Solar Wind Speed Research Articles

Photosynthetically Active Radiation Temperature Dependent Models and Its Variability with Climatic Variables over Akure, Nigeria

Photosynthetically Active Radiation (PAR) is an essential input for applications pertaining to biomass production, plant physiology, and natural lighting in greenhouses. It is the amount of light energy necessary for photosynthesis to occur, and the wavelengths of this light are typically between 400 and 700 nm. In this study, the photosynthetically active radiation for Akure (latitude 7.28°N, longitude 5.30°E, and 375 m asl.), located in the coastal region of Nigeria, was estimated and investigated using data on the monthly averaged daily global solar radiation, relative humidity, wind speed, minimum and maximum temperature from the National Aeronautics and Space Administration (NASA) archives over a thirty-eight-year period (1984–2021). In order to determine the accuracy of the models, eight (8) new temperature PAR-based models were developed for the location and statistical tests were conducted using the coefficient of determination (R2), mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE), t-test, and index of agreement (IA). The findings demonstrated that PAR for Akure is lowest in the month of August with 6.5320 MJm-2day-1 during the rainy season (April to October) and highest during the dry season (November to March), with 8.6670 MJm-2day-1 in March. The best model equation for calculating PAR for Akure was found to be the one that links the PAR and natural logarithm of temperature changes; its R2, MBE, RMSE, MPE, t-test, and IA values were 68.1%, 0.0032 MJm-2day-1, 0.5208 MJm-2day-1, -0.4479%, 0.0204, and 82.8013%, respectively.

STATISTICAL EVALUATION OF PERFORMANCE INDICATORS OF PHOTOVOLTAIC PLANTS AS A SOURCE OF ENERGY FOR WATER DESALINATION IN THE AZOV-BLACK SEA REGION OF UKRAINE

The application of statistical methods for the analysis of random processes to obtain quantitative estimates for the performance indicators of photovoltaic plants as a stochastic energy source is considered. The power generation process is represented by a set of daily random functions with their respective trends and stochastic components. One hour was taken as the minimum stationari-ty interval of trend’s statistical characteristics. A time period of 16 years was studied to obtain statistically stable estimates. The meteorological database SARAH2 was used as a source of hourly data on the density of solar irradiance, ambient temperature and wind speed in the middle part of the Azov-Black Sea region of Ukraine. For mathematical modelling of power generation and electricity production processes, specialized software PVGIS version 5.2 of the European Commission was used. Hourly quantitative estimates of expected daily power generation trends for each month of the year, as well as their correlation functions, were computed. Algorithms were developed and levels of probabilistic assurance for hourly generated power were evaluated. Statistical estimates of the expected daily, monthly, and annual volumes of electricity production by photovoltaic power plants were studied, taking into account the meteorological conditions in the said region.

Quantitative Comparisons between WSA Implementations

The Wang–Sheeley–Arge (WSA) model has been in use for decades and remains a popular, economical approach to modeling the solar coronal magnetic field and forecasting conditions in the inner heliosphere. Given its usefulness, it is unsurprising that a number of WSA implementations have been developed by various groups with different computational approaches. While the WSA magnetic field model has traditionally been calculated using a spherical harmonic expansion of the solar magnetic field, finite-difference potential field solutions can offer speed and/or accuracy advantages. However, the creation of new versions of WSA requires that we ensure the solutions from these new models are consistent with established versions and that we quantify for the user community to what degree and in what ways they differ. In this paper, we present side-by-side comparisons of WSA models produced using the traditional, spherical harmonic–based implementation developed by Wang, Sheeley, and Arge with WSA models produced using a recently open-sourced finite-difference code from the CORHEL modeling suite called POT3D. We present comparisons of the terminal solar wind speed and magnetic field at the outer boundaries of the models, weighing these against the variation of the WSA model in the presence of small perturbations in the computational procedure, parameters, and inputs. We also compare the footpoints of magnetic field lines traced from the outer boundaries and the locations of open field in the models. We find that the traced field-line footpoints show remarkable agreement, with the greatest differences near the magnetic neutral line and in the polar regions.

IMF Bx, By, and Bz dependence of energetic particle precipitation in the magnetosphere

The orientation of Interplanetary Magnetic Field (IMF B) components plays a major role in magnetospheric Energetic Particles Precipitation (EPP). This is because the IMF B components in the Geocentric Solar Magnetospheric (GSM) coordinate system control magnetic reconnection at the subsolar magnetopause. This study intends to unravel the dependence of the preceding conditions of the orientations of IMF B components that drove geomagnetic storms on the EPPs produced in the Van Allen radiation belt. We classified the geomagnetic storms based on their drivers and IMF Bz orientations prior to the storms’ onset. Energetic particle fluxes, IMF B, and solar wind data were used. The study showed that electrons are more reactive to solar wind speed than protons. Additionally, storms with southward IMF Bz orientation caused more EPP enhancement than with northward orientation. Our results also showed that protons are more influenced by magnetospheric compression than electrons at the northward orientation of IMF Bz. In fact, at this point, the proton’s motion violates adiabatic invariants. As particles interact with electric fields, a significant population of EPPs is produced by pitch angle diffusion. Generally, IMF Bx and IMF By are often anticorrelated. The preceding conditions of orientations of the IMF Bx indicated weaker dependence on auroral indices, and invariably on energetic particles precipitation than the IMF By.

Energy-water management system based on robust predictive control for open-field cultivation

Food availability has been endangered by recent global events, where agriculture, the main food source for the global population, is expected to increase even more to fulfill the growing food demand. Along with food production, water and energy consumption are also increased, leading to over-extraction of groundwater and an excess emission of greenhouse gases due to fossil fuel consumption. In this context, a balance of these three resources is crucial; therefore, the water-energy-food nexus is considered to address the previous issues by designing an energy-water management system based on robust predictive control. This controller estimates the future worst-case scenario for multiple climatic conditions, such as solar radiation, ambient temperature, wind speed, precipitation, and groundwater recharge, to define an optimal irrigation volume, maximize crop growth, and minimize water consumption. At the same time, the controller schedules daily irrigation and groundwater extraction, considering energy availability from solar generation and storage, to fulfill the previously defined irrigation volume while minimizing operating costs. Climate prediction is done through fuzzy prediction intervals, whose lower or upper bound are used as worst-case to include climate uncertainty on the controller design. The energy-water management system is tested in different experiments, where results show that considering a robust approach ensures maximum crop development, avoids over-extraction of groundwater, and prioritizes renewable energy sources. This work proposes a robust energy-water management system designed to be sustainable. Considering the water-energy-food nexus, the system ensures food security and proper resource allocation, tackling global starvation, water availability, and energy access.

Mayfly optimization algorithm: a review

This paper gives a review on the bio-inspired optimization methodology known as mayfly (MA) algorithm in order to resolve issues in optimization techniques. It is a newly formed meta-heuristic optimization algorithm that focuses on the movements of masculine and feminine mayflies. It is encouraged from flying behaviour also the methods of mating in mayflies. With the help of a realistic-world separate flow planning issue along with the coupling behaviour in numerous objective optimizations, the performance of the mayfly algorithm (MA) is well evaluated. Some of the implementations of this algorithm are discussed in this paper: Bearing fault diagnosis based on the mayfly algorithm, optimizing the performance of PEMFC, Covid diagnosis, wind speed optimization, improving the scheduling of solar wind speed using mayfly optimization, detecting fault in the wind turbine gearboxes, patterning in the array antennas with the help of optimization and so on .One of the main advantages of the MA is that it combines the other optimization algorithms namely swarm optimization (PSO) with the evolutionary optimizations (GA). The motion of the mayflies that resemble nuptial dance model along with the arbitrary flight helps in the improvement of the stability within the exploration and exploitation methods. In addition, allows escape from the community peak. All the above work reviewed shows promising results from the algorithm. More work can be carried out using this algorithm in future.

Optimal reconfiguration, renewable DGs, and energy storage units’ integration in distribution systems considering power generation uncertainty using hybrid GWO-SCA algorithms

ABSTRACT To optimize radial distribution systems, this study suggests the utilization of the Grey Wolf Optimizer (GWO), a hybrid metaheuristic optimization method, combined with the Sine Cosine method (SCA). The primary objective of this work is to enhance the distribution system by determining the most efficient network reconfiguration, sizing, and placement of various distributed energy sources in distribution system. The energy sources considered include capacitors, solar cells, wind turbines, biomass-based distributed generation units, and battery storage units. To achieve this goal, the proposed strategy incorporates the power loss sensitivity technique, which assists in identifying suitable candidate buses and accelerates the resolution process. Moreover, the model considers fluctuations in solar irradiance and wind speed using Weibull and Beta probability distribution functions, compensating for the intermittent nature of renewable energy sources and the variability in demand. To address power fluctuations, voltage surges, significant losses, and inadequate voltage stability challenges, battery energy storage, diesel generators, and dispatchable biomass DGs are employed to mitigate variability and enhance supply continuity. The proposed approach is evaluated and validated by comparing it to existing optimization strategies using IEEE 69-bus and 84-bus RDSs. The results demonstrate that the suggested technique achieves faster convergence to near-optimal solutions. The proposed methodology yields a significant reduction of up to 80% in power losses in the 69-bus system and a 35% reduction in the 84-bus system, signifying higher performance than existing methods.

Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms

In this study, a fuzzy multi-objective framework is performed for optimization of a hybrid microgrid (HMG) including photovoltaic (PV) and wind energy sources linked with battery energy storage (PV/WT/BES) in a 33-bus distribution network to minimize the cost of energy losses, minimizing the voltage oscillations as well as power purchased minimization from the HMG incorporated forecasted data. The variables are microgrid optimal location and capacity of the HMG components in the network which are determined through a multi-objective improved Kepler optimization algorithm (MOIKOA) modeled by Kepler’s laws of planetary motion, piecewise linear chaotic map and using the FDMT. In this study, a machine learning approach using a multilayer perceptron artificial neural network (MLP-ANN) has been used to forecast solar radiation, wind speed, temperature, and load data. The optimization problem is implemented in three optimization scenarios based on real and forecasted data as well as the investigation of the battery's depth of discharge in the HMG optimization in the distribution network and its effects on the different objectives. The results including energy losses, voltage deviations, and purchased power from the HMG have been presented. Also, the MOIKOA superior capability is validated in comparison with the multi-objective conventional Kepler optimization algorithm, multi-objective particle swarm optimization, and multi-objective genetic algorithm in problem-solving. The findings are cleared that microgrid multi-objective optimization in the distribution network considering forecasted data based on the MLP-ANN causes an increase of 3.50%, 2.33%, and 1.98%, respectively, in annual energy losses, voltage deviation, and the purchased power cost from the HMG compared to the real data-based optimization. Also, the outcomes proved that increasing the battery depth of discharge causes the BES to have more participation in the HMG effectiveness on the distribution network objectives and affects the network energy losses and voltage deviation reduction.

Influences of Solar Wind Parameters on Energetic Electron Fluxes at Geosynchronous Orbit Revealed by the Deep SHAP Method

AbstractSolar wind is an intermediary in energy transfer from the Sun into the Earth's magnetosphere, and is considered as a decisive driver of energetic electron dynamics at the geosynchronous orbit (GEO). Based on machine learning technology, several models driven by solar wind parameters have been established to predict GEO electron fluxes. However, the relative contributions of different solar wind parameters on GEO electron fluxes are still unclear. Recently, a feature attribution method, Deep SHapley Additive exPlanations (Deep SHAP) is proposed to open black boxes of machine learning models. In this study, we use the Deep SHAP method to quantify contributions of different solar wind parameters with an artificial neural network (ANN) model. Backpropagating the prediction results of this ANN model from 2011 to 2020, SHAP values for four solar wind parameters (interplanetary magnetic field (IMF) BZ, solar wind speed, solar wind dynamic pressure, and proton density) are calculated and comprehensively analyzed. The results suggest that solar wind speed with a lag of 1 day is the most important driver. We further investigate relative roles of different parameters in three specific electron fluxes variation events (corresponding to electron fluxes reaching a local maximum, a local minimum, and unchanged, respectively). The results suggest that high solar wind speed and southward IMF BZ (high dynamic pressures) facilitate increases (decreases) of electron fluxes. These findings help reveal the underlying physical mechanisms of GEO electron dynamics and help develop more accurate prediction models for GEO electron fluxes.

Prediction of Solar Wind Speed Through Machine Learning From Extrapolated Solar Coronal Magnetic Field

AbstractAn accurate solar wind (SW) speed model is important for space weather predictions, catastrophic event warnings, and other issues concerning SW—magnetosphere interaction. In this work, we construct a model based on convolutional neural network (CNN) and Potential Field Source Surface (PFSS) magnetic field maps, considering a SW source surface of RSS = 2.5R⊙, aiming to predict the SW speed at the Lagrange‐1 (L1) point of the Sun‐Earth system. The input of our model consists of four PFSS magnetic field maps at RSS, which are three, four, five, and six days before the target epoch. Reduced maps are used to promote the model's efficiency. We use the Global Oscillation Network Group (GONG) photospheric magnetograms and the potential field extrapolation model to generate PFSS magnetic field maps at the source surface. The model provides predictions of the quasi‐continuous test data set, which is generated by randomly assigning 120 data segments that are individually continuous in time, with an averaged correlation coefficient (CC) of 0.53 ± 0.07 and a root mean square error (RMSE) of 80.8 ± 4.8 km/s in an eight‐fold validation training scheme with the time resolution of the data as small as one hour. The model also has the potential to forecast high speed streams of the SW, which can be quantified with a general threat score of 0.39.

An Explainable Deep-learning Model of Proton Auroras on Mars

Proton auroras are widely observed on the dayside of Mars, identified as a significant intensity enhancement in the hydrogen Lyα (121.6 nm) emission at altitudes of ∼110 and 150 km. Solar wind protons penetrating as energetic neutral atoms into Mars’ thermosphere are thought to be primarily responsible for these auroras. Recent observations of spatially localized “patchy” proton auroras suggest a possible direct deposition of protons into Mars’ atmosphere during unstable solar wind conditions. Improving our understanding of proton auroras is therefore important for characterizing the interaction of the solar wind with Mars’ atmosphere. Here, we develop a first purely data-driven model of proton auroras using Mars Atmosphere and Volatile Evolution (MAVEN) in situ observations and limb scans of Lyα emissions between 2014 and 2022. We train an artificial neural network that reproduces individual Lyα intensities and relative Lyα peak intensity enhancements with Pearson correlations of ∼94% and ∼60% respectively for the test data, along with a faithful reconstruction of the shape of the observed altitude profiles of Lyα emission. By performing a Shapley Additive Explanations (SHAP) analysis, we find that solar zenith angle, solar longitude, CO2 atmosphere variability, solar wind speed, and temperature are the most important features for the modeled Lyα peak intensity enhancements. Additionally, we find that the modeled peak intensity enhancements are high for early local-time hours, particularly near polar latitudes, and the induced magnetic fields are weaker. Through SHAP analysis, we also identify the influence of biases in the training data and interdependences between the measurements used for the modeling, and an improvement of those aspects can significantly improve the performance and applicability of the ANN model.

Characteristics of new particle formation events occurred over the Yellow Sea in Springtime from 2019 to 2022

Characteristics of atmospheric new particle formation (NPF) events were investigated using the data obtained from the ship measurement campaign named Yellow Sea Air Quality (YES-AQ) over the Yellow Sea during the springtime from 2019 to 2022. NPF events occurred 15 times for the valid 128 observation days for the four-year period, and the average and standard deviation formation rate (FR) and growth rate (GR) of the NPF events were 2.38 ± 1.53 cm−3 s−1 and 4.11 ± 2.81 nm h−1, respectively. The FR and GR were close to those in urban regions, implying the unique characteristics of the Yellow Sea. Owing to the NPF events, the number concentrations of total aerosols and nucleation mode particles increased by approximately 7850 and 6002 cm−3, respectively, and those of cloud condensation nuclei at 0.2% and 0.6% supersaturation increased by about 1269 and 3269 cm−3, respectively. The NPF events mostly occurred under the meteorological conditions of low temperature and relative humidity as well as high solar radiation and wind speed. These meteorological conditions appeared predominantly when the air mass originated from the continent north of the Korean Peninsula. The current analyses based on the data measured repeatedly during a specific period each year, which were not well conducted in other maritime NPF studies, highlight the reliability of the presented characteristics of NPF events over the Yellow Sea.

Estimation of the economic impact of heat stress on the Chilean dairy regions by using two comfort thermal indices.

The study aimed to estimate economic losses associated with heat stress in the eight dairy production regions (DPR), defined by the Dairy Chilean Consortium, using two comfort thermal indices, namely, the temperature-humidity index (THI) and the THI adjusted for solar radiation and wind speed (THIa). Hourly records from 19 weather stations (Nov - Mar 2017-2022) were collected to estimate the comfort thermal indices. The economic impact was estimated considering a critical threshold of 65 for both indices and the effect of higher values on loss in milk yield, days open, culling rate, and deaths. There were differences in the number of hours above the threshold among DPRs, independent of the thermal index used (P < 0.01). The greatest values were observed in DPRs I, II, and VIII, which concentrate most dairy cows. Average losses in milk yield were between 2.0 and 6.4 times higher when THIa was used instead of THI, which also depends on the DPR (P < 0.01). These estimations coincide with those observed empirically by producers. The lowest average economic losses per cow during the summer season (5 seasons average) occurred in DPR VI (US $ 91.5), and the highest losses were observed in DPR I (US $ 184.2) both using THIa. At the country level, economic losses fluctuate between US $ 29.0 and 108.4million per summer season, depending on the comfort thermal index used. Finally, heat stress impacts negatively and significantly the Chilean dairy sector, which is highly dependent on the DPR.

Solar Activity and Cosmic Ray Intensity Variation with Geomagnetic Activity during 1996–2022

The average features of diurnal variation have been observed to change with different phases of the solar cycle, with the variance being substantially bigger at higher energies. The events were classified on the basis of different phases of solar cycles, i.e., the minimum solar activity time period, the maximum solar activity time period, and the declining phase of solar cycle. This research looks at the observed results and the influence of solar variability on cosmic rays and the geomagnetic field from 1996 to 2022. The occasional group includes a Forbush effect decline, transitory decrease, and a ground level enhancement (GLE). The 11-year fluctuation in Galactic Cosmic Rays is also known as the long-term variation, whereas the Forbush effect reduction is known as short-term variation. We investigated the long-term change in the cosmic ray intensity and its relationship to the number of Sun spots (Rz), solar wind speed (

Radial evolution of the accuracy of ballistic solar wind backmapping

Context. Solar wind backmapping is a technique employed to connect in situ measurements of heliospheric plasma structures to their origin near the Sun. The most widely used method is ballistic mapping, which neglects the effects of solar wind acceleration and corotation and instead models the solar wind as a constant radial outflow whose speed is determined by measurements in the heliosphere. This results in plasma parcel streamlines that form an Archimedean spiral (the Parker spiral) when viewed in the solar corotating frame. This simplified approach assumes that the effects of solar wind acceleration and corotation compensate for each other in the deviation of the source longitude. Most backmapping techniques so far considered magnetic connectivity from a heliocentric distance of 1 au to the Sun. Aims. We quantify the angular deviation between different backmapping methods that depends on the location of the radial probe and on the variation in the solar wind speed with radial distance. We assess these differences depending on source longitude and solar wind propagation time. Methods. We estimated backmapping source longitudes and travel times using (1) the ballistic approximation (constant speed), (2) a physically justified method using the empirically constrained acceleration profile Iso-poly, derived from Parker solar wind equations and also a model of solar wind tangential flows that accounts for corotational effects. We compared the differences across mapped heliocentric distances and for different asymptotic solar wind speeds. Results. The ballistic method results in a Carrington longitude of the source with a maximum deviation of 4″ below 3 au compared to the physics-based mapping method taken as reference. However, the travel time especially for the slow solar wind could be underestimated by 1.5 days at 1 au compared to non-constant speed profile. This time latency may lead to an association of incorrect solar source regions with given in situ measurements. Neglecting corotational effects and accounting for acceleration alone causes a large systematic shift in the backmapped source longitude. Conclusions. Incorporating both acceleration and corotational effects leads to a more physics-based representation of the plasma trajectories through the heliosphere compared to the ballistic assumption. This approach accurately assesses the travel time and provides a more realistic estimate of the longitudinal separation between a plasma parcel measured in situ and its source region. Nonetheless, it requires knowledge of the radial density and Alfvén speed profiles to compute the tangential flow. Therefore, we propose a compromise for computing the source longitude that employs the commonly used ballistic approach and the travel times computed from the derived radial acceleration speed profile. Moreover, we conclude that this approach remains valid at all radial distances we studied and is not limited to data obtained at 1 au.

Experimental and numerical modeling of photovoltaic modules temperature under varying ambient conditions

In this work, comprehensive three-dimensional computational fluid dynamics simulation, of fluid flow and heat transfer phenomena around a free-standing polycrystalline silicon photovoltaic module is carried out. The objective is to provide accurate calculation of module’s temperature as a key parameter to estimate its power output. Therefore, experiments were conducted at the university of El Oued, south-east Algeria, to collect the necessary dataset for simulations. Considering different heat transfer mechanisms, modeling absorbed solar energy within the cells, and after mesh refinement study and model validation, simulations were performed and different parameters have been investigated. Results show that more accurate module temperature (Tback) estimation can be achieved based on numerical simulations. It was also found that numerical simulation overcome other models from literature and provides better results, achieving an R2 of 0.995 and a mean absolute error (MAE) of 0.822. Results also indicate that, solar radiation (G), ambient temperature (Ta) and wind speed (Ws) tend to have the major impact on Tback, an increase of 100W/m2 in G can produce an increase of 4°C in Tback at low wind speeds, and about 2.4°C for relatively higher Ws. Ta also tends to yield linear increase in Tback, expecting 5.8°C rise, for 6°C increase in Ta at 700W/m2 and 1m/s of solar radiation and wind speed, respectively. Additionally, a regression-based model was proposed for engineering applications, providing accurate results with an R2 of 0.989, a MAE of 1.009, which is 10% more accurate than the best model from literature.

Possible Influence of Solar Activity on Lightning Occurrence: A Preliminary Study Over Nepal

One of the fundamental mysteries of lightning is its initiation mechanism. Many aspects of lightning phenomenon are well known; however, the exact processes that initiate lightning discharge are not fully understood. This study delves into a relatively underexplored domain: the relationship between solar activity specifically solar sunspots and F10.7 indices, as well as solar wind speed, on the occurrence of lightning events. The study utilizes a rich dataset spanning from 1998 to 2008, capturing an extensive period of solar activity during Solar Cycle 23. Multiple correlation analyses were conducted, comparing solar activity parameters with lightning occurrence. Lightning data was sourced from the Lightning Imaging Sensor (LIS) whereas Sunspot data was obtained from the Solar Influences Data Analysis Center (SIDC) and F10.7 as well as Solar wind data was obtained from the OMNI dataset. From this preliminary study, we observed that the correlation between solar parameters and the lightning occurrence over Nepal are found to vary at different time period from insignificant correlation to moderate correlation. Hence, further exploration is needed to shed more light to interpret the relationship between solar activity and the lightning occurrence in Nepal.

SWAN: A multihead autoregressive attention model for solar wind speed forecasting

High speed streams of solar wind can impact Earth’s magnetic environment. Current state of the art geomagnetic effect forecasting methods can predict perturbations up to two hours in advance by using solar wind measurements made in the L1 Lagrange point. By forecasting these solar wind parameters, it should be possible to extend the time range for which said methods can be used.We present Solar Wind Attention Network (SWAN), an attention-based autoregressive model for the forecasting of daily average bulk speed values. We apply a novel automatic design method to create its architecture by developing a pipeline of hyperparameter optimization techniques as a proof of concept. Usage of this pipeline leads to a robust, reliable architecture which we empirically verify the convergence of in a variety of problem configurations regarding solar wind speed forecasting.An improvement in terms of root mean squared error (RMSE) is found when compared to WindNet and convolutional neural networks (CNNs) expanding it, even when SWAN does not incorporate images of the solar disk into the input. SWAN’s RMSE of 68.79 ± 1.05 km s−1 with a lead time of three days compares to RMSE values in the order of 80.28 ± 3.05, 76.30 ± 1.87 and 71.93 ± 0.40 km s−1. From these results, we discuss the possibility of incorporating both autoregressive and image information as input to enhance predictive capability of newly developed models. Furthermore, we analyze the resulting model’s behavior to characterize its main error sources, and find its performance to be at its best in scenarios not involving newly formed coronal holes or coronal mass ejections. It is found that this behavior matches well with a priori expectations derived from the underlying physics. Finally, we briefly discuss the need for an in-depth study of different evaluation metrics to provide a more thorough understanding of modeling results on the field.

Elevating sustainability with a multi-renewable hydrogen generation system empowered by machine learning and multi-objective optimization

The global energy landscape is rapidly shifting toward cleaner, lower-carbon electricity generation, necessitating a transition to alternate energy sources. Hydrogen, particularly green hydrogen, looks to be a significant solution for facilitating this transformation, as it is produced by water electrolysis with renewable energy sources such as solar irradiations, wind speed, and biomass residuals. Traditional energy systems are costly and produce energy slowly due to unpredictability in resource supply. To address this challenge, this work provides a novel technique that integrates a multi-renewable energy system using multi objective optimization algorithm to meets the machine learning-based forecasted load model. Several forecasting models, including Autoregressive Integrated Moving Average (ARIMA), Random Forest and Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), are assessed for develop the statistical metrics values such as RMSE, MAE, and MAPE. The selected Non-Sorting Moth Flame Optimization (NSMFO) algorithm demonstrates technological prowess in efficiently achieving global optimization, particularly when handling multiple objective functions. This integrated method shows enormous promise in technological, economic, and environmental terms, emphasizing its ability to promote energy sustainability targets.

Multi-level optimal energy management strategy for a grid tied microgrid considering uncertainty in weather conditions and load

Microgrids require efficient energy management systems to optimize the operation of microgrid sources and achieve economic efficiency. Bi-level energy management model is proposed in this paper to minimize the operational cost of a grid-tied microgrid under load variations and uncertainties in renewable sources while satisfying the various technical constraints. The first level is day ahead scheduling of generation units based on day ahead forecasting of renewable energy sources and load demand. In this paper, a recent meta-heuristic algorithm called Coronavirus Herd Immunity Optimizer (CHIO) is used to solve the problem of day-ahead scheduling of batteries, which is a complex constrained non-linear optimization problem, while the Lagrange multiplier method is used to determine the set-point of the Diesel Generator (DG). The second level of the proposed EMS is rescheduling and updating the set-points of sources in real-time according to the actual solar irradiance, wind speed, load, and grid tariff. In this paper, a novel real-time strategy is proposed to keep the economic operation during real-time under uncertainties. The obtained results show that the CHIO-based bi-level EMS demonstrates an optimal economic operation for a grid-connected microgrid in real-time when there are uncertainties in weather, utility tariffs, and load forecasts.