Risk-informed Resilience Planning of Transmission Systems Against Ice Storms

TL;DR

This work tackles ice storm resilience in transmission networks by introducing a two-stage RIDDRP framework that couples line hardening and energy storage decisions with risk-informed normal and emergency operation. It explicitly models decision-dependent uncertainty and the time-varying value of predictive information, including anticipatory preparation time, and solves the resulting large-scale mixed-integer program with the Progressive Hedging Algorithm. The paper demonstrates, through a Texas 118-bus case with wind farms, that predictive information and decision-dependent uncertainty can significantly improve resilience and reduce contingency losses, while also quantifying the value and trade-offs of lead-time and forecast accuracy. The approach provides grid operators with actionable guidance for risk-informed infrastructure investments and operational strategies ahead of extreme weather events, offering a tractable method to balance economy and resilience in practice.

Abstract

Ice storms, known for their severity and predictability, necessitate proactive resilience enhancement in power systems. Traditional approaches often overlook the endogenous uncertainties inherent in human decisions and underutilize predictive information like forecast accuracy and preparation time. To bridge these gaps, we proposed a two-stage risk-informed decision-dependent resilience planning (RIDDRP) for transmission systems against ice storms. The model leverages predictive information to optimize resource allocation, considering decision-dependent line failure uncertainties introduced by planning decisions and exogenous ice storm-related uncertainties. We adopt a dual-objective approach to balance economic efficiency and system resilience across both normal and emergent conditions. The first stage of the RDDIP model makes line hardening decisions, as well as the optimal sitting and sizing of energy storage. The second stage evaluates the risk-informed operation costs, considering both pre-event preparation and emergency operations. Case studies demonstrate the model's ability to leverage predictive information, leading to more judicious investment decisions and optimized utilization of dispatchable resources. We also quantified the impact of different properties of predictive information on resilience enhancement. The RIDDRP model provides grid operators and planners valuable insights for making risk-informed infrastructure investments and operational strategy decisions, thereby improving preparedness and response to future extreme weather events.

Figures (8)

• Figure 1: Decision-dependent damage status of line $ij$ with/without hardening decision.
• Figure 2: Illustrative example of the uncertainty level evolution in predictive events.
• Figure 3: The proposed two-stage risk-informed resilience planning model.
• Figure 4: Preventive and contingency load shedding in IEEE-118 bus system.
• Figure 5: Load shedding costs in IEEE-118 bus system.