Planning for resilience in power distribution networks: A multi‐objective decision support

Abstract

Power grid response against high‐impact low‐probability (HILP) events could be enhanced by (a) hardening mechanisms to boost its structural resilience and (b) corrective recovery and mitigation analytics to improve its operational resilience. Planning for structural resilience and attempts to find the optimal location of the Tie switches in radially operated power distribution networks that enable harnessing the network topology for maximised resilience against HILP disasters are focussed. This goal is achieved through a novel resilience‐oriented multi‐objective decision making platform, which employs a k‐PEM based probabilistic power flow (PPF) algorithm. The proposed framework offers a decision making analytic embedded with the fuzzy satisfying method (FSM) that characterises the system resilience features, such as robustness, restoration agility, load criticality, and recovered capacity, to assess different network reconfiguration options and select the optimal solution for implementation. The aforementioned resilience features are formulated in nodal level and then aggregated over the entire system to characterise the system‐level objective functions. The performance of the suggested framework is analysed on the IEEE 33‐Bus test system under a designated HILP event, and the applicability on larger networks has been verified on the IEEE 69‐bus test system. The results demonstrate the efficacy and applicability of the proposed framework in boosting the network resilience against future extremes.

Integrated Risk Assessment for Robustness Evaluation and Resilience Optimisation of Power Systems after Cascading Failures

“…Cicilio et al [18] reviewed research that explores the changing generation profile, cutting-edge practices to address resilience and the combination of both topics. Other scholars have offered integrated decision-making analyses to characterise resilience, robustness, restoration agility and load criticality [19,20]. The objective of this existing work is to evaluate different infrastructure reconfiguration options and select the optimal solution for implementation in both cases.…”