Random Furrowing for a Stochastic Unit Commitment Solution

Abstract

The Unit Commitment Problem involves the inherent difficulty of obtaining optimal combinatorial power generation schedules over a future short term period. The formulation of the generalized Unit Commitment Schedule formulation involves the specific combination of generation units at several de-rated capacities during each hour of the planning horizon, the load demand profile, load indeterminateness and several other operating constraints. This largely deterministic schedule continues to find favor with several plant operators, keeping in mind the close operating time-periods involved. However, the deterministic nature of the load profile is sought to be phased out by a stochastic pattern that is realistic and mirrors real-life situations, owing to modern trends in Demand side management. This shift is in tune with the ongoing power restructuring activities of electricity power reforms. The stochastic profile is obtained by a suitably tuned 2-parameter Weibull distribution that uses appropriate shape and scale parameters. The resulting band of generated load profiles are used to evaluate net power and penal costs associated with a set of pervasive randomized probability indices. The exact UCS comprises of a specific unit absolute state corresponding to a certain time period within the planning horizon. Subsequently, regression analysis is applied to establish the correlation between the absolute states and the cumulative randomized load demand against the intervals within the planning horizon. This method is analogous to random furrowing of probabilistic demand profile.