Comprehensive Pliant Permissive Priority Optimization

Lead Performer: Pacific Northwest National Laboratory – Richland, WA
Partners:
-- Oak Ridge National Laboratory – Oak Ridge, TN
-- University of Colorado – Boulder, CO
-- Avista – Spokane, WA
-- Centrica – Windsor, UK
-- Siemens – Berlin, GER
-- UTRC – Farmington, CT
DOE Total Funding: $1,500,000
FY19 DOE Funding: $750,000
Project Term: October 1, 2018 – September 30, 2020
Funding Type: Lab Call

Project Objective

This project seeks to build a dynamic, real-time adaptive building load prioritization framework from a selected set of influential parameters, including building function and characteristics, occupancy, operational constraints from users, time of day/year, weather, and equipment-specific safety standards and operational constraints. The framework will consist of four functional elements: 1) building load utilization and utility modeling, including a list of permissive loads to be dynamically ranked based on their utilization and their occupant-specified permissive parameters, 2) parametrized building load representation characterizing the load pliability to consumption limits, 3) integration of dynamic signals from various data sources, and 4) real-time forecast-based optimization.

Together these four elements will produce a generic tool that provides real-time (with 15-minute updates) prioritization of building loads adaptive to changing user-defined operational constraints, weather, and/or dynamic grid signals. The tool will be tested in an EnergyPlus simulation with prototypical building models in different operating conditions, including demand response and emergency conditions with and without local renewable generation. Finally, the developed framework will be tested in a simulated connected community model that will include both residential and commercial loads and renewable generation.

Project Impact

The resulting system will allow utilities to monitor the potential flexibility of building services in real time and select loads for demand response. This will allow for the efficacious scheduling of critical loads in adverse or emergency situations, provide informed assistance for energy storage requirement planning, and improve end-use load selection toward frequency and voltage regulation services for microgrids.

Contacts

DOE Technology Manager: Erika Gupta
Lead Performer: Draguna Vrabie, Pacific Northwest National Laboratory