Photovoltaic Research Articles

MPPT Performance Analysis for PV Energy Harvesting Using Grey Wolf Optimization (GWO) Algorithm

Renewable energy is a key solution to meeting the growing demand for electricity while reducing reliance on non-renewable sources. Among various renewable technologies, photovoltaic (PV) systems are widely used in solar power plants (PLTS) to harness solar energy. However, PV efficiency is affected by environmental factors such as fluctuating solar irradiance and temperature, which cause instability in output voltage and power. To address these issues, Maximum Power Point Tracking (MPPT) techniques are applied to optimize power extraction. This study proposes the Grey Wolf Optimization (GWO) algorithm for MPPT and evaluates its performance through MATLAB/SIMULINK simulations under varying irradiance and temperature conditions. Inspired by the hunting behavior and social hierarchy of grey wolves, GWO dynamically adjusts the converter's duty cycle based on real-time voltage and current measurements to maximize output power. The study focuses on PV systems in Malang, Indonesia, and compares GWO with the Particle Swarm Optimization (PSO) method in terms of accuracy and stability. The results indicate that increased solar irradiance substantially enhances PV power output, while rising temperatures tend to reduce efficiency. The GWO algorithm achieves an average tracking accuracy of 94.5632%, slightly lower than the 96.9851% achieved by PSO. However, GWO demonstrates superior performance in terms of stability, with faster convergence and reduced oscillations during the tracking process. A comparison of system performance before and after applying the GWO method shows notable improvements in tracking consistency and power extraction efficiency, especially under dynamic environmental changes. The novelty of this study lies in its use of real-world environmental data collected over a 30-day period in a tropical setting, which is rarely addressed in previous GWO-based MPPT research. These findings highlight the potential of the GWO-based MPPT strategy to enhance PV system reliability and efficiency in real-time renewable energy applications.

Analysis of Photovoltaic Panel Performance Integrated with the Grid in a Load-Sharing Scheme

This paper examines the performance of photovoltaic panels integrated into the electrical grid in a load-sharing scheme at Darool Ehsan Muhammadiyah Senior High School, Sragen. The study is part of Universitas Muhammadiyah Surakarta's community service program focused on renewable energy development in educational environments. Unlike previous studies relying on simulated data, this research uses real-time primary data from direct measurements of photovoltaic power production and grid electricity consumption over a specific period. The study’s innovation lies in analyzing the photovoltaic system within a tropical climate-based load-sharing scheme and comparing energy usage when connected to photovoltaic panels versus when disconnected. It also evaluates the impact of reducing solar energy consumption from the grid. The collected data reveal an estimated daily load of 49.89 kWh, with PLN-supplied energy consistently exceeding inverter-supplied energy. Integration of photovoltaic panels into the grid reduces PLN electricity consumption by up to 30% during optimal sunlight periods, achieving an average system efficiency of 15%. This research offers valuable insights into the potential and challenges of implementing photovoltaic panels in educational settings, emphasizing the importance of climate considerations in system design and optimization. Future studies will focus on techno-economic evaluations of solar power installations and harmonic distortion (THD) analysis for larger-scale implementations.

Harmonic Distortion Analysis Of On-grid Photovoltaic Panel Integration In Load Sharing Scheme

Harmonic analysis is crucial to maintain the system power quality, especially in photovoltaic panels with load sharing scheme. Previous studies have discussed the impact of harmonic distortion on electric power systems in general, but there is still limited discussion on on-grid photovoltaic panel systems without the load-sharing scheme. Harmonic is a distortion phenomenon of current and voltage that is higher than fundamental frequency. The consumption of nonlinear loads contributes to the rising of harmonic distortion that has a bad impact on the system's power quality. This study aims to analyze and compare the levels of harmonic distortion on on-grid photovoltaic panels with a load-sharing scheme and evaluate the suitability with the IEEE 519-2014 standard. The result of this study shows that the Total Harmonic Distortion (THD) on the current (THDi) in PLN’s load and network exceeds the limit that is stipulated in IEEE 519-2014. THD current in PLN’s loads and network is higher than the output current from the on-grid inverter because of the consumption of the nonlinear load. Meanwhile, Total Harmonic Distortion (THD) on the voltage (THDv) in the on-grid inverter, electric network, and load tends to be lower and still within the safe limit. This study is expected to support the implementation of an effective preventive strategy to reduce harmonic distortion on the performance of on-grid photovoltaic panels that will be optimum in the load-sharing scheme.

Economic Analysis, Innovative Educational Models and Pragmatic Sustainability: The Case Study of a Photovoltaic System on a Public Building

The development of renewable energy has changed the utility sector, leading to a change in the interests of companies operating in the sector. The prosumer is a new emerging figure that can enable citizens and businesses to be an integral part of this change. In this context public administrations can play a key role and this study aims to assess the profitability of a photovoltaic (PV) system located in a public building (secondary school) in Italy. This analysis is integrated within an innovative teaching model that aims to stimulate the problem-solving competence of university students. The results show that the implementation of the PV system is characterised by important economic results with profits ranging from 2866 to 5670 €/kW in a 97 kW plant and from 2065 to 4012 €/kW in a 168 kW plant with a fundamental role played by the percentage of self-consumption and the avoided cost in the bill. The emission reduction in the 1st-year ranges from 56.6 to 98 tCO2eq. Pragmatic sustainability can support the decarbonisation of our energy system by involving young people in the choices and favouring social models that reward their skills.

Ecologically informed solar enables a sustainable energy transition in US croplands

United States (US) croplands are ideal recipient environments for solar photovoltaic (PV) energy because they are flat and have a high solar resource. Perceived threats of solar to agriculture have led some stakeholders to suggest that croplands be exclusively used to produce food. However, 12 million hectares of US croplands, an area about the size of New York State, are already dedicated to corn grown for ethanol (i.e., biofuel), an energy product that requires significantly more land than solar PV per unit energy. Ecosystem service benefits of an ecologically informed approach to solar development (i.e., ecovoltaics), coupled with significant land-use advantages over corn ethanol, make solar an attractive solution for a sustainable energy transition in croplands. Here, we evaluated how the conversion of a small fraction of corn-ethanol croplands into ecovoltaic solar facilities might improve land-use efficiency of energy generation, enhance ecosystem services, and provide landscape diversification. Through spatial analyses, we determined that converting just 3.2% of land currently used for corn ethanol would increase the share of utility-scale solar energy in the US from 3.9 to 13%. We also identified target locations where strategic conversion of corn ethanol to solar PV colocated with perennial vegetation could filter excess nutrients transported from adjacent farm runoff, diversify and connect agricultural landscapes, and provide local wildlife habitat. In contrast to the common perception of land-use competition and land scarcity in the energy transition, our findings highlight benefits of colocated energy landscapes that integrate fundamental principles of energy development and sustainable agroecosystems.

Performance and Environmental Impact of a 45 kWp Rooftop Grid-connected Solar PV System in a Semi-arid Academic Campus: A Case Study from Western India

Aims: This study aimed to evaluate the technical performance and environmental impact of a 45 kWp rooftop grid-connected solar photovoltaic (PV) system installed at the College of Agricultural Engineering and Technology (CAET), Junagadh Agricultural University, Gujarat. Methodology: The system was monitored over five months (August–December 2018). Real-time data on energy output, meteorological parameters, and system efficiencies were recorded. Performance metrics such as array yield, final yield, module and system efficiencies, performance ratio (PR), and CO₂ emission reduction were analyzed using IEC 61724 guidelines. Results: The system generated 22,205 kWh of DC energy and exported 20,829.26 kWh of AC energy to the grid. It achieved an average final yield of 3.37 kWh/kWp/day and a performance ratio of 71%. The average module, inverter, and system efficiencies were 11.26%, 93.84%, and 10.56% respectively. A total of 20,494 kg of CO₂ emissions were avoided. Conclusion: The solar PV system showed reliable performance and high efficiency across seasonal conditions. It proved effective for reducing institutional carbon footprint and demonstrated the feasibility of rooftop solar adoption in semi-arid academic environments.

The development of CC-TF-BiGRU model for enhancing accuracy in photovoltaic power forecasting

The development of CC-TF-BiGRU model for enhancing accuracy in photovoltaic power forecasting

PVTE system performance improvement via numerical optimization of heat sink design and fluid working conditions

In recent times, there has been increasing interest to address climate change by implementing renewable energy to enhance the energy sector as soon as possible. However, solar radiation turns into heat reducing the photovoltaic (PV) panel efficiency. The waste heat from PV panels can be utilized by thermoelectric (TE) to convert into electricity and enhance PV panel efficiency. In this study, the 3D CFD and thermal-electric numerical model was developed for thermal and electrical analysis of different heat sink designs and materials for a thermoelectric generator (TEG) to be used in a (PV) system. Heat sink was installed on the cold side of the Photovoltaic-Thermoelectric (PVTE) system to dissipate the heat from the PV panels, where varying flow inlets and convection coefficient parameters for dissipating the heat on the cold side of the PVTE system were investigated. The simulated TEG power generation with different heat sink designs, heat sink materials, convection coefficients (h) and flow inlets (v) were compared. The results showed that the TEG with pin fin heat sink design (H3) made from aluminium (Al) generated the highest power generations than the other designs. The results also showed the power generation significantly increases until saturation point around 2.01 m s− 1 for the flow inlet and also increases when the convection coefficient increases above 20 (W/m2) °C.

Prototype Design for Irradiance Estimation Using Closed-Form Models and an Optimized MPPT IC Algorithm

Measuring solar irradiance is key to assessing the conversion efficiency of photovoltaic (PV) modules. Also, PV modules can be used to estimate irradiance through their electrical response to solar radiation using closed-form models (CFMs). This paper presents a prototype design for irradiance estimation based on evaluating three CFMs by implementing a maximum power point tracking (MPPT) system and a surface temperature measurement system. The system employs an incremental conductance (IC)-based control algorithm, which is optimized to eliminate oscillations at the maximum power point (MPP) and ensure efficient MPP tracking. Experimental validation of the implemented circuits is carried out using Arduino Nano, calibrated sensors, and low-cost electronic devices. Tests in real conditions were performed for four days under different irradiance scenarios, using two monocrystalline PV modules: one with 10 years of use and one new one. The accuracy of the CFMs was evaluated using the mean absolute percentage error (MAPE) and root mean squared error indicators, comparing their estimates with measurements from a Davis Instruments pyranometer. The most accurate CFM obtained a MAPE of 4.38% with the 10-year module and 3.26% with the new module. The results show that the proposed methodology provides estimates with an error of less than 5%, which validates its applicability under various climatic conditions, even with old PV modules.

Advancements and Challenges in Photovoltaic Power Forecasting: A Comprehensive Review

The fast growth of photovoltaic (PV) power generation requires dependable forecasting methods to support efficient integration of solar energy into power systems. This study conducts an up-to-date, systematized analysis of different models and methods used for photovoltaic power prediction. It begins with a new taxonomy, classifying PV forecasting models according to the time horizon, architecture, and selection criteria matched to certain application areas. An overview of the most popular heterogeneous forecasting techniques, including physical models, statistical methodologies, machine learning algorithms, and hybrid approaches, is provided; their respective advantages and disadvantages are put into perspective based on different forecasting tasks. This paper also explores advanced model optimization methodologies; achieving hyperparameter tuning; feature selection, and the use of evolutionary and swarm intelligence algorithms, which have shown promise in enhancing the accuracy and efficiency of PV power forecasting models. This review includes a detailed examination of performance metrics and frameworks, as well as the consequences of different weather conditions affecting renewable energy generation and the operational and economic implications of forecasting performance. This paper also highlights recent advancements in the field, including the use of deep learning architectures, the incorporation of diverse data sources, and the development of real-time and on-demand forecasting solutions. Finally, this paper identifies key challenges and future research directions, emphasizing the need for improved model adaptability, data quality, and computational efficiency to support the large-scale integration of PV power into future energy systems. By providing a holistic and critical assessment of the PV power forecasting landscape, this review aims to serve as a valuable resource for researchers, practitioners, and decision makers working towards the sustainable and reliable deployment of solar energy worldwide.

BIM-Based Framework for Photovoltaic Systems: Advancing Technologies, Overcoming Challenges, and Enhancing Sustainable Building Performance

Building-integrated photovoltaic systems (BIPVs) is a strategy to achieve energy self-sufficiency in buildings. However, photovoltaic (PV) energy production presents challenges due to its intermittent nature, characterized by variations and uncertainties associated with solar radiation and interference from the building’s surroundings. Therefore, building information modeling (BIM) enables energy simulations and solar performance analyses during the design phase of buildings. In this context, this paper aims to identify the key strategies for integrating BIM and photovoltaic energy production systems and how these approaches support the development of sustainable projects. A systematic literature review was conducted, combined with bibliometric analysis, content analysis, and coding of 63 articles. Results indicate an annual research growth rate of 19.62%, with contributions from 268 authors and an international co-authorship rate of 22.22%. Core research trends in the BIM-PV context were identified through thematic maps, highlighting four dimensions—BIPV applications, parametric tools for energy simulation, challenges, and potential benefits—divided into 32 codes. The findings are synthesized into four theoretical propositions structured in an integrative framework. Additionally, five key applications of BIM-PV integration are mapped, along with their addressed problems and the limitations in establishing theoretical and managerial contributions.

Evaluation of Environmental Factors Influencing Photovoltaic System Efficiency Under Real-World Conditions

The study addresses the impact of selected environmental factors on the energy production of photovoltaic systems under real outdoor conditions, with particular emphasis on the application of evolutionary computation techniques. The experiment was carried out on a dedicated test stand, where measurements were made under natural environmental conditions. Parameters such as solar irradiance, wind speed, temperature, air pollution, and obtained PV power were continuously recorded. Initial correlation analysis using Pearson and Spearman coefficients confirmed associations between environmental factors and power output, especially solar irradiance. In order to advance the analysis beyond conventional methods, a linear regression model was developed in which the model weights were optimized using evolutionary algorithms, allowing for a more robust assessment of the contribution of each parameter. The results showed that solar irradiance accounted for 97.79% of the variance in photovoltaic power, while temperature (0.95%), air pollution (0.72%), and wind speed (0.54%) had significantly lower impacts. The implementation of evolutionary algorithms represents a novel approach in this context and has proven to be effective in quantifying environmental influence under complex real-world conditions. Furthermore, the findings highlight the indirect role of air pollution in attenuating irradiance and reducing system efficiency. These insights provide a foundation for the development of adaptive control strategies and predictive models to optimize the performance of the photovoltaic system in dynamic environmental settings.

Optimized Voltage Feed-Forward Control for Photovoltaic-Battery DC Microgrid Using Enhanced Grey Wolf Algorithm

The integration of Photovoltaic (PV) systems with battery storage in microgrids presents significant challenges in maintaining voltage stability and system efficiency due to the fluctuating nature of solar energy generation and variable load demands. Traditional control strategies, such as feedback control, often fail to respond quickly enough to these fluctuations, leading to voltage instability and reduced system performance. Therefore, this paper presents an optimized voltage feed-forward control for photovoltaic-battery DC microgrid using Enhanced Grey Wolf Algorithm (EGWO) to optimize the system's performance, stability, and energy efficiency. This work uses an Arduino microcontroller, DC-DC converter, battery, IoT device, and optocoupler. The power from a solar panel is effectively converted to the level needed for battery charging using a DC-DC converter. A DC microgrid powered by a PV system and supported by battery energy storage requires precise control to balance energy generation, storage, and consumption. EGWO is integrated with voltage feed-forward control to enhance the performance of a photovoltaic-battery DC microgrid. This work is implemented by using MATLAB, and the BLYNK web app is used to display the results. This work contributes to the advancement of smart grid technologies by providing an optimized control solution that ensures reliable operation, energy efficiency, and reduced dependency on non-renewable energy sources in DC microgrid systems.

Policy Frameworks and Competitive Dynamics in the Photovoltaic Industry: A Comparative Study of China and the United States

This study provides a comparative analysis of the photovoltaic (PV) industry in China and the United States. By tracing the development of policies, it shows the differences between China and the United States and analyses the differences between industry structure and competitiveness. The findings indicate that China has achieved global market dominance and low-cost production through stable long-term policies, strong financial support, and vertically integrated supply chains, which have allowed it to become the world's largest producer of PV modules. In contrast, the U.S. has been a leader in technological innovation but faces significant challenges, such as fragmented policies, a lack of comprehensive support for the entire PV supply chain, and heavy reliance on imported components. These factors have limited the U.S.'s ability to achieve the same scale of production as China. The study suggests that closer cooperation between the two countries could enhance both countries' competitive advantages, foster innovation, and accelerate the global energy transition to renewable sources.

Development of a Novel Low-Cost Automated Flux Chamber for Real-Time Monitoring of VOCs Emissions at Contaminated Sites.

This study introduces an innovative, low-cost static flux chamber for real-time monitoring of volatile organic compound (VOC) emissions at contaminated sites. Compared to traditional static flux chambers, the developed system is fully automated, eliminating the need for continuous operator intervention in the field. The cylindrical stainless-steel chamber (6.28 L) is equipped with internal sensors for temperature, pressure, and humidity, and a low-cost PID sensor for VOC detection (0.001-40 ppm). VOC flux is determined over 10 min measurement cycles, with two micro diaphragm pumps purging the chamber to reset concentrations. An Arduino Uno microcontroller manages the system, enabling local data storage (SD card) and a LoRa module to send real-time data to the cloud using IoT systems. Powered by a 12 V battery, rechargeable via a photovoltaic panel, the system ensures continuous operation. The prototype costs less than 1.5 k€, significantly cheaper than commercial devices. Accuracy and repeatability were assessed through lab-scale emission tests under dynamic conditions using various aliphatic and aromatic VOCs. Results closely matched those from a commercial gas analyzer and a Comsol Multiphysics numerical model, confirming the system reliability. These findings support its potential as a cost-effective alternative for continuous VOC monitoring at contaminated sites.

Photovoltaic Panel System with Optical Dispersion of Solar Light for Greenhouse Agricultural Applications

Photovoltaic Panel System with Optical Dispersion of Solar Light for Greenhouse Agricultural Applications

Validation of Designing a Photovoltaic Energy (PVE) System to Enhance Urban Life and Reduce Fossil Fuel Dependence in Tehran and Baghdad

Aim: This study aimed to design and validate a grid-connected photovoltaic (PV) system to assess its potential for reducing CO₂ emissions and enhancing urban sustainability in Tehran and Baghdad. Methods: The study employed a tilt angle of 30°/0° for both sites. A total of 3,654 photovoltaic modules were installed, covering an area of 9,990 m², with 57 inverters integrated into the system. The total system power capacity was 2,138 kW, incorporating components for storage and self-consumption. Results: The results demonstrated a significant reduction in CO₂ emissions over a 15-year system lifetime—amounting to 41,168.7 tons in Iraq and 25,814.1 tons in Iran. In terms of generated emissions, Iraq recorded 1,169.78 tons of CO₂, while the grid lifecycle emissions were 869 tons in Iraq and 575 tons in Iran. The annual system production was 1,833.9 MWh in Iraq and 1,663.5 MWh in Iran. These findings highlight the system's strong potential in mitigating carbon dioxide emissions and offering a sustainable energy solution for urban environments with high pollution levels. Conclusion: This work serves as a foundational contribution to the broader understanding of how oil and gas exporting countries can harness photovoltaic technologies to address escalating energy demands. This is particularly in densely populated urban areas while simultaneously avoiding environmental crises, such as those currently experienced in Tehran. Recommendations: There should be development and implementation of strategic approaches for integrating connected photovoltaic systems play a critical role in strengthening energy security. Addressing emerging challenges, and establishing effective pathways to reduce and prevent environmental degradation in the future.

The Rise of Chalcohalide Solar Cells: Comprehensive Insights From Materials to Devices.

While lead-halide perovskites achieve high efficiencies, their toxicity and instability drive the search for safer materials. Chalcohalides, combining chalcogen and halogen anions in versatile structures, emerge as earth-abundant, nontoxic alternatives for efficient photovoltaic (PV) devices. A wide variety of chalcohalide materials, including pnictogen metals-, post-transition metals-, mixed-metals- and organic-inorganic metals-based chalcohalides, offer diverse structural, compositional, and optoelectronic characteristics. Some of these materials have already been experimentally synthesized and integrated into PV devices, achieving efficiencies of 4-6%, while others remain theoretically predicated. Despite these advancements, significant challenges must be addressed to fully realize the potential of chalcohalides as next-generation PV absorbers. This review provides a comprehensive insight of the fundamental properties of chalcohalide materials, emphasizing their unique structures, highly interesting optoelectronic and dielectric properties, to fuel further research and guide the development of high-efficiency chalcohalide solar cells. Various synthesis techniques are discussed, highlighting important and potentially overlooked strategies for fabricating complex quaternary and pentanary chalcohalide materials. Additionally, the working principles of different device structures and recent advances in fabricating efficient chalcohalide solar cells are covered. We hope that this review inspires further exciting research, innovative approaches, and breakthroughs in the field of chalcohalide materials.

Enhanced Objectivity of AHP for More Reliable Solar Farm Site Selection

ABSTRACTThe analytic hierarchy process (AHP) is a popular decision‐making method for reliable decisions in different areas of study. Although the conventional AHP method mathematically ensures the consistency of results, the reliability of these results depends on the expert manifests. While AHP was originally proposed for subjectively relatable criteria, there may also be additional objectively relatable criteria or a consensus about the final relation of some couple of criteria. To address these objective relations and/or consensuses, this study proposes the analytic hierarchy process with optimized hierarchy (AHP‐OH). This method enhances the reliability of results by satisfying objective relations and/or consensuses about relations between criteria. The AHP‐OH methodology was applied to select optimal photovoltaic (PV) farm locations in Konya Province, Turkey, a region characterized by diverse terrain and solar radiation levels. The study incorporated geographic information systems to analyze criteria, such as solar radiation rate, land use, slope, proximity to roads and transmission lines, and restricted areas. Results demonstrated that 2.56% of Konya's terrain is highly (80%–100%) suitable and 19.34% of it has moderately high (60%–80%) suitability for PV farm development, with five highly suitable regions identified. Notably, the locations of existing PV farms aligned closely with the identified suitable zones, validating the efficacy of the AHP‐OH approach. This research underscores the importance of objectivity of decision‐making methods and proposes AHP‐OH to enhance the objectivity of the conventional AHP method. By providing a systematic and objective framework for spatial decision support systems, AHP‐OH offers significant advancements for policymakers and developers in the renewable energy sector. Future applications of this methodology can extend to other regions and renewable energy sources, contributing to global efforts in sustainable energy development.

DESIGN AND IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE BASED MAXIMUM POWER POINT TRACKING SYSTEM FOR PHOTOVOLTAIC SYSTEM USING METEOROLOGICAL DATA

The performance of photovoltaic (PV) systems is highly affected by the current-voltage (I-V) characteristics of a solar cell, which changes with sun radiation, temperature, and load conditions. Maximum power point tracking (MPPT) algorithms are vital in extracting power from PV systems under such conditions. Therefore, this study proposed an improved artificial neural network (ANN)--based MPPT controller trained using the chicken swarm optimization (CSO) algorithm to overcome these problems. This CSO uses meteorological data for the improved MPPT in varying environmental conditions. A 100W PV system was simulated in MATLAB/Simulink, and the CSO method was compared with Perturb and Observe (P&O). The result shows that the CSO-trained ANN achieves better tracking even under partial shadowing. More so, the use of hardware such as Arduino Uno interfaced with a DC-DC boost converter affirms the practical feasibility of the project. This research contributes to AI-assisted MPPT, offering a better alternative to classical MPPT methods. Future work could explore hybrid optimization techniques.