Francisco Gonzalez-Longatt Presents on Big Data and Future Power Systems

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Prof Francisco M. Gonzalez-Longatt PhD | fglongatt@fglongatt.org | Copyright © 2015 1/53
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6th October 2015
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Agenda
• Motivation
• Future Power Systems
• Bid Data and Data Analytics
• Managing Uncertainties
• Power System Security
• Challenges
• Summary
• Closure
Copyright Notice
The documents are created by Francisco M. Gonzalez-Longatt and contain copyrighted material, trademarks, and other proprietary information. All rights reserved. No part of the documents may be reproduced or copied in any form or
by any means - such as graphic, electronic, or mechanical, including photocopying, taping, or information storage and retrieval systems without the prior written permission of Francisco M. Gonzalez-Longatt . The use of these
documents by you, or anyone else authorized by you, is prohibited unless specifically permitted by Francisco M. Gonzalez-Longatt. You may not alter or remove any trademark, copyright or other notice from the documents. The
documents are provided “as is” and Francisco M. Gonzalez-Longatt shall not have any responsibility or liability whatsoever for the results of use of the documents by you.

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Motivation
• Looking beyond 2050, the challenges for electricity networks
are likely to increase.
• There exists general consensus that the challenges of climate
change, economic development and system security.
• The ability to accommodate significant volumes of decentralised
and renewable generation, require that the network
infrastructure must be upgraded to enable smart operation.
The other half of the challenge lies
in building the transport and
distribution networks
As the low-emission economy
evolves, building new generation
technologies is just half the
challenge

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Motivation: Drivers
EV
IM
Storage
PV
MTDC
AC
System
Wind Farm

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Motivation
• The future electricity networks and its potential issues
require looking beyond the existing research frontiers
irrespective of the disciplinary boundaries.
• For this reason, the discussion of the future development
on sophisticated/intelligent applications/solutions is the
key research point to provide the critical importance to
economic and social welfare into future smarter
networks.

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@fglongattBasic considerations of Future Energy Systems

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Power Network (present)  Energy Systems Future
Proliferation of
nonconventional
renewable
generation – largely
stochastic and
intermittent
(wind, PV, marine) at
all
levels and of various
sizes
• Large on-shore and offshore
wind farms
Wind Farm
Offshore wind power
Storage
Electric-vehicles
Renewable Energy Resources

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MTDC
Multi-terminal HVDC
Increased use of
HVDC lines of both,
LCC and
predominantly VSC
technology (in meshed
networks and as a
super grid)
• Liberalised market
• Increased cross-boarder bulk
power transfers to facilitate
effectiveness of market
mechanisms

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• Integrated “intelligent”
Power Electronic
devices
• Integrated ICT &
storage
• Small scale (widely
• dispersed) technologies in
Distribution networks
• Active distribution networks
• New types of loads within
• customer premises
Bi-directional energy flow
Different energy carriers
Multi-directional info flow

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What is Big Data?
• “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
governments and more .”
[1] Big Data – A Revolution That Will Transform How We Live, Work and Think. Viktor Mayer-Schonberger and Kenneth Cukier. 2013

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What is BIG DATA
• Walmart handles more than 1 million customer
transactions every hour.
• Facebook handles 40 billion photos from its user base.
• Decoding the human genome originally took 10 years to
process; now it can be achieved in one week.

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How Is Big Data Different?
• Automatically generated by a machine
(e.g. Sensor embedded in an engine)
• Typically an entirely new source of data
(e.g. Use of the internet)
• Not designed to be friendly
(e.g. Text streams)
• May not have much values
Need to focus on the important part

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Internet of Things - Capacity
• Devices connected to the Web
1970 = 13
1980 = 188
1990 = 313,000
2000 = 93,000,000
2010 = 5,000,000,000
2020 = 31,000,000,000
2050 = ???

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1 Petabyte = 1024 Terabytes

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Google = 20 Petabytes
• Google receives over 4 million search queries per
minute from the 2.4 billion strong global internet
population.
• Google processes 20 petabytes of information per
day (July 2014)

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How much data?
• Wayback Machine has 23 PB + 50-60 TB/week (2014)
• Facebook has 300 PB of user data + 600 TB/day (2014)
• eBay has 18 PB of user data + 90 TB/day (2014)
• CERN’s Large Hydron Collider (LHC) generates 30 PB a
year 640K enought to be
enough for anybody.
CERN’s Large Hydron Collider

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Big Data: This is Just the Beginning
• 2.5 quintillion bytes of data are generated every day!
• A quintillion is 1018
2010
VolumeinExabytes
9000
8000
7000
6000
5000
4000
3000
2015
Percentage of
uncertain data
Percentofuncertaindata
100
80
60
40
20
0
We are here
Sensors
& Devices
VoIP
Enterprise
Data
Social
Media
Source: IBM Global Technology Outlook - 2012

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Big Data: More Than Just Volume
• Three Characteristics of Big Data V4s
Volume
Terabytes to
exabytes of
existing data
to process
Velocity
Streaming data,
milliseconds to
seconds to respond
Variety
Structured,
unstructured,
text and multimedia
Veracity
Uncertainty from
inconsistency,
ambiguities, etc.
Volume: Large volumes of data
Velocity: Quickly moving data
Variety: structured, unstructured, images, etc.
Veracity: Trust and integrity is a challenge and a must and is important for big data just as
for traditional relational DBs

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The four dimensions of use
• Aspects of the way in which users want to interact with
their data…
– Totality: Users have an increased desire to process and analyze
all available data
– Exploration: Users apply analytic approaches where the
schema is defined in response to the nature of the query
– Frequency: Users have a desire to increase the rate of analysis
in order to generate more accurate and timely business
intelligence
– Dependency: Users’ need to balance investment in existing
technologies and skills with the adoption of new techniques
Source: IBM http://www-01.ibm.com/software/data/bigdata/

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Smart-er Grids:
Smart-er Grids: when energy meets information…
Our New Hybrid Reality
• “A permanently evolving electrical network, with a real-time, two-way flow of
energy and information, between power generation, grid operator, and end users.
It is capable of integrating all traditional and new players: renewable generation
units (wind, solar, etc.), electrical vehicles, electrical storage, or even entire
smart cities”.Past Present Future
Smarter electricity systems (Source: IEA Smart Grid roadmap 2010)

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Sources of Big Daya
• Observational datasets
• Meteorological
– Wind, rain, clouding, temperature, etc.
– Measurable at any place and at any time
– Influences demands, offers, hazards, equipment ageing
• Economics
– Prices, bids, costs of consumers and producers
– Measurable for any actor and at any time
– Influence system technical and economic performance
• Technical performance
– Failures, flows, service disruptions, quality of electricity
– Measurable component-wise and system-wise over time
Simulated datasets
– Lots of them are generated and used to replace or forecast unavailable observational quantities
• SCADA (Supervisory Control
And Data Acquisition) systems
• WAMS (Wide Area Monitoring
Systems)
• Advanced metering devices
(“Intelligent”/“Smart” meters)
• New data sources: no knowledge / expertise
• Data mining and online analytics for
interpretation

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Sources of Big-Data
WAN
People
Smart
Meters
Smart
Appliances
Data
concentrator
Applications
server
PMU PMU
• SCADA (Supervisory Control And Data Acquisition) systems
• WAMS (Wide Area Monitoring Systems)
• Advanced metering devices (“Intelligent”/“Smart” meters)
Many measurements
not just standard
Condition parameters
• New data sources: no knowledge / expertise
• Data mining and online analytics for interpretation
PQ monitoring
Customer surveys
Dynamic Thermal Rate
Environment

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Smart-er Grid: Data-Information
• Data determine the information which drives transactions
1min -15min
Grid
Operations
Business
operation
Customer
Engagement
Signal Processing and Local Automation
videos
Type V I Ph
Hz
V I Hz 10k Sw MW|MVA T, Qual V I Ph
Hz
History
100M 100k Lab
Analysis
Scada
PDM
MDN
Comm
(AMI, Tcom)

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Amounts of Big Data
Disclaimer: rough estimates, limited to transmission and distribution subsystems
• A typical EES system (of an average European country):
– AT EHV level: 103 – 104 ‘locations (nodes + lines)’
– AT MV-LV level: 106-107 ‘locations (nodes + lines)’
– Rate of individual measurements: from 107 – 109 values per
year
– i.e. up to 1016 numbers per year (up to 10 PB/year)
– Need to keep data over long periods (10-50 years)
• • European dimension
– More than 20 countries share a common interconnection
– Many physical and economic interactions among them
– Many opportunities and needs to share data and knowledge
– Need to conserve, exploit, share, ExaBytes of private data

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Data to Information to @ to Wisdom
• Data to Information to … to Wisdom
"From Data to Wisdom", Journal of Applied Systems Analysis, Volume 16, 1989 p 3-9
Graphic Original Illustration: Courtesy of Dr. Richard Candy, Eskom South Africa
Russell L. Ackoff
(February 1919 – 29 October 2009)
Identifying relationships
between aspects of each
element
Understanding the
patterns between all
relationships and
occurrences
Relating pattern to
fundamental principles
Understanding
Context
An item out of context with no relation to
other things

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Value Creation and Delivery
• Value of the grid services to end-users
• Value of the grid services to assets (e.g. DER, FACTS,
HVDC, microgrids)
• Value of assets to the grid
• Value of the grid to utilities
• Value of the grid to investors
• Value of the grid to society
Grid
Utilities
Investors
Society
Assets

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The Metrics & Value
• Performance Metrics and Maximize Value across the
Grid:
– Efficiency
– Reliability
– Sustainability
– Flexibility
– Resilience
– Security
– Safety
Controls - Signals and Actions – to Enhance…
How much?
Value
PerformanceMetrix

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Valuation of Services
Valuation of Responsive Distributed Energy Resource and
Control Actions

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Control Design and Optimization
• Determinants of Control Design and Optimization
Physic Dimension Operation Dimension
Economic and
Markets
Information

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Transactive Control
Transactive Control - Every Action Counts
• “The ability to interact with every device that connects to
the grid using price signals as a basis for monetizing
responses.” – NIST Smart Grid Advisory Committee
Report
Upstream
(toward generation, transmission,
distribution)
Downstream
(toward demand)
Source: Adapted from presentation by Dr. Ronald Melton, Pacific Northwest National Laboratory
Feedback signal
Modified Incentive
signal
Incentive signal
Modified feedback
signal
Transactive System Markets (5min-day)Control (msec-min)

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Transactive Arquitecture
Two-Way, Hierarchical, Transactive Architecture
Localizes and Balances Values & Prices

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Transactive Controls & Flexibility
Flexibility of operation—the ability of a power system to respond to change in
demand and supply—is a characteristic of all power systems
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.
Transactive Controls

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@fglongatt• Uncertainties
• Noise
• Redundancy
• Lack of data

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Big Data Conundrum
• Problems
– Although there is a massive spike available data, the
percentage of the data that an enterprise can understand is on
the decline
– The data that the enterprise is trying to understand is saturated
with both useful signals and lots of noise
Source: IBM http://www-01.ibm.com/software/data/bigdata/

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@fglongattUncertainties in Power Systems
Randomnes Incompletness
Statistical Cognitive
Stochastic FuzzyModelling
Analysis

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Sources of Uncertainties
IM
MTDC
AC
System
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• Topology, parameters & settings (e.g.,
tap settings, temperature dependent line
ratings)
• Observability & controllability • Pattern (size, output of
generators, types and
location of generators,
i.e., conventional,
renewable, storage)
• Parameters
(conventional and
renewable generation
and storage)
• Parameters of generator controllers (AVRs, Governors, PSSs, PE interface),
network controllers (secondary voltage controller), FACTS devices and HVDC line
controllers
• Contractual power flow (consequence of different market mechanisms and price)
• Faults (type, location, duration, frequency, distribution, impedance)
• Communications (noise, time delays and loss of signals)
• Time and spatial variation in load, load
composition, models and parameters

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Managing Uncertainty in Hybrid Grids
• Increasing Variability
• Data Uncertainty
• Comprehension Uncertainty
• Projection Uncertainty
• Decision Uncertainty
Coping with
Uncertainty

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The future…

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Predictive Operations
• Predictive Operations Capability in Control Rooms
Reduces the impact of variability and uncertainty on real-time
decision making in the control room
Create new value grid services, e.g. improve asset Utilization

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Transition
Analytics is the discovery and communication of meaningful patterns in data.
Especially valuable in areas rich with recorded information, analytics relies on the
simultaneous application of statistics, computer programming and operations
research to quantify performance. Analytics often favors data visualization to
communicate insight
Analysis is the process of breaking a complex topic or substance into smaller
parts in order to gain a better understanding of it.

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Advanced Analytics for Big Information
Transition from Deterministic to Probabilistic Paradigms
Source: “Incorporating Forecast Uncertainty in Utility Control Center” by Y. Markarov et..al. In Renewable Energy Integration ed. Lawrence Jones, Elsevier 2014.

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Power system states and actions

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Power System Security
• Security: the degree of risk in the ability to survive
imminent disturbances (contingencies) without
interruption of customer service
– depends on the operating condition and the contingent
probability of a disturbance
Time scales in emergency control actions

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Three Massive Challenges
Need for data-driven Operation and control
Risk = Probability  Consequence
Affecting (?)
Affecting (?)
1 2
Data mining and online analytics for interpretation
3
Analytics is the discovery and communication of meaningful patterns in data

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Three Massive Challenges
1
Analytics is the discovery and communication of meaningful patterns in data
• Volume of data: too much for engineers to handle
• Velocity of data: changing too rapidly for human effort
• Variety of data: multiple sources, on-line/off-line tests
Power System
Data Infrastructure
Field Measurements
Weather Data
Market data
GIS Data
Real Time Analysis
Stream Computing Platform
Retrospective Analysis
Data
Integration
Knowledge
Extraction
Operation-Control-Decision in the Loop AutonomousNo-supervised
Big-Data
Uncertainties
• Randomness
• Incompleteness
2
• Statistical
• Cognitive
Cloud
3
Operation
Protection
Control
Assets Management

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Thinking
about
potential
solutions

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Summary
1 2
3

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Copyright Notice
The documents are created by Francisco M. Gonzalez-Longatt and contain copyrighted material, trademarks, and other proprietary information. All rights reserved. No part of the documents may be reproduced or copied in any form or
by any means - such as graphic, electronic, or mechanical, including photocopying, taping, or information storage and retrieval systems without the prior written permission of Francisco M. Gonzalez-Longatt . The use of these
documents by you, or anyone else authorized by you, is prohibited unless specifically permitted by Francisco M. Gonzalez-Longatt. You may not alter or remove any trademark, copyright or other notice from the documents. The
documents are provided “as is” and Francisco M. Gonzalez-Longatt shall not have any responsibility or liability whatsoever for the results of use of the documents by you.

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