(Georgia Institute of Technology, 2017-11-13)
Carneiro, Gustavo Antonio
In urban regions, traditionally a main electric grid fed by centralized power plants serves the growing energy demand of residential and commercial buildings. However, the advent of new technologies, such as distributed renewable energy generation, local energy storage, and smart controls, is transforming the way buildings interact and transact with the electric grid. When operating in coordination, several buildings or households can leverage their aggregate potential and use their energy flexibility and distributed resources to improve the operation of both the main grid and the pool of integrated and intelligent buildings. Much attention has been drawn to the potential benefits of these types of integration, especially the capabilities they can provide in terms of aggregate demand management and local power resilience. Nevertheless, building energy modeling at the urban level has not yet reached the necessary computational manageability and simulation robustness to assess these novel scenarios. To address this hiatus, the current thesis presents a computer-aided energy simulation method to model the integration of multiple buildings and distributed energy resources (DER) at the neighborhood scale. The proposed methodology uses a reduced order simulation approach to achieve a reliable and tractable dynamic modeling framework that can manage multiple transacting building energy models and DER models in a single platform.
To test the modeling approach, this study first carries out a virtual experiment of a small community in Miami, FL, where it is possible to compare the outcomes of community energy consumption from our reduced order model to the outcomes from a higher order simulation approach. When using the community energy model to evaluate the performance of different DER options for community peak load shaving, we can observe that the influence of the model order reduction reveals to be very minor when compared to other uncertainties related to scenario variability and, especially, systems’ efficiencies.
Secondly, we apply the reduced order modeling approach to an existing residential community in Rancho Cordova (Sacramento County), CA, with solar energy generation and battery energy storage. With this case study, we demonstrate the viability of our approach to construct and calibrate a reduced order model of fifteen households based only on limited and general data related to energy performance of the entire neighborhood. The developed reduced order model is used to evaluate the performance of different energy storage arrangements for reducing the occurrence of community super peak loads. In this virtual experiment, we can demonstrate how the model allows for uncertainty analyses over the influence of input parameters, as well as for more sophisticated optimization studies, including stochastic optimization, in a timely and transparent fashion.
Finally, the proposed reduced order simulation approach is used to construct and test relevant energy performance measures at the neighborhood scale. Using the model unique features of manageability, reliability and flexibility, we propose the foundations for quantifying and measuring “community energy resilience” for outage situations, based on concepts of number of sustained hours and respective energy end-use convenience levels. We also measure and monetize DER options for providing “community energy flexibility”, aimed at shaping the load profile of a residential community to match the electric grid needs.