Building Load Control Using Distributionally Robust Chance-Constrained Programs with Right-Hand Side Uncertainty and the Risk-Adjustable Variants

Yiling Zhang, Jin Dong

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Aggregation of heating, ventilation, and air conditioning (HVAC) loads can provide reserves to absorb volatile renewable energy, especially solar photo-voltaic (PV) generation. In this paper,we decideHVAC control schedules under uncertain PV generation, using a distributionally robust chance-constrained (DRCC) building load control model under two typical ambiguity sets: the moment-based and Wasserstein ambiguity sets. We derive mixed integer linear programming (MILP) reformulations forDRCC problems under both sets. Especially, for the Wasserstein ambiguity set, we use the right-hand side (RHS) uncertainty to derive a more compactMILP reformulation than the commonly knownMILP reformulations with big-M constants. All the results also apply to general individual chance constraints with RHS uncertainty. Furthermore, we propose an adjustable chance-constrained variant to achieve tradeoff between the operational risk and costs.We deriveMILP reformulations under the Wasserstein ambiguity set and second-order conic programming (SOCP) reformulations under the moment-based set. Using real-world data, we conduct computational studies to demonstrate the efficiency of the solution approaches and the effectiveness of the solutions.

Original languageEnglish (US)
Pages (from-to)1531-1547
Number of pages17
JournalINFORMS Journal on Computing
Issue number3
StatePublished - May 2022

Bibliographical note

Funding Information:
History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This work was supported in part by the U.S. Department of Energy (DOE), including DOE’s Office of Electricity [Contract DE‐AC05‐00OR22725], and in part by the University of Minnesota. Supplemental Material: The online appendix is available at

Funding Information:
This manuscript has been authored in part by UT-Battelle, LLC, under Contract DE-AC05-00OR22725 with the U.S. Department of Energy (DOE). The U.S. government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. 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 U.S. government purposes. DOE will provide public

Publisher Copyright:
© 2022 INFORMS Inst.for Operations Res.and the Management Sciences. All rights reserved.


  • binary program
  • building load control
  • chance-constrained program
  • distributionally robust optimization
  • renewable energy


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