© copyright 2012 ecco international, inc. tm technical briefing for lawrence berkeley national...
TRANSCRIPT
© Copyright 2012 ECCO International, Inc.
tm
Technical Briefing for Lawrence Berkeley National Laboratory
8/31/2012
© Copyright 2012 ECCO International, Inc.
History of ColorPower
• MIT CSAIL• ZOME Energy Networks• BBN Technologies• ECCO International• ColorPower Center
© Copyright 2012 ECCO International, Inc.
Challenge
• Future retail utilities must be able to effectively manage demand fulfillment at the margin– Defer consumption– Pull forward consumption– Cancel uneconomic consumption (in
customers’ best interest!)
• Problem: Inadequate coordination between the grid and end user devices
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Economists vs. Customers
Microeconomics View
• Customers can be modeled as rational marginal demand functions for a commodity
• Customers can be modeled as virtual power plants
• Customers need to be sent price signals to modify their behavior
Customer View
• I do not have a marginal demand for power, I want reliable service
• I am not a virtual power plant
• I don’t want price volatility risk or to do laundry at midnight
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Retail Power Is a Service
• Not a hot concert ticket• Not a basket of commodity electrons
• Customers prefer to buy power as a service, not a commodity– Just like many other service industries
• Congestion pricing is not the typical way service demand peaking issues are solved
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Survey of Service Peak Demand Control Paradigms
Industry Process/Technology
Various (e.g. manufacturing) “First Come First Serve” / Backlog Queue
Hospitals “Most Urgent First” / Waiting Room
Road Transportation “Alternating Access” / Traffic Light
General Digital Communications “Best Effort Transport” / TCP/IP
Cellular Automatic Protocol / CDMA
Cable Networking Automatic Protocol / DOCSIS
DSL Automatic Protocol / ATM
Electric Power - Today Free-for-All / Circuit Breaker
Electric Power – Future Consensus Huge Real-time Auction / Smart Grid
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HOW MUCH IS THIS POWER WORTH TO YOU NOW?
HOW ABOUT NOW?
HOW ABOUT NOW?
SORRY YOU WERE JUST OUTBID BY R1CH_P0WRHAWG
YOUR POWER HAS MOVED TO A PLACE WHERE IT IS MORE APPRECIATED
[POWER OFF] THE GOOD NEWS IS YOU HAVE NOW SAVED 26 OPEN SODA CANS WORTH OF CO2 EMISSIONS
Technology As Auction Enabler
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FrigidTrader 3000Now, with high beta
coefficient strategies for those with appetites for high risk/reward energy
trading! Unlike cheap knock-offs, this one detects
when a seller is trying to walk up the clearing price
and refuses to take the bait. Cools your food and
integrated supercomputer simultaneously. Icemaker
extra.
Smart Appliance: Energy Efficient or Trading System?
© Copyright 2012 ECCO International, Inc.
WE’LL PAY YOU TO TAKE THESE PEANUTS AWAY
PEANUTS ARE VERY EXPENSIVE RIGHT NOW
The Smart Grid
The Smart Grid
Price Signal Smart Appliance/EV Stampedes
GOOD FOR SPECULATORSBAD FOR CONGESTION CONTROL
Retail Price Volatility: Be Careful What You Wish For
CATASTROPHIC FOR SYSTEM RELIABILITY
WIDE-SCALE PRICE VOLATILITY:
© Copyright 2012 ECCO International, Inc.
Human-Designed Self-Organizing Congestion Management Systems
Natural Self-Organizing Swarm Systems
Social InsectsBacteria Colonies Flocks & SchoolsNeural Networks
Queue Formation
TrafficSignals
Ethernet (CSMA/CD)
WiFi(CSMA/CA)
ColorPower(AFEM)
Cooperative Control Systems
Internet(TCP/IP)
© Copyright 2012 ECCO International, Inc.
Natural Adaptive Feedback Systems
• Bees maintain a constant hive temperature, set by instinctive hive “programming”– 35° C to produce foragers– 34° C to produce housekeepers
SUN
WIND
CREATE BREEZE
CREATE HEAT
HEAT COOL
COOL HEAT
TEMPERATURE SETTING
ADAPTIVEFEEDBACK
LOOP
© Copyright 2012 ECCO International, Inc.
• Increases road capacity by order of magnitude without building more lanes!
• Fairly distributes road access to users
• When coupled with cooperative drivers, self-organized congestion control system
• Increases transportation system utilization: higher efficiency
Engineered Self-Organization: The Traffic Light
Technical note: Roundabouts are even better than traffic lights!
© Copyright 2012 ECCO International, Inc.
Introducing ColorPower
A Distributed
Self-Organizing
Stochastic
QoS-Oriented
Power Load Balancing Protocol
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ColorPower Outsources Demand Response to the Appliance Swarms
Humans: Always In Control
Privacy Respected
ColorPower Appliance Priorities1. Obey Your Humans’ Preferences2. Donate Flexibility to Power Grid
Smart Grid: Coordinates Orderly Power
Access For Flexible Appliances & Machines
Invisible to Humans
Flexible
Emergency
Not Flexible
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A: ColorPower: Self-Identification of Flexible Demand
Leaf Button: On=Flexible
Green = Price SensitiveYellow = Reliability ResponsiveRed = Opt Out
Cloud Software With More Options
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Turn Off
Resource Tier 500
Resource Tier 499
Resource Tier 1
Resource Tier 5
Resource Tier -5
Resource Tier -1
Resource Tier -499
Resource Tier -500
Device Tiers
More Demand
Less Demand
No Feedback
Pool Pump
Run Now
Charge
Discharge Battery
Generator On
EV
Stay under 6kW
Facility Management
-3 degrees
Thermostat
-6 degrees
SolarExport Power
Default Tier Assignment Via
Color/Device Type
Measuring Demand Backlog
© Copyright 2012 ECCO International, Inc.
Tiered Aggregations
Pool Pumps - Green
HVACs - Green
Misc. Flex - Green
Pool Pumps - Yellow
HVACs - Yellow
Misc. Flex - Yellow
Battery Storage
Night Run Dishwashers
Emergency Rationing
Sheddable LoadResources
Dispatchable LoadResources
Emergency Dispatchable Load
No Inconvenience
Emergency Storage
Emergency Load Dumping
Major Inconvenience
Major Inconvenience
© Copyright 2012 ECCO International, Inc.
• Groups and Individual Devices Act Randomly—But *Precisely in Aggregate*
• Feedback loop recruits resources until demand target satisfied
DEMAND CLOUD TIER
New Demand Target -50MW
DEMAND REPORT:ON: 2034OFF: 3423SHED: 1276NOSHED: 4322
SYSTEM STATE:NEED -3.4% TOTAL DEMAND RELIEF
ADAPTIVE FEEDBACK LOOP
ColorPower Stochastic Swarm Control
Groups and Individual Devices
Local Probabilistic Cooperation Calculation
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Locational x Program Control
Pool Pumps - Green
HVACs - Green
Misc. Flex - Green
Pool Pumps - Yellow
HVACs - Yellow
Misc. Flex - Yellow
Battery Storage
Night Run Dishwashers
Emergency Rationing
Emergency Dispatchable Load
Emergency Storage
Emergency Load Dumping
Pool Pumps - Green
HVACs - Green
Misc. Flex - Green
Pool Pumps - Yellow
HVACs - Yellow
Misc. Flex - Yellow
Battery Storage
Night Run Dishwashers
Emergency Rationing
Emergency Dispatchable Load
Emergency Storage
Emergency Load Dumping
Pool Pumps - Green
HVACs - Green
Misc. Flex - Green
Pool Pumps - Yellow
HVACs - Yellow
Misc. Flex - Yellow
Battery Storage
Night Run Dishwashers
Emergency Rationing
Emergency Dispatchable Load
Emergency Storage
Emergency Load Dumping
Pool Pumps - Green
HVACs - Green
Misc. Flex - Green
Pool Pumps - Yellow
HVACs - Yellow
Misc. Flex - Yellow
Battery Storage
Night Run Dishwashers
Emergency Rationing
Emergency Dispatchable Load
Emergency Storage
Emergency Load Dumping
Locational Programs Participants
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Price signals to consumers from markets?ColorPower can send demand signals from consumers to markets.
Demand Signals To Markets
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UTILIZE IDLE CAPACITY
STAY WITHIN CAPACITY LIMITS
Flexible Load Shaping
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More Flexible Than Price Signals
Pre-Configured RulesBalance Priorities
Across the System
Customers First!
© Copyright 2012 ECCO International, Inc.
ColorPower ™ Algorithm
Challenge: fast, private, robust, non-intrusiveApproach: randomized distributed control
Aggregate flexibility information to shared modelDisseminate control signals Local decision; coin-flip for fractional colorWeight for availability, over-damped control
Control problem: long timeouts on state changes
23
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ColorPower State Transitions
• (E)nabled vs. (D)isabled• (R)efractory vs. (F)lexible
© Copyright 2012 ECCO International, Inc.
ColorPower ™ State Transitions
The evolution of each device is modeled like a modified Markov process
In each round devices in state EF randomly switch off to state DR
Once in DR device waits for certain rounds before transitions to state DF; the waiting time is a fixed number PLUS a uniform random addition to feather the distribution (so not many devices switch states at once)
The other two distributions are complementary25
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Formal Control Problem
For each ColorPower client, set pon, poff for each device group, such that the total enabled power in s(t) tracks g(t)
© Copyright 2012 ECCO International, Inc.
Formal Control Problem
The control problem is to set the transition probabilities such that the total Enabled Demand tracks the target as closely as possible, subject to the constraints
Device with lower numbered colors are shut off firstIf a color has devices that are Enabled and
Disabled, then every device is equally likely to be disabled
No device is unfairly burdened by its initial bad luck in becoming Disabled
© Copyright 2012 ECCO International, Inc.
Constraints• Goal/Forecast tracking: shape power demand
• Color priority: respect user preferences
• Fairness: no devices are favored
• Cycling: don’t keep the same devices off
© Copyright 2012 ECCO International, Inc.
Controller Design Issues
It is possible that not all constraints can be satisfied; some of them are more important than others
Customer preferences are the most important onesGoal tracking is the second most importantLeast important is the Cycling constraintThe Fairness constraint is the easiest to satisfy (simply
the same stochastic algorithm on all clients is executed)
We view the controller as having a “budget” of flexibility to spend with each color offering up to |EF|I of potential reduction in demand
© Copyright 2012 ECCO International, Inc.
Controller Design Issues
Flexibility builds up as Refractory devices finish their time outs and move to the Flexible state
The controller is formulated as a cascade of priorities of how to spend the “Flexibility budget” indicated by the state s(t)
As the controller considers each constraint in turn, it allocates flexibility to satisfy that constraint (as much as possible)
Then it attempts to satisfy the rest of the constraints with whatever flexibility remains unallocated
Any unallocated flexibility is allowed to accumulate as a reserve improving future controllability
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Control Example: Hot Summer Day
Flex
Emergency
Inflexible
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Control Example: Emergency Response
Flex
Emergency
Inflexible
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Approach Downstream Upstream Payload Size
Traditional XML Cmd/Ack Control
Ω(n+x) Ω(n+x) Large XML document
ColorPower Probabilistic Control
Ω(1) Ω(log n) Small datagrams
Algorithmic Complexity & Bandwidth Usage
ColorPower is computationally trivial—no supercomputers required
ColorPower requires an average bandwidth of 100 bytes per second per device.
Trivial for modern broadband connections
Does require massively parallel datagram traffic
Resource Requirements
© Copyright 2012 ECCO International, Inc.
Consumer Privacy
• Devices randomly and respond anonymously using local situational info + global system state info
• Load reports are anonymous and aggregatable
• 2-way anonymous information exchange
© Copyright 2012 ECCO International, Inc.
• No Infrastructure Targeting– Controllers Cannot Directly
Contact Clients– Each Client Randomly
Makes Its Own Choices
• No Wide-Area Shutdowns– Clients Ignore Unreasonable
System State Reports
• Controllers Partitioned & Firewalled
Internal Saboteur/ External Hacker
ColorPower Probabilistic
Broadcast
No Direct Attack Vectors?
Intrinsic Security Hardening
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OpenADR
• ColorPower can act as a VEN aggregating small loads
• ColorPower can act as a DRAS
© Copyright 2012 ECCO International, Inc.
Collaboration
• ColorPower needs– Research collaboration– Standardization
© Copyright 2012 ECCO International, Inc.
Thank You
• To learn more, see • http://www.colorpower.org
• To help bring about this future, please contact
• sflorek at colorpower.org