LoCal: Rethinking the Energy Infrastructure using Internet Design Principles
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Transcript of LoCal: Rethinking the Energy Infrastructure using Internet Design Principles
LoCal: Rethinking the Energy Infrastructure using Internet Design
Principles
David CullerUniversity 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
DistributionTransmissionGeneration 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
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
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
IPScomm
power
now
Load profile
w$
now
Price profile
w
now
Actual load
w
Data centerIPS
Bldg Energy
Network
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
Start with IT Equipment
9
10
Datacenters
3-19-2004 11
Server Power Consumption
230
15
248
87
190
13
190
13
200
14
161
19
287
48
0
50
100
150
200
250
300
350
Wat
ts
Pow
erE
dge
1850
Del
l Pow
erE
dge
1950
Sun
Fire
V60
x
Sun
Fire
x21
00 -
Cyb
er S
witc
hing
Sun
Fire
X22
00
Com
paq
DL3
60
HP
Int
egrit
y rx
2600
Server Power Consumption
Active
Idle Soda Machine Room Power Consumption
26.5 30.6 31
18.118.9 19
44.5
50.9 50
9.5
10.117
10
31
0
20
40
60
80
100
120
140
160
180
est kW min est kW max kW meas
KW
290 Soda
288 Soda
530 Soda
420A Soda
340 Soda
287 Soda
• x 1/PDU efficiency + ACC
• If Pidle = 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
EnergyInterconnect
LocalGeneration
Local Load
IPS
LocalStorage
IPS
IPS
IPS
IPS
IPS
Scaling Energy Cooperation
• Hierarchical aggregates of loads and IPSs• Overlay on existing Energy Grid
Energy InterconnectCommunications 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
3-19-2004 22
Cooperative Continuous Reduction
Automated Control
Facility Mgmt
User Demand
Supervisory Control
Community Feedback
High-fidelity visibility
Questions
23