Future Power Grids - DIgSILENT Americas · Future Power Grids 1 ... DGs, EV, security Complex Systems ... PSS_E, DigSILENT › Support: EPRI, ARC, HKPU, SG/EPRI, Western Power Corp,

Future Networks Research at the University of Sydney6 Sep 2013

Future Power Grids 1

David Hill* and ZY Dong**

* Director of the Centre for Future Energy NetworksARC Professorial Fellow

**Head of School of Electrical & Information EngineeringThe University of Sydney

School of Electrical and Information

Engineering

Team: Profs Z-Y. Dong, Tony Vassallo, Jin Ma; Drs Gregor Verbic, Guo Chen; PhD students David Conroy, Hesam Marzooghi, Mehdi Garmroodi

School of EIE Industry Linkage

› EIE has been providing research and educational services in the following areas

› Advanced sensor technology

› Big data and computer networks

› Defence technology

› Fibre networks

› Next generation telco technologies

› Smart/future grid technologies

› Renewable energy

Current Research & Educational Capacity

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EIE Research Matching of the IEEE / NIST SG Layered Architecture

NIST Conceptual Model

NIST System Architecture

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Communications Architecture

Power System Architecture

IT/computing Architecture

Architecture Application AMI

Architecture Application PEV … Architecture

Application N

IP Smart Grid Architecture

Layer 1

Layer 2

Layer 3

Layer 4

4-layers smart grid architecture modelSource: IEEE/NIST

EIE

has

indi

vidu

al te

chno

logi

es &

R&

D c

apac

ity

Research & 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 ToolsPSS/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, …

Future Grid

Cyber System• ICTnetwork• Network modelling, security / vulnerability

Market simulation, risk, forecasting

Power Engineering Lab

› ABB power technologies equipment

› Robotic arm

› $1m digital power system simulator

› Usage:

- Professional Training

- Teaching

- Research and

- Final year thesis project

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Equipment

New Challenges Faced by Power System Planning

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� Traditional power system planning only considers three main components.

� A number of new factors have to be taken into account in power system planning, which introduces significant challenges.

Market benefit , reliability

Investment efficiency

Problems in planning

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1. The coordination of power system planning and natural gas network planning.

2. Flexible/stochastic/hybrid methods for handling the uncertainties in the planning process.

3. Analyze the impacts of increasing customer participation (e.g. demand response and PHEVs)

Major question - resilience

› HOW to shape the whole grid so that:

- It adapts to the energy mix trajectory over decades

- with minimum cost

- specified stability margins

- limited carbon-emissions

- limited reliability

› We need to allow for controls used, including DM, reliability – orresults too conservative

› Excessive reliability costs too much – use probabilistic measures

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Distributed architectures for demand response

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Ref: Calloway and Hiskens, Proc IEEE, 2011

Grid2050 Architecture (Bakken et al.)

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Bigger question - grid structure?

� There are already extreme scenarios

1. What if consumer prices go so high and local storage cost so low that most consumers can go off grid

2. Supergrids connecting continents to major sources of renewable power, e.g. Desertec

Studies of future grids have been too driven by possible ICT developments.

Economics will decide, e.g. cost of storage vs grid costs!

Australian Transmission Network

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?

Zero Carbon Australia

› Assume:

- generation is renewable (rooftop to farms)

- fixed distributed storage (batteries to pumped storage)

- mobile storage (EVs)

› Aim: Provide an effective and efficient grid

› Real example: Masdar City, Abu Dabhi

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Planning questions

› Can we continue incrementally?

› Can we keep transmission following generation, i.e. create (government controlled) ‘electricity highways’ or ‘super grids’ and hubs based on solar, wind etc

› Need expanded computer analysis to solve ‘network of network’ problems, e.g. how electricity and gas systems interact, and water, transport

› Must always model stability and capability to control

Stability challenges

› Robustness to all the uncertainty (Hiskens)

› Dependence on structure- Find the vulnerable points for collapse

- Study motifs for the new situations (project in Aus)

- Bigger questions: Backbone networks vs weakly connected clusters for diverse generation

› For theorists: How to guarantee stability from local checks - certificates (with some exchange)

- can these be granulated?

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Renewables - case conclusions

› At this point just have a lot of observations

› All stability types affected by new dynamics, but it is hard to make many general statements

› Locations and generation types important, loads as before

› Structure of network important

Need more fundamental studies that can be applied generically

- Mechanism models, cf. as for voltage stability

- Control responses

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Computational questions

› Numerous scenarios (millions)

› How to scale up solutions to such large systems and all scenarios – ideas from optimization and learning theory

› How to find the weak spots

- Transient instability

- Oscillations

- Vulnerable points for collapse

Australia’s FG programme

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Funded by CSIRO, led by The University of Sydney - also a FG Forum for utilities on a shorter time-scale

Australia in 2050 (Ref: J.Sligar)

› Population 35million

› Large nuclear, geothermal and gas units with characteristics similar to present large units

› Thousands of small renewable generators assembled (virtual generators) at 25kv to 330kv

› Our special ‘stringy’ grid will be less so, but how?

› Still ‘no infinite bus’? cf. Denmark

› Major changes to loads: Al plants gone, new desalination plants, demand management, EVs

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Planning interactions

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Overseas

› USA DOE Future Grid university cluster

- http://www.pserc.wisc.edu/research/FutureGrid.aspx

› Fraunhofer Institut

- http://www.iwes.fraunhofer.de/en.html

› UK Grand Challenges

Outline

MotivationFuture gridsStability, control, planningResearch questions

Projects• Modelling with DM• Stability with renewables• Networked RE farm control• Probabilistiic margins• Stochastic TNEP• Generic market constraints with RE• Co-optimisation across GenTrans• Network vulnerability

Conclusions

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Possible agenda for modelling

› Identify the granulated graphs

› Taxonomies of buses, lines, ‘motifs’

› Taxonomies of dynamics

› Aggregation up the levels

Lots of work here before we can begin analysis!

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Simplied 14-generator model

› Areas

1. Snowy Mountains (hydro)

2. NSW

3. Victoria

4. Queensland

5. South Australia

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Zero Carbon Australia FG

Source: Australian Sustainable Energy Zero Carbon Australia Stationary Energy Plan 2010

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› Zero Carbon Australia proposal for HV grid upgrade

Tools for scenarios

› Scanning tool – fast simulation

› Revive direct methods, but load models no longer work

› Maybe EEAC type

› Most likely new algorithmic approaches

› Find weak points

› Network science ideas on vulnerability

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Conclusions

› ‘Future grids’ a different and bigger question to ‘smart grids’ – long-term planning vs just better control

› Should we plan or grow the network incrementally – different scenarios

› Coordinated planning and control for high RE needs development

› Algorithms for planning (and control) – computational challenges

› Networks science ideas look promising BUT only after redone on useful models

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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

...

ZY Dong, P Zhang, Y Xu and KP Wong, “intelligent system for power system security assessment”, invited paper IEEE intelligentsystems journal (2010)

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Complex Systemsapproach

Vertex (Bus)

undirected weighted graph

Vehicles（（（（vertex））））Road (Edge)

IEEE 14 bus system and the corresponding topologica l model

Edge (Branch)

The information exchange efficiency measures the network security

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

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Bus No.

V (p.

u.)

Initial Voltage profile

Resulting Voltage profile

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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

40Wind turbine selection

(HK) Wind Speed Forecast

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x 104

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- Predict- Actual

Severe Tropical Storm Kammuri (Julian)

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95 % PI of Linear Regression

Observed Wind PowerLower Bound of PIUpper Bound of PI

�Short-term wind power interval forecasting (TAS wind)

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

Capacity (MW)

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%)

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

Capacity (MW)

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Wind Power Output: Vestas V80 vs Siemens SWT-3.6

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Wind Farm LayoutOptimisation

Mean Power Output = 8.1751 MWMean Power Losses = 0.4628 MW






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

Date

kWh

PIPV

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)

CIENCentre for

Intelligent

Electricity

Networks

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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

4800 5000 5200 5400 5600 5800

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Price Duration Curve Displaying Premium Calculation

δµ 2±=vP

Feature selection

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$Mill

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Free permit ETS purchase Non-ETS

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NTL

CIENCentre for

Intelligent

Electricity

Networks

Future grid: National vs international? Transmission vs distribution? Energy, water, gas, telco, traffic, service, social, cash flow?

Thank [email protected]

Documents

Future Power Grids - DIgSILENT Americas · Future Power Grids 1 ... DGs, EV, security Complex Systems ... PSS_E, DigSILENT › Support: EPRI, ARC, HKPU, SG/EPRI, Western Power Corp,