Cut-set and Stability Constrained Optimal Power Flow for Resilient Operation During Wildfires

Resilient operation of the power system during ongoing wildfires is challenging because of the uncertain ways in which the fires impact the electric power infrastructure (multiple arc-faults, complete melt-down). To address this challenge, we propose a novel cut-set and stability-constrained optimal power flow (OPF) that quickly mitigates both static and dynamic insecurities as wildfires progress through a region. First, a Feasibility Test (FT) algorithm that quickly desaturates overloaded cut-sets to prevent cascading line outages is integrated with the OPF problem. Then, the resulting formulation is combined with a data-driven transient stability analyzer that predicts the correction factors for eliminating dynamic insecurities. The proposed model considers the possibility of generation rescheduling as well as load shed. The results obtained using the IEEE 118-bus system indicate that the proposed approach alleviates vulnerability of the system to wildfires while minimizing operational cost

Data-Driven Flow and Injection Estimation in PMU-Unobservable Transmission Systems

Fast and accurate knowledge of power flows and power injections is needed for a variety of applications in the electric grid. Phasor measurement units (PMUs) can be used to directly compute them at high speeds; however, a large number of PMUs will be needed for computing all the flows and injections. Similarly, if they are calculated from the outputs of a linear state estimator, then their accuracy will deteriorate due to the quadratic relationship between voltage and power. This paper employs machine learning to perform fast and accurate flow and injection estimation in power systems that are sparsely observed by PMUs. We train a deep neural network (DNN) to learn the mapping function between PMU measurements and power flows/injections. The relation between power flows and injections is incorporated into the DNN by adding a linear constraint to its loss function. The results obtained using the IEEE 118-bus system indicate that the proposed approach performs more accurate flow/injection estimation in severely unobservable power systems compared to other data-driven methods.Comment: 5 pages, 1 figur