Artificial Intelligence for Building Energy Management in the Electricity Market and Transmission Power Flow Planning

dc.contributorGan, Yu
dc.contributorLi, Dawen
dc.contributorSong, Aijun
dc.contributorWoodbury, Keith
dc.contributor.advisorLi, Shuhui
dc.contributor.authorGao, Yixiang
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.descriptionElectronic Thesis or Dissertationen_US
dc.description.abstractDue to the high uncertainty of building loads and customer comfort demands and extremely nonlinear building thermal characteristics, developing an effective building energy management (BEM) technology is facing great challenges. This dissertation focuses on building energy management from the day-ahead and real-time planning perspectives in the electricity market. In the day-ahead planning, this dissertation presents price-sensitive demand response strategies for smart buildings by regulating their controllable loads to minimize building electricity costs and flatten the net buildings' loads. The learning-based HVAC model and the detailed physics-based non-HVAC model are then applied to an optimization problem to determine the optimal management scheduling of building loads based on day-ahead electricity price. In addition, this dissertation proposes an hourly decoupled AC/DC power flow approach for the day-ahead planning of multi-terminal HVDC systems. The proposed method simplifies the power flow computation of multi-terminal HVDC systems while accurately reflecting the operation and control characteristics of VSC (voltage source converter) stations in an HVDC network. In real-time planning, an optimization problem in a 5-minute time frame is proposed to manage real-time building energy consumption uncertainties based on the real-time clearing price and balance the real-time deviations from the building energy consumption negotiated in the day-ahead market. To help regulate power system real-time frequency fluctuation, an economic and hierarchical control approach for multi-thermal-zone buildings is developed to participate in the ancillary service market of the electric power systems. The energy consumption of variable speed drive (VSD) fans in the HVAC system is controlled up and down to follow dynamic auto-generation control (AGC) signals from the power system operators or control centers. A decoupling method is developed to balance the power system frequency regulation requirement in 5 seconds and real-time building energy planning developed above in 5 minutes. In this dissertation, computer simulation models for building and grid integration from different power systems planning perspectives are developed. All the proposed methods are studied theoretically and evaluated via computer simulations, based on which results and conclusions for each is obtained and reported in the dissertation.en_US
dc.publisherUniversity of Alabama Libraries
dc.relation.hasversionborn digital
dc.relation.ispartofThe University of Alabama Electronic Theses and Dissertations
dc.relation.ispartofThe University of Alabama Libraries Digital Collections
dc.rightsAll rights reserved by the author unless otherwise indicated.en_US
dc.titleArtificial Intelligence for Building Energy Management in the Electricity Market and Transmission Power Flow Planningen_US
dc.typetext of Alabama. Department of Educational Leadership, Policy, and Technology Studies engineering University of Alabama
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