A Stability-Margin Projection on a Released Four-Node Grid
Abstract
The question concerns released simulated stability margin as a response surface. The secondary ordinary least squares analysis contains 10,000 observations and uses HC3 covariance. The coefficient on tau1 is 0.0036, with a 95 percent interval from 0.0035 to 0.0038. The numerical stability margin is an existing simulation output. Multi-objective and worst-case decisions need explicit preferences and protected conditions beyond an average projection. HC3 uncertainty is a working-model diagnostic on the released design collection. It should not be read as the probability of a field outage or as validation of a newly optimized robust control policy. The estimates describe the released collection and its conditioning set. No new data collection, simulator experiment, or deployment-policy evaluation is claimed.
References
Z. Shi, S. Gao, H. Xiao, and W. Chen, “A Worst-Case Formulation for Constrained Ranking and Selection with Input Uncertainty,” Naval Research Logistics, vol. 66, pp. 648–662, 2019.
W. Liu, S. Gao, and L. H. Lee, “A Multi-Objective Perspective on Robust Ranking and Selection,” Proceedings of the 2017 Winter Simulation Conference, pp. 2116–2127, 2017.
Vadim Arzamasov. Electrical Grid Stability Simulated Data . UCI Machine Learning Repository, 2018. Dataset. https://doi.org/10.24432/C5PG66.
White, Halbert. “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity.” Econometrica, vol. 48, no. 4, 1980, pp. 817–838. https://doi.org/10.2307/1912934.
Cameron, A. Colin, and Douglas L. Miller. “A Practitioner’s Guide to Cluster-Robust Inference.” Journal of Human Resources, vol. 50, no. 2, 2015, pp. 317–372. https://doi.org/10.3368/jhr.50.2.317.
Rasmussen, Carl Edward, and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006. https://gaussianprocess.org/gpml/.
Ankenman, Bruce, Barry L. Nelson, and Jeremy Staum. “Stochastic Kriging for Simulation Metamodeling.” Operations Research, vol. 58, no. 2, 2010, pp. 371–382. https://doi.org/10.1287/opre.1090.0754.
Kim, Seong-Hee, and Barry L. Nelson. “A Fully Sequential Procedure for Indifference-Zone Selection in Simulation.” ACM Transactions on Modeling and Computer Simulation, vol. 11, no. 3, 2001, pp. 251–273. https://doi.org/10.1145/502109.502111.
Frazier, Peter I., Warren B. Powell, and Savas Dayanik. “A Knowledge-Gradient Policy for Sequential Information Collection.” SIAM Journal on Control and Optimization, vol. 47, no. 5, 2008, pp. 2410–2439. https://doi.org/10.1137/070693424.
Bertsimas, Dimitris, and Melvyn Sim. “The Price of Robustness.” Operations Research, vol. 52, no. 1, 2004, pp. 35–53. https://doi.org/10.1287/opre.1030.0065.