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LoCal: Rethinking the Energy Infrastructure using Internet Design Principles

LoCal: Rethinking the Energy Infrastructure using Internet Design Principles. David Culler University of California, Berkeley Renewable Energy Microgrid Research Workshop June 5, 2009 “Energy permits things to exist; information, to behave purposefully.” W. Ware, 1997.

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LoCal: Rethinking the Energy Infrastructure using Internet Design Principles

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  1. LoCal: Rethinking the Energy Infrastructure using Internet Design Principles David Culler University of California, Berkeley Renewable Energy Microgrid Research Workshop June 5, 2009 “Energy permits things to exist; information, to behave purposefully.” W. Ware, 1997

  2. What if the Energy Infrastructure were Designed like the Internet? • Energy: the limited resource of the 21st Century • Needed: Information Age approach to the Machine Age infrastructure • Match load & supply through continuous observation and adjustment • Lower cost, more incremental deployment, able to accommodate technology innovation • Enhanced reliability and resilience through intelligence at the edges • Dumb grid, smart loads and supplies • Packetized Energy: discrete units of energy locally generated, stored, and forwarded to where it is needed; enabling a market for energy exchange

  3. Towards an Information Age Energy Infrastructure Nearly Oblivious Loads Baseline + Dispatchable Tiers Demand Generation Transmission Distribution Non-Dispatchable Sources Interactive Dispatchable Loads ???

  4. Energy Network Architecture • Information exchanged whenever energy is transferred • Loads are “Aware” and sculptable • Forecast demand, adjust according to availability / price, self-moderate • Supplies negotiate with loads • Storage, local generation, demand response are intrinsic

  5. Information Overlay to the Energy Grid Intelligent Energy Network Source IPS energy subnet Load IPS Intelligent Power Switch Generation Transmission Distribution Load Conventional Electric Grid Conventional Internet

  6. Intelligent Power Switch Host Load Power Generation Energy Storage • PowerComm Interface: Network + Power connector • Scale Down, Scale Out Intelligent Power Switch (IPS) energy flows PowerComm Interface information flows Energy Network

  7. Intelligent Power Switch • Interconnects load to power sharing infrastructure • Bundles communications with energy interconnection -- PowerComm interface • Enables intelligent energy exchange • Optionally incorporates energy generation and buffering • Potential to scale-down to individual loads, e.g., light bulb, refrigerator • Scale-up to neighborhoods, regions, etc. • Overlay on the existing power grid

  8. MultiScale Approach Load profile Price profile w w $ now now Actual load w now CT IPS IPS AHU comm Internet Bldg Energy Network Power proportional kernel IPS IPS power IPS Grid Data center IPS IPS IPS M/R Energy Net IPS Chill IPS Power proportional service manager Quality-Adaptive Service

  9. Start with IT Equipment

  10. Datacenters

  11. Server Power Consumption • x 1/PDU efficiency + ACC • If Pidle = 0 we’d save ~125 kw x 24 hours x 365 … • … Do Nothing Well

  12. Understanding Diverse Load

  13. ACme – HiFi Metering

  14. Energy Consumption Breakdown

  15. Re-aggregation

  16. By Individual

  17. Energy Aware / Adapt • Export existing facilities instrumentation into real-time feed and archival physical information base • Augment with extensive usage-focused sensing • Create highly visible consumer feedback and remediation guidance • Develop whole-building dynamic models • Basis for forecasting • And for load sculpting

  18. Scaling Energy Cooperation IPS IPS IPS • Hierarchical aggregates of loads and IPSs • Overlay on existing Energy Grid Local Storage Energy Interconnect IPS IPS Local Generation IPS Local Load Energy Interconnect Communications Interconnect

  19. Enabling Energy Markets • Information-enabled markets • Bilateral exchange  multi-lateral exchange  general markets • Aggregated load and supply models, parameterized by time and increasing uncertainty • Machine learning techniques • More degrees of freedom: • (Over) loads can be reduced • (Over) supplies can be stored • Match supply to load • Optimization algorithms vs. auction mechanisms

  20. Initial Steps

  21. “Doing Nothing Well” • Existing systems sized for peak and designed for continuous activity • Reclaim the idle waste • Exploit huge gap in peak-to-average power consumption • Continuous demand response • Challenge “always on” assumption • Realize potential of energy-proportionality • From IT Equipment … • Better fine-grained idling, faster power shutdown/restoration • Pervasive support in operating systems and applications • … to the OS for the Building • … to the Grid

  22. Cooperative Continuous Reduction User Demand High-fidelity visibility Facility Mgmt Automated Control Supervisory Control Community Feedback

  23. Questions

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