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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Plug Load Management System Field Study

In 2018, NREL performed a demonstration of the IBIS InteliNetwork(TM) plug load management system, which includes: intelligent socket devices, which plug into existing electrical outlets and collect energy usage information; a gateway, which manages communication between the intelligent sockets and the PLM cloud service; and a PLM network, which is a cloud-based measurement and control network for the entire system. Two retail stores served as the field study sites. The PLM system was found to be effective at reducing plug load energy use, with a projected annual plug load reduction of 11% and 18% at the test locations. Retail staff reported that the hardware was easy to use, but no one used the online data management system, intended to analyze and control the hardware. This PLM solution shows promise as an effective means to monitor, control, and reduce plug load energy use, but the economics of the system need to be improved.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Emerging Technologies for Improved Plug Load Management Systems: Learning Behavior Algorithms and Automatic and Dynamic Load Detection

Plug loads are responsible for a significant portion of the energy consumed in commercial buildings, yet their distributed and ever-changing nature makes them one of the most challenging building end uses to manage. Plug load management systems exist today that utilize smart plugs to meter and control devices at the outlet level, however, their uptake has been relatively slow in part due to the significant labor required for installation and maintenance. Learning behavior algorithms and automatic and dynamic load detection have been identified as two technology areas that could accelerate the adoption of plug load management systems by reducing these labor demands and providing additional energy efficiency and non-energy benefits. Learning behavior algorithms learn occupant behavior and adjust plug load management systems accordingly, allowing for the automatic creation of optimized control schedules. Automatic and dynamic load detection allows a plug load management system to identify devices as they are plugged in to a building and keeps the system up to date as devices are moved throughout a building. In this paper, we present our findings with respect to the current state of these two technologies based on a review of existing research and patents, as well as a series of interviews with companies working in the plug load space. We have found that, as of now, no commercialized solutions exist for these plug load technologies and that more work is needed to bring them to market. In addition, we summarize our findings related to the technology challenges, market barriers, drivers, and opportunities for these technologies moving forward.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2019

Device-level plug load disaggregation in a zero energy office building and opportunities for energy savings

Along with heating, cooling, ventilation, and lighting, plug loads are one of the principal consumers of energy in commercial buildings. Managing ever-changing plug loads is a great challenge given the quantity and variety of devices in commercial buildings and the cost of monitoring equipment. To address this issue, we propose a method for developing a disaggregated model of an office building's plug loads that utilizes power data from a small portion of monitored devices and a device inventory. Using data from metering and control devices in the Research Support Facility at the National Renewable Energy Laboratory, we compared our model to the building's plug load submeters. We found that the model was effective in predicting the shape of the building's average plug loads; however, it did not account for the entire magnitude of the load. With the disaggregated breakdown, we identified devices that contributed significantly to the building's unoccupied load, such as the audio visual equipment as well as devices that contributed significantly at specific times of the day, such as the microwaves at noon. This disaggregated information enables building owners to make more informed decisions with respect to plug load controls and energy efficiency upgrades. Furthermore, we highlight how the plug loads in the Research Support Facility have changed over time and offer recommendations for implementing this disaggregation method in other buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Smart Outlets: Wireless Meter and Control Systems for Plug and Process Loads

Smart outlets control the flow of power to devices plugged into them and measure their energy use. The energy use data can be accessed via an online dashboard or smartphone application, allowing the user to turn power to plug-in devices on or off based on a schedule established in the dashboard or application. This four-page resource is a fact sheet meant to educate commercial building owners on smart outlets: what they are, why we use them, and how to use them in a way that will achieve energy savings while maintaining occupant comfort. The resource also describe how to procure a smart outlet system and how to fully capture their benefits over time. This resource was developed to support the Better Buildings Alliance Plug and Process Loads Technology Research Team.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2018

Navigating Cybersecurity Implications of Smart Outlets

Plug and process loads (PPLs) consume approximately one third of U.S. buildings' energy budget. As other equipment becomes more efficient, and plug-in devices diversify in the market, the percentage of PPL energy consumption is forecasted to grow. To help combat this growth, new wireless metering and control technologies (also known as 'smart outlets') are helping building owners to collect plug load data, understand usage patterns, and implement controls. Adoption of these new connected technologies motivates serious cybersecurity consideration. Because these new technologies are developing at such a rapid pace, the aim of this paper is to inform thinking about cybersecurity issues and the risks that they impose. Through this lens, we explore the pros and cons of plug load controllers, provide a framework for assessing cybersecurity of connected devices such as plug load controllers, and offer an example of how commercial building owners could use this framework to minimize their cybersecurity risks when adopting smart products. In providing this framework, this paper aims to remove some barriers to implementation of these energy efficient technologies in the built environment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Emerging Plug Load Management Technologies that Save Energy and Time

This fact sheet introduces two emerging technologies that could streamline plug load management (PLM) for increased energy savings for building owners: learning behavior algorithms (LBA) and automatic and dynamic load detection (ADLD). Plug loads are responsible for 47% of the energy consumed in commercial buildings, yet their distributed and ever-changing nature makes them challenging to manage. PLM systems exist today that use smart plugs to meter and control devices at the outlet level, but their uptake has been relatively slow in part because of the significant labor required for installation and maintenance. LBA and ADLD may address these challenges and provide additional energy efficiency and nonenergy benefits.

Plug and process loads, PPL, plug loads, plug load↗
DOE OSTI2021

A Commercial Building Plug Load Management System that Uses Internet of Things Technology to Automatically Identify Plugged-In Devices and Their Locations

Plug and process loads (PPLs) account for a large portion of U.S. commercial building energy use. There is a huge potential to reduce whole building consumption by targeting PPLs for energy savings measures or implementing some form of plug load management (PLM). Despite this potential, there has yet to be a widely adopted commercial PLM technology. This paper describes the Automatic Type and Location Identification System (ATLIS), a PLM system framework with automatic and dynamic load detection (ADLD). ADLD gives PLM systems the ability to automatically identify devices as they are plugged into the outlets of a building. The ATLIS framework takes advantage of smart, connected devices to identify device locations in a building, meter and control their power, and communicate this information to a central database. ATLIS includes five primary capabilities: location identification, communication, control, energy metering, and data storage. A laboratory proof of concept (PoC) demonstrated all but the energy metering capability, and these capabilities were validated using a series of system tests. The PoC was able to identify when a device was plugged into an outlet and the location of the device in the building. When a device was moved, the PoC's dashboard and database were automatically updated with the new location. The PoC implemented controls to devices from the system dashboard so that devices maintained correct schedules regardless of where they were plugged in within the building. ATLIS's primary technology application is improved PLM, but other applications include asset management, energy audits, and interoperability for grid-interactive efficient buildings. An ATLIS-based system could also be used to direct power to critical devices, such as ventilators, during a brownout or blackout. Such a framework is an opportunity to make PLM more widespread and reduce the amount of energy consumed by PPLs in current and future commercial buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Plug Load Management System Field Study: Wireless Meter and Controls

This presentation offers background on GSA's Green Proving Ground (GPG) and DOE's High Impact Technology (HIT) Catalyst work, and a field study that assessed a plug load management (PLM) system in two commercial building applications. The Ibis InteliNetwork (TM) PLM system enables users to measure and analyze device energy use and identify opportunities for reduction of plug load energy use, and set times for individual devices (or groups of devices) to turn off when not in use. The presentation gives an overview of the PLM demonstration project, its results, and lessons learned.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Solid State Power Substation DC Node Optimization and Controller Hardware-In-The-Loop Demonstration

A solid state power substation (SSPS) node is a microgrid that integrates distributed energy resources and loads and injects/absorbs power to/from the SSPS distribution network. It is an essential building block of a futuristic distribution grid network. This paper presents the development and demonstration of optimization use cases of a SSPS DC node. By adopting multi-layer hierarchical control architecture and developing automatic device identification and dynamic optimization formulation algorithms, the SSPS DC node can perform plug-and-play resource integration and seamless transition of the optimized node operation under on and off grid condition without sophisticated algorithms, control mode changes, and user interactions. Four optimization use cases including economic dispatches with price signal changes, a sudden PV power drop, and a single directional meter and its associated costs with sending power back to the grid, and resiliency under a grid inverter trip condition were demonstrated through the real-time controller hardware-in-the-loop simulation.

Kim, Namwon↗