Monte Carlo sampling vs. discrete forecast error scenarios in grid reliability assessment for short-term planning

With the increasing amount of renewable and difficult-to-forecast generation units, Transmission System Operators (TSO) are facing new challenges to operate the grid properly. Indeed, given the intrinsic variability and limited predictability of most renewable generations, the application of the conventional and deterministic N-1 method becomes very costly. Moreover, this method refers to a list of discrete contingencies without considering continuous variations even if the latter challenge the grid operation. Therefore, a new approach is needed for system operational planning. The present paper presents two probabilistic methods for risk-based operational planning, estimating risk indicators while considering errors on weather (hence generation) forecasts, uncertainties on loads and timing constraints of the decision-making process in operational planning. The first method performs Monte Carlo sampling for the wind and load values and for the status of elements of the grid. The second method consists in discretizing the load and wind values in possible scenarios.