Smart grids and smart buildings are two highly interdependent energy infrastructure systems. Buildings rely on the grid to provide reliable power while their flexibility can also be utilized to enhance the reliability and efficiency of power system operations. 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 dissertation 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. Buildings that are involved in the energy market need to follow certain power profiles. The robustness of power tracking is critical to the evaluation of their quality of service. Due to the easy accessibility of building automation systems, building sensor attacks can be launched to affect the power tracking accuracy. A robust HVAC control algorithm that can handle the uncertainty of sensor attack signal distribution is proposed to enhance the building power tracking. A Wasserstein distance-based ambiguity set is defined to bound the uncertain distortion between the predicted attack signal distribution and the true distribution. The worst-case distribution within the ambiguity set that has the largest expected power tracking error is solved. Then the robust control decision is made upon this worst-case distribution. In this way, the power tracking error can be reduced by 20%. The reliability of temperature maintenance is also enhanced by the proposed distributionally robust optimization. Besides sensor attack, the control signal of building automation system can also be overwritten if the proxy aggregator is attacked. This type of attack can impact the frequency stability of the entire system by manipulating load power across the system. To study the vulnerability of the system under control signal attack, an optimization-based attack model that incorporates the grid transient model and physics-based building model is proposed. The proposed attack model solves for the time series executable control signals that coordinate the system states and building limits at the minimum cost of building temperature deviation. This attack model is used for the vulnerability assessment of the IEEE 68-bus 16-machine system from two perspectives. The vulnerability of buses and aggregators can be obtained from the trajectories of the coordinated attack signals.


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Graduation Date





Zhou Sun, Qun


Doctor of Philosophy (Ph.D.)


College of Engineering and Computer Science


Electrical and Computer Engineering

Degree Program

Electrical Engineering




CFE0009671; DP0027648





Release Date

February 2023

Length of Campus-only Access


Access Status

Doctoral Dissertation (Open Access)