Open Automated Demand Response for Small Commerical Buildings

Dudley, June Han ; Piette, Mary Ann ; Koch, Ed ; Hennage, Dan

关键词: 32;    AIR CONDITIONING;    COMMERCIAL BUILDINGS;    COMMUNICATIONS;    DESIGN;    FIELD TESTS;    HEATING;    LIGHTING SYSTEMS;    MARKET;    RESTAURANTS;    SENSITIVITY;    THERMOSTATS;    VENTILATION;    WEATHER;   

DOI  :  10.2172/965778
RP-ID  :  LBNL-2195E
PID  :  OSTI ID: 965778
Others  :  TRN: US200922%%512

美国|英语

来源: SciTech Connect

【 摘 要 】

This report characterizes small commercial buildings by market segments, systems and end-uses; develops a framework for identifying demand response (DR) enabling technologies and communication means; and reports on the design and development of a low-cost OpenADR enabling technology that delivers demand reductions as a percentage of the total predicted building peak electric demand. The results show that small offices, restaurants and retail buildings are the major contributors making up over one third of the small commercial peak demand. The majority of the small commercial buildings in California are located in southern inland areas and the central valley. Single-zone packaged units with manual and programmable thermostat controls make up the majority of heating ventilation and air conditioning (HVAC) systems for small commercial buildings with less than 200 kW peak electric demand. Fluorescent tubes with magnetic ballast and manual controls dominate this customer group's lighting systems. There are various ways, each with its pros and cons for a particular application, to communicate with these systems and three methods to enable automated DR in small commercial buildings using the Open Automated Demand Response (or OpenADR) communications infrastructure. Development of DR strategies must consider building characteristics, such as weather sensitivity and load variability, as well as system design (i.e. under-sizing, under-lighting, over-sizing, etc). Finally, field tests show that requesting demand reductions as a percentage of the total building predicted peak electric demand is feasible using the OpenADR infrastructure.