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

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

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 4

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

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

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

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

Start with IT Equipment 9

10 Datacenters

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

Understanding Diverse Load 12

ACme – HiFi Metering 13

Energy Consumption Breakdown 14

Re-aggregation 15

By Individual 16

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

Initial Steps 20

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

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

Questions 23