Transcript Energy: Corporate Challenge in a Changing World

Oahu Wind Integration Study
Dean Arakawa
Sr. Engineer, Renewable Energy Planning
Hawaiian Electric Company
Alaska Wind Integration Conference
June 29, 2010
The Challenge
2
Hawaii’s Economy in 2008
GROSS STATE PRODUCT
$63.8 BILLION
SPENDING ON ENERGY
$ 8.4 BILLION
3
Hawaii’s Energy Use Today
Primary energy: 90% fossil fuel,
Imported crude oil refined:
JET FUEL
34%
ELECTRICITY
32%
GASOLINE/
MARINE FUEL
27%
OTHER
7%
4
Hawaii’s Electricity Issues are
Fundamentally Different than
the Mainland US
U.S. Electric Power Industry
Net Generation, 2008
Hydroelectric
Conventional
6%
HECO, HELCO & MECO
Net Generation, 2008
Non-Hydro
Renewables
8%
Non-Hydro
Renewables
3%
Hydroelectric
Conventional
Less than 1%
Coal
15%
Nuclear
20%
Coal
48%
Natural Gas
21%
Petroleum
2%
Petroleum
77%
5
The Solutions
6
A Paradigm Shift is Required
 Economic drain
>
 Energy insecurity
>
 Environmental harm >
 Price volatility
>
Economic engine
Energy security
Environmental
compatibility
Price stability
7
Where Are We Today?
As of 2009 – Hawaiian Electric companies
19 % Renewable Energy & Energy Efficiency
(~50% / 50%)
State Goal by 2030 – for Hawaii’s economy
40% Renewable Energy
30% Energy Efficiency
8
How We Can Move Ahead:
 Grid transformation
 Renewable energy including liquid
fuels substitute
 Inter-island connection
9
Oahu’s Challenge
* U.S. Census
estimates as
of July 2007
Molokai
Kauai
Population
905,601 *
Maui
Oahu
Tri-island
population
141,783 *
Lanai
Wind
Geothermal
Hawaii
Solar
OTEC/
Wave
Biomass/
Biofuel
DSM/Energy
Efficiency
Population
173,057 *
MSW
10
Hawaii’s Wind Energy
Resources
11
Wind on Molokai and Lanai
7/17/2015
12
Renewable Game Plan for
Hawaii
 The load is on Oahu, but the renewable
resource is limited.
 The neighbor islands have abundant
renewable resources, but limited load.
Ultimately, the islands can
benefit by being cabled together.
13
How Can We Do It?
 ‘Interisland Wind’
Lanai & Molokai
wind farms
– 200 MW each
– Undersea cable
to Oahu
Learn more at:
www.interislandwind.com
14
HECO’s System
15
Hawaiian Electric
Isolated, stand-alone grids
ISLAND
OAHU*
PEAK DEMAND (2008)
1200 MW
MAUI
195 MW
HAWAII
200 MW
KAUAI
served by separate utility co-op
* 80% of state population
16
Big Wind Components
Successful
Big Wind
=
Wind Plant
Development &
Performance
Wind Plant Issues
 Required wind plant forecasting
and performance characteristics
 Resource intermittency
mitigation and management
(e.g. energy storage
requirements)
 Adequate capacity factor
yielding commercially
reasonable pricing
 Community acceptance of large
wind plants
+
Undersea
Cable
Intertie
Cable Issues
 Sizing and selection (AC, DC)
 Cable system reliability and
configuration (e.g. mono-pole,
bi-pole, spare cable, etc.)
 Landing sites and footprint for
converter station and supporting
equipment
 Ocean permitting and
environmental issues
 O&M responsibilities and
operating agreement
Oahu
+ Integration &
Infrastructure
Oahu Issues
 Maintain 60Hz frequency and
system stability
 Maintain adequate operating
reserves in response to wind
 Improve generator response
 Enhance system controls and
automated features
 Maintain reliable operations via
PPA commitments
 Community acceptance 17
of new
T&D infrastructure
Generation Resources on Oahu
Legend
Kahuku Wind Power (30 MW)
Firm Capacity, Net-MW
Future As-Available
Resource, MW-nameplate
Future Firm Capacity,
Net-MW
Oahu RE RFP Pending
Total Existing Firm
Capacity = 1,732 MW-net
Total Future Firm
Capacity = 35 MW-net
Waiau (473 MW)
Kahe (604 MW)
Airport DSG (8 MW)
H-POWER (46 MW)
400 MW Wind
Planned
Honolulu (108 MW)
H-POWER (27 MW)
AES (180 MW)
CIP CT-1 (113 MW)
Kalaeloa (208 MW)
Honua Power (6 MW)
18
Inter-island Wind Project
HECO Baseline
Information
Oahu Transmission Studies
Stead State Load Flow/
Transient Stability/
Short Circuit
HECO Model
Development
GE
Follow-on
Implementation
Oahu T&D Routing
Study and Engineering
Design
Submarine Cable
Architecture and
Functional Specs
Oahu Transmission
Projects
Submarine Cable
Procurement
and Permitting
EMS Upgrade
Projects
Steam Generator
Improvements
Steam Generator
Projects
EMS/AGC Capability
Analysis
Load Control
Projects
Scenario
Analysis
Standby/Quick Start
Generation
Future Generating
Resource Plan
(GE MAPS/PSLF)
Load Control
Wind Resource
Modeling
Wind Capacity
Calculation
Wind
Forecasting
PPA Negotiations/
Interconnection
19
Requirements Study
Scenario Analysis
Scenario
Baseline
Scenario #1
Scenario #3
Scenario #5
Wind
Title
Oahu
Solar
Lanai Molokai Oahu
2014 Baseline
-
-
-
-
“Big Wind”
100MW
-
-
100MW
-
100MW
Oahu only
“Big Wind”
Oahu + Lanai only
“Big Wind”
Oahu + Lanai +
Molokai
100MW 400MW
100MW 200MW 200MW 100MW
These four scenarios were
the focus of the study
(Scenarios 2 and 4 were
only moderately different
than these three scenarios).
Interest from the team to
focus effort on mitigating
strategies as opposed to
these only moderately
Oahu
600MW of new Renewables
~1200MW Peak
+100MW new Wind
+100MW new PV
sub- sea
cables
Lanai
+200MW new
Wind
Molokai
+200MW new Wind
These 3 scenarios were
analyzed to determine the
commitment/dispatch, identify
new operating characteristics,
and establish a new baseline to
assess strategies to enhance
operation with high
penetrations of renewables
20
Tools Needed For Each Timescale
Positive Sequence
Load Flow (GE PSLFTM)
1 min
1 sec
Voltage
Governor
Support
Response
Governor
Inertia
Response
Governor
Governor
Response
Response
2008
2008
50
Interval = 10min
Interval = 60min
30
20
0% percentile (10min) = -0.14
0% percentile (60min) = -0.45
0.1% percentile (10min) = -0.09
0.1% percentile (60min) = -0.27
99.9% percentile (10min) = 0.10
99.9% percentile (60min) = 0.31
100% percentile (10min) = 0.18
100% percentile (60min) = 0.48
Interval = 10min
Interval = 60min
40
Frequency (%)
Frequency (%)
40
10
1 hr
10 min
1 day
1 wk
Automatic
AGC
Generation
Regulation
Control
Statistical Wind Power
Variability Assessments
50
New tools and data
needed to properly
model and assess
system impacts within
operational time
constraints.
Long-term Dynamic
Simulations (AGC)TM
Economic
Economic
Dispatch
Dispatch
Planning
Arbitrage
Multi-Area Production
Simulation (GE MAPSTM)
Interhour Renewables
Variability AnalysisTM
100MW Oahu + 200MW Lanai + 200MW Molokai
50
30
20
0% percentile (10min) = -0.14
0% percentile (60min) = -0.45
0.1% percentile (10min) = -0.09
0.1% percentile (60min) = -0.27
99.9% percentile (10min) = 0.10
99.9% percentile (60min) = 0.31
100% percentile (10min) = 0.18
100% percentile (60min) = 0.48
Interval = 1min
Interval = 5min
Interval = 10min
45
40
10
35
-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0
0.25
-0.25
-0.2
-0.15
Power ramp (pu per interval)
0
0.05
0.1
0.15
0.2
0.25
50
Interval = 10min
Interval = 60min
30
0% percentile (10min) = -82.85
0% percentile (60min) = -269.05
20
0.1% percentile (10min) = -54.90
0.1% percentile (60min) = -163.16
99.9% percentile (10min) = 60.11
99.9% percentile (60min) = 183.45
100% percentile (10min) = 105.95
100% percentile (60min) = 289.31
10
Interval = 10min
Interval = 60min
40
Frequency (%)
40
Frequency (%)
-0.05
Power ramp (pu per interval)
50
0
-0.1
30
0% percentile (10min) = -82.85
0% percentile (60min) = -269.05
20
0.1% percentile (10min) = -54.90
0.1% percentile (60min) = -163.16
99.9% percentile (10min) = 60.11
99.9% percentile (60min) = 183.45
100% percentile (10min) = 105.95
100% percentile (60min) = 289.31
10
-150
-100
-50
0
50
Power ramp (MW per interval)
100
150
0
Frequency (%)
0
30
25
20
0.1% percentile (1min) = -12.27
99.9% percentile (1min) = 11.7685
0.1% percentile (5min) = -31.336
99.9% percentile (5min) = 33.0615
0.1% percentile (10min) = -49.305
99.9% percentile (10min) = 54.0865
Negative most (1min) = -22.479
Positive most (1min) = 22.5425
Negative most (5min) = -54.9215
Positive most (5min) = 65.0885
Negative most (10min) = -90.258
Positive most (10min) = 95.845
15
10
5
-150
-100
-50
0
50
Power ramp (MW per interval)
100
150
0
-50
-40
-30
-20
-10
0
10
20
30
40
50
Farm power (MW per interval)
21
Modeling Tools
Multi-Area
Production Simulation
(GE MAPS)
Inter-hour
Variability
Analysis
Long-term
Dynamic Simulations
(Auto Gen Control)
Positive Sequence
Load Flow
(GE PSLF)
100MW Oahu + 200MW Lanai + 200MW Molokai
50
Interval = 1min
Interval = 5min
45
Interval = 10min
40
Frequency (%)
35
30
25
20
0.1% percentile (1min) = -12.27 99.9% percentile (1min) = 11.7685
0.1% percentile (5min) = -31.33699.9% percentile (5min) = 33.0615
0.1% percentile (10min) = -49.305
99.9% percentile (10min) = 54.0865
Negative most (1min) = -22.479 Positive most (1min) = 22.5425
Negative most (5min) = -54.9215Positive most (5min) = 65.0885
Negative most (10min) = -90.258Positive most (10min) = 95.845
15
10
5
0
-50
-40
-30
-20
-10
0
10
20
30
40
50
Farm power (MW per interval)
Unit commitment/ dispatch,
representing operating rules,
benchmarked against system
operation.
Sub-hourly wind/ solar/ load
changes with respect to
reserve, based on
commitment and dispatch.
Frequency analysis, including
governors and AGC response.
Initialized from MAPS and
driven by major wind/ solar
events.
Full Transmission model for
voltage & stability
performance, governor
response, contingency
analysis.
Hourly results for 1yr
10min results for 1yr
1sec results for one 1hr
1ms results for 1min
Quantify reserve violations,
fast starts & load shed
events caused by sub-hourly
wind/ solar/ load changes…
Quantify frequency
performance during
wind/ solar variability events
and wind ramp events…
Quantify energy production,
variable cost, wind power
curtailment, emissions, etc
for each scenario to assess…
Quantify system stability
performance during
contingencies…
• Wind/solar plant
•
•
•
•
Wind/solar delivered
Unit commitment
Variable cost
Emissions, etc
• Assess reserve requirements
• Assess fast-start events
• Select windows for further
analysis
• Reserve requirements
• Types of regulating units,
• Benefit of increasing ramp
rates
requirements (freq control,
LVRT, voltage control)
• System contingencies
• Generator trip
• Load rejection
• Other
22
Wind and Solar Data
Development
 Wind and solar data monitoring units
 Develop high resolution wind and solar
time series data for modeling work
23
Model Data Requirements
Summary of Thermal Unit
24
Integration Challenges …
• Wind energy curtailment at high penetrations
• Zero marginal cost energy not being accepted
• More frequent operation of thermal units at minimum
power
• What if there is a loss of load on the system?
• Large system contingencies
• What if the undersea cable trips?
• Variability of wind energy
• Large sustained drops in wind/solar power during load rises
• Reduced thermal unit efficiency & potentially higher O&M
costs
• Higher sub-hourly maneuvering to balance wind/solar power
25
Evaluating Candidate Strategies
•
•
•
•
•
•
•
Wind power forecasting to improve unit commitment
Refine up reserve requirements based on wind power
variability
Reduce minimum power of baseload units
Seasonally cycle-off select baseload units
Reduce reserve requirement (use of fast-start units and load
control)
Increase thermal unit ramp rate capability
Consider advanced wind turbine technologies to provide
“grid support” (e.g., inertia, over-frequency control)
26
Dynamic Response Study
PREMISE
Improving the dynamic responses of
generating units on the HECO grid will
facilitate the interconnection of greater
amounts of variable generation with
reduced amounts of other technologies to
mitigate adverse operational impacts.
27
Objectives
1. Confirm I&C logic for “AGC” of
governors
2. Characterize existing inertial, droop,
and AGC (i.e., “ramp rate”) responses
3. Develop control strategies and tune
systems for improved response (model
input)
4. Identify factors and equipment that limit
unit response
5. Identify capital projects to address
limitations
28
Pre-Tuning Uninhibited Boiler Following
Response Trend
Combustion Control
at Top Load
29
Post-Tuning Uninhibited Boiler Following
Response Trend
30
Post-Tuning 3 MW/min Coordinated
Control Response Trend
31
5 MW/min Response Trend
32
System Load/Frequency Response to
125 MW Kahe 5 Trip
Theo W. Hetherington – C.S.Squared
33
(3-14-2009, 20 min response)
DYNAMIC RESPONSE – GENERATING UNITS
For Analytical Purposes Only
Unit
H8
H9
W3
W4
W5
W6
W7
W8
W9
W10
K1
K2
K3
K4
K5
K6
CIP CT-1
H-Power
ODOM
(MW/min)
1.4
1.4
0.9
0.5
1.4
1.4
2.3
2.3
3.0
3.0
2.3
2.3
2.3
2.3
2.5
2.5
na
2.0
Projected
"Everyday"
(MW/min)
3.0
3.0
2.5
2.5
3.0
3.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
7.0
6.0
10.0
1.8
Projected
"Once-in-While"
(MW/min)
5.0
5.0
4.0
4.0
5.0
5.0
7.0
7.0
10.0
10.0
7.0
7.0
7.0
7.0
10.0
8.0
13.0
2.0
Droop
(governor)
(%)
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
34
5.0
Impact of Renewables Variability
on System Frequency
Higher thermal unit ramp rates helped manage frequency
Fast Wind Power Variability
Sustained Wind Power Drop
Aug 30th 10am (995MW Load)
Oct 12th 2pm (1160MW Load)
Large and fast-wind power
variability over the 5-10min
timeframe in both directions
Largest wind forecast error. Largest
hourly wind drop (311MW; 27% of
gen.)
All fast-start units dispatched
Manageable system frequency over
fast wind variability events
Manageable system frequency
over largest wind drop 35
GE Internal – HECO Proprietary
Thermal Unit Ramp Rates & Droop
Large Wind/Solar/Load Change
Today’s Ramp Rate / Droop
10am (1108MW Load)
200MW Lanai
(curve on top of one
another)
Lanai 200 MW
Molokai 200 MW
Oahu1 50 MW
Oahu2 50 MW
Centralized PV 60 MW
Centralized PV 20 MW
Centralized PV 5 MW
Residential PV 15 MW
120
100
MW
80
MW60
60.2
60
Hz
Aug
30th
59.8
UFLS at 59.5 Hz
59.6
59.4
0
600
1200
1800
s
2400
Frequency
3000
3600
Propose Future Ramp Rate / Droop
40
60.2
20
0
0
600
1200
1800
s
2400
3000
3600
Hz
60
59.8
59.6
59.4
0
600
1200
1800
s
2400
Frequency
3000
3600
36
Results
37
Operational Strategies and
Unit Modifications
More Wind Energy Delivered & Lower Variable Cost
Benefits from…
• Operational Strategies  Wind forecasting & refine up reserve
requirement
• Thermal Unit Modifications  Reduce unit min power & seasonally cycle
off baseload units
• Modifying Reserve Req’ts  Credit load control & fast-start units for up
reserve
38
What Worked Well for HECO
 Dedicated cross-functional team
 Technical Review Committee
 Weekly meetings during scenario
analysis
 Selected the most difficult scenario
first
 Prudent use of modeling results
39
Thank You
Learn more ….

Hawaii’s Energy Future
www.hawaiisenergyfuture.com

Hawaiian Electric Company
www.heco.com

Hawaii Clean Energy Initiative
http:/hawaii.gov/gov/initiatives/2009/energy

Hawaii energy data
http://hawaii.gov/dbedt/info/energy
40
 BACK UP
41
Oahu Generating Fleet
Unit
Capability
Type
Operating
Mode
HECO Generating Units
Steam, Non-Reheat
Steam, Non-Reheat
Steam, Non-Reheat
Steam, Non-Reheat
Steam, Non-Reheat
Steam, Non-Reheat
Steam, Reheat
Steam, Reheat
Combustion Turbine
Combustion Turbine
Steam, Reheat
Steam, Reheat
Steam, Reheat
Steam, Reheat
Steam, Reheat
Steam, Reheat
Honolulu 8
Honolulu 9
Waiau 3
Waiau 4
Waiau 5
Waiau 6
Waiau 7
Waiau 8
Waiau 9
Waiau 10
Kahe 1
Kahe 2
Kahe 3
Kahe 4
Kahe 5
Kahe 6
56
57
49
49
57
56
92
94
53
54
92
89
92
93
142
142
Cycling
Cycling
Cycling
Cycling
Cycling
Cycling
Base
Base
Peaking
Peaking
Base
Base
Base
Base
Base
Base
HPOWER
Kalaeloa
AES
Major Independent Power Producers
46
Steam, Non-Reheat
Base
208
Combined Cycle
Base
180
Steam, Reheat
Base
Service
Date
Age
1954
1957
1947
1950
1959
1961
1966
1968
1973
1973
1963
1964
1970
1972
1974
1981
55
52
62
59
50
48
43
41
36
36
46
45
39
37
35
28
1990
1991
1992
19
18
17
42
How is frequency performance affected by
installed wind power and scenario
Wind Power Variability
Proposed Ramp Rates & Droops
assumptions?
Wind Power Variability
8.0
New RR and Droops
0.03
7.0
0.025
5.0
MW
Sc1
4.0
Sc3
Sc5
3.0
2.0
1.0
Frequency RMS (Hz)
6.0
0.02
Sc1
Sc3b
0.015
Sc3f3
Sc5b_FS
Sc5f3_FS
0.01
0.005
0.0
RMS20
RMS5
Scenario
Input Data
RMS1
0
fIRMS
fIRMS20
fIRMS5
fIRMS1
No solar variability, No AES governor response PSLF
• Good correlation between increased wind power variability and
associated frequency performance
• 3B and 5B scenarios have better frequency performance than 3F3
and 5F3 scenarios. This is because fewer units are against their
limits (more up regulation in 3B and 5B as compared to 3F3 and 43
5F3).
How much does maneuvering of HECO units
increase in scenarios with more wind power?
Maneuvering of HECO units w.r.t scenario 1
• System variability is higher if
solar variability is
considered. HECO units
perform most of the
maneuvering.
2.0
Sc 1
1.5
Sc 3b
Sc 3f3
1.0
Sc 5b
Sc 5f3
0.5
0.0
RMS20
RMS5
RMS1
Maneuvering of HECO units w.r.t. total system variability
1.00
Power Output RMS w.r.t. total variability
• A high percentage of total
system variability (>80%) is
counteracted by HECO units
in all scenarios and for fast
and slow variations.
Power Output RMS w.r.t. Sc1
2.5
0.90
Sc 1
0.80
Sc 3b
Sc 3f3
0.70
Sc 5b
Sc 5f3
0.60
0.50
RMS20
RMS5
RMS1
Proposed AGC ramp-rates, no solar variability, no AES governor
response
Maneuvering of HECO units doubled in scenarios with offshore
44
wind for slow and fast variations
What units increase maneuvering in
scenarios with more wind power?
KALAELOA CT1
2.5
2.5
2
2
Sc1
1.5
Sc3b
Sc3f3
Sc5b_FS
1
Sc5f3_FS
Power Output RMS (MW)
Power Output RMS (MW)
AES
Sc1
1.5
Sc3b
Sc3f3
Sc5b_FS
1
Sc5f3_FS
0.5
0.5
0
0
RMS20
RMS5
RMS20
RMS1
RMS1
KAHE 4
2.5
2.5
2
2
Sc1
1.5
Sc3b
Sc3f3
Sc5b_FS
1
Sc5f3_FS
0.5
Power Output RMS (MW)
Power Output RMS (MW)
KAHE 6
RMS5
Sc1
1.5
Sc3b
Sc3f3
Sc5b_FS
1
Sc5f3_FS
0.5
0
0
RMS20
RMS5
RMS1
RMS20
RMS5
RMS1
Proposed AGC ramp-rates, no solar variability,
no AES governor response PSLF
Variability of HECO units increased to counteract additional wind
45
power variability in scenarios 3 and 5