Energy Yield Estimation Research Articles

Optimizing cropping patterns and resource allocation for sustainable agricultural development: A case study of Ilam province, Iran

Establishing connections between ecosystems and agricultural mechanization can help address erroneous decision-making in agricultural management and prevent further harm to ecosystems. It is essential for authorities and decision-makers in the agricultural sector to understand ecosystem dynamics and influencing factors to develop appropriate policies for sustainable development. This study focuses on identifying optimal conditions for determining crop patterns and input allocation in Ilam province's agricultural practices, considering technical and energy parameters. Cultivating one hectare of wheat and barley in the region requires an average energy consumption of 1.79 GJ from various sources, with an estimated energy output of 38.04 GJ ha−1 for these crops. The research involved farmers cultivating wheat, barley, fodder maize, and canola in the southern region of Ilam province, covering cities like Abdanan, Dareshahr, and Dehloran. The study suggests adjustments to cropping patterns, including reducing wheat cultivation and increasing irrigated fodder maize cultivation under optimal conditions. It also recommends exploring rainfed cultivation or enhancing the yield of rainfed wheat to offset the reduced wheat cultivation. The goal planning model indicates a shift towards sustainability, with changes in cropping patterns focusing on wheat, barley, fodder maize, and canola crops, including expanding the cultivation areas of irrigated wheat, barley, and fodder maize. Efforts aim to enhance the productivity of irrigated crops to promote sustainability and improve agricultural practices in the province for long-term sustainability goals.

Assessment of Wind Energy Potential at Three Prime Locations in Saudi Arabia: Analysis of Sharma, Qurayyat and Sakaka Sites

Purpose: The research aims to assess the wind energy potential at three chosen sites in Saudi Arabia Sharma, Al Qurayyat, and Sakaka over a five-year period from 2019 to 2023. The main goal is to determine the most favorable locations for wind farm development based on wind speed characteristics, Weibull distribution parameters, and wind power density (WPD). Materials and Methods: The wind speed data for the chosen locations was obtained from NASA's wind website and analyzed using the Weibull distribution to determine the shape (k) and scale (c) parameters, which are indicative of wind speed distribution. Critical metrics such as mean wind speed, WPD, and the most probable wind speed were computed to evaluate the wind energy potential. The Vestas V150-4.2 MW turbine was utilized to estimate potential energy output, considering the variability and reliability of wind resources at the specified locations. The study utilized tables, figures, and wind rose diagrams to highlight important wind parameters like mean wind speed, Weibull parameters, WPD, and Vmax for each location. Flowcharts were also used to summarize the methodology and conclusions, ensuring a clear and comprehensive presentation of the data. Findings: The analysis showed that Qurayyat consistently had the highest mean wind speed and WPD, indicating excellent wind energy potential. Sharma and Sakaka also displayed significant wind resources, with high mean wind speeds and substantial WPD values. The Weibull parameters indicated that all three sites have favorable wind characteristics for energy production. The estimated energy output using the Vestas V150-4.2 MW turbine ranged from 12,809 to 17,807 MWh/yr at Site 18, 14,095 to 15,170 MWh/yr at Site 24, and 14,013 to 15,832 MWh/yr at Site 31. Implications to Theory, Practice and Policy: The study presents a thorough evaluation of the wind energy potential in the Al-Jawf and Tabuk regions of Saudi Arabia. It contributes to a deeper understanding of wind resource distribution and its significance for the development of renewable energy. The study provides valuable insights for wind farm developers and policymakers by identifying the most promising locations for investment in wind energy infrastructure. Furthermore, it emphasizes the need for detailed site-specific analyses to optimize wind energy deployment, aligning with Saudi Arabia's broader renewable energy objectives.

Analysis and Selection of Optimal Perovskite/Silicon Tandem Configuration for Building Integrated Photovoltaics Based on Their Annual Outdoor Energy Yield Predicted by Machine Learning

Accurate estimation of annual output energy yield () for perovskite/silicon (PSK/cSi) tandem solar cells is pivotal in assessing their suitability for building‐integrated photovoltaics (BIPV). This study pioneers five machine learning models of ensembles of trees, Gaussian process regressions, regression trees, support vector machines, and artificial neural networks (ANN) to predict output power density and compute for 2T, 3T, and 4T PSK/cSi tandem configurations in Japan's outdoor conditions. Seven predictive inputs of visible‐light solar irradiance, near‐infrared‐light solar irradiance, incident solar spectrum angle, solar module temperature, perovskite thickness, perovskite bandgap, and terminal of tandem configuration (T) drive the machine learning models. These models optimize predictions using k‐fold cross‐validation and Bayesian algorithms, showcasing superior precision in prediction compared to prior models. The ANN model emerges as the best model, displaying the minimal error in predicting , used to estimate across five Japanese locations (Gifu, Naganuma, Okinoerabu, Tosu, and Tsukuba). Results from these locations in blue‐rich solar spectrum zones identify the 4T PSK/cSi tandem configuration, featuring the most outstanding mean maximal (93.63, 263.02, 153.59, and 91.75 kWh m−2 for the east, rooftop, south, and west directions), as the prime candidate for BIPV applications.

Biomass and energy potential of Erianthus arundinaceus and Saccharum spontaneum-derived novel sugarcane hybrids in rainfed environments

BackgroundEnergy canes are viable feedstocks for biomass industries due to their high biomass production potential, lower susceptibility to insects and diseases, better ability to adapt to extreme conditions and clean bioenergy. Interspecific hybrids (ISH) and intergeneric hybrids (IGH) have great potential to meet the growing demand of biomass, biomass-derived energy and feedstock.ResultsIn this study, two types of energy canes, Type I and Type II, derived from S. spontaneum and E. arundinaceous background were evaluated for high biomass, fiber and bioenergy potential under subtropical climate along with the check varieties Co 0238 and CoS 767. Out of 18 energy canes studied, six energy canes, viz., SBIEC11008 (204.15 t/ha), SBIEC11005 (192.93 t/ha), SBIEC13008 (201.26 t/ha), SBIEC13009 (196.58 t/ha), SBIEC13002 (170.15 t/ha), and SBIEC13007 (173.76 t/ha), consistently outperformed the check varieties under Type-I, whereas in type-II, SBIEC11004 (225.78 t/ha), SBIEC11006 (184.89 t/ha), and SBIEC14006 (184.73 t/ha) energy canes produced significantly higher biomass than commercial checks, indicating their superior potential for cogeneration. Estimated energy output from the energy canes (700–1300 GJ/ha/year) exceeded the range of co-varieties (400–500 GJ/ha/year) and energy utilization efficiency in plants and ratoon crops for energy canes viz., SBIEC11008 (3%, 1.97%), SBIEC14006 (1.93%, 2.4%), SBIEC11005 (1.7%, 1.9%), and SBIEC11001 (1.01%, 1.03%), was higher than best checks Co 0238 (0.77, 0.9%). Additionally, energy canes SBIEC 13001 (22.35%), SBIEC 11008 (22.50%), SBIEC 14006 (28.54%), SBIEC 11004 (30.17%) and SBIEC 11001 (27.03%) had higher fiber contents than the co-varieties (12.45%).ConclusionThe study gives insight about the potential energy canes for higher biomass and energy value. These energy cane presents a vital option to meet the future demand of bioenergy, fiber and fodder for biomass due to their versatile capacity to grow easily under marginal lands without competing with cultivated land worldwide.

Evaluation of solar PV-based microgrids viability utilizing single and multi-criteria decision analysis

Feasibility analysis is crucial for solar projects to minimize risk and ensure goal achievement. Essential factors include the PV system's location, elevation, land slope, energy yield, soil texture, mean annual cloud frequency map, and distance from urban areas. This study examines the performance of a 3 MW PV system across five remote Egyptian regions characterized by desert weather conditions. The influence of available weather data sources and different yield estimation methods on the feasibility of the PV system is treated as a Single-Criteria Decision Making (SCDM) scheme. Meteorological data from Meteonorm 8.1, PVGIS, and NASA-SSE satellite data are utilized as input data for the designed PVsyst and ANSYS-based energy yield estimation models and for empirical formulas. Significant variations in estimated energy yield are observed, depending on the weather data source. The study also underscores the importance of diligently evaluating and comparing the accuracy and reliability of different weather data sources and estimation methods for simulating PV-based microgrid system performance. The yield estimation-based SCDM indicates that the New Valley PV system consistently outperformed the other systems. However, a Multi-Criteria Decision Making (MCDM) indicates that the Red Sea performs well in technical criteria evaluation, while alternative Aswan City excels in practical and feasibility criteria assessment. The study employs the MCDM approach to provide a more detailed evaluation. Specifically, the technical criteria focus on energy yield, system efficiency, and reliability.In contrast, a decision-maker prioritizing long-term sustainability might lean towards a system that excels in technical criteria, even if it is economically less attractive in the short term. Conversely, a decision-maker constrained by budget limitations might opt for a system that, while not the most technically efficient, offers the most cost-effective solution. This dual-criteria approach reveals that while one region may excel in technical performance, another is more financially viable. The choice between these two regions hinges on the specific priorities of the decision-maker.

Statistical analysis of wind speed distribution based on sixWeibull Methods for wind power evaluation in Garoua, Cameroon

Wind data analysis and accurate wind energy potential assessment are critical factors for suitable development of wind power application at a given location. This paper explores wind speed distribution to select the two-parameter Weibull methods that provide accurate and efficient estimation of energy output for Wind Energy Conversion Systems (WECS).The dimensionless shape parameter k and the scale parameter C are determined based on measured hourly mean wind speed data in times-series from 2007 to 2012, collected at the Garoua International Airport, main meteorological station, in Garoua, Cameroon. Six numerical methods, namely Empirical Method (EM), Energy Pattern Factor method (EPF), Graphical Method (GM), Maximum Likelihood Method (MLM), Moment Method (MM) and Modified Maximum Likelihood Method (MMLM) are examined to estimate the Weibull parameters. To analyze the efficiency of the methods and to ascertain how closely the measured data follow the Weibull methods, goodness of fit tests were performed using the chi-square test (X2), correlation coefficient (R2), root mean square error (RMSE) and Kolmogorov-Smirnov test (KOL). The results revealed that the EPF followed by the MM were the most accurate and efficient methods for determining the value of C and k to approximate wind speed distribution. The statistical tests rejected the GM as an adequate method and revealed as well that the EM, MLM and MMLM ranked respectively third, fourth and fifth. Furthermore, the potential for wind energy development in Garoua is not fitted for generating electricity and a very fruitful result would be achieved if windmills were installed for producing community water supply, livestock watering, and farm irrigation.

On the errors in annual energy yield estimation due to monodirectional wave spectra assumption

The wave energy sector has made significant progress, and its potential is tangible. However, in order to make wave energy production a key tool in the energy transition, it is crucial to minimize the uncertainty associated with the energy production assessment. Trustworthy evaluations are reachable only through detailed analyses based on reliable, accurate and representative wave data.The use of synthetic parameters exclusively derived from a frequency spectrum leads to the neglect of directional information and the combination of wind waves and swells is treated incorrectly. This study investigates errors associated with relying solely on the frequency spectrum and points out the relevance of directionality.The island of Pantelleria serves as a case study, and the results show that PeWEC (Pendulum Wave Energy Converter) performance is overestimated when the monodirectionality of the waves is assumed. Neglecting the directional information contained in the frequency-direction spectra results in an overestimation of energy production of 40%.

Performance Evaluation of Burkina Faso’s 33 MW Largest Grid-Connected PV Power Plant

This study conducted an in-depth analysis of the performance of the largest Grid-Connected Solar Photovoltaic System in Burkina Faso from 2019 to 2021. The research utilized measured data and simulated the plant’s performance using the PVGIS database. The results revealed that the months with high solar radiation were the most energy-productive, indicating a direct correlation between solar irradiance and energy generation. During the rainy season (July and August), the PV plant exhibited the highest conversion efficiency. Conversely, the hot season (March and April) was associated with the lowest conversion efficiencies, with module temperatures reaching approximately 47 °C. Efficiency decreased from 12.29% in 2019 to 12.10% in 2021. The system’s performance ratio ranged from 80.73% in 2019 to 79.36% in 2021, while the capacity factor varied from 19.89% in 2019 to 19.33% in 2021. The final yield, measured in hours per day, was 4.89 h/d in 2019, 4.61 h/d in 2020, and 4.92 h/d in 2021. These findings highlight the deterioration in the performance of the Zagtouli PV plant over time. The study emphasizes the utility of using PVGIS-SARAH2 to forecast solar radiation and estimate energy output in PV systems. A semi-automatic cleaning system is used to clean the modules. This cleaning mechanism is inefficient because it is inconsistent. To increase the PV plant’s effectiveness, improved cleaning systems with more advanced mechanisms are required. This research, the first of its kind on the largest PV power plant connected to Burkina Faso’s national grid, serves as a valuable model for other power plants currently under construction or in the planning stages.

Albedo measurements and energy yield estimation uncertainty for bifacial photovoltaic systems

AbstractThe deployment of bifacial photovoltaic (PV) modules for utility‐scale PV plants has quickly proliferated in recent years. As they can harness the irradiance received at the rear side of bifacial generators—which depends on the albedo of the reflector ground—bifacial modules provide higher productivity than monofacial modules, in exchange for a low increase in manufacturing costs. However, the reduction of PV performance estimations uncertainty still represents a main concern for both PV and financial actors, which seek to reduce financial risks of bifacial PV systems. The aim of this work is to provide a better understanding of the impact that albedo measurements have on PV yield estimations uncertainty. We analysed the albedo sensitivity coefficient relating albedo measurement and subsequent energy yield estimations' uncertainties through a representative yield simulation exercise performed for three different sites (Chile, Spain and Sweden) and two types of PV plants: static and single‐axis tracker. A formula considering the impact of Ground Coverage Ratio and Inverter Loading Ratio is proposed for this coefficient. We performed an experimental campaign to evaluate the uncertainty associated to different albedo measurement conditions. Surface reflectance of five different ground surfaces has been measured using four sensors, which allowed to obtain three useful types of albedos that we defined and described. A series of recommendations for short‐term ground‐based albedo measurements that entail a maximum of 1.5% yield uncertainty contribution in the frame of commercial PV plants are then derived. A representative case of actual albedo measurement practices at the current PV market scene is also presented in the form of a commercial albedo measurement project for a future 50 MW bifacial PV plant. Despite being a local characteristic, the most relevant uncertainty source found is associated to the non‐homogeneity of the future PV plant's surface. An important consideration is that changes in the use of land caused by the construction of a PV plant lead to an albedo uncertainty that predominates over all the uncertainty sources associated to the albedo measurement conditions. This can also be the case for long‐term seasonal variations, which can become the dominant uncertainty source for high latitude locations.

Energy yield estimation of on-vehicle photovoltaic systems in urban environments

Greenhouse gases from the propulsion systems of road transportations constitute a significant obstacle to achieve the Paris Agreement objectives. Nowadays, the substitution of conventional internal combustion engines with electric motors, along with electrochemical storage systems are the leading efforts to reduce the use of fossil fuels in road transportations. However, their limited driving range and the long charging times are the main technical factors that hinder the development of electromobility. Thus, energy harvesters and regeneration systems are increasingly incorporated in road vehicles, in order to increase their driving range. In this context, Vehicle Integrated and Applied Photovoltaics (VIAPVs) constitute an attractive prospect. The electricity yield for VIAPVs depends strongly on the route, the shadings due to the urban environment, the applied Maximum Power Point (MPPT) algorithm and the traffic conditions. In this paper, four commonly used commercial MPPT algorithms are experimentally evaluated, regarding their ability to extract the maximum available power simulating realistic city routes. The results show notable discrepancies in the performance of the studied algorithms, between terrestrial and VIAPV applications, highlighting the impact of poor MPPT performance in terms of power generation in moving vehicles.

Simulation of Colored BIPV Modules Using Angular-Dependent Spectral Responsivity

An accurate energy yield estimation for building-integrated photovoltaic (PV) applications with colored glass under field conditions is a key factor for architects, building planners, and potential investors. The power output of these installations depends not only on the angle of incidence but also on the incident solar spectrum. The aim of this work is to simulate the short-circuit current of PV single-cell laminates with colored solar glass under field conditions. Therefore, we use angular-dependent spectral responsivity measurement data from a physical setup. Test samples with gold-, blue-, blue-green-, and gray-colored solar glass and a reference with standard solar glass, oriented on a tilted surface, are simulated. The results for one month of field validation indicate a good agreement with a relative root mean square error between 1.9% and 2.5% for the test samples. A model comparison reveals larger errors for the blue, blue-green, and gold test samples when the spectral responsivity at normal incidence and the incident angle modifier for broadband direct irradiation are used instead. It is concluded that short-circuit current simulation using indoor characterization from spectrally resolved measurement setups provides improved accuracy that is particularly suitable for bankable energy yield estimates of colored building-integrated PV modules.

Productivity of giant miscanthus plantations depending on the applied elements of cultivation technology in the conditions of the Forest Steppe of Ukraine

Purpose. To improve the technology of caring for giant miscanthus plantations, starting from the second year of the crop vegetation, and to establish the peculiarities of the formation of biomass yield with the use of different types of fertilizers. Methods. The research was carried out in the years 2020–2021 in the conditions of the Bila Tserkva Experimental and Breeding Station of the Institute of Bioenergy Crops and Sugar Beet of the National Academy of Agrarian Sciences located in the zone of unstable moisture of the Right Bank Forest Steppe of Ukraine. The design of the experiment involved the application of mineral fertilisers (ammonium nitrate N24 kg/ha a. i., ammonium nitrate + ammonium sulfate N24 + S6 kg/ha a. i.) in row in the spring, foliar application of humates (Vermisol, 8.0 l/ha, Humifield VR-18, 0.4 l/ha), and complex fertiliser with amino acids (Quantum Aminomax, 0.5 l/ha). Mineral fertilizers were applied locally in row before the emergence of miscanthus seedlings. Split foliar application of humates and amino acids was carried out in the five-leaf stage and two weeks later. Results. Productivity of miscanthus plants differed significantly over growing seasons, while the main regularities of the influence of the investigated elements of plantation care technology on the productivity formation were maintained. In the second year of vegetation (2020), each plant formed from 18 to 37 shoots and in the third year from 37 to 50 shoots. Four-year-old plants had 28–50 shoots each. The yield of raw biomass at the end of the growing season in 2020 varied from 14.5 to 43.5 t/ha. Compared to 2020, in 2021 the biomass yield increased 1.5–2 times – up to 31.0–74.6 t/ha. According to the experiment, the average yield of biomass for the fourth year of the growing was 17% lower compared to the respective indicators of 2021 and varied according to the experiment treatments from 25.3 to 67.1 t/ha. The yield of dry matter varied from 9.78 to 28.30 t/ha in 2020, from 17.50 to 37.85 t/ha in 2021, and from 12.11 to 32.46 t/ha in 2022. The estimated energy output from miscanthus biomass, depending on the influence of the experimental factors, was 174.8–509.6 GJ/ha in 2020, 315.5–683.2 GJ/ha in 2021, and in 2022 – 219.0–587.3 GJ/ha. Conclusions. The minimum values of all studied productivity indicators of miscanthus were obtained in the control treatment of the experiment. Application of ammonium nitrate (N24) and ammonium nitrate with ammonium sulfate (N24 + S6) in spring contributed to a significant increase in the number of shoots per plant, as well as biomass yield and estimated energy yield per hectare. Similarly, significant increase in all indicators of productivity was ensured by foliar application of humates (Humifield BP-18, 0.4 l/ha and Vermisol, 8,0 l/ha). In general, over the years of research, the maximum indicators of the number of shoots per plant, the yield of biomass and the estimated energy output from biomass were obtained under the combined application of mineral fertilisers [ammonium nitrate + ammonium sulfate (N24 + S6)] with two-time foliar application of humates (Vermisol, 8.0 l/ha or Humifield VR-18, 0.4 l/ha) and complex fertiliser Quantum Aminomax, 0.5 l/ha.

A framework to estimate generating capacities of PV systems using satellite imagery segmentation

The growing interest in global warming has led to various efforts to rely on new renewable energy such as solar power. More accurate energy generation calculation is on-demand with the rise of publicly accessible residential photovoltaic (PV) panels. PV panel area computation is the first step towards the image-based estimation of energy generation from the residential solar arrays. Satellite image segmentation provides a low-cost and simple solution to calculate solar panel area installed on rooftops and the ground. The most relevant approach is to acquire high-quality labels rather than network architectures in many scenarios. This research aims to estimate potential solar energy from satellite images using deep learning segmentation techniques. We compared five architectures such as UNet, UNet++, Feature Pyramid Network (FPN), Pyramid Scene Parsing Network (PSPNet), and DeepLabV3, with different encoders. We converted segmented image pixels into square meters regarding the average slope of panels. We considered the average solar panel efficiency, global tilted irradiation, and the loss coefficient to estimate the annual energy output from the calculated area. UNet with the EfficientNetB7 encoder performed the best with a dice similarity coefficient of 0.9982 for segmentation and a Mean Absolute Percentage Error (MAPE) of 3.5 for energy output estimation.

Pragmatic analysis of solar photovoltaic system design in an institutional building in eastern India

PV systems connected to the grid are primarily designed to generate clean energy to fulfil the rising energy demand. To predict the performance and efficacy of PV systems, several installation aspects are employed, such as location, local meteorological data, orientation, optimal tilt angle, varying losses, and energy yield estimation. Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar (Latitude 20.24°N and Longitude 80.85°E) has taken one such initiative by installing a 13 kWp rooftop grid-connected solar photovoltaic system in one of its institutional buildings. The solar PV system is fixed at an angle of 21° and is installed on the rooftop of a 25-m height building. The measured performance characteristics were analysed and compared to those of PVSyst software to determine its feasibility in campus buildings. From the result, the annual electricity supplied to the grid is 17.79 MWh, final yield of 3.80 h/d, reference yield of 4.62 h/d and performance ratio of 0.82. The solar PV system installed at the institution is predicted to have reduced 17443.02 kg of CO2, 22.07 kg of SO2, and 46.1 kg of NOx from the atmosphere per year. The lebelised cost of energy (LCOE) of the installed PV system is 9.408 INR/kWh with a payback of 9.8 years. This study provides the techno-economic feasibility of installing grid-connected solar PV systems on the roof of an institutional building and help the solar installers in establishing the most efficient solar photovoltaic system’s sizing and design.

Impact of high constant charging current rates on the charge/discharge efficiency in lead acid batteries, for residential photovoltaic system applications

The charging and discharging of lead acid batteries using Traditional Charge Controllers (TCC) take place at constantly changing current rates. These techniques do not permit the accurate estimation of energy input and energy output during charge and discharge processes. In this work, the main objective is to investigate the effect of high constant charging current rates on energy efficiency in lead acid batteries, extending the current range to 8A from 5A already reported in literature. We also present the experimental proof of the variation of electrolyte temperature with magnitude of the constant electric charging current. Firstly, a Constant Current Circuit (CCC), capable of charging the battery at current rates ranging from 0.5A to 8A was built and used to run experiments on two sample lead acid batteries, battery sample 01, the Vanbo battery and battery sample 02, a Winbright battery. Charge and discharge processes were conducted on these batteries through the CCC and efficiencies computed per current rate tested. Secondly, to compare the results of the CCC built with those of a TCC, the very batteries (battery sample 01 and battery sample 02) were charged and discharged through a TCC and their corresponding efficiencies computed. Thirdly, three constant charging current regimes (0.5A, 5A and 8A) were chosen within the tested current rates for which further electrolyte temperature monitoring tests were carried out, using two other lead acid battery samples of different health states. This was carried out on battery sample 03 which was a SIGA lead acid battery type with a compromised state of health and on battery sample 04, another brand new Winbright battery with relatively good state of health. It was noticed that, for the CCC, efficiencies between 62 % and 82 %, were recorded for the Vanbo Battery (Sample 01) while efficiencies of 53 % to 91 % were recorded for the Win Bright Battery (Sample 02). On the other hand, the efficiency of the Vanbo Battery (Sample 01) through the TCC was found to be 66 % while that for the Winbright battery (Sample 02) was found to be 79 %. As for electrolyte temperature monitoring, battery sample 03 with compromised health state registered higher temperatures for higher charging rates while battery sample 04 of good state of health registered no noticeable increase in temperatures even for constant charging current rates up to 8A. Since existing literature had tackled lower current values from 0.5A to 5A, this work therefore comes in with an extension of the current rates, testing higher current magnitudes and obtaining the same results with conclusion that, if the same energy is stored in a lead acid battery at precise rates, the charge/discharge efficiency of the battery increases as the charging current rate increases. As regards electrolyte temperature monitoring, the work provides further proof that even at charging current rates as high as 8A, battery electrolyte temperatures still remain within acceptable limits(below 50 °C).

Design of International Airport Hybrid Renewable Energy System

This paper presents the design and simulation of a hybrid renewable energy system utilizing solar and wind energy sources with a backup generator. The demand for reliable electric energy in support of investments in large social and economic development activities such as airport operations has been an agenda worldwide. In Tanzania, Mwanza International Airport (MIA) expects to consume about 18 MVA of electric power annually to support its operations for the next 25 years. About 78-80% of the world's commercial energy comes from fossil fuels. Non-renewable fuels and other negative effects contribute to global warming through greenhouse gas emissions and carbon dioxide emissions. Additionally, most centralized conventional power generation methods require transmission systems, adding complexity and poor power quality. Therefore, the proposal to use a mixed-coupled hybrid renewable energy source to power the airport is necessary. The energy mix considered is solar photovoltaic (PV), wind, diesel generator and a battery. There is an average solar irradiation of 5.38 kWh/m2 and a wind speed average of 4.20 m/s that could be converted to electricity by installing a 10-kW wind turbine (this is enough to generate power for MIA). The diesel generator and the battery designed at 140 kVA and 400 Ah, respectively, take the intermittency. The project will be in operation for 25 years; hence its costs are reasonable, and the justification is the potentiality of harvesting that estimated energy output of 18 MVA, which will meet the load for MIA. Some mathematical computations were performed, and, in the end, simulation results displayed different techno-economic Hybrid Renewable Energy Source (HRSE) configurations. The selected system's complete design would include a 78.48 kW PV system comprising 314 pieces of 200 W poly-crystalline modules, 608 batteries of 83.4 Ah, 12 V rating, 140 kVA diesel generator, and 41.64 kVA bidirectional converter. The net present cost of the selected design is US$357,780.8, the energy cost is 0.93US$/kW, and the minimum renewable fraction is 40.2%.

The Study of Fish Remains from the Late Roman Era from Argamum, Tulcea County

The studied fish remains come from the excavations carried out by the Ștefan Honcu archaeologist between 2017 and 2020 within the Argamum archaeological site. The study methodology consisted mainly of anatomical and taxonomic identifications based on archaeozoological atlases, quantification of the data, taphonomical evaluation, osteometry, size and weight estimation, and energy yield estimation. The archaeozoological material represents food waste. The only osteological remains registered with butchering marks was a fragment of a caudal vertebra from Silurus glanis (catfish). The studied material consists of 72 fish remains coming from carp – 28 fragments, catfish – 19 remains and the rest of 25 fragments couldn`t be identified up to species level. The faunal material is dated as follows: second half of the V century level – 9 remains, 4 remains from carp, a fragment from catfish and 4 fragments from unidentifiable fish; first half of the VI century – 17 osteological remains coming from carp – 7 remains, catfish – 8 remains and two unidentified fragments; second half of the VI century were discovered the most fish remains – 41 osteological fragments; late VI – early VII century – 5 fragments, 2 remains coming from carp and 3 remains from catfish. Most of the carp individuals range between 60-80 cm and 80-100 cm. For catfish, most of the individuals have sizes between 220 -240 cm. Based on the estimated amount of meat for each species, the energy yield (kcal) was calculated, the assemblages dated to the VI century being the richest.

Estimation and comparison of annual energy yield of total cross tied- in- series and conventional PV array configuration under unshaded and commonly occurring partial shading conditions in urban areas

Estimation and comparison of annual energy yield of total cross tied- in- series and conventional PV array configuration under unshaded and commonly occurring partial shading conditions in urban areas

Implantation, Operation Data and Performance Assessment of An Urban Area Grid-Connected Small Wind Turbine

Over the last few decades, and more prominently currently, many countries have launched and reinforced campaigns to reduce CO2 emissions from all human activities and, in the area of energy, promote energy generating technologies from low carbon, renewable sources, especially wind and solar. In recent years, this promotion of renewables can be seen in statistics as well as an extraordinary increase in plants using renewable sources. There is more activity surrounding the use of small devices installed close to consumers, such as small wind turbines (SWT). In cities, the best places to install SWT are tall buildings. The Institute of Energy and Environment (IEE-USP) has installed a 1.8 kW SWT on the University of São Paulo campus in São Paulo, Brazil. Even with low-magnitude winds at the site, the SWT installation was carried out to serve as a didactic apparatus and demonstration initiative of wind energy generation connected directly to the University’s electric grid, which already has other embedded renewable sources installed, namely photovoltaic and biogas plants. The turbine was placed on the roof of the existing High Voltage Laboratory building, leading to an operating height of 35 m. This paper presents previous local wind data measurements using a Lidar system, annual energy yield estimation calculations, and measurements, also bringing all implementation details. It reports and analyzes the operation and energy production data from three full operational years, from 2018 to 2020, discussing and concluding with further improvements of SWT from technical and economic aspects.

The estimation and forecast of solar energy yield with Weibull distribution

Abstract In the first and second chapter the article provides an overview of the currently used energy sources in Hungary and the most popular renewable energies. In addition, the Weibull estimation is presented, too. The subsequent chapter looks at some of the research results about the solar energy optimization with Weibull distribution. The study presented is a mathematical solution of the solar energy optimization with distribution. The final chapter contains a brief explanation of the results. This publication briefly summarizes a prototype solution for an estimation and forecast of solar energy and yield with Weibull distribution.