变电站防洪减灾的两阶段模型比较
Comparisons of two-stage models for flood mitigation of electrical substations
AI总结：
本文比较随机规划与鲁棒优化两阶段模型，用于飓风前变电站防洪资源部署，发现减灾预算和不确定性视角影响显著，而DC与LPAC潮流模型选择影响甚微。
摘要：
我们比较了随机规划与鲁棒优化决策模型，用于在即将来临且不确定的飓风之前指导临时防洪措施的部署，以保护变电站。在我们的模型中，第一阶段刻画固定数量防洪资源的部署，第二阶段刻画可能受损电网的运行，其主要目标是最小化负荷削减。为了对电网运行建模，我们引入了直流（DC）与线性化交流（LPAC）潮流近似模型的适应性改进，这些改进通过一个指示变量具备相对完全补偿特性。我们将模型应用于两个地理上真实的洪水案例研究，一个基于飓风哈维，另一个基于热带风暴伊梅尔达。我们研究了减灾预算、潮流模型选择以及不确定性视角对最优减灾策略的影响。结果表明，减灾预算和不确定性视角影响显著，而在DC与LPAC潮流模型之间进行选择几乎没有影响。为验证模型，我们在交流（AC）潮流模型中评估了它们所给出的减灾方案的性能。
英文摘要：
We compare stochastic programming and robust optimization decision models for informing the deployment of ad hoc flood mitigation measures to protect electrical substations prior to an imminent and uncertain hurricane. In our models, the first stage captures the deployment of a fixed quantity of flood mitigation resources, and the second stage captures the operation of a potentially degraded power grid with the primary goal of minimizing load shed. To model grid operation, we introduce adaptations of the DC and LPAC power flow approximation models that feature relatively complete recourse by way of an indicator variable. We apply our models to a pair of geographically realistic flooding case studies, one based on Hurricane Harvey and the other on Tropical Storm Imelda. We investigate the effect of the mitigation budget, the choice of power flow model, and the uncertainty perspective on the optimal mitigation strategy. Our results indicate the mitigation budget and uncertainty perspective are impactful whereas choosing between the DC and LPAC power flow models is of little to no consequence. To validate our models, we assess the performance of the mitigation solutions they prescribe in an AC power flow model.
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