Stochastic Pricing Game for Aggregated Demand Response Considering Comfort Level

Yang Chen, Kadir Amasyali, Byungkwon Park, Mohammed M. Olama, Bhagyashri Telsang, Seddik M. Djouadi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In recent years, demand response (DR) has been explored as a fundamental strategy for demand-side management due to its advantages in mediating intermittency of renewable energy generation, load shifting, etc. To engage customers in DR programs, several deterministic price-based DR strategies have been developed and implemented. However, the stochastic weather conditions and occupants' consumption behaviors often make the deterministic solution less robust to uncertainties. In this paper, with the consideration of the uncertainties, a stochastic Stackelberg game is proposed to model the price-demand negotiation between a distributed system operator and load aggregators, where the virtual battery constraints are extracted from the building thermostatically controlled loads (TCLs)' characteristics to guarantee comfortable TCLs' levels. Following the negotiation, a priority-based control method is used to allocate the optimal aggregated power DR profile at the building level and track the power signal. Several groups of experiments have demonstrated the effectiveness and robustness of the stochastic solutions.

Original languageEnglish
Title of host publication2021 North American Power Symposium, NAPS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665420815
DOIs
StatePublished - 2021
Event2021 North American Power Symposium, NAPS 2021 - College Station, United States
Duration: Nov 14 2021Nov 16 2021

Publication series

Name2021 North American Power Symposium, NAPS 2021

Conference

Conference2021 North American Power Symposium, NAPS 2021
Country/TerritoryUnited States
CityCollege Station
Period11/14/2111/16/21

Funding

Y. Chen is with the Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, USA (email: [email protected]) K. Amasyali, B. Park, and M.M. Olama are with the Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, USA (email: [email protected], [email protected], [email protected]) B. Telsang and S.M. Djouadi are with the Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, USA (email: [email protected], [email protected]) This manuscript has been authored 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) This study was supported by the US Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy, Building Technologies Office under contract DE-AC05-00OR22725.

Keywords

  • Backward induction
  • Demand response
  • Load aggregator
  • Stackelberg game
  • Virtual battery

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