Wind Resource Potential for Electricity Generation Using Micro - Hybrid Systems in Northern Kenya
Njiru, David Murithi
MetadataShow full item record
Currently the world is embracing renewable energy not only to enhance energy capacity but also to mitigate problems associated with green house gas emissions. North eastern Kenya region lagged behind others parts of the republic in social economic indices. This may be attributed to the fast distance between the region and the electricity generation areas. Therefore northern Kenya is not connected to national electricity grid. Renewable energy from the wind can be used to supply electricity using off grid micro hybrid systems in the region. The study examined how variability in wind speeds may affect the potential for electricity generation using micro grid systems. The study therefore evaluated the spatial and temporal wind resource distribution, trends and the variability, assessed the electricity consumption per capita and the contribution of wind power resource to hybrid systems in the region. The study used the simulated model outputs extracted from Global Circulation Models (ECMWF-ERA) for winds at surface and 850mb level. Data period was from 1981- 2015 for baseline with projections up to 2050. Other data considered were the electricity supply from the Kenya power company and the demographic totals for each of the five counties in the region sourced from the Kenya bureau of statistics Nairobi. The analyses were done using Grid Analysis and Display System, XLSTAT and SPSS. Findings indicated that region has good wind power potential to generate wind energy. Marsabit County had the highest long term mean wind velocity at 9m/s for the surface and 11m/s at the 850mb level. The models depicted wind speeds greater than the lowest wind speed (3-5m/s) that can be used to turn any rated turbine to generate useful wind energy. Data revealed low electrification rates with highest being 2.4% in Marsabit County where consumption was moderate at 556kWh per capita and the highest wind power density was 807W/m2. Trend test posted p-values between 0.00 and 0.57 for 0.05 alpha. Mann Kendall test indicated positive but insignificant trends for baseline and projected winds up to 2050. The models also depicted low standard deviations at 0.22 - 0.46 with Coefficient of variation between 3.9 % - 5.8% for baseline and model projections. Thus study concluded that the northern Kenya regions have good wind power resource potential with low but insignificant long term mean wind speeds variability patterns. The seasonal wind patterns were intertwined with interseasonal and intraseasonal wind variability characteristics which influenced the distribution of the wind power resources across the region. However, June July August was the most dominant season compared to other seasons. The seasonal wind variability patterns were therefore significant. HadGEM2 model correlated well with baseline data. The correlation coefficients ware 0.5 for the surface and 0.9 for the upper level winds, therefore modelling could be a better method for simulating winds for the region. The study recommended to both the national and the county governments to take advantage of good wind power potentials to put up wind farms to generate electricity using off grid micro grid systems. This will increase electricity access and trigger the much needed social and economic growth and development for people in the region.