usa-argentina collaboration: smart grid applications in ......the 1996 blackout in the western north...

USA-Argentina Collaboration: Smart Grid Applications in Transmission Systems PRESENTER: MARCELO A. ELIZONDO, RESEARCH ENGINEER

September 18, 2013 1

Electricity Infrastructure Group, Energy and Environment Division Buenos Aires, Argentina

PNNL-SA-98382

USA-Argentina Collaboration

Smart Grids (SG): Advances in communications and computation to address power systems challenges U.S. DOE and PNNL expertise developing SG technologies in both transmission and distribution SG technology to address Argentina’s needs Summary of smart grid activities at PNNL on transmission systems. Several PNNL staff contributing. Marcelo’s background in USA and Argentina

UNSJ, San Juan, Argentina. Power system engineer. CONICET, Argentina. Scholarship

UNSJ, Argentina. PhD, power systems engineering. Carnegie Mellon University, USA. 2-year visitor.

Mercados (ME-C), Argentina. Consultant engineer. PNNL, USA, research engineer (Electricity Infrastructure Group)


Outline

PNNL and its Electricity Infrastructure Group Four examples of developments

Catalyst role in PMU technology development & mode meter Model validation Technology enabling finer selection of load shedding Control center tools for renewable integration and dynamic interchange

Open discussion: opportunities in Argentina


Who is PNNL?

4

The Pacific Northwest National Laboratory is a DOE Office of Science laboratory in Richland, WA. Operated since 1965 by Battelle, a global non-profit research and development organization committed to science and technology for the greater good.

Mission: Transform the world through courageous discovery and innovation. Vision: PNNL science and technology inspires and enables the world to live prosperously, safely, and securely. Values: Integrity, creativity, collaboration, impact and courage provide the foundation for all we do.

PNNL employs nearly 5,000 staff and has an annual operating budget of $1.1 billion. PNNL has over 100 people working in Electric Infrastructure – over 50 Power System Engineers.

Slide from: Paul Skare, PNNL September 18, 2013

PNNL draws upon core capabilities, facilities, and investments in Electric Infrastructure


Power system operation, planning and security

Power markets

Demand response

Renewable integration

Advanced analytic methods, HPC-based simulations, visualization

Staff Capabilities

Live PMU data from all three interconnections

PMU data archive

PowerNET lab

EMS/DMS displays

T&D-level data displays

Platform for tool evaluation, operator training

Physical Control Center (EIOC)

Live security data streams

Visual analytics

Co-located with classified assets that accelerate threat recognition and appropriate response

Emergency Response

Public / Private

Cyber Security / Resilience Center (EICC)

Networking and data management

Advanced analytic methods and HPC approaches for real-time modeling and simulation

Visualization and decision support

Next Generation EMS

Next Generation Simulation

Future Power Grid Initiative

Slide from: Paul Skare, PNNL

Outline





7

Impact: Mode Meter

If used in ‘96, the mode meter would have provided operators the necessary lead time (4-6 min) to potentially avoid a $2B blackout .

Our approach: Improve power system performance and transmission reliability by extracting greater value from grid measurements and data. Key elements include:

U.S. Department of Energy (DOE) lead for the North American Synchrophasor Initiative (NASPI) – joint effort with the North American Electric Reliability Council (NERC) and industry to build out phasor measurement units (PMUs) across North America, enabling increased situational awareness and control Planning models validation through measurement-based analysis Decision support tools for operators

Mode meter – uses PMU data to improve detection of grid disturbances, enabling greater asset use and preventative measures; deployed in Western Interconnection Synchrophasor Project

Electricity Infrastructure Operations Center – providing utilities, vendors and researchers access to real-time grid data for testing in realistic operations environment

National Challenge

Ensure a reliable U.S. power system by

leveraging new data streams that provide

wide-area visualization,

monitoring and control.

Overview Transmission Reliability

Impact: Mode Meter

If used in ‘96, the mode meter would have provided operators the necessary lead time (4-6 min) to potentially avoid a $2B blackout .

Graphical Contingency Analysis

Real-time power flow visualization identifies/prioritizes issues, recommends corrective actions

NASPI

7 September 18, 2013 Slide from: Jeff Dagle, PNNL

North American SynchroPhasor Initiative

8

“Better information supports better - and faster - decisions.”

DOE and NERC are working together closely with industry to enable wide area time-synchronized measurements that will enhance the reliability of the electric power grid through improved situational awareness and other applications

April 2007 November 2012

http://www.naspi.org

Lessons Learned from August 10, 1996 Blackout in the Western Interconnection of North America

9

4000

4200

4400

4600

0 10 20 30 40 50 60 70 80 90

4000

4200

4400

4600

Time in Seconds

Simulated COI Power (initial WSCC base case)

Observed COI Power (Dittmer Control Center)

Data captured from high-speed time-synchronized Wide Area Measurement Systems were essential to support the blackout investigation

The need for better model validation was demonstrated

September 18, 2013 Slide from: Jeff Dagle, PNNL

Mode meter: new grid disturbance detection system using real-time phasor data

Challenge Today: limited real-time monitoring of grid oscillations that cause instability and result in constrained operations (renewables integration/demand response will increase instability) PNNL Approach: use real-time phasor data to improve oscillation detection, enabling increased grid asset utilization and preventative measures Status:

Mode meter to be used in Western Interconnection Synchrophasor Project to monitor oscillations Licensed to commercial energy management system providers for incorporation into utility software

The 1996 blackout in the western North American power system illustrated the critical need for an advanced view of grid disturbances before they cascade to outages. If used in 1996, the mode meter would have provided operators the necessary lead time (4-6 min) to potentially avoid the blackout.

Slide from: Jeff Dagle, PNNL

Situational awareness

Congestion management

Renewable integration

Increase in operating transfer capacity

System protection

REAL-TIME SYNCHROPHASOR APPLICATIONS AND THEIR PREREQUISITES

FUTURE Prerequisites

Applications

ANALYSIS

COMMUNICATIONS

Good data collection

Interconnection-wide baselining

System studies

High availability, high speed

TODAY

Functions

Appropriate physical & cyber-security

Redundant, fault-tolerant

Outage avoidance

Wide-area Monitoring Visualization Frequency and voltage monitoring Oscillation detection

Event detection Alarming

Operator decision support

Automated wide-area controls Reliability Action Schemes

USERS

Pattern detection

Model validation – system & elements

Familiarity Good visual interface Training

Interoperability standards

Synchrophasor Deployment Summary

DOE and PNNL has played a key catalyst role in the development and implementation of synchrophasor technology PNNL will continue to support DOE’s efforts to promote and enable widespread adoption of advanced monitoring technologies to ensure grid reliability DOE is actively supporting needed R&D to ensure that the full value of a North American phasor network will be realized • Hardware – measurement technologies • Network – data access and security • Software Applications – focus on reliability management objectives • Demonstrations – regional in scope

The American Recovery and Reinvestment Act of 2009 has provided

funding for investment grants and demonstration projects. This has enabled unprecedented advancement of synchrophasor technology deployment


Slide from: Jeff Dagle, PNNL

Outline





Some Definitions

Model Validation – diagnosis of model problems Determine mismatch between model and measurement

Model Calibration – solution to model problems

Tune parameters to minimize the mismatch Model Verification – confirmation of good solutions

Compare model against measurement from multiple disturbance recordings to ensure reliable estimation

September 18, 2013 14 Slide from: Henry Huang and Ruisheng Diao, PNNL

Model Validation and Calibration Methodology

Model validation through event play-in using phasor data

15

Remainder of Power system Model

Subsystem for hybrid modeling

Remainder of Power system Model

(external)

Measurement boundary

Subsystem for hybrid modeling

Measured V,f(θ)

P,Q

Simulated P,Q

Measured P,Q = ?

Calibration

“Prediction” Dynamic

Simulation dx/dt = f(x, y, α)

Dynamic states x(k-1)

Parameters α(k-1)

Measurement z(k)

x(k), α(k)

x’(k)

“Correction” Measurement

Equations ∆ z = z – h(x, α)

k: time step

Event Playback

Model calibration using Kalman Filter

Slide from: Henry Huang and Ruisheng Diao, PNNL

Model Validation and Calibration

Conventional power plant model validation and calibration Device-level model validation and calibration Renewable generation model validation and calibration System-wide model validation and calibration Real-time model validation and calibration

September 18, 2013 16 Slide from: Henry Huang and Ruisheng Diao, PNNL

Power Plant Model Validation


Mismatch between Simulation and Measurement

-790

-785

-780

-775

-770

-765

-760

-755

-750

-745

-7405 10 15 20 25 30 35 40

t (s)

P (M

W)

P3_ref P3_sim

P4_ref P4_sim-50

-30

-10

10

30

50

70

90

5 10 15 20 25 30 35 40

t (s)Q

(MW

)

Q3_ref Q3_sim

Q4_ref Q4_sim

(Data from August 18 2002, 1500 MW Navajo units #2 & #3 drop)


Calibration of Inertia Constant and Exciter Gain

18

0 10 20 30 40-7.8

-7.6

-7.4

t in seconds

real

pow

er fl

ow p

er u

nit

0 10 20 30 403.5

4

4.5

5

5.5

t in seconds

Iner

tia C

onst

ant

0 10 20 30 40300

320

340

360

380

t in seconds

Exc

iter G

ain

0 10 20 30 40-0.6

-0.4

-0.2

0

0.2

t in seconds

reac

tive

pow

er p

er u

nit Measurement

Calibration

4.98

311

(Data from August 18 2002, 1500 MW Navajo units #2 & #3 drop)


Methodology

Automated model validation wrapping around PSLF BPA EPCL codes + PNNL MATLAB codes

Standalone MATLAB codes for model calibration A GUI is available User-selectable for what parameters to be calibrated.

Goal: Integrate calibration with PSLF (PSSE, …)

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MATLAB

PSLF Play-in .sav .dyd .csv

PLOT

• Plots: model validation

• Calibrated parameters • Plots: model validation,

model calibration, model verification

Play-in Calibration

Data Handling

Call PSLF Call PLOT

Model Validation Model Calibration

Outline





Grid Friendly™ Appliances Provide Fast, Autonomous Reliability Resource


Autonomously detects under-frequency events and sheds load 150 new Whirlpool clothes dryers, 50 retrofitted water heaters No one noticed in hundreds of curtailment events! Can displace spinning reserves and increase reliability Reacts within 1/2 second Delays & randomizes service restoration to avoid grid shock Low cost: no communications required

Slide from: Robert Pratt, PNNL

“When the inevitable occurs … people get stuck in elevators and high-value uses of power are shut off along with all the lowest priority uses of energy. It's the meat-ax approach to interrupting power flows.” Dr. Vernon Smith, 2002 Nobel Prize Winner, Economics

Outline





23

December 2008

Simulation, Modeling, and Control

Integration of forecasting and renewable energy production tools into grid resource planning and operation tools

Forecasting generation capacity and ramp ranges needed to balance the system (orange bands)

Incorporating all sources of uncertainty/variability: wind and solar generation and demand

Example Outcome: predicting generation deficiency above the available range (gray band)

Tool is installed at the CAISO Control Center to help real-time dispatchers anticipate and address ramping needs.; Planned deployment to other ISOs

* Patricia Hoffman (Assistant Secretary, DOE ), “Maximizing Renewable Energy in the US Electric Grid,” Presentation at The Road to a 100% Renewable Energy System Workshop, August 1, 2011

Ramp and Uncertainty Prediction Tool

Slide from: Yuri Makarov and Pavel Etingov, PNNL

Focus Areas


Sources of uncertainty (discrete and continuous): • Traditional:

Load forecast Unexpected forced

outages Generation units failure

to startup • New challenges:

Wind generation forecast Solar generation

forecast Wind ramps

Balancing process: • Day-ahead schedules

Unit commitment Economic dispatch

• Real-time dispatch Unit commitment Economic dispatch

• Spinning reserve • Regulation • Contingency reserve

• Control performance • Reliability


Models for Balancing Requirements Uncertainty: Multidimensional Uncertainty Analysis

25

Existing approaches are frequently limited to one dimension of the uncertainty problem – capacity. But capacity is not a single sufficient descriptor of the problem. Operational performance of a power system can be demonstrated through four basic metrics, forming the “first performance envelope”.

Capacity (π). Ramp rate (ρ). Ramp duration (δ). Energy (Є).

Ramp Duration, min

Energy, MWh

Capacity,MW

Time

MW

Ramp Rate, MW/min

Net Load ORLoad Following ORegulation Curve

* Y.V. Makarov, C. Loutan, J. Ma, and P. De Mello. “Operational impacts of wind generation on California power systems,” IEEE Transactions on Power Systems, vol. 24, pp.1039–1050, May, 2009.

Probabilistic Tool Design


Forecast error, %

-20 -10 0 10 20

Statistical analysis Cumulative distribution function

1. Data acquisition 2. Uncertainty assessment through

statistical analysis 3. Prediction of future grid

balancing requirements for specified time horizons and confidence levels

1

2

3


Real-Time Requirements Screen

27 Slide from: Yuri Makarov and Pavel Etingov, PNNL

Dynamic Interchange Adjustment (DINA) Tool


Objectives of PNNL dynamic interchange adjustment (DINA) tool: Provide an online estimation of the secure range for possible ISO-NE intra-hour net interchange adjustments within the next dispatch interval. Consider all contributing factors, such as:

expected changes to system load and interchange, spinning reserve requirements, system ramping capability, relevant uncertainties significantly impacting this estimation, including:

load forecast errors, random interchange variations, uninstructed deviations on generators, uncertainties in generation dispatch and reserve requirements.

Use ISO-NE market and other data in an agreed form Slide from: Yuri Makarov and Pavel Etingov, PNNL

Example of Actual Interchange Adjustment Range Estimation

29

Summary

PNNL experience Catalyst role in PMU technology development & Mode meter Model validation Technology enabling finer selection of load shedding Control center tools for renewable integration and dynamic interchange

Advancements to improve system reliability and resilience Opportunities in Argentina?


Thank you

Marcelo A. Elizondo, PhD Research Engineer

[email protected]

www.pnnl.gov


PNNL-SA-98382

usa-argentina collaboration: smart grid applications in ......the 1996 blackout in the western north...

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