Author ORCID Identifier
Year of Publication
Doctor of Philosophy (PhD)
Electrical and Computer Engineering
Dr. Dan M. Ionel
Smart homes can operate as a distributed energy resource (DER), when equipped with controllable high-efficiency appliances, solar photovoltaic (PV) generators, electric vehicles (EV) and energy storage systems (ESS). The high penetration of such buildings changes the typical electric power load profile, which without appropriate controls, may become a “duck curve” when the surplus PV generation is high, or a “dragon curve” when the EV charging load is high. A smart home may contribute to an optimal solution of such problems through the energy storage capacity, provided by its by battery energy storage system (BESS), heating, ventilation, and air conditioning (HVAC) system, and electric water heater (EWH), and the advanced controls of an home energy management (HEM). The integrated modeling of home energy usage and electric power distribution system, developed as part of this dissertation research, provides a testbed for HEM control methods and prediction of long-term scenarios.
A hybrid energy storage system including batteries and a variable power EWH was proposed. It was demonstrated that when the operation of the proposed hybrid energy storage system was coordinated with PV generation, the required battery capacity would be substantially reduced while still maintaining the same functionality for smart homes to operate as dispatchable generators. A newly developed co-simulation framework, INSPIRE+D, enables the dynamic simulation of smart homes and their connection to the grid.
The equivalent thermal model of a reference house was proposed with parameters based on the systematic study of experimental data from fully instrumented field demonstrators. Energy storage capacity of HVAC systems was calculated and an equivalent state-of-charge (SOC) was defined. The aggregated HVAC load was calculated based on special HVAC parameters and a sequential DR scheme was proposed to reduce both ramping rate and peak power, while maintaining human comfort according to ASHRAE standards. A long short-term memory (LSTM) method was applied to for the identification of HVAC system from the aggregated data.
The generic water heater load curves based on the data retrieved from large experimental projects for resistive EWHs and heat pump water heaters (HPWHs) were created. A community-level digital twin with scalability has been developed to capture the aggregated hot water flow and average hot temperature in the tanks. The potential electricity saving of shifting from EWH to HPWH was calculated. The energy storage capacities for both EWHs and HPWHs were calculated.
Long term load prediction by considering different fractions of smart homes with HEM for at the power system was provided based on one of the largest rural field smart energy technology demonstrators located in Glasgow, KY, US. Also demonstrates was the ability of EWH to provide ancillary services while maintaining customer comfort. The minimum participation rates for EWH and batteries were calculated and compared with respect to different peak reduction targets.
The aggregated charging load for EV in a community was calculated based on data from the National Travel Household Survey (NHTS). The EV charging and RESS operation were scheduled to reduce the daily utility charge. Building resilience was quantified by analyzing the self-sustainment duration for all possible power outages throughout an entire year based on the annual electricity usage of a typical California residence. The influence of factors such as energy use behavioral patterns, BESS capacity, and an availability of EV was evaluated.
A concept of generalized energy storage (GES) model for BESS, EWH and HVAC systems was proposed. The analogies, including SOC versus water/indoor temperature differential, were identified and explained, and models-in-the-loop (MIL) were introduced, which were compatible with the Energy Star and Consumer Technology Association (CTA)-2045 general specifications and command types. A case study is included to illustrate that the “energy content” and “energy take” for BESS and EWH.
The main original contributions of this dissertation include the comprehensive simulation of the total building energy usage and the development of the co-simulation framework incorporating building and power system simulators. Another contribution of the dissertation is the quantification of building resilience based on the building energy usage model. The dissertation also contributes to the concept of GES which regards the HVAC and EWH as virtual energy storage and their unified controls with BESS. The GES facilitates the employment of industrial standards, e.g., CTA-2045, and the hybrid ESS reduces required BESS capacity.
This dissertation contributes to the modeling of aggregated load for EWH, HVAC, and EV using different methods and long term forecasting of power profile at the system level. The aggregated generic load for EWH was calculated based on large amount of field data, the aggregated EV charging load was estimated based on national survey results, and the aggregated HVAC load was simulated based on the modeling of every residences, where the model parameters were populated according to special distributions. The methods based LSTM for the identification of HVAC power from the aggregated load was developed.
Digital Object Identifier (DOI)
The Ph.D. research studies were supported by the Department of Energy (DOE), projects #DE-EE0009021 and #DE-EE0008352, the National Science Foundation (NSF), award #1936131, Louisville Gas & Electric Company and Kentucky Utilities (LG&E and KU), and Tennessee Valley Authority (TVA).
Gong, Huangjie, "Models and Optimal Controls for Smart Homes and their Integration into the Electric Power Grid" (2022). Theses and Dissertations--Electrical and Computer Engineering. 179.