Quantifying the value of building demand response: Introducing a cross-sectoral model framework to optimize demand response scheduling

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mcpherson_madeleine_EnergyRep_2024.pdf (7.39 MB)

Date

2024

Authors

Stanislaw, Lauren
Seatle, Madeleine
McPherson, Madeleine

Journal Title

Journal ISSN

Volume Title

Publisher

Energy Reports

Abstract

Co-optimization of demand-side electrification and supply-side variable renewable energy integration in electricity systems can lead to dramatically reduced emissions due to synergies between the two sectors. However, few models exist that represent both sectors in sufficient operational detail. To bridge this gap, this paper proposes a novel framework for transferring information from a building stock model to an electricity system model. First, demand response (DR) events are simulated within a model of building stock energy use. Then, the energy characteristics of these events are used to inform a series of constraints within the electricity system model. At the same time, hourly electricity use predictions from the building stock are also incorporated into the total demand met by the electricity system. This allows the electricity system model to determine the grid-optimal times for the building stock to enact DR. To demonstrate the utility of this framework, a case study into the effects of building efficiency increases, DR, and variable renewable capacity expansion in the city of Regina, Saskatchewan is performed, and various ways to reduce the costs and emissions associated with electricity use in Regina are compared.

Description

The authors would like to thank Mohammad Miri, Jack Pawlyna, and Harold Stanislaw for insightful feedback during the manuscript editing process.

Keywords

urban building energy modeling, electricity system model, cross-sectoral model integration, demand response

Citation

Stanislaw, L., Seatle, M., & McPherson, M. (2024). Quantifying the value of building demand response: Introducing a cross-sectoral model framework to optimize demand response scheduling. Energy Reports, 11, 2111-2126. https://doi.org/10.1016/j.egyr.2024.01.063

URI

https://doi.org/10.1016/j.egyr.2024.01.063
 https://hdl.handle.net/1828/16319

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