Distributed Security Constrained Economic Dispatch

TL;DR

This paper compares two decomposition methods, LR and ALR, for solving security constrained economic dispatch, highlighting their convergence rates and privacy tradeoffs using a 6-bus test network.

Contribution

It introduces a novel tie-line modeling method and provides a comparative analysis of LR and ALR in terms of convergence and privacy.

Findings

ALR converges faster than LR due to more shared data

A new tie-line modeling method is proposed for decomposed networks

Tradeoff identified between convergence speed and information privacy

Abstract

In this paper, we investigate two decomposition methods for their convergence rate which are used to solve security constrained economic dispatch (SCED): 1) Lagrangian Relaxation (LR), and 2) Augmented Lagrangian Relaxation (ALR). First, the centralized SCED problem is posed for a 6-bus test network and then it is decomposed into subproblems using both of the methods. In order to model the tie-line between decomposed areas of the test network, a novel method is proposed. The advantages and drawbacks of each method are discussed in terms of accuracy and information privacy. We show that there is a tradeoff between the information privacy and the convergence rate. It has been found that ALR converges faster compared to LR, due to the large amount of shared data.