PNNL’s Variable Resources Integration Projects
Yuri MakarovChief Scientist, Power Systems
Northwest Wind Integration Forum Technical Work GroupPortland, OROctober 29, 2008
1
PNNL Renewables Integration Program Objectives
Pave the road for a wider penetration of renewable resources in the national energy portfolio
Support renewable integration efforts of the US & State Governments, utilities, grid operators, developers and other interest groups
Pursue technologies and solutions that allow our nation to rely on renewable energy, surpassing a 25% penetration level by 2025
Help to minimize impacts of intermittency and poor predictability on operational performance characteristics, reliability, other generation resources, transmission, energy production & grid operations costs
Program Manager: Ross Guttromson
Chef Scientist and PI: Yuri Makarov
PNNL Renewables Integration Team & CooperationTeam
ProjectsAbout $6M project volumeAbout 5 major reports, 20 research papers per yearClients: DOE, BPA, Columbia Grid, CEC, CAISO, Hawaiian Utilities, CERTS, LDRD
Cooperation:NREL (Dr. Michael Milligan, Mr. Brendan Kirby PE), LBNL (Dr. Joe Eto), Sandia, ArgonneAREVA, Beacon PowerArizona State University (Prof. Vittal and Prof. Heydt), University of Illinois (Prof. Pai), University of Washington (Prof. Christie), University College, Ireland (Prof. O’Malley), Queensland University of Technology, Australia (Prof. Ledwich), Hong Kong Polytechnic University (Prof. Zhao Yang Dong)WECC VGS, NERC IVGTF, UWIG
Mr. Ross Guttromson, PE, Program ManagerDr. Yuri Makarov, PI, Chief ScientistDr. Zhenyu (Henry) Huang, PE - PMDr. Kris Subbarao - PMMr. John BowerDr. Kevin Schneider, PEDr. Shuai Lu - PMDr. Ning Lu - PMDr. Tony NguyenDr. Jian MaDr. Pengwei Du Dr. Nader Samaan – PMDr. Vilayanur Viswanathan… and others
Dr. Mark Weimer, Chief EconomistsDr. Harold KirkhamDr. Ram Sastry - PMDr. Pavel Etingov Dr. Ruisheng DiaoDr. Sunita MalharaDr. Marcelo ElizondoProf. Evgeniy Toroptsev (Stavropol University, Russia)Dr. Bhujanga Chakrabarti (Transpower, New Zealand)Ms. Marianna Ettorre (PhD student, Italy)Mr. Hjortur Johannsson (PhD student, Iceland/Denmark)Mr. Preben Nyeng (PhD student, Denmark)
Wind Integration Model(Co-funded by BPA and DOE/EERE)
Ross GuttromsonPNNL Project Manager
Stan CalvertDOE Project Manager
Mary JohannisBPA Project Manager
Project Objectives
Develop a model to evaluate the impacts of wind on grid operationsThe simulation model will analyze:
Control Performance Standards complianceCongestionRamping and operating reserve requirementsProbabilistic characteristicsMitigation measures to address variability and forecast errors:
Improved scheduling process (e.g., intra-hour schedules)Better forecasting algorithms and systems Coupling the intermittent resources with hydro resources, energy storage, and demand management, etc.
Conduct what-if studies:Impacts of new technologies, such as variable speed turbinesNew operational methods, such as BA consolidation, etc.
Wind Integration ModelBasic Functional Overview
Weather model
Historical weather info
Wind production modelBy 3Tier
(Framework is done )
Capacity De-rating
information (availability )
Data Module--------------------------------Wind Turbine placing / Hills/
geographic layout--Building /Tower--Library of Turbine models--Determine how to combine all
things for any particular site
Load Model
Load growth factor
Regulation load following
Energy Storage
Load curvesFuture year load curve
Scheduling model
Forecast error
Regional only
Balancing Model
Interchange model
Time
Inte
rch
an
ge
sum
Agreements
Weather and Site Model
Postprocessor
Weather model
Historical weather info
Wind production modelBy 3Tier
(Framework is done )
Capacity De-rating
information (availability )
Data Module--------------------------------Wind Turbine placing / Hills/
geographic layout--Building /Tower--Library of Turbine models--Determine how to combine all
things for any particular site
Load Model
Load growth factor
Regulation load following
Energy Storage
Load curvesFuture year load curve
Scheduling model
Forecast error
Regional only
Balancing Model
Interchange model
Time
Inte
rch
an
ge
sum
Agreements
Weather and Site Model
Postprocessor
Developing Tools for Online Analysis and Visualization of Operational Impacts of Wind and Solar Generation
(Funded by CEC through CIEE)
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Kris SubbaraoPNNL Project Manager
Larry MillerCIEE/CEC Project Manager
Uncertainty Models Development
Uncertainties addressed by the projectLoad forecast uncertaintiesWind generation forecast uncertaintiesForced outages and reserves activationUnictructed deviations of conventional generators
Uncertainties in capacity, ramp, ramp duration and energy requirements
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Objective and Scope
Generation requirement assessment tool incorporating load and wind generation uncertainties
Capacity (MW)Ramp (MW/min)Ramp Duration (min)Energy (MWh)VisualizationAlerts and alarmsIntegration with the CAISO systems
Mapping and transmission congestion analysis toolPower, voltageAlerts and alarmsVisualization (maps)Alerts and alarmsIntegration with the CAISO systems
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“Flying brick” method
Capacity, ramp rate, ramp duration, and energy (first performance envelope)Cycling (second performance envelope)
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π
ρΔπ
Δρ
Δδ
δ
MW
tt1
93%
93%
95%
95%
t2 t3 t4 t5 t6 t7t0
1h
2h
15 min
1 hour
Swinging door algorithm for ramp rate
Example Screen
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12
Visualization of Wind Power Production & Its Impacts on Congestion
EMS Integration ofProbabilistic Wind Forecasts(Funded by DOE/EERE)
Henry HuangPNNL Project Manager
Stan CalvertDOE Project Manager
EMS Integration Project PurposeDevelopment and integration of wind and load uncertainty forecasting into energy management systems (EMS) to improve operationsProject includes demonstrating the effectiveness of the implemented system with one or more utility partners
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SCADA
SE
PNNL Wind/Load
Confidence
Viz
AGC ED
EMS
UC
Tail event mitigation
New Methods (ED, UC)
Reserve sharing
Proactive
Passive
Active
data Interface
Data Base
XML Interface
CA
Load Actual load 1(2,3,4,5) hour ahead load forecast (hourly, daily)
-
Day - ahead load forecast Generation
Generator status Generator scheduled output Actual MW generation
Forced outage rate and duration
Startup time and startup cost Maximum/minimum capacity
Wind Actual wind generation
- 1(2,3,4,5)hour ahead wind forecast (hourly, daily) Day - ahead wind generation forecast
Wind/load confidence intervals
>1/min
1/min
Market System
Operation Planning
Data Mgmt
Potential linkage
EMS – energy management system; UC – unit commitment; ED – economic dispatch;
AGC – automatic generation control; CA – contingency analysis; VIZ – visualization;
Wind/load Confidence
intervals
Major Tasks
Probabilistic forecast of wind and load uncertainties (5 minute intervals, 3-8 hour ahead)Forecast uncertainty evaluation with off-line dataIntegration of probabilistic forecast to AREVA EMSLevels of EMS integration of wind and load uncertainties
Passive integration Active integrationProactive integration
Virtual Balancing AuthorityJoint PNNL/NREL Project (Funded by DOE/EERE)
Michael MilliganNREL Project Manager
Ning ZhouPNNL Project Manager
Stan CalvertDOE Project Manager
Some of the Analyzed Options
Actual consolidation of BAsDynamic scheduling for incorporating more renewable resources and/or delivering more ancillary servicesAdvanced ADIWind-only BARegulation resource sharingWide area tail events and their mitigation
Imbalances Transmission events
Wide area “flexibility market (e.g., bilateral market for load following service)For most of these options, we have industry involvement or offspring projects (e.g., our project with Columbia Grid, PNNL PM – Ram Sastry, CG PM – Paul Arnold)
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Advanced ADI
18
+
10BA
NIA(A)
NIS(A)
-
+
FS
FA(A)
-
+-
Raw ACEA
Gen
eratio
n A
llocation
Gen Control (A)
Balance Authority A
+
10BB
NIA(B)
NIS(B)
-
+
FS
FA(B)
-
+-
Raw ACEB
Gen
eratio
n A
llocation
Gen Control (B)
Balance Authority B
AG
C
Alg
orithmA
GC
A
lgorithm
Other participating
BAs
+
10BB
NIA(B)
NIS(B)
-
+
FS
FA(B)
-
+- Raw
ACEB
Generation
Allocation
Gen Control (B)
Balance Authority B
+ADI
ACEB
ADI Adj
(ΔPB)
-
ADI approach
+
10BA
NIA(A)
NIS(A)
-
+
FS
FA(A)
-
+-
Raw ACEA
Generation
Allocation
Gen Control (A)
Balance Authority A
+
ADI ACEA
ADI Adj
(ΔPA)
-
Reliability
Fairness
ADI Adj (ΔPi)
Raw ACEi
Coordination Center
AG
C
Algorithm
AG
C
Algorithm
ACE diversity interchanges (ADI) provides a tool for reducing the generation control requirement through sharing ACE among all participating balancing areas.
Advanced ADI
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9.91 9.92 9.93 9.94 9.95 9.96 9.97 9.98 9.99 10
x 104
-50
0
50
100
150
200
time
AC
E,
MW
Raw ACE vs Adjusted ACE, CAISO, Year 2006
Raw ACE
Adjusted ACE
Advanced ADI
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Reg UP Reg DOWN0
2
4
6
8
10
12
14x 10
6
MW
h
BPA total regulation
No ADI
ADI 25MW LimADI
ADI+conjestion
Advanced ADI
21
0 1 2 3 4 5 6 7 8
x 105
-4000
-3000
-2000
-1000
0
1000
2000
3000
4000
5000
P,
MW
COI power flow
limit
actual
Value of Fast Regulation for Intermittent Resources(Funded by CEC through CERTS)
Yuri MakarovPNNL Project Manager
Joe EtoCERTS PM
Mike GravelyCEC PM
Value of Fast Regulation for Intermittent Resources
Develop methodology to assess the relative value of generation resources used for regulation and load following CAISO functions. Help the CAISO to build a framework for incorporating more fast responsive resource into its balancing systems.
Importance
Fast responsive regulation resources are more valuable to the system (more efficient) because they allow applying controls at the exact moment and in the exact amount as needed.Fast responsive resources can help to reduce CAISO’s regulation procurement by about 40%.
Objectives
Value of Fast Regulation for Intermittent Resources
Unique methodology accounts for the relationship between the regulation capacity, ramping capability and ramp duration/energy.
IncreasingRegulationCapacity
IncreasingRampingCapability
IncreasingPercentile of“Sufficiency”
Estimate of Optimal Size & Placement of Energy Storage for 300GW of Wind in the U.S. Grid(Funded by PNNL)
Michael Kintner-MeyerPNNL Project Manager
Key steps in the methodology
Build the data set based on future renewable energy technology projectionsAnalyze the theoretical maximum of the storage capacity applicable for each component of the system balancing process (scheduling, load following, and regulation) Analyze the system requirements to the balancing service (system performance envelope) Analyze the performance envelope for each storage technologyOptimize the economical percentage of energy storage additions compared to the theoretical maximum and based on system and storage performance envelopes for:
Balancing servicesEnergy arbitrage
Wide Area EMS Control for Renewable Energy(Funded by CEC and BPA)
Ning LuProject Manager
John PeaseBPA Project Manager
Larry MillerCIEE Project Manager
Wide Area EMS Control for Renewable Energy
Purpose: This project targets operational principles, algorithms, market integration rules, functional design and technical specification for an energy storage that mitigates the intermittency and fast ramps that occur at higher penetration of renewable generation.
Technology: Field experiment design and monitoring of the flywheel energy storage for existing and future renewable penetration.
Selected System Configuration:Configuration with two ESDs flywheel & a hydroVertical configuration that would integrate the wide area EMS with the BPA and CAISO AGC systemsBPA’s and CAISO ACE or “conventional regulation unit” signals will be used Dynamic schedules are used to incorporate the ESD regulation into the corresponding neighboring control area AGC system.Control algorithms have been designed to mimic behavior of a conventional unit on regulation & to coordinate functions of participating ESDs.
Controller
COICOICOI
Hydro 1
Flywheel 2
Wide Area EMS Control for Renewable Energy
Shared Regulating Resource PerformanceUsing PNNL Control Algorithm(using Beacon Power Flywheels)
Tail Event Prediction and Analysis(Funded by BPA)
Shuai LuProject Manager
John PeaseBPA Project Manager
Low Probability Tail Event Analysis and Mitigation in BPA Control Area
Tail eventThe situation when forecast errors for load and wind result in large wind ramps in a power system
The imbalance between generation and load becomes very significantThis type of events occurs infrequently Always appear on the tails of a histogram showing the distribution of system power imbalance
This project analyzes characteristics of tail events in the BPA system, including
Frequency of occurrence, severity, patterns, etc, The quantity of system reserves needed for tail eventsMeasures to mitigate impacts of tail events on system operations.
Low Probability Tail Event Analysis and Mitigation in BPA Control Area
-2500 -2000 -1500 -1000 -500 0 500 1000 15000
100
200
300
400
500
600
700
800
900Distribution before extreme cases are removed
Capacity, MW
Num
ber
of
Da
ta P
oin
ts
Distribution of load following capacity requirement
Tail events (for projected wind in BPA system in 2010:
Temperature(t)
Storm(t)
Wind Power (t)
Gen (t)
Load (t)
LFE (t+1) WFE (t+1)
System Imbalance(t+1)
Line Congestion
CPS Violations
Wind Curtailment(t+1)
Transmission Curtailment (t+1)
LFE: Load Forecast ErrorWFE: Wind Forecast ErrorSCE: Generation Schedule Control Error
SCE (t+1)
LFE (t)
WFE (t)
System Reserve(t)
Transmission Outage(t)
Gen Startup Failure(t)
Load Curtailment Gen Curtailment
Gen Outage(t)
SCE (t)
Bayes Net Model Predicting System Imbalance – Assisting Decision-making Based on Policies
Real-time System Imbalance Prediction
Optimizing Efficiency & Emissions with High RE Penetration(Funded by PNNL)
Shaui LuProject Manager
Optimizing Efficiency and Emissions with High RE Penetration
This project develops key elements of a coordinated approach by:
Developing an optimal mix for the new generation additions, Increasing the flexibility and dispatchability of the generation mix Using energy storage, grid responsive load and inter-area balancing schemes, Developing better unit commitment, dispatch, and load balancing strategies.
Optimizing Efficiency and Emissions with High RE Penetration
Wind Plants
Fossil-fuel Fired Generators
(Coal and natural gas)
Hydro Generation
Energy Storage Devices
(pumped hydro, fly wheels, battery)
Demand Response(A/C, PHEV)
Day-ahead Schedule (hourly)
Load Following/Real-time dispatch (5 to
15 minutes)
Regulation(every 2 or 4
seconds)
Power System Operations
1. Energy production cost;
2. Emissions; 3. Number of unit
startups;4. Etc.
Simulation platform:
Wide Area Multidimensional Security Region (Co-funded by BPA and DOE/through CERTS)
Shuai LuPNNL Project Manager
Jim BurnsBPA Project Manager
Phil OverholtDOE Project Manager
Research Questions
Develop a state-of-the-art approach for analyzing wide-area security conditions of an interconnected power system in real time based on an idea of wide-area multidimensional nomograms (WAMN) or, which is the same, wide-area multidimensional security region.
Provide an open platform for incorporating all sorts of security and other operational constraints within a single approach
Provide real time actionable information concerning the system security margin and best controls to increase it.
Develop new procedures for Offline calculation of the approximated security region with a given accuracy
(automatic procedure) Extremely fast evaluation of the available security margin in real time Calculating optimal controls in real time Identification of important system parameters affecting security margins
THIS IS NOT A VISUALIZATION TOOL!
Method: Example of Existing Nomogram
Widely used by power system operations and grid planning
2-D or 3-D plots
Piecewise linear approximations
Include thermal, voltage, voltage stability, oscillatory and transient stability constraints
Already used by EMS and market applications (such as Security Constrained Economic Dispatch).
North of John Day vs. COI + NW/Sierra or PDCI Flow(Summer 2008 N-S Nomogram)
2400
2500
2600
2700
2800
2900
3000
3100
3200
3300
3400
3500
3600
3700
3800
3900
4000
4100
4200
4300
4400
4500
4600
4700
4800
4900
7000 7100 7200 7300 7400 7500 7600 7700 7800 7900 8000
North of John Day Cutplane Flow (MW)
PD
CI
or C
OI
+ N
W/S
ierra
Flo
w (
MW
)
Midpoint - Summer Lake
400 MW East to West
MW East to West
Midpoint - Summer Lake400 MW West to East
Midpoint - Summer Lake0 MW
Midpoint to Summer Lake flows are shown in increments of 200 MW
Solid lines are for COI + NW/Sierra limits and Dashed line is for PDCI limit.
Method: Security Nomogram in a 3-D View
North of John Day vs. COI + NW/Sierra or PDCI Flow(Summer 2008 N-S Nomogram)
2400
2500
2600
2700
2800
2900
3000
3100
3200
3300
3400
3500
3600
3700
3800
3900
4000
4100
4200
4300
4400
4500
4600
4700
4800
4900
7000 7100 7200 7300 7400 7500 7600 7700 7800 7900 8000
North of John Day Cutplane Flow (MW)
PD
CI o
r C
OI +
NW
/Sie
rra
Flo
w (
MW
)
Midpoint - Summer Lake
400 MW East to West
MW East to West
Midpoint - Summer Lake400 MW West to East
Midpoint - Summer Lake0 MW
Midpoint to Summer Lake flows are shown in increments of 200 MW
Solid lines are for COI + NW/Sierra limits and Dashed line is for PDCI limit.
Method: A Conceptual View of Multidimensional Security Region
Approximate the actual nonlinear security region using a set of hyperplanes.Number of parameters (dimensions) in each constraint is nd Number of hyperplanes/constraints is m
Security Region
d 1
d 3
d 2
ξd D0
Hd 11 1 1 ,1
21 1 2 ,2
1 1 ,
...
...
......
nn d
nn d
mn nm d m
d d
d d
d d
n n L
n n L
n n L
Situational Awareness Visualization Tool – Virtual Reality
(funded by DOE)John BowerPNNL Project Manager
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Virtual Conference Room
45
System Observation
46
Virtual Security Region
47
Virtual Transmission Planning
48
Thanks!