---
abstract: 'Controllable building loads have the potential to increase the flexibility of power systems. A key step in developing effective and attainable load control policies is modeling the set of feasible building load profiles. In this paper, we consider buildings whose source of flexibility is their HVAC loads. We propose a data-driven method to empirically estimate a robust feasible region of the load using coarse data, that is, using only total building load and average indoor temperatures. The proposed method uses easy-to-gather coarse data and can be adapted to buildings of any type. The resulting feasible region model is robust to temperature prediction errors and is described by linear constraints. The mathematical simplicity of these constraints makes the proposed model adaptable to many power system applications, for example, economic dispatch, and optimal power flow. We validate our model using data from EnergyPlus and demonstrate its usefulness through a case study in which flexible building loads are used to balance errors of wind power forecasts.'
author:
- 'Jesus E. Contreras-Ocaña,  Miguel A. Ortega-Vazquez,  Daniel Kirschen,  and Baosen Zhang,  [^1]'
bibliography:
- 'bibliography.bib'
title: Tractable and Robust Modeling of Building Flexibility Using Coarse Data
---

Buildings, flexibility, data-driven modeling.

Nomenclature {#nomenclature .unnumbered}
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Introduction
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Preliminaries {#sec:underlying_bldg}
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A tractable and robust approximation of the feasible region {#sec:TRAFL}
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Estimating the feasible region {#sec:data}
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Model Validation {#sec:case}
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Conclusion {#sec:conclsion}
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In this work, we propose a method to estimate the feasible region of the building load using simple linear relations that is robust to temperature prediction errors. Our method ensures that a building’s HVAC system is able to maintain acceptable occupant comfort while providing flexibility to the power system. Its mathematical simplicity makes our model tractable in the sense that it can be easily incorporated into optimization and control environments that are common in power systems applications. For instance, the proposed model can be seamlessly incorporated into problems such as the economic dispatch, demand response scheduling and control, optimal power flow, and unit commitment, among others. The proposed model requires relatively little data to be trained and can be applied to all types of buildings, without requiring detailed sensor data. We compare our model to the RC circuit model and demonstrate a practical application in which an aggregator uses three different buildings (two offices and a supermarket) to balance wind generation forecast errors.

[^1]: The authors are with the Department of Electrical Engineering at the University of Washington (emails: {jcontrer, kirschen, zhangbao}@uw.edu) and with the Electric Power Research Institute (email: mortegavazquez@epri.com). This work is partially supported by the University of Washington Clean Energy Institute.
