Dane Christensen

Electric Energy Management in the Smart Home: Perspectives on Enabling Technologies and Consumer Behavior

Smart homes hold the potential for increasing energy efficiency, decreasing costs of energy use, decreasing the carbon footprint by including renewable resources, and transforming the role of the occupant. At the crux of the smart home is an efficient electric energy management system that is enabled by emerging technologies in the electricity grid and consumer electronics. This paper presents a discussion of the state of the art in electricity management in smart homes, the various enabling technologies that will accelerate this concept, and topics around consumer behavior with respect to energy usage.

Using EnergyPlus to Perform Dehumidification Analysis on Building America Homes

A parametric study was conducted using EnergyPlus version 6.0 to investigate humidity issues on a typical mid-1990s reference home, a 2006 International Energy Conservation Code home, and a high-performance home in a hot–humid climate. The impacts of various dehumidification equipment and controls are analyzed on the high-performance home. The study used the Walker and Wilson (1998) infiltration model to examine the combined effects of infiltration and mechanical ventilation with balanced and unbalanced mechanical ventilation systems. Indoor relative humidity excursions were examined—specifically, the number of excursions, average excursion length, and maximum excursion length. Space relative humidity, thermal comfort, and whole-house source energy consumption were analyzed for indoor relative humidity set-points of 50%, 55%, and 60%. The study showed and explained why similar trends of high humidity were observed in all three homes, regardless of energy efficiency, and why humidity problems are not necessarily unique in high-performance homes. Thermal comfort analysis indicated that occupants are unlikely to notice indoor humidity problems. The study confirmed that supplemental dehumidification should be provided to maintain space relative humidity below 60% in a hot–humid climate. All modeled supplemental dehumidification options successfully controlled space relative humidity excursions, yet the increase in whole-house energy consumption was much more sensitive to the humidity set-points than the chosen technology option.

NILM Applications for the Energy-Efficient Home

We present a forecast for systems-focused applications of non-intrusive load monitoring (NILM), which meet the needs of homeowners, the technology sector, the service sector, and/or utilities. We discuss both near- and long-term applications.

Field Monitoring Protocol. Mini-Split Heat Pumps

The report provides a detailed method for accurately measuring and monitoring performance of a residential Mini-Split Heat Pump. It will be used in high-performance retrofit applications, and as part of DOEs Building America residential research program.

Modeling stationary lithium-ion batteries for optimization and predictive control

Accurately modeling stationary battery storage behavior is crucial to pursuing cost-effective distributed energy resource opportunities. In this paper, a lithium-ion battery model was derived for building-integrated battery use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. To achieve these goals, a mixed modeling approach incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. The proposed battery model is validated through comparison to manufacturer data. Additionally, a dynamic test case demonstrates the effects of using regression models to represent cycling losses and capacity fading. A proof-of-concept optimization test case with time-of-use pricing is performed to demonstrate how the battery model could be included in an optimization framework.

User-preference-driven model predictive control of residential building loads and battery storage for demand response

This paper presents a user-preference-driven home energy management system (HEMS) for demand response (DR) with residential building loads and battery storage. The HEMS is based on a multi-objective model predictive control algorithm, where the objectives include energy cost, thermal comfort, and carbon emission. A multi-criterion decision making method originating from social science is used to quickly determine user preferences based on a brief survey and derive the weights of different objectives used in the optimization process. Besides the residential appliances used in the traditional DR programs, a home battery system is integrated into the HEMS to improve the flexibility and reliability of the DR resources. Simulation studies have been performed on field data from a residential building stock data set. Appliance models and usage patterns were learned from the data to predict the DR resource availability. Results indicate the HEMS was able to provide a significant amount of load reduction with less than 20% prediction error in both heating and cooling cases.

Frequency Regulation Services from Connected Residential Devices: Short Paper

This paper demonstrates potential benefits that residential buildings can provide for frequency regulation services in the electric power grid. In a hardware-in-the-loop (HIL) implementation, simulated homes and a physical laboratory home are coordinated via a grid aggregator, and it is shown that their aggregate response has the potential to follow the regulation signal on a timescale of seconds. Connected (communication-enabled) devices in the National Renewable Energy Laboratorys (NRELs) Energy Systems Integration Facility (ESIF) received demand response (DR) requests from a grid aggregator, and the devices responded to meet the signal while satisfying comfort bounds and physical hardware limitations. Future research will address the issues of cybersecurity threats, participation rates, and reducing equipment wear-and-tear while providing grid services.

Grid Connected Functionality

Dataset demonstrating the potential benefits that residential buildings can provide for frequency regulation services in the electric power grid. In a hardware-in-the-loop (HIL) implementation, simulated homes along with a physical laboratory home are coordinated via a grid aggregator, and it is shown that their aggregate response has the potential to follow the regulation signal on a timescale of seconds. Connected (communication-enabled), devices in the National Renewable Energy Laboratorys (NRELs) Energy Systems Integration Facility (ESIF) received demand response (DR) requests from a grid aggregator, and the devices responded accordingly to meet the signal while satisfying user comfort bounds and physical hardware limitations.