Clearing the Fog of
Assumptions
Dynamic Line Ratings Provides Real Time
Visibility and Market Efficiency
Eric Hsieh
Carnegie Mellon Electricity Conference
February 5, 2014
Results from DOE SGDP
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Project Description and Scope
Economic Cost of Thermally Constrained Lines
Comparison of Static Limits to Dynamic Limits
Integration of Dynamic Line Ratings into Economic Dispatch
Results from Deployment in Texas
Expansion of DLR into additional areas
February 4, 2014
2
VALUE OF DLR CAPACITY: ONCOR EXAMPLE
Oncor System Attributes
Oncor System Attributes

180 congested lines

30 lines responsible for
80% of congestion

$172m average annual
Oncor congestion
February 4, 2014
3
TECHNICAL AND FINANCIAL BASIS FOR PROJECT
Problem and Opportunity
Oncor System Congestion
$250,000,000
$200,000,000
$150,000,000
Market
Outage-Driven
$100,000,000
$50,000,000
$0
2011



2012
Situation: Overhead thermal lines responsible for significant congestion costs
Obstacle: Static assumptions force operators to use artificially low limits
Next Generation SCADA Hypothesis:
Real time line information can significantly reduce congestion costs
February 4, 2014
4
LOCATION OF DYNAMIC LINE RATING EQUIPMENT
Oncor SGDP Project Scope
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February 4, 2014
8 Lines (138kV-345kV)
Installation 2010-2011
ERCOT Go-Live 5/2012
5
Static Ratings
Sun
IEEE 738
ΔT = I2R + Qs - Qr - Qc
Assumed
I2maxR = Tmax - Qs + Qr + Qc
February 4, 2014
6
Ambient Adjusted Ratings
Sun
IEEE 738
ΔT = I2R + Qs - Qr - Qc
Assumed
I2maxR = Tmax - Qs + Qr + Qc
Measured
February 4, 2014
7
Full Dynamic Line Ratings
Sun
IEEE 738
ΔT = I2R + Qs - Qr - Qc
Qc = I2measR - Qs + Qr - ΔTcalc
Calculated
I2maxR = Tmax - Qs + Qr + Qc
Measured
February 4, 2014
8
Comparison of Ratings Methodologies
Parameters
Static
Ratings
Ambient Adjusted
Ratings
Dynamic
Ratings
Solar and Ambient
Conditions
Assumed
Measured
(regional)
Measured (corridor
specific)
Wind Speed
Assumed
Assumed
Calculated
Tension
Assumed
Assumed
Measured
Sag or Clearance
Assumed
Assumed
Calculated
Maximum Rating
Does not take into
Artificially limited to account wind
worst case scenario (greatest cooling
factor)
February 4, 2014
Calculated based
on measured
conditions
9
Importance of Wind on Conductor Temp
1000
100
10
1
1
4
7
10
13
16
19
22
25
28
31
34
37
40
43
46
49
52
55
58
61
64
67
70
73
76
79
82
85
88
91
94
97
100
log(Number of Observations)
Weather-Based Cooling Distribution 1/5/2010
Cooling Power (W/ft)
Radiative Cooling
February 4, 2014
Wind Cooling
Based on Seppa, Mohr, Stovall, “Error Sources of Real-Time Ratings Based on Conductor Temperature Measurements,
Report to CIGRE WG B2.36, May 20, 2010
10
Integrated Dynamic Line Rating Systems
Net Radiation Sensor measures
conditions as seen by an
unloaded conductor
Load Cell measures tension
CAT Unit transmits data
from tower to substation
CAT Master loads sensor
data onto utility network
Operator Display
Capacity Engine
CAT-1™ and line
load data from EMS
Line capacity and
temperature to EMS
Oncor SCADA
SGDP Breakthrough:
live streaming to ERCOT SCED
February 4, 2014
11
DLR Integration Into ERCOT SCED
Adjustment Period & Real-Time Operations
QSE Deadline:
Update Energy Offers
Submit HRUC Offers
Update Output Schedules
Update Inc/Dec Offers for DSRs
QSE Deadline:
Update Output Schedules for DSRs
Provide SCADA Telemetry
Preparation for
Real-Time Ops
Adj Period
Real-Time
Operations
Operating Period
Operating Hour
T
18:00
(D – 1)
60 Minutes
Prior to
Op Hour
ERCOT Activity:
Snapshot Inputs &
Execute HRUC
February 4, 2014
Clock
Hour
ERCOT Activity:
Communicate
HRUC Commitments
ERCOT Activity:
LFC Process every 4 secs
Execute SCED every 5 mins
Communicate Instructions
& Prices
Source: ERCOT Nodal Protocols – Adjustment Period & Real-Time Operations
12
INDICATIVE RESULT FROM A SINGLE MONITORED LINE
Capacity Increases from DLR
Dynamic Line Rating (DLR) And Ambient Adjusted Rating (AAR)
Rogers Hill-Elm Mott, September, 2011
Daily Distribution
2000
1800
MW Rating
1600
1400
1200
1000
800
Time
SLR
February 4, 2014
DLR 90%ile
DLR Median
DLR 10%ile
AAR 90%ile
AAR Median
AAR 10%ile
13
23:20
22:40
22:00
21:20
20:40
20:00
19:20
18:40
18:00
17:20
16:40
16:00
15:20
14:40
14:00
13:20
12:40
12:00
11:20
10:40
10:00
9:20
8:40
8:00
7:20
6:40
6:00
5:20
4:40
4:00
3:20
2:40
2:00
1:20
0:40
0:00
600
SIMULATION OF ADDITIONAL CAPACITY ON 5% OF ERCOT LINES
Savings in Day-Ahead Market
Congestion Cost Per Hour 11/1/11 DAM
February 4, 2014
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SIMULATION OF ADDITIONAL CAPACITY ON 5% OF ERCOT LINES
Savings in SCED (5-min Dispatch)
Extrapolated Congestion Cost Per Hour 11/1/11 SCED
February 4, 2014
15
Congestion Reduction Results
Peak Day
Annual
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Congestion Reduction Projections
Direct Line
Peripheral Lines
Control
Plus 5%
68%
54%
Plus 10%
78%
100%
Control
Plus 5%
60%
56%
Plus 10%
100%
58%
Control: dispatch with selected (direct) lines rated at static capacity
Plus 5%/10%: dispatch with selected lines at “dynamic” capacity
Peripheral Lines: any line sharing a substation with a direct line
Percentage Reduction: change in congestion cost attributable to line(s)
Peak Day: 7/27/2011
Annual: interpolation based on 12 sample days throughout the year
February 4, 2014
16
EXTRAPOLIATION TO A BUSINESS CASE
Indicative Oncor DLR Value and ROI
Value Analysis for DLR in Oncor
€20.0M
Cost/Savings Systemwide
€15.0M
€10.0M
€5.0M
€0.0M
1
2
3
€5.0M
€10.0M
€15.0M
Year
DLR Cost
February 4, 2014
4
5
If DLR installed on 30 of
Oncor’s likely congested
lines:
 NPV Cost (Installation):
€12.0m
 NPV Savings
(Congestion over 5 years):
€68.0m
 ROI: 467%
 Payback: 9.6 Months
Congestion Savings
17
FOLLOW-UP PROJECT IN ERCOT TO ADDRESS URGENT NEEDS
Second Deployment After SGDP Success
February 4, 2014
Source: ERCOT, “Update on Transmission Planning for West Texas Congestion”, May 14 2013
18
Fast Deployment for Critical Needs
2013 Jan
West Texas
Need Identified
2013 May
System
Calibration
2013 Mar
System Shipped
2013 Feb
DLR System
Ordered
2013 Apr
System
Installation
2013 June
Ratings
streamed to
ERCOT
From shipment to operations in
less than 90 days
February 4, 2014
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Conclusions: Drawing New Connections
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Connection: Sensor to Next-Generation SCADA
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Incorporation of real time, individual line capacity data into SCED
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First stages of grid operations that rapidly adapt to changing conditions
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Framework for future, high-definition closed-loop systems
Connection: Concept to SGDP to Business Case
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Theory: dynamic line ratings can provide customer savings
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Results: significant efficiencies from reduced congestion costs
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Practice: sufficient evidence to justify full commercial deployments
February 4, 2014
20
Integrated Dynamic Line Rating Systems
eric.hsieh@nexans.com
Eric Hsieh
February 4, 2014
21