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

Abstract

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.