Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 37, Issue 5, Pages 3862-3874Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2021.3136464
Keywords
HVAC; Buildings; Real-time systems; Atmospheric modeling; Load modeling; Fans; Computational modeling; HVAC flexibility quantification; demand response; convex optimization
Categories
Funding
- U.S. Department of Energy's Office of Energy Efficiency, and Renewable Energy (EERE) under the Building Technology Office [DE-EE0009152]
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This paper presents a novel HVAC flexibility quantification method based on semidefinite programming. By reformulating the non-convex problem of HVAC power optimization, the proposed method provides real-time flexibility quantification results. Physic-based HVAC model is incorporated to ensure the reliability and accuracy of the solutions. The quantification results are organized into a table, providing response strategies for flexibility adjustment.
The quantification of heating, ventilation, and air condition (HVAC) system flexibility is critical to the operations of both the grid and buildings in demand response (DR) programs. However, the flexibility quantification is challenging due to the non-linearity and non-convexity of thermal dynamics associated with HVAC components. This paper proposes a novel HVAC flexibility quantification method based on a semidefinite programming (SDP) formulation. The SDP is reformulated from the non-convex problem of HVAC power optimization, and can be solved efficiently in real-time. The physics-based HVAC model is incorporated to ensure the reliability and accuracy of solutions. The quantification results are organized into an HVAC flexibility table that can provide response strategies on adjusting HVAC setpoints in response to the grid signals received. The developed response strategies minimize occupant discomfort while satisfying grid requirements. A case study of a test building model is carried out to illustrate the flexibility quantification framework and compares the performance of two DR strategies.
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