Hybrid Power Systems for Commercial Application in Kenya

The cost, availability and stability of power are parameters that greatly define the quality of energy supplied by a generating system. A hybrid power system with different sources of energy must be properly designed in order to capitalize on the positive features of the deployed resources. Hybrid power system optimization is a process that allows for deliberate attainment of desired output from a power system. In this paper, a learning institution, East African School of Aviation (EASA) was selected as a case study for investigating the outcome of harnessing local energy resources to serve a commercial consumer in Kenya. A cost competitive hybrid power system was obtained through Simulation and optimization processes

Hybrid Power System Sizing and Design for Commercial Application in Kenya

Hybrid power system sizing involves determination of local load and energy resources’ conditions as well as availability of generating system components. Each component of the system is preliminarily sized individually using prevailing load, resource and conversion system’s conditions. The Net Present Cost (NPC), Cost of Energy (COE) may be included in determining the configuration of the most optimal system that can meet all the desired power system’s goals. In order to refine the sizing and design process, a simulation software is used to select and size system components that can serve the load effectively. In this study, a Solar PV, Wind and Grid hybrid power system was systematically sized to meet the demand of a commercial consumer, East African School of Aviation (EASA). HOMER software was used to simulate the operation of the proposed HPS. The most suitable HPS was found to consist of Wind generation of an installed capacity of 200 kW, solar PV of 120 kW and the Grid. This system had an NPC of KSh 77,684,050 and a COE of KSh 8.34/kWh

Wind Power potential in Kigali and Western provinces of Rwanda

Wind speed and wind direction are the most important characteristics for assessing wind energy potential of a location using suitable probability density functions. In this investigation, a hybrid-Weibull probability density function was used to analyze data from Kigali, Gisenyi, and Kamembe stations. Kigali is located in the Eastern side of Rwanda while Gisenyi and Kamembe are to the West. On-site hourly wind speed and wind direction data for the year 2007 were analyzed using Matlab programmes. The annual mean wind speed for Kigali, Gisenyi, and Kamembe sites were determined as 2.36m/s, 2.95m/s and 2.97m/s respectively, while corresponding dominant wind directions for the stations were ,  and  respectively. The annual wind power density of Kigali was found to be  while the power densities for Gisenyi and Kamembe were determined as and . It is clear, the investigated regions are dominated by low wind speeds thus are suitable for small-scale wind power generation especially at Kamembe site