Connecting Risk and Resilience for a Power System Using the Portland Hills Fault Case Study

Abstract

Active seismic faults in the Pacific Northwest area have encouraged electric utilities in the region to deeply contemplate and proactively intervene to support grid resilience. To further this effort this research introduces Monte Carlo (MC)-based power system modeling as a means to inform the Performance Based Earthquake Engineering method and simulates 100,000 sample earthquakes of a 6.8 magnitude (M6.8) Portland Hills Fault (PHF) scenario in the Portland General Electric (PGE) service territory as a proof of concept. This paper also proposes the resilience metric Seismic Load Recovery Factor (SLRF) to quantify the recovery of a downed power system and thus can be used to quantify earthquake economic risk. Using MC results, the SLRF was evaluated to be 19.7 h and the expected economic consequence cost of a M6.8 PHF event was found to be $180 million with an annualized risk of $90,000 given the event’s 1 in 2000 year probability of occurrence. The MC results also identified the eight most consequential substations in the PGE system—i.e., those that contributed to maximum load loss. This paper concludes that retrofitting these substations reduced the expected consequence cost of a M6.8 PHF event to $117 million.

Resilience in an Evolving Electrical Grid

“…Regardless of category, all methods must first determine the location and severity of the damage to understand the scale and consequences of the HILF event. Modeling the damage mechanism and modeling the response of the power system should be decoupled [46].…”

“…Examples of physics-based methods are implemented in the General Fragility Model [49], which uses calculations (e.g., bending moment of wood poles during sustained gusts) to predict the likelihood of collapse. In the study carried out by Chalishazar et al [46] the likelihood of each substation asset-failures (e.g., circuit breakers, transformers) is determined by using resonance frequency calculations based on peak ground acceleration values. Examples of statistical modeling are described in Reference [50,51], where predictor variables (e.g., soil moisture, population density, maximum sustained gust speeds) are used to predict the number of outages or collapsed poles within cells of a spatial grid.…”

“…This work notes that reliability metrics are inadequate to quantify resilience due to their inability to address topological flexibility and identify critical infrastructure, cooperation with customers, and potential preventative measure evaluation. The valuable contributions of Reference [113] provide insight into and classification of resilience in power systems and highlighting the need for cost-benefit studies of proposed resiliency improvements (e.g., those carried out in Reference [46]). Bhusal et al [43] identify key attributes of power system resilience and provide a survey of resilience metrics (also known as performance-based metrics) based on the "5 Rs" of the DIRE curve seen in Figure 4.…”

“…Performance-based metrics, based on a chosen performance indicator, estimate (1) how the system would perform during a HILF event, (2) how severe the impacts of the event would be, and (3) how quickly the system would recover and be restored to the pre-event state. Examples of such metrics are time and cost of recovery [46], number of customers offline [114], and DNS [52].…”

An Interval Optimization-Based Approach for Electric–Heat–Gas Coupled Energy System Planning Considering the Correlation between Uncertainties

“…As the uncertainty of RES output will directly affect the electricity selling price of the system operator, and thus affect the user's energy usage, there is a correlation between uncertainties. An integrated energy system considering the uncertainty and correlation of RES and DR can effectively reduce the influence of fluctuations of external factors on EH, and thus increase the overall resilience of the energy hub [22][23][24][25]. In this paper, a multidimensional parallelepiped interval model is used to deal with the correlation between uncertainties [26].…”