National Renewable Energy Laboratory Research Articles

Time-domain equivalent model for harmonic simulations of wind and photovoltaic plants

Equivalent models of wind power plants or photovoltaic systems reduce the computational burden relative to detailed models (DMs) during power quality studies. However, existing time-domain equivalent models are ineffective for predicting harmonic currents because they do not consider switching behavior from power converters. This paper proposes a novel time-domain equivalent model, the aggregated harmonic model (AHM), to perform harmonic current prediction in large plants. The AHM uses the National Renewable Energy Laboratory method to equalize the collector system and applies the superposition principle to include the dead time and switching effects. Simulations were executed using the DM and AHM of actual plants. Field measurements were performed to validate the AHM. The results show that the proposed model produced well-matched curves for current waveforms, frequency responses, and harmonic spectra. Consequently, this model is an accurate tool for transmission system operators.

Wind farm structural response and wake dynamics for an evolving stable boundary layer: computational and experimental comparisons

Abstract. The wind turbine design process requires performing thousands of simulations for a wide range of inflow and control conditions, which necessitates computationally efficient yet time-accurate models, especially when considering wind farm settings. To this end, FAST.Farm is a dynamic-wake-meandering-based mid-fidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. This work is an extension of a study that addressed constructing a diurnal cycle evolution based on experimental data (Quon, 2024). Here, this inflow is used to validate the turbine structural and wake-meandering response between experimental data, FAST.Farm simulation results, and high-fidelity large-eddy simulation results from the coupled Simulator fOr Wind Farm Applications (SOWFA)–OpenFAST tool. The validation occurs within the nocturnal stable boundary layer when corresponding meteorological and turbine data are available. To this end, we compared the load results from FAST.Farm and SOWFA–OpenFAST to multi-turbine measurements from a subset of a full-scale wind farm. Computational predictions of blade-root and tower-base bending loads are compared to 10 min statistics of strain gauge measurements during 3.5 h of the evolving stable boundary layer, generally with good agreement. This time period coincided with an active wake-steering campaign of an upstream turbine, resulting in time-varying yaw positions of all turbines. Wake meandering was also compared between the computational solutions, generally with excellent agreement. Simulations were based on a high-fidelity precursor constructed from inflow measurements and using state-of-the-art mesoscale-to-microscale coupling.

Open-source simulation platform for air source heat pump integrated with thermal energy storage

This article introduces a modular simulation platform for assessing thermal energy storage (TES) integrated with air source heat pumps (ASHP). The Python platform is an open-source library that includes classes for modeling air-air and air-water heat pumps, TES devices, and the heating load of residential buildings. To validate the ASHP model, the study utilized experimental data obtained from a commercial heat pump evaluated at the National Renewable Energy Laboratory (NREL). The results indicate a mean deviation of 0.7% for COP across the operating range, with a maximum relative deviation of 12.6%. In terms of system heating capacity, the model had an average deviation of 4.3% compared to experimental results, with a maximum deviation of 8.2%. Three classes were implemented for modeling distinct types of TES devices: a generic TES based on energy balances, a sensible isothermal water tank, and a stratified water tank. Details of the mathematical models are provided, along with their respective strengths and limitations. An example is provided showcasing the integration of a residential 10 kWh thermal storage unit with an ASHP operating in Denver, CO. The comparison of two different discharge criteria for the TES unit highlights the importance of control strategies in the system performance.

Prediction of ultimate tensions in mooring lines for a floating offshore wind turbine considering extreme gusts

The design of mooring line parameter for a floating offshore wind turbine (FOWT) should consider the ultimate limit state. Typhoons should be considered as a threatening condition for mooring design, and the characteristics of transient changing wind should be studied. Currently, few extreme value predictions consider gust features, and extreme mooring tensions may be underestimated in practical FOWT design. We conducted the simulations of the 5 MW wind turbine from the National Renewable Energy Laboratory (NREL) in the environmental conditions of extreme gusts, waves, and currents. Extreme gusts were found to cause dramatic increases in mooring tensions under 100-year typhoon conditions. The average conditional exceedance rate (ACER) method is used to predict extreme mooring tensions. The impacts of simulation duration and sample size on the prediction results are then discussed. The results show that in extreme gust conditions, the tail data of the average conditional exceedance rate of mooring tension presents a different extrapolation trend from stable wind conditions. Short-term predicted values in gust conditions are 14–20% larger than those in stable wind conditions. The research shows the necessity of considering threatening gust conditions in the ultimate limit state design of mooring system of FOWT.

Worldwide scientific landscape on fires in photovoltaic

The rapid growth of photovoltaic (PV) technology in recent years called for a comprehensive assessment of the global scientific landscape on fires associated with PV energy installations. This study examines the scientific literature indexed in Scopus from 1983 to 2023. It reveals a striking increase in output since 2011, with nearly one hundred publications in the most recent year under review. This growth of interest has occurred in parallel with the global expansion of photovoltaics. The majority of studies in this field are classified as engineering, with 34% of publications in this area. The USA leads the way with over 160 publications, followed by China with 125. Two institutions in the USA are particularly prominent in this field: Sandia National Laboratories in New Mexico with 22 publications, and the National Renewable Energy Laboratory in Colorado with 16 publications. The second institution is the University of Science and Technology of China, which has published 17 articles on the subject. A close examination of the evolution of keywords reveals a remarkable transformation in the scientific landscape over the past 10 years, from 2013 to 2023. The evolution of keywords suggests a maturation in the understanding of fire risks associated with photovoltaic energy. A total of seven scientific communities have been identified in which these works are grouped according to their keywords. These include Fire and Energy Storage, PV faults, Fire resistance, Fire hazard, Fire detectors, Deep learning, and Fire safety. It has been found that fires caused by PV installations are not listed as a cause of fire starts. This should be taken into account when conducting preventive analyses of this potential danger, particularly in light of the possible development of agrivoltaics, where facilities will be mainly located in the natural environment.

Short-to-Medium-Term Wind Power Forecasting through Enhanced Transformer and Improved EMD Integration

Accurate wind power forecasting (WPF) is critical in optimizing grid operations and efficiently managing wind energy resources. Challenges arise from the inherent volatility and non-stationarity of wind data, particularly in short-to-medium-term WPF, which extends to longer forecast horizons. To address these challenges, this study introduces a novel model that integrates Improved Empirical Mode Decomposition (IEMD) with an enhanced Transformer called TransIEMD. TransIEMD begins by decomposing the wind speed into Intrinsic Mode Functions (IMFs) using IEMD, transforming the scalar wind speed into a vector form that enriches the input data to reveal hidden temporal dynamics. Each IMF is then processed with channel attention, embedding, and positional encoding to prepare inputs for an enhanced Transformer. The Direct Embedding Module (DEM) provides an alternative viewpoint on the input data. The distinctive perspectives of IEMD and DEM offer interaction through cross-attention within the encoder, significantly enhancing the ability to capture dynamic wind patterns. By combining cross-attention and self-attention within the encoder–decoder structure, TransIEMD demonstrates enhanced proficiency in detecting and leveraging long-range dependencies and dynamic wind patterns, improving the forecasting precision. Extensive evaluations on a publicly available dataset from the National Renewable Energy Laboratory (NREL) demonstrate that TransIEMD significantly improves the forecasting accuracy across multiple horizons of 4, 8, 16, and 24 h. Specifically, at the 24 h forecast horizon, TransIEMD achieves reductions in the normalized mean absolute error and root mean square error of 4.24% and 4.37%, respectively, compared to the traditional Transformer. These results confirm the efficacy of integrating IEMD with attention mechanisms to enhance the accuracy of WPF.

Exploring Motion Stability of a Novel Semi-Submersible Platform for Offshore Wind Turbines

The stability of offshore floating wind turbine foundation platforms is a fundamental requirement for the efficiency and safety of wind power generation systems. This paper proposes a novel small-diameter float-type semi-submersible platform to improve system stability. To evaluate the superior motion stability of the proposed floating platform, a comprehensive frequency–domain response analysis and experimental study were conducted in comparison with the OC4-DeepCwind platform developed by the National Renewable Energy Laboratory (NREL). The respective comparison of the frequency–domain response analysis and the experimental results demonstrated that the proposed floating wind turbine platform shows better hydrodynamic characteristics and resonance avoidance capability. This not only reduces the Response Amplitude Operators (RAOs), but also enhances the system stability, namely, effectively avoiding the regions of concentrated wave loading and low-frequency ranges. Furthermore, the proposed small-diameter semi-submersible platform has the potential to reduce manufacturing costs, providing valuable insights for the manufacturing of offshore floating wind turbine systems.

Effects of an Owl Airfoil on the Aeroacoustics of a Small Wind Turbine

Aerodynamic noise emitted by small wind turbines is a concern due to their proximity to urban environments. Broadband airfoil self-noise has been found to be the major source, and several studies have discussed techniques to reduce airfoil leading-edge and trailing-edge noises. Reduction mechanisms inspired by owl wings and their airfoil sections were found to be most effective. However, their effect/s on the tip vortex noise remain underexplored. Therefore, this paper investigates the effects of implementing an owl airfoil design on the tip vortex noise generated by the National Renewable Energy Laboratory (NREL) Phase VI wind turbine to gain an understanding of the relationship, if any, between airfoil design and the tip vortex noise mechanism. Numerical prediction of aeroacoustics is employed using the Ansys Fluent Broadband Noise Sources function for airfoil self-noise radiation. Detailed comparisons and evaluations of the generated acoustic power levels (APLs) for two distinguished inlet velocities were made with no loss in torque. Although the owl airfoil design increased the maximum generated APL by the baseline model from 105 dB to 110 dB at the lower inlet velocity, it significantly reduced the surface area generating the noise, and reduced the maximum APL generated by the baseline model by 4 dB as the inlet velocity increased. The ability of the owl airfoil to mitigate the velocity effects along the span of the blade was found to be its main noise reduction mechanism.

The Decision to Install a Rooftop Photovoltaic System by a Small Business: A Case Study

The potential of rooftop solar power has been identified as a main driver of clean energy adoption in an urban environment. While residential solar projects have lower capacity than commercial systems, residential solar represents most of the installation base for rooftop solar projects. Rooftop solar adoption in the commercial market lags behind residential solar installation. To better understand why this is the case, we conduct a case study of a small manufacturing firm. Based on the firm’s energy demand and the physical attributes of its location, we study a 25-kilowatt solar array using the National Renewable Energy Laboratory’s (NREL) System Advisor Model (SAM). Our empirical study evaluates the economic prospects of a rooftop solar installation project for the firm under study. This analysis sheds light on the financial ramifications of the adoption of solar panels by small, commercial firms in New York state.

A structural depth network embedding stacking model based on Moran’s index and seasonal trend for short-term solar irradiance prediction

Solar irradiance prediction of multi-station is influenced by complex spatial characteristics and temporal fluctuations, so how to obtain accurate short-term irradiance prediction by using photovoltaic cluster data becomes a challenge. In order to address the challenge, a structural depth network embedding stacking model based on Moran’s I and seasonal trend for short-term solar irradiance prediction is proposed. Firstly, the deep spatial characteristics of photovoltaic clusters are analyzed by using bivariate Moran index to capture strong correlations between stations. Secondly, seasonal trend decomposition is used to smooth the seasonal factors in time series, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise is used to decompose the residual components to reduce the fluctuation of signals and the complexity of data feature mapping. Finally, based on the Stacking framework, Long Short-term Memory Network, Least Absolute Selection and Shrinkage Operator, Gradient Boosted Decision Tree, and Artificial Neural Network are superimposed. And the aggregated sub-modal components are introduced into the robust integration model to predict the solar irradiance in multiple steps. Through the case study of the National Renewable Energy Laboratory open-source dataset in the United States, the conclusions are as follows: (1) The spatio-temporal method in this paper can effectively extract the deep spatial characteristics of photovoltaic stations. (2) The fusion strategy of embedded structured deep network effectively improves the stacking effect. (3) Comparing with many classical forecasting models, it shows that this method can effectively improve the forecasting effect.

Spatial modeling sensitivity analysis: Copula selection for wind speed dependence

As the adoption of wind energy as a key renewable energy source accelerates, precise power flow analysis becomes crucial for accurate power delivery forecasting. This paper addresses the inherent uncertainties in wind speed data at different wind farm locations by conducting a sensitivity analysis to assess wind farm pairs. The analysis accommodates various data sizes, namely, short, medium, and large, and diverse spatial relationships between wind farms. By leveraging National Renewable Energy Laboratory wind speed data from nine distinct wind farms, the dependence structure between wind farm pairs is modeled using copulas. This modeling takes both the wind speed knowledge level and the various spatial interplays among the wind farm pairs into consideration. The findings indicate an inverse proportionality between the strength of dependence and the distance separating the wind farm pairs.

Recurrent neural network for pitch control of variable-speed wind turbine.

Wind is one of the most widely used renewable energy sources due to its cost-effectiveness, power requirements, operation, and performance. There are many challenges in wind turbines, such as wind fluctuation, pitch control, and generator speed control. When the wind speed exceeds its rated value, the pitch angle controller limits the generator output power to its rated value. In this research work, several soft computing techniques have been implemented for pitch control of variable-speed wind turbine. The data is collected for the National Renewable Energy Laboratory offshore 5 MW baseline wind turbine. Wind speed, tip speed ratio, and power coefficient are taken as inputs, and pitch angle as output. Machine learning and artificial intelligence-based techniques such as recurrent neural networks (RNNs), adaptive neuro-fuzzy inference system (ANFIS), multilayer perceptron feed-forward neural network (MLPFFNN), and fuzzy logic controller (FLC) are implemented on MATLAB, and their results are evaluated in terms of mean square error (MSE) and root mean square error (RMSE). The controllers have been implemented in MATLAB/Simulink to schedule the wind turbine blade pitch angle and keep the output power stable at the rated value. The experimental results show that RNN provided the best results for 15 neurons in hidden layers and 1000 epochs with MSE of 3.28e-11 and RMSE of 5.54e-06, followed by MLPFFNN with MSE of 2.17e-10 and RMSE of 1.56e-05, ANFIS with MSE of 8.5e-05 and RMSE of 9.22e-03, and FLC with MSE of 6.25e-04 and RMSE of 0.025. The proposed scheme is more reliable and robust and can be easily implemented on a physical setup by using interfacing cards such as dSPACE, NI cards, and data acquisition cards.

Reduction in Emissions by Massive Solar Plant Integration in the US Power Grid

Fossil fuels, the predominant energy source in the United States, have been identified as major contributors to environmental pollution through the release of harmful emissions. As a countermeasure, there has been an increasing focus on the exploration and development of cleaner energy alternatives to alleviate the environmental degradation caused by fossil fuels and to satisfy the growing energy needs. This study conducted scenario analyses to evaluate the impact of integrating solar energy into specific US power grids on reducing carbon emissions. The analysis encompassed electrical systems within California, New England, New York, and the Southwest, utilizing datasets from the Energy Information Administration and National Renewable Energy Laboratory. The Energy Information Administration dataset includes information on net generation according to each source and carbon emissions according to fuel type, whereas the National Renewable Energy Laboratory dataset provides hourly projections for 6000 theoretical photovoltaic installations and detailed solar energy output data every five minutes over a year. Our findings indicated a notable decrease in carbon dioxide emissions following the introduction of solar power facilities. The most significant reductions were observed in the Southwest and California, attributed to solar plant integration. Conversely, New York and New England were identified as regions requiring additional policy measures and incentives to meet the emission reduction goals.

Offshore low-level jet observations and model representation using lidar buoy data off the California coast

Abstract. Low-level jets (LLJs) occur under a variety of atmospheric conditions and influence the available wind resource for wind energy projects. In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas administered by the Bureau of Ocean Energy Management: Humboldt and Morro Bay. The wind profile observations from the lidars and collocated near-surface meteorological stations (4–240 m) provide valuable year-long analyses of offshore LLJ characteristics at heights relevant to wind turbines. At Humboldt, LLJs were associated with flow reversals and north-northeasterly winds, directions that are more aligned with terrain influences than the predominant northerly flow. At Morro Bay, coastal LLJs were observed primarily during northerly flow as opposed to the predominant north-northwesterly flow. LLJs were observed more frequently in colder seasons within the lowest 250 m a.s.l. (above sea level), in contrast with the summertime occurrence of the higher-altitude California coastal jet influenced by the North Pacific High, which typically occurs at heights of 300–400 m. The lidar buoy observations also validate LLJ representation in atmospheric models that estimate potential energy yield of offshore wind farms. The European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) was unsuccessful at identifying all observed LLJs at both buoy locations within the lowest 200 m. An extension of the National Renewable Energy Laboratory (NREL) 20-year wind resource dataset for the Outer Continental Shelf off the coast of California (CA20-Ext) yielded marginally greater captures of observed LLJs using the Mellor–Yamada–Nakanishi–Niino (MYNN) planetary boundary layer (PBL) scheme than the 2023 National Offshore Wind dataset (NOW-23), which uses the Yonsei University (YSU) scheme. However, CA20-Ext also produced the most LLJ false alarms, which are instances when a model identified an LLJ but no LLJ was observed. CA20-Ext and NOW-23 exhibited a tendency to overestimate the duration of LLJ events and underestimate LLJ core heights.

Coupled Dynamic Characteristics of a Spar-Type Offshore Floating Two-Bladed Wind Turbine with a Flexible Hub Connection

To reduce manufacturing, transportation, lifting and maintenance costs of increasingly larger and larger floating wind turbines, a Spar-type floating two-bladed wind turbine based on the 5 MW OC3-Hywind floating wind turbine model from the National Renewable Energy Laboratory (NREL) is studied in this paper. The two-bladed wind turbine can cause serious problems with large dynamic loads, so a flexible hub connection was introduced between the hub mount and nacelle carrier to alleviate the dynamic effect. The paper focuses on studying the dynamic responses of the proposed Spar-type floating two-bladed wind turbine with a flexible hub connection at rated and extreme environmental conditions. Fully coupled time-domain simulations are carried out by integrating aerodynamic loads on blades, hydrodynamic loads on the spar, structural dynamics of the tower, blades and mooring lines, control system and flexible hub connection. The analysis results show that the application of a flexible hub connection between the hub mount and nacelle carrier can make a contribution to enable the Spar-type floating two-bladed wind turbine to effectively dampen the motion of the floating platform, while significantly reducing the tower load and blade deflection.

An Economic Analysis of In-Situ Hydrogen Production from Natural Gas Wells with Subsurface Carbon Retention

Summary An economic analysis for a wellbore methodology that promotes sustainable natural gas conversion to hydrogen is presented. The methodology uses at-source, wellbore gasification of methane for hydrogen production, incorporating the simultaneous in-situ sequestration of carbon for both climate and economic benefit. The proposal is for a wellbore completion tool, to take natural gas (methane) production from the reservoir and perform gasification within the wellbore tool (not within the reservoir). This would not interfere with reservoir management, allowing standard reservoir management practices to be used. The proposed process is for natural gas fields and not for use in the gasification of heavy oils (which is covered by other “combustion type” reservoir management processes performed deep within the reservoir geology). The proposed methane gasification tool, when located deep within the wellbore, takes maximum advantage of the “free” energy provided by the elevated temperatures and pressures of the surrounding fluid-connected geology. The combination of surface-injected fluids and geofluids, mixed inside the wellbore gasification tool at depth, significantly reduces the excess process energy input from the surface and lessens feedstock consumption for power. The proposed system is neither electricity cost dependent nor fuel cost dependent, as both are provided in situ and through heat recovery and reserves. There are therefore several process steps and significant energy and cost savings to be gained by this method when compared with surface-based methane reformation facilities, as well as infrastructure longevity benefits. In addition, CO2 life cycle climate savings are made, as zero carbon is produced to the surface, eliminating the harm greenhouse gases (GHGs: CH4 and CO2) do while transitioning through the environment. The proposed methodology therefore avoids the expense and energy consumption of the subsequent, only partial, downstream recapture of the CO2 released from the combustion of this same methane. To help maintain consistency and ensure comparability for hydrogen production types, the standardized H2A template of the National Renewable Energy Laboratory (NREL) of the U.S. Department of Energy was used in our analysis. This economic template contains several cost model scenarios used to illustrate the possible magnitudes of economic advantages using this wellbore methodology. Based on the model’s comparative cost analyses, such a proposed system could produce hydrogen from natural gas wells consistently below 1 USD/kg H2, leading to cost-competitive wellbore hydrogen production when compared with surface-based steam methane reformation facilities. Using several scenarios for cost analysis, we found that the cost cannot be higher than 2 USD/kg H2. In our uncertainty quantification, we included the effects of the number of wells that can be used as well as mixing H2 with CH4 (v/v%).

Technology Focus: Artificial Lift (March 2024)

The industry is now in its second year beyond the COVID-19 pandemic, and the workforce has changed in many ways, but issues facing production performance remain. Major conferences saw 116 submissions from 28 countries, with 64% regarding electrical submersible pumps (ESPs), and 18% and 8% devoted to gas lift and rod lift, respectively. Many of the papers dealt with case history successes. That is all well and good, but it doesn’t necessarily drive reader interest if it can’t be applied to their asset. The top three papers were selected on the guidelines of clarity of the abstract in addressing the scope, methods/procedures/process, results/observations/conclusions, and novel or additive information. The papers described here focus on novel unconventional horizontal downhole pump card signatures, carbon reduction through alternative energy to pump wells, and predicting downhole-discharge pressure of ESPs. Paper SPE 214723 presents a discussion of the design, evaluation, and field testing of a new type of mechanical gas separator with what the authors term a “hydrohelical” design. The separator, in Permian Basin tests, almost doubled the flow capacity of conventional gas separators with a high level of efficiency. The authors draw particular attention to the improvement of ESP operations under multiphase conditions. Carbon reduction is a hot topic, and paper OTC 32671 does not disappoint as it looks at the use of a 10-MW floating solar photovoltaic power system to meet the energy needed for 10 ESP wells on an oil platform in the Brazil equatorial region. Collaboration with the National Renewable Energy Laboratory and its System Advisor Model was used with extensive meteorological and wind data to examine the sustainability of green energy use for the petroleum industry and offer valuable guidance for future renewable energy projects in regions with similar characteristics. Finally, paper SPE 216598 comes from Sudan and Saudia Arabia and focuses on digital solutions for the challenge of knowing the downhole-discharge pressure of an ESP system. Most ESP systems do not have downhole gauges, so an artificial neural network (ANN) approach is used to predict the ESP discharge pressure for performance optimization. Forty wells and more than 12,000 data points were reduced to 16 inputs plus one hidden layer of seven neurons to provide a high degree of accuracy in prediction of discharge pressure. The authors state that the ability to accurately predict discharge pressure can lead to the early detection of possible anomalies, which can prevent costly failures and reduce downtime. Artificial intelligence technology may improve the accuracy of ANNs, but, for now, ANNs are worth a look to obtain much-needed ESP discharge pressure. Other notable papers dealt with gas separation, ESP reliability, and the increasing challenges of low rates, low pressures, and high gas fractions. Recommended additional reading at OnePetro: www.onepetro.org. SPE 214913 Solved: ESP Shutdowns and Restart Issues—Novel Pump Protection and Active 3D Dispersion System Improve ESP Performance in Multiple Wells by A. Bhattacharjee, SLB, et al. SPE 215474 A New Artificial Lift Approach in Telisa Low-Productivity Reservoir: Utilized Very Low-Rate ESP by Agus Aryanto, Medco E&P Indonesia, et al. SPE 215170 A Comprehensive Study of a Novel Submersible Pump by Hattan Banjar, Saudi Aramco, et al. SPE 214722 Case Study: Predicting Electrical Submersible Pump Failures Using Artificial Intelligence and Physics-Based Hybrid Models by Shejuti Silvia, Baker Hughes, et al. SPE 214733 Gas-Flow-Management Technology Designed To Decrease Downtime and Improve ESP Efficiency by S. Fulwider, ConocoPhillips, et al.

Hybrid Energy Holds Promise for Artificial Lift for Offshore Brazilian Fields

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 32671, “Technical Evaluation of the Use of Hybrid Energy (Solar and Offshore Wind) To Supply Artificial Lift Pumps on an Oil Platform on the Equatorial Margin,” by W.K. Silva, Ana L.R. Cunha, SPE, and Ana C. Alves, Federal University of Pará, et al. The paper has not been peer reviewed. _ The complete paper investigates the relevance of wind and solar energy for the supply of artificial lift systems on offshore platforms in the Brazilian equatorial region. The scope of the authors’ study encompassed a detailed technical and environmental assessment, with an emphasis on the integration of a 10-MW floating solar photovoltaic (FSPV) power system to meet demand for 10 pumps. The results obtained highlight the high viability of the system in meeting the demand of these pumps and ensuring an adequate and sustainable energy supply. Area of Study The equatorial margin is an area in northern Brazil covering part of the sedimentary basins of the equatorial Atlantic. The region is home to important sedimentary basins such as the Amazon Basin, the Pará-Maranhão Basin, the Barreirinhas Basin, the Potiguar Basin, and the Ceará Basin, all known for their hydrocarbon reserves. Offshore oil-exploration activities in the equatorial margin involve the installation of drilling and production platforms that include artificial lift systems. To power these systems, a reliable and continuous power source is required. The use of green energies such as wind and solar can be advantageous in meeting this purpose. For the present study, the coastal city of Salinópolis in the state of Pará was selected because of its plentiful meteorological data and its location in the Pará-Maranhão Basin region. Methodology The methodology of the work consists of the evaluation of the energy required for artificial lifting systems with 10 pumps, using the nine steps developed by Baker Hughes’ Artificial Lift Research and Technology Center for the calculation of the power required to drive the pumps. Additionally, the study uses the System Advisor Model (SAM), November 2022 version, developed by the National Renewable Energy Laboratory to simulate the FSPV. Information related to wind potential was obtained through the measuring stations of the National Institute of Meteorology (INMET).

Chemical, Thermogravimetric and Elemental Characterization of Nigerian Groundnut and Cowpea Shells for Sustainable Valorization

In this study, the chemical, thermogravimetric, and elemental composition of Nigerian groundnut (Arachis hypogaea L.) and cowpea shells (Vigna unguiculata) were investigated to understand their potential as valuable waste resources. Analysis of the carbohydrates and lignin content was performed using the National Renewable Energy Laboratory method. Fourier transform infrared (FTIR) spectroscopy was used to determine the chemical structure of the substrates. Elemental analysis was performed using X-ray fluorescence (XRF) and an elemental analyzer to determine the carbon, hydrogen, nitrogen, and sulphur. In addition, thermogravimetric analysis was used to assess the thermal stability of the substrates. The chemical composition analysis revealed that the cowpea shells contain 21.32% cellulose, 21.46% hemicellulose, and 28.37% lignin. On the other hand, groundnut shells comprise 26.31% cellulose, 19.5% hemicellulose, and 38.33% lignin. The XRF results indicated the presence of significant elemental compositions in both substrates, including Si, Al, Mg, Na, P, Fe, K, Ca, and S. The carbon content of both substrates was found to be 43%. Thermogravimetric analysis revealed that groundnut shells exhibit a higher cellulose decomposition temperature at 350 °C, whereas cowpea shells manifest this phenomenon at 322 °C. The results show that the cowpea shells had a higher heating value as they exceeded that of the groundnut shell by 0.89 per cent. These comprehensive findings show the substantial potential of Nigerian groundnut and cowpea shells as valuable waste materials, which can be effectively used to produce valuable products such as bioethanol, biochemicals, biochar, and bio-composite materials. This research contributes to understanding the composition and properties of these agricultural by-products, thus paving the way for their sustainable use in various industrial applications.

Fluid–Structure Interaction Simulations of Wind Turbine Blades with Pointed Tips

The aerodynamic shapes of the blades are of great importance in wind turbine design to achieve better overall turbine performance. Fluid–structure interaction (FSI) analyses are normally carried out to take into consideration the effects due to the loads between the air flow and the turbine structures. A structural integrity check can then be performed, and the structural/material design can be optimized accordingly. In this study, three different tip shapes are investigated based on the original blade of the test wind turbine (Phase VI) from the National Renewable Energy Laboratory (NREL). A one-way coupled simulation of FSI is conducted, and results with a focus on stresses and deformations along the span of the blade are investigated. The results show that tip modifications of the blade have the potential to effectively increase the power generation of wind turbines while ensuring adequate structural strength. Furthermore, instead of using more complicated but computationally expensive techniques, this study demonstrates an effective approach to making quality observations of this highly nonlinear phenomenon for wind turbine blade design.