 30.41%. Photovoltaic scheduling alone reduces loss by 15.40%. When combined, PV scheduling and DSR achieve a 38.29% loss reduction, demonstrating strong synergy. Full integration including IPL further improves voltage deviation by 40.26% compared to the base case, while maintaining loss reduction at 36.20%. Full article

High penetration of distributed photovoltaic (PV) generation has intensified voltage violations and stochastic voltage fluctuations in distribution networks, while existing voltage–var control methods still have limitations in terms of communication dependence, scalability, and edge deployment. To address these issues, this paper proposes a stability-constrained voltage–var self-optimizing control method for distribution networks based on the Bandit-Guided Online Self-Tuning Group Relative Policy Optimization (BOST-GRPO) algorithm. First, based on the LinDistFlow linearized power-flow model, a communication-free, decentralized, and locally observable reinforcement learning control environment is constructed, enabling each node to independently generate reactive power regulation commands using only local voltage measurements. Second, a contraction-mapping-based stability constraint is embedded into the policy output layer, theoretically guaranteeing the local exponential convergence of nodal voltage deviations around the equilibrium point and reducing the risk of voltage instability caused by overly aggressive policy actions. Meanwhile, device capacity constraints are incorporated into the policy output through a tanh-based action mapping, ensuring the physical feasibility of control commands. On this basis, BOST-GRPO realizes the online self-tuning of key hyperparameters within a single training process through a Bandit-guided mechanism, thereby avoiding the repeated training overhead caused by traditional offline hyperparameter tuning. Simulation results on the IEEE 33-bus system show that the proposed method outperforms benchmark reinforcement learning algorithms in final test cost, voltage deviation suppression, steady-state error, and regulation speed. Further tests under sensitivity matrix mismatch, different initial voltage disturbance intensities, and the extended IEEE 69-bus system demonstrate that the proposed method achieves good robustness and scalability. Full article

Two semiflexible piezoelectric composite plate structures were developed, incorporating 1 × 9 and 2 × 9 arrays of PZT elements mounted on brass discs and mechanically secured by pop rivets within a thin plastic foil spacer positioned between two copper-clad PCB layers. This configuration provides reliable electrical contact, adequate mechanical compliance, and efficient conversion of mechanical vibration energy into electrical energy. In addition, a multifunctional thermoelectric device was realized, consisting of four cubic modules arranged around a rectangular tube and enabling both handheld operation and coupling to hot or cold surfaces. Each cube is equipped with optimized finned heat sinks and integrates four thermoelectric elements on each face. Experimental results show that each cube generates approximately 6 mW, when handheld and with icy water injected into the central tube, demonstrating its suitability as a compact and versatile thermal energy harvester. Under low-light conditions, a solar panel is supplemented by this hybrid piezoelectric–thermoelectric energy harvesting system that combines the output of a piezoelectric composite plate with the dual outputs of a thermoelectric device using an electronically isolated summing block to ensure source decoupling. Energy storage and management are implemented using a capacitor buffer for the piezoelectric device, two voltage boosters for the thermoelectric outputs, and an automatic ultra-low-power pulse width modulation buck regulator for charging supercapacitors at 5 V. Full article

Agrivoltaics is a promising technique, especially in view of the rapid population growth associated with the expansion of cultivated areas to satisfy the food demands of the population, and the increase in solar power plants, which require considerable space to supply the population with energy. Thus, the transition from agricultural to agrivoltaics systems and the transition from PV power plants to agrivoltaics systems can enable more efficient use of land for energy and agricultural production. However, the configuration of agrivoltaics systems, namely panel elevation, spacing between panels and between rows of panels, and panel size, defines the amount of material used. As a result, configuration can have a major impact on the environment. The aim of this study is to highlight the environmental impact from converting 1 ha of land used entirely for agricultural production to 1 ha of an agrivoltaic system, and from converting 1 ha of land used entirely for solar photovoltaic energy production to 1 ha of an agrivoltaic system through a life cycle assessment. Three different configurations of agrivoltaics systems are considered to assess the environmental potential of agrivoltaics configurations. This analysis is performed with SimaPro 9.4 software, using the ReCiPe Midpoint (H) method and the Eco-invent database. The study determined impacts on global warming, stratospheric ozone depletion, ionizing radiation, ozone formation, mineral resource scarcity, fossil resource scarcity, water consumption, and land use through the determination of the Land Equivalent Ratio (LER). The results show that impacts are highest for PV power plants, followed by the agrivoltaic system with the largest PV panels for all indicators, except for stratospheric ozone depletion, where impacts are highest for agrivoltaics and agricultural use systems. The results of the land evaluation showed that the agrivoltaic system Case 3 gave the best performance, with a Land Equivalent Ratio of 148.7%. Full article

The purpose of this study was to investigate the adverse effects of 1.5 μg·L⁻¹ environmentally relevant chlorantraniliprole (CAP) on oxidase biomarkers (juvenile, 2.5 g) for 2, 4, and 8 h and transcriptomic response (adult, 254.8 g) for 96 and 192 h in American shad Alosa sapidissima (Wilson, 1981). American shad is sensitive to pollutants and has become an important economic fish in China, especially for recirculating the aquaculture system and photovoltaic farming. For juvenile shad under short-time CAP exposure, acid phosphatase (ACP) and aryl hydrocarbon receptase (AHR) at the protein level significantly increased at 2 h, and for longer-time exposure, alkaline phosphatase (AKP), polyphenol oxidase enzyme (PPO), and tumor necrosis factor alpha (TNFα) at the protein level significantly decreased; ryanodine receptase (RYR) at the protein level was significantly increased at 8 h. Interestingly, malondialdehyde (MDA) contents, biomarkers of oxidative stress, were significantly decreased for depletion at 2 h and 4 h, while they increased for eliminating free radicals at 8 h via longer-time CAP exposure duration. With the same CAP exposure for adult shad, the number of congested and dilated sinuses of the liver changed, with fine granular brown pigmentation and vacuolization of hepatocytes at 96 h, while the sinuses and central veins were dilated and edematous degeneration occurred at 192 h for longer-time exposure. The detected enzymatic activities, except for adenosine 5′-monophosphate (AMP)-activated protein kinase (AMPK), significantly decreased, and MDA contents significantly increased in adult shad at 96 and 192 h. Ribosome, proteasome, spliceosome, protein processing in endoplasmic reticulum, oxidative phosphorylation, glycerophospholipid metabolism, biosynthesis of amino acids, ferroptosis, peroxisome, apoptosis, necroptosis, and mTOR signaling pathways were the most significantly enriched pathways. For qPCR verification, the genes ppa2, pla1a, psmb13a, pkz and stat1b were significantly upregulated, while hspa8b, capn2, tram2, asns, bcl2l1, diablo, and prkcb were downregulated in adult shad. The results reveal elevated oxidative stress causing time-dependent hepatic damage via 1.5 μg·L⁻¹ CAP exposure both in juvenile and adult shad. Full article

The building sector accounts for nearly 30% of global energy use and 28% of CO₂ emissions, with residential buildings in Canada contributing about 17% of national energy demand. In cold regions such as Labrador, approximately 82% of this consumption is associated with space heating and domestic hot water, making heating the dominant residential load, while fossil-fuel furnaces and electric baseboard heaters remain common. These conditions highlight the need for efficient and sustainable heating alternatives for cold-climate residential buildings. This study examines the design and performance of a hybrid solar photovoltaic (PV) and geothermal heat pump (GTHP) system for a typical detached home in L’Anse-au-Loup, Labrador, Newfoundland and Labrador, Canada (51.52° N, 56.84° W), with the goal of improving energy efficiency and reducing dependence on the electrical grid. Heating and cooling loads were developed using the Hourly Analysis Program (HAP 6.1), while system operation and economic performance were assessed through the Hybrid Optimization Model for Electric Renewables (HOMER Pro 3.18.3). The proposed design combines a rooftop PV array, a ground-source heat pump, and second-life lithium-ion batteries repurposed from retired electric vehicles to lower costs and support short-term energy storage. The system is modelled under grid-connected conditions to reflect realistic operation for northern households. Results show that the hybrid system can meet annual electrical and thermal needs while reducing grid consumption by more than half. Annual carbon emissions decrease by roughly 4–5 tonnes, and repurposed batteries offer a cost-effective alternative to new storage. Overall, the study demonstrates that PV–GTHP systems can provide reliable, efficient, and practical energy solutions for cold-climate homes. Full article

To address fault-induced boundary variations and conflicting commands among heterogeneous controllers in microgrid clusters with high distributed generation penetration, this paper proposes a multilayer multiagent control strategy based on cross-coordinated multiagent control and conflict coordination. The method uses a hierarchical distributed hybrid architecture. Local grid-forming (GFM) energy storage and photovoltaic (PV) converters provide autonomous voltage source support, microgrid coordination controllers generate distributed candidate commands, and the system-level coordination controller performs event-triggered arbitration. Unlike consensus-based cooperative control with fixed exchanged variables, the proposed method enables overlapping supervisory authority, weighted command fusion, explicit conflict classification, and feasible command projection under resource, state-of-charge (SOC), ramping, and load priority constraints. Direction, capacity, and objective conflicts are resolved through system-level arbitration, which converts multiple candidate commands into a single executable command. Comparative simulations show that the proposed method reduces frequency and voltage deviations, shortens power recovery time, improves SOC balancing among energy storage units, and enhances constrained hydropower coordination compared with conventional droop control and one-to-one hierarchical control. These results verify its effectiveness in improving dynamic stability and coordinated support capability in microgrid clusters. Full article

Early identification of thermal and electrical anomalies in grid-connected photovoltaic (PV) systems is becoming increasingly important to reduce energy losses, limit power quality (PQ) degradation, and avoid excessive operating stress on power electronic converters. Conventional electrical monitoring methods can provide overall performance information, but they are generally unable to detect and localize early-stage defects occurring at module or cell level. In this context, the present study proposes an integrated diagnostic framework that combines non-destructive infrared thermography (IRT) with advanced electrical signal processing techniques for PV condition monitoring. The proposed approach correlates thermographic information, capable of revealing defects such as hotspots, cell cracks, and bypass diode failures, with high-frequency electrical signal analysis based on frequency-domain and time–frequency methods, together with deep learning-driven thermographic segmentation. By associating thermal acquisitions with electrical PQ indicators, the framework enables the early detection of physical defects linked to inefficient Maximum Power Point Tracking (MPPT) operation and progressive degradation of PV system performance. The methodology was experimentally validated on a grid-connected photovoltaic installation under different fault conditions, including hotspots, bypass diode anomalies, and localized overheating effects, demonstrating the potential of the proposed approach for predictive maintenance and intelligent PV monitoring applications. The obtained results indicate that the proposed framework improves the reliability of photovoltaic fault detection by combining thermographic inspection with advanced electrical signal analysis and AI-based defect interpretation, thus supporting predictive maintenance strategies in smart PV infrastructures. The proposed approach demonstrates image segmentation capabilities, as evidenced by a precision (PA) of 96.88%, a mean IoU (mIoU) of 77.83% and a macro F1-score of 87.47%. The proposed framework maintained reduced computational requirements compatible with real-time monitoring applications. Full article

To address missed detections, false alarms, and deployment limitations in thermal defect detection of photovoltaic modules from unmanned aerial vehicle (UAV) infrared images, this paper proposes an improved detection method based on You Only Look Once version 8 nano (YOLOv8n). The proposed method is optimized according to the characteristics of UAV infrared photovoltaic inspection, including small thermal targets, weak and diffuse thermal responses, complex backgrounds, and lightweight deployment requirements. Specifically, a P2 shallow feature layer is introduced to enhance fine-grained feature perception for small thermal defects, while Ghost Convolution (GhostConv) is incorporated into the backbone to reduce model complexity. In addition, C2f-Large Separable Kernel Attention (C2f-LSKA) is embedded in the neck to strengthen contextual and spatial feature modeling under complex infrared backgrounds, and Wise-IoU version 3 (WIoUv3) is adopted to improve bounding box regression and localization stability for boundary-ambiguous thermal anomalies. Experiments are conducted on a self-constructed UAV infrared thermal imaging dataset. From nearly 10,000 inspection images, 3000 representative images are selected and manually annotated, covering typical challenges such as small hot spots, low-contrast defects, complex background interference, and diffuse abnormal temperature-rise regions. Compared with the baseline YOLOv8n, the proposed method improves Precision, Recall, mean average precision at an IoU threshold of 0.5 (mAP@0.5), and mean average precision averaged over IoU thresholds from 0.5 to 0.95 (mAP@0.5:0.95) by 5.1, 11.4, 9.6, and 13.2 percentage points, respectively, while reducing the number of parameters and model size by 65.8% and 61.9%, respectively. These results indicate that the proposed method improves detection accuracy and localization quality under the evaluated UAV infrared inspection setting while maintaining lightweight characteristics. Full article

This paper presents a bidirectional power flow control strategy for a grid-connected solar photovoltaic (PV)-based water pumping system employing a brushless DC (BLDC) motor drive. The proposed system enables continuous water pumping operation under varying solar irradiance conditions without the use of phase-current sensors while maintaining the motor at its rated operating speed. A single-phase voltage source converter (VSC) employs a unit vector template (UVT)-based control scheme that regulates bidirectional power flow between the utility grid and the dc-link, thereby supporting both grid-to-load and PV-to-grid power transfer. Excess photovoltaic energy can be exported to the utility grid during periods of reduced pumping demand, improving overall utilization of the available solar power. The voltage source inverter (VSI) driving the BLDC motor employs a PWM_ON_PWM switching scheme to reduce torque ripple while operating at fundamental frequency to minimize switching losses. The proposed system also incorporates maximum power point tracking (MPPT), power factor correction, and harmonic mitigation to improve power quality and ensure compliance with IEEE-519 requirements. The effectiveness of the proposed control strategy is evaluated through detailed MATLAB/Simulink R2023a simulations under various operating conditions. The simulation results demonstrate stable dc-link voltage regulation, bidirectional power flow capability, continuous pumping operation, and reduced torque ripple, highlighting the suitability of the proposed system for grid-interactive solar water pumping applications. Full article

With the increasing demand for low-carbon energy, automated defect detection using unmanned aerial vehicle (UAV)-based thermal inspection has become essential for maintaining the reliability of photovoltaic systems. However, existing methods still suffer from low-contrast thermal imagery, large-scale variations of defects, and subtle thermal anomalies. To address these challenges, this study proposes Grouped-Hypergraph-Modulation DEIM (GHM-DEIM), a robust end-to-end detection framework based on an improved DEIM architecture. Specifically, a grouped multi-scale aggregation attention network is introduced to enhance global thermal perception and recover discriminative features from blurred backgrounds. In addition, an enhanced encoder incorporating a hypergraph-based context encoding mechanism is designed to model high-order non-local relationships and improve feature representation across different defect scales. Furthermore, a modulation fusion module is employed to adaptively refine multi-scale feature responses and suppress environmental noise interference. Extensive experiments conducted on the ThermoSolar-PV and PV-HSD-2025 datasets demonstrate that the proposed method consistently outperforms state-of-the-art detectors, achieving mAP@50 values of 88.6% and 74.2%, respectively, with improvements of 4.7% and 2.9% over the baseline. These results demonstrate the effectiveness and robustness of GHM-DEIM for UAV-based PV thermal defect inspection. Full article

Watermelons account for 7% of the world’s fruit vegetable production. In the European market, Spain contributes around 35% of total watermelon supply, with the majority grown in greenhouses in Almería, Southern Spain. This study presents experimental results from the first agrivoltaic watermelon trial conducted in a raspa-y-amagado greenhouse during the 2024 growing season in Almería, Spain. Watermelons were cultivated under two shading treatments with 30% and 50% of the roof area covered with PV modules and compared against an unshaded control group. Throughout the experiment, temperature values in the 30% and 50% zones were $2.2$${}^{\circ }\mathrm{C}$ and $4.3$${}^{\circ }\mathrm{C}$ lower than in the control zone, respectively. The unshaded control zone and the 30% shading treatment maintained DLI conditions within the optimal range between 21/$\mathrm{m}$/day and 32/$\mathrm{m}$/day for most of the crop cycle, while the 50% shading zone remained largely above the minimum threshold of 15/$\mathrm{m}$/day required for adequate crop growth. No statistically significant differences were observed in fruit weight, rind width, fruit firmness, or soluble solids content at harvest. In addition, the experimentally measured irradiance data from this study were compared with simulations from a previously established irradiance model. The model was applied to the raspa-y-amagado greenhouse, and the experimental data were used to perform a long-term comparison between simulated and measured irradiance for 265 days of data. The irradiance model accurately reproduced shading effects from both the PV modules and greenhouse structure, achieving nRMSE values of 0.09, 0.18, and 0.27 for the control, 30% shading, and 50% shading zones, respectively. Full article

To address the operational challenges posed by the high penetration of photovoltaic systems in distribution networks—such as system congestion, voltage violations, and increased distribution losses—this study proposes a spatiotemporal sparsified dynamic reconfiguration scheduling method considering practical implementation in real distribution system operations. The proposed framework comprises two complementary sparsification mechanisms. Spatial sparsification is achieved by clustering hourly net-load distributions in a high-dimensional net-load space to aggregate characteristic net-load patterns, thereby restricting power flow evaluations and configuration screening to a small set of representative patterns and substantially reducing the computational burden. Temporal sparsification is realized by solving an integer linear programming problem to optimize the reconfiguration schedule under a daily reconfiguration frequency constraint, which optimizes the reconfiguration timing while mitigating excessive switching operations. Numerical experiments under deterministic forecast assumptions demonstrated that the proposed method can effectively eliminate congestion and voltage violations while achieving loss reduction by 4.56% and 27.4% respectively in two scenarios from the conventional method with the computational scalability significantly improved. Full article

This paper presents a comprehensive evaluation of control strategies for a highly energy-efficient plus-energy terraced housing complex equipped with photovoltaic generation, modulating ground-source heat pumps, electrical and thermal energy storage systems, and activation of building thermal mass. The study combines long-term monitoring data, annual simulations, and hardware-in-the-loop (HiL) experiments to assess modulating heat-controlled operation (HC), PV-controlled (PVC), and predictive control strategies, including simple predictive control (SPC) and model predictive control (MPC). The simulation results show that the baseline HC operation already achieves a high load cover factor (LCF), defined as the fraction of total electrical demand covered by local PV generation (direct use + battery discharge) of 65.6% and a seasonal performance factor (SPF) of the central heat pumps of 5.8. PVC increases LCF (71.0%) by shifting heat pump operation toward PV-rich periods but leads to elevated storage temperatures up to 5 K and a reduced SPF of 4.8. MPC further enhances LCF by 4–7 percentage points in simulated and HiL environments. However, its real-world performance is strongly influenced by forecast quality and the limited controllability of the heat pump system. In addition, building thermal mass activation is investigated as a complementary flexibility option. Simulation and monitoring results demonstrate that moderate room temperature set-point (2 K) increases during PV availability significantly improve LCF from 20% to 55% while maintaining thermal comfort. Overall, the findings indicate that in highly efficient plus-energy buildings, robust rule-based strategies combined with thermal mass activation can achieve a large share of the attainable benefits, while the added complexity of MPC must be carefully weighed against practical limitations. Full article

This study presents a gate-to-gate life cycle assessment (LCA) of an industrial sweet cherry sorting and packing facility in Greece, directly addressing environmental sustainability in agri-food supply chains through data-driven impact quantification and improvement pathways in post-harvest operations. The assessment focuses on a gate-to-gate system boundary encompassing all processes inside the cherry sorting and packing facility, while upstream cherry production and downstream waste management are modeled and reported separately to provide system-level context. Core-stage hotspots are then analyzed in detail in the Results section, highlighting the dominant role of electricity use compared with packaging materials. The functional unit is defined as 1 kg of packed, market-ready cherries at the factory gate. Primary data are obtained from high-resolution, batch-level measurements of mass flows, energy use, water consumption, packaging materials and waste streams over a full processing season, structured as virtual sensor outputs. These sensor-informed operational data are combined with secondary life cycle inventory information from established databases to quantify climate change impacts and identify environmental hotspots across materials, energy, water, and waste, thereby delivering a quantified picture of environmental performance in the post-harvest stage. The results show that corrugated cardboard and associated packaging components are among the main contributors within the facility-level, gate-to-gate system, while the Core stage accounts for 28.43% of total GWP100. Upstream cherry production dominates the overall Upstream–Core–Downstream climate footprint with 70.61% of total impacts. Moreover, practical mitigation scenarios are modeled, including packaging optimization, partial substitution of grid electricity with photovoltaic generation, and increased water recirculation. Ιn the combined mitigation scenario, where packaging optimization, low-carbon electricity and improved water management are implemented simultaneously, total GWP100 decreases from 114,207.32 to 92,500.27 kg CO₂-eq (−19.0%) relative to the baseline, providing actionable sustainability improvements for industry stakeholders and supporting Sustainable Development Goals (SDGs) related to climate action and resource efficiency. In addition, the proposed virtual sensor architecture and data workflow support continuous monitoring, eco-efficiency management and near-real-time LCA implementation in post-harvest agri-food systems, enabling operational sustainability. Full article