In November 2019, a joint team from Colorado State University and the University of Rwanda intervened to repair a failed pico-hydropower plant in southwestern Rwanda. In a common outside-intervention model, the plant had been constructed by U.S. university team that later, for unknown reasons, couldn't provide additional intervention. The system had failed in 2016 after three years of operation. The qualitative analysis completed during the repair visit identified three primary issues which led to system failure: shortage of local management and technical skills resulting in poor maintenance and no financial reserves, poor local availability of common materials and tools needed for maintenance, and lack of community education to manage the plant. The analysis also includes an assessment of progress on these issues. While immediate technical issues have been resolved and training improved, fundamental operational processes require substantial additional work. The objective of this paper is to highlight, as is too infrequently the case, a failed village electrification project. Only by illuminating what isn't working can the development community learn the best practices.
Godwin Norense Osarumwense Asemota , Aphrodis Nduwamungu
Accurate and precise prediction of power output plays a great significance in power system industry, as it provides the basic outlooks and views for making future decisions in power system planning and operation. This paper used monthly and annual dataset of output power and solar irradiance from a stand-alone solar system to compare nonlinear autoregressive neural network (NARNET) and nonlinear autoregressive with External (Exogenous) input neural network algorithms in predicting (short term and long term prediction) the output power from photovoltaic (PV) system. During the execution of prediction, The Levenberg-Marquardt method was adopted and used for training. Comparison results reveals that NARNET and NARX neural network are both suitable for performing PV output power prediction, but NARX was found to be more accurate. NARNET best prediction result was achieved with MSE (Mean Square Errors) equal to 1.6153 and coefficient R equals to 0.47643 while for annual NARNET prediction model, the MSE and R were found to be equals to 0.84891 and 0.82244. For NARX monthly prediction model, the MSE and R were equal to 0.99206 and 0.53094 while for the case of NARX annual prediction model, the MSE and R were 0.8008 and 0.85248 respectively. The lower the MSE indicates few errors and the more R is close to unity indicates the better correlation and great relation between actual and predicted values.
he provision of energy to all citizens has been an outstanding need in most developing countries in order to meet Sustainable Development Goals (SDGs) by 2030. Uganda as a developing country has put mechanisms in place to provide electricity to her people and be able to realize the national vision of 2040 and be able to address other SDGs by 2030. Solar PV mini-grids are emerging as popular alternatives to address rural electrification shortfall after other renewable energy mini-grid types proved desirable for some period. Uganda's expectations of renewable mini-grids are high with the Ministry of Energy and Mineral Development, the World Bank, and the Government of Uganda showing much interest to support the mini-grid development. This paper, therefore, looks at general mini-grid development and operation models and finds out arguments making renewable energy mini-grids popular at the expense of non-renewable based mini-grids. The methodology focuses on exploring ideas and formulating a hypothesis by summarizing, categorizing, and interpreting both the primary and secondary data. The paper concludes looking at the strength, benefits, and challenges of renewable mini-grids.
South Sudan is experiencing serious shortage in electricity supply with only 1% of the population having access to grid electricity. The country has plenty of renewable energy resources which can possibly be exploited to generate electricity. In spite of the abundance in resources, renewable energy resources are not popular or commonly used in South Sudan. Solar energy has shown success in the domain of electric power generation. Light from the sun, or solar radiation, is the “fuel” which powers solar energy technologies. Therefore knowing the potential of solar radiation at a location, an exercise known as solar resource assessment (SRA), is very important for the selection, design and accurate economic analysis of solar energy technologies for power generation. The research work presented in this paper aims at investigating solar energy resource potential in South Sudan, to help identify potential sites for future solar power plants. Radiation data for 20 locations, covering the period from 2005 to 2018, are requested through Copernicus Atmospheric Monitoring Service Radiation (CAMS-RAD) Service user interface. CAMS-RAD Service uses Heliosat-4 method to calculate solar energy radiation at the earth's surface from Meteosat satellite images. Output data are evaluated and analyzed and annual daily average global (G) and direct (B) solar irradiation calculated for each of the 20 locations together with long term average monthly global irradiance. Results show that 99% of locations receive annual average global irradiation above 5.0 kWh/m 2 . The solar resource in South Sudan is considered favorable for the development of photovoltaic solar power plants. However, it might not be economically viable or only marginally so for concentrated solar systems.
One of the most important factors to achieving sustainable development is the need for clean and reliable energy. Renewable energy systems produce clean energy from renewable sources and convert them into energies such as thermal, electric energy and others. The purpose of this study is to select the most appropriate off-grid renewable energy systems for urban and rural areas in South Sudan. Three scenarios are developed based on the potential of the renewable energy resource in each area. The selection of renewable energy systems is done using the Analytic Hierarchy Process (AHP), a mathematical analysis tool used for the assessment of multi-criteria decision-making problems. AHP tackles complex and unstructured decision-making problems involving multiple alternatives by developing a hierarchy for the decision. Super Decisions, a software used for building and assessing decision models, is used for implementing AHP to select the best off-grid renewable energy system for each scenario. A model is developed by structuring the problem into a three-level hierarchy of goal, alternatives and criteria. Criteria weights, local priorities of the alternatives with respect to each criterion and overall priority and ranking of alternatives are calculated and determined. Results show the best alternative for each scenario and the overall most appropriate alternative.
The research focuses on two modes of wireless power transfer, sine and pulse, respectively. In this paper, a particular signal was injected through input of a transmitting coil and compared the recorded power transferred to a receiving coil with respect to each displacement. The resonant frequency of 150 kHz generated by an external oscillator was used to conduct the experiments. The two solutions were compared and analyzed in order to figure out their behavior, with respective to the efficiency reduction, as the distance increases. A simple implemented circuit model of wireless power transfer system was explored with sine and pulse modes. Results of experimental tests were measured using an oscilloscope and presented numerically and visually with the help of tables and graphs to demonstrate that efficiency of wireless power transfer systems can be improved by using a pulse mode rather than sine mode. With pulse mode, the radiated range is longer, compared to that of input sine wave.
Many scholars have been focusing on the energy management by Integrating a smart grid into a conventional electrical grid. They have showed that to meet a certain power demand of the consumers, using energy management, the electric utility can turn on some generators, which may have the least operation cost, while the generators with high operation cost are left to supply extra load demand in specific peak periods. Henceforth, the operation cost of its generation units is minimized. The issue remains at a level of relating the energy management to CO2 emission. The present paper briefly discusses the Rwandan electrical network that still integrates the use of diesel generators. It estimates the amount of CO2 emission that can be avoided once a PV system is integrated into the electrical network. The paper as well proposes an algorithm for energy management with consideration of CO2 emission.
Faults in electrical power systems are among the key factors and sources to network disturbances, however control strategies are among key faults clearing techniques for the sake of safe operational mode of the system.Some researchers have shown various limitations of control strategies such as slow dynamic response,inability to switch Off and On network remotely and fault clearing time. For a system with wind energy technologies, if the power flow of a wind turbine is interrupted by a fault, the intermediate-circuit voltage between the machine-side converter and line-side converter will fall in unacceptably high values.To overcome the aforementioned issues, this paper used a Matlab simulations and experiments in order to analyze and validate the results.The results showed that fault ride through (FRT) with SCADA Viewer software are more adaptable to the variations of voltage and wind speed in order to avoid loss of synchronism. Therefore at the speed of 12.5m/s a wind produced a rated power of 750W and remained in synchronization before and after a fault created and cleared but worked as generator meanwhile at speed of 3.4m/s wind disconnected from grid and started working as a motor and consumed active power (P=-25watts) and voltage dip at 100% .For the protection purpose, the DC chopper and crowbar should be integrated towards management of excess energy during faults cases.
Deploying off-grid generations systems that are based on clean energy sources would enhance electricity generation capacity in rural areas. This research study adopted the Pico-hydropower plant (HPP) and substitute the dump loads by electrical services that support the economic development of public society. Smart sensors and metering systems will be incorporated in management aiming at making these power plants smarter. Further, this system will increase energy efficiency and reduce carbon emissions caused by fossil fuels like kerosene and diesel, which are used in lamps for domestic lighting in absence of HPP. The significance of this study is to provide access to affordable, reliable, clean and modern energy for all, thereby meeting the UN SDG 7.
The understanding of the impacts caused by electrical power systems on environment is important in order to mitigate the environmental impacts. A literature review study on the direct non-generation greenhouse gas emissions in the construction and operation of the national electrical power transmission and a distribution (T &D) system was conducted in order to understand its impact on climate change. The results revealed that the impact of the distance of T &D lines to the greenhouse gas emissions, due to vegetation removal, is insignificant. The finding show that, Kenya is the lowest emitter in study area with the highest average flow of electricity, compared to Rwanda with the lowest average flow of electricity. The study contributes to an improvement of the understanding of the life cycle emission inventory and the impacts caused by electrical power T &D on greenhouse gases due to vegetation removal in particular. However, further studies are recommended, to cover a wider scope of the environmental impact and electrical power systems backed by primary data on materials consumption by the power systems and power losses in the system.