PNNL

Nanoinformatics workshop report: Current resources, community needs, and the proposal of a collaborative framework for data sharing and information integration

Evaluating the Functionality and Streamflow Impacts of Explicitly Modelling Forest-Snow Interactions and Canopy Gaps in a Distributed Hydrologic Model

Methane and Ethane Steam Reforming over MgAl2O4-Supported Rh and Ir Catalysts: Catalytic Implications for Natural Gas Reforming Application

In the Absence of Writhe, DNA Relieves Torsional Stress with Localized, Sequence-Dependent Structural Failure to Preserve B-form

Protonated Sites of Sulfate-Promoted Zirconium Oxide Catalysis: A Fourier Transform-IR, Thermal Analysis, and Solid State 1H NMR Study

Analysis of Respirator Cartridge Performance Testing on the 702-AZ Primary Exhauster for the Hanford AY/AZ Tank Farms

To achieve deep cuts in U.S. emissions, state-driven policy is only slightly more expensive than nationally-uniform policy

Preliminary Workforce Development and Environmental and Co-use Management Plans for a Floating Offshore Wind Platform - CRADA 609 (Final Report)

Modeling the effects of carbon payments and forest owner cooperatives on carbon storage and revenue in Pacific Northwest forestlands.

Controller for Thermostatically Controlled Loads

This centralized load controller is used to provide continuous regulation reserves (CRRs) using thermostatically controlled appliances (TCAs). A temperature-priority-list method is developed to dispatch TCA loads to meet CRR requirements while maintaining customer-desired indoor temperatures and the load diversity. The key steps are to group and prioritize TCA resources based on a forecaster prediction of the HVAC behaviors for the next time step. Before the controller starts the dispatch algorithm, all the controllable HVAC units are first divided into two groups based on their forecasted on/off status. For example, if space heating units are used, the units in the “on” group are prioritized in descending order based on their room temperatures, i.e., if the room temperature is closer to the upper thermostat setting T+, the unit is at the top of the queue to be turned off. The units in the “off” group are prioritized in ascending order based on their room temperatures, i.e., if the room temperature is closer to the lower thermostat setting , the unit is at the top of the queue to be turned on. The HVAC units that are “on” under direct load control will switch off immediately when they receive an “off” signal from the central controller, and vice versa. The forecaster is used to estimate room temperatures of the HVAC units for the next time step, determine the on/off status of the HVAC units, and create two priority (turn-on and turn-off) lists for the two groups of HVAC units. The forecasts, , can be tuned and corrected based on actual measurement, , collected from HVAC units every 15 minutes. The forecaster can use a simplified HVAC thermal model as described in Section II.A. An update process can be seen in Fig. 10. This forecast-and-update process will reduce the amount of data traffic from each controlled HVAC unit to the central controller. 1000 HVAC units in their heating modes can be controlled by the central controller to provide a ±1-MW CRRs 24 hours a day. The controller is robust to forecasting errors, minimum HVAC turn-off times, response delays, and consumer overrides.