Patrice Chetangny

This article presents the results of the suitability assessment and identification of favorable sites for photovoltaic solar power plants connected to the electricity grid in northern Benin. The integration of renewable energies into Benin's energy park constitutes a major challenge for which those in power are seeking an optimal solution. The north of the country has strong solar potential, we based our study on this region in order to evaluate the technical and economic feasibility of photovoltaic power plants. The method adopted consists first of analyzing the opportunity to choose potential sites through the combination of a geographic information system (GIS) and a hierarchical analytical process (AHP) approach. In this process, nine (9) factors were weighted, namely solar irradiation, grid connection infrastructure, topography, land cover and use, surface and soil characteristics, environmental risks, flooding, restricted areas and distances from the road and power grid to determine potential sites. Then thanks to the Technique of Order of Preference by Similarity with the Ideal Solution (TOPSIS), the distances were optimized for the identification of the most favorable sites. The results of the application of the selection criteria applied to the northern zone around the substations of the Beninese Energy Electrical located in Bembereke, Djougou, Kandi, Natitingou and Parakou made it possible to identify 25 potential sites then 15 favorable sites. This hybrid method has the advantage of determining both favorable sites and the capacities of the resulting power plants.

The expansion of mobile networks generates high energy demand, especially in remote areas. In Benin, the use of diesel generators in these areas leads to pollution and energy losses on site in the event of fuel shortages. To solve these problems, the integration of renewable energy sources such as solar in mobile base stations is a promising solution. The objective of this study is to optimize the integration of intermittent renewable sources, powering the base transceiver stations (BTS). This leads to the reduction of CO2 gaze emissions and the reliability of power supply to mobile networks in remote areas. The work is based on optimizing a PV/Diesel/battery hybrid system in terms of energy production to permanently power the telecommunication systems. To achieve this goal, the NSGA-II genetic algorithm is employed, taking into account a non-linear load profile that perfectly reflects the energy demand of a BTS site. The results show that the optimal power system for BTS in Benin is composed of a 12 kW diesel generator and 50 solar panels with a peak power of 540 Wc and 30 modular 48V/150Aah batteries. This system would emit 3571.3282 kg/year CO2 with a Lost Power System Probability (LPSP) equal to 4%. The average monthly consumption of the existing site, which is the subject of this study and operates on a diesel generator, is 1500 liters per month, or 18,000 liters per month. This diesel consumption results in annual pollution of 53460 tons of CO2. With the integration of solar energy in this system, the theoretical results show a consumption of 1202 liters of diesel for a production of greenhouse gases of 3571 tonnes of CO2. We note a significant reduction of about 93% on the consumption of fuel oil and on the production of greenhouse gases.

The uneven distribution of primary sources of electric power generation in Economic Community of West African States (ECOWAS) compelled the heads of states to create the West African Power Pool (WAPP). The vision of this system is to set up a common electrical energy market to satisfy the balance between supply and demand at an affordable price using the interconnected network. Forecasting maximum power demand and energy consumption is essential for planning and the coordination of new power plant and transmission lines building. This work consists of predicting maximum power demand and total energy that must transit through the WAPP interconnected network by the year 2032. We compare the performances of three time series models namely the Long Short-Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA) and Fb Facebook Prophet. Electric power and energy data used for training the systems comes from the WAPP authorties. The results show that, for monthly peaks, the Facebook (Fb) Prophet model is the best, with a MAPE (mean absolute error percentage) of 3.1% and a low RMSE (root mean square error) of 1.225 GW. For energy prediction, ARIMA performances are the best compared to others with (RMSE 1.20 TWh, MAPE 1.00%). Thus, the forecast for total annual energy consumption and annual peak demand will be, respectively, 96.85TWh and 13.6 GW in 2032.

Africans in general and specially Beninese’s low rate access to electricity requires efforts to set up new electricity production units. To satistfy the needs, it is therefore very important to have a prior knowledge of the electrical load. In this context, knowing the right need for the electrical energy to be extracted from the Beninese network in the long term and in order to better plan its stability and reliability, a forecast of this electrical load is then necessary. The study has used the annual power grid peak demand data from 2001 to 2020 to develop, train and validate the models. The electrical load peaks until 2030 are estimated as the output value. This article evaluates three algorithms of a method used in artificial neural networks (ANN) to predict electricity consumption, which is the Multilayer Perceptron (MLP) with backpropagation. To ensure stable and accurate predictions, an evaluation approach using mean square error (MSE) and correlation coefficient (R) has been used. The results have proved that the data predicted by the Bayesian regulation variant of the Multilayer Perceptron (MLP), is very close to the real data during the training and the learning of these algorithms. The validated model has developed high generalization capabilities with insignificant prediction deviations.

A model of the higher heating value on a dry basis from the proximity analysis of agricultural wastes in Benin has been proposed in this article. This model was developed using agricultural residues such as shea shells and cakes, cotton and soybean stalks, corn cobs and peanut shells identified as part of the implementation of an experimental system. The validity of this model has been established for the Higher Heating Value (HHV) between 18.07 MJ/kg to 25.91 MJ/kg, Volatile Matter rate (%VM) 66.8% to 79.87%, Fixed Carbon rate 13.83% to 29.59%, and Ash content (%Ash) 3.47% to 6.3%. The model has an average absolute error of 2.79% and a bias error of 0.034%, significantly better than the most accurate literature prediction model, which offers a mean absolute error of 5.97% and –4.66% for the bias error. This work presents as well the first data from the proximity analysis of agricultural residues in Benin. These analyzes are carried out using a well-structured methodology that respects the standards and measures of simple random sampling forsample collection. Samples prepared under appropriate conditions are analyzed using standardized protocols for the agricultural wastes studied.

Within the framework of the Economic Community of West African States (ECOWAS) countries power networks interconnection project carried by the West African Power Pool (WAPP), operating guidelines have been defined by WAPP and ECOWAS Regional Electricity Regulatory Authority (ERERA) to ensure efficient operation of the interconnected network. Each operator is obliged to comply with these directives to guarantee the stability and reliability of energy supply to ECOWAS countries. The purpose of this paper is to carry out the study of the power flow on the existing Togo and Benin power transmission network and to propose solutions for compliance with the requirements relating to the voltage level required on the interconnection lines or at connection points with neighboring power network operators. The dynamic stability of generators will also be studied to assess the behavior of the network during major disturbances. CEB (Communauté Electrique du Bénin in french) is the power transmission network operator of the Republic of Togo and the Republic of Benin. After the inventory of CEB transmission and production infrastructure and the consumption report, the model of CEB power network is produced as well as the single-line diagram. The power flow calculation is performed with the Newton-Raphson algorithm and the node voltages were calculated using the Cyme software. From the voltage values obtained at the various nodes and depending on the behavior of each generator, solutions are proposed to improve the voltage plan of the power network in accordance with the recommendations of the Regulatory Authority.

The choice of a machine for an application and a given specification remains a complex problem. This will involve, for example, bringing together criteria such as: performance, space saving, economical, reliable, little acoustic noise and others. The best machine selection to fulfill all constraints is an important step for the project to be realized. This work focus on Stirling Engine based Generator and study all types of rotating machines that can be employed for maximum electric power production. Analytical electromagnetic models where developed for all types of rotating machines that satisfied minimum requirement for the project by solving Maxwell equations. The purpose is to develop the design model and combine electromagnetic and thermal study of the machines. Finite Element Method is used to compare the performances of the generators for the best choice. Results show that for applications not requiring bigger output power, the major criteria for the selection is the optimal magnetic induction created by the inducer in the stationary part of the machine. For application such as Stirling generators, permanent magnet (PM) machine satisfy many comparison criteria such as maximum power at low speed, torque density, high efficiency. Beyond exposing a selection method for a project, this work lay down a step-by-step method for engineers and scientists for the crucial stage of design and conception work