Photovoltaic Power Production Research Articles

COMPARATIVE ANALYSIS OF CLOT-ADJUSTED AHI-8/9 AND FY-4A SOLAR RADIATION DATA FOR SOLAR PV POWER POTENTIAL ASSESSMENT IN THE PHILIPPINES

Abstract. The theoretical solar photovoltaic (PV) power (PPV) potential in the Philippines has been estimated to range from 1.87 to 3.19 kWh/kWp daily and 681.80 to 1,162.58 kWh/kWp yearly using the shortwave radiation (SWR) product derived from the Advanced Himawari Imager-8 and 9 (AHI-8/9) satellite data. However, solar radiation measurements, especially for satellite-derived data, must be accurate when used to estimate the maximum solar energy available for solar PV power production. The objective of this study is to evaluate the satellite-derived measurements of AHI-8/9 SWR and Fengyun 4A surface solar irradiance (SSI) data products in the estimation of theoretical PPV output in the Philippines. Moreover, selected actual solar PV installation sites located across the Philippines, such as in the cities of Baguio, Cebu, and Tagum, are included in the analysis PV power production data. Both SWR and SSI products were adjusted using the cloud optical thickness (CLOT) product for cloud effects and consequently, obtain the maximum solar PV power potential. In this study, SWR and SSI products were analyzed to determine which factors may have contributed to the differences in the estimated values. The in-situ PV power production data was compared to the two satellite-based solar radiation data to assess the degree of correlation between the two. Diurnal trends for representative areas in Luzon, Visayas and Mindanao show that for all sites, the SWR data agrees with the actual PV production data better than the SSI data. Results show that the range of CLOT-adjusted solar irradiance values are higher when using SSI than SWR. It was also observed that CLOT reduces the solar radiation by an average of 19.36%, 16.08% and 10.54% during the cool dry season (December to January), rainy season (June to November) and hot dry season (March to May), respectively. In future studies, the adjusted solar radiation (R') data may be used instead of the raw AHI8 SWR or FY-4A SSI to estimate the theoretical maximum solar PV power potential (PPV). Geomorphological and meteorological factors can also be considered in the estimation of effective PPV.

Demand response of an Electric Vehicle charging station using a robust-explicit model predictive control considering uncertainties to minimize carbon intensity

This paper presents a novel approach to address uncertainties and enable demand response in Electric Vehicle (EV) charging station optimization. A two-stage optimization strategy is proposed, integrating Robust Optimization and explicit Model Predictive Control (eMPC). The first stage involves day-ahead planning using Robust Optimization technique to limit the hourly power consumption of EVs, considering worst-case scenarios caused by uncertainties in EV consumption and CO2 emissions. The objective is to minimize environmental impact by reducing CO2 emissions. An Explicit Model Predictive Control strategy is developed in the second stage for real-time operation. The explicit solution, calculated offline, models uncertainties such as the initial state of charge of the battery energy storage, photovoltaic power production, and EV power consumption. During real-time operation, the explicit solution is accessed using measured data from the charging station, refining the schedule derived from the first stage. The proposed solution is implemented and evaluated at an EV charging station in Trieste, Italy. The results demonstrate a significant 69% reduction in CO2 emissions compared to a deterministic approach while maintaining a real-time computation time of less than 0.1 s.

Solar radiation modification challenges decarbonization with renewable solar energy

Abstract. Solar radiation modification (SRM) is increasingly being discussed as a potential tool to reduce global and regional temperatures to buy time for conventional carbon mitigation measures to take effect. However, most simulations to date assume SRM to be an additive component to the climate change toolbox, without any physical coupling between mitigation and SRM. In this study we analyze one aspect of this coupling: how renewable energy (RE) capacity, and therefore decarbonization rates, may be affected under SRM deployment by modification of photovoltaic (PV) and concentrated solar power (CSP) production potential. Simulated 1 h output from the Earth system model CNRM-ESM2-1 for scenario-based experiments is used for the assessment. The SRM scenario uses stratospheric aerosol injections (SAIs) to approximately lower global mean temperature from the high-emission scenario SSP585 baseline to the moderate-emission scenario SSP245. We find that by the end of the century, most regions experience an increased number of low PV and CSP energy weeks per year under SAI compared to SSP245. Compared to SSP585, while the increase in low energy weeks under SAI is still dominant on a global scale, certain areas may benefit from SAI and see fewer low PV or CSP energy weeks. A substantial part of the decrease in potential with SAI compared to the SSP scenarios is compensated for by optically thinner upper-tropospheric clouds under SAI, which allow more radiation to penetrate towards the surface. The largest relative reductions in PV potential are seen in the Northern and Southern Hemisphere midlatitudes. Our study suggests that using SAI to reduce high-end global warming to moderate global warming could pose increased challenges for meeting energy demand with solar renewable resources.

Enhanced Solar Photovoltaic Power Production Approach for Electric Vehicle Charging Station: Economic and Environmental Aspects

In recent years, Electric Vehicles (EVs) are contributing a major share in Thailand and benefit the environment. Most of the EV charging stations are sourced from solar energy as it becomes a carbon-free source of energy production. Secondly, Thailand is rich in solar irradiance, and higher irradiance leads to higher power production. On the other hand, in tropical conditions, solar Photovoltaic (PV) module temperature increases following the solar irradiance due to high ambient temperature, resulting negative impact on the efficiency and lifespan of photovoltaic (PV) modules. Further, to increase PV power production, in this study, different rates of cooling strategies are proposed. The study found that reducing the temperature by 5% to 25% resulted in increased average power outputs of 5947.94W, 6021.43W, 6094.92W, 6168.41W, and 6241W, respectively. Notably, 25% of the cooling rate achieved higher production. However, it is lower than the nominal power production. Following that, economic analysis and environmental impacts are analyzed for Thailand’s EV charging station using a different cooling rate of PV module. Overall, it is concluded that, depending on the economic viability of the EV charging station, cooling technology can be applied, and it will benefit the EV charging station both economically and environmentally. To further enhance the solar PV power production approach for EV charging stations in Thailand, it is imperative to prioritize future endeavors towards optimizing cooling technology, integrating energy storage, and implementing supportive policies.

Enhanced PV Power Prediction Considering PM10 Parameter by Hybrid JAYA-ANN Model

The demand for electrical energy is continuously increasing in these days, particularly due to advancements in the industrial sector. This surge in demand has underscored the importance of seeking alternative energy sources, with solar energy emerging as a standout option due to its low investment costs and environmental friendliness. However, the variability in photovoltaic power production, influenced by meteorological data, necessitates accurate prediction methods. To enhance the precision of these predictions, incorporating new parameters alongside existing meteorological data is advantageous. In this regard, this study explores the impact of the particulate matter (PM10) parameter on photovoltaic power prediction using artificial neural network (ANN) model and JAYA-ANN. Comparing the prediction results based on root mean squared and mean absolute percentage errors reveals that the hybrid JAYA-ANN model consistently outperforms the ANN and persistence models. Notably, the PM10 parameter proves to be a significant input in forecasting daily photovoltaic power.

Prediction of Electricity Generation Using Onshore Wind and Solar Energy in Germany

Renewable energy production is one of the most important strategies to reduce the emission of greenhouse gases. However, wind and solar energy especially depend on time-varying properties of the environment, such as weather. Hence, for the control and stabilization of electricity grids, the accurate forecasting of energy production from renewable energy sources is essential. This study provides an empirical comparison of the forecasting accuracy of electricity generation from renewable energy sources by different deep learning methods, including five different Transformer-based forecasting models based on weather data. The models are compared with the long short-term memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models as a baseline. The accuracy of these models is evaluated across diverse forecast periods, and the impact of utilizing selected weather data versus all available data on predictive performance is investigated. Distinct performance patterns emerge among the Transformer-based models, with Autoformer and FEDformer exhibiting suboptimal results for this task, especially when utilizing a comprehensive set of weather parameters. In contrast, the Informer model demonstrates superior predictive capabilities for onshore wind power and photovoltaic (PV) power production. The Informer model consistently performs well in predicting both onshore wind and PV energy. Notably, the LSTM model outperforms all other models across various categories. This research emphasizes the significance of selectively using weather parameters for improved performance compared to employing all parameters and a time reference. We show that the suitability and performance of a prediction model can vary significantly, depending on the specific forecasting task and the data that are provided to the model.

Soiling impact and cleaning techniques for optimizing photovoltaic and concentrated solar power power production: A state-of-the-art review

Nowadays, renewable energies are capturing the world's attention, particularly in light of the phenomenon of climate change and carbon dioxide emissions, which have caused major environmental damage. As a result, many investors have recently focused on developing investments in renewable energy projects worldwide, specifically photovoltaic and concentrated solar power plant projects. These solar technologies are considered among the most profitable solutions for generating power from a natural, free, and unlimited energy source. This review paper discusses one of the most significant issues affecting the performance of these solar systems, which is known as soiling. It has been supported by several studies in various nations with different climatic conditions, which offered accurate empirical data on the degradation rate of photovoltaic and concentrated solar power systems’ production due to the soiling effect. Furthermore, it provides various mitigating soiling ways, including manual and autonomous cleaning methods for both solar technologies. Ultimately, it summarizes each cleaning technique's main advantages and drawbacks, specifying its applicability according to the location characteristics and climatic conditions. Additionally, the review results reported in this work are intriguing enough to warrant further development of concentrated solar power and photovoltaic technologies.

A climatology of weather-driven anomalies in European photovoltaic and wind power production

Weather causes extremes in photovoltaic and wind power production. Here we present a comprehensive climatology of anomalies in photovoltaic and wind power production associated with weather patterns in Europe considering the 2019 and potential 2050 installations, and hourly to ten-day events. To that end, we performed kilometer-scale numerical simulations of hourly power production for 23 years and paired the output with a weather classification which allows a detailed assessment of weather-driven spatio-temporal production anomalies. Our results highlight the dependency of low-power production events on the installed capacities and the event duration. South-shifted Westerlies (Anticyclonic South-Easterlies) are associated with the lowest hourly (ten-day) extremes for the 2050 (both) installations. Regional power production anomalies can differ from the ones in the European mean. Our findings suggest that weather patterns can serve as indicators for expected photovoltaic and wind power production anomalies and may be useful for early warnings in the energy sector.

Long-term power prediction of photovoltaic panels based on meteorological parameters and support vector machine

<span>Solar energy is the most generally accessible energy in the entire globe. Proper solar panel maintenance is necessary to reduce reliance on imported energy. Continuous monitoring of the solar panel's power output is required. The deployment of internet of things (IoT) monitoring of solar panels for maintenance is the basis for the current research. A multi-variable long-term photovoltaic (PV) power production prediction approach based on support vector machine (SVM) is developed in this study with the aim of completely evaluating the influence of PV panels performance and actual operational state factors on the power generation efficiency. This study examines the use of SVM and climatic factors to forecast the long-term output of power from solar panels. A solar power facility in a semi-arid area provided the data utilized in this investigation. Temperature, humidity, wind speed, and sun radiation are some of the meteorological variables that were considered in the study. To anticipate the power generation of the panels, the SVM is trained using the climatic factors and the power generation data. The findings demonstrate that the SVM model consistently predicts the panels' long-term power generation with a high degree of accuracy.</span>

Effect of Saharan dust episodes on the accuracy of photovoltaic energy production forecast in Hungary (Central Europe)

In order to meet global sustainability goals, in particular, the rapid decarbonisation of the energy sector in combination with geopolitical energy security issues, as well as further improvement in regional air quality-the so-called renewable energy sources are becoming increasingly critical and important. Photovoltaic power (PV) generation is clearly the most widely deployed (non-hydro) renewable energy source globally, which still has a significant growth potential in many countries, including Hungary.However, due to the intermittent nature and the strong dependency of photovoltaic power production on meteorological factors, continuous adjustment of electric power in the electric grid is needed. This, in turn, requires the most accurate forecast of the photovoltaic energy to be produced in the ultra-short term (15 min) and short term (1 day) in order to minimise last-minute and high-price energy acquisition or unplanned kicks-in of gas-fired auxiliary power plants.In this paper, we evaluate the impacts of large-scale dust transport episodes on the accuracy of forecasts of PV power generation. The numbers and intensities of Saharan dust storm events reaching Central Europe have increased over the last decade, hitting a new record in 2022 with 16 (!) African dust episodes observed over Hungary. We have shown that the semi-direct effect of atmospheric dust particles on high-level cloud formation rather than their direct irradiance-reducing effect is responsible for the reduced accuracies of e short-term (24-h) PV energy production forecasts during these events.

Solar parks can enhance bird diversity in agricultural landscape

Solar photovoltaic power parks are a relatively new anthropogenic habitat that will become more widespread in the future. The greatest potential for solar photovoltaic power production is on arable land and grassland. Knowledge on the impacts of solar parks on biodiversity is scarce and spatially limited. We investigated the impact of ground-mounted solar parks on species richness, abundance, Shannon diversity and composition of bird communities in Slovakia (Central Europe), taking into account pre-construction land cover, elevation and landscape context. We recorded breeding, foraging or perching birds on 32 solar park plots and 32 adjacent control plots (two hectares each) during single breeding season. We found that solar parks supported higher total bird species richness and diversity, and richness and abundance of invertebrate-eaters, and that the abundance of ground-foragers was higher in solar parks developed on grassland than in grassland control plots. Ordination analysis showed that solar parks had a different composition of bird communities and thus increased overall species diversity and beta diversity in the agricultural landscapes studied. Plot type and landscape context accounted for most of the variation in bird community composition. Black redstart, European stonechat, white wagtail and Eurasian tree sparrow were identified as indicator species for solar parks. The observed pattern could be due to the higher structural diversity of solar parks. The solar parks studied were designed and managed exclusively for electricity production. It can therefore be assumed that solar parks designed and managed in synergy with a stronger focus on wildlife would have an even greater positive impact on bird diversity in an agricultural landscape.

What drives historical and future changes in photovoltaic power production from the perspective of global warming?

We investigate the drivers of global and regional changes in the potential for photovoltaic (PV) power production from the pre-industrial (1850) to present-day (1985–2014) and until the end of the century (2071–2100), based on output from the Coupled Model Intercomparison Project phase six (CMIP6). Our assessment separates regional contributions from changes in clouds, humidity, temperature, aerosols, and wind speed to the changes in PV power potentials for the first time. Present-day PV power potentials are adversely affected by anthropogenic aerosols compared to the pre-industrial, with a global decrease of the PV power potential by −1.3%. Our results highlight a globally averaged decrease in future PV power potentials primarily driven by temperature and humidity increases by −1.2% to more than −3.5%, depending on the scenario. Regionally different contributions of changes in clouds and aerosols cause heterogeneous spatial patterns in changes of PV potentials, with typically stronger (weaker) influences from clouds (aerosols) in SSP5-8.5 compared to SSP1-2.6. Our results imply that the uncertain response of clouds to warming and aerosol effects are hurdles in quantifying changes in the regional potentials for PV power production.

Offshore floating PV–DC and AC yield analysis considering wave effects

The growing global energy demand increases the need for renewable energy sources. This increase requires land to be occupied, competing with other activities such as agriculture and residency. In such a situation, renewable energy sources expand to other environments like the ocean. However, this new scene poses some challenges, such as the effect of waves on photovoltaic (PV) performance. Consequently, this study aims to evaluate the power output of an Offshore Floating PV (OFPV) system located in the North Sea considering the effect of the waves. A 3D mechanical movement model, which has been validated with data from a real system, is developed for this purpose. A sensitivity analysis is conducted to determine how the size of fluctuations depends on the dimensions of the floater. The main outcome is that a heavy and wide floater aligned with the most common wind direction reduces angle variations. Results from DC power simulations show that sea fluctuations have a negative yet small influence on PV power production. Over the course of the year, these losses amount to just 0.1% of the annual energy yield. However, a hypothetical optimally-tilted PV system placed on water would still generate 14.6% more DC power output than the floating one. On the AC side, laboratory experiments show that these oscillations negatively affect the inverter efficiency during rough sea conditions by a decrease of over 2 percentage points compared to a still system.

Neural Network-Based Solar Irradiance Forecast for Edge Computing Devices

Aiming at effectively improving photovoltaic (PV) park operation and the stability of the electricity grid, the current paper addresses the design and development of a novel system achieving the short-term irradiance forecasting for the PV park area, which is the key factor for controlling the variations in the PV power production. First, it introduces the Xception long short-term memory (XceptionLSTM) cell tailored for recurrent neural networks (RNN). Second, it presents the novel irradiance forecasting model that consists of a sequence-to-sequence image regression NNs in the form of a spatio-temporal encoder–decoder including Xception layers in the spatial encoder, the novel XceptionLSTM in the temporal encoder and decoder and a multilayer perceptron in the spatial decoder. The proposed model achieves a forecast skill of 16.57% for a horizon of 5 min when compared to the persistence model. Moreover, the proposed model is designed for execution on edge computing devices and the real-time application of the inference on the Raspberry Pi 4 Model B 8 GB and the Raspberry Pi Zero 2W validates the results.

Cost-effective energy management of an islanded microgrid

The interest in the integration of distributed energy resources in microgrids increased significantly in the last decade. The stochastic nature of some energy sources and the dynamic power demand has brought difficulties regarding the optimal control of microgrids. This current study addresses the energy management challenge in an islanded hybrid energy microgrid that includes three types of renewable energy resources (photovoltaic, geothermal and biomass) and a battery storage system, using intelligent management methods that optimize energy production costs. The proposed intelligent management methods are based on cost function minimization. These ensure optimal energy management operation under uncertain weather conditions, keeping to the minimum the price of the energy produced by the microgrid. Due to the erratic nature of solar irradiance and considering that solar energy is cheaper than its counterparts (geothermal and biomass in this case), forecasting the photovoltaic power production is an essential task for guaranteeing optimal microgrid operation. Day-ahead photovoltaic power forecasts are issued based on solar irradiance forecasts provided by the Global Forecasting System and further integrated into the day-ahead scheduling of the microgrid. This algorithm, together with the cost-optimization algorithms, gives intelligence to the management protocol generating the input parameters (power and operating time for each generating unit) for the microgrid Energy Management Controller. Different cost optimization methods are studied and compared, including mathematical models, such as Mixed Integer Linear Programming, or nature-inspired optimization algorithms, such as Genetic Algorithm, Harmony Search Algorithm, and Particle Swarm Optimization. It is shown that these algorithms provided suitable solutions for the energy balance and scheduling ensuring minimal operation costs for the microgrid. The study also highlights the differences and limitations of these algorithms and shows a method on how these energy management methods can be integrated into a real-world microgrid providing a step forward in the development of intelligent microgrids.

Predictive Modeling of Photovoltaic Panel Power Production through On-Site Environmental and Electrical Measurements Using Artificial Neural Networks

Weather disturbances pose a significant challenge when estimating the energy production of photovoltaic panel systems. Energy production and forecasting models have recently been used to improve energy estimations and maintenance tasks. However, these models often rely on environmental measurements from meteorological units far from the photovoltaic systems. To enhance the accuracy of the developed model, a measurement Internet of Things (IoT) prototype was developed in this study, which collects on-site voltage and current measurements from the panel, as well as the environmental factors of lighting, temperature, and humidity in the system’s proximity. The measurements were then subjected to correlation analysis, and various artificial neural networks (ANNs) were implemented to develop energy estimations and forecasting models. The most effective model utilizes lighting, temperature, and humidity. The model achieves a root mean squared error (RMSE) of 0.255326464. The ANN models are compared to an MLR model using the same data. Using previous power measurements and actual weather data, a non-autoregressive neural network (Non-AR-NN) model forecasts future output power values. The best Non-AR-NN model produces an RMSE of 0.1160, resulting in accurate predictions based on the IoT device.

Effectiveness of neural networks and transfer learning to forecast photovoltaic power production

Artificial Neural Networks (ANNs) can successfully be integrated into smart models for energy prediction, but require large datasets for training. This investigation presents an innovative methodology for photovoltaic power generation forecasting with ANNs, when only a limited amount of real data is available, and has been tested and validated on a real-life photovoltaic installation. Feature selection identifies which meteorological features most impact photovoltaic power generation. A simulator, which accurately models a real photovoltaic installation, is used to create an artificial, but accurate and realistic, dataset of power generation large enough to effectively train and test different ANNs. These are then exploited on a portion of real, but limited, dataset of power generated by the real photovoltaic installation on which the simulator is modeled. Finally, different transfer learning techniques are used to tune the ANN models with the remaining portion of the real, but limited, dataset of photovoltaic power generation.

Research on the Dynamic Characteristics of Photovoltaic Power Production and Sales Based on Game Theory

This paper mainly studies the dynamic characteristics of production and sales of distributed photovoltaic power. This is beneficial for the construction and development of a green power system, and it promotes the sustainable development of the social economy. First, the production and sales laws of the photovoltaic power are analyzed, and the trade process between photovoltaic power producers (PVPs) and photovoltaic power demanders (PVDs) is modeled as a game. Different from the existing relevant literature, two kinds of payoff bimatrices are provided, which correspond to the PVD market and PVP market, respectively. Then, the dynamic characteristics of the model are revealed by using the semitensor product method. The results present a more reliable theoretical basis for the sustainable development of the green electricity market. Finally, through an illustrative example, it can be seen that the strategies of all participants are constantly changing to obtain more profits rather than stable at a traditional Nash equilibrium point. It is worth pointing out that the method and results are applicable to other distributed low-carbon energies, contributing to the development of sustainable energy systems.

Geographical information system as an approach for PV power plants allocation with 3E criteria

One of the major issues in developing photovoltaic (PV) power production farms is choosing suitable locations for their installment, since it has substantial effects on efficiency of power production and consequently, environmental and economic viability of that. Taking this point into consideration, the geographical Information system (GIS) approach was used to eliminate unsuitable locations for PV power plant installation. After the elimination process, the remaining locations are assessed from energy, economic, and environmental (3E) perspectives. 3E criteria were obtained for each possible location in two scenarios, i.e., constant area and constant capacity, in which the former investigates three different panel capacity in a constant area, while the latter models three constant farm capacities without any restrictions for land. In each scenario, potential installation sites are ranked based on their suitability for PV farm installation using Multi-Criteria Decision-Making (MCDM). Iran was selected as the case study, and using a weighted Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method, ideal locations in each scenario for installing PV plants were found. The suggested locations offer noticeably improved objective functions as compared to farms that have already been installed with 9, 20, 8, and 60% improvement in GE, PR, LCOE, and environmental parameters, respectively. A detailed report for environmental impact of using PV farms instead of fossil energy is presented. Furthermore, a comparison of mono and polycrystalline panels is carried out, and it was found that in all of the parameters, the mono-crystalline panel has better 3E performance.

Optimal Allocation and Sizing of Decentralized Solar Photovoltaic Generators Using Unit Financial Impact Indicator

A novel financial metric denominated unit financial impact indicator (UFII) is proposed to minimize the payback period for solar photovoltaic (PV) systems investments and quantify the financial efficiency of allocation and sizing strategies. However, uncontrollable environmental conditions and operational uncertainties, such as variable power demands, component failures, and weather conditions, can threaten the robustness of the investment, and their effect needs to be accounted for. Therefore, a new probabilistic framework is proposed for the robust and optimal positioning and sizing of utility-scale PV systems in a transmission network. The probabilistic framework includes a new cloud intensity simulator to model solar photovoltaic power production based on historical data and quantified using an efficient Monte Carlo method. The optimized solution obtained using weighted sums of expected UFII and its variance is compared against those obtained by using well-established economic metrics from literature. The efficiency and usefulness of the proposed approach are tested on the 14-bus IEEE power grid case study. The results prove the applicability and efficacy of the new probabilistic metric to quantify the financial effectiveness of solar photovoltaic investments on different nodes and geographical regions in a power grid, considering the unavoidable conditional and operational uncertainty.