LoCal RetreatWinter 2010

Eric Brewer, David Culler, Randy Katz, Seth Sanders

EECS Department

University of California, Berkeley, CA 94720-1776

Presentation Outline

• Retreat Purpose and Agenda

• What is LoCal?

• Summary and Conclusions

Retreat Goals &Technology Transfer

UC Berkeley Project Team Industrial CollaboratorsGovernment Sponsors

Friends

PeopleProject Status

Work in ProgressPrototype Technology

Early Access to TechnologyPromising Directions

Reality CheckFeedback

Retreat Purpose

• First LoCal retreat away from Berkeley

• Early stage where industrial input is critical!– Lots of discussion on the “Smart Grid”– Where is the real opportunity for impact?

• Review initial progress

• Posters, breakouts over dinner, panel

Who is Here?

• Industrial– ArchRock

– Cisco

– Fujitsu Labs USA

– Intel

– Microsoft

– National Semiconductor

– People Power

– PG&E

– QualComm

– Samsung Electronics

– Siemens

• Government/Labs– LBNL

– ORNL

– Innovation Center Denmark

• Academic– University of California, Berkeley

– University of Michigan, Ann Arbor

Retreat Schedule

• Wednesday, January 130730 – 1130 Travel from Berkeley to

Granlibbakkan

1200 - 1330 Lunch

1330 - 1500 LoCal Introduction and Overview, RandyOpportunities and Challenges, David

1500 - 1530 Break

1530 - 1700 Redesigning the Datacenter from the Green Up

Power-Proportional Web Farm : Andrew, Laura, Sara

Power-Proportional Switch Design: David, Ganesh

Power-Proportional HPC Platform: Prashanth, Himanshu

1700 - 1715 Poster Previews

1800 – 1930 Dinner (Organized around discussion topics)

1930 – 2100 Poster Session

Retreat Schedule

• Thursday, January 14:0730 - 0830 Breakfast

0830 - 1000 Building Scale Monitoring and Modeling

Cory Hall CEC Bldg-to-grid Testbed: Fred, Jorge, Ken

First Cut at a Physical Information Bus: Jorge

Learning from Whole-Building Data: Sam, Omar

1000 - 1030 Break

1030 - 1200 New Ideas in Storage and the Grid

Fly Wheel Storage: Seth

Stirling Engine Storage: Mike

AC/DC Conversion: Evan

1200 - 1800 Lunch/Ski Break

1800 - 1930 DinnerEnergy in the Developing World: Eric Brewer

1930 - 2100 Industry Panel Discussion

Paths to Innovation and Impact

Retreat Schedule

• Friday, January 15:0730 - 0830 Breakfast

0830 - 1000 Modeling, Analysis and Understanding

Metering/Measurement as Simple Web-services: Stephen, Fred

Towards Slack Analysis: Jay, Prabal

Energy Efficient MapReduce: Yanpei

1000 - 1030 Break & Check-out

1030 - 1200 Feedback Session

1200 - 1300 Lunch

1300 - Depart Granlibakkan

Breakout Topics

• Closing the User Loop• Sculptability• Drivers and Barriers• Cost vs. Karma• Technology Game Changers• Economics/Policies vs. Technology• Grid Stability• Appropriate Grid Abstractions• Defining Success• Energy Utility Markets• LoCal Security

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Posters

• Energy Efficient Platform-tools and tradeoffs: Steve, Andrew• Modeling and Mitigating Energy Demands of Hadoop Jobs:

Yanpei• ACme: Fine-Grain Building Energy Monitoring: Fred• IS4-Integrated Sensor-Stream Storage System: Jorge• Personal Energy Visualization & Feedback: Sushant, Jeff• Energy Market Simulator: Mike/Fred/Evan• Wind Project: Ken• LoCluster Design: Sara, Laura, Andrew• Application Protocol for Veris E30 Panel-board Monitoring

System: Jaein and Prashanth

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What is LoCal?

• Boosting the IQ of the Smart Grid: Information-centric Energy Infrastructure– “Energy permits things to exist; information, to

behave purposefully.” W. Ware, 1997– Concept of Energy Networks: bits follow

where current flows– Pervasive information: monitor, model,

manage– Multiscale aggregates: nodes, racks,

buildings, grids11

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Generation

Transmission

Distribution

Machine Age Energy Infrastructure

Characteristics of the Grid

• Topology– Long distance transmission– Unidirectional distribution

• Coupling– Dispatchable supply and oblivious loads– Supply-to-load synchronization

• Managing Load Uncertainties– Baseload + Intermediate + Peaking Plants– Minimize outages

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The Big Switch: Clouds + Smart Grids

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Computing as a Utility

Computing in the UtilityLarge-scale industrializationof computing

EnergyEfficient

Computing

EmbeddedIntelligence in

CivilianInfrastructures

Energy + Information Flow =Third Industrial Revolution

“The coming together of distributed communication technologies and distributed renewable energies via an open access, intelligent power grid, represents “power to the people”. For a younger generation that’s growing up in a less hierarchical and more networked world, the ability to produce and share their own energy, like they produce and share their own information, in an open access intergrid, will seem both natural and commonplace.”

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Jeremy Rifkin

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What if the Energy Infrastructure were Designed like the Internet?

• Energy: the limited resource of the 21st Century• Information Age approach: bits follow current flow

– Break synchronization between sources and loads: energy storage/buffering is key

– Lower cost, more incremental deployment, suitable for developing economies

– Enhanced reliability and resilience to wide-area outages, such as after natural disasters

• Exploit information to match sources to loads, manage buffers, integrate renewables, signal demand response, and take advantage of locality

Information Overlay to the Energy Grid

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Conventional Electric Grid

Generation

Transmission

Distribution

Load

Intelligent Energy Network

Load IPS

Source IPS

energy subnet

Intelligent Power Switch

Conventional Internet

Aware Co-operative Grid

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• Monitor, Model, Manage• Deep instrumentation• Waste elimination• Efficient Operation

• Shifting, Scheduling, Adaptation

• Forecasting• Tracking• Market

• Availability• Pricing• Planning

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Intelligent Power Switch

(IPS)

Energy Network

PowerComm Interface

EnergyStorage

PowerGeneration

Host Load

Intelligent Power Switch

(IPS)EnergyStorage

Intelligent Power Switch

(IPS)EnergyStorageEnergyStorage

Intelligent Power Switch

(IPS)EnergyStorage

Intelligent Power Switch

(IPS)EnergyStorageEnergyStorage

Intelligent Power Switch

(IPS)EnergyStorage

Intelligent Power Switch

(IPS)EnergyStorageEnergyStorage

Intelligent Power Switch

(IPS)EnergyStorage

Intelligent Power Switch

(IPS)EnergyStorageEnergyStorage

Host LoadHost Load

energy flows

information flows

Intelligent Power Switch

• PowerComm Interface: Network + Power connector• Scale Down, Scale Out

Multi-Scale Energy Internet

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comm

power

now

Load profile

w$

now

Price profile

w

now

Actual load

w

Datacenter

Bldg Energy

NetworkIPS

IPS

IPS

IPSInternet

Grid

IPS

IPS

Power proportional kernel

Power proportional service manager

Quality-Adaptive Service

M/R Energy

Net

IPS

IPS

IPS

AHU

Chill

CT

IPS

LoCal Energy Network

• Investigation of Internet DCs as a design instance of a LoCal Energy Network– “Doing Nothing Well”: Better processing and network

node designs that exhibit more agile transitions into lower energy states during idle times

– Scheduling: identify workloads time shifted to use fewer resources at higher/more efficient levels of utilization

– (Energy) Storage: decoupling production from usage, thus shifting activity in time

– Building-Machine Room Co-Design: Co-management of building facilities (e.g., power, cooling) given usage patterns

Increasing the Effectiveness of Non-Dispatchable Supply

LoCal Energy Network Methodology

• Workload modeling and scheduling– When: peak shifting/filling

valleys of processing load– Where: energy implications

of topology, replicas, multi-DC distribution

• Low power processing and network platform– Processing: Agility in entering

low power states when idle– Facility: Couple cooling with

predicted DC peaks, e.g., in advance of need

• Doing Nothing Well

• Scheduling

• Storage

Tools and Techniques

Scheduling

Forecasting Supply

Shifting

Prioritizing

Storage

Monitoring

Modeling

Mitigation/Reduction

Consumption

Smart Buildings

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Servers / Clusters

HVAC / CRU / PDU support

Lighting

HVAC & Plug Loads

Instrumented Buildings

Building OS and Evolving Information Model

Slack and Non-Dispatchable Sources

Non-dispatchable sources can exhibit a high degree of variability

“Slack” is a measure of thisvariability that we can quantify over a unit of time

Loads also exhibit Slack, we can use it toexpress the accepted degree of variability

Real-time measurement and communications allow the Slack in a source to be best matched with that available in a load

LoCal Testbeds

• Loads (with storage/supply/transport)– LoCalized Rack– LoCalized Machine Room/Datacenter– LoCalized Building– LoCalized Buildings/Campus/Local Grid

• Storage/Supplies– LoCalized Energy Storage– LoCalized Renewable Energy Source

• Beyond– Standalone Testbed (aka “Burning Man”)

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CS294-49 Fall 2009

• “Creating the Grid OS: A Computing Systems Approach to Energy Problems”– Physical Layer: Power Systems– Device Layer: Loads (Datacenters and

Buildings)– Information Flows and Protocols– Resource Allocation and Control– System Architecture– Projects

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Summary and Conclusions

• LoCal: a scalable energy network– Inherent inefficiencies at all levels of electrical

energy distribution– Integrated energy generation and storage– IPS and PowerComm Interface– Energy matching at small, medium, large scale

• Datacenters Buildings Grid