Peak Reduction in a Residential Community through Bayesian Optimization of Transactive Control Signals

Ian Schomer; Fangxing Li; Ben Ollis

doi:10.1109/NAPS50074.2021.9449734

Peak Reduction in a Residential Community through Bayesian Optimization of Transactive Control Signals

Ian Schomer, Fangxing Li, Ben Ollis

Energy and Control Systems Security

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Daily residential power consumption in aggregate tends to have a large peak that places stress on the distribution system and induces high operational costs. This work shows that coordinated transactive control of noncritical loads within a residential community or microgrid can help to alleviate peak-time stress on the distribution system by flattening the aggregate load curve. Treating the load forecaster as a high-fidelity, expensive black-box function, a new algorithm utilizing Bayesian optimization (BO) is proposed to achieve the best solution under uncertainty with minimal computing effort. The proposed BO algorithm manipulates the shape of the load based on transactive signals sent to each home. The thermostatically controlled loads (TCLs) act out of self-interest in response to the given price while maintaining comfort, and the optimizer exploits the thermal energy retention of the homes for the benefit of the community. Simulations confirm consistent neighborhood-level peak power reduction and energy cost savings.

Original language	English
Title of host publication	2020 52nd North American Power Symposium, NAPS 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728181929
DOIs	https://doi.org/10.1109/NAPS50074.2021.9449734
State	Published - Apr 11 2021
Event	52nd North American Power Symposium, NAPS 2020 - Tempe, United States Duration: Apr 11 2021 → Apr 13 2021

Publication series

Name	2020 52nd North American Power Symposium, NAPS 2020

Conference

Conference	52nd North American Power Symposium, NAPS 2020
Country/Territory	United States
City	Tempe
Period	04/11/21 → 04/13/21

Funding

This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

Bayesian optimization
dynamic pricing environment
peak reduction
transactive load control

Access to Document

10.1109/NAPS50074.2021.9449734

Cite this

Schomer, I., Li, F., & Ollis, B. (2021). Peak Reduction in a Residential Community through Bayesian Optimization of Transactive Control Signals. In 2020 52nd North American Power Symposium, NAPS 2020 Article 9449734 (2020 52nd North American Power Symposium, NAPS 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NAPS50074.2021.9449734

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title = "Peak Reduction in a Residential Community through Bayesian Optimization of Transactive Control Signals",

abstract = "Daily residential power consumption in aggregate tends to have a large peak that places stress on the distribution system and induces high operational costs. This work shows that coordinated transactive control of noncritical loads within a residential community or microgrid can help to alleviate peak-time stress on the distribution system by flattening the aggregate load curve. Treating the load forecaster as a high-fidelity, expensive black-box function, a new algorithm utilizing Bayesian optimization (BO) is proposed to achieve the best solution under uncertainty with minimal computing effort. The proposed BO algorithm manipulates the shape of the load based on transactive signals sent to each home. The thermostatically controlled loads (TCLs) act out of self-interest in response to the given price while maintaining comfort, and the optimizer exploits the thermal energy retention of the homes for the benefit of the community. Simulations confirm consistent neighborhood-level peak power reduction and energy cost savings.",

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doi = "10.1109/NAPS50074.2021.9449734",

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Schomer, I, Li, F & Ollis, B 2021, Peak Reduction in a Residential Community through Bayesian Optimization of Transactive Control Signals. in 2020 52nd North American Power Symposium, NAPS 2020., 9449734, 2020 52nd North American Power Symposium, NAPS 2020, Institute of Electrical and Electronics Engineers Inc., 52nd North American Power Symposium, NAPS 2020, Tempe, United States, 04/11/21. https://doi.org/10.1109/NAPS50074.2021.9449734

Peak Reduction in a Residential Community through Bayesian Optimization of Transactive Control Signals. / Schomer, Ian; Li, Fangxing; Ollis, Ben.
2020 52nd North American Power Symposium, NAPS 2020. Institute of Electrical and Electronics Engineers Inc., 2021. 9449734 (2020 52nd North American Power Symposium, NAPS 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Daily residential power consumption in aggregate tends to have a large peak that places stress on the distribution system and induces high operational costs. This work shows that coordinated transactive control of noncritical loads within a residential community or microgrid can help to alleviate peak-time stress on the distribution system by flattening the aggregate load curve. Treating the load forecaster as a high-fidelity, expensive black-box function, a new algorithm utilizing Bayesian optimization (BO) is proposed to achieve the best solution under uncertainty with minimal computing effort. The proposed BO algorithm manipulates the shape of the load based on transactive signals sent to each home. The thermostatically controlled loads (TCLs) act out of self-interest in response to the given price while maintaining comfort, and the optimizer exploits the thermal energy retention of the homes for the benefit of the community. Simulations confirm consistent neighborhood-level peak power reduction and energy cost savings.

AB - Daily residential power consumption in aggregate tends to have a large peak that places stress on the distribution system and induces high operational costs. This work shows that coordinated transactive control of noncritical loads within a residential community or microgrid can help to alleviate peak-time stress on the distribution system by flattening the aggregate load curve. Treating the load forecaster as a high-fidelity, expensive black-box function, a new algorithm utilizing Bayesian optimization (BO) is proposed to achieve the best solution under uncertainty with minimal computing effort. The proposed BO algorithm manipulates the shape of the load based on transactive signals sent to each home. The thermostatically controlled loads (TCLs) act out of self-interest in response to the given price while maintaining comfort, and the optimizer exploits the thermal energy retention of the homes for the benefit of the community. Simulations confirm consistent neighborhood-level peak power reduction and energy cost savings.

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Peak Reduction in a Residential Community through Bayesian Optimization of Transactive Control Signals

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