Globally, electricity systems are transitioning from marginal to dominant renewable energy systems in terms of installed capacity and electricity generation shares. This transition has led to the situation of matching dynamic supply with dynamic demand. For effective management, electricity system planners and operators must have a clear understanding of the dynamics of the supply sources. Knowing these would enable them to identify periods of
constrained supply and manage resources optimally. Studying 365 supply profiles for a year is a cumbersome exercise and it is impossible to observe the supply peculiarities from such a huge volume of data. Therefore, there needs to be a mechanism in place to capture these patterns along with magnitude, span, temporal effects and influential factors of supply-side variabilities. In this research, we propose a methodology for characterizing variations in supply profiles of solar and wind energy technologies by deploying a simulation-based approach. First, a logical clustering method is employed to form a smaller groups of supply profiles. Next, probability distribution-based Monte Carlo simulation is adopted to refine these groups of supply curves and arrive at a representative supply curve, which is called Representative Supply Profile (RSP). This approach is validated using data from Karnataka (a state in India) electricity system, and technology specific RSPs are developed. With this, we could represent the 365 days’ hourly generation into 10 RSPs for solar and 14 RSPs for wind. The results show that solar and wind supply profiles represent different seasonal cycles.

Keywords
Electricity System Planning, Electricity Transition, Representative Supply Profiles, Electricity Modelling, Renewable Energy.