Co-optimization of Short- and Long-term Decisions for the Transmission Grid's Resilience to Flooding

Abstract

We present and analyze a three-stage stochastic optimization model that integrates output from a geoscience-based flood model with a power flow model for transmission grid resilience planning against flooding. The proposed model coordinates the decisions made across multiple stages of resilience planning and recommends an optimal allocation of the overall resilience investment budget across short- and long-term measures. While doing so, the model balances the cost of investment in both short- and long-term measures against the cost of load shed that results from unmitigated flooding forcing grid components go out-of-service. We also present a case study for the Texas Gulf Coast region to demonstrate how the proposed model can provide insights into various grid resilience questions. Specifically, we demonstrate that for a comprehensive yet reasonable range of economic values assigned to load loss, we should make significant investments in the permanent hardening of substations such that we achieve near-zero load shed. We also show that not accounting for short-term measures while making decisions about long-term measures can lead to significant overspending. Furthermore, we demonstrate that a technological development enabling to protect substations on short notice before imminent hurricanes could vastly influence and reduce the total investment budget that would otherwise be allocated for more expensive substation hardening. Lastly, we also show that for a wide range of values associated with the cost of mitigative long-term measures, the proportion allocated to such measures dominates the overall resilience spending.

Figures (6)

• Figure 1: Tiger Dam protecting a substation in North Carolina after Hurricane Florence
• Figure 2: A sample MEOW generated using category 5 storms approaching the Texas Gulf Coast in the north-west direction with a forward speed of 5 mph
• Figure 3: (a) The ACTIVSg2000 synthetic grid for Texas. (b) The reduced grid obtained after performing the network reduction. The red elements represent the new nodes and branches that were introduced as the artifacts of the reduction procedure to maintain equivalence in the grid characteristics
• Figure 4: A schematic diagram representing the scenario tree used in the case study. Here, D, C, and F represent the set of storm directions, intensity categories, and forward speeds.
• Figure 5: First- and second-stage solutions: \ref{['fig:granular_voll_250']} and \ref{['fig:granular_voll_6000']} depict the solutions of the BAM when VOLL is $250/MW-hr and $6000/MW-hr, respectively. \ref{['fig:decoupled_6000']} shows the same for the BAM-D for a VOLL of $6000/MW-hr. \ref{['fig:max_prep_15']} represents the BAM solutions for the maximum preparedness level of 15 feet. For more details, refer to Section \ref{['figure_discussion']}