Date of Award
5-12-2024
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Mechanical and Aerospace Engineering
Advisor(s)
Bing Dong
Keywords
building control;building-to-grid;EV charging and discharging;human mobility;model predictive control;vehicle-to-grid
Subject Categories
Engineering | Mechanical Engineering
Abstract
In the United States (U.S.), the buildings sector consumes about 76% of electricity use and 40% of all primary energy use and associated greenhouse gas emissions. Occupant behavior, which includes the presence of people in the space, interactions between the occupant and building systems, and occupant adaptations to the built environment, has a significant impact on the energy consumption of buildings. Many researchers have investigated occupant behaviors at a single building scale, however, very few studies have been conducted at the community scale or urban scale. Our previous review study introduced human mobility modeling approach from other domains into the building science domain. With the development of information technologies, such as mobile technology, urban sensing, and IoT, big data that was generated by those technologies provides opportunities to better understand occupant behavior at urban scale. Our pilot study uses a novel mobility-based approach to derive urban scale building occupant profiles, the building energy simulation results show up to 40% reduction of cooling energy demand and up to 60% reduction of heating energy demand. However, the study of human mobility modeling techniques is still limited. Across the U.S., there is an increasing demand for EV charging infrastructure, such as in office space, retail stores, and multifamily buildings. From the government level, the U.S. president signed an executive order in December 2021 to direct the federal government to achieve five carbon neutral goals and set the roadmap for the U.S. to net-zero emissions from overall federal operations by 2050. Meanwhile, the Building Technologies Office from the United States Department of Energy (DOE) has initiated research on Grid-interactive Efficient Buildings (GEB), to make building operations coordinate with the grid regarding the amount and timing of energy use and reduce greenhouse gas emissions from buildings. By promoting GEB nationally, DOE aims to triple the energy efficiency and demand flexibility of buildings sector by 2030 relative to 2020 levels. To facilitate the transition of GEB, new technologies are needed to achieve seamless smart integrated control of buildings and grid (building-to-grid). With the development of electrical vehicles (EV) and distributed energy resources (DERs) such as solar photovoltaic (PV) panels and battery energy storage systems, challenges become even greater because of the disturbances introduced by EVs and DERs to the power grid. Most studies only focus on building-to-grid integration or vehicle-to-grid integration respectively. Furthermore, the understanding of urban scale building occupant behavior and EV charging/discharging behaviors is very limited. This study examines the effects of Electric Vehicle (EV) charging/discharging behaviors, informed by human mobility, on the flexibility of grid distribution networks and the energy demand of buildings integrated with Distributed Energy Resources (DERs) and EVs. It hypothesizes that an advanced control framework, specifically Model Predictive Control (MPC), can optimally regulate building HVAC systems, on-site PV generation, Battery Energy Storage Systems (BESS), and EV charging/discharging, while simultaneously providing grid services such as load profile stabilization and lowering utility costs for users. Accordingly, this study developed a Vehicle-to-Building-to-Grid (V2B2G) framework that incorporates urban-scale human mobility modeling. This framework is designed to enable advanced building controls and smart EV charging/discharging, enhancing grid support services. Mobility models, derived from real-world mobile phone data, identify crucial user locations and utilize Recurrent Neural Network (RNN) models to forecast future movements. These models enrich our understanding of occupant behavior and EV charging/discharging practices. The study results demonstrated that we successfully predicted urban-scale daily human mobility patterns with a 12-hour prediction horizon, achieving an average accuracy of approximately 85% and a mean precision of about 86%. A comprehensive MPC framework has been developed to reduce energy costs and enhance grid flexibility through demand-side management, all while preserving occupant comfort. The simulation results indicate that the developed smart controller, under various control objectives and EV charging/discharging configurations, can achieve: 1) a reduction of the total grid net load by up to 81.55%; 2) a decrease in users’ total utility costs by up to 110.47%, effectively generating profit; 3) smoothing of the grid net load curve, achieving a 56.18% reduction in on-peak time peak demand and a 56.18% decrease in peak load rebound. This work enhances our understanding in three key areas: (1) modeling and predicting urban-scale occupant behavior through human mobility analysis; (2) optimizing EV charging and discharging based on user behavior insights; (3) integrating control mechanisms between vehicles, buildings, and the grid. The proposed framework offers a sophisticated control strategy that supports further research into GEB.
Access
Open Access
Recommended Citation
LIU, YAPAN, "Urban Scale Vehicle-To-Building-To-Grid Integration Leveraging Human Mobility Modeling" (2024). Dissertations - ALL. 1888.
https://surface.syr.edu/etd/1888
