Distributed Load Algorithms LBNL Demand Response Automated Server 1 Siemens Smart Energy Box Internet OpenADR ClientWeather data APOGEE BASWattStopper.

Distributed Load AlgorithmsLBNL Demand Response

Automated Server

1

Siemens Smart Energy Box

Internet

OpenADR Client Weather data

APOGEE BAS WattStopper Distributed Load Control Gateway

BMS Adapter 3rd Party Plug-in

Energy Simulation

Air handlers/fansChillersDIADR Mid-Project Demonstration, April 27, 2011

Jay TanejaNathan Murthy

UC Berkeley

Distributed DR Algorithms• Goal: Testing and evaluation of distributed DR

strategies• Dense deployment of metering devices on

appliances and office equipment, with actuation by the energy gateway– Thermostatically-controlled loads (e.g. refrigerators,

space heaters, etc.)– Battery-powered loads (e.g. laptop computers,

desktop computers with UPS units, etc.)– Lighting (e.g. overhead lights, lamps, etc.)– Other office equipment (e.g. printers, routers, etc.)

2

Smart Office (464 SDH)

3

Sensor Data Management: sMAP

• Interface for gathering and storing heterogeneous, unsynchronized physical data

• Includes data from zone lights and two types of plug meters

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http://green.millennium.berkeley.edu:8080/media/graph/demo.html

Skipping Refrigerator Cycles

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Devices with Onboard Batteries

• Case Study: Laptops• Collected traces to build empirical model of

charge and discharge behavior• Power delivered is a function of battery capacity• Developing metrics to design laptop charge

schedule during DR period• Mix of known state (power consumption, maybe

battery capacity) and unknown state (mobility, computation load)

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7

power

battery capacity

Curtailment of Battery Charging in a DR Event

• Assume N laptops with uniform distributed capacity states

• Assume laptops leave and enter zone both at a Poisson rate with λ=1

• Define duration of DR Event• Throughout DR event, set curtailment ratio c (%

of baseline load) and select laptops to charge• Choose c to minimize projected peak power for

remainder of DR event

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Charging Curtailment Simulation Results

• 30% curtailment possible• Choice of curtailment ratio is crucial to how

load management throughout DR event• Aggressive initial curtailment may offset peak

load reduction towards end of DR event• Aggregate distributed load in a zone can be

shaped using device energy storage

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Desktop Power Management

• Desktop + UPS is similar to laptop

• Collaboration with Dhaani Systems– Using network appliance to manage state (and

power) of Windows machines– Machines put to sleep remotely when not in use– During DR event, aggressiveness can be increased

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Lighting

• Lighting zones on SDH 4– Actuate using Wattstopper via BACnet– High (50W) and low-power (25W) ballasts in each zone

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Other Loads

• Printers– High peak-to-idle ratio (> 75:1)– Idea: DR-aware print queue• Avoid concurrent printing (and

resulting high peak load)• Modify existing print server

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Next Steps

• Application of techniques to similar loads• Integrated management of heterogeneous

loads• Occupant light control

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Questions?

AcknowledgementThis material is based upon work supported by the Department of Energy under Award Number DE-EE0003847

DisclaimerThis report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

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