CIEN Centre for Intelligent Networks Smart Grid & Future ... Grid & Future Grid – Challenges and...

Smart Grid & Future Grid –

Challenges and Opportunities

South Africa

07 June 2013

Prof ZY Dong

[email protected]

Ausgrid Chair, Director Centre for Intelligent Electricity Networks, NIER

The University of Newcastle, Australia

CIEN Centre for Intelligent Electricity Networks


Joint Research Excellence

in Intelligent Electricity Networks

• Centre for Intelligent Electricity Networks

CIEN is actively working with Ausgrid under NIER on a number of new initiatives


National Wealth Building

• Joint Research Centre/research partners Centre for Intelligent Electricity Networks

TNSPs, DNSPs, research institutions & universities, smart grid technology vendors, CISCO, Ericsson, …

• Delivering to National Infrastructure Priorities Building on data from Smart Grid Smart City projects at Nelson Bay

R&D work on new products for the National Broadband Network by 2015-2016

Customer Applications: Smart Meters, Home Area Network, Electric Vehicles

Grid Applications: Active Volt VAR Control, Fault Detection Isolation and Restoration, Substation Feeder Monitoring, Wide Area Measurements

Energy Resource Management: Storage Batteries, Solar, BlueGen, Distributed Generation, Micro Grid Control

• Funding / clients ARC, CSIRO, EPRI (USA), Defence Dept, Dept of Broadband and Digital Economy, US

Defence, …

Ausgrid, AEMO, AGL, Aurecon, Transgrid, Powerlink, Western Power Corp, Clean Energy Council, TRUenergy, …


Smart Grid

Distributed/grid computing facility Control Centre

Network node

Network node

Firewall

Operator

Next gen. communication

network

(Internet, wireless comm’s

network)

GenTrans & Dist Networks

Network node

Network sensors &

control

SCADA/EMS

Intelligent

building

Intelligent

appliances

E.V.

users

D.G.

Measurement and

control for DG

Infrastructure

Applications

Standards and Security

Communications and Networks

Sensors, Monitoring and Control

Dynamic Rating

Self Healing

Cost Reflective Pricing

Electric Vehicles

Distributed Renewables

Demand Response

Distributed Generation

Islanding & MicroGrids

Communications and Networks

Source: Energeia

Source: Ausgrid / DRET

CIEN Centre for Intelligent Electricity Networks Source: Ausgrid

Technical Solutions

CIEN Research

methodologies & tools

Power System / market modeling,

operations and planning

Optimization

• Optimal Power Flow

• System planning & operations

• Economic Dispatch / Unit C’t

• ATC Calculation

• Wind farm optimisation

Monitoring

• Load modeling and

parameter estimation

• Contingences

Power system

stability / vulnerability

• Transient, Voltage

Small-signal/osc. stab.

• RE / DG/EV impact

Control Design

• Power System Stabilizer

• FACTS Device

• Wind power/PV control

Soft Computing & Data

Mining

• Computational intelligence

• Learning

• Multi-agents

• Data mining

Computing Platform

• HPC Server / Cluster

• Parallel Computing

• Grid Computing

• Cloud computing

Software Tools PSS/E,DSA,DigSILENT

, PSCAD /EMTDC,

GridLAB-D, ASPEN,

SINCAL, OpenDSS,

Prophet, Plexos,

OPNET, NS-2/3

Smart Grid • Micro-grid

• Grid Apps, Energy

storage, Peakdemand,

DGs, EV, security

Complex Systems

• Vulnerability, security,

topological analysis

• Power network,

comm’s & internet, …

Smart Grid

Cyber System

• ICTnetwork

• Network modelling,

security / vulnerability

Market simulation, risk, forecasting


Layered Smart Grid Roadmap – NIST 4-layer SG Architecture Model

Conceptual Model

System Architecture

Generic domains: Power System, Telecommunications & IT architectures

Specific: AMI, EV, WAMS, AVVC, AFR, (Ausgrid: Grid Apps, ERM, FDIR)

CIEN Smart Grid Research

Power systems

Telecommunications

IT/computing

Generation, conventional and

renewable, centralized and distributed

Transmission and subtransmission

networks, interconnections and

system security

Distribution, supply reliability and

operations

Wireless solutions WAN, LAN, 3G, LTE

Broadband

Enterprise services bus

CIM – common information model

Power model

Data cleansing

Historian, and Data mining,

Visualisation

DMS – distribution management

system (GE staff)

OMS - Outage management system

SCADA/EMS


CIEN & partners’ smart grid

research projects – A Data-Driven Intelligent Distribution System for Smart Grid – Dynamic line rating in a smart grid – Distributed forecasting. – Knowledge discovery from system measurement data. – Distribution Network Reconfiguration. – Volt VAR Planning and Real-Time Control – Real Time Measurement Data Based Dynamic Line Rating – Modelling, Analysis and Networked Control of Smart

Distribution Grids – Wide area composite coordination of frequency regulation

strategy in Smart Grids – Autonomous Decentralized Systems – Grid Computing Platform for Power System Stability

Assessment. – Wind power prediction, planning and wind farm impact on

system stability. – Smart grid (cyber) security & vulnerability modelling tool

and assessment algorithms – Future grid – Load modelling

Power system control &

reliability and telco

Peak demand &

reliability

Market / financial /

investment efficiency

enviro

nm

ent

Source: Ausgrid


Some research/industry projects 2012-13 Ausgrid project: STATCOM Study 2012-13 Ausgrid project: ERM Battery Trial Management and Study 2012-13 Aurecon projects: Yallourn Excitor modeling; Eraring Gas Turbine Connection

Study; Yallourn Generator Modelling, … 2012-2015 CSIRO Future Grid Flagship Grant 2012 - 2013 Generator Grid Connection Compliance Studies 2012-2013 EPRI - Power system reactive power prediction and control 2012-2015 Security Assessment for Smart Grids Involving Cyber-Physical Interdependency 2011- 2014 NETWORK VULNERABILITY ASSESSMENT AND RISK MANAGEMENT STRATEGY FOR

A SMART GRID 2012-2014 Increased power transfer capacity through SVC control 2013-2015 Robust electricity networks accommodating high levels of renewables 2011-2014 An investigation of the impacts of increased power supply to the national grid by

wind generators on the Australian electricity industry 2011-2016 Power system data analysis and self-healing 2011-2015 Cyber Physical System and Networked Control based Electric Vehicle Optimal

Dispatch and Control 2010-2011 Power system reactive power prediction and sensitivity analysis (PI) 2010-2011 Dynamic power system load modelling studies 2010-2011 Analysis on CEM MV Distribution Loss


System security and vulnerability

assessment • System security assessment to avoid cascading failure

requires high computational efficiency – Deterministic vs probabilistic stability assessment

– Grid computing

– Load modelling & its impact

• In addition to conventional time domain methods and energy based methods, new methods have been proposed – Data mining & intelligent system & WAMS based approach

– Complex system based approach

– Sensitivity based approach EPRI funds, IEEE Taskforce member on cascading failure, State Grid funds, PM&C funds


Intelligent system based security

assessment

Knowledge Base

Generation

Input and Output

variables

Significant-

Feature selection

Intelligent

Algorithm training

Results utilization

Robustness

Reliability

Accuracy

.

.

.

.

.

.

Development Implementation

Intelligent Stability

Assessment

System

Knowledge Base

Off-Line Simulation

Historical archives

Significant-Feature

Selection

Off Line

Real-time System

Measurements

Classifier/Predictor

Stability Assessment

Results

Real Time

Decision Making

Learning

...……

input output

Other Useful Information

Updating

ZY Dong, P Zhang, Y Xu and KP Wong, “intelligent system for power system security assessment”, invited paper IEEE intelligent

systems journal (2010)


13

Complex Systems

approach Vertex (Bus)

undirected weighted graph

Vehicles（vertex）

Road (Edge)

IEEE 14 bus system and the corresponding topological model

Edge (Branch)

The information exchange efficiency measures the

network security

2-VulnerabilitySlidesExtracted_cien.ppt


Load Modelling (load model parameter

identification & generalisation)

• Research Problems: – Different values of parameters describe different dynamic

properties of load model. – using different dynamic response data in the task of parameter

identification will obtain different parameter values. – how the real dynamic properties of load model can be reflected by

the appropriate selection of load model parameters. – Specific measurement based load modelling, PSS_E, DigSILENT

• Support: EPRI, ARC, HKPU, SG/EPRI, Western Power Corp, AEMO • CIGRE C4.605: Modelling and Aggregation of Loads in Flexible

Power Networks


On-line automatic var predictive

control (EPRI)

• Operational data (CIM format)

• Predict system Var & recommend reactive power switching to maintain overall grid voltage stability

15

Learning Model

Data Base Data cleansingFeature

Selectiontraining

Learning/Mining

Algorithm

Reactive power

forecasting module

Q-V quantitative

analysis module

Correlation

Analysis

Human

Expert input

0 20 40 60 80 100 1200.9

0.95

1

1.05

1.1

0 20 40 60 80 100 1200.9

0.95

1

1.05

1.1

0 20 40 60 80 100 1200.9

0.95

1

1.05

1.1

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0.95

1

1.05

1.1

0 20 40 60 80 100 1200.9

0.95

1

1.05

1.1

Bus No.

V (

p.u.

)

Initial Voltage profile

Resulting Voltage profile


16

Wind Resource Analysis and

Prediction Package –

OptiWIND© • Short term wind forecast (min, hours, days)

– For operations

• Long term wind resource analysis (n x years)

– For wind farm planning, and wind Atlas

• Stand alone at client PC

• Server based

• Interactive graphic user interface

• Most commonly used wind turbines (Nordex, Vestas, SWT, GE)

Identified offshore suitable regions for wind energy in HK

Map of the cumulative tracks of all tropical cyclones during 1985–2005

A wind turbine destroyed by typhoon in Taiwan

http://en.wikipedia.org/wiki/File:Global_tropical_cyclone_tracks-edit2.jpg


17

Wind turbine selection

(HK) Wind Speed Forecast

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

x 104

0

5

10

15

20

25

30

35

40

Wind

Speed

Time (Minutes)

- Predict - Actual

Severe Tropical Storm Kammuri (Julian)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

x 104

0

2

4

6

8

10

12

14

16

460 480 500 520 540 560 580 600

500

1000

1500

2000

2500

3000

Time (5 mins)

Win

d P

ow

er

(MW

)

95 % PI of Linear Regression

Observed Wind Power

Lower Bound of PI

Upper Bound of PI

Short-term wind power interval

forecasting (TAS wind)

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

5

10

15

20

25

30

35

40

45

Vestas V80 (2000 KW) Mean Power Production = 0.2029 (MW)

Capacity (MW)

Fre

quency (

%)

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.60

5

10

15

20

25

30

35

40

45

SWT-3.6-107 (3.6 MW) Mean Power Production = 0.3653 (MW)

Capacity (MW)

Fre

quency (

%)

Wind Power Output:

Vestas V80 vs Siemens SWT-3.6


-1000 -800 -600 -400 -200 0 200 400 600 800 1000-1000

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-600

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Wind Farm Layout

Optimisation

Mean Power Output = 8.1751 MW

Mean Power Losses = 0.4628 MW











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11-Aug-2012 12-Aug-2012 13-Aug-2012 14-Aug-2012 15-Aug-2012 16-Aug-2012 17-Aug-2012 18-Aug-2012 19-Aug-20120

20

40

60

80

100

120

140

160

180

200kWh per 30 minute interval on 11/08/2012 to 18/08/2012

Date

kW

h

PI

PV

Solar PV

• PV panel coating technology:

– Improve efficiency upto 20%

– Absorbs light from all angles

– Self-cleaning, scratch resistant

– Low maintenance costs

• PV impact studies (Ausgrid)

– Peak demand

– Protection

– Energy storage

Microgrid

• How to schedule a microgrid to maximise the utilisation of RE while maintaining reliability

• point of common coupling (PCC) power, electric heater and boiler to accommodate the fluctuation of wind power

(in collaboration with Denmark)


0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-80

-60

-40

-20

0

20

40

60

80

Time (s)

Roto

r angle

(degre

e)

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6-200

-100

0

100

200

300

400

500

Time (s)

Roto

r angle

(degre

e)

Future smart grid:

a social-cyber-physical system

Smart grid (cyber-

physical system)

simulation and

security assessment

tools

Physical (power system) Cyber (ICT) and physical system social –cyber-physical system


Smart Grid Disaster Management

Disaster

System Failure

Market Failure


4800 5000 5200 5400 5600 5800

50

100

150

200

250

Time of day (5 mins)

RR

P (

$ | M

Wh)

RRP

forecast RRP

3000 4000 5000 6000 7000 8000

10

20

30

40

50

60

70

Time unit (5 mins)

Reg

iona

l Ref

eren

ce P

rice

($ |

MW

h) Real RRP

Forecasted RRP

Market Modelling

2000 2500 3000 3500 4000

10

20

30

40

50

60

70

80

Time unit (5 mins)

Reg

iona

l Ref

eren

ce P

rice

($ |

MW

h) Real RRP

Forecasted RRP

% Time

Pri

ce

($

)

SRMC

Price Duration Curve Displaying

Premium Calculation

2vP

PriceDemand

Supply

Confidence

Trading

Strategy

Production

Costs

Weather

Forecast

Accuracy

Demand

Forecast

Accuracy

Temprature

Wind and

Rainfall

Plant

ClosureNew

Capacity

Installed

Plant

Reliability

Emission

Constraints

Fuel Prices

Political &

Economic

FactorsGovernment

Policy

New

Technology

Modeling

Capacity

Taxes

Feature selection

-200.00

0.00

200.00

400.00

600.00

800.00

1,000.00

1,200.00

Sta

nw

ell

CS

QP

TC

Taro

ng

Macquarie

Earing

Delta

Redbank

Snow

y

SE

CV

AG

L

Energ

y

Loy Y

ang

Org

in

NR

G

TR

U

Hydro

QLD NSW SNOWY VIC SA TAS

$M

illi

on

Free permit ETS purchase Non-ETS

0 5 10 15 20 25 30 35 40 45 500

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NTL


Future grid: National vs international? Transmission

vs distribution? Energy, water, gas, telco, traffic,

service, social, cash flow?

Thank you

[email protected]

CIEN Centre for Intelligent Networks Smart Grid & Future ... Grid & Future Grid – Challenges and...

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Transcript of CIEN Centre for Intelligent Networks Smart Grid & Future ... Grid & Future Grid – Challenges and...