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 E

nerg

y Co

nsum

ption

(kW

h)

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

0

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400

600

800

1000

1200

0 2 4 6 8 10 12 14 16 18 20 22 24

Tota

l Hou

rly E

nerg

y Co

nsum

ption

(kW

h)

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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600

700800

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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 E

nerg

y Con

sum

ption

(kW

h)

Hour of Day

Load Shapes for Single-Family (Gas) Homes on 7-18-2006Fixed 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

Olympic Peninsula RTP Market

Customer participation

$35

ComfortEconomy

15

Economic Cooling Response

k

TmaxTmin

k

Temperature

Pric

e

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

Peak energy uncertainty

19

Gross margin volatility

20

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

Dai

ly S

et-U

p F

ile D

ata

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

RepositoryDynamic 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 AheadDay 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

Questions/Comments

Contact:David P. Chassin

Pacific Northwest National Laboratorydavid.chassin@pnl.gov