Search

[Display omitted] • Electricity consumption is mapped using a combined cartographic methodology. • State of the sky impact is explored for the first time on energy management strategies. • LF strategy provides the best load monitoring by minimizing the generator uptime. • Energy management strategies play a pivotal role as hybrid solar system control tools. A novel optimized sizing and management strategy of a grid-connected hybrid photovoltaic (PV)-solar-battery-group system were proposed for the electrification of residential consumers in Northwest Africa (a case of Mauritania), and the influence of the state of the sky (clear, moderately overcast, and overcast) was analyzed according to the load flowing (LF) and the cycle charging (CC) strategies. In order to mitigate the pressure on the national grid, consolidate the consumer autonomy, minimize the cost of medium- and long-term consumption bills, and CO 2 -related emissions, a cartographic approach was conducted as the first attempt to map the electricity consumption potential for buildings in the city of Nouakchott (Mauritania) using a geo-referenced database. ArcGIS®, HOMER Pro®, and MATLAB® softwares were used for the establishment of the load profile, optimized sizing of the PV-batteries-group-grid system, and calculation of the lightness index, respectively. The LF strategy provided the best monitoring of the load throughout the day by minimizing the generator uptime. The techno-economic analysis revealed the values of cost of energy (COE) and net present cost (NPC) as follows: COE = $0.0549/kWh and NPC = $24,796 for the LF PV-batteries-grid strategy, COE = 0.0646 $/kWh and NPC = $23,380 for the CC PV-batteries-grid strategy, and COE = $0.17/kWh and NPC = $23,262 for the case of the grid on its own. [ABSTRACT FROM AUTHOR]

This study proposes an original adaptive neuro-fuzzy inference system modeling approach to predict the output power of a wind turbine. The model's input includes the wind speed, turbine rotational speed, and mechanical-to-electrical power converter's temperature. The structure of the adaptive neuro-fuzzy inference system-based model was first identified using the processed data gathered from wind turbine number 1 of a 30-MW wind farm in Nouakchott (Mauritania). Then, the proposed data-driven model was trained and validated according to two new scenarios based on the data set from four identical wind turbines operated in the same climatic conditions and the data set from the same wind turbines operated under different climatic conditions. Benchmarking involved the proposed model, existing approaches in the literature, and five adaptive neuro-fuzzy inference system-based models, including grid partition, subtractive clustering, fuzzy C-means clustering, genetic algorithm, and particle swarm optimization, on the same data set to validate their prediction performance. Compared with existing adaptive neuro-fuzzy inference system-based models, the proposed approach was proven to be a promising methodology with higher accuracy for estimating the output power of wind turbines operating in different climatic conditions. According to the results from two different scenarios, the lowest value of the fitting rate and the highest values of the normalized mean square error, normalized mean absolute error, and root mean square error for the validating period were 0.9977, 0.0047, 0.0473, and 46.5831 kW, respectively. Moreover, the proposed model showed superior forecasting performance and thus better accuracy in estimating wind power output compared to other adaptive neuro-fuzzy inference system-based models. [Display omitted] • Original ANFIS approach was developed for wind turbine (WT) output power prediction. • Scenarios-based validation with real data from wind farm enhanced model building. • Machine learning embedded fuzzy logic boosted the model identification strategy. • Benchmarking of existing models corroborated the superiority of preprocessed ANFIS. • Neuro-fuzzy tool accurately estimated WT output power in various climatic conditions. [ABSTRACT FROM AUTHOR]

Despite the remarkable potential for biogas production from livestock and organic wastes, the number of constructed biogas digesters for the continent is in the order of thousands and in case of Mauritania is lower than the number of the provinces. While majority of the existing digesters left far behind the expected efficiency, lack of a comprehensive economic assessment along with an efficient design questions their practical application. The present study introduced the first assessment to evaluate the biogas potential from livestock manures and waste from slaughterhouses in Northwest Africa (a case of Mauritania). Using ArcGIS® software, a database was conducted to build maps that show the amount of waste products, the potential of biogas, and equivalent amounts of energy. These were used to assess the potential of biogas and the corresponding potential energy for each geographical department in Mauritania. The results indicated that the southern provinces had the highest biogas potential with the maximum and the average values of 520 and 258.7 (±125.8) × 106 m3/year. On the other hand, the lowest biogas production potential (27.7 × 106 m3/year) was recorded for northern provinces with the maximum and the average values of 135 and 76.4 (±39.7) × 106 m3/year. The results showed that 63,579 × 106 kg of waste associated with livestock (cattle) and slaughterhouse applications were annually produced in the country. It was determined that this quantity could generate 2451 × 106 m3 of biogas per year, corresponding to an energy potential of 52,704 × 106 MJ/year. Considering the rapid depletion of conventional energy sources and the significance of biogas as a renewable fuel, a detailed feasibility analysis (for the biogas production in each province of Mauritania by means of community type fixed-dome digesters) was also performed in the scope of this study. The results of the comprehensive cost breakdown analysis revealed that the revenues obtained from sale of biogas-generated electricity and digested slurry (as fertilizer) could able to pay the initial investment within approximately 6.5 years without subsidy. The findings of this study, as the first of its own, could be used to comprehend how utilization of information such as slaughterhouse and livestock population, nutrition habitats, land-usage maps and geographic information system (GIS) can be employed to germinate a model for more comprehensive assessment of biogas production potential from livestock manure and slaughterhouse wastes, specifically in case of northern African countries. Moreover, in case of biogas plant, this model could be employed as a decision-making tool to identify the highly qualified location for construction of the biogas plant. • Mapping of biogas production potential is conducted in Mauritania for the first time. • 63,579 × 106 kg of waste can generate 2451 × 106 m3 of biogas per year in the country. • The southern provinces are found to have approximately 4 times more biogas potential. • Energy production in Mauritania heavily depends on oil, natural gas, and wood fuels. • A nationwide biogas digester project can pay for itself in 6.5 years without subsidy. [ABSTRACT FROM AUTHOR]