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Optimizing Flood Mitigation Strategies for Electrical Substations Using Two-Stage Stochastic Programming and Robust Optimization Models


Core Concepts
Effective deployment of temporary flood barriers like Tiger Dams™ to mitigate flood-induced power grid component outages prior to an imminent hurricane.
Abstract
The paper introduces and compares two-stage stochastic programming (SP) and robust optimization (RO) models for informing the deployment of temporary flood mitigation resources to protect electrical substations before an imminent hurricane. The first-stage decisions involve allocating a limited budget to reinforce substations to different resilience levels. The second-stage recourse problems capture the operation of the potentially degraded power grid, with the primary goal of minimizing load shed. The authors adapt the classical DC power flow approximation and several variants of the more sophisticated LPAC approximation to model the grid operation. The authors investigate the impact of the mitigation budget, the choice of power flow model, and the uncertainty perspective (SP vs. RO) on the optimal mitigation strategy. Their results indicate that the mitigation budget and uncertainty perspective are impactful, whereas the choice between the DC and LPAC power flow models has little to no consequence. To validate their models, the authors assess the performance of the mitigation solutions prescribed by their models in an AC power flow model. The authors also introduce an "infeasibility indicator variable" in their recourse problems to ensure relatively complete recourse when the power flow model does not admit a feasible solution for certain contingencies.
Stats
The number of widespread outages caused by severe weather and natural disasters has increased from around 40 annually in the early 2000's to about 80 by 2010 to 100 or more in recent years. Many of the worst outages and economic losses are due to tropical cyclones. Climate models project the frequency and intensity of the most extreme tropical cyclones (i.e., Category 4 and Category 5 storms) to increase globally.
Quotes
"resilience is the ability to prepare and plan for, absorb, recover from, and more successfully adapt to adverse events." "Resilience metrics be formed with consideration of three factors: the threat, the likelihood, and the consequences."

Deeper Inquiries

How could the proposed models be extended to consider the restoration of power grid components in addition to the initial mitigation decisions

To extend the proposed models to consider the restoration of power grid components in addition to the initial mitigation decisions, we can introduce a second stage in the optimization models. The first stage would focus on the deployment of flood mitigation measures to protect electrical substations, similar to the current setup. The second stage would then address the restoration of any components that were damaged or affected by the hurricane. In the second stage, decision variables could be introduced to represent the restoration actions, such as repairing damaged substations, re-energizing transmission lines, and bringing generators back online. These decisions would need to consider the availability of resources, time constraints, and the interdependencies between different components of the power grid. The objective function in the second stage could aim to minimize the time taken to restore full functionality to the power grid while considering the costs associated with the restoration efforts. By incorporating a two-stage approach that includes both mitigation and restoration decisions, the models would provide a more comprehensive framework for addressing the challenges posed by hurricane events on the power grid. This extension would allow for a more holistic and effective decision-making process in ensuring the resilience and reliability of the power system in the face of natural disasters.

What are the potential drawbacks or limitations of the robust optimization approach compared to the stochastic programming approach in this context

While robust optimization offers a conservative and risk-averse approach to decision-making under uncertainty, it also comes with certain drawbacks and limitations compared to stochastic programming in this context. One potential limitation of the robust optimization approach is its tendency to be overly conservative, leading to suboptimal solutions in scenarios where the actual outcomes are less severe than the worst-case assumptions. This can result in higher costs and resource allocations than necessary, as the model is designed to withstand the most adverse conditions without considering the likelihood of those conditions occurring. Additionally, robust optimization may not fully capture the probabilistic nature of uncertainties, as it focuses on the worst-case scenarios rather than considering the entire probability distribution of outcomes. This can lead to a lack of flexibility in decision-making and may not account for the varying levels of risk associated with different scenarios. Furthermore, robust optimization models can be computationally intensive and complex, especially when dealing with large-scale systems and multiple sources of uncertainty. This complexity can make it challenging to solve and interpret the results, especially in real-time decision-making scenarios where quick and efficient solutions are required. In contrast, stochastic programming allows for a more nuanced and probabilistic approach to decision-making under uncertainty. By considering the entire probability distribution of outcomes, stochastic programming can provide more balanced and realistic solutions that take into account the likelihood of different scenarios occurring. It also allows for the incorporation of risk preferences and trade-offs between costs and performance metrics in a more flexible manner.

How might the models be adapted to consider the interdependencies between the power grid and other critical infrastructure systems during and after a hurricane event

To consider the interdependencies between the power grid and other critical infrastructure systems during and after a hurricane event, the models can be adapted to incorporate a multi-sector approach that accounts for the interactions and dependencies between different systems. This holistic perspective would enable a more comprehensive analysis of the cascading effects of a hurricane on various infrastructure systems and facilitate better decision-making to enhance overall resilience and recovery efforts. One way to adapt the models is to include additional constraints and variables that capture the interdependencies between the power grid, water, transportation, communication, and other critical infrastructure systems. This could involve modeling the impact of power outages on water supply and treatment facilities, transportation networks, and emergency communication systems, and vice versa. Furthermore, the models could be expanded to optimize resource allocation and restoration efforts across multiple sectors, considering the priorities and dependencies between different systems. By incorporating a multi-sector optimization framework, decision-makers can identify synergies, trade-offs, and potential bottlenecks in the recovery process and develop more effective strategies to enhance overall system resilience. Additionally, the models could integrate real-time data and information sharing mechanisms between different infrastructure systems to improve coordination and response efforts during and after a hurricane event. This would enable a more coordinated and efficient response to disruptions and facilitate the timely restoration of critical services to the affected communities.
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