Graduate Thesis Or Dissertation


Estimating The Value of Jointly Optimized Electric Power Generation and Residential Electrical Use Public Deposited
  • Automatic electric load shaping and modulation can increase the efficiency of existing thermal power plants and facilitate a transition to carbon-free generation from renewables. The ongoing electrification of buildings, industry, and transportation presents an ever-expanding set of opportunities to use the Internet of Things to introduce load elasticity and alter the traditional electricity supply-follows-demand paradigm. The problem addressed was creating a globally applicable framework to model the value of flexible residential load across a range of geographies in terms of electricity production cost and carbon dioxide emissions. This research extended models of optimal supervisory control of residential building thermal masses by investigating the complementary aspects of the model predictive control (MPC) of multiple degrees of freedom including air-conditioning, electric domestic hot water heating, and battery storage. The framework uses historical load, weather, building stock attributes, operating schedules of electrical devices, distribution feeder models, and generator constraints to quantify the value of residential load shaping for decision and policymakers. Three phases completed the research:Phase 1 adopted a data-driven perspective to analyze, synthesize, and statistically simulate diversity in residential load observed in empirical data associated with home appliances. Phase 2 quantified feeder-wide electric grid benefits resulting from the aggregation of flexible loads, with time-of-day sensitivities investigated across electrical distribution networks, seasons, and U.S. climate regions.Phase 3 estimated the system-wide impacts likely to result from jointly optimizing residential load with existing generation fleets and feasible penetrations of renewable generation in an annual case study of the large electric grid managed by the Electric Reliability Council of Texas. Results indicate a 1/3 reduction in annual generation costs and a 1/5 reduction in CO2 emissions at high renewable energy penetrations.

Date Issued
  • 2019-09-19
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Last Modified
  • 2022-05-04
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