Strategic Investments for Enhancing Power System Resilience through Zonal Microgrids

Abstract

The existing power system is confronted with a myriad of challenges and encompassing issues such as ageing infrastructure, dynamic shifts in energy demand patterns and disturbances induced by climate change. Given the pivotal role played by the power sector in contemporary society by providing essential services and supporting economic activities, ensuring the resilience of power systems is a top priority for governments, utilities and other stakeholders like consumers. The extant power grid characterized by its aging components, requires a complete overhaul to enhance its resilience in the wake of increasing weather-induced power disruptions attributed to climate change. However, outright replacement of the existing is presently deemed economically impractical, entailing significant costs and negative social impacts. Instead, a more pragmatic strategy to augment the overall system resilience involves identifying and reinforcing critical sectors of the grid at a reasonable cost and with reasonable disruptions. This paper explores the concept of smart investment as a strategic framework to enhance power system resilience. We propose a vulnerability assessment framework, where N-1 contingency criteria are used to identify the most vulnerable lines to help determine the critical areas to be prioritized for resilience improvement investments. After identification of the critical areas, a resilience enhancement strategy which involves partitioning of the existing distribution network in zonal microgrids and optimally sizing and placing DERs in the individual microgrids is implemented. A real-life existing distribution network is used as a case study for the proposed framework to prove its effectiveness. OpenDSS a power system distribution network simulator is paired with MATLAB in implementing the proposed strategies which resulted in a significant improvement in the resilience of the systems, measured in terms of unserved loads.