<p>The seismic risks to a structure or a set of structures in a region are usually determined by generating fragility curves that provide the probability of a building responding in a certain manner for a given level of ground motion intensity. Developing fragility curves, however, is challenging as it involves the computationally expensive task of obtaining the maximum response of the selected structures to a suite of ground motions representing the seismic hazard of the region selected. </p><p>This study presents a methodology to develop surrogate models for the prediction of the maximum responses of buildings to ground motion excitation. Data-driven surrogate models using simple machine learning techniques and physics-based surrogate models using the space mapping technique to map the low-fidelity responses obtained using a multi-degree of freedom shear building model to the high-fidelity values are developed for the prediction of the maximum roof drift ratio and the maximum story drift ratio of a chosen 15-story steel moment-resisting frame building with varying structural properties in California. The predictions of each of these surrogate models are analyzed to assess and compare the performance, capabilities, and limitations of these models. Best practices for developing surrogate models for the prediction of maximum responses of structures to ground motion are recommended.</p><p>The results from the development of data-driven surrogate models show that the spectral displacement is the best intensity measure to condition the maximum roof drift ratio, and the spectral velocity is the best intensity measure to condition the maximum story drift ratio. Fragility analysis of the structure is thus conducted using maximum story drift as the engineering demand parameter and spectral velocity as the intensity measure. Monte Carlo simulation is conducted using the physics-based surrogate model to estimate the maximum story drifts for ground motions that are incrementally scaled to different intensity levels. Maximum likelihood estimates are used to obtain the parameters for a lognormal distribution and the 95% confidence intervals are obtained using the Wald confidence interval to plot the fragility curves.</p><p>Fragility curves are plotted both with and without variations in the structural properties of the building, and it is found that the effects of variability in ground motions on the fragility are far higher than the effects of the randomness of structural properties. Finally, it is found that about 65 ground motion records are needed for convergence of the parameters of the lognormal distribution for plotting fragility curves by using Monte Carlo simulation.</p>

This dissertation explores the idea of inducing grid peak demand curtailment by turning commercial buildings into interactive assets for building owners during the demand control period. The work presented here is useful for both ab initio design of new sites and for existing or retrofitted sites. An analytical hierarchy process (AHP)-based framework is developed to curtail the thermal load effectively across a group of commercial buildings. It gives an insight into the amount of peak demand reduction possible for each building, subject to indoor thermal comfort constraints as per ASHRAE standards. Furthermore, the detailed operation of buildings in communion with the electric grid is illustrated through case studies. This analysis forms an outline for the assessment of transactive energy opportunities for commercial buildings in distribution system operations and lays the foundation for a seamless building-to-grid integration framework. The contribution of this dissertation is fourfold – (a) an efficient method of developing high-fidelity physics-based building energy models for understanding the realistic operation of commercial buildings, (b) identification of minimal dataset to achieve a target accuracy for the building energy models (c) quantification of building peak demand reduction potential and corresponding energy savings across a stipulated range of thermostat setpoint temperatures and (d) AHP-based demand curtailment scheme. By careful modeling, it is shown that commercial building models developed using this methodology are both accurate and robust. As a result, the proposed approach can be extended to other commercial buildings of diverse characteristics, independent of the location. The methodology presented here takes a holistic approach towards building energy modeling by accounting for several building parameters and interactions between them. In addition, parametric analysis is done to identify a useful minimal dataset required to achieve a specified accuracy for the building energy models. This thesis describes the concept of commercial buildings as interactive assets in a transactive grid environment and the idea behind its working. / Doctor of Philosophy / This dissertation titled "An Expert-based Approach for Grid Peak Demand Curtailment using HVAC Thermostat Setpoint Interventions in Commercial Buildings" tackles two important challenges in the energy management domain: –electric grid peak demand curtailment and energy savings in commercial buildings. The distinguishing feature of the proposed solution lies in addressing these challenges solely through demand-side management (DSM) strategies, which include HVAC thermostat setpoint interventions and lighting control. We present a methodology for developing highly accurate building energy models that serve as digital twins of actual buildings. These digital replicas can be used to quantify the impact of various interventions and reflect the realistic operation of commercial buildings across varied conditions. This enables building owners to control demand intelligently and transact energy effectively in the electricity market. The development of Internet of Things (IoT) market and advanced technologies such as smart meters and smart thermostats allows for the design of novel strategies that address traditional challenges faced by electric grid operators. This dissertation elaborates on how smart buildings can leverage IoT-based solutions to participate in the electricity market during demand control periods. We also developed an expert opinion-based demand curtailment allocation scheme resulting in grid peak demand reduction. The numerical results obtained reinforce the effectiveness of the proposed solution across varied climatic conditions.