reliability and risk engineering - set-nav.eu · reliability and risk engineering prof. dr....

||

RREReliability and Risk Engineering

Prof. Dr. Giovanni Sansavini

Reliability and Risk Engineering Laboratory

ETH Zurich, Switzerland

4/3/2017Prof. Dr. Giovanni Sansavini 1

Risk Modelling for Interdependent Energy Carriers

||


Wifi network public-5

Browse to enter.ethz.ch

Login SET-Nav

Password setnav


Internet Connection

||


Renewables

Increased interconnectivity

Power flows over long distances

Redundant energy pathways

New technologies: storage

Flexibility

More complex system to operate

Potential for failure propagation up to

systemic failure

3

Motivation – Energy Transitions: Opportunities and Challenges

Prof. Dr. Giovanni Sansavini 4/3/2017

||



Motivation – Interdependent Systems

Source: Report on Outages and Curtailments During the Southwest Cold Weather Event of February 1-5, 2011

Transition to RES portfolios

increases interdependencies

-> Gas as flexibility provider

GFPP balance the volatility of renewable generation in absence of storage

Unavailable generation [MWh]

UK’s electricity supply by fuel type [TWh]

Gas supply problems account for

10% of generation unavailability

in SW US

||


Risk-based Security Assessment of Power Systems

Integrated model for the risk assessment

Cascading failure analysis model

Application: Proximity to Cascading Outages

Gas network model

Application: Extreme conditions, Failure analysis, Large ramps & Correlations

Conclusions

5

Outline


||



Risk-based Security Assessment of Power Systems

Ni, McCalley, Vittal

and Tayyib, 2003

||


Electrical instability – Fast

Cascade

Weather fluctuations

Thermal contingencies –

Slow Cascade

Dynamics of the gas flow

Generation planning

Energy Markets

Infrastructure planning


Challenges – Energy Carriers (Dependencies) & Time Scales

Electric power network

Gas network

…

Coal supply chain

Oil supply chain

…

||


8

Cascading Failure Analysis Model

Trigger Event


Power flow

Identify and trip

overloaded lines4/3/2017

||


9


Island detection


Trigger Event

Power flow

Identify and trip


For each island:

Frequency stability

Under/Over-voltage

problems

||


10


Island detection


Safety Interventions

Trigger Event

Power flow

Identify and trip


Power imbalance?

Primary/Secondary frequency

control

Load shedding

For each island:

Frequency stability

Under/Over-voltage

problems

||


11


Consequences:

Load Shedding

Cascade

stops?

Yes


Trigger Event

Island detection Safety Interventions

Power flow

Identify and trip


Power imbalance?


control

Load shedding

For each island:

Frequency stability

Under/Over-voltage

problems

||


12


Trip new

overloaded

lines

No


Trigger Event

Consequences:

Load Shedding

Cascade

stops?

Yes

Island detection

Power imbalance?


control

Load shedding

Safety Interventions

Power flow

Identify and trip


For each island:

Frequency stability

Under/Over-voltage

problems

||


13


Validation (I)


System characteristics:

• 240 bus, 448 lines,179

generators

• Hourly profiles for load

demand and RES power

generation for 2004

• 17 coal plants & 4 nuclear as

base-loaded

• Capacity and cost information

for 50 aggregated

dispatchable gas-fired

generators

Test System: reduced WECC network

||


14


Validation (II)


Historical data for outages:

Observed blackout size in MW

(1984 – 2006)

Line outage (1999 – 2008)

Test System:

reduced WECC network

4/3/2017

||



Testing Proximity to Cascading Outages

Idea:

Triggering cascading outages for different operating conditions, designs,…

Identify which conditions are more prone to cascading outage propagation

Switzerland and neighboring

Countries

||



Testing Proximity to Cascading Outages – Load Factors

Demand not served [% total]

CC

DF

CCDF(Sev(Ei)| Ei, Xt,j)

Ei : independent line failures, p

Xt,j: hourly conditions for one

year

500’000 MC trials

Change in load factor

influences large outages

||



Testing Proximity to Cascading Outages – Impact by Country


CC

DF



Xt,j: hourly conditions for one year

500’000 MC trials

Influenced by:

Ration Demand over Generation

capacity

Transmission capacity

||



Testing Proximity to Cascading Outages – Mitigation Strategies


CC

DF



Xt,j: hourly conditions for one

year

500’000 MC trials

Strategy 1: new transmission

Strategy 2: new generation

Strategy 3: storage

||



Regional Impact and Line Criticality

DNS at each canton

Bing, Barker & Sansavini, ESREL, 2015

||


Pipelines Transient one-dimensional flow

Non-pipeline elements

Offtakes: non-electric demand

Gas-fired power plants: electric demand

Storage

Pressure governors

Compressors


Gas Network Model - I

||


Safety interventions - mitigations:

Curtailment is progressively performed at

GFPP close to minimum pressure violation

locations

Gas storage in proximity of pressure violations

is activated to restore pressure levels


Gas Network Model - IIConstraint Value Effect/Correction

Maximum pressure 85 bar Storage withdrawal

Minimum pressure 38 bar Gas curtailment

Storage injection

Compressor

envelope

3000 ÷9500 rpm Shutdown/ Outlet

pressure reduction

Compressor

envelope

0.2÷1.4 m3/s Shutdown

Power required by

compressors

Depending on

working set point

Shutdown

Ramp rate of PP 0.001÷10 p.u./min Ramp up/down

limits

Storage Operations Different for each

storage

Injection/extraction

limitations

Pressure regulators

mass flow

Depending on

pressure regulator

Outlet pressure

reduction

||



Gas Network Model - Validation

||



The Interdependent Model

Gas NetworkElectrical

Network

GFPPs

Compressors

𝑃𝐺𝐹𝑃𝑃 = 𝑀𝑔𝑎𝑠 ∗ 𝐻𝐻𝑉 ∗ 𝜂

𝑃𝑐𝑜𝑚𝑝 =𝑝𝑖𝑛𝑙𝑒𝑡 ∗ 𝑄

η ∗ 𝑚∗ β𝑚 − 1

Gas flow required for GFPP

Electric power required for

compressor operations

||



Case Study

Great Britain’s power and gas systems

1. Extreme working conditions analysis

2. Failure analysis of single components

3. Investigation of system robustness

against large wind fluctuations

Assessments:

High pressure gas network (red), high voltage electrical network

(green), GFPP (purple) and compressors (blue)

Gas Network

• 89 pipes

• 9 pressure regulators

• 9 storage facilities

• 21 compressor stations (5

electrically driven)

Electric Network

• 98 lines, 29 nodes

• 57 power plants (23 gas fired

PP)

• Generation capacity 80 MW

• Peak demand 52.7 MW

||



System Reaction to Minimum Pressure Violations

Condition of depleted line

pack

Line pack management

essential for economic

operations

Compressor flexibility

reduces gas curtailments

||


No effect before

30% increase

Past 30% only

power redispatch

but no cascading

failure


Extreme Working Conditions (I)

δ: non-electric gas demand (δ=1 current peak)

||



Extreme Working Conditions (II)

Location of pressure violation and

compressor issues

Curtailment to GFPP

||



Failure Analysis of Single Components – Removal of PP 17

||



Failure Analysis of Single Components (I)

Complete single component failure analysis

Pow

er R

e-D

ispat

ch [

GW

e]

||



Failure Analysis of Single Components (II)

Redispatch Analysis

||


Ramp down of 14 GW / h

High-gas utilization

No violation before 9 due

to line pack build up

during night


Robustness Against Large Wind Fluctuations

||



Correlations in Wind Power Output

D-vine Copula truncated at level 72

PD

F

||



Correlations in Wind Power Output

||


34

Conclusions – What we did not see… (I)


Energy Systems Resilience: minimize deviations and quickly recover service

||


35

Conclusions – What we did not see… (II)


Energy Systems Resilience: predicting negative conditions and adapt/prevent

||


36

Conclusions – What we did not see… (III)


Cyber-Physical Interdependencies

||


37

Conclusions – What we did not see… (IV)


Water-Energy Nexus – When water becomes a constraint

TPP

Hea

t ex

chan

ge w

ith

en

viro

nem

ent

Pri

mar

y st

ream

Secondary stream

R

R – Reservoir N – Natural inflow G – Generator(s) Pc – Condensate pump Pci – Circulation pump Pmu – Makeup water pump C – TPP condenser In – River water inlet Out – River water outlet

GG

In

OutMea

sure

d

Tem

per

atu

re

Known temperatures

Mixing of flows

N

TPP

Pc

PciPmu

C

C

Alternative

Pc

Once-trough cooling

Wet tower cooling

Mai

n s

trea

m

1.5

1.85

2.2

2.55

2.9

10000

12000

14000

16000

18000

20000

22000

24000

21.5

21.6

5

21.8

21.9

5

22.1

22.2

5

22.4

22.5

5

22.7

22.8

5

23

(⁰C)

TPP

en

ergy

(MW

h)

(⁰C)

1.5

1.85

2.2

2.55

2.9

12000

14000

16000

18000

20000

22000

24000

21.5

21.6

5

21.8

21.9

5

22.1

22.2

5

22.4

22.5

5

22.7

22.8

5

23

(⁰C)

TPP

en

ergy

(MW

h)

(⁰C)

||


10.05.2016Giovanni Sansavini 38

Thanks!

reliability and risk engineering - set-nav.eu · reliability and risk engineering prof. dr....

Documents