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)

7

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

11

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!