introduc)on–chrisdevelderusers.atlantis.ugent.be/cdvelder/papers/2015/develder2015uci.pdf ·...

Introduc)on*–*Chris*Develder*

! PhD,%Ghent%University,%2003%•  “Design%and%analysis%of%op<cal%packet%switching%networks”%

! Professor%at%Ghent%University%since%Oct.%2007%•  Research(Interests:%smart*grids%(op<miza<on/scheduling%algorithms%for%DSM/DR;%data%analy<cs),%informa)on*retrieval/extrac)on*(e.g.,%knowledge%base%popula<on,%event%rela<ons%in%news%archives);%op)cal*networks*(dimensioning,%resilience%schemes,%ILP)%%

•  Visi<ng%researcher%at%UC%Davis,%CA,%USA,%JulVOct.%2007%%(op<cal%grids)%•  Visi<ng%researcher%at%Columbia%Univ.,%NY,%USA,%2013V14%%(IR/IE)%

!  Industry%Experience:%network%planning/design%tools%•  OPNET%Technologies%(now%part%of%Riverbed),%2004V05%

! More%info:%h_p://users.atlan<s.ugent.be/cdvelder%%

%

C.(Develder,("Smart(grid(algorithms:(Knowing(and(controlling(power(consump>on",(Invited(talk(at(UCI,(5(Jun.(2015(

Smart*grid*algorithms:*Knowing*and*controlling*power*consump)on*

Chris%Develder,%Kevin%Mets,%Ma_hias%Strobbe%%%%Ghent%University%–%iMinds%Dept.%of%Informa<on%Technology%–%IBCN%

Smart*Grids*

Distributed*genera)on*(large*scale)*Green*energy*sources*(fluctua)ng)*

Distributed*genera)on*(small*scale)*

Local*energy*storage*

PHEV*charging*

(residen)al)*

PHEV*charging*(car*parks)*

Demand*side*management*

ICT*infrastructure*

New*services*&*business*models*Fault*detec)on?*Restora)on?*

Data*processing?*Privacy,*security?*Pricing*schemes?*

…*


Power*grid*structure*


Transmission*network*(operated*by*TSO)*

Distribu)on*network*(operated*by*DSO)*

Outline*

1.  Introduc<on%Part*I:*Algorithms*for*DSM/DR*2.  Example%1:%Peak%shaving%%3.  Example%2:%Wind%balancing%4.  Tools%to%study%smart%grid%cases%Part*II:*Data*analy)cs*5.  Clustering%smart%metering%data%


K.(Mets,(R.(D'hulst(and(C.(Develder,("Comparison+of+intelligent+charging+algorithms+for+electric+vehicles+to+reduce+peak+load+and+demand+variability+in+a+distribu9on+grid",(J.(Commun.(Netw.,(Vol.(14,(No.(6,(Dec.(2012,(pp.(672M681.(doi:10.1109/JCN.2012.00033((

Example*case*study:*EV*charging*

! Research%ques<ons:%1.  Impact%of%(uncontrolled)%EV%charging%in%a%residen<al%environment?%2.  Minimal%impact%on%load%peaks%we%could%theore<cally%achieve?%3.  How%can%we%minimize%the%impact%of%EV%charging%in%prac<ce?%


!  Sample%analysis%for%150%homes,%x%%of%them%own%a%PHEV%! BAU%=%maximally%charge%upon%arrival%at%home%

Impact*of*EV*charging*

0%

50%

100%

150%

200%

250%

12:00%

13:30%

15:00%

16:30%

18:00%

19:30%

21:00%

22:30%

00:00%

01:30%

03:00%

04:30%

06:00%

07:30%

09:00%

10:30%

12:00%

Total*loa

d*(kWh)*

Time*(h)*

No%PHEVs%BAUV10%%BAUV30%%BAUV60%%

0%%

20%%

40%%

60%%

80%%

100%%

10%% 30%% 60%%PHEV*Penetra)on*(%)*

Addi<onal%Power%Consump<on%Addi<onal%Peak%Load%


! Objec<ves:%•  Reduce%peak%load%•  Fla_en%(total)%load%profile%(=%reduce%<meVvariability)%

•  Avoid%voltage%viola<ons%

Controlling*EV*charging?*

Time Slot

Charging Rate (W)

Charging*schedule*

0%

50%

100%

150%

200%

250%

12:00%

14:15%

16:30%

18:45%

21:00%

23:15%

01:30%

03:45%

06:00%

08:15%

10:30%

Total*loa

d*(kWh)*

Time*(h)*

0%

50%

100%

150%

200%

250%

12:00%

14:15%

16:30%

18:45%

21:00%

23:15%

01:30%

03:45%

06:00%

08:15%

10:30%

Total*loa

d*(kWh)*

Time*(h)*


Quadra)c*Programming*(QP)*! Offline%algorithm%! Planning%window%!  “Benchmark”%!  Three%approaches:%

•  Local%•  Itera<ve%•  Global%

Mul)YAgent*System*(MAS)*! Online%algorithm%! No%planning%window%"%current%<me%slot%info%only%(but%EV%bidding%changes%when%charging%deadline%approaches)%

!  “Realis<c”%!  Single%approach%

Smart*charging*algorithms*

Reference*scenario:%Uncontrolled%charging%


Smart*charging:*QP*

BAU%(uncontrolled)%

Local*control*(QP)%Global*control*(QP),*

Market*MAS%


Case*study*

!  63%Households%•  Randomly%distributed%over%3%phases%

•  Spread%over%3%feeders%

!  Electrical%vehicles%•  PHEV:%15%kWh%ba_ery%•  Full%EV:%25%kWh%ba_ery%•  Randomized%arrivals%(~5pm)%and%departures%(~6am)%

Scenario* PHEV*3.6*kW*

PHEV*7.4*kW*

EV*3.6*kW*

EV*7.4*kW*

Light% 4% 3% 2% 1%

Medium% 10% 10% 5% 4%

Heavy% 17% 16% 7% 7%


Results*(1)*–*Load*profiles*

0%

50%

100%

150%

200%

250%

300%

12:00% 15:00% 18:00% 21:00% 0:00% 3:00% 6:00% 9:00% 12:00%

Power*con

sum)o

n*(kW)*

Time*(hh:mm)*

Heavy*

0%

20%

40%

60%

80%

100%

120%

12:00% 15:00% 18:00% 21:00% 0:00% 3:00% 6:00% 9:00% 12:00%

Power*con

sum)o

n*(kW)* Light* Uncontrolled% Local%

Global% MAS%


Results*(2)*–*Load*peaks*&*variability**

QP1 = local QP2 = iterative QP3 = global


Not%solved%en<rely!%(No(explicit(part(of(objec>ve(func>on!)%%

Note: 10 slots ~ 3.4% of the time

Results*(3)*–*Voltage*devia)ons*


Outline*



Distributed*genera)on*(DG)*


Distributed*genera)on*(DG)*

! Mo<va<on%for%DG%•  Use%renewable%energy%sources%(RES)%⇒%reduc)on*of*CO2*

•  Energy*efficiency;%e.g.,%Combined%Heat%and%Power%(CHP)%•  Genera<on%close%to%loads%•  Deregula<on:%Open%access%to%distribu<on%network%•  Subsidies%for%RES%• …%

!  Technologies%• Wind%turbines%•  Photovoltaic%systems%•  CHP%(based%on%fossil%fuels%or%RES)%•  Hydropower%•  Biomass%• …%


Technical*impact*of*DG?*


Wind*turbines*


! Horizontal%axis%%•  Upwind%vs%downwind%•  Needs%to%be%pointed%into%the%wind%•  High%rota<onal%speed%(10V22%rpm)%•  Needs%a%lot%of%space%(cf.%60V90m%high;%blades%20V40m)%

! Ver<cal%axis%•  Omnidirec<onal%•  No%need%to%point%to%wind%•  Lower%rota<onal%speed%•  Can%be%closer%together%

((((E.g.,(hZp://www.inflowMfp7.eu/((Savonius(Darrieus(

A*typical*wind*profile*


Case*Study*


K.(Mets,(F.(De(Turck(and(C.(Develder,("Distributed+smart+charging+of+electric+vehicles+for+balancing+wind+energy",(in(Proc.(3rd(IEEE(Int.(Conf.(Smart(Grid(Communica>ons((SmartGridComm(2012),(Tainan(City,(Taiwan,(5M8(Nov.(2012,(pp.(133M138.(doi:10.1109/SmartGridComm.2012.6485972(

High*peak*loads*

Wind*balancing*with*EV*charging*

Supply/demand*imbalance*!  Inefficient%use%of%RES%!  Imbalance%costs%! High%peak%loads%

Undesirable!%

0%

10000%

20000%

30000%

40000%

50000%

60000%

70000%

0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00%

Power*(W

)*

Time*Uncontrolled% Wind%energy%

Day%2% Day*3* Day*4* Day*5* Day*6* Day*7*Day%1%


Distributed*control*

Balance%Responsible%Party%

EV% EV%

Coordinator Coordinator%

Exchange%of%control%messages%to%itera<vely%nego<ate%

charging%plans%for%a%specific%period%of%<me%


Electric*vehicle*model*

! Minimize%disu<lity:%•  Charging%schedule%variables:%xt

k%=%%charging%rate%for%user*k*at%)me*t•  Spread%demand%over%<me,%preferably%at%the%“preferred%charging%rate”%(pk),%

which%is%the%maximum%supported%charging%rate%in%our%case%•  Model%behavior/preferences%of%the%subscriber%(βk)%

•  Charging%schedule%for%a%window%of%T%<me%slots:%Minimize%disu<lity%

! Respect%energy%Requirement:%

•  Vehicle%can%only%be%charged%between%arrival%<me%Sk and%departure%<me%Tk

(1)%

(2)%

(3)%


Balance*Responsible*Party*(BRP)*Model*

!  Imbalance%Costs%•  Minimize%imbalance%costs:%%%Penalty%cost%if%supply%≠%demand%•  Supply:%%Wind%energy%(wt)%•  Demand:%%Total%of%all%electric%vehicles%(dt)%•  Tuning%parameter:%α•  Cost%func<on:%

!  For%a%planning%window%of%T%<me%slots,%minimize:%


Centralized*Op)miza)on*Model*

! Based%on%social%welfare%maximiza<on%• Minimize%imbalance%costs%• Minimize%user%disu<lity%

! Objec<ve:%

! Global%constraints:%

!  Local%constraints:%•  BRP:%supply%<%limit%•  EV:%energy%&%<me%constraints%

Drawbacks:%1)  Privacy:%sharing%of%

cost%&%disu<lity%func<ons,%arrival/departure%info,%…%

2)  Scalability+


! Move%demandVsupply%constraint%into%objec<ve,%w/%Lagrange%mul<plier%λt

! No<ce:%Objec<ve%func<on%is%separable%into%K+1%problems%that%can%be%solved%in%parallel%(assuming(λt(are(given)%

!  Itera<vely%update%pricing%vector…%%

Distributed*op)miza)on*model*

1%BRP%problem%

K%subscriber%problems%


original(objec>ve( constraint(

Distributed*op)miza)on*model*scheme:*

1.  Coordinator%distributes%virtual%prices%2.  BRP%solves%local%problem%3.  Subscribers%solve%local%problem%4.  Coordinator%collects%schedules:%

•  BRP:%%•  EVs:%

5.  Coordinator%updates%virtual%prices:%%

6.  Repeat%un<l%demand%=%supply%

in%parallel%


Case*study:*Assump)ons*

! Wind%energy%supply%≈%EV%energy%consump<on%!  Energy%supply%=%6.8%MWh%

!  100%Electric%vehicles%!  Ba_ery%capacity:%10%kWh%ba_ery%! Maximum%charge%power:%3.68%kW%!  Arrivals%&%departures:%sta<s<cal%model%!  Charging%at%home%scenario%

!  Time%!  Simulate%4%weeks%!  Time%slots%of%15%minutes%!  Planning%window%of%24%hours%


Case*study:*Algorithms*

! Uncontrolled*business*as*usual*(BAU)*•  EV%starts%charging%upon%arrival%•  EV%stops%charging%when%stateVofVcharge%is%100%%•  No%control%or%coordina<on%

%

! Distributed*algorithm*•  Executed%at%the%start%of%each%<me%slot%

!  “Ideal*world”*benchmark*•  Offline%allVknowing%algorithm%determines%schedules%for%ALL%sessions%•  No%EV%disu<lity%func<on%"%maximum%flexibility%•  Objec<ve:%

min


Results:*Uncontrolled*BAU*vs.*Distributed*

0%

10000%

20000%

30000%

40000%

50000%

60000%

70000%

0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00%

Power*(W

)*

Time*

Uncontrolled% Wind%energy%


0%

5000%

10000%

15000%

20000%

25000%

30000%

0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00%

Power*(W

)*

Time*

Distributed% Wind%energy%



Results:*Distributed*vs.*Benchmark*

0%

5000%

10000%

15000%

20000%

25000%

30000%

0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00% 6:00%12:00%18:00%0:00%

Power*(W

)*

Time*

Benchmark% Distributed% Wind%energy%



Results:*Energy*Mix*

!  Total%energy%consump<on%≈%6.8%MWh%!  Substan<al%increase%in%the%use%of%renewable%energy%! Reduced%CO2%emissions%

2.72%

4.62% 4.99%

4.07%

2.18% 1.80%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

Uncontrolled% Distributed% Benchmark%

Energy*su

pply*(M

Wh)*

Charging*strategy*

Renewables% Non%renewables%

1450%

798%

633%

0%

200%

400%

600%

800%

1000%

1200%

1400%

1600%

Uncontrolled% Distributed% Benchmark%

CO2*em

ission

s*(kg)*

Charging*strategy*

Renewables:%%%%%%%%%%%%%7.4%CO2%g/kWh%Non%Renewables:%351.0%%CO2%g/kWh%

−45%*

40%% 68%% 73%%


−56%*

Contribu)on*from*RES* Reduc)on*of*CO2*emissions*

Conclusions*

! Objec)ve:%balance%wind%energy%supply%with%electric%vehicle%charging%demand%

! Method:%Distributed%coordina<on%algorithm%where%par<cipants%exchange%virtual%prices%and%energy%schedules%

! Performance:%Distributed%coordina<on%significantly%be_er%than%BAU,%close%to%“ideal%world”%benchmark%•  Increased%usage%of%renewable%energy%sources%•  Reduc<on%of%CO2%emissions%


Outline*



K.(Mets,(J.(Aparicio(and(C.(Develder,("Combining+power+and+communica9on+network+simula9on+for+costAeffec9ve+smart+grid+analysis",(IEEE(Commun.(Surveys(Tutorials,(Vol.(PP,(2014,(pp.(1M26.(doi:10.1109/SURV.2014.021414.00116(

Problem*Statement*

!  Simulators%are%already%used%in%the%two%domains:%•  Communica)on%network%engineering%•  Power%engineering%

%!  In%a%coYsimula)on*approach,%power%&%communica<on%are%loosely%coupled%•  Requires%careful%synchronisa<on%•  Drawback:%no%integra<on%of%tools%

ns-2 / ns-3

OpenDSS

OMNeT++

Matlab tools

…

…

Power Grid Simulation

Communication Network Simulation


Challenge*for*coYsimula)on:*Synchronisa)on*


Figure: [H.Lin et al., 2012]

Integrated%(combined)%power%grid%and%communica<on%network%simula<on%"%Large%scale%smart%grid%simula<ons%

Our*proposed*solu)on*

Power Grid Simulation

Communication Network Simulation

Middleware Layer

Application Layer

DSM/DR% AMI% PMU% …


Outline*



K.(Mets,(F.(Depuydt.(and(C.(Develder,("TwoAstage+load+paGern+clustering+using+fast+wavelet+transforma9on",(IEEE(Trans.(Smart(Grid,(2015,((to(appear(

Clustering*smart*metering*data*

! Goal:*Iden<fy%different%types%of%daily%power%consump<on%<me%series*1.  Single%household:%dis<nct%types%of%daily%load%pa_erns?%2.  Over%whole%popula<on:%dis<nct%groups%of%users?%

! Why?*•  Demand%analysis%(na<onVwide,%distribu<on%substa<ons,%…%single%houses)%•  Customer%segmenta<on,%tariffs,%billing…%•  Power%system%planning%•  Load%forecas<ng%•  Demand%response%programs%• …%


TwoYstage*load*paoern*clustering*

< >

(i) typical patterns per user (ii) overall load pattern clustering

user 1...

user N

...

. . .

. . .

global cluster 1

global cluster 2

global cluster 3

global cluster K

n 1Kn 11, ... ,

< >n NKn N1, ... ,

cluster selectrepresentative

pattern


Can%run%in%parallel,%simultaneously%for%all%users%

Representa<ve%pa_ern%=%real%pa_ern%closest%to%center%

Core*ideas*

! Hierarchical%scheme%! Wavelet%transforma<on:%

•  Dimensionality%reduc<on%•  Invariance/tolerance%to%<me%shi|ing%

! GVmeans%(instead%of%kVmeans)%[Hamerly2003]%


128%features/day% 128%features/day% 7Y8*features/day*

x( f(

96%features/day%

Upsampling%(Linear%

Interpola<on)%

Fast%Wavelet%Transform%(Haar%wavelet)%

Energy%per%detail%scale%

xu( xw(

G.(Hamerly,(C.(Elkan,(“Learning(the(k(in(kMmeans”,(NIPS(2003(

Cluster 1 Cluster 2 Cluster 3

0

1

2

3

4

00:00 06:00 12:00 18:00 00:0000:00 06:00 12:00 18:00 00:0000:00 06:00 12:00 18:00 00:00Time

Powe

r con

sum

ptio

n (k

Wh)

meanrepresentativereal

Sample*result:*Single*user*


For%alpha%=%0.01%%"%low%number%of%clusters%Note:(representa>ve(≠(arithme>c(mean(

Time*vs*wavelet*domain:*Number%of%clusters%

alpha = 15.00% alpha = 10.00% alpha = 5.00%

alpha = 2.50% alpha = 1.00% alpha = 0.01%0

10

20

30

0

10

20

30

40

50

0

10

20

30

40

0

10

20

30

40

50

0

20

40

0

25

50

75

0 25 50 75 100 0 25 50 75 100 0 25 50 75 100cluster size

num

ber o

f clu

ster

s

wavelet featurestime series


Time%shi|ing%"%distance%metric%differences%in%<me%greater%than%in%wavelet%feature%domain%

Smaller%alpha%"%convergence%to%%the%same%clusters%

Time*vs*wavelet*domain:*Cluster%quality%(in%<me%domain)%

J MIA CDI

SMI DBI WCBCR

0.5

1.0

1.5

2.0

2.5

0.050

0.075

0.100

0.125

0.150

0.175

1

2

3

4

0.0

0.2

0.4

0.6

0.8

0.0

0.1

0.2

0.3

0.4

0

500

1000

1500

0 25 50 75 100 0 25 50 75 100 0 25 50 75 100Number of clusters

Met

ric v

alue

time serieswavelet features


Time%shi|ing:%distance%metric%differences%in%<me%domain%greater%than%in%wavelet%feature%domain%"%Metrics%calculated%in%<me%domain%worsen%

Conclusions*

!  Totally%unsupervised%clustering%process%•  No%a%priori%defini<on%of%‘typical%day’,%groupings%into%weekday/weekend%…%•  Cluster%quality%not%%

! Note%on%scalability:%•  Stage%1%=%executed%per%user%(in%parallel)%•  Stage%2%=%number%of%profiles%to%cluster%is%limited,%by%reducing%‘representa<ve’%profile%%

•  Vector%space%dimensionality%is%reduced%by%FWT%(96%"%7%or%8%features)%


WrapYup*


Summary*

! Challenge:%deal%with%renewable%sources%! Demand%response%algorithms:%ini<al%feasibility%studies%

•  How%close%to%“best”%possible?%scalable?%• What%are%achievable%benefits?%

! Get%insight%in%consump<on/produc<on:%e.g.,%clustering%as%first%step%! Quan<fy%flexibility%=%amount%of%“shi|able”%power%+%what%<me?%

! What’s%next?%•  Can%we%learn/predict%flexibility,%e.g.,%from%smart%metering%data?%•  Can%we%infer%user*behavior,%and%from%there%(contextVaware)%preferences?%•  Evaluate%business*case*of%flexibility?%•  Convincingly%demonstrate%flexibility%exploita<on%in%the%real%world?%


E.g.,%refine%“disu<lity”%from%user;%“imbalance”%from%business%perspec<ve;%evaluate%using%real(is<c)%data…%

Thank*you*…*any*ques)ons?*


Thank*you*…*any*ques)ons?*


Chris%Develder%[email protected]%%Ghent%University%–%iMinds%%

%

introduc)on*–*chris*develder*users.atlantis.ugent.be/cdvelder/papers/2015/develder2015uci.pdf ·...

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