How Retail Markets Can Optimize Electricity Distribution D. P. Chassin Pacific Northwest National...
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Transcript of How Retail Markets Can Optimize Electricity Distribution D. P. Chassin Pacific Northwest National...
How Retail Markets Can Optimize Electricity Distribution
D. P. ChassinPacific Northwest National Laboratory
Overview
Introduction to real-time capacity marketsPurpose, theory, basic examples, issues
Examine Olypen market design/results– Objectives, implementation, results, insights
• Preview AEP NE Columbus RTP-DA rate– Rate design and valuation process
Purpose of Retail Real-Time Pricing
• Discover retail price of energy– Time-varying value of (constrained) supply– Incorporates time-varying value of demand response– Addresses 3 major distribution issues:
Load growth, distributed resource control, demand response
Markets as optimizers
• Auctions solve allocation problem – Computationally efficient (parallelizable)– Equilibrium assignment of buyers and sellers– Interative (either explicit or implicit)
• Linear program discovers price– Maximizes total benefit (primal)– Minimize local costs (dual)
• Price solution is Pareto optimal
See DP Bertsekas , Linear Network Optimization: Algorithms and Codes, MIT Press, 1991
Buyer surplus
Seller surplus
Retail Capacity Market
Power [MW]
Energy price [$/MWh]
Cleared price
Cleared load
Incorporate Day-Ahead Schedule
Day-aheadPrice is low
Real-timeprice is high
RTP customers’actual response
Retail price between DA and RT
Load (MW)
Pric
e ($
/MW
h)
ScheduledLoad
MaximumLoad
UnresponsiveLoad
Cleared price
Some potential issues/FAQs
• Should utility be allowed to own/coordinate distributed resources (analog to generation/transmission conflict)?
• How to ensure costs are not double-embedded?• How is seller surplus from feeder congestion used?• How does utility fairly compensate consumers?• Are there any subsidies built into the rate scheme?• How is misbid/misresponse handled?• What kind of security is really needed?• How is rebound managed?
Rebound peaks occur with load control
0
200
400
600
800
1000
1200
0 2 4 6 8 10 12 14 16 18 20 22 24
Tota
l Hou
rly
Ener
gy C
onsu
mpti
on (
kWh)
Hour of Day
Load Shape for Single-Family (Gas) Homes on 7-18-2006
Fixed_A TOU_A_Group_1Fixed price Time-of-use price
Complex pricing strategies mitigate rebound
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1200
0 2 4 6 8 10 12 14 16 18 20 22 24
Tota
l Hou
rly
Ener
gy C
onsu
mpti
on (k
Wh)
Hour of Day
Load Shape for Single-Family (Gas) Homes on 7-18-2006
TOU_A_Group_1 TOU_A_Group_2 TOU_A_Group_3
TOU_A_Group_4 TOU_A_Group 5 TOU_A_Group 6
Time-of-use group 1Time-of-use group 4
Time-of-use group 2Time-of-use group 5
Time-of-use group 3Time-of-use group 6
At some point a capacity market is easier
0
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500
600
700
800
900
1000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Tota
l Hou
rly En
ergy
Con
sum
ption
(kW
h)
Hour of Day
Load Shapes for Single-Family (Gas) Homes on 7-18-2006
Fixed TOU/CPPReal-time priceFixed price
11
GridWise Testbed ParticipantsBonneville Power Administration IBMPacificorp Whirlpool/Sears KenmorePortland General Electric Clallum County Public Utility DistrictCity of Port Angeles Municipal Utility
Pacific NW GridWise™ Testbed Projects
Virtual Distribution Utility Operation
12
Invensys
JohnsonControls
IBM
$
MW
MarketMarket
Internet broadband communications
15
Economic Cooling Response
k
TmaxTmin
k
Temperature
Pri
ce
Tcurrent
Pbid
Pavg
Pclear
Tset Tdesired
User sets: Tdesired, comfort (based on occupancy calendar)
These imply: Tmax, Tmin, k (price response parameters)
Price is expressed as std. deviation from mean (over a short period, e.g., 24 hrs)
16
Managing Constraints
DG required above feeder limit
Market failed to cap demand for one 5-min. interval in 12 months of operation
Price ($/MWh)
Load (kW)
Hour
17
Load Shifting RTP Customers
• Winter peak load shifted by pre-heating
• Resulting new peak load at 3 AM is non-coincident with system peak at 7 AM
• Illustrates key finding that a portfolio of contract types may be preferred – i.e., we don’t want to just create a new peak
Mixing rates also manages uncertainty
18
It is impossible to choose a portfolioin this white region because no combinationof contracts can yield such risk/return
21
Response Manages New Resources
normal fluctuations in loadDemand management to a capacity cap with real-time prices eliminated load fluctuations for 12 hours!
Regulation: one or more fast-responding power plants continually throttle to match normal fluctuations in load
• Highest cost generation in markets (zero net energy sales, wear & tear, fuel consumption)
• Intermittency of wind output can exceed regulation capability and reduces cost effectiveness of wind
Hour
Load (kW)
AEP NE Columbus Project
• Many tariffs are planned• Fixed Rate (standard)• Interruptible Tariff (direct load control)• 2-Tier Time of Use (2-TOU)• 3-Tier Time of Use (3-TOU)
• Real Time Price Double Auction (RTPDA)
• Each tariff enable a difference kind of response
RTP Rate Design
• Determine RTP-DA pricing method– PJM DA Hourly LMP– 5-minute RTP LMP– Customer bids (Heating, AC, hotwater)– Feeder constraints (physical limits)– System limits not expressed in LMP
• Residential (exc. RR1), small commercial– May include special terms (e.g., 1 yr harmless)
• May also include other resources TBD• PUCO approval required
System requirements
• Advanced Metering Infrastructure (AMI)• Home Energy Manager (HEM)• Advanced equipment controls
– Heating systems (electric only)– Air-conditioning system– Hotwater heaters (electric only)
• Resource control (e.g., CES strategies)• Smart Grid Dispatch engine
RTP-DA Valuation
• Values included– Wholesale energy
production– Generation capacity– Ancillary services (regulation
and reserves)– Transmission congestion– Distribution congestion
• Values excluded– Scarcity pricing– Subtrans. constraints– Environment constraints– Wind/bundling/firming– Reactive power– Emergency/reliability– Financial transmission rights
Determine costs/benefits of RTP-DA
How Does RTPDA work?
MDM
MACSS MAINFRAME
CustomerAEP.COM
AEP OHIO BATTELLE RTP PROJ ECT
ccs
CALCULATIONWATCHDOG
ENGINE
Send Register Reads
2/23/2010
BATTELLEApplication
Circuit loads(80) Usage Summarized
Da
ily S
et-
Up
File
Da
ta
Send Bill Trigger Data & Retrieve Summary Level
Changes
Data Store
Interval Usage
Interval Rate
Interval Amount* =
Interval Usage
Interval Amount
Cirucuit Loads
Detail View for each
5 minute interval
Summary View Marginal Energy
Cost
Circuit Load View
Appliance Loads
Appliance Load View
DynamicPrices
Repository
Dynamic Prices Repository
Summary Detail
RTP Display Data
Graph
RTP Display Data
AEP - DAS
M
M
PJM LMP
Transm. Node
ApplianceLoad
(14 Nodes)
DISTLMP
D Nodes(80 Nodes)
H
AMIHead-END Interval Data
Demand Input
Home AreaNetwork
Meter
Enterprise Integration (EI)
Deliver AEP Zone LMP’s
Source ServicesEI Broker
Day Ahead
Real Time(Both 5 Min & Hourly)
Daily Settled
Target Services
Day Ahead
5 Min RT
Hourly RT
Settled
GuaranteedDelivery
Real TimeReal TimeRetrieve Real Time
LMP Prices
Retrieve Day Ahead (Projected) LMP Prices
Retrieve Daily (Settled) LMP Prices
OPC Scheduler(Mainframe)
OPC Scheduler(Mainframe)
ftp://ftp.pjm.com/pub/account/lmp InvokeSettled
InvokeDay Ahead
CSP Web Services
Daily Settled
Hourly RT
5 Min RT
Day Ahead
Day Ahead
5 Min RT
Hourly RT
SettledAEP Firewall
Internet
RT
DA
S
FTP Server
DA
eDataFeed (XML API)
eMarket (XML API)
Common SolutionPlatform (CSP)
Integ
Key: DA=Day Ahead, RT=Real Time, S=Settled
Interval Data
Send RTP
Prices
Conclusions
• Retail capacity markets – Energy price of Pareto-optimal allocation
• Olypen project a simple/full example– Demonstrated basic concept– Showed important of enabling technology
• AEP NE Columbus project – Significant scaling up of implementation– Stronger integration into wholesale operations