Photovoltaic Energy Production Research Articles

An Empirical Approach to Optimize non-linear Problems of Domestic Energy Management Systems

Energy management system for domestic houses comprising photovoltaic energy production and battery electrical storage combined with time-variant electricity prices use to be controlled mean numerical optimization algorithms. The modelling of the system comprises the different involved devices, energy flows and their constrains, and an objective function, which parametrizes the object of the optimization (usually the minimization of the operative costs and/or the CO2 emissions). The solution of the optimization problem provides the optimal operation (charging/discharging) of the battery along the prediction horizon considered. Power inverter efficiencies are usually modelled by assuming that they have constant values, and hence, that charging and discharging energy-flows lie on the most probably operating region of the inverter. A more realistic modelling of the power inverter efficiencies should consider a non-linear parametrization of the efficiency curves. This consideration converts the optimization problem into a non-linear one. It this paper, we propose a method to solve non-linear optimization problems means iterations of linear optimization problems. At the first step, an optimization problem will be solved by using the values of the efficiencies of the battery inverter provided by the manufacturer for the values. The values of the solution of that problem will be the seed of an iterative process: with help of measured (dis)charging power curves and the optimized (dis)charging energy flows, new values of (dis)charging efficiencies will be determined, and a new optimization problem will be defined and solved. The process stops after a certain number of iterations, or once a convergence is achieved. The results of the experimentation show that the modified method improves the performance of the system by reducing the operative costs with respect to the original method. The results summarized in the table correspond to a whole year with a 15-minutes time resolution and a 24-hour prediction horizon. The second method has the same time-steps, and for each time step triggers an iterative process. Simulations were implemented in Matlab and run on a COREi7 processor (8th generation) platform with 16 GB RAM. A wide experimentation by selecting representative cases with shorter periods (one week) will be done. The main objective of these selected cases is to analyze the dependencies between both methods under different circumstances (seven consecutive days of the year with maximal and minimal solar radiation, seven consecutive days of the year with maximal and minimal standard deviation of the buy and sell prices).

Air quality impacts on rooftop photovoltaic energy production during the 2019–2020 Australian bushfires season

The ”Black Summer” 2019–2020 bushfires covered large areas of eastern Australia, causing substantial health impacts, animal deaths and damage to infrastructure. The smoke-related aerosols produced from these fires can also impact solar energy production by attenuating useable radiation. This study assesses the impacts of high particulate matter (PM) concentrations in photovoltaic (PV) energy production in New South Wales (NSW), from November 2019 to January 2020, during the bushfire season. We use in-situ measurements of commercial rooftop PV systems, pollution data from NSW Air Quality Monitoring Network and the Schools Weather and Air Quality, and satellite information from Himawari-8. We focus the study on clear days to eliminate the effects of clouds and concentrate on the impact of PM alone. We compare the energy generated by rooftop PV systems during clean/polluted conditions with a statistical conditional analysis approach. Results show that most locations exhibit similar behaviour, with PV generation reductions in polluted environments compared to clean conditions. The air quality impacts are stronger in locations near bushfire burning areas. For instance, in Katoomba, mean reductions up to 20% in the generated PV energy are found during high PM episodes. High-speed westerly winds transported the smoke several hundreds of kilometres, influencing the air quality and PV energy generation in most stations across Sydney. On a particular day of high bushfire activity (10 December 2019), we use satellite information to show how smoke caused increases in aerosol levels, increased PM concentrations, attenuated radiation and produced up to 65% reductions in hourly PV energy.

Daily Prediction Model of Photovoltaic Power Generation Using a Hybrid Architecture of Recurrent Neural Networks and Shallow Neural Networks

In recent years, photovoltaic energy has become one of the most implemented electricity generation options to help reduce environmental pollution suffered by the planet. Accuracy in this photovoltaic energy forecasting is essential to increase the amount of renewable energy that can be introduced to existing electrical grid systems. The objective of this work is based on developing various computational models capable of making short-term forecasting about the generation of photovoltaic energy that is generated in a solar plant. For the implementation of these models, a hybrid architecture based on recurrent neural networks (RNN) with long short-term memory (LSTM) or gated recurrent units (GRU) structure, combined with shallow artificial neural networks (ANN) with multilayer perceptron (MLP) structure, is established. RNN models have a particular configuration that makes them efficient for processing ordered data in time series. The results of this work have been obtained through controlled experiments with different configurations of its hyperparameters for hybrid RNN-ANN models. From these, the three models with the best performance are selected, and after a comparative analysis between them, the forecasting of photovoltaic energy production for the next few hours can be determined with a determination coefficient of 0.97 and root mean square error (RMSE) of 0.17. It is concluded that the proposed and implemented models are functional and capable of predicting with a high level of accuracy the photovoltaic energy production of the solar plant, based on historical data on photovoltaic energy production.

Performance Analysis of Designed and Implementation Dual-Axis Solar Tracker with Monitoring System

Renewable energy has been the prime focus in recent times for mitigating the ever-rising energy demand. Because renewable energy doesn’t emit carbon into the environment, it is considered the cleanest form of energy source. Solar energy has always been the most popular renewable energy source in our country for its geological advantages. Solar cells need to use sunshine as effectively as possible. The continuous change in the relative angle of the sun concerning the earth reduces the watts delivered by the solar panel. At present, fixed arrangement of solar panels is used to harvest solar energy from the sun, which only receives few amount of its effectiveness from the sun because it encounters the sun for a limited period of time per day. In this context, the solar tracking system is the best alternative to increase the efficiency of the photovoltaic (PV) panel. Solar trackers move the payload toward the sun throughout the day. In this paper, we suggested a Light Dependent Resistor (LDR) based dual-axis solar tracking system to increase tracking movements as well as PV energy production by leveraging the benefits of sun radiation.

Multifunctional Control for PV Integrated Battery Energy Storage System with Enhanced Power Quality

Abstract: This modest paper presents a study on the energy quality produced by a multifunction system consisting of a Photovoltaic (PV) power source connected to a battery. A three-level inverter was used in the system studied for the purpose of improving the quality of energy injected into the grid and decreasing the Total Harmonic Distortion (THD). A Maximum Power Point Tracking (MPPT) algorithm based on a Fuzzy Logic Controller (FLC) is used for the purpose of ensuring optimal production of photovoltaic energy. In addition, another FLC controller is used to ensure DC bus stabilization. The considered system was implemented in the Matlab/Simpower environment. The results show the effectiveness of the proposed inverter at three levels in improving the quality of energy injected from the system into the grid.

Thermal management of photovoltaic-thermoelectric generator hybrid system using radiative cooling and heat pipe

Temperature regulation of photovoltaic modules is crucial for improving their efficiency. Nowadays, radiative cooling is a widely adopted passive thermal management technique for photovoltaic systems. Heat pipe and radiative cooling are two primary passive photovoltaic cooling methods employed in photovoltaic-thermoelectric generator hybrid systems. Therefore, this study proposes a novel photovoltaic-heat pipe-thermoelectric generator-radiative cooling hybrid system by applying heat pipe and radiative cooling simultaneously to control the temperature of the photovoltaic-thermoelectric generator hybrid system. A detailed computational model of the proposed system is developed and analyzed in the COMSOL Multiphysics. A comparative analysis of the photovoltaic module temperature drop and its efficiency enhancement between the proposed and the reference system consisting of only a photovoltaic module is performed. The effects of solar radiation, ambient temperature, and wind speed on both systems are studied under the Atacama Desert and Las Vegas climatic conditions. This proposed configuration reduced the photovoltaic temperature by efficiently evacuating its residual heat using a heat pipe and then rejecting it to the atmosphere using a radiative cooler. Results show that in contrast to the reference system, the proposed system reduced the average photovoltaic operating temperature by 2 °C for both the summer and winter seasons of the Atacama Desert and by 13 °C for Les Vegas. The maximum and minimum reductions of the photovoltaic temperature in the proposed system, as compared to the reference system, are 4 °C and 1.5 °C respectively, in June and 9 °C and 1 °C respectively, in January for the Atacama Desert. The maximum PV conversion efficiency and energy production improvements as compared to the reference system are 0.8% & 1.03% respectively, (for summer), and 0.3% & 0.94% respectively, (for winter) for the Atacama Desert, and 1.8% & 7.2% respectively, for the Las Vegas environment. The expected range of LCOE for a hybrid PV-HP-TEG-RC system is found to be between 0.065-0.089 USD/kWh. This study can help in improving the energy conversion efficiency by controlling the photovoltaic temperature using passive cooling methods in harsh environmental conditions such as the desert locations.

Machine learning and analytical model hybridization to assess the impact of climate change on solar PV energy production

The United Nations' (UN) Sustainable Development Goals (SDGs) agenda has triggered numerous countries to harness solar energy from solar photovoltaic (PV) modules to increase the share of renewable energy in the global energy mix. However, geographical and climatic factors have a significant impact on the electrical performance of solar PV modules. In addition, since solar PV energy production models are the only physics-based approach to transferring ground-measured PV energy production to other locations, the authors developed 294 physical models from six different PV power technologies and validated them for the model's adaptability. To facilitate the possible determination of PV electric energy generation in the unique geographical and climatic environment of the experiment site, these models were built using machine learning, Gumbel's probabilistic approach (GPM), and hybridization of the two. The major challenges in this study are in developing the hybridized machine learning with the Gumbel probabilistic functional model, which resides in the mathematical transformation process, which required a great deal of repeated mathematical science knowledge to arrive at the final transformed and efficient model for predicting the potential of solar PV output. With a thorough coefficient of determination (R2) of 0.9998% and a root mean square error (RMSE) of 0.0063 kwh, the hybrid model with only the measurable solar radiation parameter is the closest to the measured PV energy production of all technologies. The best hybridized model was used to explore the potential impacts of climate change on the different solar PV technologies. This was achieved by using energy parameters from the Australian Community Climate and Global System Simulation (ACCESS-CM2) in Phase 6. On an annual basis, the effects of climate change on various PV technologies have had a small adverse impact (less than 1%) on these renewable energy technologies. It was also found that, compared to other technologies, CIGS thin film technology produced the least negative effects on climate change, with 10.94%–36.75% in the best-case, 35.71%–36.36% in the moderate-case, and 33.33–40.00% in the worst-case scenario for shared socioeconomic pathways (SSP126, SSP245, and SSP585) emissions. This suggests the intrinsic properties of Copper Indium Gallium Selenide (CIGS) thin film modules are more effective at withstanding high temperatures as they contribute 60.00–89.66% of their intrinsic module properties to PV energy production compared to other technologies. However, taking into account the time, resource availability, cost-effectiveness, commercialization, and consumption of various PV technologies studied in this era of global sustainability, poly-crystalline (p-Si) technology is highly recommended for harvesting solar PV energy products in Alice Springs, Australia.

Is Manila Clam Farming Environmentally Sustainable? A Life Cycle Assessment (LCA) Approach Applied to an Italian Ruditapes philippinarum Hatchery

Italy supplies approximately 96% of EU-farmed Manila clams. Following a reduction in wild seed availability, farmers started to depend on hatchery-produced seed, mainly imported from other countries. Indeed, only one hatchery is currently operating in Italy. This study quantifies the environmental impacts of seed production in this Italian hatchery facility to inform future planning for improving the sustainability of the supply chain. The environmental performance of the Manila clam hatchery was evaluated using the Life Cycle Assessment methodology. A cradle-to-gate analysis was performed, covering the following production phases: (1) microalgae production, (2) broodstock maintenance and conditioning, and (3) larval rearing until marketable size. The functional unit adopted was 1 kg of live clam seed. The main driver of the environmental impacts was electricity consumption, contributing over 80% for all impact categories. Other inputs showed minor contributions to different impact categories, including liquid oxygen, water pumps, and high-density polyethylene. This study highlights that the environmental burden associated with seed production could be reduced by switching to alternative technologies to meet energy needs, such as investments in photovoltaic and wind energy production systems.

How Does Neural Network Model Capacity Affect Photovoltaic Power Prediction? A Study Case.

The use of models capable of forecasting the production of photovoltaic (PV) energy is essential to guarantee the best possible integration of this energy source into traditional distribution grids. Long Short-Term Memory networks (LSTMs) are commonly used for this purpose, but their use may not be the better option due to their great computational complexity and slower inference and training time. Thus, in this work, we seek to evaluate the use of neural networks MLPs (Multilayer Perceptron), Recurrent Neural Networks (RNNs), and LSTMs, for the forecast of 5 min of photovoltaic energy production. Each iteration of the predictions uses the last 120 min of data collected from the PV system (power, irradiation, and PV cell temperature), measured from 2019 to mid-2022 in Maceió (Brazil). In addition, Bayesian hyperparameters optimization was used to obtain the best of each model and compare them on an equal footing. Results showed that the MLP performs satisfactorily, requiring much less time to train and forecast, indicating that they can be a better option when dealing with a very short-term forecast in specific contexts, for example, in systems with little computational resources.

Smart monitoring method for photovoltaic systems and failure control based on power smoothing techniques

In recent years, photovoltaic energy production has experienced significant progress, being integrated into the grid through large-scale distributed systems. The intermittent nature of solar irradiance coupled with the presence of photovoltaic failures causes fluctuations that could compromise the quality and stability of electrical grid. This paper presents a novel photovoltaic power smoothing method in a combination with moving averages and ramp rate to reduce fluctuations with hybrid storage systems (supercapacitors/batteries), the main novelty involves optimizing the number of charging/discharging cycles under PV failures. To achieve this goal, a photovoltaic failure detection method is proposed that uses machine learning to process big data by monitoring the behavior of photovoltaic. The experiments have been done under controlled conditions in the microgrid laboratory of the University of Cuenca. The results show the reduction of the supercapacitor operation with respect to other power smoothing methods. Moreover, the monitoring system is capable of detecting a failure in photovoltaic systems with a root mean squared error of 0.66 and the computational effort is reduced using the new smoothing technique. In this sense, the initial execution time is 4 times lower compared to the moving average method.

Prediction of Power Generation of a Photovoltaic Power Plant Based on Neural Networks

Photovoltaic energy production is an important factor for increasing the electricity supply. The ability to predict the electric power production (EPP) of a photovoltaic (PV) farm supports from the management process of the power grid to the trade in the energy market and much more. Also, by predicting the production of PV power (PVP), it is possible to monitor the lifetime of the solar cells that form the backbone of any solar PV system. As a critical result, sudden failures of the PV plant can be avoided. Using a long short-term memory recurrent neural network (LSTM-RNN) model, this work evaluates the prediction accuracy of two forecasting strategies: the recursive strategy and the non-recursive Multiple-Input and Multiple-Output, respectively. The dataset consists of 5-years in-filed production data measurements collected from the CETATEA photovoltaic power plant, a research site facility for renewable energies located in Cluj-Napoca, Romania. The high granularity of the electric power production dataset values recorded each 1 hour guarantees the overall prediction accuracy of the system. The impact of the dataset size, the number of previous observations, and the forecast horizon on the neural network prediction accuracy is evaluated for each strategy. The performance metrics used to evaluate the prediction accuracy are the root mean square error, the mean bias error, and the mean average error. The results analysis demonstrates the ability of the implemented machine learning models to predict electric power production, as well as their importance in the energy loss management process.

Potentiality of Solar Photovoltaic Energy in Paraiba, Brazil, and the Relation with Sustainable Development

The use of non-renewable energy has been a major environmental concern and, therefore, there is a need to look for other renewable energy sources, especially photovoltaic’s. In view of this, an attempt was made to quantify the potential of solar irradiance in the State of Paraiba, as an alternative source for conversion and use in electrical energy, these determinations being the main objectives. Global solar irradiance and solar photovoltaic data were extracted from scientific publications and/or made available on the websites of the National Institute of Meteorology (INMET), the Ministry of Mines and Energy and the National Electric Energy Agency, among others. For the case study, semi-structured questionnaires were applied in different business establishments in Campina Grande, with questions related to socioeconomic aspects and photovoltaic technology. Data were analyzed using descriptive statistics criteria and using an Excel spreadsheet. The main results indicated that the Brazilian energy matrix is predominantly from renewable sources. The Northeast is the second region with the highest production of photovoltaic solar energy and the State of Paraiba occupies its fourth position in the generation of this type of energy. The option of photovoltaic technology is a promising alternative, especially for rural areas, where there is not always a conventional electricity grid. The high availability of solar energy in northeastern Brazil, in almost all months of the year, especially in the state of Paraiba, demonstrates the existence of a high potential to generate electricity from photovoltaic systems. This technology contributes to local sustainable development, as it is an activity that generates employment and income, without degrading the environment.

Benchmark of eight commercial solutions for deterministic intra-day solar forecast

Continuous increase of the production of photovoltaic energy requires precise forecasting to ensure grid stability. This paper presents a detailed benchmark of eight commercial forecasting solutions for intra-day solar forecasts. The comparison was carried out on a period of six months, from November to May 2021, on seven different PV plants located in different countries of the northern hemisphere. Performance evaluation metrics MAE, RMSE and MBE are used in order to analyze the forecasting precision. It is shown that forecasting solar power remains challenging, as shown by the important dispersion between the actors that we have observed.

An Empirical Approach to Optimize Nonlinear Problems of Domestic Energy Management Systems

Abstract Numerical optimization methods are used to reduce the operative costs and emissions of domestic houses comprising photovoltaic energy production and battery electrical storage combined with time-variant electricity prices. The modelling of the system comprises the different involved devices, energy flows and their constraints, and an objective function, which parametrizes the object of the optimization. The solution of the optimization problem defines the most adequate charging and discharging strategy of the battery into the future (prediction horizon). Power inverter efficiencies are usually modelled by assuming that they have constant values, and hence, that charging and discharging energy-flows lie on the most probably operating region of the inverter. A more realistic modelling of the power inverter efficiencies should consider a nonlinear parametrization of the efficiency curves. This consideration converts the optimization problem into a nonlinear one. It this paper, we modify a method to solve nonlinear optimization problems means iterations of linear optimization problems. The first iteration uses as seed values the solution of an optimization problem, which considers constant efficiencies of the battery inverter provided by the manufacturer of the battery. With the values of the solution of the optimization problem and with help of measured (dis)charging power curves and the optimized (dis)charging, new values of the efficiencies of the inverter of the battery will be determined, and the optimization problem will be with these values again computed. If a certain number of iterations is achieved or the values of the efficiencies converge, then the process stops.

Climate Vulnerability of Photovoltaic Energy Systems using GIS: Case of the Plateau Department

Benin has a large potential (3.5-5.5 kWh/m2 /day) for solar photovoltaic energy production. This daily energy production, which mainly depends on solar radiation, also varies considerably, depending on climatic parameters. The Plateau department is an industrial zone where mainly clinker and cement are mined and processed. In such an environment of dust production, meteorological data are very dynamic and act as input parameters or sometimes disruptors of the photovoltaic energy conversion chain. The aim of this paper is to determine the appropriate location of the photovoltaic field for optimal production of electrical energy, in the plateau department of Benin. The analysis is based on the multicriteria decision-making method (MCDM) and Analytic Hierarchical Process (AHP), using a Geographic Information System (GIS). ArcGIS 10.8 software was used to classify and weight the different vulnerability criteria (Global Horizontal Irradiation, Temperature, Wind Speed, Wind Direction, Precipitation, Relative Humidity, Cloud cover, and Aerosol), in order to determine the optimal photovoltaic power generation area by overlaying the layers. The result shows that solar irradiation is the most important criterion for better production of photovoltaic energy whose weight of 46.06% is the highest, and aerosol (dust), the lowest weight of 2.43%, considerably reduced energy production. The northern zone from 7°35'0"N-7°39'0"N of the commune of Ketou is therefore the best site for optimal production, considering the parameters studied.

Maximum Power Point Tracking Method Application to Increase the Efficiency of Photovoltaic Installations

Photovoltaic systems using boost converters based on the traditional incremental conduction method have delayed convergence dynamics to the maximum power point (MPP). This article presents a simulation and hardware implementation of a variable step size MPPT with automatic scaling at low cost and low power consumption. The production of photovoltaic energy is highly dependent on weather and environmental conditions such as temperature and solar radiation. Due to any change in the external environment and the only control condition, the response of the first step of the working cycle of the converter of the traditional incremental conduction MPPT algorithm is not accurate, resulting in an incorrect estimate. Furthermore, this study clarifies the usual technique and presents a modified incremental conductance approach that appropriately responds as solar radiation levels rise. In order to improve the economy and efficiency of photo-voltaic systems, we proposed an improved incremental conduction algorithm for the MPP control strategy. This tech-nique is simpler in structure than the classic incremental conduction algorithm, and it can assign immediate incre-ments of power, voltage, and current as the environment changes, as well as increase tracking performance. The MATLAB/Simulink program runs under conditions of rapidly changing solar radiation levels, and the results of the enhanced and conventional incremental conductance algorithm are compared. The results show that the pro-posed algorithm can effectively detect erroneous judgments and prevent their occurrence. This not only optimizes the system, but also improves the efficiency, response speed, and tracking efficiency of the PV system, thus ensuring the stable operation of the power system.

Combining data envelopment analysis and Random Forest for selecting optimal locations of solar PV plants

Solar photovoltaic (PV) energy has emerged as a potential alternative to carbon-based energies to meet the Paris agreement commitment. This study investigates the effect of environmental variables on the efficiency of solar PV panels. Data Envelopment Analysis (DEA) is used to estimate efficiencies of 91 solar PV panels located in Australia during the time period 2010–2020. The effects of environmental variables on the estimated efficiencies are quantified using the truncated regression model. Random forest is then used to predict efficiency of solar PV panel in every city of Australia. The results allow to determine the most suitable location and regions for solar PV energy production in Australia. This study provides an interesting and easily interpretable tool for policy decision makers.

Photovoltaic Energy Production Forecasting in a Short Term Horizon: Comparison between Analytical and Machine Learning Models

The existing trend towards increased penetration of renewable energies in the traditional grid, and the intermittent nature of the weather conditions on which these energy sources depend, make the development of tools for the forecasting of renewable energy production more necessary than ever. Likewise, the prediction of the energy generated in these renewable production plants is key to the implementation of efficient Energy Management Systems (EMS) in buildings. These will aim both to increase the energy efficiency of the building itself, as well as to encourage self-consumption or, where appropriate, collective self-consumption (CSC). This paper presents a comparison between four different models, the former one being an analytical model and the remaining three machine learning (ML) based models. All of them will forecast the photovoltaic (PV) production curve for the next day. In order to validate these models, a case study of a PV system installed on the roof of a university building located in Bidart (France) is proposed. The model that most accurately forecasts the PV production during the period of July 2021 is the support vector regression (SVR), which has a mean R2 of 0.934 for July, being 0.97 on sunny days and 0.85 on cloudy ones. This is an improvement of 5.14%, 4.07%, and 4.18% over the nonlinear autoregressive with exogenous inputs (NARX), feedforward neural network (FFNN), and analytical model, respectively.

Photovoltaic Energy Production Forecasting through Machine Learning Methods: A Scottish Solar Farm Case Study

Photovoltaic solar energy is booming due to the continuous improvement in photovoltaic panel efficiency along with a downward trend in production costs. In addition, the European Union is committed to easing the implementation of renewable energy in many companies in order to obtain funding to install their own panels. Nonetheless, the nature of solar energy is intermittent and uncontrollable. This leads us to an uncertain scenario which may cause instability in photovoltaic systems. This research addresses this problem by implementing intelligent models to predict the production of solar energy. Real data from a solar farm in Scotland was utilized in this study. Finally, the models were able to accurately predict the energy to be produced in the next hour using historical information as predictor variables.

Photovoltaic Production Management in a Hall of Residence with High Energy Consumption

A hall of residence with low energy performance was subjected to an extensive retrofitting program due to its high energy consumption, to enhance the building’s energy efficiency and reduce its energy costs and greenhouse gas emissions. The retrofitting program included the integration of a solar photovoltaic (PV) system installed on the building’s flat roof, among other factors. Nevertheless, the electricity supply provided by the system during the daytime proved insufficient to cover the building’s energy demand. Based on this, a study was implemented to analyze the contribution of the electricity produced by the solar PV system throughout the year to reduce power consumption under distinct solar radiation conditions, and to define a strategy to optimize renewable energy use by drawing up a set of organizational measures to be implemented to manage the PV solar system energy strategically. The strategic measures are mainly related to selecting the residence tasks with higher energy consumption to fit into higher PV energy production periods. Additionally, it is fundamental to raise the residence’s occupants’ education and awareness of energy efficiency, optimize the excess PV energy produced in specific periods by converting it into other energy forms, and install complementary storage systems for surplus production.