future energy system: big-data+uncertainties = risk, arequipa, peru 6 oct2015

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Prof Francisco M. Gonzalez-Longatt PhD | [email protected] | Copyright © 2015 1/53

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@fglongatt

6th October 2015

@fglongatt

mailto:[email protected]

http://www.fglongatt.org/


http://scholar.google.co.uk/citations?user=4dkpMnEAAAAJ&hl=en


http://orcid.org/0000-0002-7157-9844

http://orcid.org/0000-0002-7157-9844

http://www.scopus.com/authid/detail.url?authorId=24922792900


http://www.linkedin.com/pub/francisco-gonzalez-longatt/12/660/43b


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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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http://orcid.org/0000-0002-7157-9844

http://orcid.org/0000-0002-7157-9844





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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

EVIM

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






http://orcid.org/0000-0002-7157-9844

http://orcid.org/0000-0002-7157-9844





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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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@fglongatt






http://orcid.org/0000-0002-7157-9844

http://orcid.org/0000-0002-7157-9844





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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

year640K 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

Vo

lum

e in E

xa

byte

s

9000

8000

7000

6000

5000

4000

3000

2015

Percentage of

uncertain data

Pe

rce

nt o

f unce

rtain

da

ta

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/


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

operationCustomer

Engagement

Signal Processing and Local Automation

videosType V I Ph

Hz

V I Hz 10k Sw MW|MVA T, Qual V I Ph

Hz

History

100M 100kLab

Analysis

Scada

PDM

MDN

Comm

(AMI, Tcom)


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Amounts of Big DataDisclaimer: 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

Co

nte

xt

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

Perf

orm

ance M

etr

ix


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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

MarketsInformation


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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

ReportUpstream

(toward generation, transmission,

distribution)

Downstream

(toward demand)

Source: Adapted from presentation by Dr. Ronald Melton, Pacific Northwest National Laboratory

Feedback signal

Modified Incentive

signalIncentive signal

Modified feedback

signal

Transactive SystemMarkets (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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http://orcid.org/0000-0002-7157-9844





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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/


http://www-01.ibm.com/software/data/bigdata/

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@fglongattUncertainties in Power Systems

Randomnes Incompletness

Statistical Cognitive

Stochastic FuzzyModelling

Analysis






http://orcid.org/0000-0002-7157-9844

http://orcid.org/0000-0002-7157-9844





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Sources of Uncertainties

IM

MTDC

AC

System

@fglongatt

• 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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http://orcid.org/0000-0002-7157-9844





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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 RoomsReduces 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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@fglongatt






http://orcid.org/0000-0002-7157-9844

http://orcid.org/0000-0002-7157-9844





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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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@fglongatt






http://orcid.org/0000-0002-7157-9844

http://orcid.org/0000-0002-7157-9844





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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

1Analytics 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 AnalysisStream 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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@fglongatt

Thinking

about

potential

solutions






http://orcid.org/0000-0002-7157-9844

http://orcid.org/0000-0002-7157-9844





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Summary

1 2

3


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@fglongatt

@fglongatt

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.






http://orcid.org/0000-0002-7157-9844

http://orcid.org/0000-0002-7157-9844





future energy system: big-data+uncertainties = risk, arequipa, peru 6 oct2015

Engineering