Photovoltaic System Research Articles

Enhancing energy autonomy of greenhouses with semi-transparent photovoltaic systems through a comparative study of battery storage systems

Effective energy management is crucial in greenhouse farming to ensure efficient operations and optimal crop growth. This study investigates the energy autonomy—defined as the ratio of on-site energy generation to the total energy demand—of greenhouses equipped with semi-transparent photovoltaic (STPV) systems under two scenarios: with and without a Battery Energy Storage System (BESS). STPV systems are beneficial because they generate energy while still allowing enough light to pass through for healthy plant development. Seasonal variations in energy autonomy during summer and winter were analyzed. Results show that incorporating BESS significantly reduces reliance on grid electricity, with energy autonomy improving from 43.43% to 24.17% in summer and 81.36% to 69.45% in winter. The system’s performance was highly sensitive to the transmittance rate of STPV panels and the minimum Daily Light Integral (DLI) required for crops. These findings highlight the potential of BESS to enhance energy independence and promote sustainable agricultural practices. The study provides insights into optimizing renewable energy systems in greenhouses, emphasizing practical implications for scalability and economic feasibility.

Balanced dung beetle optimization algorithm based on parameter substitution and escape strategy

Dung Beetle algorithm is an intelligent optimization algorithm with advantages in exploitation ability. However, due to the high randomness of parameters, premature convergence and other reasons, there is an imbalance between exploration and exploitation ability, and it is easy to fall into the problem of local optimal solution. The purpose of this study is to improve the optimization performance of dung beetle algorithm and explore its engineering application value. A balanced dung beetle optimization algorithm was proposed, and parabolic adaptive parameter R was introduced to broaden the exploration range and slow down premature convergence. Gaussian distributed phase parameter β is introduced to reduce the randomness of parameters and stimulate the potential of algorithm exploitation. Levy flight escape strategy is introduced to balance the global exploration ability of the algorithm and fully explore the solution space. The effectiveness of the improved strategy is verified by comparing the CEC2017 benchmark function with the single strategy variant. The experimental results show that BDBO algorithm is superior to other algorithms in terms of convergence accuracy and generalization ability, and the accuracy improvement percentage is 35.29% compared with DBO algorithm. Wilcoxon rank sum test was used to evaluate the experimental results, which proved that the improvement strategy was statistically significant. Finally, the BDBO algorithm is applied to the tracking technology of the maximum power point of the photovoltaic system, and the experimental results show that the application effect of the BDBO algorithm is better and has more engineering application value.

Design of Non-isolated DC–DC Converters for Maximum Power Point Tracking in Stand-Alone Photovoltaic System

Design of Non-isolated DC–DC Converters for Maximum Power Point Tracking in Stand-Alone Photovoltaic System

Nanogrids in Modern Power Systems: A Comprehensive Review

Nanogrids are becoming an essential part of modern home power systems, offering sustainable solutions for residential areas. These medium-to-low voltage, small-scale grids, operating at medium-to-low voltage, enable the integration of distributed energy resources such as wind turbines, solar photovoltaics, and battery energy storage systems. However, ensuring power quality, stability, and effective energy management remains a challenge due to the variability of renewable energy sources and evolving customer demands, including the increasing charging load of electric vehicles. This paper reviews the current research on nanogrid architecture, functionality in low-voltage distribution systems, energy management, and control systems. It also explores power-sharing strategies among nanogrids within a microgrid framework, focusing on their potential for supplying off-grid areas. Additionally, the application of blockchain technology in providing secure and decentralized energy trading transactions is explored. Potential challenges in future developments of nanogrids are also discussed.

An Approach Based on Golden Eagle Optimization Algorithm for Maximum Power Point Tracking of PV Panel Under Partial Shading Conditions

This study introduces an innovative Maximum Power Point Tracking (MPPT) technique utilizing the Golden Eagle Optimization (GEO) method, specifically designed to enhance the efficiency of photovoltaic (PV) systems under partial shading conditions. Unlike traditional MPPT approaches that struggle with local peaks in power-voltage curves caused by shading, the GEO method leverages the hunting behavior-inspired algorithm to accurately locate the global maximum power point (GMPP). The effectiveness of the GEO MPPT technique is demonstrated through extensive simulations across three diverse case scenarios, each representing different partial shading patterns. In all scenarios, the GEO method outperforms conventional MPPT techniques, showcasing its adaptability and superior performance in challenging conditions. The successful implementation of GEO MPPT leads to substantial improvements in PV panel energy extraction efficiency, even when faced with the complexities of partial shading. This research contributes significantly to the advancement of solar PV systems, enhancing their reliability and performance in real-world environments. By mitigating the impact of partial shading, this work promotes the wider adoption of solar energy as a viable and sustainable power solution.

Optimal parameter identification of photovoltaic systems based on enhanced differential evolution optimization technique

Identifying the parameters of a solar photovoltaic (PV) model optimally, is necessary for simulation, performance assessment, and design verification. However, precise PV cell modelling is critical for design due to many critical factors, such as inherent nonlinearity, existing complexity, and a wide range of model parameters. Although different researchers have recently proposed several effective techniques for solar PV system parameter identification, it is still an interesting challenge for researchers to enhance the accuracy of the PV system modelling. With the above motivation, this article suggests a stage-specific mutation strategy for the proposed enhanced differential evolution (EDE) that adopts a better search process to arrive at optimal solutions by adaptively varying the mutation factor and crossover rate at different search stages. The optimal identification of PV systems is formulated as a single objective function. It appears in the form of the Root Mean Square Error (RMSE) between the PV model current from the experimental data and the current calculated using the identified parameters considering the parameter constraints (limits). The I-V (current-voltage) characteristics/data with identified parameters are validated with the experimental data to justify the proposed approach’s accuracy and efficacy for different cells and modules. Extensive simulation has been demonstrated considering two different PV cells (RTC France & PVM-752-GaAs) and three different PV modules (ND-R250A5, STM6 40/36 & STP6 120/36). The results obtained from the proposed EDE technique show Root Mean Square Errors (RMSE) of 7.730062e-4, 7.419648e-4, and 7.33228e-4 respectively, in parameter identification of RTC France PV cell models based on single, double, and triple diodes. Also, the RMSE involved in parameter identification of PVM-752-GaAs PV cell models based on single, double, and triple diodes are 1.59256e-4, 1.408989e-4, and 1.30181e-4, respectively. The parameters identification of ND-R250A5, STM6 40/36 and STP6 120/36 PV modules involve RMSE values of 7.697716e-3, 1.772095e-3, and 1.224258e-2, respectively. All these RMSE values obtained with proposed EDE are the least as compared to other well-accepted algorithms, thereby justifying its higher accuracy.

Review on topologies of quasi Z-source inverter in grid-connected solar photovoltaic system

Review on topologies of quasi Z-source inverter in grid-connected solar photovoltaic system

Zinc Grid Based Transparent Electrodes for Organic Photovoltaics

AbstractZinc is the fifth most electrically conductive metal and is available at a fraction of the cost of the most widely used transparent electrode materials; silver, indium‐tin oxide, and poly(3,4‐ethylenedioxythiophene) polystyrene sulfonate, but has been surprisingly overlooked as a current carrying element in organic photovoltaics. Here, a transparent flexible electrode based on an embedded zinc grid with ≈1 µm linewidth is reported and its utility as a drop‐in replacement for indium‐tin oxide coated glass electrodes in model organic photovoltaic devices is demonstrated. The zinc grids are fabricated using the unconventional approach of condensation coefficient modulation, using a micro‐contact printed patterned layer of poly(perfluorooctylmethylmethacrylate) to resist zinc condensation in the gaps between grid lines, together with a copper acetylacetonate seed layer to nucleate zinc condensation where grid lines are required. Density functional theory calculations of the strength of the interaction between zinc atoms and this fluorinated polymer provide fundamental insight into why the latter is so effective at resisting zinc condensation. The resulting zinc grid is embedded in a flexible polymer support and transferred to a flexible plastic substrate by delamination, which enables recovery and reuse of the fluorinated polymer.

Analysis and Modeling of Photovoltaic Arrays for Sustaining Power Generation in Geostationary Satellite Solar Panels using Machine Learning

Analysis and Modeling of Photovoltaic Arrays for Sustaining Power Generation in Geostationary Satellite Solar Panels using Machine Learning

Enhancing efficiency and sustainability: a combined approach of ANN-based MPPT and fuzzy logic EMS for grid-connected PV systems

Enhancing efficiency and sustainability: a combined approach of ANN-based MPPT and fuzzy logic EMS for grid-connected PV systems

Case Study on Photovoltaic System: Impact of Solarimetric Stations on Simulations and Anomaly Detection

Local solarimetric stations (LSS) are essential for collecting data to evaluate photovoltaic (PV) plant performance and improve simulation accuracy. When unavailable, commercial solar databases (CSD), typically derived from satellite-based typical years, are used. This study compared the impact of LSS and CSD data on PV simulations and explored the use of LSS data for anomaly detection. Two LSS were analyzed: one at a small-scale PV system (minigeneration in Brazil) and another at a large-scale PV plant. The minigeneration station measured irradiance components to calculate Plane of Array (POA) irradiance, while the large-scale station directly measured POA. For the minigeneration system, simulations using LSS data showed a lower discrepancy (0.21%) compared to CSD (3.13%). For the large-scale plant, a -5.99% discrepancy using LSS revealed anomalies in energy generation. MAE and RMSE improved significantly with LSS for the large-scale system, with MAE decreasing from 660.25 MWh (CSD) to 348.08 MWh. Additionally, an unsupervised anomaly detection flagged 2.88% and 4.47% of data for two inverters, showcasing LSS potential for predictive models. These findings suggest that while LSS data are valuable for PV plant performance analysis, their effectiveness may depend on spectral range, averaging intervals, and irradiance transposition in simulations.

Transition of Perovskite Solar Technologies to Being Flexible.

Perovskite technologies has taken giant steps on its advances in only a decade time, from fundamental science to device engineering. The possibility to exploit this technology on a thin flexible substrate gives an unbeatable power to weight ratio compares to similar photovoltaic systems, opening new possibilities and new integration concepts, going from building integrated and applied photovoltaics (BIPV, BAPV) to internet of things (IoT). In this perspective, the recent progress of perovskite solar technologies on flexible substrates are summarized, focusing on the challenges that researchers face upon using flexible substrates. A dig into material science is necessary to understand what kind of mechanisms are limiting its efficiency compare to rigid substrates, and which physical mechanism limits the upscaling on flexible substrate. Furthermore, an overview of stability test on flexible modules will be described, suggesting common standard procedure and guidelines to follow, showing additional issues that flexible modules face upon bending, and how to prevent device degradation providing an ad-hoc encapsulation. Finally, the recent advances of flexible devices in the perovskite market will be shown, giving an outline of how this technology is exploited on flexible substrates, and what are still missing that need stakeholders' attention.

New geographic information system based sustainability metric for isolated photovoltaic systems

The integration of photovoltaic (PV) technologies is vital for achieving sustainable energy solutions in isolated systems. However, A critical challenge that remains is maintaining the sustainability of these systems under the fluctuating conditions of solar irradiance, which is key for isolated energy systems. This study hypothesizes that the sustainability of PV systems can be accurately assessed through a new metric that incorporates performance consistency, variability, and resilience, using real-time energy production data alongside GIS-based solar radiation models. By analyzing fixed PV, concentrated PV (CPV), and dual axis tracking PV (DATPV) systems over a three-year period (2017–2019), The analysis indicates that DATPV systems achieved the highest energy output, with energy ratios exceeding 300% in 2019, though this was accompanied by substantial variability in performance. Fixed PV systems demonstrated the most stable performance, with a consistency term reaching 0.93 and a sustainability score of 0.87 in 2019. CPV systems performed moderately, with a sustainability score of 0.66 in 2017. These results highlight the trade-off between energy capture and operational stability, which is critical for sustainable energy management in isolated systems.

Pumped hydro energy storage to support 100% renewable energy

Abstract The rapidly growing scale of solar photovoltaics and wind energy coupled with electrification of transport, heating and industry offers an affordable pathway for achieving deep decarbonization. Massive integration of variable solar photovoltaics and wind energy requires large-scale adoption of short (seconds-hours) and long (hours-days) duration energy storage. Currently, long-duration pumped hydro energy storage (PHES) accounts for about 95% of global energy storage for the electricity sector. This paper discusses the Global PHES Atlases developed by the Australian National University which identify 0.8 million off-river (closed-loop) PHES sites with a combined 86 million Gigawatt-hours of storage potential, which is about 3 years of current global electricity production. These Atlases show that most global jurisdictions have vast potential for low-cost PHES with small water and land requirements, and that do not require new dams on rivers. The low capital cost of premium PHES systems ($ per kilowatt-hour) is pointed out. Methods for creating shortlists of promising PHES sites from the Atlases for detailed investigation are developed.

Designing EV Charging Energy Hubs to Meet Flexibility Requirements in Smart Grids

Energy hub stations represent a transformative approach to modern energy systems, functioning as flexible nodes within distribution networks. By seamlessly integrating electric vehicles (EVs) and battery energy storage systems (BESSs), these hubs address critical challenges such as grid stress, renewable energy utilization, and peak load management. This study introduces a novel mathematical optimization model designed to maximize the operational flexibility of EV charging stations by transforming them into fully functional energy hubs. The model incorporates key parameters such as energy demands, load profiles, and grid conditions to optimize the sizing and operation of distributed energy resources, including photovoltaic (PV) systems and BESSs. The proposed approach minimizes grid dependency, enabling energy hubs to efficiently operate varying levels of operational flexibility, from full reliance on grid power to complete independence. The results reveal that, by effectively shifting energy usage away from peak periods and leveraging PV generation, energy hubs emerge as a critical component of sustainable energy systems and can independently support approximately 45% of their load, with minimal PV and BESS capacities. It also reveals a direct correlation between higher flexibility levels and increased infrastructure requirements for PV systems and BESSs. The proposed model underscores the critical role of energy hubs in facilitating the global shift toward decarbonization, aligning with contemporary goals for resilient and environmentally sustainable energy ecosystems. This work provides a scalable framework for future energy systems, with significant implications for policymakers, researchers, and practitioners in the fields of renewable energy and sustainable mobility.

Capacity optimization of photovoltaic storage hydrogen power generation system with peak shaving and frequency regulation

To solve the problem of power imbalance caused by the large-scale integration of photovoltaic new energy into the power grid, an improved optimization configuration method for the capacity of a hydrogen storage system power generation system used for grid peak shaving and frequency regulation is proposed. A hydrogen storage power generation system model is established, and the photovoltaic power generation and hydrogen fuel cell power generation is calculated. A two-layer hydrogen storage power generation system capacity optimization configuration model was established, an improved particle swarm optimization algorithm was used to solve the improved hydrogen storage power generation system capacity optimization configuration model, and the capacity optimization configuration results were obtained. The experimental results show that this method can obtain the best capacity allocation result based on local lighting conditions and the cost of the power generation system, whereas the improved particle swarm optimization algorithm can solve the model faster and reduce the cost of the power generation system. After applying this method, the net income of the solar hydrogen storage power generation system has almost doubled.

A Robust Linear Active Disturbance Rejection Control for Renewable Energy Grid-Connected System Based on APF

With the energy transition and changes in electrical load structures, harmonic distortion in power systems is increasingly threatening grid stability, especially during the integration of renewable energy sources. Active power filters (APFs) are commonly used to improve power quality, but current fluctuations and voltage instability caused by DC-side capacitor charging and discharging hinder effective harmonic compensation. To address this, this paper presents a method combining a variable-step-size adaptive linear predictor with linear active disturbance rejection control (VALP-LADRC). By integrating an adaptive linear predictor (ALP) with LADRC, the proposed method effectively reduces the impact of disturbances on control, improving voltage regulation and harmonic compensation, and enhancing renewable energy grid integration. To address delays during the adjustment of initial parameters in the adaptive predictor, we combine it with a variable-step-size algorithm, significantly improving disturbance rejection and tracking accuracy, while solving voltage drop issues. The simulation results in a photovoltaic grid-connected system show that VALP-LADRC effectively mitigates disturbances, ensures voltage stability, and enhances power quality. This approach offers a promising solution for supporting sustainable development through better renewable energy integration and improved power quality.

Optimization of Microgrid Dispatching by Integrating Photovoltaic Power Generation Forecast

In order to address the impact of the uncertainty and intermittency of a photovoltaic power generation system on the smooth operation of the power system, a microgrid scheduling model incorporating photovoltaic power generation forecast is proposed in this paper. Firstly, the factors affecting the accuracy of photovoltaic power generation prediction are analyzed by classifying the photovoltaic power generation data using cluster analysis, analyzing its important features using Pearson correlation coefficients, and downscaling the high-dimensional data using PCA. And based on the theories of the sparrow search algorithm, convolutional neural network, and bidirectional long- and short-term memory network, a combined SSA-CNN-BiLSTM prediction model is established, and the attention mechanism is used to improve the prediction accuracy. Secondly, a multi-temporal dispatch optimization model of the microgrid power system, which aims at the economic optimization of the system operation cost and the minimization of the environmental cost, is constructed based on the prediction results. Further, differential evolution is introduced into the QPSO algorithm and the model is solved using this improved quantum particle swarm optimization algorithm. Finally, the feasibility of the photovoltaic power generation forecasting model and the microgrid power system dispatch optimization model, as well as the validity of the solution algorithms, are verified through real case simulation experiments. The results show that the model in this paper has high prediction accuracy. In terms of scheduling strategy, the generation method with the lowest cost is selected to obtain an effective way to interact with the main grid and realize the stable and economically optimized scheduling of the microgrid system.

Solar parking lot capacity: an abundant dual-use alternative to meet demand for the renewable energy transition

Abstract Most utility-scale solar facilities (USSFs) in the United States (US) are planned or installed in grasslands, pastures, agriculture land, and timberland, with aggressive future expansion plans. Investor-owned electricity providers have lobbied aggressively to remove obstacles to approval of USSFs on agricultural lands and for limits on regulations that support distributed solar installation. Rural USSF expansion may have potentially significant local, state, and regional impacts on agricultural production and the economy, property values and affordability, and conservation of habitats, connectivity, and endangered species. The potential use of parking lots in urban and suburban areas for solar development to meet demands is poorly studied and parking lot solar has not gained much traction in the US. The objective of this research was to estimate whether solar PV systems installed in parking lots can meet electricity demands across multiple urban and suburban metropolitan areas within the US. Parking lot data across four municipalities with variation in latitude, solar radiation, and population density was obtained, along with modeled annual electricity demand by sector. Parking lot area and potential generating capacity was calculated for canopy and carport designs using standard and premium efficiency panels, and annual electricity production was estimated using the National Renewable Energy Laboratory’s PVWatts Calculator. Premium efficiency canopy designs can meet ~100% of demand in some large car dependent low to medium density municipalities. High density urban areas may receive marginal direct benefit from parking lot solar development within their boundaries. Dual-use of lands without any ecosystem or productive value can substantially contribute to renewable energy mixes to meet demands.

Recovery strategies for EoL solar panels: sustainable and circular economy practices

The rapid expansion of solar PV technology highlights the need for sustainable End-of-Life (EoL) management to address resource scarcity and environmental sustainability. This study, aligned with SDG 7 and SDG 12, proposes an integrated EoL recycling approach combining thermal, chemical, and green methods to maximize material recovery while minimizing environmental impact. Thermal treatment delaminates panels, preserving silicon wafers and glass, followed by eco-friendly chemical treatments to recover metals like silver and aluminum. This method achieves high-purity material recovery for reuse in solar panel manufacturing and high-value products. By overcoming limitations of traditional methods and incorporating green solvents, it supports circular economy principles and offers cost-effective solutions for global solar panel waste management. Future research should focus on industrial scalability and economic feasibility to advance solar energy’s role in sustainable development.