An Analysis on the Impact of Large Wind Farms on the Sizing and Allocation of Power Systems Spinning Reserve Reauirement
MetadataShow full item record
"The need to diversify power generation sources, increase security of supply, incorporate sustainability in energy sources and reducing fuel usage and emissions has significantly led to the integration of variable renewable energy sources such as wind to power grids globally. Kenya, for instance, targets to increase wind power generation to 2,000 MW by 2031. However, increased integration of wind power into a grid necessitates the need to update unit commitment and operating reserve algorithms since wind power is highly variable, intermittent and non-dispatchable. In addition, operational decisions in these grids are made on the basis of wind and load forecasts which are not perfect. Therefore, variability and uncertainty due to wind power introduced in the system is expected to have adverse effect on power system operation. It is important then to predict the effects of increased Variable renewable energy generation on various technical aspects of the power system. One such concern is what increased intermittent renewable energies would mean on the reliability of the power system given their fluctuating nature. Such generators are not only prone to equipment failure but also to absence of the “fuel” e.g. the wind resource during certain periods. The rule of thumb when operating a number of generators in a power system is to ensure that the spinning reserve is greater than the largest online generator (the N-1 criterion). However, extra reliability considerations have to be factored in due to the intermittency of the wind resource when it forms a significant proportion of the system generation mix. In this research, the effects of increased wind power generation on the power system spinning reserve resource requirements were analyzed. The first step was to model the variability of typical wind and load data in MATLAB/Simulink software, the results of which were plotted in time series to assess the variability of the two. Next, variable wind speed data were converted into wind power data using an appropriate model also designed in the MATLAB/Simulink software. Monte Carlo Simulations were performed by use of the Unit Commitment formulation method and a probabilistic approach so as to analyze the amount of spinning reserve resource required and the optimal cost of operation for a model having three different levels of wind power generation: 20MW, 40MW and 60MW of wind power. Finally, the spinning reserve resource requirements were quantified for the three different levels of wind power generation, both with and without the reserve constraint being considered. The vi study demonstrates that operating reserve is quantified considering the largest online generator, system generation margins and a fraction of the wind power integrated to the system. The study findings reveal that increased wind power integration should be met by an increase in spinning reserve resource allocation to cater for inherent variability, intermittency and uncertainty that characterize wind power. The study recommends that the cost of provision of additional operating reserve due to wind integration should be compared to the benefit of introduction of wind to the grid."