Search Results (5,886)

Agriculture is increasingly challenged by water scarcity, climate change, and rising energy demands, requiring more efficient and sustainable irrigation solutions. Conventional irrigation systems often lack the capability to adapt their operation to changing soil conditions and renewable energy availability, resulting in inefficient water and energy use. This study proposes a digital twin-based framework for energy management and irrigation optimization in photovoltaic (PV)-powered smart agriculture systems using Internet of Things (IoT) soil monitoring. The proposed system integrates a physical irrigation infrastructure, an IoT monitoring network, a fuzzy logic control layer, and a digital twin environment that periodically synchronizes the virtual model with IoT measurements to support the system representation and decision-making. The digital twin models soil moisture, temperature, nutrient levels, PV energy generation, battery state of charge, and irrigation water consumption. The virtual representation was periodically aligned with the physical system using measurements transmitted through the long-range (LoRa)-based network. An energy-aware irrigation scheduling strategy was developed to optimize irrigation timing based on soil conditions, battery status, and solar energy availability. The framework was evaluated using field data collected in a real apple orchard through an ESP32-based IoT platform and a standalone PV-powered irrigation system; quantitative experimental validation was performed for the soil twin. The results demonstrate high soil twin synchronization accuracy, with an overall RMSE of 1.47 percentage points and R² of 0.981, based on experimental field measurements. The energy twin and irrigation twin were evaluated using experimentally acquired sensor data together with model-based performance assessment, demonstrating the potential of the proposed digital twin framework for integrated water–energy management in smart agriculture. Full article

Driven by low-carbon mandates, hybrid power vessels integrating diesel, battery, shore-power, and photovoltaic vessel (PV) systems are emerging as a key green shipping pathway. However, operation scheduling is essentially complex due to the integration between supply-side routing and load-side energy dispatch, which is compounded by multi-dimensional uncertainties in PV generation, port-grid loads, and feeder delays. To address this, we formulate a unified two-stage robust optimization model. The objective is to simultaneously minimize operating costs and enhance port-grid friendliness by coordinating on-board energy management and shore-power interactions. In detail, the first stage determines routing and sailing speeds, while the second stage allocates multi-source power under a worst-case budgeted polyhedral uncertainty set. A piecewise-linearization scheme handles the cubic speed–power relation, rendering a tractable mixed-integer linear programming problem. The problem is efficiently solved via a tailored Benders decomposition algorithm, utilizing a genetic-algorithm warm-start to substantially accelerate convergence. Validated on three real-world networks via 1000 Monte Carlo scenarios, the proposed model reduces mean operating costs by 20.2–23.5% and suppresses cost variance by over 60% compared to deterministic approaches. Full article

Driven by the rapid expansion of the new energy industry and the growing demand for photovoltaic (PV) power plant construction, optimizing site selection to ensure operational efficiency and stability has emerged as a critical imperative. Addressing the lack of precise zonation assessment systems for PV development in China’s arid and semi-arid regions, this study introduces a multi-dimensional, five-tier suitability evaluation framework. By integrating Boolean logic, the Bayesian Best–Worst Method (B-BWM), and Equal Interval classification, we developed a multi-resolution integrated assessment framework where spatial boundaries are mainly constrained by 30 m topographic and land-cover data. Results indicate that the candidate PV construction space (the upper two suitability classes, PDSI ≥ 3.40) spans 0.85 million km² concentrated in central-western Inner Mongolia and southeastern Xinjiang, and model validation achieves an Area Under the Curve (AUC) of 0.79. The annual technical potential reaches 19,069 TWh, equivalent to approximately 207% of China’s total electricity consumption in 2023. If fully developed, this potential offers a theoretical annual CO₂ emission reduction ranging from 12.69 to 21.48 billion tons under different conversion efficiency scenarios, with a baseline estimate of 14.65 billion tons. These multi-resolution integrated findings provide a useful spatial reference for preliminary site screening in arid and semi-arid regions, support China’s “Dual Carbon” goals, and offer a practical methodological approach for renewable energy planning on marginal lands. Full article

High photovoltaic (PV) penetration in distribution networks can cause significant voltage deviations, making effective voltage regulation a critical issue for distribution system operators. Step voltage regulators (SVRs) provide a cost-effective solution; however, their performance strongly depends on control parameter settings that should account for PV-induced voltage fluctuations. Because installing high-resolution sensors at all PV sites is impractical, this paper proposes a method for determining SVR control parameters using short-term PV output estimation. The proposed approach assumes that only large-capacity PV systems are equipped with high-resolution sensors and estimates the outputs of other PV systems using the inverse distance weighting (IDW) method. Based on historical power flow data and estimated PV outputs, the method determines 48 sets of SVR control parameters for the following day while capturing rapid voltage rises caused by PV fluctuations. Numerical simulations using a practical distribution network model developed from an actual Chubu Electric Power Grid Co., Inc. system demonstrate the effectiveness of the proposed method. The results show that the allowable PV hosting capacity increases by 25% compared with a method without short-term PV output estimation, while the increase in SVR tap operations remains within an acceptable range. Full article

Against the backdrop of increasing renewable energy penetration, distribution networks face the dual challenges of spatiotemporal mismatch between sources and loads, as well as the surging costs of physical capacity expansion. To address these issues, this paper proposes a joint planning method for distribution network expansion and hybrid energy storage (electrical, thermal, and cooling) in green power parks, aimed at enhancing renewable energy accommodation. With the objective of minimizing the total life-cycle cost, a comprehensive optimization model integrating network topology upgrades and hybrid storage capacity configuration is established, which is then formulated and solved as a Mixed-Integer Linear Programming (MILP) problem. The effectiveness of the proposed strategy is validated through simulation comparisons with traditional solely line-based expansion schemes on the standard IEEE 33-bus system. The results indicate that substituting network expansion with energy storage can significantly alleviate the hardware investment burden on the grid. Moreover, leveraging the spatiotemporal “buffer” effect of multi-energy hybrid storage and the operation mechanism of cooling-heating-power substitution, surplus photovoltaic generation is efficiently absorbed locally, maintaining a renewable energy accommodation rate consistently above 50%. Economic evaluation further confirms that this joint planning framework reduces the total life-cycle cost by 21.2% compared to conventional transmission expansion approaches. It significantly enhances the system’s flexible bidirectional regulation capabilities, providing a solid theoretical foundation for the coordinated development of networks and storage in green power parks with high renewable energy penetration. Full article

Perovskite tandem solar cells (TSCs) have emerged as a transformative photovoltaic technology, offering a viable pathway to surpass the Shockley-Queisser limit of single-junction devices by enabling broader solar spectrum utilization and reduced thermalization losses. This review provides a comprehensive overview of recent progress in perovskite-based TSCs, covering four major device architectures: perovskite/silicon, perovskite/CIGS, all-perovskite, and perovskite/organic TSCs. We systematically discuss the fundamental working principles, including bandgap engineering, charge generation and separation, and current-voltage matching, followed by an in-depth analysis of strategies for perovskite layer regulation, interface engineering, and transport-layer optimization. Key advancements, such as compositional engineering, defect passivation, crystallization control, and optical management, have synergistically pushed power conversion efficiencies (PCEs) beyond 34% for perovskite/silicon TSCs and over 28% for all-perovskite and perovskite/organic configurations. Despite these achievements, critical challenges remain, including material instability, halide phase segregation, lead toxicity, scalable fabrication, and cost-effective integration. This review also outlines future perspectives, emphasizing the development of lead-free perovskites, novel charge-transport materials, advanced encapsulation techniques, and large-area manufacturing processes. With continued interdisciplinary efforts, perovskite TSCs hold great promise for driving the global transition toward sustainable and low-carbon energy systems. Full article

High-penetration renewable energy integration intensifies net-load fluctuations, renewable energy curtailment, and carbon-emission control challenges in power system dispatch. To improve low-carbon operation and source-load-storage coordination, this study proposes a two-layer alternating coordinated dispatch method with response-gap negative-feedback dual incentives. The dispatch-side layer jointly optimizes thermal generation, wind and photovoltaic power, carbon capture and storage, and ladder-type carbon trading, while the load-storage layer coordinates demand response and energy storage through peak-shaving and renewable-accommodation incentives. Unlike static incentives or dynamic schemes driven only by demand indices, the proposed mechanism updates incentive prices according to the deviation between dispatch-side expected response and load-storage-side realized response. The reconstructed net load is then fed back to the dispatch side, forming a closed-loop alternating coordination process. A typical-day case study shows that, compared with Scenario 1, the proposed method reduces the dispatch-side operating cost by 2.89% and net carbon emissions by 20.4%, while increasing the overall renewable energy accommodation rate by approximately 21.5 percentage points. Sensitivity and uncertainty tests further indicate that the main economic, emission-reduction, and renewable-accommodation benefits remain robust under different lower-layer preference weights and moderate demand-response uncertainty. Full article

The problem of load frequency control (LFC) becomes more severe with the extensive integration of photovoltaic (PV) generation owing to the intermittent nature of the source. In this study, a machine learning approach is developed to design the proportional–integral–derivative (PID) controller of a two-area PV–thermal LFC system, extending a prior proportional–integral (PI) benchmark to full PID action. A Random Forest model is trained to predict the relationship between the six PID gains and the closed-loop integral of time-multiplied absolute error (ITAE), yielding an accurate performance model (test R² = 0.933) that is subsequently searched by a metaheuristic optimizer to determine the controller gains; the resulting controller is termed ML-PID. The novelty of the approach lies in employing the learned model not as a controller or a physical-quantity predictor, as in existing ML-based LFC studies, but as a reusable performance model that maps the controller gains directly to the closed-loop index and guides the PID design. To isolate and quantify the contribution of the learned model, the same three optimizers, namely the Cheetah Optimizer (CO), the Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO), are also applied directly to the plant, yielding purely metaheuristic controllers (CO-PID, GWO-PID, and PSO-PID) that are compared against the machine learning-assisted designs under identical algorithms and computational budget, with CO selected on the basis of the Friedman and Wilcoxon tests. The proposed ML-PID-CO controller attains the minimum ITAE under a step-load disturbance, approximately 70% lower than that of the reference SCHO-PI controller and comparable to the directly optimized controllers, with reduced control effort. Under a simultaneous variation in the plant time constants, it is the most robust of all controllers, exhibiting the smallest Δf₁ undershoot and a performance that degrades about 4.2 times less than that of the reference. The results show that a learned performance model provides a good and reusable basis for PID design. It can be searched over repeatedly once built and reduces the per-design simulation burden relative to direct metaheuristic tuning, while the design is largely independent of the optimizer used. Full article

This study considers the multi-objective optimization of a standalone hybrid nanogrid system (HNGS) providing electricity power to a residential load in an off-grid area of Djelfa Province, Algeria. The focus of this study is to obtain the optimum design of a standalone hybrid nanogrid system consisting of photovoltaic (PV) panels, wind turbines (WTs), battery storage (BT), diesel generators (DGs), and power converters to satisfy the energy demand of residential consumers in Djelfa Province, Algeria. In this context, four multi-objective optimization algorithms (MOPs), NSGA-II, MOPSO, MOSSA, and MODE, are used to solve the optimal sizing problem of the proposed system. The formulated multi-objective optimization problem takes into account multiple performance criteria such as cost of energy (COE), loss of power supply probability (LPSP), renewable energy penetration, and diesel generator usage reduction, balancing economic, reliability, and sustainability aspects. The optimization process optimizes critical design parameters, including the size of the PV system, the number of wind turbines, and the size of the battery storage system, for a realistic operating scenario. The optimization algorithms are combined with an energy management strategy (EMS) that helps to coordinate the power flow distribution between various parts of the system to achieve optimum system performance. The effectiveness of each of the proposed approaches is analyzed based on the obtained results, where it was found that the MODE algorithm provides the best compromise solution, with a COE of 0.167 USD/kWh and an LPSP of 6.372%, and the lowest carbon dioxide emissions of 205.1 kg/year compared to MOPSO, NSGA-II, and MOSSA. Moreover, the results obtained from this process will provide a set of feasible design solutions, which will allow decision-makers to choose the most suitable design solution based on technical and economic specifications. Full article

After high-penetration distributed photovoltaics (DPVs) are integrated into AC/DC hybrid distribution networks, stochastic source-load fluctuations, AC-DC coupling, and heterogeneous response characteristics of voltage-regulation devices jointly aggravate voltage violations and rapid voltage fluctuations. This paper proposes a spatio-temporal coordinated hierarchical distributed voltage-optimization framework. Spatially, the AC and DC regions are first separated according to voltage-source-converter (VSC) interfaces, and an electrical-coupling-aware modularity index is then constructed for the AC network by combining normalized bidirectional reactive-power-voltage sensitivities, available fast reactive-power support, and intra-cluster compactness. Temporally, an 1 h day-ahead model coordinates slow, discrete, or intertemporally coupled resources, including on-load tap changers, capacitor banks, energy storage systems, and flexible loads, while a 15 min intra-day rolling model coordinates DPV inverters, static var generators, and VSCs. The synchronous alternating direction method of multipliers (SADMM) is tailored to the resulting AC clusters, DC subnetworks, and VSC boundary variables to enable synchronous regional solution and boundary-consensus coordination. Studies on a modified 50-node AC/DC test system show that, under the investigated operating conditions, the framework mitigates voltage violations and intra-day fluctuations while obtaining favorable network-loss, DPV-curtailment, and solution-time performance. Full article

The digitalization of photovoltaic (PV) systems can support sustainable energy deployment by improving operational efficiency, system visibility, and energy extraction. However, many existing Internet of Things (IoT)-enabled solutions address monitoring and maximum power point tracking (MPPT) separately or depend on proprietary platforms, remote cloud services, and relatively costly hardware, which may restrict their accessibility and replication in small-scale and resource-constrained applications. This study presents the implementation and laboratory-scale experimental evaluation of an edge-IoT architecture that integrates real-time PV monitoring, embedded adaptive MPPT control, local data management, and visualization using low-cost hardware and open-source software. The proposed architecture combines an ESP32 microcontroller with a Raspberry Pi (RPi) local server to enable environmental and electrical sensing, edge-based control, message queuing telemetry transport (MQTT) communication, local data storage, and interactive visualization through the open-source Node-RED, InfluxDB, and Grafana platforms. An adaptive perturb-and-observe (AP&O) algorithm is implemented on the ESP32 to dynamically adjust the duty cycle of a DC–DC boost converter in response to changing operating conditions. The system is experimentally evaluated using a PV test bench equipped with a custom boost converter and sensing modules measuring eleven electrical and environmental parameters. The architecture achieved an average communication latency of 193 ± 23 ms and an average MPPT efficiency of 97.3 ± 0.54%. It also provided a power gain of 0.7 ± 0.5% compared with the conventional fixed-step perturb-and-observe method. By combining local processing, open-source software, low-cost components, and integrated monitoring and control, the proposed system reduces dependence on external cloud infrastructure while supporting responsive and accessible PV energy management. These results demonstrate its potential as a replicable technological framework for improving the operational sustainability and digital management of small-scale PV installations. Full article

Photovoltaic (PV) technology is now one of the main options for expanding the power of low-carbon electricity generation. However, in practical operation, PV systems still face several persistent difficulties, including the variability of solar irradiance, gradual performance degradation, fault occurrence, suboptimal control, and the growing complexity of grid-connected operation. These issues explain why artificial intelligence (AI) has become increasingly relevant in PV research, not only as a prediction tool, but also to improve monitoring, control, diagnosis, and decision-making. This review investigates the applications of AI in the major stages of the PV system lifecycle: solar resource assessment, power forecasting, fault detection, condition monitoring, system sizing, maximum power point tracking (MPPT), and grid integration. Rather than treating these applications as separate research topics, the review attempts to connect them through the common factors that determine their practical value: data quality, sensing configuration, model complexity, physical operating conditions, and deployment constraints. The reviewed studies indicate that AI-based MPPT methods can achieve tracking efficiencies close to 99%, while recent forecasting models, particularly LSTM, CNN–LSTM, and transformer-based architectures, can reduce prediction errors under changing weather conditions. At the same time, PV fault detection is moving beyond electroluminescence image classification toward more practical multimodal strategies that combine infrared thermography, RGB and drone imagery, electrical measurements, and SCADA/IoT data. Nevertheless, the progress reported in the literature should be interpreted with caution. Many proposed models are still evaluated on limited or non-standardized datasets, and their performance may decrease when they are transferred to different PV technologies, climates, fault severities, or operating conditions. Other recurring limitations include class imbalance, high computational cost, weak generalization, and the limited interpretability of deep-learning models. For this reason, hybrid neural networks, explainable AI, physics-informed learning, edge-AI, federated learning, and quantum machine learning are discussed as possible directions for making AI-based PV solutions more reliable and deployable. This review aims to critically synthesize recent advances and remaining gaps in order to support the practical integration of AI into efficient, reliable, and sustainable PV systems. Full article

Photovoltaic (PV)/biomass systems offer stable/continuous power by overcoming solar-energy intermittency with biomass dispatchable energy. Considering gaps in the scientific literature, this article sets out to present information on PV/biomass installations and the eco-profiles of different feedstocks for biogas generation. To this end, this article is split into two parts. The first one outlines some key elements of the literature on PV/biomass systems, highlighting factors that determine feasibility and performance. The second one presents the eco-profiles of three feedstocks. The methodology is based on literature review and Life-Cycle Assessment (LCA). Regarding the first part, the results show that the majority of the prior research placed emphasis on techno-economic analysis and the design/modelling of PV/biomass systems, and there is a dearth of LCA studies on PV/biomass installations. As for the second part, the findings demonstrate that, among the feedstocks examined (manure; waste cooking oil; grass), in most categories, animal waste shows the highest environmental impacts. For instance, considering the total impact of these three feedstocks and based on Environmental Product Declaration (EPD), the results indicate that, in many categories, manure surpasses the percentage values of 40%. Grass exhibits minor percentage shares, with the exception of the “Eutrophication” (54%) and “Acidification” (33%) categories. Full article

Molybdenum carbide (MoC) thin films were deposited by dual-target magnetron co-sputtering and investigated as platinum-free counter electrodes (CEs) for dye-sensitized solar cells (DSSCs). The effects of Mo target power and film thickness on structural evolution, electrical transport properties, and photovoltaic performance were systematically examined. Raman analysis revealed progressive modifications in the carbon bonding structure, accompanied by variations in the G-band position and an overall reduction in the I_D/I_G ratio. These structural changes were correlated with increased hardness, reduced electrical resistivity, and decreased surface wettability, indicating improved structural integrity and electrical transport characteristics of the MoC films. The optimized films exhibited a resistivity as low as 2.04 mΩ·cm and improved charge-transport behavior. DSSCs employing the optimized MoC CEs achieved a maximum power conversion efficiency of 4.13%. The photovoltaic performance trends were consistent with the evolution of the electrical transport properties of the MoC thin films, suggesting a close relationship between electrode structure, charge transport, and device operation. The results demonstrate that sputtered MoC thin films are promising functional materials for Pt-free DSSC CEs and provide insight into structure–transport–performance correlations relevant to sustainable photovoltaic energy-conversion systems. Full article

Existing grid-connected photovoltaic (PV) systems with grid-following (GFL) control rely on phase-locked loop (PLL)-based grid synchronization, which may become unstable under weak grid conditions. By contrast, grid-forming (GFM) retrofits are regarded as promising solutions for improving the stability of GFL-based PV integration. However, existing retrofit methods often require comprehensive replacement or substantial modification of the existing GFL controller or hardware, thereby increasing retrofit costs and maintenance complexity. To address these issues, a non-intrusive configuration with a battery-backed GFM branch comprising a charging rectifier and a GFM inverter is proposed for retrofitting implemented GFL PV power plants in this paper. With the proposed configuration, the existing GFL PV inverters retain their original hardware and GFL control. The charging rectifier branch absorbs surplus PV power from the PV inverter output, whereas the proposed battery-backed GFM branch is connected at the point of common coupling (PCC) to provide voltage- and frequency-forming supports. To utilize the available branch capacity under normal grid conditions, selected auxiliary functions, i.e., harmonic compensation and Volt–VAR support, are incorporated subject to the available inverter operating margin. The effectiveness of the proposed configuration and the corresponding control scheme is validated through different operating scenarios, including PV/load mismatch charging, nonlinear load harmonic compensation, reactive load Volt–VAR support, grid frequency and grid voltage disturbances under different grid strength conditions, and combined dynamic operating disturbances at weak grids. Full article