Enhancing Power System Resilience Through Evolutionary Programming for High Impact Low Probability Events

• Published in: Lecture Notes in Electrical Engineering
• Main Author: Zakaria F.; Musirin I.; Kamari N.A.M.; Aminuddin N.; Johari D.; Shaaya S.A.; Bajwa A.A.; Kumar A.V.S.
• Format: Conference paper
• Language: English
• Published: Springer Science and Business Media Deutschland GmbH 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205087944&doi=10.1007%2f978-981-97-3851-9_19&partnerID=40&md5=f065053b51ec342ef74a28168c61a333

Ensuring the sustainability of power systems is of utmost importance for modern societies. It is a fundamental necessity that directly impacts the well-being and functioning of communities and economies. The increasing frequency of power shutdowns triggered by severe weather events, which are worsened by the effects of climate change, has intensified research efforts aimed at enhancing the resilience of power systems. Remedial action needs to be planned for improving the power system’s resilience. The installation of distributed generation (DG) is one of the suitable efforts to alleviate this phenomenon. This paper presents enhancing power system resilience through evolutionary programming for high-impact low probability (HILP) events. Validation on IEEE 30-Bus Reliability Test System (RTS), solved using Evolutionary Programming (EP) under extreme weather demonstrates its capability in improving the power system resilience. In this study, the EP technique is used to identify the best configuration of DG placement and capacity that can effectively improve the system's ability to withstand and recover from such extreme events. After the installation of DG, the system's resilience was significantly enhanced across three different scenarios of HILP events. In scenario 1, the resilience increased from 0.713 to 1. Similarly, in scenario 2 and scenario 3, the resilience improved from 0.174 to 0.257 and from 0 to 0.302, respectively. The results demonstrate that this algorithm effectively quantifies the system’s resilience under HILP events. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.