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Power distribution grids are currently challenged by observability issues due to limited metering infrastructure. On the other hand, smart meter data, including local voltage magnitudes and power injections, are collected at grid nodes with renewable generation and demand response programs. A power flow-based approach using these data is put forth here to infer the unknown power injections at non-metered grid nodes.

This poster presents a real-time decentralized temperature control scheme via Heating Ventilation and Air Conditioning (HVAC) systems for energy efficient buildings, which balances user comfort and energy saving. Firstly, we introduce a thermal dynamic model of building systems and its approximation. Then a steady-state optimization problem is formulated, which aims to minimize the aggregate deviation between zone temperatures and their set points, as well as the building energy consumption.

Energy storage systems are becoming a key component in smart grids with increasing renewable penetration. Storage technologies feature diverse capacity, charging, and response specifications. Investment and degradation costs may require charging batteries at multiple timescales, potentially matching the control periods at which grids are dispatched. To this end, a microgrid equipped with slow- and fast-responding batteries is considered here. Energy management decisions are taken at two stages.

This paper presents a new methodology to detect low-frequency
oscillations in power grids by use of time-synchronized data
from phasor measurement unit (PMU). Principal component analysis
(PCA) is first applied to the massive PMU data to extract the
low-dimensional features, i.e., the principal components (PCs). Then
based on persistent homology, a \emph{cyclicity response function}
is proposed to detect low-frequency oscillations through the use of
PCs. Whenever the cyclicity response exceeds a numerically robust

Optimal power flow (OPF) is one of the key electric power system optimization problems. "Moment" relaxations from the Lasserre hierarchy for polynomial optimization globally solve many OPF problems. Previous work illustrates the ability of higher-order moment relaxations to approach the convex hulls of OPF problems' non-convex feasible spaces.

Wide-area-measurement systems (WAMSs) are used in smart grid systems to enable the efficient monitoring of grid dynamics. However, the overwhelming amount of data and the severe contamination from noise often impede the effective and efficient data analysis and storage of WAMS generated measurements. To solve this problem, we propose a novel framework that takes advantage of Multivariate Empirical Mode Decomposition (MEMD), a fully data-driven approach to analyzing non-stationary signals, dubbed MEMD based Signal Analysis (MSA).