Distributionally robust building load control to compensate fluctuations in solar power generation

Yiling Zhang, Jin Dong, Teja Kuruganti, Siqian Shen, Yaosuo Xue

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


This paper investigates the use of a collection of dispatchable heating, ventilation and air conditioning (HVAC) systems to absorb low-frequency fluctuations in renewable energy sources, especially in solar photo-voltaic (PV) generation. Given the uncertain and time-varying nature of solar PV generation, its probability distribution is difficult to be estimated perfectly, which poses a challenging problem of how to optimally schedule a fleet of HVAC loads to consume as much as local PV generation. We formulate a distributionally robust chance-constrained (DRCC) model to ensure that PV generation is consumed with a desired probability for a family of probability distributions, termed as an ambiguity set, built upon mean and covariance information. We benchmark the DRCC model with a deterministic optimization model and a stochastic programming model in a one-day simulation. We show that the DRCC model achieves constantly good performance to consume most PV generation even in the case with the presence of probability distribution ambiguity.

Original languageEnglish (US)
Title of host publication2019 American Control Conference, ACC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781538679265
StatePublished - Jul 2019
Event2019 American Control Conference, ACC 2019 - Philadelphia, United States
Duration: Jul 10 2019Jul 12 2019

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Conference2019 American Control Conference, ACC 2019
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2019 American Automatic Control Council.


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