Embed Size (px)
Citation preview
Alstom Grid Inc.- P 11 ALSTOM © 2011
Seminar on Renewable Integraton
Lund University
December 17, 2013
Sweden
Lawrence Jones, Vice President,
Utility Innovations and Infrastructure Resilience
BIG DATA – Enabling the Integration of RES, DER,
DR, µGrids & Smart Cities
Alstom Grid Inc.- P 2
Big Data is the Old, New and Future Normal
1 exabyte = 1, 000 000 000 000 000 000
Today we create approximately 5 exabytes every two days
Alstom Grid Inc.- P 3
Power Generation
Thermal & Renewables
Low & No-Carbon Energy Solutions
Renewables
Efficiency Improvements
Carbon capture & Storage
Transport
Faster, Cleaner and less congestion
High-speed Rail
Light Rail
Urban Tramways
Grid
Smarter, more Reliable, more
Flexible Power Transmission
Up to 1200 kV substations
Advanced Network Management
Renewable Integration
Smart Grid and Super Grid
technologies
Clean Energy and Transport Solutions
Alstom Grid Inc.- P 4
EU
Meshed DC
(WP2013)
EU
Energy + Nhood
(Cooperate)
Smart GridA new demonstration/pilot tsunami
Manitoba Hydro
On-line Stability
ERDF
DERMS (Vendée)
ERDF
DERMS (NiceGrid)
Veolia
DERMS (Reflexe)
GDF Suez
EMBIX (Greenlys)
UVSQ
EMBIX
(Smart Campus)
Strategy, Early Concept (11)
Pilots (13)
Commercial (22)
Issy City
EMBIX (IssyGrid)
BOUYGUES
EMBIX (Eco2charge)
BOUTGUES
EMBIX (EPIT)
MAUI
iDMS
Tres Amigas
DC Grid – VSC
(multi-terminal)
Southern
iDMS
WECC
On-line Stability
ISONE
On-line Stability
Manitoba Hydro
On-line Stability
Dunneill Wind Farm
Smart FACTS
BATTELLE
DERMS (DOE)
Duke Energy
DERMS (DOE)
PJM/Philadelphia
DERMS (Navy yard)
EDF SEI
iDMS
CFE
Digital Substation
RTE
Digital Substation
RTE
Renewable Portfolio
WPD
Distribution
Automation (Falcon)
UKPN
Smarter Network
Storage
UKPN
Distribution
Automation (FPP)
SNCF
DERMS (Prog Gare)
CEA
Meshed DC
(Winposer)
ESB
Smart Grid VSC
(Green e-motion)
Svenska Krafnat
Cyber Security
Energinet.dk
Cell Controller
EcoGrid.dk
Energinet.dk
Digital Substation
Energinet.dk
On-line Stability
STEDIN
iDMS
MEW - Kuwait
On-line Asset Mgt
MEW - Kuwait
iDMS
Transpower
DERMS
SGCC
On-line Stability
GAMESA
Renewable Portfolio
MSETCL
On-line Stability
MEW - Kuwait
On-line Stability
(AFTER)
MEW - Kuwait
DERMS (e-storage)
ELIA
Digital Substation
Infrax
DERMS (Slim)
SGCC
Digital Substation
CSG
On-line Stability
ESKOM
On-line Stability
UKPN
On-line Stability
(Volga)
FSK
Digital Substation
(Nadezhda)
EU
DC breakers, WAMS
(Twenties1)
Alstom Grid Inc.- P 5
“Big data refers to things one can do at a large scale that cannot be done
at a smaller one, to extract new insights or create new forms of value, in
ways that change markets, organizations, the relationship between
citizens and goverments and more .”Big Data – A Revolution That Will Transform How We Live,
Work and Think. Viktor Mayer-Schonberger and Kenneth Cukier. 2013
What is Big Data?
Alstom Grid Inc.- P 6
3Vs of Big Data
Source: Big-Data Tutorial. Marko Grobelnik Stavanger, May 8, 2012
Alstom Grid Inc.- P 7
Big Data Analytics Evolution
• 1880 – U.S. census
• Collected and categorized information for 50 million people• It took 7 years to process the census data manually
• 1890 – U.S. census
• World War II - The Manhattan project
• 1950s – U.S. space program
• Big Science – Hadron Collider at CERN can generate 1 petabyte per second
• Big Medicine
• Big Business
• Big Grid
• . . .
Alstom Grid Inc.- P 8Adapted from Big Data: The Next Frontier for Innovation,
Competition, and Productivity. Mckinsey Global Institute, May 2011
Alstom Grid Inc.- P 9
Big Data Everywhere
Adapted from Big Data: The next Frontier for Innovation, Competition, and Productivity. Mckinsey Global Institute, May 2011
Alstom Grid Inc.- P 10
Source of Data Explosion in Electricity Distribution
Source: Electric Light and Power, http://www.elp.com/
Alstom Grid Inc.- P 11
Four Dimensions of Integrating Renewables
Physics Operation
Economics
/MarketsInformation
Flexibility
Alstom Grid Inc.- P 12
Flexibility, Flexibility, Flexibility
Existing and new flexibility needs can be met by a range of resources in the electricity system – facilitated by power system markets,
operation and hardware.
Source: Adapted from Harnessing Variable Renewables, International Energy Agency
Alstom Grid Inc.- P 13
Big Data in the Utility Industry
Field level PMU IED MeterDA DG CMU HANLine sensor
Others photos,
videos,
labs analysis,
site reports,
Financial
Qty
Time
Resolution
Type
1k 100k 10k 100k 10k 10M 100M10k
Weather
100k
1ms 100ms 10ms 1s 10min 1min
to 15min100M10k 100k
V I Ph
Hz
V I
HzSw MW MVA T°, Qual
MWh
V I ph
Hz
History
100M10k 100k
Files
100Tb
Comm(AMI, Tcom)
Signal Processing and Local Automation
GridOperations
BusinessOperations
CustomerEngagement
Real Time
Data Management
Transactional
13
ScadaPDCMDM
Alstom Grid Inc.- P 14
Synchrophasors: The New Heartbeat of the Grid!Enabling Intelligent Decentralized Grid Monitoring & Control
EMS
Dynamic Security
Assessment
State Estimation
Contingency
Analysis
Offline
Analysis
Modelling
Root Cause
Analysis
Wide Area
Controls
Post-Event
Generator & ISO
Compliance
New Smart Grid
Applications
Distribution
DMSDistributed
Generation
Demand
Response
SynchroPhasors:‘the new heartbeat of the grid’
Local
Controls
Synchrophasor
Analytics
Alstom Grid Inc.- P 15
Making Sense of Big Data
• Data Correlation or Scientific Models
• How Should Theories be Crafted in an
Age of Big Data
• Visualization as a Sense-Making Tool
• Bias-Free Interpretation of Big Data
• Is More Actually Less
• Correlations, Causality and Strategic
Decision-making
Source: The Promise and Peril of Big Data, David Bollier Rapporteur, The Aspen Institute, 2010
Alstom Grid Inc.- P 16
Operational time dimensions changing
Greater flexibility and more reliable customer resources are needed
Courtesy of Paul De Martini , Newport Consulting Group LLC
Alstom Grid Inc.- P 17
Valuation of Responsive Distributed Energy
Resource and Control Actions
Courtesy of Paul De Martini , Newport Consulting Group LLC
Alstom Grid Inc.- P 18
Typical
Flexible
MWDuration
Reaction
timeTypical Energy Transactions (1000GW System)
Time
horizon
Regulation
Reserve
Emergency
Grid
Operation
< 200
Seconds
< 10
Minutes
Hours
< 200
Grid
security
Power Quality
Minutes
Seconds
Hours
Minutes
Minutes
Market
Operation
Minutes
Peak/Off Peak Day Ahead 100 - 1,000
Energy Infra Day < 100 MinutesHours
Need to define Revenue model across energy transaction
New TSO/DSO regulatory framework required
Peak/Off Peak Week Ahead 10,000
HoursHoursDay
DaysDaysWeek
Consumer
Islanding
Quality
Power
Quality
500 Minutes
Seconds
< 10 Minutes
Load Management
Minutes
Seconds
MinutesHours
Seconds
Minutes
10,000 DaysEnergy Efficiency DaysWeek
< 100 Minutes Minutes
Minutes
Alstom Grid Inc.- P 19
Next G
eneration Market
Place (N
GM) Local
Balance Place
National
Balance Place
Regional
Balance Place
Offers
OffersAwards
Awards
Schedules
Constraints
RT Resources
conditions
RT Dispatch
instructions
VPP
Local Market Place
TSO
DSO
Consumers and
producers
Regional Market PlaceRegional Coordination
DERMS (DER +DR)
Commercial Operation
Physical Operation
New Regulatory step : towards multi-tier markets
RT Dispatch
instructions
RT Resources
conditions
Central Market Place
Alstom Grid Inc.- P 20
Examples of Big Data Analytics Use Cases
Revenue Protection • Identifying and locating tamper, malfunction, bypass meters
• Loss reduction opportunities by region
• Customer statistical outliers
Outage Analysis • Track outage impacts and restoration activities
• Service territory and feeder reliability
Asset Management • Track asset history, health, and investments
• Predictive maintenance
• Quantify network and customer risks
Volt var optimization • Track min/max voltages across distribution network
• Estimate energy/financial savings with CVR
• Identify feeder/DER remediation opportunities
• Compare with SCADA data at feeder, cap banks, tap changers, voltage
regulators
Demand response • Peak load forecasting, monitoring, deviation tracking
• Event dispatch and aggregate response monitoring
• Tracking of customer participation, individual customer response
Predictive Manhole
MaintenanceCon Ed applied correlational analysis to historical power grid big data and
was able to predict which manhole to be prioritized for maintenance
Alstom Grid Inc.- P 21
Demand Response Use Case
Time Scale of DRSource: LBNL
• Most operational use of
demand response (DR)
assets today involves many
time-consuming manual
steps.
• Use of DR cannot scale
unless the manual steps are
eliminated through
automation, and integrated
to control center
applications.
• Integration and automation
is also essential for DR to
provide real-time energy and
ancillary services.
Alstom Grid Inc.- P 22
And it is becoming a reality !
Over 10 Nuclear Plan equivalent interacting into PJM Market
Alstom Grid Inc.- P 23
Big Data Will Transform the Utility Workforce
• Data Scientists, e.g. Wall street ‘quants’
• Statisticians
• Visualization engineers
• Cognitive engineers
• Behavioral scientists
• . . .
Alstom Grid Inc.- P 24
Critical Skills of a Data Scientist
• Data mining
• Data visualization
• Data analysis
• Data manipulation
• Data discovery
Alstom Grid Inc.- P 25
Forthcoming Book
“Renewable Energy Integration: Practical Management of Variability, Uncertainty and Flexibility in Power Grids.”
Editor, Lawrence E. Jones
Elsevier, 2014
![Data and Society Lecture 2: Big Data 1bermaf/Data Course 2018/Lecture 2...About Big Data [Strata] •Value of big data: analytical use, enabling new products •Ways that big data](https://img.dokumen.tips/doc/110x75/5ec9e5b78a455f04c75660aa/data-and-society-lecture-2-big-data-1-bermafdata-course-2018lecture-2-about.jpg)
