The distribution networks are more and more heavily loaded due to economic growth, industrial development and housing. The operation of these networks under these conditions generates voltage instabilities and excessive power losses. The present work consisted in the optimal integration of multi-GED (Decentralized Energy Generators) (Photovoltaic (PV), Fuel Cell (FC or PAC) and Wind Generator (WG)) and FACTS (SVC) in a Medium Voltage distribution's departure of the Beninese Electrical Energy Company (SBEE), with a view to improve its technical performances. The diagnostic study of the Ouidah 122-nodes test network, before optimization, revealed that the active and reactive losses are 457.34588 kW and 625.41503 kVAr respectively. This network has high voltage instability with a minimum voltage of 0.80455 p.u. and a minimum VSI of 0.41897 p.u. The optimization of the size and positioning of GED and FACTS was based on the Nondominated Sorting Genetic Algoritm II (NSGA II). After optimization with the NSGA II, a comparative study of the different combinations between the three GEDs and the SVC, made it possible to choose that of the placement of a 121 kW Wind Generator at node 75, a PV of 131 kW at node 51, a system of Fuel Cell (FC, PAC in french) of 700 kW at node 34, and an SVC of 2.126 MVAr at node 94 of the network. This positioning enabled a reduction of 65.11% in active losses and 65.12% in reactive losses. The voltage profile and the voltage stability are clearly improved, with a minimum voltage of 0.96993 p.u. and a minimum VSI of 0.88505 p.u. The initial investment for this project is seven hundred and seven million three hundred and fifty-two thousand three hundred and fifty-eight point seven CFA francs (707,352,358.7 CFA francs). The technical and economic evaluation shows that the payback period is approximately 4 years 6 months and 14 days. The relevant results obtained show that the method used is efficient and effective, and can be applied to other MV departures of the SBEE.

This paper presents the design and construction and control of a quadcopter drone for Aerial Data Collection (ADC). The frame of the drone was designed using CadDian Software and the parts were printed using a 3D printer. The flight controller was based on Arduino board using an Atmega328p microprocessor with GSM, GPS and GPRS for sending data over the internet and also enhancing long range flight. A feedback control system was developed and tested to control the stability of drone. The proposed control strategy of the drone was tested for a case of pursuit of trajectory and also for speed of response and the findings were very positive confirming the appropriateness of the control measures for independent and autonomous flying with promising precision. This Unmanned Aerial Vehicle (UAV) fitted with IoT has the capability of collecting and sending data over the internet and therefore can be used in many applications including risk assessment, forestry management, urban planning, coastal zone management, infrastructure monitoring, post-disaster damage assessment and delivery of medical supplies.

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This paper presents the sizing of run-of-river small hydro-PV hybrid power system using the Non-dominated Sorting Genetic Algorithm (NSGA-II). The two objective functions are the total generated energy and the energy production cost of hybrid system. The total energy has been maximized whereas the energy production cost of hybrid system has been minimized. The nominal turbine flow rate ( ), the number of hydropower units ( ), and the number of PV modules () are considered as decision variables in this problem. The Yeripao site in Benin has been considered as case study. The optimal solutions converge to Pareto front which represent the best trade-offs between total generated energy and energy production cost. The results have shown that energy production cost increases with the total generated energy. Thus, minimizing the energy production cost is contradictory with maximizing the total generated energy. Moreover, the sensitivities analysis of and on the total generated energy and on energy production cost have been conducted in this study. It is relevant to note that the optimal solutions are grouped into four categories according to = , , . For each category, the total generated energies, energy production costs and cost per kWh increase with the . For = the lowest values of energy production costs, total generated energies and costs per kWh have been recorded. Moreover, = match with the solutions that present the highest total generated energies and costs. The lowest overall cost per kWh is € 0.363/kWh. The conducted study can be applied to other sites by using their hydro and solar resources characteristics.

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The distribution networks of African cities are for the most part in degradation of the required quality due to the exponential evolution of the loads due to demography and industry. In this paper, a hybrid approach based on an analyticmetaheuristic method called (BVSI-GA) developed in the MATLAB environment was used to dimension and position in an ultra-optimal way a DSTATACOM in a quasi-disaster network of Maradi in Niger. Indeed, the positioning of a DSTATCOM with a power of 1840 kVAr at node 40 in this 123-bus distribution network has contributed to reduce the losses in this network by 19.08% with a voltage profile improvement of 2.44%. On the other hand, the exploration of a multiple positioning in order to better secure and make this 123-node network more reliable in case of failure of any of the multiple DSTATACOMs contributed to reducing losses by 25.76%, i.e., a performance improvement of 6.68% compared to the mono-DSTATACOM. As for the tension profile in this case was improved by 2.78% compared to the initial case and by 0.34% to the mono positioning case. The method used in this study is accessible to operators and is effective in predicting and performing distribution networks in African countries, which are nowadays confronted with various contingencies that must be controlled within the limits of tolerable time.