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

Abstract

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 decide HVAC 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 for DRCC problems under both sets. Especially, for the Wasserstein ambiguity set, we use the right-hand side (RHS) uncertainty to derive a more compact MILP reformulation than the commonly known MILP 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 derive MILP 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. Summary of Contribution: The problem studied in this paper is motivated by a building load control problem that uses the aggregation of heating, ventilation, and air conditioning (HVAC) loads as flexible reserves tomore » absorb uncertain solar photovoltaic (PV) generation. The problem is formulated as distributionally robust chance-constrained (DRCC) programs with right-hand side (RHS) uncertainty. In addition, we propose a risk-adjustable variant of the DRCC programs, where the risk level, instead of being predetermined, is treated as a decision variable. The paper aims to provide tractable reformulations and solution algorithms for both the (general) DRCC and the (general) adjustable DRCC models with RHS uncertainty.« less

Authors:

Zhang, Yiling  ^[1];

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• Search DOE PAGES for ORCID "0000-0002-1077-198X"

Dong, Jin  ^[2]

• Search DOE PAGES for author "Dong, Jin"
• Search DOE PAGES for ORCID "0000-0002-5753-1588"

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+ Show Author Affiliations

1. Univ. of Minnesota, Minneapolis, MN (United States)
2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Publication Date:

Mon Feb 07 00:00:00 EST 2022

Research Org.:

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:

USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Electricity (OE)

OSTI Identifier:

1878716

Grant/Contract Number:  

AC05-00OR22725

Resource Type:

Accepted Manuscript

Journal Name:

INFORMS Journal on Computing

Additional Journal Information:

Journal Volume: 34; Journal Issue: 3; Journal ID: ISSN 1091-9856

Publisher:

INFORMS

Country of Publication:

United States

Language:

English

Subject:

97 MATHEMATICS AND COMPUTING; building load control, renewable energy, distributionally robust optimization, chance-constrained program, binary program