Solar Power Production Research Articles

Genetic algorithm selection of the weather research and forecasting model physics to support wind and solar energy integration

To make a future run by renewable energy possible, we must design our power system to seamlessly collect, store, and transport the Earth's naturally occurring flows of energy – namely the sun and the wind. Such a future will require that accurate representations of wind and solar resources and their associated variability permeate power systems planning and operational tools. Practically speaking, we must merge weather and power systems modeling. Although many meteorological phenomena that affect wind and solar power production are well-studied in isolation, no coordinated effort has sought to improve medium- and long-term power systems planning using numerical weather prediction (NWP) models. One modern open-source NWP tool – the weather research and forecasting (WRF) model – offers the complexity and flexibility required to integrate weather prediction with a power systems model in any region. However, there are over one million distinct ways to set up WRF. Here, we present a methodology for optimizing the WRF model physics for forecasting wind power density and solar irradiance using a genetic algorithm. The top five setups created by our algorithm outperform all of the recommended setups. Using the simulation results, we train a random forest model to identify which WRF parameters contribute to the lowest forecast errors and produce plots depicting the performance of key physics options to guide energy researchers in quickly setting up an accurate WRF model.

Atomistic simulation framework for molten salt vapor-liquid equilibrium prediction and its application to NaCl.

Knowledge of the vapor-liquid equilibrium (VLE) properties of molten salts is important in the design of thermal energy storage systems for solar power and nuclear energy production applications. The high temperatures involved make their experimental determination problematic, and the development of both macroscopic thermodynamic correlations and predictive molecular-based methodologies are complicated by the requirement to appropriately incorporate the chemically reacting vapor-phase species. We derive a general thermodynamic-based atomistic simulation framework for molten salt VLE prediction and show its application to NaCl. Its input quantities are temperature-dependent ideal-gas free energy data for the vapor phase reactions and density and residual chemical potential data for the liquid. If these are not available experimentally, the former may be predicted using standard electronic structure software, and the latter may be predicted by means of classical atomistic simulation methodology. The framework predicts the temperature dependence of vapor pressure, coexisting phase densities, vapor phase composition, and vaporization enthalpy. It also predicts the concentrations of vapor phase species present in minor amounts (such as the free ions), quantities that are extremely difficult to measure experimentally. We furthermore use the results to obtain an approximation to the complete VLE binodal dome and the critical properties. We verify the framework for molten NaCl, for which experimentally based density and chemical potential data are available in the literature. We then apply it to the analysis of NaCl simulation data for two commonly used atomistic force fields. The framework can be readily extended to molten salt mixtures and to ionic liquids.

Dual Battery Storage Technique for Remote, Location-Based Solar PV System and Standalone Applications

Nowadays, the usage of renewable energy resources (RER) is growing rapidly, but at the same time, the effective utilization of RER is also a challenging task. For the better usage of RER and the reduction of loss, the dual battery storage is proposed. The main aim of this work is to focus on the design and implementation of a reliable and renewable power generating system under a robust situation, along with a battery storage system. The perturb and observe (P&O) maximum power point tracking (MPPT) technique has been applied to improve the solar photovoltaic power production. In addition, the dual battery storage system is being introduced to improve the life cycle of the primary storage system. The proposed dual storage system is highly preferable for remote, location-based application systems, space applications and military operations. In the dual battery storage system, the batteries are working effectively with a good lifespan, when compared with the existing methods. To determine the state of charge (SOC) and depth of discharge (DOC), those batteries’ input charging and discharging levels were monitored closely. MATLAB Simulink (R2013) is used for simulation; finally, a real-time, three-phase inverter was designed and validated. Under this dual battery storage mode, the life time of battery is improved.

Performance comparison between fixed tilt angle and solar tracking systems at Basra governorate: A case study

Theoretical <span lang="EN-US">calculations and online system simulations using PVWatts and global solar atlas simulators, were conducted in this study to find the difference in solar power production between fixed solar power systems and tracking systems at Basra Governorate. The research included the analysis of geographical location and weather conditions and their effect on output power. The reliant power resource types and power generation of the southern region of Iraq as well as load demands were demonstrated and discussed in this research. Furthermore, the sun path, solar angles and solar radiations were considered in this study, in addition to the mathematical calculations of optimum tilt angles. The methodology used in this study was based upon theoretical and online measures of real-time weather factors, solar angles, solar radiations and model properties of the examined system. The results and factors of different systems including: peak sun hours per day, dc to ac derate factors, tilt angles, solar radiations and power production were compared to multiple similar research elements that were accomplished around the same region and some other countries. The study concluded that solar tracking system absorbs more radiations and produces an annual production of 15–30% higher than fixed tilt angle system.</span>

Gradient refractive index-based broadband antireflective coatings and application in silicon solar modules

Gradient refractive index-based broadband antireflective coatings (ARCs) could potentially improve solar power production by reducing the amount of sunlight reflected from the surface of photovoltaic (PV) modules. In this study, refractive index tunable hybrid SiO2 sols with refractive indices from 1.20 to 1.44 were prepared by adjusting different volume ratios and pH values and mixing acid-catalyzed SiO2 with base-catalyzed SiO2. Based on Essential Macleod simulation, five-layer graded refractive index broadband ARCs (FGBA) (n = 1.10–1.44) were prepared using hybrid sols derived from the sol-gel method as the building blocks. The morphological and structural characteristics of each coating layer were systematically investigated with different characterization methods. The average transmittance of the glass substrate coated with FGBA reached 97.57% (380–1800 nm), much higher than that of soda-lime glass (88.49%), and exhibited excellent broadband transmittance enhancement performance. The hardness performance of FGBA reached 5H, and the water contact angle of the surface at annealed 400 °C was 149°. The FGBA exhibited excellent durability performance in thermal cycling tests and abrasion tests. According to the external quantum efficiency test and I-V and P-V curve results, the short-circuit current density gain was 4.83% and the photoelectric conversion efficiency gain was 5.81% for the mini-silicon cell solar modules dipped with FGBA, compared to the undipped modules. This indicates positive prospects for application in outdoor PV plants. Meanwhile, it proves that ARCs are essential components of PV modules and could provide additional solar gains.

An adaptive approach-based ensemble for 1 day-ahead production prediction of solar PV systems

The world is becoming more reliant on renewable energy sources to satisfy its growing energy demand. The primary disadvantage of such sources is their significant uncertainty in power production. As appropriate energy production planning and scheduling necessitate a solid and confident assessment of renewable power production, the necessity for developing reliable prediction models grows by the day. This paper proposes an adaptive approach-based ensemble for 1-day ahead production prediction of solar Photovoltaic (PV) systems. Different ensembles of Artificial Neural Networks (ANNs) prediction models are established, whose architectures (number of the ANNs that comprise the ensembles) and configurations (number of hidden nodes required by the ANNs models of the ensembles) change adaptively at each hour h, h∈ [1, 24] of a day, for accommodating the hour seasonality in the solar PV data and, thus, enhancing the 1 day-ahead predictions accuracy. The suggested approach is tested on a 264 kW solar PV system installed at Applied Science Private University, Jordan. Its prediction performance is evaluated, particularly for different weather conditions (seasons) experienced by the concerned PV system, using standard performance metrics. Results show the effectiveness of the suggested approach in predicting solar PV power production and its superiority compared to another prediction approach of the literature that uses single ANNs at each hour h of the day. Specifically, for 1-day ahead prediction, the obtained enhanced accuracy, on average, was around 8%–10% on the test “unseen” datasets.

Smoke from 2020 United States wildfires responsible for substantial solar energy forecast errors

The 2020 wildfire season (May through December) in the United States was exceptionally active, with the National Interagency Fire Center reporting over 10 million acres (40 000 km2) burned. During the September 2020 wildfire events, large concentrations of smoke particulates were emitted into the atmosphere. As a result, smoke was responsible for ∼10%–30% reduction in solar power production during peak hours as recorded by the California Independent System Operator (CAISO) sites. In this study, we focus on a 9 d period in September when wildfire smoke had a profound impact on solar energy production. During the smoke episodes, hour-ahead forecasts utilized by CAISO did not include the effects of smoke and therefore overestimated the expected power production by ∼10%–50%. Here we use multiple observational networks and a numerical weather prediction (NWP) model to show that the wildfire events of 2020 had a significantly detrimental influence on solar energy production due to high aerosol loading. We find that including the contribution of biomass burning particles greatly improves the day-ahead solar energy bias forecast of both global horizontal irradiance and direct normal irradiance by nearly ∼50%. Our results suggest that a more comprehensive treatment of aerosols, including biomass burning aerosols, in NWP models may be an important consideration for energy grid balancing, in addition to solar resource assessment, as solar power reliance increases.

Spatial merit order effects of renewables in the Italian power exchange

Estimating the merit order effect of renewable energy sources provides key information inputs in several domains, notably in the design of green energy policies. Given the spatial nature of electricity supply and demand, it is important to disentangle the direct effect of renewables on zonal electricity prices from spatial spillovers. In this paper, we estimate spatial econometric models of the Italian zonal electricity prices, using IPEx daily average prices in the time window 2009–2015, for 6 market zones. The best specification is a spatial Durbin model including wind power, solar power, and hydropower production, as well as control variables. The model is estimated under different assumptions on the spatial-weighting matrix and in 3 sub-periods. Direct and indirect merit order effects are found stronger over time, especially for wind power; but the estimated merit order effect is smaller than in non-spatial models, suggesting that our analysis is capable of accounting for spatial spillovers.

Can Forest Fires Be an Important Factor in the Reduction in Solar Power Production in India?

The wildfires over the central Indian Himalayan region have attracted the significant attention of environmental scientists. Despite their major and disastrous effects on the environment and air quality, studies on the forest fires’ impacts from a renewable energy point of view are lacking for this region. Therefore, for the first time, we examine the impact of massive forest fires on the reduction in solar energy production over the Indian subcontinent via remote sensing techniques. For this purpose, we used data from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO), the Satellite Application Facility on support to Nowcasting/Very Short-Range Forecasting Meteosat Second Generation (SAFNWC/MSG) in conjunction with radiative transfer model (RTM) simulation, in addition to 1-day aerosol forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). The energy production during the first quarter of 2021 was found to reach 650 kWh/m2 and the revenue generated was about INR (Indian rupee) 79.5 million. During the study period, the total attenuation due to aerosols and clouds was estimated to be 116 and 63 kWh/m2 for global and beam horizontal irradiance (GHI and BHI), respectively. The financial loss due to the presence of aerosols was found to be INR 8 million, with the corresponding loss due to clouds reaching INR 14 million for the total Indian solar plant’s capacity potential (40 GW). This analysis of daily energy and financial losses can help the grid operators in planning and scheduling power generation and supply during the period of fires. The findings of the present study will drastically increase the awareness among the decision makers in India about the indirect effects of forest fires on renewable energy production, and help promote the reduction in carbon emissions and greenhouse gases in the air, along with the increase in mitigation processes and policies.

Fluid-structural analysis of modular floating solar farms under wave motion

Floating solar farms (FSFs) are emerging to be a viable option for large scale solar power production. The present study develops an original approach for the design assessment of the maximum stress/strain and displacement of massively connected modular FSFs under wave action. The scope includes two parts: (1) numerical simulations for the fluid structural analysis with two-way coupling of a global array of connected modular floaters that hold the massive number of solar panels; and (2) experimental validation of the numerical predictions for the dynamic response of the global array under wave action. The numerical approaches are based on finite element simulations. Both static and dynamic simulations are carried out with and without using the Fluid Structure Interaction (FSI) method, under different incident wave characteristics, rheological properties of the global array, and mooring configurations. The experiments were performed with a flexible perforated sheet using Froude scaling for dynamic similitude, with ultrasonic measurements to quantify the wave profile as well as the displacement responses of the array under wave action. The comparison shows reasonable agreement between the predictions and measurements at discrete locations along the array. Finally, a summary is provided on how the new approach can aid in the design assessment of modular FSFs under wave motion.

Angle Dependence of Solar Power Capacity: The Case of Setit Humera

The solar power capacity depends on orientation angle of the solar panel. This study evaluates the angle dependence of solar power capacity for a summer season in 2017. Using three solar panels, with 10 Watt capacity each placed on a metallic frame to facilitate changing tilt and azimuth angles independently. Experiments were done for the first six days of three months, and the first three days of each month were used to measure voltages for different tilt angles by changing from 0° to 26° in steps of 2o. The remaining three days of a week of every month, the middle solar panel was fixed at a tilt angle that yielded maximum power while the two panels were moved with azimuth angles from 0o to 270 in steps of 3o to clockwise and counterclockwise with respect to the middle solar panel. A digital multimeter was used to record the circuit voltage of the solar panels with 10 ohms resistor serving as a load. Data gathering was done every ten minutes, during the daytime from 9:30 am to 16:30 pm. The study has shown that the maximum power output was obtained at the tilt angle of 4o in June and July, 2o in August and a season tilt was found at 4o. Maximum solar power outputs were obtained when solar panels facing east and west were oriented between 0o and 3o. This recommends that the maximum solar power production depends on the tilt angle of 4o and clockwise and counterclockwise of 3o orientation for this geographical location.

Organic solar powered greenhouse performance optimization and global economic opportunity

This work integrates greenhouse energy demand, solar power production, and plant growth modeling to assess the economic opportunity of organic solar powered greenhouses. Results show these systems have positive economic outlook across broad climates.

Integration of solar energy supply on a smart distribution board based on IoT system

Modern electricity management of solar power supply at the smart distribution board is heavily dependent on the Internet of Things (IoT). It is still difficult to create practical, affordable intelligent systems for condition monitoring, protection and control at the smart distribution board of distribution networks. Smart grids use the capabilities of information and communication technologies, especially the Internet of Things, to improve the sustainability, quality and productivity of energy production and demand forecasting, as well as to reduce resource consumption and increase the integration of renewable energy sources. The performance of the developed system is evaluated for a variety of residential electrical loads with different energy consumption patterns on a smart distribution board. In this project, the photovoltaic tracking system as well as the cleaning system will be connected to the smart board. All the input data will be collected and sent via Bluetooth/WI-FI by a working prototype built on an Arduino board, which can independently collect and monitor the real-time energy and environmental conditions of solar plants. The mobile application is also used to allow individual users to monitor the system in real time, notify users of electrical system problems, display real-time energy consumption, and provide other important notifications and alerts to the remote user when there is a deviation of solar power production quality parameters from a predefined set of standard values. A number of improvements are also proposed for further integration with Internet of Things (IoT) and smart home technologies.

Solar Power Production Forecasting with Solar Irradiance Estimated by Similar Days: A Case Study in Taiwan

Solar Power Production Forecasting with Solar Irradiance Estimated by Similar Days: A Case Study in Taiwan

Modelling of Solar Power Production in Dry and Rainy Seasons Using Some Selected Meteorological Parameters

In this paper, we deployed the multiple linear regression method in developing a solar power output model for solar energy production, where the meteorological parameters are the independent variables. We fitted the model and found that the meteorological variables considered accounted for 94.88% and 99.61% of the power output in both dry and rainy seasons. We observed from the work that the solar panel performs well in all seasons but slightly better in the rainy seasons. This could be attributed to the washing away of dust particles from solar panels by the rain and higher operating temperature different from the specified manufactured temperature of 25°C. We observed that other factors such as the cloud slightly affect the optimal performance of the system. Panels inclined at an angle of 5° (Tilt) and facing south azimuth performs optimally, periodic washing of the surface of solar panels enhances optimal performance.

Finite State Machine-Based Load Scheduling Algorithm for Smart Home Energy Management

The problem of scheduling household appliances with the availability of renewable energy is the biggest challenge in the smart home energy management system. The components such as renewable energy resources, household appliances, utility grid, storage batteries are pooled into a nonlinear, time-varying, indefinite, and dynamic structure that is impossible to control and refine. For this, real-time pricing is applied in most nations to withstand the burden on the grid. This requires attention to utilize renewable energy effectively. In this paper, a load scheduling method to schedule the loads based on the availability of solar energy and customer preferences is presented. First, the availability of solar energy is forecasted ahead one day using Regression Analysis. Second, the finite state machine approach-based load scheduling algorithm is implemented and tested using MATLAB Simulink and Lab VIEW. LabVIEW-based GUI is developed to visualize the MATLAB schedule for loads. The problem is divided into several states with the availability of solar power, and if solar power is unavailable, grid power is utilized. The loads preferred by the consumers are scheduled in alignment with the production of solar power with the finite state machine scheduling algorithm. Also, the loads considered are able to consume instantaneous energy with the instantaneous production of energy, thereby reducing CO2 emission by not consuming power from the grid. Finally, the loads are scheduled accordingly, and it is concluded that coordination can be established between energy providers, and the system proposed can flatten out the load profile.

Predicting rare events of solar power production with the analog ensemble

Some revised versions of the analog ensemble (AnEn) algorithm to improve its performance when predicting rare events of solar power production are presented. Rare events are considered both extremely cloudy situations and clear sky conditions with very low aerosol concentrations. In these cases, the solar power production can be respectively unusually low or high, making it more difficult to find good analog dates in a historical archive of past forecasts. The lack of suitable quality analogs determines conditional biases from the AnEn when predicting these rare events. Here, an application of the AnEn to predict solar power generated by two photovoltaic plants located in Kuwait is presented. The modified versions of the AnEn are compared to the standard algorithm. We focus the verification of the AnEn’s performance over the rare conditions outlining the improvements achieved with the new AnEn models.

Simultaneous Production of Solar Thermal Heat and Power for Industrial Applications

Currently most of the energy production is supported by fossil fuels, however, renewable sources contribution on worldwide demand of energy has been in constantly growth. One of the main challenges in the use of solar thermal energy in industrial processes is their cost, especially when is compared to energy generation by fossil fuels. This cost delimits the investment payback time for a thermo-solar installation to be feasible. This fact makes crucial the development of methodologies for the integration of solar energy for large-scale industrial applications. This work deals with the thermo-economic evaluation of the heat and power production through a low-temperature flat-plate solar collector system. Also, strategies were developed to increase energy efficiency by evaluating different process integration scenarios. The proposed strategies for energy efficiency combined with renewable energy were applied to an integrated solar thermal energy system into a 1G and 2G (first and second generation) bioethanol process from sugarcane. It was found that to replace the fully process heat and power with low-temperature solar thermal energy, a heat recovery network ? ?? ?????? of 5 °C, a heat duty of 131 kW and a supply temperature of 105 °C are required. All these results lead to a cost of 0.2112 USD/kWh of the integrated system. The power system operates independently by mean an Organic Rankine Cycle and the heating system is supplied by a solar collector network with a cost of 0.0477 USD/kWhele.

Thermal energy autonomy study for a reference house equipped with PV panels, a heat pump and PCM storage elements

Despite their rapid growth, renewable energies cannot provide energy on demand, which is an essential requirement for heating buildings. To this end, we developed heat exchangers with Phase Change Materials that allow the on-demand charge, storage and discharge of thermal energy. In this paper we evaluate the storage capacities needed to achieve thermal energy autonomy. An existing reference house, meteorological data from MeteoSwiss and solar radiation intensity were used to evaluate the thermal needs and the solar power production for the year 2019. To heat the building an air-water heat pump is preferably powered by solar energy. The calculations have been performed from October to March. Using 1200 litres of PCM, a thermal autonomy of 100% was achieved for March and October. In February, November and December, 24 days could not reach a thermal autonomy. For the month of January that was studied in detail 15 days are self-sufficient. By increasing the PCM volume to 2’600 litres 5 more days become self-sufficient. To achieve total building heating autonomy, seasonal storage is necessary.

Space-based Solar Power as a Catalyst for Space Development

Robust space development as usually envisioned by its advocates—the persistent and self-sustaining presence of humans and industry in space—is stymied by a fundamental paradox: for there to be a reason to develop space, there must already be people and industry there. To the extent that space development is a priority for any government or entity, this paradox must be surmounted. Space-based solar power (SBSP) production may represent the best way to overcome this paradox because of the technology's inherent scalability, rising demand for terrestrial clean baseload energy, and potential for self-funding. However, technical, regulatory, legal (domestic and international), and geopolitical challenges to deploying this technology on a large scale remain. In this article, the authors describe how SBSP development can potentially overcome the development paradox and offer an analysis of the challenges to this concept and potential approaches to resolving them. Furthermore, they provide a tentative research and commercialization roadmap for a private-public partnership for SBSP to deliver sustainable space development, identifying relative advantages and disadvantages.