Case-study of probabilistic risk assessment on the Icelandic power system

Samuel Perkin

Overview

The EU FP7 GARPUR Project:

Generally

Accepted

Reliability

Principle with

Uncertainty modelling and through probabilistic

Risk assessment

4 years (2013 – 2017)

Landsnet‘s focus in GARPUR:

- RT/ST application

- Near real-life pilot test

NOTE: discussion points will appear hereDiscussion points will appear here...

Is the Icelandic system a useful/relevant test case to EU TSOs or vendors?

• Low inertia system

• ‚two-body‘ inertia distribution

• Bad weather

• High proportion of industrial end-users

• Hydro-dominated generation

• Barriers to new transmission infrastructure

• WAMS (34 PMUs at 21 nodes)

• Dynamic load control

The Icelandic System

Load centre

Connection to Grid

Where the N-1 fails

• Many radial regions are not N-1 secure

• New lines would fail at the same time as existing lines (large investment for no gain in reliability)

• Installed microgrid system to improve reliability

• Still not N-1 secure

• Need new ways to quantify/justify such investments

Does N-1 bias us to solving reliability issues with new infrastructure, rather than new controls?

Landsnet‘s pilot test

Live Weather Data

Live System Data

Every minute

Every 20 minutes

Reimagine EMS as a platform? APIs in future EMS software for in-house tool integration

Weather-dependent failure rates (all components)

Should weather-dependent failure rate models be built in-house by TSOs or externally?

Weather-dependent failure rates (single OHL)

Should weather-dependent failure rate models be built in-house by TSOs or externally?

Landsnet‘s pilot test

Live Weather Data

Live System Data

Every minute

Every 20 minutes

Landsnet‘s pilot test

Live Weather Data

Live System Data

Pilot Test Models

Every minute

Every 20 minutes

Models shown in Appendix of slides (also see: http://www.garpur-project.eu/deliverables)

Live Weather Data

Live System Data

Pilot Test Models

Every minute

Every 20 minutes

Live Reliability and Risk Measures

Every 1-2 mins

Landsnet‘s pilot test

100 to 5000 contingencies

Models shown in Appendix of slides (also see: http://www.garpur-project.eu/deliverables)

Presently debugging the method in the control room

Are risk measures useful to operators? Is there value in assessment without control?

Goal: Provide useful risk information to operators

Are risk measures useful to operators? Is there value in assessment without control?

Goal: Provide useful risk information to operators

Are risk measures useful to operators? Is there value in assessment without control?

Vs.

Importance of data visualisation

Is there a better way to convey small probabilities (0.01% = 14 heads in a row, 6 sixes with dice)?

Narrative outputs

Useful when the contingency list is small, but need clever UI/UX solutions for large lists (1000+)

Showing the ‚story‘ behind each contingency helps to create a sense of transparency in the models (also helps debugging process).

Less Information

Show only important and

useful information

More Information

Enough detail to understand the

system state

Vs.

How do we prevent information overload?

Information overload is already an issue, how do we manage this tension when adding new tools?

Cost

Reliability Level

Total CostsInterruption CostsOperational Costs

Base case (2013)

Revisiting the N-1 critique

Cost

Reliability Level

Total CostsInterruption CostsOperational Costs

New Transmission Line

Base case (2013)

Revisiting the N-1 critique

Cost

Reliability Level

Total CostsInterruption CostsOperational Costs

New Transmission Line

Base case (2013)

Revisiting the N-1 critique

Microgrid

Is it easier to justify new investments as N-1 neccessary or as socially beneficial?

1) Continue development: prototype -> operational tool

2) Pilot test control of the system using probabilistic risk

3) Improve models to allow for short-term risk forecasting

4) Move from interruption costs to social welfare

5) Consider more threats and vulnerabilities in failure models

Ideally, what’s next?

How do we expediate the development and testing of such tools, and maximise collaboration?

1) Balancing accuracy with computational speed

a) Capturing dynamic instabilities (iTesla?)

b) ‚smart‘ software architecture (case filtering?)

c) Base model inaccuracies/typos (not CIM)

2) Validation of economic and probabilistic models

3) Control action data and models

4) Human barriers

a) Arriving at common understanding of Risk (not gambling!)

b) The argument for implementation (see: Flow-based MC)

c) Cognitive biases when dealing with probability/risk

Some barriers

How do we expediate the development and testing of such tools, and maximise collaboration?

Thank yousamuelp@landsnet.is

The research leading to these results has received funding from the European

Union Seventh Framework Programme (FP7/2007-2013) under grant

agreement No 608540.

Is the Icelandic system a useful/relevant test case to EU TSOs or vendors?

Does N-1 bias us to solving reliability issues with new infrastructure, rather than new controls?

Reimagine EMS as a platform? APIs in future EMS software for in-house tool integration

Should weather-dependent failure rate models be built in-house by TSOs or externally?

Are risk measures useful to operators? Is there value in assessment without control?

Is there a better way to convey small probabilities (0.01% = 14 heads in a row, 6 sixes with dice)?

Information overload is already an issue, how do we manage this tension when adding new tools?

Is it easier to justify new investments as N-1 neccessary or as socially beneficial?

How do we expediate the development and testing of such tools, and maximise collaboration?

Appendix: Flow Charts of modelling approach

Main pilot test loop

Probabilistic Risk Assessment Loop

System Response loop