Huangjie Gong

As the smart grid involves more new technologies such as electric vehicles (EVs) and distributed energy resources (DERs), more attention is needed in research to general energy storage (GES) based energy management systems (EMS) that account for all possible load shifting and control strategies, specifically with major appliances that are projected to continue electrification such as the electric water heater (EWH). In this work, a methodology for a modified single-node model of a resistive EWH is proposed with improved internal tank temperature for user comfort modeling and capabilities for conservation voltage reduction (CVR) simulations as well as Energy Star and Consumer Technology Association communications protocol (CTA-2045) compliant controls, including energy storage calculations for “energy take”. Daily and weekly simulations are performed on a representative IEEE test feeder distribution system with experimental load and hot water draw (HWD) profiles to consider user comfort. Sequential controls are developed to reduce power spikes from controls and lead to peak shavings. It is found that EWHs are suitable for virtual power plant (VPP) operation with sustainable tank temperatures, i.e., average water temperature is maintained at set-point or above at the end of the control period while shifting up to 78% of EWH energy out of shed windows per day and 75% over a week, which amounts to up to 23% of the total load shifted on the example power system. While CVR simulations reduced the peak power of individual EWHs, the aggregation effect at the distribution level negates this reduction in power for the community. The EWH is shown as an energy constant load without consistent benefit from CVR across the example community with low energy reductions of less than 0.1% and, in some cases, increased daily energy by 0.18%.

Smart homes and virtual power plant (VPP) controls are growing fields of research with potential for improved electric power grid operation. A novel testbed for the co-simulation of electric power distribution systems and distributed energy resources (DERs) is employed to evaluate VPP scenarios and propose an optimization procedure. DERs of specific interest include behind-the-meter (BTM) solar photovoltaic (PV) systems as well as heating, ventilation, and air-conditioning (HVAC) systems. The simulation of HVAC systems is enabled by a machine learning procedure that produces ultra-fast models for electric power and indoor temperature of associated buildings that are up to 133 times faster than typical white-box implementations. Hundreds of these models, each with different properties, are randomly populated into a modified IEEE 123-bus test system to represent a typical U.S. community. Advanced VPP controls are developed based on the Consumer Technology Association (CTA) 2045 standard to leverage HVAC systems as generalized energy storage (GES) such that BTM solar PV is better utilized locally and occurrences of distribution system power peaks are reduced, while also maintaining occupant thermal comfort. An optimization is performed to determine the best control settings for targeted peak power and total daily energy increase minimization with example peak load reductions of 25+%.

A novel co-simulation framework was developed and demonstrated through virtual power plant (VPP) simulations that include hundreds of unique building models randomly populated into a modified IEEE 123-bus feeder system. The framework employs ultra-fast models for heating, ventilation, and air-conditioning (HVAC) systems as well as building thermal envelopes that are satisfactorily accurate for both electric power and indoor temperature. The approach circumvents generic control time limits typically in conventional implementations by enabling occupant thermal comfort monitoring. The HVAC and building models contain parameters by which they are characterized as generalized energy storage (GES) systems based on Energy Star definitions. This enables their compatibility with the Consumer Technology Association (CTA) 2045 standard control commands and event types. Example CTA-2045 “shed” events are illustrated to exemplify this feature and to analyze power distribution system effects in terms of power flow and voltages.

This article proposes a generalized energy storage model for battery energy storage systems, electric water heaters (EWH), and heating, ventilation, and air-conditioning (HVAC) systems to enable demand response control complying to Energy Star and CTA-2045 standards. The demand response control has been implemented in the DER integration testbed, which was originally developed by Electric Power Research Institute, to demonstrate that the “energy content” and “energy take” for BESS and EWH with mixing valve technology are comparable for typical residential ratings. A distribution power system was modeled using the modified IEEE 123-bus feeder system, measured residential loads, and EWH power simulated based on realistic hot water draws from CBECC-Res software. The demand response control, which complies to CTA-2045 standards was implemented to the EWHs considering the energy take values. Results demonstrate that the EWHs can be controlled to postpone the peak power at the distribution system level and provide a large amount of energy storage, while maintaining system robustness. The impact on occupant comfort was also analyzed.

The forecast and estimation of total electric power demand of a residential community, its baseload, and its heating ventilation and air-conditioning (HVAC) power component, which represents a very large portion of a community electricity usage, are important enablers for optimal energy controls and utility planning. This paper proposes a method that employs machine learning in a multi-step integrated approach. An LSTM model for total electric power at the main circuit feeder is trained using historic multi-year hourly data, outdoor temperature, and solar irradiance. New key temperature indicators, TmHAVC, corresponding to the standby zero-power operation for HVAC systems for summer cooling and winter heating are introduced using a V-shaped hourly total load curve. The trained LTSM model is additionally run with TmHVAC and zero irradiance inputs yielding an estimated baseload, which is representative of typical occupancy patterns. The HVAC power component is disaggregated as the difference between total and baseload power. Total power forecasts of an aggregated residential community as seen by major distribution lines are experimentally validated with a satisfactory MAPE error below 10% based on a 4-year dataset from a representative suburban community with more than 1800 homes in Kentucky, U.S. Discussions regarding the validity of the separation method based on combined considerations of fundamental physics, statistics, and human behavior are also included.

Separating the HVAC energy use from the total residential load can be used to improve energy usage monitoring and to enhance the house energy management systems (HEMS) for existing houses that do not have dedicated HVAC circuits. In this paper, a novel method is proposed to separate the HVAC dominant load component from the house load. The proposed method utilizes deep learning techniques and the physical relationship between HVAC energy use and weather. It employs novel long short-term memory (LSTM) encoder-decoder machine learning (ML) models, which are developed based on future weather data input in place of weather forecasts. In addition to being used in the proposed HVAC separation method, the LSTM models are employed also for accurate day-ahead HVAC and solar photovoltaic (PV) energy forecasts. To test and validate the proposed method, the SHINES dataset, a publicly available dataset spanning three years at 15-minute time resolution from a large-scale DOE experimental project, is used. Two computational case studies are constructed with a test HEMS to investigate the power regulating capability of smart home virtual operation as dispatchable loads or generators. Prediction results obtained with the proposed method show hourly and daily CV(RMSE) of 29.4% and 11.1%, respectively. These results are well within the bounds of error established by academia and the ASHRAE building model and calibration standards.

Heating, ventilation, and air-conditioning (HVAC) systems use the most electricity of any household appliance in residential communities. HVAC system modeling facilitates the study of demand response (DR) at both the residential and power system levels. In this article, the equivalent thermal model of a reference house is proposed. Parameters for the reference house were determined based on the systematic study of experimental data obtained from fully instrumented field demonstrators. Energy storage capacity of HVAC systems is calculated and an equivalent state-of-charge is defined. The uniformity between HVAC systems and battery energy storage system is demonstrated by DR control. The aggregated HVAC load model is based on the reference house and considers a realistic distribution of HVAC parameters derived from one of the largest smart grid field demonstrators in rural America. A sequential DR scheme as part of a virtual power plant control is proposed to reduce both ramping rate and peak power at the aggregated level, while maintaining human comfort according to ASHRAE standards.

Advanced control techniques may be used to establish a virtual power plant to regulate the operation of electric water heaters, which may be regarded as a “uni-directional battery” and a major component of a hybrid residential energy storage system. In order to estimate the potential of regulating water heaters at the aggregated level, factors including user behavior, number of water heaters, and types of water heaters must be considered. This study develops generic water heater load curves based on the data retrieved from large experimental projects for resistive electric water heaters (EWHs) and heat pump water heaters (HPWHs). A community-level digital twin with scalability has been developed to capture the aggregated hot water flow and average hot temperature in the tank. The results in this paper also include the “energy take” in line with the CTA-2045 standard and Energy Star specification. The data from the experiments demonstrated that changing from an EWH to an HPWH reduces electricity usage by approximately 70%. The case study showed that daily electricity usage could be shifted by approximately 14% and 17% by EWH and HPWHs, respectively, compared to their corresponding average power. Another case study showed that both EHWs and HPWHs, coordinated with PV to reduce morning and evening peaks, could shift approximately 22% of the daily electricity.

Buildings with solar photovoltaic (PV) generation and a stationary battery energy storage system (BESS) may self-sustain an uninterrupted full-level electricity supply during power outages. The duration of off-grid operation is dependent on the time of the power fault and the capabilities of the home energy management system (HEMS). In this paper, building resilience is quantified by analyzing the self-sustainment duration for all possible power outages throughout an entire year. An evaluation method is proposed and exercised on a reference house in California climate zone 9 for which the detailed electricity usage is simulated using the EnergyPlus software. The influence of factors such as energy use behavioral patterns, energy storage capacity from the BESS, and an electric vehicle (EV) battery on the building resilience is evaluated. Varying combinations of energy storage and controllable loads are studied for optimally improved resilience based on user preferences. It is shown that for the target home and region with a solar PV system of 7.2 kW, a BESS with a capacity of 11 kWh, and an EV with a battery of 80 kWh permanently connected to the home, off-grid self-sustained full operation is guaranteed for at least 72 h.

Domestic load profiles in the residential sectors are being modified with the adoption of smart home management systems and solar generation. In addition, houses with rooftop PV behave like local generators, contributing to the growth of the penetration of PV energy. Hence, the demand for power is declining day by day. However, the increasing PV penetration causes technical challenges for the power system, such as the “duck curve”. This can be addressed through home energy management (HEM) techniques including peak shaving, load shifting with smart home devices. In this regard, electric water heaters (EWH), with high thermal mass and being ubiquitous, are attractive and low-cost energy storage systems. In this article, a case study for one of the largest rural field smart energy technology demonstrators involving business, industries, and more than 5,000 residences, located in Glasgow, KY, US, is presented. Furthermore, a HEM system, which aims to minimize the total energy usage and peak demand by regulating the heating, ventilation, and air-conditioning (HVAC) systems, water heaters, and batteries, thereby benefiting both the utility and the consumer is proposed. This work also demonstrates the ability of EWH to provide ancillary services while maintaining customer comfort. The minimum participation rates for EWH and batteries are calculated and compared with respect to different peak reduction targets. Long term load prediction by considering different fractions of smart homes for the utility is also provided.