Mitigating Energy System Vulnerability by Implementing a Microgrid with a Distributed Management Algorithm

Abstract

This work presents a management strategy for microgrid (MG) operation. Photovoltaic (PV) and wind generators, as well as storage systems and conventional units, are distributed over a wide geographical area, forming a distributed energy system, which is coordinated to face any contingency of the utility company by means of its isolated operation. The management strategy divides the system into three main layers: renewable generation, storage devices, and conventional units. Interactions between devices of the same layer are determined by solving an economic dispatch problem (EDP) in a distributed manner using a consensus algorithm (CA), and interactions between layers are determined by means of a load following strategy. In this way, the complex behaviour of PV and wind generation, the battery storage system, and conventional units has been effectively combined with CA to solve EDP in a distributed manner. MG performance and its vulnerability are deeply analysed by means of an illustrative case study. From the observed results, vulnerability under extreme conditions could be reduced up to approximately 30% by coupling distributed renewable generation and storage capacity with an energy system based on conventional generation.

Advancing a New Generation of Sustainability-Based Assessments for Electrical Energy Systems: Ontario as an Illustrative Application—A Review

“…The development and use of modular technologies can greatly enhance prospects for the integration of electricity obtained from diverse sources in a safe, effective, and efficient manner [92,93]. Modular designs can also significantly lessen the severity of consequences of natural disasters, accidents, and other threats to system integrity [94,95].…”

Distributed System Vulnerability Model Based on Reliability Theory

“…Gautam P used the opportunity graph to describe the attacker's opportunistic advancement process, represented the different paths to the attacker's goal as the attacker's different attack strategies, and used a dynamic algorithm to calculate the average attack cost of behavioral attacks. It is not difficult to see that this method It is "attack-centric", and the method of calculating the average attack cost is obtained empirically, without scientific basis, so the quantitative results cannot well demonstrate the security of the system [4].…”