Solar PV Output Research Articles

Roofing Highways With Solar Panels Substantially Reduces Carbon Emissions and Traffic Losses

AbstractPhotovoltaic (PV) installations are a leading technology for generating green electricity and reducing carbon emissions. Roofing highways with solar panels offers a new opportunity for PV development, but its potential of global deployment and associated socio‐economic impacts have not been investigated. Here, we combine solar PV output modeling with the global highway distribution and levelized cost of electricity to estimate the potential and economic feasibility of deploying highway PV systems worldwide. We also quantify its co‐benefits of reducing CO2 equivalent emissions and traffic losses (road traffic deaths and socio‐economic burdens). Our analysis reveals a potential for generating 17.58 PWh yr−1 of electricity, of which nearly 56% can be realized at a cost below US$100 MWh−1. Achieving the full highway PV potential could offset 28.78% (28.21%–29.1%) of the global total carbon emissions in 2018, prevent approximately 0.15 million road traffic deaths, and reduce US$0.43 ± 0.16 trillion socio‐economic burdens per year. Highway PV projects could bring a net return of about US$14.42 ± 4.04 trillion over a 25‐year lifetime. To exploit the full potential of highway PV, countries with various income levels must strengthen cooperation and balance the multiple socio‐economic co‐benefits.

IMC-PI control-based solar PV output voltage regulation for battery charging

IMC-PI control-based solar PV output voltage regulation for battery charging

ASSESSMENT OF REANALYSIS DATA FOR SOLAR PV OUTPUT FORECASTING IN THE PHILIPPINES: CASE OF PANGASINAN, NEGROS OCCIDENTAL, AND DAVAO DEL NORTE

Abstract. The sustainable energy transition in the Philippines requires accurate forecasting of solar PV output to optimize energy efficiency and grid management. While existing studies have emphasized the positive correlation between solar irradiance and PV production, this study aims to explore whether forecasting improves with the inclusion of weather data. This research conducts a comparative analysis between relying solely on solar irradiance against integrating various weather parameters to enhance solar PV output forecasting. The study focuses on three distinct locations (Pangasinan, Negros Occidental, and Davao Del Norte) and employs two models per each site: Model 1 (M1), which relies only on solar irradiance as predictors, and Model 2 (M2), which incorporates solar irradiance and weather parameters. Using Fifth Generation ECMWF Reanalysis (ERA5) Data, Principal Component Analysis (PCA) is conducted on the significant weather parameters. Extreme Gradient Boosting (XGBoost) with 5-fold nested cross-validation is applied for solar PV output forecasting. Models are assessed using Mean Absolute Percentage Error (MAPE) and skill scores. Results showthat while solar irradiance alone suffices for predicting solar PV output in Negros Occidental, incorporating weather parameters improves forecasting accuracy in Davao Del Norte and Pangasinan. This paper recommends caution in generalizing the findings to different regions with varying weather patterns, as the forecasting performance of the models is influenced by data quality, specific location, and prevailing weather conditions.

Day-Ahead Hourly Solar Photovoltaic Output Forecasting Using SARIMAX, Long Short-Term Memory, and Extreme Gradient Boosting: Case of the Philippines

This study explores the forecasting accuracy of SARIMAX, LSTM, and XGBoost models in predicting solar PV output using one-year data from three solar PV installations in the Philippines. The research aims to compare the performance of these models with their hybrid counterparts and investigate their performance. The study utilizes the adjusted shortwave radiation (SWR) product in the Advanced Himawari Imager 8 (AHI-8), as a proxy for in situ solar irradiance, and weather parameters, to improve the accuracy of the forecasting models. The results show that SARIMAX outperforms LSTM, XGBoost, and their combinations for Plants 1 and 2, while XGBoost performs best for Plant 3. Contrary to previous studies, the hybrid models did not provide more accurate forecasts than the individual methods. The performance of the models varied depending on the forecasted month and installation site. Using adjusted SWR and other weather parameters, as inputs in forecasting solar PV output, adds novelty to this research. Future research should consider comparing the accuracy of using adjusted SWR alone and combined with other weather parameters. This study contributes to solar PV output forecasting by utilizing adjusted satellite-derived solar radiation, and combining SARIMAX, LSTM, and XGBoost models, including their hybrid counterparts, in a single and comprehensive analysis.

Toward the Development of an Empirical Model of Air Pollution Impact on Solar PV Output for Industry Use

Toward the Development of an Empirical Model of Air Pollution Impact on Solar PV Output for Industry Use

Predicting Solar PV Output Based on Hybrid Deep Learning and Physical Models: Case Study of Morocco

Background: In recent years, the integration of renewable energy sources into the grid has increased exponentially. However, one significant challenge in integrating these renewable sources into the grid is intermittency. Objective: To address this challenge, accurate PV power forecasting techniques are crucial for operations and maintenance and day-to-day operations monitoring in solar plants. Methods: In the present work, a hybrid approach that combines Deep Learning (DL) and Numerical Weather Prediction (NWP) with electrical models for PV power forecasting is proposed Results: The outcomes of the study involve evaluating the performance of the proposed model in comparison to a Physical model and a DL model for predicting solar PV power one day ahead and two days ahead. The results indicate that the prediction accuracy of PV power decreases and the error rates increase when forecasting two days ahead, as compared to one day ahead. Conclusion: The obtained results demonstrate that DL models combined with NWP and electrical models can improve PV Power forecasting compared to a Physical model and a DL model.

Analisis Efektivitas Daya Dan Energi Pada Sistem Pembersih Solar PV 2 × 50 Wp dengan Metode Lateral Movement

The implementation of the solar cell itself often has problems in its operation. One of the factors that affect the performance of PLTS is that the dirty solar panel cover glass is covered with dust or dirt so that it blocks the intensity of sunlight entering the solar panel which results in a reduced output of the solar panel. A 2x50 Wp solar pv cleaning tool was made to overcome the problem of reduced solar pv output due to the influence of dirt adhering to the pv cover glass. The cleaning tool that has been designed is expected to maximize the quality of power and energy that will be generated by solar pv. The solar pv dirt cleaning tool is used on the research object of the PLTS system in the Renewable Lab, Navy Building, State Polytechnic of Malang. Making a cleaning tool design using Solidworks software, with the reason being able to get a 3D picture of the planned pv dirt cleaner. The cleaning tool works with the help of a battery/power supply as its electrical power. There are 3 dc motors connected to brushes, wheels and water pumps. The cleaning tool moves in a lateral movement (sideways movement) from corner to corner 3 times, then the brush rotates to clean the solar cell cover glass from dirt. In the cleaning process, it can be assisted by using water provided and sprayed by a water pump. The average Vmp and Imp values are 19.72 V and 1.64 A. From the Vmp and Imp values, the average Pmp calculation results are 32.60 W for solar pv after cleaning. Before cleaning, the average maximum voltage (Vmp) and maximum current (Imp) values are 18.43 V and 1.05 A. From the Vmp and Imp values, the average Pmp calculation is 19.65 W.

Smart energy monitoring and power quality performance based evaluation of 100-kW grid tied PV system

Globally, the demand for energy from renewable sources is growing due to the increasing electricity consumption and the pollution of fossil fuels. The government has framed various policies to facilitate green energy generation, encouraging renewable energy source usage through PV installations in multiple sectors, including educational institutions. The primary objective of this paper is to propose a methodological approach for analysing the performance of the installed PV system on the rooftop of a university building in Tamil Nadu, India. The site selected is favourable for electricity generation from PV systems with an average global solar radiation of 5.82 kWh/m2day. Solar energy changes periodically with annual and daily variations and is not steady due to seasonal changes. The step-by-step performance assessment and the annual performance of the 100-kW solar PV system, which was instituted in 2019, with the forecasted parameters, are presented in this paper. Therefore, the assessment analysis is carried out in four phases: feasibility assessment, Energy yield assessment, Life cycle assessment, and Power quality assessment. To improve the solar PV output and efficiency, considering the solar irradiation, temperature, wind velocity, etc., PV yield is measured to evaluate the PV system's energy metrics. This paper also considers the carbon credits earned, solar power generated in the location, and the payback period. The power quality assessment is carried out in this paper to test the PV plant's compliance with effective grid integration.

A multi-model ensemble-based CMIP6 assessment of future solar radiation and PV potential under various climate warming scenarios

The ability of the hybrid evolutionary auto-regression integrated moving average (ARIMA) with a transformed probabilistically optimized Gumbel (GP) model (ARIMA-GP) and empirical technique (EM-GP) to predict global solar radiation (H) was investigated in Nigeria. The outcome showed that the hybrid ARIMA-GP2 (0, 0, 0) significantly outperformed the empirical and other ARIMA configuration models. Additionally, the potential for PV generation was estimated using six different PV power technology models that were fitted to Nigeria's comparable climatic conditions. It was found that, compared to other technologies, poly-crystalline silicon (p-Si) technology yielded the highest increase in solar PV output compared to other modules in both moderate forcing-case scenarios (SSP245) and strong forcing-case scenarios (SSP585) between 2015–2050 and 2051–2099. However, in the mitigation-case scenario (SSP126), amorphous silicon (a-Si) technology produces less than a 1% increase in solar PV production, while other technologies produce less than a 1% decrease in solar PV output. The CMIP6 climate model was also used to assess the effects of climate change on global solar radiation (insolation). With the exception of the far-future sequencing period (2051–2099), as the impact of climate change intensifies, there may be a corresponding increase in insolation in the moderate forcing- and strong forcing-case scenarios in all seasons between 2015–2050 and 2051–2099, as well as in the annual resolution. However, under the mitigation-case scenario (SSP126), with the exception of the DJF season during the 2015–2050 and 2051–2099 sequencing periods, impacts of climate change resulted in a maximum decline in insolation of 2.988% in Nigeria. This suggests that solar energy should be Nigeria's primary source of renewable energy and low-carbon economic planning, but Nigeria may not achieve the net-zero energy transition by 2050 if prompt climate mitigation and adaptation measures are not implemented.

Potential impacts of climate change on global solar radiation and PV output using the CMIP6 model in West Africa

In this study, six different PV power technology models from similar climatic environments in West Africa were used to estimate the potential of PV generation and apply the model to evaluate the effects of climate change on PV output in the region. This was achieved by using historical and projected satellite datasets obtained from the ECMWF database. It was found that, compared to other technologies, poly-crystalline silicon (p-Si) technology yielded the highest increase in solar PV output compared to other modules in both best-case and moderate-case scenarios. However, in the worst-case scenario, amorphous silicon (a-Si) technology produces less than a 1% increase in solar PV production, while other technologies produce less than a 1% decrease in solar PV output. The CMIP6 climate model was also used to assess the effects of climate change on the region's global solar radiation. With the exception of the near- and far-future Boreal winter seasons (DJF), as the impact of climate change intensifies, there may be a corresponding decrease in solar radiation in the moderate- and worst-case scenarios in different seasons between 2015-2050 and 2051–2100, as well as in the annual resolution implying that solar energy should be the main contributor to renewable energy and low-carbon economic planning in the region. In addition, the authors used historical datasets to develop and test 171 theoretical models to estimate West Africa's solar radiation potential. According to the error metrics, the CARIMA-LG hybrid surpassed other models developed in this study as well as those obtained from the literature.

EVB-Supportive Energy Management for Residential Systems with Renewable Energy Supply

This study examines the potential role that an Electric Vehicle Battery (EVB) can play in Home Energy Management System (HEMS) based on a future development on the performance and costs of batteries. The value of EVB in an HEMS with different home connection settings and energy consumption/storage/generation capacities are investigated to advise the optimal future HEMS setups. Solar PV are considered as the residential renewal energy supply, which is the main resource of uncertainty of the system. A novel forecasting model is deployed which incorporates geographical information, solar radiation forecast and weather-related conditions into an exponential-based method to simulate day-ahead solar PV output. Optimal flows of energy and usage of storage (batteries) are then captured by a Stochastic Programming (SP) model and solved by CPLEX. Managerial insights and optimal designs of the HEMS are drawn based on the results obtained.

Enhancing solar PV output forecast by integrating ground and satellite observations with deep learning

Accurate output forecasts are essential for photovoltaic projects to achieve stable power supply. Traditional forecasts based on ground observation time series are widely troubled by the phase lag issue due to the incomplete consideration of the impacts of cloud motion. With the consensus that this issue can be addressed by introducing satellite-derived cloud information, we propose an innovative framework that integrates ground and satellite observations through deep learning to enhance PV output forecasts. Cloud motion patterns are captured from satellite observations using convolutional neural networks, and the long-range spatio-temporal cloud impacts on subsequent PV outputs are established by long short-term memory network. The forecast accuracy of real-time PV output is significantly improved, with a minimum (maximum) relative root mean square error of 16% (29%). The ratio of phase lag is reduced to 15% on average. This work provides a potential for alleviating the power intermittency of solar PV system and making advance planning in solar energy utilization.

Rooftop solar PV and the peak load problem in the NEM's Queensland region

Over the period 2016–2021 Australia's National Electricity Market (NEM) experienced an investment supercycle comprising 24,000 MW of renewables. One of the more intriguing aspects of the supercycle was a partial shift of investment decision-making from utility boardrooms to households – rooftop solar PV comprised 8000 MW of the 24,000 MW total. In NEM regions such as Queensland, take-up rates have now reached 41.8% of households, currently the highest take-up rate in the world. There is a distinct mismatch between residential peak demand and solar PV output, which tends to suggest any peak load problem will be exacerbated. When the contribution of rooftop solar PV is abstracted to the power system level these results reverse. The partial equilibrium framework of Boiteux (1949), Turvey (1964) and Berrie (1967) has historically been used to define the optimal plant mix to satisfy demand growth. In this article, their partial equilibrium framework is used to define conventional plant ‘dis-investment’ in the presence of rising rooftop solar PV and utility-scale renewables in an energy-only market setting. Queensland's 4400 MW of rooftop solar displaces 1000 MW of conventional generation in equilibrium, 500 MW of peaking plant and somewhat counterintuitively, 500 MW of baseload coal plant – falling ‘minimum system demand’ being a driving factor. The NEM's energy-only market and its $15,000/MWh price cap proves tractable through to a 50% renewable market share, but relies critically on frictionless coal plant divestment and bounded negative price offers.

Economic feasibility investigation of integrated Nano-Grid with solar PV for community deployment based on load pattern survey

The building sector consumes a significant part of the total primary energy sources. Hence, energy-saving techniques and renewable energy deployment reduce conventional energy consumption. As a result, they need quantitative data based on energy usage audits. The solution derived from the analysis of energy audit and survey done for load pattern study is used to perform economic investigation. A time zone-based survey of a group of residential and commercial buildings in an area is conducted to carry this out. Individual buildings are equipped with Solar PV considering the maximum energy required per day and battery designed based on average energy consumption. If the same buildings under study are allowed to share their solar PV output and battery storage while getting connected to a common grid/community grid, the required battery and solar PV for the whole system is computed again to find the savings in investment for PV and battery. Cumulative of investment for individual nanogrid for each building is compared with the community grid system investment. The result of the analysis with the suggestion to be incorporated for better energy savings is discussed in this paper.

PV Output forecasting based on weather classification, SVM and ANN

The expansion in solar power is expected to be dramatic soon. A number of solar parks with high capacities are being set up to harness the potential of this renewable resource. However, the variability of solar power remains an important issue for grid integration of solar PV power plants. Changing weather conditions affect the PV output. Thus, developing methods for accurately forecasting solar PV output is essential for enabling large-scale PV deployment. This paper proposes a model for forecasting PV output based on weather classification, using a solar PV plant in Maharashtra, India, as the sample system. The input data is first classified using RBF-SVM into three types based on weather conditions, namely, sunny, rainy and cloudy. Then, the neural network model corresponding to that weather type is applied to forecast the solar PV output. The result obtained for the overall model is studied for its effectiveness and are compared with existing research.

The uncertainties involved in measuring national solar photovoltaic electricity generation

The measurement of near real-time national solar PV electricity output is an increasingly important component for managing electricity systems. PV systems embedded in the distribution network are problematic for system operators since their output is not directly visible through existing transmission system metering. Typically, national solar PV output is measured indirectly by upscaling the output from a sample of reference PV systems using an estimate of the national capacity. National PV output is then used, along with similar measurements for other embedded technologies such as wind, to train and validate electricity forecasts which ensure efficient electricity market operation. For the first time and using Great Britain as a case study, we investigate the accuracy of this general approach by characterising different sources of uncertainty in national PV output measurements. We find that the capacity error, at ±5%, dominates the yield calculation error, at < ±1% and leads to an overall error in GB solar PV output estimates of ±5.1%. We conclude that solar PV measurements, and consequently national electricity demand forecasts, are currently limited by the state of national PV capacity registers. To make PV output measurements more accurate nations must develop a coherent and comprehensive approach to PV system registration, and output measurement methods must move away from using static solar PV capacity registers towards estimating an operational grid-connected solar PV capacity. This could involve moving away from capacity registers altogether and instead estimating capacity from other sources, for example, from network power flows, satellite imagery, or more likely, a combination of complementary datasets.

Performance Analysis of 120 kWp Grid-Connected Rooftop Solar Photovoltaic System in Central Java

This study examines the performance of a solar power plant with a total capacity of 125 KWp, which operates for one year in Blora, Central Java. The ability of this power plant is a total of PV modules with 20 kWp, ten kWp, and five kWp capacities spread across eight locations. The annual performance of the rooftop solar power plant is measured automatically by the converter installed in each module. The resulting data from inverters are compared to the meteorological conditions from the meteorological agency. This research will investigate the influence of climate on the power generated, the efficiency of the equipment in a power plant, and the effect of pseudo motion of the sun. It was found that there were variations in energy output throughout the year, and it was concluded that the maximum annual energy was produced in July-August. In addition to weather, other factors need to be investigated further to determine the causes of variations in solar PV output.

Fuzzy Based MPPT Controller For Solar Photovoltaic Systems

In this work, a Fuzzy Logic Control (FLC) based MPPT technique is proposed to improve the performance of a stand-alone solar energy system. The Fuzzy logic controller is used as an intelligent way of tracking the maximum power point (MPP). The Taguchi method is adopted in this study to analyze multiple operating conditions of solar PV array. Solar PV output changes with Atmospheric conditions. The change in PV Current and Power are measured and fed to the Fuzzy logic controller as input. The Fuzzy controller is designed with 25 fuzzy rules and the Mamdani fuzzy inference is performed to obtain the aggregation which will be defuzzified by Center of gravity method. Based on the change in PV Current and Power, the Fuzzy logic controller generate the duty cycle for the boost converter (DC-DC converter). The variation of the duty cycle is from 0 to 1. The signal of change in duty ratio from the Fuzzy logic MPPT algorithm is fed to the PWM for switching the IGBT to dynamically update the duty cycle of the boost converter for extracting the maximum power from the solar PV array. A stand-alone Photovoltaic system with a boost converter is simulated in MATLAB Simulink to demonstrate the results and applicability of the proposed method.

A Solar PV-Based Inverter-Less Grid-Integrated Cooking Solution for Low-Cost Clean Cooking

The cost of solar PV has been reduced to a level such that the levelized cost of solar electricity is either cheaper or competitive relative to the grid electricity. So, a low-cost integration of solar PV with grid can be a cost-effective solution for clean cooking. The usual technique of using grid-tied inverters contribute ~20% towards the energy cost. The proposed system incorporates a control circuit that connects grid electricity to the solar PV via a DC link and provides a DC output eliminating the requirement of grid-tied inverters. Most of the cooking utensils either have a resistive heating element or an electronic control circuit that is insensitive to input AC or DC and no modification is needed for the cooking utensils while using with DC voltage. In the proposed system, preference for power delivery is always given to the solar PV and the grid effectively operates as the backup for the system when solar PV output fluctuates due to varying weather and climatic conditions. As the absence of a grid-tied inverter in the system restricts the excess solar energy to be transferred to the grid, some kind of energy storage device is essential to run the system efficiently. A novel idea of storing solar PV energy in the form of hot water has been presented in this paper, with a cost-effective clean cooking concept. A simple and low-cost heat preservation technique has been suggested that requires a minimal change in habit for the users. Experimental results with multiple cooking utensils and foods have been presented and energy cost for cooking has been found to be as low as 4.75 USD/month, which is significantly lower (32%) than that of the grid-connected regular cooking system.

Virtual Oscillator Control of Multiple Solar PV Inverters for Microgrid Applications

This paper proposes the inverter control strategy for multiple solar PV generation sources based on the two-stage converters with a combination of the modified virtual oscillator control (VOC) and the cascaded sliding mode control (SMC). With this proposed control strategy, the load power-sharing in proportion to the inverter rating is guaranteed when the solar PV output satisfies the power-sharing requirement. On the other hand, the control algorithm autonomously forces the solar PV to operate at the maximum power point if the solar PV output is lower than the power-sharing requirement. Various operating scenarios have been simulated to appreciate the effectiveness of the proposed control scheme for ensuring the load-power sharing and maintaining the voltage and frequency stability of the islanded microgrid containing a 100% solar PV generation.