smartgrid - nano-tera 2016
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SmartGridReal time monitoring and management ofsmart grids with validation in the EPFLcampus
Lausanne, 26 of April 2016
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Real-Time State Estimation of Active Distribution Networks(ADNs)
Network
monitoring
Optimal
V/P control Congestionmanagement
Losses
minimization
Optimal dispatch
of DERs
Islanding
operation
a!lt detection
and location
Real"#ime $tate
Estimation of %DNsacc!raterelia&lefast (sub-second)low latenc'
!
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Power sstem state estimation
"tate estimation of power s#stems refers to a statistical
!rocedure that processes the measurements together with thenetwork model in order to provide the most li$el# network
state%Statistical !rocedure & e%g% 'eighted Least "(uares or )alman *lter%Network model& networ$ topolog# and line electrical parameters%
Network state & phase+to+ground nodal voltages%
State EstimationEstimate
dstate "
etwor$ model
-easurements. /ncertainties
Real+time state estimation& state estimation at #i$# refres#
rate several tens of frames+per+second, e%g% enaled # the
use of snc#ro!#asor measurements provided # P%&s%
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ower sstem state est mat on'vs* +,(eighted Least $)!ares
• static algorithm: uses only
meas!rements and network model• widely-used in power systems
*alman filter
• rec!rsi+e algorithm, i.e. it uses also aprocess model to represent the time-evolution of the system
• we ha+e de+eloped and +alidated aprocess model s!ita&le for powers'stems state estimation
Concl!sions
* has &etter acc!rac' than (L$ when the
system is in normal operating conditions
Numerical results:
Time evolution of the state
Estimation errors
[1] Sarri, Zanni, Popovic, Paolone, Le Boudec, “ Performance
Assessment of Linear State Estimators using
Synchrophasor Measurements”, EEE Trans! on
nstrumentation and "easurement!
.P
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uplicated networ!
stem arc tecture .Pcommunication
Dedicated communication networ$, resilient topower outages% Reliabilit through an innovative pac$et duplication
method& iPRP' internet Parallel Redundanc# Protocol% E3isting P-/s and P45s ta$e advantage of
redundant networ$s without eing aware of pac$etreplication% "upports P routed networ$s and multicast% o changes in the applications that run on P45 and P-/s% o changes in the networ$ devices%
ADN Communication Network: Reliability with
iPRP
7
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%ulticast aut#entication& per P-/ authentication withno shared secret is re(uired for an A48 we evaluated
state+of+the art schemes and found that E54"A with pre+computed to$ens is feasile%
"ecurit# of the time snc#roni/ation !rotocol remainsan issue, even with cr#ptograph# and authentication,
ecause of dela attacks* 'e demonstrated that a dela#attac$ is possile and, so far, undetectale%
Securit for ADNs
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"y inserting asymmetric delays
in lin!s to appropriately chosen
pairs of #$%, we can modify
the measured phasor angles in
ways that are undetectable. &e
induce the grid operator intounder- or over-estimating
power flows.
t
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' - ase mon tor n$sstems
:
'ith the infrastructure deplo#ed in the EPFL campus&
-onitoring of the state of the s#stem voltage levels with highrefresh rate and low latenc#8
4evelopment of the *rst application& ,ault Detection and,aulted ine .denti3cation*
4etects the e"istence of a fault8 denti*es the faulted line, fault t!e and estimate the fault
current4
5ompatile with real-time !rotection*
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P%&-based monitorin$ sstems
'pplication: ault etection and aulted ine *dentification
using #$%-based +eal-ime State stimation
mplemented # using& Real-time simulator reproduces real+networ$
ehavior in real+time Real P%& al$orit#m used in EPFL to generate the
measurements Real measurement noise model otained in EPFL
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P%&-based monitorin$ sstems
E3tensivel# tested for several scenarios&• Real distribution or transmission test networ$8
• Low, high or ver# high im!edance faults 1 ;, 10 ;, 1000 ;8• "everal fault t!es and fault locations4• Earthed or unearthed neutral4• Large presence of distributed $eneration and di
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Smart buildin$ environment
1
• oads are organied in
clusters for management
purposes.
• Intelligent Loads (iLoad )• oads identification/• Specific loads
monitoring 0 control/
• 1entralied low powercommunication layer.
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owPowe
r
Smart 5uildin$ Environment
1
2 3 3 2 2 3
3 3 2 3
2 3 2
222
ioads :• agging• $onitoring• 1ontrol
nergy $anagement System:• aily forecasting• Smart-4rid dialog
E-"
'5
#sers
"uilding $anagement System:• ' representative segment of
the unifying database model• ata mining 0 users interface
"$S
$iddleware:• istributed messaging• 6irtual emulator for
"uilding
owatenc
6uman5asedearni
n$
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Distributed-Scalable Arc#itecture
1
e"mart=s
eplugs
• iLoads• HES-S
sensors
>"4? speciali@ed for
storing time var#ingdata&
• "pace ecient• "amples (ueries
optimi@ed
Hermes: !he "istribute" messagingmi""leware
"olution for&• Latenc#• Reduced Power• 4istriuted message dispatching B
processing
openBMS: !he centrali#e"
management ser$er "calale solution
• Cigh+Level integration of allinfrastructure
• "ervice provides AP . 'esoc$ets
>he actual applications and interfacingwith the 4D, C4, E-" and general the
e%ternal stakehol"ers
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P7' .nte$rated c#i! desi$n
Transient Simulation 6ardware
Arc#itecture
Transient Simulation 6ardware
Arc#itecture .8 Desi$n
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P7' .nte$rated c#i! desi$n
Transient Simulation 6ardware
Arc#itecture .8 Desi$n
AS.8 Part (2nodes)
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AS.8 A:9&T
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8onclusion
+eal-time monitoring system for one feeder of the mediumvoltage networ! of the # campus.
evelopment of the first application: a!lt Detection and
a!lted Line Identification,
1ommunication layer with strong reliable and secure
mechanism.
"ottom-up approach for Smart-"uilding environment usingioad technology with dedicated environment for human
interaction with large granularity monitoring and control.
smart$rid*e!;*c#
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