A Self-Updating K-Contingency List for Smart Grid System

TL;DR

This paper introduces a real-time, self-updating contingency list for smart grids using a dependency model, enabling operators to identify critical entities and mitigate damage effectively.

Contribution

It proposes a novel heuristic method for generating contingency lists based on MIIM, improving real-time decision-making in smart grids.

Findings

The heuristic method effectively identifies critical entities in the IEEE 14-Bus system.

The MIIM-based predictions of network damage are more realistic than previous models.

Validation shows the approach is computationally efficient for real-time applications.

Abstract

A reliable decision making by the operator in a smart grid is contingent upon correct analysis of intra-and-interdependencies between its entities and also on accurate identification of the most critical entities at a given point of time. A measurement based self-updating contingency list can provide real-time information to the operator about current system condition which can help the operator to take the required action. In this paper, the underlying intra-and-interdependencies between entities for a given power-communication network is captured using a dependency model called Modified Implicative Interdependency Model (MIIM) [1]. Given an integer K, the event-driven self-updating contingency list problem gives the list of K-most critical entities, failure of which maximizes the network damage at the current time. Owing to the problem being NP complete, a fast heuristic method to generate a real-time contingency list using system measurements is provided here. The validation of the work is done by comparing the contingency list obtained for different K values using the MIIM model on a smart grid of IEEE 14-Bus system with that obtained by simulating the smart grid using a co-simulation system formed by MATPOWER and Java Network Simulator (JNS). The results also indicate that the network damage predicted by both the ILP based solution [2] and the proposed heuristic solution using MIIM are more realistic compared to that obtained using another dependency model called Implicative Interdependency Model (IIM) [3].