2011 RESIN Workshop
Making the Concepts of Robustness,
Resilience and Sustainability Useful Tools for
Power System Planning, Operation and
C t l
Control
L i Mili
Lamine
Bradley Department of Electrical and Computer Engineering
Virginia Tech
Tech, Northern Virginia Center
Falls Church, VA 22043, USA
NSF EFRI Grant 0835879
A. Urken
Social Science
1 GRA
S. Shukla
Communications
1 GRA
L. Mili (PI)
L
Power Systems
2 GRAs
B. Hobbs
Economics
2 GRAs, 1 SRP
M. von Spakovsky
Sustainability
2 GRAs
R. Pires
S Brazilian
S.
B ili utilities
tiliti
2 GRA, 2 SRP
2
Definition of Robustness and Resilience
•
Robustness to a class of perturbations is defined as
the ability of a system to maintain its function (normal
state)) when it is subject
j
to p
perturbations of this class.
•
Resilience to a class of unanticipated failures is
defined as the ability of a system to gracefully degrade
and to quickly self-recover to a normal state.
Normal
Robustness
Secure
Insecure
Self-reconfigure
Restorative
Alert
Resilience
In Extremis
Resilience
Emergency
3
Achieving Resilience via Modularity
•
Resilience is achieved
• via a segmentation of the system into weakly
coupled subsystems to prevent the propagation
of local failures to large areas via cascading
events;
• and via distributed and coordinated control
actions
•
A trade-off between robustness and resilience can
be formulated as an optimization problem subject
j
to
a bound on the cost.
•
This optimization will indicate where to segment the
t
transmission
i i system
t
via
i HVDC li
links
k and
d will
ill give
i
us the desired level of penetration of microgrids.
4
Power System Segmentation (EPRI)
A metric of system flexibility is being developed using
the potential energy function.
HVDC
Subsystem 1
Subsystem 3
HVDC
Subsystem 2
HVDC
Microgrid
Microgrid
Microgrid
Microgrid
5
Enhancement of the Stability Margin
Bus_8
A
V
#2
100.0 [MVA]
13.8 [kV] / 230[kV]
#2
PI
A
V
COUPLE
ED
bus_gen3
COUPLED
PI
SECTION
Load C
A
A
V
Tim ed
Breaker
Logic
Clos ed@t0
bus_6
Gen3
V
A
Tim ed
Breaker
Logic
Open@t0
#2
BRK1
30 [MVAR]
#1
50.0 [MVAR]
0.12[H]
0.12 [H]
0.12[H]
100 [MVA]
16.5[kV] / 230[kV]
C
B
A
BRK1
125.0 [MW]
A
V
0.1 [ohm]
0.1 [ohm]
0.1 [ohm]
bus_4
Load B
90.0 [MW]
A
V
PI
SECTION
PI
A
V
Load A
COUPLED
SECTION
COUPLED
A
V
-77.15 [MW]
-18.89 [MVAR]
BRK
V_bus
bus_5
#1
PI
SECTION
V
Timed
Fault
Logic
A
V
A
V
BRK
PI
SECTION
-78.99 [MW]
5.294 [MVAR]
BRK
Tm 2
Tm _s teady
A
V
A
#1
El
ABC
Ia
COUPLED
SECTIO
ON
A
V
bus_9
COUPLED
V
Bus_7
100 [MVA]
18 [kV] / 230 [kV]
100.0 [MW] 35.0 [MVAR]
Bus_2
Gen2
bus1
A
B
Gen1
C
C
A
V
Microgrid
Main : Graphs
Rotor Angle Deviatio
on(degrees)
600
Time...
200
Delta13
500
400
300
200
100
0
0
10
20
30
40
50
60
70
80
System without microgrid: critically
unstable; Critical clearing time is 833 ms
90
R o t o r A n g le D e v ia t io n (d e g re e s )
Delta12
Delta12
Delta13
150
100
50
0
-50
-100
The microgrid increases the stability
margins. Critical clearing time is 1.3 s
6
Mitigating Hurricanes’ Impacts
•
•
Following hurricanes, microgrids can provide electric
energy to customers in an islanding mode for several
weeks.
A cost-benefit analysis is being carried out in a case
study in Florida that integrates energy, transportation,
water, and communications infrastructures
7
Agent Based Supervision of Zone 3 Relays
• Master agents co-ordinate with slaves in a peer-to-peer manner
• Monitor and control power system through interconnected network
8
Maximum Delays in the Network
Maximum delays in the network sharing same topology with the power lines
po
e
es
Maximum delay in the hierarchical network with LANs in the substation
9
Modeling of Cascading Failure in Power Systems
The IEEE reliability test system has 9 different types of 32 generating
units ranging from 12MW to 400MW.
The variance of the Loss of Load Expectation (LOLE) for the direct
method and the importance sampling algorithm
10
Sustainability of Power and Communications Systems
•
Development of theoretical foundations of a twolevel sustainability
y assessment framework ((SAF))
Multicriteria Analyses
and Optimizations
Plant 1
Environomic
analysis/optimization
Plant n
Environomic
analysis/optimization
11
Sustainable Power System
•
Selection of environomic sustainability indicators to assess the
sustainability of the NW European regional transmission/distribution
grid interacting with a set of micro-grids (Frangopoulos et al., 2010).
Selection
Grouping
Judging
Environomic Sustainability Indicators
Technical
Group
Environmental
Group
Indicators of positive sustainable development (SD)
Normalizing
Economic
Group
Social
Group
Indicators of ppositive SD
Indicators of negative SD
Normalized Indicators
Indicators of negative SD
Weighting factors
Weighting Factors of Indicators
Weighting
Calculating
Sub-indices
Combining
Technical
Sub-index
Environmental
Sub-index
Economic
Sub-index
Social
Sub-index
Sustainability Sub-index of group j, and
relative weight of indicator i in group j.
Composite Sustainability Index
Composite sustainability index and relative
weight of the indicator in group j.
12
Sustainability of Microgrids in the NW European Market
•
Goal: Assess the sustainability of
microgrids in the NW European
market that consists of 15 nodes.
•
Market Simulator: COMPETES
(COmprehensive Market Power in
Electricity Transmission and
Energy Simulator)
www ecn nl/ps/tools/modellingwww.ecn.nl/ps/tools/modellingsystems/competes/
•
This Model was enhanced to
quantify emissions and
thermodynamic efficiency
•
Collaborators:
C
ll b
t
O Ozdemir
O.
O d i and
d S.
S
Hers, ECN (The Netherlands)
13
Sustainable Power System
•
Development of a 20 MW residential
micro-grid configuration including
renewable and non-renewable
technologies with cogeneration tied
to a NW European power network
and used in an upper-level SAF.
•
50 Residential MGs are aggregated at
the node Krim in the Netherlands
The daily load demand is divided into
b
base
load,
l d intermediate
i t
di t load,
l d and
d peak
k
load.
•
20 MWe
Emission rates in the MG by
t h l
technology
(ton/MWh)
(t /MWh)
Characteristics of MG
technologies
14
Impacts of Microgrids
•
Microgrids (MGs) lead to an
improvement in the
energetic
g
and exergetic
g
efficiency.
•
MGs enhance the
sustainability of a power
system because they yield a
reduction in both CO2 and
NOx emissions.
•
MG scenarios are less
economically sustainable
due to the high capital costs
of the MG technologies,
g
especially fuel cells.
Application of an Upper-level SAF to
the NW European Power Network
S1 – no MG, no CO2 price
S3 – no MG, CO2=25 €/ton
S2 – MG, no CO2 price
S4 – MG, CO2=25 €/ton
15
Conclusions
•
Microgrids will allow a host of small-scale
renewable energy resources to be interconnected
with a power system.
•
Microgrids will provide a power system with
enhanced flexibility and agility during emergency
conditions.
•
Microgrids will represent a paradigm shift in power
system operation and control. They are one of the
key components of a smart grid
grid.
16