@misc{babc7e72bae04dda84d6fea8ae6dfc76,
title = "Grid Optimization with Solar (GO-Solar) Experiences With: Data-Driven and Machine Learning Approaches for High-Pen PV Grids",
abstract = "Provides an overview of the innovations and challenges from the algorithm development portion of the GO-Solar project, including plain language introductions to matrix-completion based state estimation, multi-kernel learning based state forecasting, 'slow-scale' voltage-load sensitivity matrix linearized optimal power flow, and 'fast-scale' OPF plan following using on-line Provides an overview of the innovations and challenges from the algorithm development portion of the GO-Solar project, including plain language introductions to matrix-completion based state estimation, multi-kernel learning based state forecasting, 'slow-scale' voltage-load sensitivity matrix linearized optimal power flow, and 'fast-scale' OPF plan following using on-line multi-objective state.",
keywords = "distributed PV, OMOO, online multi-objective optimization, predictive state estimation, PSE, solar",
author = "Bryan Palmintier and Yingchen Zhang",
year = "2019",
language = "American English",
series = "Presented at the Challenges for Distribution Planning Operational and Real-time Planning Analytics Workshop, 16-17 May 2019, Washington, D.C.",
type = "Other",
}