Aggregate Flexibility of Thermostatically Controlled Loads

By He Hao, Borhan Molazem Sanandaji, Kameshwar Poolla, Tyrone L. Vincent. 2015

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Citations in the corpus

Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting (2017, background): “The use of ancillary services such as frequency regulation and load following to compensate for such imbalances is facilitated by accurate forecasts (Hao et al., 2013), (Sanandaji et al.”
Smart Grids: A Cyber–Physical Systems Perspective (2016, background): “Another effective approach is the distributed model predictive control which considers future trends into control actions [44], [45].”
An overview of Demand Response: Key-elements and international experience (2017, methodology): “For example, [69] investigates the potential of a household equipped with a HEMS to provide frequency response, [70] examines the potential of load flexibility provided by smart appliances in order to participate in reserve services, [71] employs a model of ACs in order to provide reserves by DLC th”
Distributionally Robust Chance-Constrained Optimal Power Flow With Uncertain Renewables and Uncertain Reserves Provided by Loads (2017, background): “We assume that the flexible loads are aggregations of electric heat pumps, and each load aggregation can be modeled as a thermal energy storage unit [7], [8] with power capacity Pc , energy capacity Ec , and baseline power consumption PL , which all vary with (uncertain) outdoor air temperature ̃ θ”
A Geometric Approach to Aggregate Flexibility Modeling of Thermostatically Controlled Loads (2016, methodology): “Our approach (Theorems 2–4) includes the approach proposed in [1], [2] as a special case.”