Identifying Optimal Portfolios of Resilient Network Investments Against Natural Hazards, With Applications to Earthquakes

Abstract

Although extreme natural disasters have occurred all over the world throughout history, power systems planners do not usually recognize them within network investment methodologies. Moreover, planners had historically focused on reliability approaches based on average (rather than risk) performance indicators, undermining the effects of high impact and low probability events on investment decisions. To move towards a resilience centred approach, we propose a practical framework that can be used to identify network investments that offer the highest level of hedge against risks caused by natural hazards. In a first level, our framework proposes network enhancements and, in a second level, uses a simulation to evaluate the resilience level improvements associated with the network investment propositions. The simulator includes 4 phases: threat characterization, vulnerability of systems components, system response, and system restoration, which are simulated in a sequential Monte Carlo fashion. We use this modeling framework to find optimal portfolio solutions for resilient network enhancements. Through several case studies with applications to earthquakes, we distinguish the fundamental differences between reliability-and resilience-driven enhancements, and demonstrate the advantages of combining transmission investments with installation of backup distributed generation.

Resilience‐based tri‐level framework for simultaneous transmission and substation expansion planning considering extreme weather‐related events

“…By the same token, the authors in [25] provided a cost-effective operating and design strategy for strengthening the power transmission network infrastructure against the EWEs. In [26], a hierarchical framework was reported for increasing the resilience level pertaining to the power transmission assets exposed to natural disasters. To optimally protect the power distribution network versus the EWEs, a two-level mixed-integer linear model was formulated for identifying resilience-oriented strategies by a progressive hedging algorithm in [27] and an effective greedy search algorithm in [12].…”

“…The admittance matrix of the transmission network according to the expansion plans is periodically updated using constraints (24) and (25). The maximum allowable number of newly installed transmission lines in each corridor of the transmission network during the planning horizon is stated by constraint (26). Constraints (27) and (28) indicate the maximum permissible number of newly installed transmission switches in each corridor and in all corridors of the transmission network during the planning horizon, respectively.…”

Identification of critical lines for enhancing disaster resilience of power systems with renewables based on complex network theory

“…In [6], with application to the impact modelling of severe windstorms on the transmission network, a comprehensive approach is presented to assess and enhance the transmission system resilience under extreme weather conditions. In [7], a mathematical framework for determining the resilient network investments is presented. The proposed framework in [7] can hedge the risks caused by natural disasters and can be applied to various natural disasters.…”

“…In [7], a mathematical framework for determining the resilient network investments is presented. The proposed framework in [7] can hedge the risks caused by natural disasters and can be applied to various natural disasters. In order to assess and enhance the disaster resilience of power systems, the concept of the fragility curve of a specific power system component is presented and applied in [4–7].…”

Contribution of emergency demand response and emergency loading of overhead lines in network resilience enhancements

“…The authors use the fragility models, for overhead lines (OHLs) and towers, to assess the damage of windstorms on network performance and the effect of hardening to mitigate the damage. The same authors extend their work to include the strategies of network hardening measures against floods [13] and earthquakes [14]. Another solution is to smarten the network through timely anticipation of HILP events and enhanced emergency coordination, by using preventive and defensive smart grid technology (e.g.…”