Photovoltaic System Research Articles

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.

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.

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

AbstractPerovskite 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.

Enhancing Flexible Perovskite Photovoltaic Cells and Modules Through Light-Trapping and Light-Shifting Strategies.

Flexible perovskite photovoltaic devices are typically constructed on flexible polyethylene naphthalate (PEN) substrates, which exhibit near-ultraviolet absorption and high visible-light reflection, leading to significant optical losses. To address this issue, a reusable optical-management sticker tailored for flexible substrates has been proposed in this work. The sticker incorporates a light-shifting material that converts near-ultraviolet light into visible light, enabling photoelectric conversion of near-ultraviolet light. Additionally, the sticker features a light-trapping microstructure that creates a refractive index gradient from PEN to air, thereby achieving a significant anti-reflection effect. As a result, the efficiency of a flexible perovskite solar cell reached 23.05% (certified 22.46%) under 1 sun AM1.5G illumination and 36.65% (certified 34.03%) under 1000 lux artificial light illumination. Furthermore, scaling this solution to large-area modules has yielded remarkable improvements, achieving a breakthrough certified efficiency of 20.48% (aperture area 21 cm2) in flexible perovskite photovoltaic modules.

Improved BRBFNN-based MPPT algorithm for coupled inductor KSK converter for sustainable PV system applications

Improved BRBFNN-based MPPT algorithm for coupled inductor KSK converter for sustainable PV system applications

Reconfigured single- and double-diode models for improved modelling of solar cells/modules

Proper modeling of PV cells/modules through parameter identification based on the real current-voltage (I-V) data is important for the efficiency of PV systems. Most related works have concentrated on the classical single-diode model (SDM) and double-diode model (DDM) and their parameter extraction by various metaheuristic algorithms. In order to render more accurate and representative modeling, this paper adds a small resistance in series with the diodes in SDM and DDM. The new models are named reconfigured SDM (Reconfig-SDM) and reconfigured DDM (Reconfig-DDM), and they have not been studied so far as we know. A squirrel search algorithm (SSA) is employed to globally find the parameters of the new models. The performance achieved is experimentally tested on both a commercial RTC France solar cell and a CS6P-220P polycrystalline PV module located at Düzce University in Türkiye. A vivid comparison of experimental findings, observation, and analysis clearly demonstrates that the proposed Reconfig-SDM and Reconfig-DDM tuned by the SSA have better capacity and effectiveness for modeling PV devices than some cutting-edge approaches. Specifically, compared with the best-performing approach in the literature, Reconfig-SDM and Reconfig-DDM could reduce the error rate up to 0.37% and 2.58% for the solar cell, and 3.21% and 29.0% for the solar module.

Identification of Parameters for Solar Photovoltaic Systems Using a Human Optimization Algorithm

By increasing the global demand for electricity, there is a heightened need for power generation. The rising costs of natural gas and regulatory pressures to limit greenhouse gas emissions have escalated the expenses associated with electricity production from fossil fuels. Consequently, there is a growing inclination to utilize alternative energy sources for electricity generation, particularly solar power through photovoltaic systems. Evolutionary algorithms are among the most favored methods for identifying and optimally estimating the parameters of photovoltaic systems, and this article examines their functionality. A critical issue in these systems is the appropriate selection of photovoltaic system parameters. This paper presents a new method for optimal parameters identification of the photovoltaic (PV) systems using a newly modified version of a metaheuristic algorithm. The propose algorithm is based on adapting the performance of the Human Evolutionary Optimizer and is called AHEO algorithm. Simulation results indicate that the estimation of the photovoltaic cell's circuit model parameters has been effectively accomplished, with the mean square error (MSE) associated with the AHEO demonstrating a high level of accuracy and robustness in parameters identification of the PV system.

Evaluation of Energy Potential in a Landfill Through the Integration of a Biogas–Solar Photovoltaic System

The integration of biogas and photovoltaic solar energy systems in sanitary landfills represents a promising strategy for sustainable energy generation and efficient urban waste management. This study evaluates the potential for biogas and photovoltaic energy production in two cells of the Municipal Landfill of Chihuahua, Mexico. Using the LandGEM and MMB models (Landfill Gas Emission Model and the Mexican Biogas Model), biogas generation was estimated by considering the composition of the landfill gas and the characteristics of the cover in each cell, revealing notable differences due to their operational periods and waste deposition. Photovoltaic simulations, conducted with the HelioScope software 2020, evaluated spatial configurations and solar radiation data. The generation potential for 2025 was simulated using predictive models, yielding results between 25.48 and 26.08 MW for the biogas–photovoltaic system, depending on the orientation of the panels and the optimization of the coverage. The novelty of this work lies in the combined evaluation of biogas and photovoltaic potential within a single landfill site, integrating advanced modeling tools to optimize system design. By demonstrating the feasibility and benefits of this hybrid system, the study contributes to clean energy solutions, environmental mitigation, and improved waste management strategies. Our findings emphasize the importance of site-specific management practices and predictive modeling to enhance renewable energy production and reduce greenhouse gas emissions, supporting sustainable urban development initiatives.

Thermal imaging-based fault detection and energy efficiency analysis in a 1.6 MW photovoltaic system in Bağyurdu OIZ, Türkiye

The present study assesses the influence of thermal imaging defect detection on the energy efficiency of a 1.6 MW solar power facility in the Bağyurdu Organized Industrial Zone (OIZ) in İzmir, Turkey. Thermal imaging has demonstrated efficacy in detecting serious problems in photovoltaic (PV) panels, including hot spots, inoperative modules, faulty connections, and shadowing, which substantially impact system performance. A comprehensive investigation revealed that around 15% of the photovoltaic panels displayed defects, resulting in a 16% decrease in system performance and an estimated yearly energy loss of 0.35 GWh. The study emphasizes the benefits of thermal imaging compared to conventional fault detection techniques, including its capacity for swift and non-invasive identification of localized overheating, which may lead to fires, and its ability to discern fluctuations in energy output due to shading or malfunctioning modules. The results underscore the necessity for routine thermal evaluations and maintenance to guarantee photovoltaic systems’ operational efficacy and dependability. This study enhances the sparse data on large-scale photovoltaic systems in Türkiye and illustrates the effectiveness of thermal imaging as an economical and accurate diagnostic instrument. Future studies should amalgamate thermal imaging with sophisticated diagnostic techniques, like electroluminescence testing and machine learning, to augment fault detection precision and optimize photovoltaic system efficacy.

Renewable Energy Community Sizing Based on Stochastic Optimization and Unsupervised Clustering

Renewable Energy Communities (RECs) are emerging as significant in the global paradigm shift towards a smart and sustainable energy environment. By empowering energy consumers to actively participate in local energy generation, and sharing, using renewable energy sources, energy storage, and flexible loads, REC participants can reduce costs, and also contribute to low-carbon objectives, providing the flexibility needed to address modern smart grid challenges. This article presents a mixed integer linear programming model for optimal sizing of the solar PVs and battery energy storage systems (BESS) of REC participants who engage in P2P energy exchange. The model is formulated using a two-stage stochastic optimization to address load and PV uncertainty, and unsupervised clustering to structure the data for the stochastic optimization process. The model enables sizing solar PVs for different rooftop geometries and the objective function includes comprehensively defined electricity, operational, and scaled investment costs for solar PV and BESS, where economic fairness constraints are analyzed and implemented. The model is validated on real solar and atmospheric measured data from Zagreb, Croatia, and publicly available household consumption data from Northern Germany. The article also analyzes how tariff models, and electricity prices affect PV and BESS sizes, cost reductions, and P2P energy exchange for different REC participants with varying consumption and production profiles.