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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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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

A Data-driven Control Method for Operating the Commercial HVAC Load as a Virtual Battery

Building loads have a great potential to provide ancillary services because of its large power consumption. In order to quantify their flexibility, methods of modeling building loads as virtual battery (VB) have been proposed. The VB model provides an effective way to coordinate building loads and energy storage systems together when providing service to grid. However, after grid send a power signal to VB, the control strategy has not been fully studied to let building consumes this desired amount of power yet. This paper introduces a method to control commercial heating, ventilation, and air conditioning (HVAC) system. First, the thermal model of commercial building is introduced. We then used several data-driven models to approximate this thermal dynamic and the operation of different HVAC components. Base on these data-driven models, we developed a centralized control algorithm to utilize HVAC system consuming the desired amount of power. Finally, we modeled a realistic commercial building in EnergyPlus to test the performance of this proposed control algorithm

Wang, Jiyu↗
DOE OSTI2017

Transactive Control of Commercial Buildings for Demand Response

Transactive control is a type of distributed control strategy that uses market mechanism to engage self-interested responsive loads to achieve power balance in the electrical power grid. In this paper, we propose a transactive control approach of commercial building Heating, Ventilation, and Air- Conditioning (HVAC) systems for demand response. We first describe the system models, and identify their model parameters using data collected from Systems Engineering Building (SEB) located on our Pacific Northwest National Laboratory (PNNL) campus. We next present a transactive control market structure for commercial building HVAC system, and describe its agent bidding and market clearing strategies. Several case studies are performed in a simulation environment using Building Control Virtual Test Bed (BCVTB) and calibrated SEB EnergyPlus model. We show that the proposed transactive control approach is very effective at peak clipping, load shifting, and strategic conservation for commercial building HVAC systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Gnu-RL: A Precocial Reinforcement Learning Solution for Building HVAC Control Using a Differentiable MPC Policy

Reinforcement learning (RL) was first demonstrated to be a feasible approach to controlling heating, ventilation, and air conditioning (HVAC) systems more than a decade ago. However, there has been limited progress towards a practical and scalable RL solution for HVAC control. While one can train an RL agent in simulation, it is not cost-effective to create a model for each thermal zone or building. Likewise, existing RL agents generally take a long time to learn and are opaque to expert interrogation, making them unattractive for real-world deployment. To tackle these challenges, we propose Gnu-RL: a novel approach that enables practical deployment of RL for HVAC control and re- quires no prior information other than historical data from exist- ing HVAC controllers. To achieve this, Gnu-RL adopts a recently- developed Differentiable Model Predictive Control (MPC) policy, which encodes domain knowledge on planning and system dynam- ics, making it both sample-efficient and interpretable. Prior to any interaction with the environment, a Gnu-RL agent is pre-trained on historical data using imitation learning, which enables it to match the behavior of the existing controller. Once it is put in charge of controlling the environment, the agent continues to improve its policy end-to-end, using a policy gradient algorithm. We evaluate Gnu-RL on both an EnergyPlus model and a real- world testbed. In both experiments, our agents were directly de- ployed in the environment after offline pre-training on expert demonstration. In the simulation experiment, our approach saved 6.6% energy compared to the best published RL result for the same environment, while maintaining a higher level of occupant comfort. Next, Gnu-RL was deployed to control the HVAC of a real-world conference room for a three-week period. Our results show that Gnu-RL saved 16.7% of cooling demand compared to the existing controller and tracked temperature set-point better.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Potential Demand Reduction from Buildings in a Simulated Utility

The availability of large-area sensing, scalable algorithms, and high-performance computing has enabled the possibility of urban-scale building energy modeling using new methods not limited to the scalability of manual building energy model creation or retrieval of county-by-county tax assessor’s data. Automatic Building detection and Energy Model creation (AutoBEM) has created 178,368 building energy models for the Electric Power Board (EPB) of Chattanooga, TN, and compared simulation performance to 15-minute data from each building. These models leverage several data sources (e.g. imagery, GIS, utility), software tools to extract building properties (e.g. footprint, height, façade type, window-to-wall ratio, occupancy, building type), simulate at scale on two of the world’s #1 fastest supercomputers, and provide web-based visual analytics.Grid-interactive efficient buildings offer the potential to reduce utility and rate-payer energy costs during each calendar month’s hour of critical generation – when the least efficient, most costly, and often dirtiest generation assets must be brought online. In this paper, EnergyPlus is used to simulate over 150,000 buildings to assess the technical potential of utility-controlled smart thermostats. This is analyzed under a couple scenarios leveraging buildings as thermal batteries via pre-conditioning to coast through peak hours. Results are provided in box and whisker plots assessing the distribution of peak demand reduction at the utility-scale along with breakouts of energy and demand savings by building type and vintage.

Bass, Brett↗
DOE OSTI2015

Autotune

The Autotune software code uses an island-based evolutionary algorithm to solve multi-objective optimization problems. For the code provided, this has been applied to the whole-building simulation engine EnergyPlus to allow a software description of a building to be calibrated to measured data.

Joshua New, AaronGarrett↗
DOE OSTI2016

OpenStudio® Fault Models Gem [SWR-15-23]

Overview of the OpenStudio® Fault Models Gem: Creating a comprehensive library of OpenStudio® fault models. Features: - Physics-based fault impact simulation within the capability of EnergyPlus®/OpenStudio® - Fault intensity implementation simulating how severe faults are (e.g., how much portion is fouled in the condenser heat exchanger) and estimating the impact of a fault in different fault intensities. - Fault evolution implementation simulating evolving/increasing fault intensity through certain period of time. Future goals: - what are common/typical faults in different sample spaces (e.g., HVAC system type, building operation type, building location etc.)? - how often (e.g., incidence and prevalence) faults occur in different sample spaces (e.g., HVAC system type, building operation type, building location etc.)?

Frank, Stephen↗
DOE OSTI2016

OPENSTUDIO® [SWR-07-40]

OpenStudio® is a cross-platform (Windows, Mac, and Linux) collection of software tools to support whole building energy modeling using EnergyPlus and advanced daylight analysis using Radiance. OpenStudio is an open source (LGPL) project to facilitate community development, extension, and private sector adoption. OpenStudio includes graphical interfaces along with a Software Development Kit (SDK). In addition to the graphical interface, OpenStudio allows building researchers and software developers to quickly get started through its multiple entry levels, including access through C++, Ruby, and C#. Users can leverage the Ruby interface to create OpenStudio Measures that can be easily shared and applied to OpenStudio Models. OPENSTUDIO® IS A REGISTERED TRADEMARK OF ALLIANCE FOR SUSTAINABLE ENERGY, LLC, THE MANAGER AND OPERATOR OF THE NATIONAL RENEWABLE ENERGY LABORATORY.

Ball, Brian↗
DOE OSTI2017

Transactive Energy Simulation Platform

Transactive Energy Simulation Platform (TESP) combines existing domain simulators in the electric power grid, buildings, and communication systems, with new transactive agents, growth models and evaluation scripts. The existing domain simulators include GridLAB-D for the distribution grid and single-family residential buildings, MATPOWER for transmission and bulk generation, EnergyPlus for large buildings and ns-3 for communication systems. More are planned for subsequent versions of TESP. The new elements are: TEAgents - simulate market participants and transactive systems for market clearing. Some of this functionality was extracted from GridLAB-D and implemented in Python for customization by PNNL and others. Growth Model - a means for simulating system changes over a multiyear period, including both normal load growth and specific investment decisions. Customizable in Python code. Evaluation Script - a means of evaluating different transactive systems through customizable post-processing in Python code. TESP will run on Linux, Windows and Mac OS X.

McDermott, Tom↗
DOE OSTI2018

NREL/OpenStudio

OpenStudio is a cross-platform collection of software tools to support whole building energy modeling using EnergyPlus and advanced daylight analysis using Radiance. This disclosure includes two OpenStudio measures: (1) data center prototype models, and (2) occupant schedule generator. The data center models cover two sizes (small computer server rooms, stand-alone large data centers) and two IT load densities (average and high).

Li, Han↗
DOE OSTI2016

OpenStudio® Resources [SWR-07-40]

This repository includes a suite of simulation tests that can be used to validate new OpenStudio® model objects as well as ensure that objects continue to work after they are added. Each new OpenStudio Model object should ideally have two simulation tests associated with it: - a Ruby one which verifies the Ruby API and, - an OSM one that verifies the OSM file can be loaded with future versions of OpenStudio. Both tests should result in a simulation-ready OpenStudio Model that can be simulated using EnergyPlus®. Both of these tests are located in the /model/simulationtests directory, the easiest way to add a new test is to find a related existing test and modify it. When new tests are added they must be added to the model_tests.rb file.

Benne, Kyle↗
DOE OSTI2018

View3D

This is View3D, version 3.2, a C program for the computation of geometric view factors. This version of View3D is included as part of the EnergyPlus installer package. The program utilizes a number of integration techniques to compute view factors between planar triangles or quadrilaterals in three dimensions.

DeGraw, Jason↗
DOE OSTI2018

Alfalfa [SWR 18-17]

Alfalfa is a web based software service that implements a virtual building control system using the EnergyPlus simulation as a backend. Conventional building energy simulation is non interactive, meaning that a simulation is started and run to completion without any user input during the simulation. Alfalfa allows a client to start a building simulation, then interactively read sensor locations and dynamically command actuators in realtime. The Alfalfa web service conforms to the Project Haystack standard, which is a specification adopted by many building control system vendors. The service enables Haystack compliant control system vendors to test their control modules and algorithms without the need for a physical building or modifying their hardware and software, enabling faster and cheaper development cycles. Alfalfa has use cases beyond control system testing. One example is building fault detection, where physical building performance can be compared to simulated values in realtime, indicating where there may be a problem in the building. Another use case is informing predictive control systems, where near future events are simulated to premptively guide control decisions in the real building. This software is web based and designed to be self service. Users of Alfalfa can upload an OpenStudio building model and begin communicating with the simulated building without any advanced setup or configuration, as if they were communicating a real building control system. Behind the scenes, Alfalfa makes extensive use of Amazon Web Services, in order to scale from a very small number of users and buildings, to extremely large numbers in a cost effective way.

Benne, Kyle↗
DOE OSTI2019

Commercial Building Energy Saver, API (CBES API) v2.0

The CBES API provides Application Programming Interface to a suite of functions to improve energy efficiency of buildings, including building energy benchmarking, preliminary retrofit analysis using a pre-simulation database DEEP, and detailed retrofit analysis using energy modeling with the EnergyPlus simulation engine. New capabilities in version 2.0 include renewable technologies (PV, storage), advanced HVAC systems, demand response measures, TDV (Time Dependent Valuation energy) metrics, as well as shading from adjacent buildings, trees or roof obstructions. The CBES API is used to power the LBNL CBES Web App. It can be used by third party developers and vendors.

Hong, Tianzhen↗
DOE OSTI2019

AERCalc v1.2.10

AERCalc is a software tool for evaluating the energy performance of window attachments (e.g., shading systems, attached window panels, etc.). All the annual energy modeling is performed by the EnergyPlus OS engine. The program incorporates a user-friendly GUI.

Mitchell, Robin↗
DOE OSTI2019

Modelica Buildings Library Extension for AC and DC Building Comparison (Modelica DC Buildings) v1

This software simulates and compares the losses in AC and DC buildings. It uses formatted load profile data from EnergyPlus, PV generation data from PVWatts, realistic converter efficiency curves based on product data, a battery controller based on excess PV, and a realistic wire model. The software includes Modelica code, which is an extension of the Buildings Library Electrical models. It also includes Python code, which manages the Modelica parametric simulations and plots results.

Gerber, Daniel↗
DOE OSTI2020

OpenStudio-Occupant-Variability-Gem v1.0

This gem contains methods to introduce occupant-related variability to extend core OpenStudio SDK. The goal of introducing the variability is to improve the assumptions related to stochastic occupant movement, occupant-related lighting and electric equipment, and thermostat set points schedules in traditional OpenStudio and EnergyPlus models.

Li, Han↗
DOE OSTI2021

FlexDRL v1.0

FlexDRL is a framework that can be used as a benchmarking tool to evaluate different Deep Reinforcement Learning algorithms in a building energy management setting. OpenAI Gym was used as wrapper to the co-simulation environment (EnergyPlus for envelope and HVAC and Modelica for battery and PV) to interface with DRL algorithms, which are developed in PyTorch. In order to facilitate the usage of FlexDRL, all the required dependencies have been packaged into a Docker container.

Touzani, Samir↗
DOE OSTI2020

BEEAM (Building Electrical Efficiency Analysis Model) [SWR-20-107]

Modern high-performance buildings exhibit an increasing number of building loads that use direct current (DC) electricity internally, rather than alternating current (AC), due to the advent of low-cost computing and advanced power electronics. Powering DC devices from the AC grid requires AC/DC power conversion, which introduces energy losses and reduces efficiency. As DC loads proliferate, the cumulative wasted energy associated with hundreds of millions of AC/DC converters has become one of the broadest energy savings opportunities in buildings. DC power distribution systems have been proposed as an elegant and transformative solution to the problem of DC devices. In a DC distribution system, the building's wires carry DC electricity, rather than AC; and a few centralized, highly efficient AC/DC converters replace the many smaller, less efficient converters that serve individual DC loads. Unfortunately, the trade-offs associated with DC distribution systems are not well understood. Reported energy savings associated with DC power distribution differ widely and have not been well validated. The Building Electrical Efficiency Analysis Model (BEEAM) is a Modelica library that simulates the efficiency of building electrical distribution systems using harmonic power flow. BEEAM can model a wide variety of building distribution topologies, including three-phase AC, single-phase AC, unipolar DC, bipolar DC, and hybrid networks under both balanced and unbalanced load conditions. BEEAM accurately models power electronic converter losses, provides granular estimates of losses throughout the distribution system, and properly models efficiency at part load conditions. Users can package BEEAM within a functional mockup unit (FMU), enabling co-simulation with other modeling platform, such as EnergyPlus. In summary, BEEAM provides a tool suite for fair and accurate comparison of the efficiency of building electrical distribution systems, including AC, DC, and hybrid systems.

Frank, Stephen↗