Data Perturbation-Based Sensitivity Analysis of Real-Time Look-Ahead Economic Dispatch

In this paper, the sensitivity of look-ahead economic dispatch in real-time power markets with respect to data perturbation is studied. In the look-ahead dispatch optimization problem, a small change in the data that are used for setting the interspatial and temporal equality/inequality constraints and the objective function may negatively affect normal operations, such as the calculation of real-time wholesale electricity prices and operating costs. This could lead to more distorted prices and larger operating costs with the look-ahead dispatch than with a static dispatch that use data for a single future time. We perturb Karush-Kuhn-Tucker conditions of the look-ahead dispatch optimization formulation and then, using them, derive a linear sensitivity matrix that assesses the impact of data corruption on look-ahead dispatch. This matrix illustrates the changing optimal solution of look-ahead dispatch subject to potential corruption in various types of spatial and temporal data-generator's bidding cost coefficients, capacity limits for generators and transmission lines, ramp rates and the estimate of initial generation output with ramp constraints, and multiple-time series of the forecast load for the look-ahead horizon. The results of the simulation are illustrated with numerical examples in the IEEE 14-bus system.