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Enabling Deep Demand Response With Supply-Following Loads Jay Taneja (with Prabal Dutta and David Culler) University of California, Berkeley.

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Presentation on theme: "Enabling Deep Demand Response With Supply-Following Loads Jay Taneja (with Prabal Dutta and David Culler) University of California, Berkeley."— Presentation transcript:

1 Enabling Deep Demand Response With Supply-Following Loads Jay Taneja (with Prabal Dutta and David Culler) University of California, Berkeley

2 2 Instrumentation Models Controls Building OS Plug Loads Lighting Facilities Building Instrumentation Models Routing/Control Grid OS Demand Response Load Following Supply Following Grid Facility-to- Building Gen-to- Building Instrumentation Models Control Load Scheduling Temperature Maintenance Supply-Following Loads Storage- to-Building Instrumentation Models Power-Aware Cluster Manager Load Balancer/ Scheduler Web Server Web App Logic DB/Storage Machine Room MR-to- Building Multi-scale Energy Network Gen- to-Grid uGrid- to-Grid Building- to-Grid Wind Modeling

3 3 Sources and Loads Dispatchable Sources Oblivious Loads Non-Dispatchable Sources Aware Loads

4 California Energy Mix Renewables Portfolio Standard (RPS) 36 of 50 states have set goals Near-term goals range from 10% to 25% California RPS 2008: 10% 2010: 20% 2020: 33%

5 Wind Ramping From a wind farm in Minnesota

6 How to Solve This? Co-locate with energy storage Companion dispatchable energy sources Increase demand-side management – Supply-following Loads – Dispatch energy loads using deeply embedded sensors and actuators

7 Dispatchable Loads Thermostatically-Controlled Loads Maintain the temperature between predetermined constraints Duty-cycled or variable-drive load often using closed-loop feedback Scheduled with Slide Loads Schedule for completion at some point in the future Flexibility of load determined by user – requires change of expectation

8 Measuring “Slack” Slack is the potential of an energy load to be advanced or deferred Measurement in energy (kWh, J, etc.) Provides a metric for control decisions, allows optimization against different criteria

9 Fridge Deployment 18 cu. ft. General Electric Refrigerator/Freezer (at my house!) Four Telos motes with Rel. Humidity, Temperature, and Light Sensors One ACme electricity meter for power measurements Devices on an ad-hoc, IPv6-compatible network (6LoWPAN - blip)

10 Thermostatically-Controlled

11 Scheduled with Slide

12 Heating a House

13 Heater Guardband

14 Supply-Following Heater ObliviousSupply-Following Supercool Supply-Following Wide Guardband

15 Heater Energy Comparison

16 A Population of Fridges

17 Future Directions Reinforcement Learning controller Modeling slack of large-scale Cory Hall loads (e.g. chillers, air handlers, etc.) Actuation on Cory Hall thermostats Building the Intelligent Power Switch using an appliance-level battery

18 Questions? Contact: Jay Taneja

19 Renewables By Hour of Day


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