A methodological research on software engineering applied to design of smart grids using a complex system approach
Abstract
El reto de conseguir una red eléctrica más eficiente pasa por la introducción masiva de energías renovables en la red eléctrica, disminuyendo así las emisiones de CO2. Por ello, se propone no sólo controlar la producción, como se ha hecho hasta ahora, sino que también se propone controlar la demanda. Por ello, en esta investigación se evalúa el uso de la Ingeniería Dirigida por Modelos para gestionar la complejidad en el modelado de redes eléctricas, la Inteligencia de Negocio para analizar la gran cantidad de datos de simulaciones y la Inteligencia Colectiva para optimizar el reparto de energía entre los millones de dispositivos que se encuentran en el lado de la demanda.
Full text
A me hodological esea ch on so wa e enginee ing applied o he design o Sma
G ids using a Complex Sys em app oach
Au ho : Jos´
e´
E o a-G´
omez
Ad iso : F ancisco Ma io He n´
andez-Teje a
Ad iso : Jos´
e Juan He n´
andez-Cab e a
Tex p in ed in Las Palmas de G an Cana ia
Fi s edi ion, Oc obe 2014
Dedico es a esis a mis se es m´
as que idos.
A mi mad e, mi pad e y mi he mano Chenko
po apoya me siemp e. A Paula po da me ´
animos,
escucha me y much´
ısimo m´
as. A mi abuela Mamen y
mi ´
ıa Ma ga i a que siemp e se han p eocupado po mi.
CONTENTS
9 Simula ion esul s analysis 101
9.1 Business In elligence me hodologies o analysing da a . . . . . . 103
9.2 On-Line Analy ical P ocessing o Sma G ids . . . . . . . . . . 106
10 Demand Side Managemen policy design 115
10.1 Swa ming In elligence echniques o designing policies . . . . . 116
10.2 Pa icle Swa m Op imisa ion . . . . . . . . . . . . . . . . . . . . 116
10.3 Mul i Objec i e Op imisa ion . . . . . . . . . . . . . . . . . . . . 117
10.4 Mul i Objec i e Pa icle Swa m Op imisa ion . . . . . . . . . . . 119
IV Expe imen a ion 121
11 Expe imen a ion conside a ions 123
11.1 Modelling in Ta a . . . . . . . . . . . . . . . . . . . . . . . . . 124
11.2 Expe imen s compa ing di e en simula ion imings . . . . . . . 132
12 Agen -Based Modelling o Elec ical Load a Household Le el 137
12.1Desc ip ion ............................. 138
12.2CaseS udy ............................. 141
12.3Resul s................................ 142
12.4Discussion.............................. 146
13 Modelling li es yle aspec s in luencing he esiden ial load-cu e 149
13.1Desc ip ion ............................. 150
13.2Cases udy.............................. 151
13.3Resul s................................ 154
13.4Discussion.............................. 158
14 A Mul i-Objec i e Pa icle Swa m Op imisa ion me hod o Di ec Load
Con ol in Sma G id 161
14.1Desc ip ion ............................. 162
14.2CaseS udy ............................. 169
14.3Resul s................................ 173
14.4Discussion.............................. 178
III
CONTENTS
15 A la ge-scale elec ical g id simula ion o massi e in eg a ion o dis-
ibu ed pho o ol aic ene gy sou ces 181
15.1Desc ip ion ............................. 182
15.2Cases udy.............................. 184
15.3 Simula ion esul s . . . . . . . . . . . . . . . . . . . . . . . . . . 185
15.4Discussion.............................. 186
16 C i icali y in complex socio echnical sys ems, an empi ical app oach 187
16.1Desc ip ion ............................. 188
16.2Cases udy.............................. 191
16.3Resul s................................ 193
16.4Discussion.............................. 203
17 Vehicle o G id 205
17.1Desc ip ion ............................. 205
17.2Cases udy.............................. 210
17.3Resul s................................ 215
17.4Discussion.............................. 218
18 F equency Managemen wi h Swa m In elligence: a case s udy in Sma
G ids 219
18.1Desc ip ion ............................. 219
18.2Cases udy.............................. 220
18.3Resul s................................ 222
18.4Discussion.............................. 226
19 Agen -based modelling o designing an EV cha ging dis ibu ion sys-
ems: a case s udy in Sal ado o Bahia 227
19.1Desc ip ion ............................. 227
19.2Cases udy.............................. 229
19.3Resul s................................ 230
19.4Discussion.............................. 233
IV
CONTENTS
V Conclusions 235
20 Resul s 237
20.1 Abou Sma G id modelling . . . . . . . . . . . . . . . . . . . . 239
20.2 Abou da a analysis . . . . . . . . . . . . . . . . . . . . . . . . . 240
20.3 Abou s a egy design . . . . . . . . . . . . . . . . . . . . . . . . 241
20.4 Empi ical hypo heses alida ion . . . . . . . . . . . . . . . . . . 241
20.5T ans e ence............................. 243
20.6P ojec s ............................... 244
20.7Publica ions............................. 244
21 Discussion 249
21.1Con ibu ions ............................ 249
21.2O he ema ks............................ 256
21.3Fu u ewo k............................. 259
Bibliog aphy 265
V
Lis o Figu es
1.1 DSM policy design me hod . . . . . . . . . . . . . . . . . . . . . 9
1.2 Model o e alua ing DSM policies . . . . . . . . . . . . . . . . . 10
1.3 Addi ion o laye s o implemen ing he policy . . . . . . . . . . 11
2.1 Powe g ids’ challenges . . . . . . . . . . . . . . . . . . . . . . . 18
3.1 Powe consump ion a iabili y . . . . . . . . . . . . . . . . . . . 29
3.2 Demand modi ica ion wi h DSM . . . . . . . . . . . . . . . . . . 34
4.1 Complex sys em illus a ion . . . . . . . . . . . . . . . . . . . . 39
5.1 MDE abs ac ion le els . . . . . . . . . . . . . . . . . . . . . . . 53
5.2 MDEexample............................ 54
6.1 DSS concep e olu ion . . . . . . . . . . . . . . . . . . . . . . . 58
6.2 Decision making p ocess unde a DSS en i onmen . . . . . . . . 59
6.3 BIda a amewo k ......................... 62
8.1 Abs ac ion le els o modelling powe g ids . . . . . . . . . . . . 76
8.2 Ta a amewo k a chi ec u e . . . . . . . . . . . . . . . . . . . . 78
8.3 A Me amodel example o a powe g id . . . . . . . . . . . . . . 81
8.4 Dependencies examples be ween objec s . . . . . . . . . . . . . . 88
8.5 Modelexample ........................... 89
8.6 Synch onous s Asynch onous simula ion . . . . . . . . . . . . . 90
8.7 Da a eques ............................. 91
8.8 Reques ge s blocked . . . . . . . . . . . . . . . . . . . . . . . . 92
VII
LIST OF FIGURES
8.9 The da a is deli e ed . . . . . . . . . . . . . . . . . . . . . . . . 92
8.10Cyclicdependency ......................... 93
8.11Messagesending .......................... 94
8.12 Message is ecei ed and applied . . . . . . . . . . . . . . . . . . 94
8.13 Simula ion Time p opaga ion . . . . . . . . . . . . . . . . . . . . 95
8.14 Simula ion Time ecep ion . . . . . . . . . . . . . . . . . . . . . 95
8.15Scallingup ............................. 96
8.16 Ta a asynch onous simula ion a chi ec u e . . . . . . . . . . . . 98
8.17 In e ac ion example wi h agen s . . . . . . . . . . . . . . . . . . 99
9.1 S uc u e o expo simula ion esul s . . . . . . . . . . . . . . . . 102
9.2 Cube o esiden ial consump ion . . . . . . . . . . . . . . . . . . 104
9.3 An OLAP Cube s uc u e . . . . . . . . . . . . . . . . . . . . . . 104
9.4 Desc ip ion o a ac . . . . . . . . . . . . . . . . . . . . . . . . . 106
9.5 Scena io composi ion . . . . . . . . . . . . . . . . . . . . . . . . 107
9.6 Householdcube........................... 107
9.7 Household dimension . . . . . . . . . . . . . . . . . . . . . . . . 108
9.8 TVcube............................... 108
9.9 TVdimension............................ 109
9.10Radia o cube............................ 109
9.11 Radia o dimension . . . . . . . . . . . . . . . . . . . . . . . . . 110
9.12 Radia o da a mine . . . . . . . . . . . . . . . . . . . . . . . . . 111
9.13 In o ma ion isualisa ion example . . . . . . . . . . . . . . . . . 112
11.1 Case s udy’s me amodel . . . . . . . . . . . . . . . . . . . . . . 125
11.2 A e aged cu e a e 100 simula ion uns. . . . . . . . . . . . . . 129
11.3 Cu es o 100 simula ion uns. . . . . . . . . . . . . . . . . . . . 130
11.4 Cu e o wo simula ion uns wi h signi ican di e ences. . . . . . 130
11.5 Cu es o 100 simula ions uns wi h ixed numbe o agen s. . . . 131
11.6 Cu es o wo simula ions uns wi h ixed numbe o agen s. . . . 132
11.7Couplingde ails1.......................... 133
11.8Couplingde ails2.......................... 133
11.9 Times in which each en i y kind inished . . . . . . . . . . . . . . 135
VIII
LIST OF FIGURES
12.1 Load cu e example . . . . . . . . . . . . . . . . . . . . . . . . . 139
12.2 Agen a chi ec u e . . . . . . . . . . . . . . . . . . . . . . . . . . 140
12.3 Socio-demog aphic g oups used in he case s udy . . . . . . . . . 141
12.4 Simula ed load cu e . . . . . . . . . . . . . . . . . . . . . . . . 143
12.5 Model cons uc ion pa e n . . . . . . . . . . . . . . . . . . . . . 144
12.6 Compa ison wi h eal da a . . . . . . . . . . . . . . . . . . . . . 145
13.1 Absence om home on weekdays . . . . . . . . . . . . . . . . . 155
13.2 Simula ed load cu e wi h eal dis ibu ions . . . . . . . . . . . . 156
13.3 Simula ed load cu e o conse a i e well-o . . . . . . . . . . . 157
13.4 Simula ed load cu e o en e ainmen seeke s . . . . . . . . . . 158
14.1DLCs uc u e............................ 163
14.2MOPSOpa icles.......................... 165
14.3Sea chspace............................. 169
14.4 Con olle s and consume s . . . . . . . . . . . . . . . . . . . . . 170
14.5 Res ic ions sen by he g id ope a o . . . . . . . . . . . . . . . . 171
14.6 Indi idual appliances consump ion . . . . . . . . . . . . . . . . . 174
14.7 Indi idual appliances consump ion wi h DLC . . . . . . . . . . . 175
14.8 Neighbou hoods consump ion . . . . . . . . . . . . . . . . . . . 176
14.9 Neighbou hoods consump ion wi h DLC . . . . . . . . . . . . . . 176
14.10To al consump ion . . . . . . . . . . . . . . . . . . . . . . . . . . 177
14.11To al consump ion wi h DLC . . . . . . . . . . . . . . . . . . . . 177
14.12Non-DLC sDLC.......................... 177
15.1 GIS laye s ela ed o he p o ided da a . . . . . . . . . . . . . . . 182
15.2 DLC s a egy lowcha . . . . . . . . . . . . . . . . . . . . . . . 183
15.3 Powe o e -demand unbalances . . . . . . . . . . . . . . . . . . 184
15.4 Non-DLC s DLC o a building . . . . . . . . . . . . . . . . . . 185
15.5 Non-DLC s DLC o all cus ome s . . . . . . . . . . . . . . . . 186
16.1S abili y egimes .......................... 192
16.2 Pa ame e s and hei classi ica ion . . . . . . . . . . . . . . . . . 194
16.3 S abili y conside ing he e ige a o sha e . . . . . . . . . . . . . 196
16.4 Numbe o oscilla ion s sha es . . . . . . . . . . . . . . . . . . . 197
IX
LIST OF FIGURES
16.5 S abili y conside ing he doo opening a e . . . . . . . . . . . . . 198
16.6 S abili y conside ing he equency h eshold . . . . . . . . . . . 199
16.7 Edge o chaos conside ing he equency h eshold . . . . . . . . 200
16.8 S abili y conside ing he ex e nal empe a u e (scena io 4-a) . . . 201
16.9 Edge o chaos conside ing he ex e nal empe a u e (scena io 4-a) 202
16.10S abili y conside ing he ex e nal empe a u e (scena io 4-b) . . . 203
16.11Edge o chaos conside ing he ex e nal empe a u e (scena io 4-b) 203
17.1 Cha ging s a ions consump ion o bo h s a egies . . . . . . . . . 217
18.1 F equency eac ion acing a gene a ion uni ailu e . . . . . . . . 221
18.2 Indica o s ha a e used o e alua e policies . . . . . . . . . . . . 222
18.3 F equency eac ion o a ailu e wi h sma e ige a o s . . . . . . 223
18.4 F equency eac ion o he policy #2 . . . . . . . . . . . . . . . . 223
18.5 F equency eac ion o he policy #3 . . . . . . . . . . . . . . . . 224
18.6 Bes policy s base case . . . . . . . . . . . . . . . . . . . . . . . 225
19.1 EVs consump ion when hey s a cha ging as soon as hey a e
plugged ............................... 231
19.2 Compa ison o he o iginal load cu e o 2014 (g ey) wi h he o al
consump ion including EVs cha ging o 2030 (black) . . . . . . . 232
19.3 EVs consump ion acco ding o he RTP based policy which makes
cheape cha ging om 1am o 8am . . . . . . . . . . . . . . . . . 232
19.4 Compa ison be ween o iginal load cu e o 2014 (g ey) wi h he
o al consump ion including EVs cha ging (black) using he RTP-
basedpolicy o 2030........................ 233
20.1 Policies design p oblems . . . . . . . . . . . . . . . . . . . . . . 239
21.1Kuhn’scycle ............................ 250
X
Lis o Tables
11.1 Con igu a ion o he agen s ha uses he shopping cen es. . . . . 127
11.2 Timing o e e y de ice a each simula ion . . . . . . . . . . . . . 134
11.3 Pe o mance compa ison be ween synch onous and asynch onous
cases. ................................ 135
13.1 O e li es yle g oups in S u ga . . . . . . . . . . . . . . . . . . 152
14.1 Elemen s exis ing in he model scene. . . . . . . . . . . . . . . . 172
14.2 MOPSO pa ame isa ion used. . . . . . . . . . . . . . . . . . . . 173
14.3 MOPSO execu ion imes. . . . . . . . . . . . . . . . . . . . . . . 178
17.1 PSO pa ame isa ion used. . . . . . . . . . . . . . . . . . . . . . 216
18.1 Indica o s o bo h cases. . . . . . . . . . . . . . . . . . . . . . . 225
19.1Vehicles............................... 229
XI
1.1 The u u e o powe g ids
su icien elec ici y [Mas13]. This solu ion is e ec i e bu no e icien since hese
backup uni s mus be ope a i e e en when enewable ene gy is being p oduced.
On he one hand, in as uc u es (buildings, powe lines, ans o me s, gene a o s)
should eplica e he enewable ins alled powe . This in as uc u e mus be ins alled
and main ained. On he o he hand, backup gene a o s keep bu ning ossil- uels
while enewable sou ces a e p oducing ene gy. The e o e, enewable ene gy is no
ze o-emission, since i is indi ec ly p oducing GHG.
An al e na i e solu ion in ol es he use o ba e ies o o he ene gy s o age
echnology. This is he case o El Hie o island whe e an ene gy s o age in a-
s uc u e has been buil wi h a bidi ec ional hyd oelec ic powe plan [BC05]. The
main p oblem wi h his solu ion is he in es men cos which is e y high. How-
e e , he cos o ene gy s o age echnology could dec ease in he coming yea s and
his solu ion could become mo e compe i i e [And09].
Howe e , SGs could p o ide al e na i e solu ions ha could be mo e e icien .
Tha means, he same esul s could be achie ed wi h less expense. Fo example,
when he sun is hidden by clouds, PV p oduc ion may be a ec ed, making p oduc-
ion lowe han consump ion. In his case, some loads can be selec i ely discon-
nec ed in o de o ebalance consump ion and p oduc ion. In ano he case, when
clouds disappea and p oduc ion is eco e ed, loads can be econnec ed. These
ac ions a e espec i ely known as load shedding and load shi ing [S 08] and hey
a e cen al opics in his hesis. Since hese policies ac di ec ly on he consump ion
side, hey a e classi ied as Di ec Load Con ol wi hin Demand Side Managemen
(DSM) [GMR+03].
F om a gene al poin o iew, DSM includes all he policies ha adap he de-
mand o g id equi emen s [GMR+03, NNdGW09]. Some o DSM policies a e
based on making he people awa e o e icien ene gy use. O he policies a e e-
la ed o he modi ica ion o p ices, encou aging consump ion in o -peak hou s and
discou aging consump ion in peak hou s.
Howe e , nowadays he e is in o ma ion echnology (IT) ha could au oma -
ically modi y he consump ion o speci ic loads in o de o ebalance he g id
[NNdGW09]. This echnology is an oppo uni y ha could bene i u u e g ids.
The main p e equisi e o his solu ion is de eloping an IT ne wo k o moni o -
ing and ac ing o e he g id componen s. In his way, powe g ids could become
5
1. CONTEXTUALISATION
sma e since hey would be able o pe cei e he en i onmen and ac acco dingly.
This is a dis up i e concep ion ha in oduces a new powe g id pa adigm, SGs,
whe e he managemen o bo h sides, p oduc ion and consump ion, can be ca ied
ou .
Nowadays, many ins i u ions ela ed o powe g ids a e esea ching and design-
ing he ansi ion owa ds SGs. This esea ch and design p ocess is no an easy
ask since he e a e many ac o s ha ha e o be de ined: in as uc u e, ma ke ,
de ice, communica ion, s a egies and conce ns, among o he s. All o hese ac o s
need o be well de ined and s udied since hey will be implemen ed in eal powe
g ids which a e c i ical sys ems ha wo k a ound he clock. Conside ing all hese
conce ns men ioned abo e, he ocus o his hesis is made on he esea ch o new
DSM policies.
IT-based DSM policies need o be designed, e alua ed and alida ed p io o
hei implemen a ion in a eal powe g id in as uc u e. This is necessa y because
hey may in ol e isks o he s abili y and pe o mance o powe g ids. The sys em
could spin ou o con ol, p o oking mal unc ioning and in he wo s case, powe
ailu es. This could happen in he case o a p oduc ion-consump ion unbalance
when DSM policies a e modi ying he consump ion.
Since i is necessa y o s udy he impac o DSM policies on SGs, his esea ch
con ibu es o he ield o compu e simula ions ha add ess his kind o s udies.
The e a e speci ic challenges ha mus be aced when simula ing DSM policies.
1.2 Simula ing u u e scena ios
To illus a e he need o simula ions o DSM policies, some examples o DSM in
SGs a e p esen ed. In all hese examples, i is assumed ha he g id unde s udy
has a communica ion ne wo k which allows he emo e con ol o he appliances
on he demand side.
Le us hink o a policy whe e he c i e ia o make decisions is based on he s a e
o he powe g id. When he e is an unbalance be ween gene a ion and demand,
his policy will eac . When he e is mo e gene a ion han demand, he policy will
y o inc ease consump ion in appliances (e.g. swi ching wa e hea e s on). In he
6
1.2 Simula ing u u e scena ios
opposi e case, when he e is mo e demand han gene a ion, he policy will ac on
de ices o dec ease he demand (e.g. swi ching wa e hea e s o ).
In he case o a powe g id unbalance in which demand is highe han gen-
e a ion, he applica ion o he policy educes he demand o all e ige a o s o
he g id. Since e e y household has a e ige a o , his ac ion has a huge impac
on he powe demand causing he opposi e unbalance: mo e gene a ion han de-
mand. In his si ua ion, he applica ion o he policy swi ches all he e ige a o s
on again, ying o balance he g id. Ob iously, going back o he o iginal posi ion,
i.e. when all e ige a o s a e on, unbalances he g id since demand is once again
highe han gene a ion. In his case, he applica ion o he policy may lead o an
uns able si ua ion whe e e ige a o s a e con inuously being swi ched on and o
and he consequences could include a powe ailu e o damage o he e ige a o s
[VKE+13]. An expe imen o his si ua ion has been done and mo e in o ma ion
can be ound in chap e 16.
Ano he example o policy is educing demand when he e is an unbalance by
con olling he in ensi y o ligh ing. Le ’s assume ligh ing in ensi y can be emo ely
modi ied by he policy applie . Then, whene e he e is a g id unbalance, his policy
educes he in ensi y o all ligh ing by 30%. A i s glance, his seems like a good
idea and much ene gy demand can be educed in he ace o a powe g id unbalance
p oblem. Ne e heless, wha happens i his occu s a nigh ? Would all cus ome s
be in a ou o ha ing ligh s a 70% o hei no mal in ensi y? Ligh s may be
conside ed as an essen ial powe usage, especially a nigh . This policy does no
ake in o accoun he quali y o he se ice ha is o e ed o cus ome s.
These examples poin ou he need o s udy DSM policies p io o hei im-
plemen a ion. E en hough he policy designs a e ob iously w ong, he e may be
many o he issues (e.g. echnological, social, e c.) ha a e no as easy o in e as
hese. These issues a e no easy o in e due o he ac ha powe g ids a e complex
sys ems ha con ain many di e en ac o s whose own sel -in e es ed decisions a -
ec he g id [PAB12]. The e o e, hese issues mus be iden i ied and aken in o
accoun be o e implemen ing policies in eal g ids.
Simula ion is a way o es hese policies. Th ough simula ion, policies can
be es ed on a la ge-scale making i possible o see e ec s a di e en le els o
agg ega ion. Simula ions o es ing DSM policies may be de eloped by using
7
1. CONTEXTUALISATION
disagg ega ed models since hese policies, such as Di ec Load Con ol, a e de-
o ed o ac ing a he lowes le el o consump ion (de ices on he demand side).
Examples o hese simula ions ha e been ci ed in many documen a y i ems as in
[CGLP94, PKZK08, EKM+11, RVRJ11].
When many decisions a e being made in a local and dis ibu ed manne , he
agg ega ed e ec s o hese decisions esul in an eme gen beha iou ha canno
be easily p edic ed. In gene al, his is a common cha ac e is ic o complex sys ems
and he bes way o s udy hem is h ough simula ions. These simula ions may be
use ul o analysing he policies in SGs and e ining hei design [PFR09].
Complexi y in SGs a ises when many componen s a e ac ing and small a i-
a ions in hei beha iou may cause he sys em o e ol e in an unp edic able man-
ne . The challenges ha a e add essed in his documen a e ela ed o deal wi h
his complexi y when de eloping simula ions o es hese policies. Powe g ids a e
huge enginee ing cons uc ions which consis o many di e en elemen s linked o
each o he . In his sense, di e en app oaches ha e been esea ched in his docu-
men o deal wi h hese le els o complexi y.
1.3 P oblem de ini ion
In he p e ious sec ions, he di icul ies o simula ing DSM policies ha e been
desc ibed. Ne e heless, hey a e also challenges ha mus be aced. On he one
hand, he need o disagg ega ing he demand in o de o s udy DSM policies has
been jus i ied. In addi ion, he need o unning he s udies h ough simula ion
expe imen s has been jus i ied as well.
The design o complex sys ems is usually app oached h ough a ial and e o
me hod. In [Sim91], his is exp essed: “ he mo e di icul and no el he p oblem,
he g ea e is likely o be he amoun o ial and e o equi ed o ind a solu ion”.
This is he case in he design o DSM policies which a e bo h di icul and no el.
This ial and e o me hod is no comple ely andom o blind; i is ac ually highly
selec i e, since he knowledge acqui ed in p e ious expe imen s is used o design
new ones.
Following his idea, he way in which DSM policies a e designed can be based
on an i e a i e p ocess (Figu e: 1.1). This p ocess s a s by de ining he objec i es
8
1.3 P oblem de ini ion
Figu e 1.1: A ial and e o me hod o designing DSM policies.
ha he applica ion o he policy mus achie e. Acco ding o hese, he beha iou
o he indi iduals ha make decisions is de ined. This beha iou mus be o ien ed
o achie e he objec i e o he policy.
Thus, he powe g id needs o be modelled and simula ed in o de o e alua e
he policy. A e he simula ions a e execu ed, he da a hey p o ide is analysed in
o de o compa e i o he objec i es p e iously de ined. Depending on he esul s
o his compa ison, he i e a i e p ocess may be comple ed o no , s a ing a new
i e a ion.
The execu ion o he s ages o his ial and e o me hod may be e o -consuming.
The main di icul ies ha hese s ages ha e a e discussed in he ollowing pa a-
g aphs h ough he desc ip ion o a case s udy ca ied ou in his esea ch.
Some expe imen s de eloped wi hin he con ex o he Millene p ojec [EDFb,
CSMJ13] we e o ien ed o es ing DSM policies on a F ench island called La
R´
eunion [AER]. These expe imen s equi ed he modelling and simula ion o he
island’s en i e g id in ol ing he demand side.
Conc e ely, he expe imen was o ien ed o simula ing he e ec s o applying
DSM policies o e de ached homes. To his end, e e y de ached home has o be
modelled including, a leas , he appliances ha can be emo ely con olled. This
in ol ed a o al numbe o elemen s, including hei espec i e beha iou al models,
o app oxima ely 2.5 million.
Facing his complexi y is no a i ial p oblem. The managemen o such com-
plex models is an a duous ask. Modelling a scena io such as he one p esen ed
equi es he in eg a ion o mul iple da a coming om mul iple sou ces (Figu e:
1.2).
9
1. CONTEXTUALISATION
Figu e 1.2: Base model enabled o he e alua ion o Demand Side policies.
Fi s o all, da a o ep esen ing he powe lines o he g id has o be in eg a ed.
La e on, a da abase o buildings h oughou he island mus be included in he
model. A his poin , appliances ha a e equi ed o simula ing he demand need
o be modelled. Fu he mo e, he beha iou o he people li ing in homes mus be
designed and implemen ed in o de o simula e he way people use he appliances
a he esiden ial le el.
This modelling ask desc ibed abo e is jus one pa o he whole p ocess since
i only p o ides he base scena io on which DSM policies can be es ed. The in-
clusion o a policy like his may in ol e he implemen a ion o a communica ion
ne wo k which enables he ansmission o messages among di e en ac o s in he
g id o command he demand side (Figu e: 1.3). In his ne wo k a se o de ices
ha a e esponsible o making decisions, ansmi ing messages, applying com-
mands, moni o ing he powe g id, e c. has o be placed. This is, he addi ion
o he elemen s ha a e necessa y depends on how he policy is designed and i s
equi emen s.
In his kind o simula ion in which he e a e millions o elemen s in ol ed he
quan i y o esul s p o ided by he simula o is huge. E e y single elemen o
10
1.3 P oblem de ini ion
Figu e 1.3: Addi ion o a communica ion and a decision making laye o he base
model.
he simula ion is p o iding i s own a iable pa ame e s a each ime s ep. Ha ing
2.5 million elemen s ha a e ou pu ing h ee di e en pa ame e s each o one
simula ion day wi h a ime s ep o minu es (so 1440 minu es / day), he o al numbe
o esul s eaches 1010. Wi h so many esul s, modelle s usually ocus only on
ce ain ea u es o he simula ion since i is almos impossible o check all he
ou pu . The e o e, many impo an conclusions ha may be d awn om he esul s
may be missed since he whole da a ou pu is no mally no being e iewed.
Ano he issue ha has been obse ed is ela ed o he concep ion o DSM
policies. The oo p oblem, and he eason why hese policies a e c ea ed, is ha
he e a e limi ed esou ces (ene gy) and many di e en ac o s ha wan o ha e
access o hose esou ces (cus ome s). Fu he mo e, he e is an en i onmen which
has limi ed esou ces and he e ogeneous ac o s ha ha e hei own pa icula in-
e es s. The e o e, he challenge is how o e icien ly dis ibu e hese esou ces
acco ding o ce ain objec i es and cons ain s.
In conclusion, h ee majo p oblems ha e been desc ibed in his sec ion:
• SGs modelling and simula ion: he complexi y o dealing wi h a huge num-
be o elemen s.
• Simula ion esul s analysis: he complexi y o dealing wi h a huge numbe
o esul s om simula ions.
• DSM policies design: he complexi y o dealing wi h so many ac o s ha
wan access o he limi ed ene gy esou ces.
11
1. CONTEXTUALISATION
1.4 Resea ch ques ions
Complexi y is a common issue in he p oblems desc ibed in he p e ious sec ion.
The ques ions p esen ed in his sec ion add ess he p oblems ha ha e been p esen-
ed.
In igu e 1.1, he way in which DSM policies a e designed is p esen ed. Fi s ,
a e de ining he policy’s objec i es, he way in which decision make s will ac
mus be de ined. Second, o es he policy, i is necessa y o de elop “in silico”
expe imen s. Ne e heless, ca ying ou hese expe imen s is an a duous ask i he
powe g id ep esen ed is huge. Thi d, hese simula ions can ou pu huge quan i ies
o da a which mus be analysed.
Al hough he selec i e ial and e o me hod is assumed o be he base o
designing DSM policies, he e is a gene al ques ion ha a ises: how o imp o e
he execu ion o his me hod? Tha is o say: how he e o o design a policy can
be lessened. In o he wo ds, his is a ques ion o e iciency and p oduc i i y. This
can be add essed by making less i e a ions o educing he e o o ca y ou each
i e a ion.
This esea ch is ocused on he educ ion o he e o o ca y ou he i e a ions
as his issue can be deal wi h me hodological and echnological app oaches. The
p oblem o educing he numbe o i e a ions has o do wi h de ining heu is ics ha
sugges he pa hs ha should be ied i s [Sim91]. This has no been add essed in
his esea ch, al hough i could be u he explo ed in u u e esea ch. Thus, his
esea ch is o ien ed o explo ing me hods and echnology o imp o ing e iciency
and p oduc i i y in he design o DSM policies.
To be mo e speci ic, his gene al ques ion is elabo a ed h ough ques ions ha
add ess he p oblem o e iciency and p oduc i i y a each s age: de ini ion o he
indi idual’s beha iou ; modelling and simula ion o complex sys ems; and analysis
o simula ion esul s.
Rega ding he de ini ion o he indi idual’s beha iou : he app oach esea ched
is based on na u al compu ing, ega dless o o he ideas ha could be explo ed.
“Na u al compu ing” me hods ake inspi a ion om na u e o he de elopmen
o p oblem-sol ing echniques [RBK11]. These me hods ha e been p e iously
12
1.4 Resea ch ques ions
applied in o he domains by ou esea ch g oup 1. Fo example, Swa m In elli-
gence me hods ha e been used in op imisa ion p oblems. Fo his eason, he ques-
ion “can Swa m In elligence help o deal wi h he complexi y o designing DSM
policies?” has been used o s udy hese speci ic me hods in he ield o DSM.
In addi ion, his ques ion is also ela ed o he eme gen beha iou o he powe
g id as he idea consis s in using Swa m In elligence o ob ain he powe g id ob-
jec i es o consump ion. In o he wo ds, Swa m In elligence echniques could be
used o ob ain a desi ed eme gen beha iou by ac ing on he indi iduals. The e-
o e, ano he ques ion could be: “can Swa m In elligence help ob ain a desi ed
eme gen beha iou in a SGs?” The e may be many di e en echniques o ad ess
his issue. Howe e , apa om he ac ha we ha e used hese echniques in he
pas , we ha e obse ed ha o he esea che s ha e used hem in he powe g ids
ield (Sec ion: 4.4).
Conce ning he modelling and simula ion o complex sys ems, se e al ques-
ions can be de ined. The mos gene al one would be, how can he complexi y be
add essed? Howe e , his ques ion is oo gene al and i mus be mo e speci ic. A
common s a egy o sol ing a p oblem is using me hods. So, a di e en ques ion
could be: “which me hod would be app op ia e o de ining complex models?”
This ques ion assumes as an axiom ha i is be e o apply a me hod a he han
no hing. Ne e heless, his ques ion ocuses on sea ching o a me hod.
Since his esea ch has been de eloped by a g oup 1wi h esea ch lines in Model
D i en Enginee ing and Business In elligence me hodologies, i is logical o ex-
plo e how hese me hodologies could be applied o he p oblem o complexi y in
SGs. The e o e, he abo e ques ion can be p ecisely exp essed: “could Model
D i en Enginee ing help o deal wi h he complexi y o modelling and simula ing
SGs?” and “could Business In elligence me hodology help o deal wi h he com-
plexi y o he da a analysis om simula ions?”
The na u e o hese ques ions de ines an explo a o y esea ch, whe e hese
me hodologies can be adap ed o his p oblem. I is no possible o explo e how
hese me hodologies a e applied o all he p oblems ela ed o complexi y in SGs.
1CES (Calidad, E iciencia y Sos enibilidad - Quali y, E iciency and Sus ainabili y) di ision o
SIANI (Sis emas In eligen es y Aplicaciones Num´
e icas en la Ingenie ´
ıa - In elligen Sys ems and
Nume ical Applica ions in he Enginee ing) ins i u e, Uni e si y o Las Palmas de G an Cana ia.
13
1. CONTEXTUALISATION
Howe e , i is possible o explo e hem in ce ain cases. The goal ha has been
de ined o his esea ch is o p o ide e idence o o agains hese me hodologies
ins ead o e i ying hem. Fu he mo e, he p oblem canno be exp essed as he
disco e y o he bes me hodology o dealing wi h complexi y. Such ques ions a e
no wo hwhile because i is no possible o de ine sui able expe imen a ion.
1.5 S uc u e o he documen
The es o his documen is o ganised as ollows: a e his pa ha was in ended
o con ex ualise he esea ch, he “s a e o he a ” o opics ela ed o his hesis
a e e iewed: SGs, DSM, modelling and simula ion, model d i en enginee ing
and da a analysis. This pa aims o expand upon he in o ma ion p o ided in he
in oduc ion. I also shows how o he esea che s a e ca ying ou he DSM expe i-
men a ion. Fu he mo e, i desc ibes modelling app oaches, applica ions o model
d i en enginee ing and echnology o da a analysis.
The hi d pa p esen s he hypo heses. These hypo heses a e ela ed o explo -
ing he ques ions p e iously de ined. The e o e, his pa is de o ed o desc ibing
he hypo heses and hei de elopmen in e ms o how he esea ch p ocess has
been add essed and he desc ip ion o he ools ha ha e been de eloped as a con-
sequence o he esea ch.
The ou h pa is de o ed o alida ing he esea ch p ocess desc ibed in he
hi d pa by applying i o case s udies. In his pa o he documen , wo syn he ic
case s udies a e p esen ed o show ea u es ela ed o he modelling and simula ion
o SGs. The ollowing case s udies desc ibed he e a e based on eal da a. They a e
so ed om case s udies in which only he demand is simula ed o hose in which
he e a e policies ha ac o e he demand.
The las pa o he documen , he conclusion, shows he esul s and discus-
sions o his esea ch. This is o say, in he “ esul s” chap e , he ou comes o his
esea ch a e desc ibed in e ms o wha has been done, amewo ks, p ojec s, pa-
pe s, e c. The “discussion” chap e p o ides some in e es ing e lec ions and u u e
conside a ions ela ed o his esea ch.
14
2.3 Scope
Technology Pla o m and he o he om he USA Depa men o Ene gy. They a e
ci ed below:
In [ p eno ], he Eu opean Technology Pla o m o Sma G ids s a ed ha
SGs a e “elec ici y ne wo ks ha can in elligen ly in eg a e he beha iou and ac-
ions o all use s connec ed o i – gene a o s, consume s and hose ha do bo h – in
o de o e icien ly deli e sus ainable, economic and secu e elec ici y supplies”.
In [Dep], he Depa men o Ene gy o US poin s ou ha SGs mus include
he ollowing ea u es: “sel -healing om powe dis u bance e en s; enabling ac -
i e pa icipa ion by consume s in demand esponse; ope a ing esilien ly agains
physical and cybe a ack; p o iding powe quali y o 21s cen u y needs; accom-
moda ing all gene a ion and s o age op ions; enabling new p oduc s, se ices, and
ma ke s; op imizing asse s and ope a ing e icien ly”.
As i can be seen, he Depa men o Ene gy is mo e p ecise when de ining
he aims assigned o a SG, highligh ing he impo ance o add essing sa e y issues
[F e08].
2.3 Scope
SGs we e ini ially concei ed o add ess he imp o emen o he DSM, ene gy e i-
ciency and sa e y h ough he cons uc ion o g ids ha a e obus agains sabo age
and na u al disas e s [RPT07]. Howe e , new equi emen s ha e expanded he
ini ial scope o he SGs o some hing b oade which includes he c ea ion o ame-
wo ks o achie e he in e ope abili y o all he ac o s wi hin he SG [FMXY12].
An in e es ing way o analyse wha SGs a e supposed o add ess is h ough he
p oposal o amewo ks. These amewo ks a e usually de eloped by ins i u ions
and one o he main poin s is he de ini ion o he scope o SGs. The nex pa a-
g aphs will summa ise he scope ha some ele an amewo ks ha e de ined o
SGs.
a) NIST F amewo k p oposal. Acco ding o a epo om NIST (Na ional In-
s i u e o S anda ds and Technology o US) [JG12] de eloped in 2010, SGs scope
mus be ocused on:
• Imp o ing powe eliabili y and quali y
21
2. SMART GRIDS
• Op imizing acili y u iliza ion and a e ing cons uc ion o back-up plan s
• Enhancing capaci y and e iciency o exis ing elec ic powe ne wo ks
• Imp o ing esilience o dis up ion
• Enabling p edic i e main enance and sel -healing esponses o dis u bances
• Facili a ing expanded deploymen o RES
• Accommoda ing dis ibu ed powe sou ces
• Au oma ing main enance and ope a ion
• Reducing GHG emissions by enabling EVs and new powe sou ces
• Oil usage by educing he need o ine icien gene a ion in demand peaks
• P esen ing oppo uni ies o imp o e g id secu i y
• Enabling ansi ion o plug-in EVs and new ene gy s o age op ions
• Inc easing consume choice
• Enabling new p oduc s, se ices, and ma ke s.
b) ETP amewo k p oposal. The Eu opean Technology Pla o m (ETP) con-
side s ha SGs ha e been concei ed o mee he challenges and oppo uni ies o
he 21s cen u y [C+06]. The use o e olu iona y new echnologies, p oduc s and
se ices is conside ed as he main pilla o SGs. In pa icula , he SGs scope mus
be o ien ed o educe peaks and was e, encou age manu ac u e s o de elop mo e
ene gy-e icien appliances and sense and p e en blackou s by isola ing dis u b-
ances on he g id.
c) EEGI amewo k p oposal. The Eu opean Elec ici y G id Ini ia i e (EEGI)
conside s ha SGs scope mus include inc easing hos ing capaci y o enewable
and dis ibu ed gene a ion, in eg a ion o na ional ne wo ks in o ma ke -based ne -
wo ks, ac i e pa icipa ion o use s in ma ke s and ene gy e iciency, and opening
business oppo uni ies and ma ke s [EE10] including he s anda disa ion and in e -
ope abili y.
d) IEA DSM Task XVII amewo k p oposal. The In e na ional Ene gy Agency
DSM ask XVII conside s ha SGs mus be o ien ed o ace in eg a ion o DSM,
Dis ibu ed Gene a ion, RES, ene gy s o ages [In 09]. This in eg a ion is con-
side ed as impo an since Dis ibu ed Gene a ion, dis ibu ed ene gy s o ages and
Demand Response can be seen as dis ibu ed ene gy esou ces [HHM11] ha may
help o in eg a e in e mi en RES.
22
2.4 Challenges
In eg a ing all iews and de ini ions om he li e a u e ha has been e iewed,
SGs can be in ex enso de ined by lis ing hei main obse ed ea u es:
• SGs a e de o ed o imp o e ene gy e iciency, eliabili y and secu i y.
• SGs will inco po a e capaci ies o moni o ing and con olling de ices p o id-
ing lexibili y.
• SGs will edesign he way in which ene gy ma ke s a e wo king nowadays,
including he pa icipa ion o many new ac o s in he decision making p o-
cess o e he g id.
2.4 Challenges
In he li e a u e e iew, di e en s a egies ha SGs may be add essing in he u u e
ha e been shown. This las sec ion o he e iew is in ended o summa ise SG
s a egies ollowing he li e a u e e iew made in [XABO+14].
a) Sma me e s and demand lexibili y. Sma me e s appea o be a genuinely
c oss cu ing componen o SGs, al hough hey a e no uni e sally pe cei ed as
necessa y o a SG [Eu 10]. This echnology is expec ed o help educe demand
based on be e in o ma ion and by shi ing load consump ion o o -peak imes
[LC11].
Nowadays, he e is a massi e in oduc ion o he mal loads in he powe g id
such as hea ing sys ems, ai condi ioning sys ems (HVAC) and e ige a o s. In a
nea u u e he massi e in oduc ion o Elec ical Vehicles (EVs) is p edic ed. Bo h
he mal loads and EVs may become a majo d i e o SGs [XABO+14]. This is
due o he ac ha bo h may help sa is y demand peaks by ei he injec ing s o ed
ene gy o s opping consump ion [CMG11]. Howe e , hei in oduc ion in he g id
in ol es a conside able inc emen in he o e all ene gy consump ion. In his si u-
a ion, he challenge is di ided in o wo sub-challenges: imp o ing he capaci y o
powe g ids o suppo hei in oduc ion and adding mechanisms o con ol hese
kinds o de ices o suppo demand peaks. The second one is ela ed o a concep
ha is u he de eloped in chap e 3 called DSM.
b) Secu i y o supply. As men ioned be o e, his is an impo an opic since
he in oduc ion o RES may in ol e secu i y o supply weaknesses due o hei
23
2. SMART GRIDS
in e mi en na u e. As said in [Spe10], we a e s a ing o become awa e o he
h ea o supply dis up ion which may ame he de elopmen o SG a ound ene gy
secu i y.
c) Cybe secu i y, p i acy, and con ol. The in oduc ion o an in as uc u e o
con ol and moni o makes i possible o cap u e da a ha may be sensi i e. Fo his
eason, da a secu i y becomes an impo an conce n bo h om a da a go e nance
and a cybe -secu i y poin o iew [Ho 11]. The i s one conce ns he p i ileges o
access his da a, whe eas he second one conce ns how ha da a mus be handled
o p e en i om being accessed by in ude s [TM11]. The in oduc ion o Sma
Me e s makes his issue e en mo e u gen and mus be app oached by SG s a egies.
d) Sys em de agmen a ion. The non-coo dina ed ope a ion o companies ha
ope a e in he ene gy sec o in ol es se e al di e en s anda d echnologies and
p o ocols. This leads o di e en business models ha equi e o be me ged in o de
o o e come such di e ences [Jac11]. A he same ime, his makes a ansi ion o
a decen alised sys em mo e di icul , which is co e o SGs [XABO+14]. This
agmen a ion p oblem mus be add essed by SGs.
e) Mic ogene a ion and decen aliza ion. Mic o-gene a ion is gene ally e y
impo an o low ca bon elec ici y sys ems, e.g. o alle ia e sys em conges ion
[BMW10]. Mic ogene a ion o e s bene i s o educing he demand, especially in
a wide decen alised con ex . Indi iduals would be esponsible o hei ene gy
p oduc ion and consump ion and, he e o e, would be awa e o how hey ha e o
use ene gy in o de o op imise bo h a iables [DW07].
Howe e , apa om he high cos s hese echnology ha e, he e a e wo main
ac o s which a e p e en ing he de elopmen o hese echnologies [XABO+14].
On he one hand, Dis ibu ion Ne wo k Ope a o s discou age hese in es men s on
inno a ion. On he o he hand, many ene gy ma ke s a e based on cen al gene -
a ion. The inclusion o hese echnologies in he g id would open his ma ke o
small ene gy selle s which, on he agg ega e, may become impo an playe s o he
ma ke .
An in e es ing idea [PRS08] o add ess his kind o egula o y issues is he use
o Vi ual Powe Plan s. Vi ual Powe Plan s can eme ge gi en he comme cial
and egula o y suppo . These plan s will depend on he coope a ion o hose who
a e in con ol [Wol12].
24
2.4 Challenges
) In e ope abili y. P e iously p esen ed amewo ks a e no only in ended o
de ine SGs scope, bu many mo e hings. One o he mos impo an is he de ini-
ion o in e ope abili y. Fo ins ance, NIST has de ined a SG In e ope abili y Panel
(SGIP). This panel is o ien ed o p o ide a o um ha suppo s s akeholde pa icip-
a ion and ep esen a ion wi h he aim o de eloping and e ol ing in e ope abili y
s anda ds [JG12]. SGIP has h ee p ima y unc ions:
• To o e see ac i i ies in ended o expedi e he de elopmen o in e ope abili y
and cybe -secu i y speci ica ions.
• To p o ide echnical guidance o acili a e he de elopmen o s anda ds o
a secu e and in e ope able SG, and
• To speci y es ing and ce i ica ion equi emen s necessa y o assess he in-
e ope abili y o SG- ela ed equipmen .
25
CHAPTER
3
Demand side managemen
In he li e a u e, he e a e many sou ces ha e iew DSM. F om all o hem,
[S 08] is he one ha has been ollowed, due o i s cla i y, o conduc he s uc-
u e o his chap e . A e e iewing DSM, di e en app oaches o simula e DSM
policies a e p esen ed as his is a co e concep in his hesis.
Wi hin he SG concep , moni o ing and con olling he demand is one o he
key aspec s o imp o e powe sys em e iciency. Then, he app oach o new powe
g ids may consis in, besides ac ing on he p oduc ion, ac ing on he demand oo.
Powe g ids may ac upon bo h he p oduc ion and demand side, he e o e hey
bo h become lexible g id elemen s. The objec i es o DSM a e, among o he s, he
minimisa ion o peak demand and he imp o emen a he sys em ope a ion and
planning le el [GMR+03].
3.1 Oppo uni ies o Demand Side Managemen
The e a e di e en sec o s whe e oppo uni ies o he DSM can be ound: gene a-
ion, ansmission, dis ibu ion and demand. These sec o s a e e iewed below.
27
3. DEMAND SIDE MANAGEMENT
3.1.1 Gene a ion
Powe g ids a e designed o suppo he maximum peak o demand. This peak o
demand a ies o e ime on a daily and seasonal basis. Apa om his, powe g ids
a e buil wi h a 20% ma gin o gene a ion capaci y o deal wi h he unce ain y
o gene a ion capaci y and unp edic ed demand inc eases [S 08]. The a e age
u ilisa ion o he gene a ion capaci y in a yea is below 55%.
The e is ano he issue a ising wi h he in oduc ion o in e mi en enewable
sou ces as wind o pho o ol aic. These sou ces o ene gy a e no p edic able and
his causes he in oduc ion o unce ain y in he powe g id gene a ion [BI04].
Nowadays, his is deal by using backup gene a ion uni s ha a e a ailable o sub-
s i u e in e mi en enewable gene a ion in case i is needed. This equi es a high
in es men [Mas13].
This is an oppo uni y o DSM o apply load shi ing (mo e some usages o
ene gy) om peak o o -peak pe iods [GMR+03]. Load shi ing can help, on he
one hand, o educe he equi ed gene a ion capaci y since peaks a e smalle and,
on he o he hand, imp o e he e iciency by inc easing he a e age u ilisa ion o
gene a ion capaci y. Conce ning he in oduc ion o in e mi en enewable gene -
a ion, DSM can help o abso b ene gy in cases in which he e is a high p oduc ion
o in e mi en enewable sou ces and a low consump ion.
3.1.2 T ansmission and dis ibu ion
F om he poin o iew o he ansmission and dis ibu ion, powe g ids a e de-
signed o be obus in case a ci cui is los . When his happens, emaining ci cui s
ake o e he load o he aul y one. Howe e , hese ci cui s canno become o e -
loaded. Tha is, unde peak-load condi ions, hese ci cui s a e usually loaded below
50% [S 08].
An oppo uni y eme ges o he DSM in his ield when powe g ids a e de-
signed o ha e a disagg ega ed gene a ion. Nowadays, mos o he gene a ion ca-
paci y is cen alised. Howe e , he dis ibu ion o gene a ion a oids he need o
ha ing low-used ci cui s jus in case a ci cui ails [S 08]. DSM may help in his
ask o make an ac i e con ol o he dis ibu ed p oduc ion acco ding o he eal-
ime needs o he g id.
28
3.1 Oppo uni ies o Demand Side Managemen
Figu e 3.1: Le : he consump ion o a win e weekday. Righ : he consump ion o a
summe weekday. (Sou ce: Red El´
ec ica de Espa˜
na).
3.1.3 Demand
Demand side is la gely uncon ollable in cu en powe g ids and a ies wi h ime
o he day and season [S 08]. Fo ins ance, in G ea B i ain, he minimum con-
sump ion occu s in summe nigh s and is abou a 30% o he win e peak. The
a iabili y o he consump ion o a powe g id depends on egional en i onmen al
condi ions.
In Spain, as in G ea B i ain and many o he powe g ids, he e is a high a i-
abili y in he amoun o ene gy consumed du ing he day and be ween seasons.
In igu e 3.1, on he le side, he consump ion o a win e weekday in he Spanish
mainland is p esen ed and, on he igh side, a cu e o a summe weekday (Sou ce:
Red El´
ec ica de Espa˜
na [REE]). On he one hand, a iabili y conce ning he day-
ime can be obse ed since he e a e isible alleys and peaks. On he o he hand,
a iabili y be ween seasons can be obse ed since no only he amoun o ene gy
changes bu also he shape o he cu e.
DSM can ha e an oppo uni y o balance his unbalanced condi ion ha exis s
be ween peaks and o -peaks [PD11]. This can be made by shi ing loads om
peak pe iods o consume when he g id is in an o -peak pe iod. A way o add ess
a load shi ing is h ough he use o elec ic ene gy s o ages (ba e ies) so ha hey
can injec ene gy in peak-pe iods and consume ene gy in o -peak pe iods.
Since he massi e in oduc ion o ba e ies is nowadays e y expensi e, o he
ene gy s o ages ha a e ound in he demand side can be used, such as he mal
loads [PD11]. These loads can be shi ed om peak-pe iods o o -peak pe iods.
29
3. DEMAND SIDE MANAGEMENT
The ene gy consump ion o hese loads is no mally no educed bu pos poned.
3.2 Re iew o Demand Side Managemen echniques
DSM is di ided in se e al kinds o in e en ions a he cus ome le el including
Di ec Load Con ol (DLC) and Demand Response (DR) [NNdGW09]. DLC is an
in e en ion a he de ice le el in o de o shi cus ome s’ consump ion ega ding
g id s a e objec i es. DR consis s in modi ying he cus ome s’ ene gy usage om
hei no mal consump ion pa e ns in esponse o changes (e.g. p ice o elec ici y
o e ime) [AES07]. The nex subsec ions in oduce di e en DSM echniques.
3.2.1 Di ec load con ol
The nex sec ions p esen se e al echniques in which a di ec -load con ol ap-
p oach is being used. In hese echniques, appliances a e di ec ly add essed by
emo e de ices in o de o modi y hei consump ion acco ding o ce ain c i e ia.
a) Nigh - ime hea ing wi h load swi ching. The idea o his echnique is o se
hea e s o consume ene gy a nigh in o de o balance he consump ion o he g id.
This echnique equi es he use o addi ional de ices which allows o swi ch hea ing
de ices emo ely by using adio ele-swi ching. This echnique can i in o wha is
known as Di ec -Load Con ol.
b) Comme cial and indus ial p og ammes. The e a e some p og ammes ha
a e aimed a comme cial and indus ial pu poses. Pa icula ly popula a e load-
in e up ible p og ammes which a e o ien ed o p o ide ese e se ices enhancing
he sys em eliabili y. O he p og ammes ha a e no mally a ailable o comme -
cial cus ome consis in con olling loads by using building con ol sys ems o
HVAC. These de ices can be connec ed o powe g id agg ega o s o command o
ei he educe o inc ease consump ion acco ding o he s a e o he g id.
3.2.2 Demand esponse
The nex sec ions p esen se e al echniques in which a demand esponse app oach
is being used. These echniques a e cha ac e ized o using ex e nal s imulus, such
30
4.1 Powe g id simula o s
TEFTS [Uni00]: “p og am has been designed o do ansien s abili y and en-
e gy unc ion analyses o educed dynamic models o ac/dc powe sys ems, wi h
addi ional capabili ies o ol age s abili y (bi u ca ion) s udies based on con inu-
a ion me hods”.
MATPOWER [ZG97]: “is a package o MATLAB M- iles o sol ing powe
low and op imal powe low p oblems. I is in ended as a simula ion ool o
esea che s and educa o s ha is easy o use and modi y”.
Vol age S abili y Toolbox (VST) [Nwa02]: “de eloped a he Cen e o Elec ic
Powe Enginee ing, D exel Uni e si y combines p o en compu a ional and analy -
ical capabili ies o bi u ca ion heo y and symbolic implemen a ion and g aphical
ep esen a ion capabili ies o MATLAB and i s Toolboxes. I can be used o ana-
lyze ol age s abili y p oblem and p o ide in ui i e in o ma ion o powe sys em
planning, ope a ion, and con ol”.
Powe Sys em Analysis Toolbox (PSAT) [Mil05]: “is a Ma lab oolbox o elec-
ic powe sys em analysis and simula ion. The main ea u es o PSAT a e: Powe
Flow; Con inua ion Powe Flow; Op imal Powe Flow; Small Signal S abili y Ana-
lysis; Time Domain Simula ion; Comple e G aphical Use In e ace; Use De ined
Models; FACTS Models; Wind Tu bine Models; Con e sion o Da a”.
In e PSS (In e ne echnology based Powe Sys em Simula o ) [Zho]: “is a ee
and open so wa e de elopmen p ojec . Simula ion is key o enhancing powe
sys em design, analysis, diagnosis, and ope a ion”.
AMES Ma ke Package [Tesa]: “is ou so wa e implemen a ion, in Ja a, o
he AMES Wholesale Powe Ma ke Tes Bed. Ou objec i e is he acili a ion o
esea ch, eaching, and aining, no comme cial-g ade applica ion”.
DCOPFJ [Tesb]: “is a ee open-sou ce Ja a sol e o bid/o e -based DC op-
imal powe low (DC-OPF) p oblems sui able o esea ch, eaching, and aining
applica ions”.
OpenDSS [ADHM]: “is a simula o speci ically designed o ep esen elec-
ic powe dis ibu ion ci cui s. OpenDSS is designed o suppo mos ypes o
powe dis ibu ion planning analysis associa ed wi h he in e connec ion o dis ib-
u ed gene a ion (DG) o u ili y sys ems”.
TSAT [Pow]: “is a leading-edge ull ime-domain simula ion ool designed o
comp ehensi e assessmen o dynamic beha io o complex powe sys ems”.
37
4. MODELLING AND SIMULATION FOR SMART GRIDS
G idLAB-D [Pac]: “is a new powe dis ibu ion sys em simula ion and analysis
ool ha p o ides aluable in o ma ion o use s who design and ope a e dis ibu ion
sys ems, and o u ili ies ha wish o ake ad an age o he la es ene gy echnolo-
gies”.
Some o hese simula o s a e able o simula e he demand side a he le el o
disagg ega ion ha is necessa y. Howe e , hey p esen some limi a ions ha a e
discussed in he hi d pa o his documen .
4.2 Powe g ids as complex sys ems
Powe g ids a e composed o many elemen s a di e en le els connec ed o each
o he , a a physical le el, h ough a ne wo k in as uc u e [K e13]. The pa adigm
shi in he ene gy sec o which is cha ac e ised by new ma ke ules, oge he wi h
he in oduc ion o enewable ene gies and dis ibu ed sys ems ha e inc eased i s
deg ee o complexi y. The implemen a ion o SG echnologies may in ol e he
in oduc ion o a ne wo k laye which enhances he capabili y o communica ion
among he di e en ac o s o he g id.
SG concep s, such as DSM, dis ibu ed gene a ion and ene gy e iciency usage,
encou age new s udies ha equi e new app oaches. P e ious s udies on powe
g ids we e mainly ocused on how o be mo e e icien om he poin o iew o
scheduling powe gene a ion uni s acco ding o a demand which was conside ed
as an agg ega ed load. New s udies ha a e ocused on demand side, dis ibu ion
and local e ec s o applying demand side s a egies equi e he use o app oaches
whe e he powe g id mus be ep esen ed as a complex sys em [PKZK08].
A complex sys em is comp ised o a (usually la ge) numbe o (usually s ongly)
in e ac ing en i ies, p ocesses o agen s, he unde s anding o which equi es he de-
elopmen , o he use, o new scien i ic ools, nonlinea models, ou -o equilib ium
desc ip ions and compu e simula ions [Wo 10] (Figu e: 4.1).
An in e es ing conce n is ha he complex sys em e e s o he way in which
eali y is ep esen ed. Tha is, e e y eal sys em is complex on i s own. Fo ex-
ample, a sys em composed by a ca going h ough a pa h is a complex sys em.
Howe e , i s beha iou can be modelled as a simple equa ion which conside s
38
4.2 Powe g ids as complex sys ems
Figu e 4.1: Complex sys em illus a ion. Ne wo k o di e en ypes o en i ies and
links (colou s) as example o a complex sys em.
some ew ac o s. In his case, he eal complex sys em is simpli ied in o a equa-
ion. Ne e heless, his sys em can be ep esen ed as a complex sys em in which all
main componen s a e sepa a ely modelled and in e connec ed wi h each o he . Fo
example, he ca can be modelled by i s componen s (wheels, engine, s uc u e, e c)
and each o hem will ha e an in luence in he way he ca beha es d i ing along
he pa h. Models and simula o s a e no mally de eloped o a end he equi emen s
o he expe imen hypo heses.
Simila ly, his may be applied o powe g ids. Ini ially, hey we e simply mod-
elled since he manageable sec ion o he powe g id was he p oduc ion side. To
his end, many models and simula ions o hese powe g ids we e ep esen ed by
a sys em o equa ions. Nowadays, he in oduc ion o new echnologies which
enhance he capabili y o ha ing a disagg ega ed con ol on he g id whe e many
di e en ac o s can in luence i boos he s udy o powe g ids o a highe le el o
complexi y [KdH09]. This way, we can unde s and be e he way in which SG
concep s can impac on powe g ids conce ning a wide a ie y o anges: p oduc-
ion, demand, ma ke s, dis ibu ed gene a ion, on-line mic og ids, e c.
The nex pa ag aphs a e in ended o p o ide some insigh s on he mos im-
po an p ope ies o complex sys ems in o de o ully unde s and he in e es o
inco po a ing his app oach o he simula ion o powe g ids. These p ope ies a e
he e ogenei y, ne wo ks and eme gence [BY97].
One o he main p ope ies o complex sys ems is he he e ogenei y o he ele-
men s ha con o m he sys em. This p ope y is one o he ac o s ha de e mine
39
4. MODELLING AND SIMULATION FOR SMART GRIDS
how complex a sys em is.
In his sense, e e ing back o he example o he ca d i ing along a pa h,
we can app ecia e he pa icipa ion o di e en and he e ogeneous en i ies: ou
wheels, an engine, a ca s uc u e (which can be decomposed) and a pa h. Le ’s
assume ha he expe imen is aimed a measu ing how long i akes he ca o go
om one poin o he pa h o ano he . In his sense, hese elemen s which ha e
conc e e beha iou s will in luence on he o al amoun o ime ha i will ake he
ca o do his ajec o y.
Powe g ids a e also composed o he e ogeneous en i ies such as gene a o s,
consume s, dis ibu ion echnologies, e c. Mo eo e , each ca ego y o hem con-
ains mo e he e ogeneous elemen s inside. Each elemen wi hin he powe g id has
an in luence on he sys em beha iou (which is an eme gen beha iou . Eme gence
in complex sys ems is u he de eloped in his sec ion).
Ne wo ks a e ano he impo an p ope y wi hin complex sys ems. This p op-
e y also conce ns he he e ogenei y p ope y as connec ions wi hin complex sys-
ems may be he e ogeneous oo. This he e ogenei y a he ne wo k le el is mainly
due o wo ac o s: ype o connec i i y and kind o connec ions. Connec i i y
ypes in ne wo ks may be any o he lis below [K e13]:
• Fully connec ed ne wo ks: e e y elemen o he sys em is connec ed o all
o he nodes o he sys em.
• Dis ance based ne wo ks: elemen s a e connec ed among hem acco ding o
dis ance c i e ia. Fo ins ance, e e y elemen is connec ed o i s wo closes
elemen s.
• Random ne wo ks: in his kind o ne wo ks, elemen s a e connec ed o each
o he ollowing a andom c i e ia which is based on a ce ain p obabili y.
• Scale ee ne wo ks [B+09]: all sys em ne wo ks ha canno be modelled
ollowing any o he app oaches abo e. Fo example, social ne wo ks do no
wo k ollowing he kind o ne wo ks exposed be o e. In his sense, scale
ee ne wo ks ha e been iden i ied in o de o ca ego ise all hose sys em
ne wo ks which do no i in he o he ca ego ies. In social ne wo ks, iend-
ship among people does no ollow he pa e ns desc ibed abo e. As hey
ollow di e en shapes, hey a e conside ed o be scale ee ne wo ks. Unde
40
4.2 Powe g ids as complex sys ems
hese ne wo ks, elemen s a e connec ed eely o each o he acco ding o he
sys em ha is being modelled.
In he same way ha he e is he e ogenei y among componen s wi hin a com-
plex sys em, connec ions can be he e ogeneous om he poin o iew o he ela-
ion hey exp ess.
Re e ing back o he example o he social ne wo k, links among people wi hin
he social ne wo k may be o di e en na u e: iendship, ela ionship, amily, e c.
In his sense, links a e he e ogeneous as hey exp ess di e en kinds o ela ion
among en i ies and, he e o e, hey de ine he way in which componen s will in e -
ac wi h each o he . Especially in he case o SGs, we can ind many di e en kinds
o links among componen s as, o ins ance, powe lines, communica ion links o
con ainmen ela ions.
The coope a ion among en i ies h ough hese links leads us o he las p ope y:
eme gence. Eme gence is an impo an key issue wi hin complex sys ems. Com-
plex sys ems o en beha e in unexpec ed ways ha canno be in e ed di ec ly om
he beha iou o hei componen s; his is known as eme gen beha iou [NN12].
Eme gen phenomena a e a consequence o complexi y. The ope a ing mode o
a sys em ha is being analysed ollowing a adi ional app oach can be p edic ed
om he sys em model as his ope a ing mode has had o be p og ammed [SPT06].
In he case o complex sys ems, eme gen beha iou s a e no p edic able h ough
ools de o ed only o he da a analysis, which is he eason why simula ion becomes
an essen ial ool o he analysis.
In he case o a complex sys em app oach, e e y single componen beha iou
o he sys em is indi idually modelled acco ding o he beha iou ha each uni
mus ha e. Howe e , he esul s a mac o-scale a e he consequence o all o hese
indi idual beha iou s unning and in e ac ing h ough hei links o each o he .
In o de o explain he main di e ence, a case based on he demand side o a
powe g id is used. Demand side on powe g ids can be modelled in many di e -
en ways ha a e discussed in he nex pa ag aphs. A i s app oach can consis in
aking in o accoun p o iles o consump ion on agg ega ed le els o he g id o be
simula ed (e.g. p o iles modelled h ough an equa ion). E en hough his equa ion
41
4. MODELLING AND SIMULATION FOR SMART GRIDS
may p o ide di e en esul s wi h espec o he p o iles o consump ion, he be-
ha iou o he sys em can be di ec ly in e ed. Ano he app oach can be based on
collec ing da a om appliances and consume s in o de o ully ep esen he de-
mand o a egion. On he one hand, da a om appliances can be used o, acco ding
o his da a, model each indi idual appliance ha is in he g id. On he o he hand,
da a om cus ome s can be used o model he way in which use s in e ac wi h he
appliance wi hin a household.
In his sense, e e y single componen o his complex sys em has been modelled
om hei own poin o iew. Ne e heless, he coope a ion o all o hem wi hin
he sys em p o ides di e en esul s in he o e all consump ion o he g id. This
consump ion canno be in e ed di ec ly om he way in which appliances and
cus ome s can be modelled.
Then, he demand in powe g ids is he esul o he agg ega ion o all he
consump ions o he powe g id. Should dis ibu ed gene a ion be inco po a ed o
he powe g id, his gene a ion can be conside ed as nega i e consump ion. Taking
in o accoun ha demand side policies a e o ien ed o play a ole a he highes
le el o de ail (appliances), i is necessa y o ep esen no only he model o each
di e en kind o de ice, bu also he social beha iou o he people li ing in he
household [PKZK08]. Since people li ing in he household a e sel -in e es ed,
hey a e conside ed as agen s whose ac s cause an e ec a he mac o-scale. This
way o concei ing he modelling o a powe g id as a complex sys em in which
he e a e in elligen agen s is known as agen -based modelling (ABM).
4.3 Agen -based modelling and simula ion
ABM is he compu a ional s udy o social agen s as e ol ing sys ems o au onom-
ous in e ac ing agen s [Jan05]. ABM is a ool ha allows o he s udy o social
sys ems om he poin o iew o an adap i e complex sys em. The e o e, he
esea che is in e es ed in he way in which mac o phenomena a e eme ging as a
consequence o he he e ogeneous indi idual beha iou s ha a e aking place a
he mic o le el [Hol92]. This app oach makes i possible o sys ema ically es di -
e en hypo heses ha a e ela ed o he a ibu es o he agen s, hei beha iou al
ules, hei ypes o in e ac ions and he way in which hey a ec he sys em.
42
4.3 Agen -based modelling and simula ion
An in e es ing discussion which conce ns he use ulness o his app oach o
unning expe imen s is gi en in [Jan05]. The e a e esea che s ha wonde why an
app oach based on ABM may be needed o unning expe imen s. Is i no possible
o app oach he expe imen s based on equa ions? The au ho ’s answe is ha his
decision may depend on he ypes o issue ha a e add essed. Many p oblems
may be aced by using equa ion-based models. Howe e , p oblems ha conce n
coo dina ion o s a egy in e ac ion whe e mul iple agen s a e pa icipa ing need o
be add essed in a di e en way using ABM.
One o he main issues in ABM is he possibili y o ep esen he complex s uc-
u es o social in e ac ions. In some sys ems (e.g. a powe g id), he mac oscale
p ope ies (e.g. o e all demand o ene gy) a e sensi i e o he s uc u e o in e -
ac ions among agen s and social ne wo ks (e.g. cus ome s making decisions ha
a ec ene gy consump ion). Howe e , using an equa ion-based app oach, hese
agen s a e supposed o be implici in hese equa ion models making i impossible
o esea ch on he sensi i i ies o he s uc u e o in e ac ions [Jan05].
The esea ch de eloped on mul iagen sys ems in A i icial In elligence (AI) has
in luenced much he a chi ec u e o agen s p esen in ABM. Mul i-agen esea ch
s udies he adap i e beha iou o au onomous agen s in a conc e e en i onmen
[Jan05]. In elligen agen s a e able o ac lexibly and au onomously [Woo02].
This means ha agen s a e goal-di ec ed (sa is ying o maximizing hei u ili y),
eac i e (adap ing hemsel es o en i onmen al changes) and capable o in e ac ing
wi h o he agen s.
The use o ABM o esea ch and managemen is g owing apidly in a num-
be o ields [RLJ06]. This g ow h is due o he abili y o hese models o add ess
p oblems by he heo y o e olu ion [GRB+05] and s a egies [GR05]. Howe e ,
his kind o modelling is s ill an obs acle o esea che s since i equi es an in-
ensi e so wa e de elopmen in o de o be ca ied ou . Fo his eason, simula-
ion pla o ms ha e been de eloped o make expe imen s using an ABM app oach.
These lib a ies ollow a amewo k and lib a y pa adigm, p o iding a amewo k 1
along wi h a lib a y o so wa e which implemen s he amewo k and p o iding
simula ion ools. These pla o ms ha e succeeded since hey p o ide s anda dised
so wa e designs and ools enabling he simula ion o di e en kinds o models.
1a se o s anda d concep s o designing and desc ibing ABM
43
4. MODELLING AND SIMULATION FOR SMART GRIDS
Howe e , hese pla o ms ha e well-known limi a ions. F om [RLJ06], i e di -
e en pla o ms a e e iewed in he nex pa ag aphs: Ne Logo, Mason, Repas ,
Swa m o Objec i e-C, Swa m o Ja a. Fu he mo e, Anylogic and Flame a e
also e iewed as hey a e also popula solu ions o agen -based simula ion.
a) Ne Logo [TW04]. I is sugges ed o de elop models ha a e compa ible
wi h i s pa adigm o sho - e m, local in e ac ion o agen s and a g id en i onmen
which a e no ex emely complex. Highly ecommended as a ool o p o o yping
models ha can be, la e on, implemen ed in lowe -le el pla o ms. Howe e , i s
simpli ied p og amming en i onmen may make expe ienced p og amme s eel un-
com o able since all code mus be placed in jus one ile (which is con a y o good
p ac ices in objec -o ien a ion p og amming) and he lack o a s epwise debugge .
b) Mason [LCRPS04]. This is a good al e na i e o expe ienced p og amme s
who wo k on models ha a e compu a ionally in ensi e since hey p o ide a good
pe o mance and he bes execu ion ime o he i e pla o ms es ed. Things ha
can be imp o ed a e i s non-s anda dised e minology, i s incompa ible classes wi h
he schedule and i s lack o a e minal window o debugging pu poses.
c) Objec i e-C Swa m [MBLA96]. This e sion o Swa m is s able p o iding
a ai ly comple e se o ools, a clea concep ual basis and cle e design, whe e he
model can be sepa a ed om he in e aces. This ool is o ien ed o help he model
o ganisa ion by allowing he design and implemen a ion o modules in sepa a ed
swa ms, each o one owns objec s and schedules o hei ac ions. This helps o
manage he complexi y o he models which is in e es ing when dealing wi h high
complex sys ems. The d awbacks o his pla o m a e he lack o iendly de elop-
men ools, lack o ga bage collec ion, weak e o handling and low a ailabili y o
documen a ion.
d) Ja a Swa m [MBLA96]. They p o ide he Swa m implemen a ion o Ja a
use s bu i con ains signi ican d awbacks: clumsy wo ka ounds o implemen
Swa m ea u es in Ja a, di icul y on debugging e o s ha happen on Objec i e-C
lib a ies and slow execu ion speed, among o he s.
e) Repas [Col03]. Apa om implemen ing mos o Swa m’s unc ions, hey
ha e added capabili ies like ese and es a models om he g aphical in e ace
and he mul i- un expe imen manage . The execu ion speed is good when com-
pa ed o o he pla o ms. They also p o ide geog aphical and ne wo k suppo .
44
4.4 Swa m In elligence
Howe e , i p esen s weaknesses like he di icul y o ge s a ed on i , especially
o ama eu de elope s, and poo documen a ion.
) Anylogic [BF04]. AnyLogic is a mul i-pa adigm simula o suppo ing ABM
as well as Disc e e E en modeling. I p o ides suppo o de elop lowcha s,
Sys em Dynamics and s ock-and- low desc ip ions. AnyLogic can cap u e a bi -
a y complex logic, in elligen beha iou , spa ial awa eness and dynamically chan-
ging s uc u es. AnyLogic is objec o ien ed and based on he Ja a p og amming
language. To a ce ain deg ee his ensu es a compa ibili y and eusabili y o he
esul ing models [ZLB07].
g) Flame [HCS06]. FLAME (Flexible La ge-scale Agen -based Modelling En-
i onmen ) is an ABM amewo k which allows modelle s om a ious disciplines
like economics, biology and social sciences o easily w i e agen -based models and
simula e hem on pa allel ha dwa e a chi ec u es. The en i onmen allows o c ea e
agen -based models ha can be un in high pe o mance compu e s and g aphical
p ocessing uni s. The simula ion code is gene a ed by p ocessing a model de ini-
ion [KRH+10].
4.4 Swa m In elligence
The Swa m in elligence (SI) concep comes om he ields o AI, Dis ibu ed In-
elligence and Robo ics, whe e one o he main challenges is coo dina ing se e al
obo s. This concep o SI was i s ly in oduced in [BW89]. The au ho was in-
e es ed in how obo s p og ammed wi h simplis ic beha iou s may ou pu in elli-
gence as a esul o he eme gence coming om hei collec i e beha iou . The nex
pa ag aph p esen s how he au ho de ined hese sys ems in which se e al obo s
we e p esen :
“Sys ems o non-in elligen obo s exhibi ing collec i ely in elligen beha iou
e iden in he abili y o p oduce unp edic ably ‘speci ic’ ([e.g.] no in a s a is ical
sense) o de ed pa e ns o ma e in he ex e nal en i onmen .” [BW93]
Based on hese ideas, he au ho in oduced a new concep : SI. Then, Swa m
in elligen sys ems e e o hose sys ems in which each indi idual, o agen , has
been implemen ed wi h simple ules. The main cha ac e is ic o hese sys ems
45
4. MODELLING AND SIMULATION FOR SMART GRIDS
is ha hei eme gence is no p edic able. Once mo e, he au ho ’s de ini ion is
p esen ed o SI:
“Basically swa m in elligen sys ems a e unp edic able o hei de ini ion o
unp edic able (which is ho ough) and hey p oduce esul s ha a e imp obable so
a e in some way su p ising o unexpec ed.” [BW93]
Ano he impo an au ho in he ield o SI is E ic Bonabeau. In [BM01], he
explains SI by exposing main cha ac e is ics o li ing sys ems as o ins ance an
insec colony:
• Flexible: he colony can espond o in e nal pe u ba ions and ex e nal chal-
lenges
• Robus : asks a e comple ed e en i some indi iduals ail
• Decen alised: he e is no cen al con ol(le ) in he colony
• Sel -o ganised: pa hs o solu ions a e eme gen a he han p ede ined
In [BM01], SI is conside ed a mindse a he han a echnology ha uses a
bo om-up app oach o con ol and op imise dis ibu ed sys ems. This bo om-up
app oach ha p o ides an eme gen beha iou is suppo ed by he use o esilien ,
decen alised and sel -o ganised echniques.
In his hesis, he e alua ion o his kind o in elligence is made as a way o
design and pe o m DSM policies. I can be conside ed ha e e y single load o
he demand side is an an . Then, he sys em has a huge amoun o an s which can be
con olled a any ime o balance he powe g id. In he li e a u e e iew he e a e
di e en au ho s ha ha e app oached he DSM policy design and implemen a ion
h ough SI algo i hms.
SI ha e been p e iously used o con ol di e en aspec s in powe g ids. In
[YKF+00], he con ol o eac i e powe and ol age is made using Pa icle Swa m
Op imisa ion. In [Cao04], he powe g id is balanced h ough a collec ion o local
in e ac ions using An Colony Op imisa ion. An ecosys em o in elligen , au onom-
ous and coope a i e an s is p esen ed in [SD06]. These an s make decisions when
he sys em is unbalanced. An imp o ed e sion o he An Colony Op imisa ion is
used in [SS03]. In his e sion, mul iple colonies a e used o op imising he pe -
o mance o a conges ed ne wo k by ou ing ene gy ia se e al al e na i e pa hs.
46
5.3 Model D i en Enginee ing abs ac ion le els
Figu e 5.1: P oposal o MDE abs ac ion le els based on ou laye s.
In o de o be e unde s and hese abs ac concep s, an example ela ed wi h
ilms is explained (Figu e: 5.2). In his example, he ilm Casablanca is in he
lowes -le el o abs ac ion (M0). In his le el, he e a e objec s o he eali y, so
his ilm, Casablanca, e e s o a conc e e ins ance o he ilm (e.g. a conc e e
DVD). This DVD is an ins ance o a ilm which is de ined in M1. M1 may be
de o ed o ep esen he domain o he ilms. To do so, he ilm concep is de ined
he e. Howe e , his ilm is an ins ance o a concep which is coming om he
le el M2. In his le el, a me amodel is de ined in o de o allow de ining concep s
o he ilm domain. Fu he mo e, in M2, a class called A ibu e is de ined. This
class is o ien ed o p o ide he possibili y o gi ing a ibu es in he le el M1. This
is he case o he a ibu e yea in he concep ilm. Finally, a ibu e and concep
a e ins ances o classes, elemen ha is p o ided om M3 allowing o desc ibe and
de ine he me amodel in M2.
Ha ing hese ou le els is powe ul om he poin o iew o he de elopmen .
M0 and M1 le els do no need jus i ica ions as hey a e he common le els ha a e
suppo ed by all objec -o ien ed languages in which he class de ini ion would be
simila o he concep s p esen in M1 and he objec ins ances a e in M0.
In his example, M2 con ains classes ha a e necessa y o de ine elemen s in
M1. Thanks o hese classes, he ilm concep can be de ined. This le el is neces-
53
5. MODEL DRIVEN ENGINEERING
Figu e 5.2: Example o he use o MDE abs ac ion le els (using UML no a ion).
54
5.4 Model D i en Enginee ing applica ion cases
sa y no only o his eason, bu also o ha ing a o mal desc ip ion ha will be
used o gene a o s and ansla o s so ha hey can in e p e M1 models. The e-
o e, based on his M2, gene a o s and ansla o s will be able o p ocess all M1
models ha a e M2 compliance, gene a ing as many so wa e solu ions as di e en
M1 models a e de eloped.
A he same ime, M2 equi es a se o mechanisms o de ine all i s elemen s. To
his end, he me ame amodel, M3, p o ides one mechanism which allows de ining
classes in he M2 le el. This M3 le el is no mally called me ame amodel bu i is
usually a language ha allows de ining classes in M2.
Ano he example can be in o ma ion sys ems. Unde his con ex , M3 may
p o ide a se o mechanisms o de ine classes in M2. These classes de ined in M2
may desc ibe all needed elemen s o building in o ma ion sys ems. Then, M1 may
desc ibe a speci ic de ini ion o an in o ma ion sys em (e.g. in o ma ion sys em o
a speci ic ci y council) being suppo ed by M2. M0 may ep esen he in o ma-
ion sys em in exploi a ion. The e o e, many M1 models ep esen ing in o ma ion
sys ems o se e al o ganisa ions can be de eloped based on classes ha a e in M2.
Gene a o s and ansla o s can build a so wa e solu ion o each o hese M1 mod-
els.
5.4 Model D i en Enginee ing applica ion cases
The e a e many success ul MDE applica ion cases. Fo ins ance, Mo o ola has
been wo king using MDE [BLW05]. In one way o ano he , hey ha e been using
MDE o nea ly wo decades. They ha e ound ha h ough he coo dina ed and
con olled in oduc ion o MDE echniques, signi ican quali y and p oduc i i y
gains can be consis en ly achie ed, and he issues encoun e ed can be handled in a
sys ema ic way.
Ano he example is he applica ion o MDE in eBusinesses [He 09]. This e-
sea ch is o ien ed o help small o ganisa ions ha ha e limi ed esou ces o de ine
hei o ganisa ional and echnological p ojec ion. A conclusion o his esea ch
is ha his app oach is no only use ul o his kind o o ganisa ions bu also o
modelling se ice s uc u es o public adminis a ions.
55
5. MODEL DRIVEN ENGINEERING
MDE has also been ied in he ield o mobile applica ions de elopmen . In
his ield he e a e new challenges ha mus be aced, pa icula ly, he p edic ion
o pe o mance o a gi en design [TWDS09]. The au ho s ha e seen in MDE a
p omising app oach o add ess hese challenges. Using MDE i is possible o
de elope s o quickly unde s and he consequences o a chi ec u al decisions.
A esea ch made in [RMMG08] e alua es he use o MDE in designing adap -
i e mul i-agen sys ems. This is a opic ha is eally close o wha his hesis is
add essing. They conclude ha i wo ked as expec ed o an ad-hoc case bu ha
he e is s ill some wo k o be done o gene alising.
In [SCF+06], he au ho s ackle he main p oblems o au o-gene a ed use in-
e aces. To his end, MDE echniques a e e alua ed. Thei conclusion s a es ha
eusing MDE echnologies may be p omising.
56
CHAPTER
6
Da a analysis and decision
making suppo
Decision making p ocesses ake place in e e y company o o ganisa ion. In
hese ins i u ions, many decisions ha mus be made a e o ien ed o sol e he p ob-
lems o he domain in which hey a e wo king. Since hese decisions ha e epe -
cussions in each ins i u ion, hey mus be made wi h maximum accu acy so ha
bene i s can be maximized.
One o he main keys o succeeding in decision making p ocesses consis s in
ha ing as much in o ma ion as possible which conce n he domain o he decisions
o be made. Howe e , ge ing his in o ma ion may equen ly be impossible o oo
cos ly. Fo his eason, he use o s a egies o ga he in o ma ion o o educe he
unce ain y is impo an in o de o imp o e decision making p ocesses.
The e a e wo key concep ha mus be de ined: decision and decision making.
A decision is de ined as he choice o one among a numbe o al e na i es. Decision
Making e e s o he whole p ocess o making he choice [Boh03]
O e ime, di e en concep s ha e eme ged o suppo decision making p o-
cesses [HW05]. The e a e many o he classi ica ions o all hese concep s ha a e
57
6. DATA ANALYSIS AND DECISION MAKING SUPPORT
CONCEPT EVOLUTION
MIS
Managemen
In o ma ion
Sys ems
DSS
Decision
Suppo
Sys ems
EIS
Execu i e
In o ma ion
Sys em
DW
Da a
Wa ehouse
BI
Business
In elligence
1960 1970 1980 1990 2000 2010
Figu e 6.1: E olu ion o concep s ha ha e add essed he decision making suppo
[HW05].
ela ed o suppo decision making p ocesses. Howe e , checking he li e a u e e-
iew, i can be obse ed ha mos ele an li e a u e o each concep is mainly
concen a ed in he yea s ha igu e 6.1 p esen s.
In his chap e , wo o he concep s a e e iewed in a high le el o de ail: De-
cision Suppo Sys ems (DSS) and Business In elligence (BI). The i s has been
included because i is one o he esea ch opics ha has had mo e ele ance in he
pas o decision making p ocesses. The second one has been included oo because
i is he one ha is nowadays in ogue. Ne e heless, he o he h ee a e b ie ly
desc ibed in he nex pa ag aphs:
a) Managemen In o ma ion Sys em [Swa74]. The main idea is o ha e a da a
bank con aining all he ele an in o ma ion conce ning he company. To his end,
each execu i e o he company will be equipped o emo ely connec h ough e -
minal o a la ge scale compu e ha con ains his da a bank.
b) Execu i e In o ma ion Sys em[Sp 80]. EIS p o ides a se o capabili ies in-
cluding epo p epa a ion, inqui y capabili y, a modelling language, g aphic dis-
play commands, and a se o inancial and s a is ical analysis sub ou ines.
c) Da a Wa ehouse[Gup97]. A da a wa ehouse is a eposi o y o in eg a ed
in o ma ion a ailable o que ying and analysis [IK93, Wid95]. The in o ma ion is
s o ed in se s o iews de i ed om he da a e ie ed om he sou ces.
58
6.1 Decision Suppo Sys ems
Figu e 6.2: Decision making p ocess unde a DSS en i onmen .
6.1 Decision Suppo Sys ems
DSS a e compu e echnology solu ions ha a e used o suppo complex decision
making and p oblem sol ing [SWC+02]. Classic DSS ool design is comp ised
o componen s o (i) sophis ica ed da abase managemen capabili ies wi h access
o in e nal and ex e nal da a, in o ma ion, and knowledge, (ii) powe ul modelling
unc ions accessed by a model managemen sys em, and (iii) powe ul, ye simple,
use in e ace designs ha enable in e ac i e que ies, epo ing, and g aphing unc-
ions. Much esea ch and p ac ical design e o has been conduc ed in each o
hese domains.
The p ocess ha is usually ollowed o making decisions unde a DSS en i on-
men is shown in igu e 6.2 [SWC+02]. The main ocus is made on he model de-
elopmen and p oblem analysis. Once he p oblem is ecognised, his is desc ibed
in e ms ha make he c ea ion o models easie . Models a e c ea ed ep esen ing
al e na i e solu ions o he p oblem aised. Then, one o hese solu ions is selec ed
and implemen ed. This p ocess is i e a i e and has looping backs o p e ious s ages
acco ding o he be e ecogni ion o he p oblem, solu ions ha ail, e c.
This esea ch ield has e ol ed o he las 50 yea s p o iding di e en ap-
p oaches o DSS. The di e en app oaches o DSS de eloped du ing his pe iod can
59
6. DATA ANALYSIS AND DECISION MAKING SUPPORT
be classi ied in one o he ollowing g oups: model-d i en, da a-d i en, communica ion-
d i en, documen -d i en and knowledge-d i en [Pow07].
a) Model-d i en DSS. The i s app oaches o DSS we e model-d i en. Ex-
amples o hem can be seen in [MS84] and [FJ69]. Model-d i en DSS is ocused on
he accessibili y and manipula ion o inancial, op imisa ion and simula ion mod-
els. This is, simple models ha p o ide he mos elemen a y le els o unc ionali y.
Model-d i en DSS use limi ed da a and pa ame e s p o ided by decision make s in
o de o allow he analysis o a si ua ion by he decision make s, no being neces-
sa y la ge da a bases [Pow02]. IFPS (In e ac i e Financial Planning Sys em) is he
i s comme cial ool ha allows o de elop model-d i en DSS based on inancial
and quan i a i e models. This ool, IFPS, was de eloped by Ge a ld R. Wagne
and his s uden s a he Uni e si y o Texas in he 1970s. Ano he DSS ool based
on a model-d i en app oach was Expe Choice [exp]. VisiCalc [ is], a ool de-
eloped by Dan B ickling and Bob F anks on, p o ided he oppo uni y o he
analysis and decision suppo a a easonably low cos . This ool was he i s kille
applica ion o pe sonal compu e s and made possible he de elopmen o many
model-o ien ed, pe sonal DSS o manage s o use.
b) Da a-d i en DSS. Da a-d i en DSS is ocused on he access and manipula-
ion o empo al da a. This da a can be ei he in e nal o ex e nal om he poin
o iew o he company and some imes i is equi ed o deal wi h eal- ime da a.
In [CCS93], he au ho s conside ha Da a-D i en DSS wi h On-Line Analy ical
P ocessing (OLAP) p o ide he highes le el o unc ionali y and decision suppo
o analyse la ge collec ions o da a. In [Nyl99], he de elopmen o BI ela ed o
P oc e & Gamble’s e o was conside ed in 1985. They buil a DSS ha linked
in o ma ion o sales and e ail scanne da a. BI became popula as a e m ha was
coined and p omo ed by Howa d D esne o he Ga ne G oup in 1989. BI was
desc ibed as a se o concep s and me hods ha a e o ien ed o imp o e he decision
making o businesses by using ac -based suppo sys ems.
c) Communica ion-d i en DSS. The use o he ne wo k and communica ion
echnologies in o de o make easie he decision- ele an collabo a ion and com-
munica ion is known as Communica ion-d i en DSS. The dominan elemen o he
a chi ec u e in hese sys ems is he communica ion echnologies such as g oup-
wa e, ideo con e encing and based bulle in boa ds [Pow02]. Apa om hese
60
6.2 Business In elligence and On-Line Analy ical P ocessing
p ima y echnologies, he massi e expansion o he in e ne has made possible
he inc emen o echnologies o synch onous communica ion-d i en DSS such
as oice and ideo deli e ed h ough he in e ne .
d) Documen -d i en DSS. A documen -d i en DSS p o ides documen e ie al
and analysis using a compu e s o age in conjunc ion wi h p ocessing echnologies.
These compu e s o ages may con ain scanned documen s, hype ex documen s,
images, sounds and ideos. This ma e ial can be accessed by documen -d i en
DSS. An example o his kind o DSS is a sea ch engine [Pow02].
e) Knowledge-d i en DSS. Knowledge-d i en DSS solu ions p o ide ecom-
mended ac ions o manage s. These sys ems a e pe son-compu e sys ems wi h
specialized p oblem-sol ing expe ise. Such expe ise consis s o knowledge abou
a pa icula domain, unde s anding o p oblems wi hin ha domain and skills o
sol ing some o hese p oblems [Pow07]. These sys ems a e no mally implemen-
ed based on AI echniques.
6.2 Business In elligence and On-Line Analy ical P o-
cessing
BI is a se o me hodologies, p ocesses, a chi ec u es and echnologies ha com-
bine da a ga he ing, da a s o age, and knowledge managemen wi h analy ical ools
o p esen complex in e nal and compe i i e in o ma ion o planne s and decision
make s [EN08, Neg04]. The objec i e is, he e o e, o imp o e he imeliness and
quali y o inpu s o he decision p ocess by making he access o he in o ma ion
easie . These sys ems a e conside ed o be p oac i e and a e composed by he
ollowing essen ial componen s [LV03]:
• eal- ime da awa ehousing,
• da a mining,
• au oma ed anomaly and excep ion de ec ion,
• p oac i e ale ing wi h au oma ic ecipien de e mina ion,
• seamless ollow- h ough wo k low,
• au oma ic lea ning and e inemen ,
• geog aphic in o ma ion sys ems
61
6. DATA ANALYSIS AND DECISION MAKING SUPPORT
Figu e 6.3: BI da a amewo k.
• da a isualisa ion
The way in which BI ools wo k is h ough con e ing da a in o use ul in o m-
a ion which, using human analysis, is hen con e ed in o knowledge [Neg04]. A
main ask consis s in c ea ing o ecas s based on his o ical da a, pas and cu en
pe o mance, and es ima es ega ding u u e ends. Based on an analysis o he u-
u e, al e na i e scena ios can be designed in o de o e alua e hei impac (Wha i
analysis). Ano he necessa y ask is accessing he da a in o de o answe speci ic
ques ions. This is, ha he da a wa ehouse mus be lexible enough o answe -
ing his kind o ques ions. Fu he mo e, BI ools mus p o ide s a egy insigh s as
esul s o he que ies ha a e launched on he ool.
BI is de o ed o assis in s a egical and ope a ional decision making. An in e -
es ing summa y o his kind o decision makings is p o ided in [Wil02]:
• Co po a e pe o mance managemen
• Op imizing cus ome ela ions, moni o ing business ac i i y, and adi ional
decision suppo
• Packaged s andalone BI applica ions o speci ic ope a ions o s a egies
• Managemen epo ing o BI
One o he main issues wi hin BI is he managemen o semi-s uc u ed da a.
The asks ha a e de eloped by he analys s in o de o deal wi h bo h s uc u ed
and semi-s uc u ed da a is equi ed [RC03].
Semi-s uc u ed da a do no i in o ela ional o la iles, which is called s uc-
u ed da a. A su ey pe o med in [BA03] indica ed ha 60% o Chie In o ma-
ion O ice and Chie Technology O ice conside semi-s uc u ed da a as c i -
ical o imp o ing ope a ions and c ea ing new business models. Examples o
62
7.1 Hypo hesis #1
all sys em. Howe e , he way in which he o e all sys em will beha e canno be
in e ed om hese indi idual beha iou s. The e o e, as said be o e, his sys em
needs o be s udied as a complex sys em.
Fu he mo e, among all he elemen s, he e a e also agen s which a e cha ac-
e ised by being able o pe cei e he en i onmen and ac au onomously acco ding
o hei pa icula goals. Thus, powe g ids may be ep esen ed using an ABM
app oach. This app oach uses he same p inciples as complex sys ems, conside -
ing he inclusion o agen s as an impo an pa o he modelling and simula ion
p ocess.
In he s a e o he a , se e al agen -based pla o ms we e s udied in o de o
pe o m SG simula ions. As a esul , we ha e ound some limi a ions. Ini ially, he
lack o an explici seman ic ep esen a ion in models does no allow he euse, sha -
ing o combina ion. This is due o he ac ha each model has i s own seman ic, so
di e en de elope s can p oduce models ha canno be eused. The e o e, wo king
in a modula way is no acili a ed by hese pla o ms due o hei lack o seman ic
suppo .
Ano he limi a ion is he lack o suppo o de eloping la ge scale models. I is
possible o build la ge scale models o hese pla o ms, bu he de elopmen p ocess
could esemble he complexi y o he models. These pla o ms do no p o ide a
me hodology ha suppo s he de elopmen p ocess in la ge scale models. This is
o say, we ind he e is a lack o a chi ec u al suppo o de eloping la ge-scale
models.
Fu he mo e, hese pla o ms p o ide languages o desc ibing ha a e isola ed
om he p oblem domain. This means a seman ic gap be ween he concep s o
pla o m and modelle s. Ideally, modelle s would be mo e com o able exp essing
hei models in a language close o he p oblem domain.
The app oach o his hesis ocuses on complexi y. MDE has been explo ed as
a me hodology o de eloping an agen -based complex sys em o SGs. The ap-
plica ion o his me hodology in his ield has been esea ched in o de o ep esen
la ge scena ios h ough models. This hypo hesis is s a ed as ollows:
“MDE helps o de elop models o la ge powe g ids using an agen -based ap-
p oach wi h a high le el o disagg ega ion and can also gene a e simula o s acco d-
ing o hese models.”
69
7. MANAGING COMPLEXITY
7.2 Hypo hesis #2
La ge-scale simula ions p o ide a la ge amoun o da a ha needs o be analysed.
This analysis is necessa y since p oblems in he modelling p ocess can be de ec ed
and co ec ed, and consequences o DSM policies can be analysed. These con-
sequences will e u e o alida e he ideas de ined o a DSM policy and based on
his, new ideas can eme ge o imp o e i s design.
New app oaches o da a analysis and managemen de ine he p oblem o dealing
wi h da a as he challenge o he 3 Vs: olume, a ie y and eloci y [Lan01]. This
“3 Vs” challenge was c ea ed by Doug Laney, a Ga ne analys . “Volume” e e s
o he la ge amoun o da a, “ a ie y” o he da a he e ogenei y and “ eloci y” o
he need o ex ac ele an in o ma ion om he da a as as as possible.
The applica ion o BI me hodologies has been esea ched o add ess hese p ob-
lems o dealing wi h simula ion da a ou pu . The use o hese me hodologies o
analyse da a coming om la ge-scale simula ions may be help ul, om he poin
o iew o he design o DSM policies, o iden i y p oblems in he design o he
policy o “bugs” in i s implemen a ion o in he base scena io in which he policy
is es ed. This hypo hesis is s a ed as ollows:
“BI helps o analyse la ge amoun o he e ogeneous da a coming om simula-
ions.”
7.3 Hypo hesis #3
DSM policies aim o modi y he way in which ene gy is consumed in o de o im-
p o e he e iciency o powe g ids. Ne e heless, his modi ica ion o demand may
a ec he consume s by no mee ing hei needs. The e o e, hese policies, espe-
cially hose ha di ec ly ac on demand appliances, mus be designed o conside
consume needs in o de o ensu e a minimum quali y o se ice. Thus, he en i -
onmen in which policies a e ope a ing consis s o many di e en sel -in e es ed
agen s.
This p oblem can be o mula ed as an op imisa ion p oblem in which esou ces
mus be dis ibu ed e icien ly. In his sense, policies based on op imisa ion me h-
ods can be designed in o de o deal wi h eal p oblems on he demand side such
70
7.4 Resea ch conce ns
as he massi e in oduc ion o EVs in o he g id o he modi ica ion o he demand
side acco ding o g id s a es. SI o e s a p omising app oach o dealing wi h his
kind o op imisa ion p oblems. In his documen , he applica ion o his app oach
in he design o DSM policies is explo ed and e alua ed. This hypo hesis is s a ed
as ollows:
“SI helps o design DSM policies by dealing wi h he in e es s o many ac o s
in ol ed in he p ocess.”
7.4 Resea ch conce ns
The hypo heses p esen ed in ol e he applica ion o me hodologies. Thus, he main
esea ch conce n is how o alida e ha hese me hodologies a e going o be help ul
in sol ing he p oblems s a ed.
I is impo an o dis inguish he concep o “me hodology” om he concep o
“me hod”. In his sec ion, bo h concep s a e in oduced and ela ed o each o he o
cla i y he seman ic o he e ms.
On he one hand, a “me hodology” in enginee ing can be conside ed as a guideline
o sol ing a ce ain kind o p oblem. Me hodologies a e gene ally comp ised o
he ollowing ou elemen s: desc ip ion o he p oblem ha needs o be sol ed,
de ini ion o which echniques a e o be used and when hey will be used, gi ing
ad ice on p oduc quali y managemen as well as p o iding ools o acili a e he
p ocess [RH97].
On he o he hand, a me hod is a p ocedu e ha de ines a egula and sys ema ic
way o accomplishing some hing [How12].
Gene ally speaking, me hodologies do no desc ibe speci ic me hods. A me hod
ho oughly de ines he s eps ha ha e o be pe o med in acco dance o a me h-
odology guideline. The e o e, many me hods can be based on one me hodology
p o ided hey ollow he me hodology di ec i es [Vac12].
In gene al, me hodologies canno be alida ed h ough he scien i ic me hod.
This means, he ce ain y o he hypo heses canno be demons a ed wi h expe i-
men s since i is no possible o execu e he same p ojec wi h wo o mo e di e en
me hodologies in o de o compa e hem. Mo eo e , his conce n becomes mo e
de e minan since he execu ion o he p ojec depends on many o he a iables:
71
7. MANAGING COMPLEXITY
human knowledge, he na u e o he sys em, budge , ime, e c. The e o e, i is
almos impossible o isola e he me hodology a iable in he success o a p ojec .
Ne e heless, his wo k is an expe imen al esea ch wi h he goal o p o iding
e idence so as o he alidi y o hese me hodologies o sol ing he p oblems o
enginee ing SGs h ough case s udies. “Case s udy esea ch design” is p oposed as
a aluable and impo an empi ical app oach o alida ing a me hodology [LR04,
ZW97]. This is a use ul me hod o alida ing me hodologies by using hem in eal
wo ld si ua ions.
Gene ally, in expe imen al esea ch, i is necessa y o es ablish e idence o
causali y h ough in e nal alidi y [SAA+02]. Howe e , his is no su icien when
conduc ing expe imen s using enginee ing me hodologies. These expe imen s may
also s udy he ex e nal alidi y o hypo heses, which ensu es hei applica ion in a
b oade spec um o cases a he han only in expe imen al si ua ions.
An expe imen has in e nal alidi y i i demons a es a causal ela ion be ween
wo o mo e a iables [B e00]. A causal in e ence can be based on a ela ion when
h ee c i e ia a e sa is ied [SCC02]: empo al p ecedence (cause p ecedes e ec );
co a ia ion (cause and e ec a e ela ed) and nonspu iousness ( he e a e no mo e
explana ions o he obse ed co a ia ion).
In e nal alidi y is easy o demons a e h ough he esul s. I is also impo an
o demons a e ex e nal alidi y. Ex e nal alidi y is a gene alisa ion o causal
in e ences in scien i ic s udies [MJ12]. Tha is o say, esul s can be ex apola ed
om an expe imen o o he si ua ions.
A his poin , i is necessa y o de ine wha can be unde s ood by “ex e nal
alidi y” in he con ex o his esea ch. In his sense, wo di e en concep s o
ex e nal alidi y can be conside ed. In he i s , i can be said ha he hypo heses o
his esea ch ha e ex e nal alidi y i hey can be applied o mo e han one expe -
imen ela ed o SGs. The second, i could be ha he hypo heses o his esea ch
ha e ex e nal alidi y i hey can be applied o he enginee ing o o he ields apa
om SGs.
In his esea ch, a se o expe imen s has been de ined o alida e he hypo-
heses. Some o hese expe imen s ha e been join ly de ined wi h ou pa ne s
and colleagues om EIFER [ei ] and EDF [ed a]. These expe imen s analyse eal
p oblems in eal powe g ids. These con ibu ions o he expe imen al pa o his
72
7.4 Resea ch conce ns
wo k a e aluable because he alida ion has been made using expe imen s based
on eali y ins ead o only syn he ic p oblems.
Fu he mo e, hese expe imen s a e impo an because hey a e eal cases which
helps o demons a e bo h in e nal alidi y and ex e nal alidi y. The in e nal alid-
i y is demons a ed h ough he esul s o he execu ion o expe imen s. Conce ning
ex e nal alidi y, he i s de ini ion p esen ed is also demons a ed since se e al ex-
pe imen s ha e been conduc ed using he same me hodologies. The second de ini-
ion is no demons a ed in his esea ch bu i could be p oposed as u u e wo k.
73
CHAPTER
8
Sma G id modelling
As said be o e, a simula o is equi ed o each expe imen al s udy. Fo small
expe imen s, he cons uc ion o a simula o is usually simple and as . Howe e ,
la ge expe imen s equi e he cons uc ion o a mo e complex simula o since di -
e en beha iou s mus be implemen ed. Fu he mo e, in la ge expe imen s, he
ini ial expe imen condi ions a e changing cons an ly, so i is necessa y o modi y
he simula o in a as and easy way [PHS+08].
As he simula o cons uc ion may be an a duous ask du ing he expe imen al
s udy, gaining p oduc i i y in his ask is e y impo an . F om he poin o iew
o So wa e Enginee ing, his simula o mus be suppo ed by a good a chi ec u e.
This a chi ec u e should acili a e he simula o cons uc ion and allow he execu-
ion o con inuous changing o equi emen s ha ake place du ing he expe imen al
s udy.
Since he so wa e de elopmen o hese simula ions is ime-consuming, i is
necessa y o ind a way o imp o e de elopmen pe o mance. In he nex sec ions,
a amewo k o de elop simula o s based on MDE app oach is p esen ed. This
amewo k, known as Ta a [EKM+11, EHHK12, EHH13c], is in ended o speed up
he c ea ion o simula o s o domains in which a complex sys em app oach and a
disc e e iming can be applied. Among o he ad an ages, he use o MDE enhances
75
8. SMART GRID MODELLING
Figu e 8.1: Abs ac ion le els o modelling powe g ids.
he capabili y o componen euse and hides implemen a ion de ails p o iding a
high le el language o de elop simula o s.
The adap a ion o MDE o he ield o complex sys em simula ions is he base
ha suppo s he Ta a amewo k. The design o he abs ac ion applied o his
case consis s o h ee le els o abs ac ion. The i s le el, conside ed he lowes
abs ac ion le el, consis s o he conc e e elemen s ha can be ound in a speci ic
domain. The second and hi d le els p opose a way o abs ac he de ini ion o his
conc e e elemen .
In igu e 8.1 hese h ee le els a e ep esen ed o a conc e e case which is e-
la ed o he wo ld o powe g ids. In a i s le el, which is ela ed o he model de-
sc ip ion, conc e e elemen s a e loca ed. This is, a conc e e household, powe line
and cus ome . Howe e , hese conc e e elemen s can be abs ac ed in o a second
le el. The second le el, known as Me amodel, he concep o a household, a powe
line and a cus ome is ep esen ed wi hou conside ing speci ic de ails o a conc e e
ins ance. Based on his concep speci ica ion, he conc e e elemen s can be de ined
by pa ame ising he elemen s o he second le el. A hi d le el o abs ac ion sim-
pli ies he complex sys em wo ld in o h ee di e en ypes o elemen s: en i ies,
connec ions and agen s. The e o e, in his case, a building is conside ed as a en i y,
a powe line as a connec ion and a cus ome as an agen o he powe g id sys em.
76
8.1 Model D i en Enginee ing o modelling complex sys ems
8.1 Model D i en Enginee ing o modelling complex
sys ems
Ta a allows building models o complex sys ems whe e he o e all scene is de-
composed in o di e en componen s. Each componen o he model is s a ically
ep esen ed by means o a ibu es and a iables. In his way, i is modeled a s uc-
u al iew o he sys em. In o de o model he beha iou al iew o he sys em,
each componen may include se e al beha iou s ha desc ibe how his componen
changes o e ime and he way in which i in e ac s wi h o he componen s. Tha
is, he dynamic o he sys em.
This sepa a ion be ween s uc u al and beha iou al iew is a majo design con-
ce n in Ta a since i allows modeling a complex sys em in a modula app oach. A
he same ime, his design echnique is o ien ed o ace he challenge o building
la ge scale simula ions. Basically, when a simula ion is unning, all he beha iou s
a e concu en ly execu ing and modi ying he a iables o hei componen s.
8.1.1 A chi ec u e
The co e componen o Ta a a chi ec u e is he Me amodel (Figu e 8.2) which de-
sc ibes he ypes o ins ances ha could exis in he complex sys em which shall be
ep esen ed. The Me amodel is o ien ed o a speci ic domain and hus es ablishes
a common seman ic which allows modele s o sha e simula ions and models. The
Me amodel allows a ep esen a ion in e ms o en i ies, connec ions, agen s, a ib-
u es, a iables and con ex , among o he s.
The Me amodel can be ansla ed in o se e al o ma s: HTML, XSD and Ja a.
The i s one, HTML, allows he obse a ion o he Me amodel elemen s, as well
as hei p ope ies, in an easy and com o able way by using a b owse . The XSD
ansla ion e u ns a XML scheme model ha helps o alida e he model cons uc-
ion om a seman ic poin o iew and p o ide alid nex okens o add when
w i ing models. Finally, he mos impo an one, he Ja a classes ha implemen s
he Me amodel elemen s and hei ea u es. These Ja a classes a e used in com-
bina ion wi h he Simula o Engine and beha iou s ( eposi o y) in o de o build
simula o s.
77
8. SMART GRID MODELLING
Figu e 8.2: A chi ec u al diag am o he amewo k.
78
8.1 Model D i en Enginee ing o modelling complex sys ems
can widely a y om one o ano he . The e o e, a main ask o de eloping
simula ions consis s in p epa ing da a in o de o use hem in he model.
Depending on he da a o ma , cohe ence, comple eness, complexi y, e c. he
e o o de elop his ask may a y. The inal goal is o ha e he da a wi h
a o ma ha allows i o be pa sed by he P o ile . Howe e , some small
expe imen s may no ha e a complica ed p ocess o p epa ing he da a.
• Model c ea ion. This s ep is di ided in wo main pa s: c ea ion o he sim-
ula ion model and c ea ion/modi ica ion o model elemen s o beha iou s.
The i s one is manda o y o de eloping a simula ion since in his simu-
la ion model he scena io o he expe imen is desc ibed. The second one
depends on he expe imen equi emen s (e.g. a new elemen ha is no in
he Me amodel may be needed o a new beha iou , e c.). Based on he da a
o ma ed in he s ep be o e, he P o ile could help o gene a e he scena io,
especially, i i is a la ge one.
• Model simula ion and calib a ion. The model simula ion s ep inalises wi h
he esul s ga he ing. Howe e , his does no only consis o unning he
model in he simula o and wai ing o he esul s, bu also e i ying and al-
ida ing ha his p ocess is co ec (known as calib a ion). This calib a ion
p ocess conce ns he e i ica ion o he co ec wo king o each simula ion
elemen by checking ha he ou pu s hey ha e a e he expec ed ones. This
can be app oached in se e al ways: using es ing amewo ks, ou pu check-
ing by hand, e c. Fu he mo e, he expe imen equi emen s mus be con on-
ed wi h he simula ion ea u es, so ha , i is checked whe he he simula ion
ea u es ma ch he expec ed equi emen s o no .
• Resul analysis. Acco ding o he expe imen goals, he esul s mus be e al-
ua ed in o de o ob ain conclusions. Some imes, his e alua ion may in ol e
he simula ion o o he simula ion expe imen s in o de o check how he
sys em wo ks unde di e en condi ions. E en i he expe imen planning is
ho oughly de ailed, many condi ions can be in e es ing o be modi ied a e
wa ching he simula ion esul s so ha new simula ion expe imen s may be
ca ied ou .
85
8. SMART GRID MODELLING
8.2 Simula ion pe o mance
Mos o he ools p esen ed in his documen execu e he simula ion wi h a syn-
ch onised app oach. Synch onous simula ions ha e he ad an age o simple ime
managemen as all objec s o he modelled sys em a e unning in he same ime
ins an . I o ces objec s o always pe o m calcula ions, in e e y ime s ep. Some-
imes hese calcula ions a e unnecessa y due o he ac hey canno p o ide new
esul s. Fo example, a washing machine is usually wai ing o an agen o be u ned
on, conside ing his as an e en . La e on, i de elops some washing cycles whe e
he powe may a y along he ime. Whene e he washing machine s a e does no
change, calcula ions could be a oided.
In his sec ion, i is p oposed ha an asynch onous simula ion app oach is in-
cluded in Ta a which would allow objec s o de elop hei own ime as desi ed.
They could beha e bo h e en and ime-based acco ding o hei na u e. Fu he -
mo e, hey could use a iable s eps om one calcula ion o ano he . Fo example,
in an asynch onous simula ion whe e he powe consump ion o a washing ma-
chine is analysed, calcula ions would be done only when he washing machine s a e
changes. The ad an age wi h espec o a synch onous simula ion is clea since in
he synch onised case, calcula ions a e done e e y ime s ep.
In he con ex o disc e e e en simula ion he asynch onous concep has dual
conno a ion. One o hem consis s in a iable ime-inc emen p ocedu es as op-
posed o a “synch onous” o ixed ime-inc emen p ocedu es o simula ion con-
ol. This conno a ion is ela ed o he known concep Dis ibu ed Disc e e E en
Simula ions [Kau87, Mis86]. Fo ins ance, Simula [Poo87], a simula ion-o ien ed
p og amming language, is based on his asynch ony concep whe e he ime man-
agemen is mainly e en -based. This kind o asynch ony was al eady conside ed in
Ta a h ough using di e en ime s eps o each mode o beha iou [EKM+11]. On
he o he hand, he asynch ony can be unde s ood as a non-sequen ial p ocessing
whe e simula ion pa s may no be execu ed in he p ope empo al o de . Tha is
o say, la e pa s o he simula ion may be execu ed be o e p e ious ones [Gho84].
The las conno a ion is he one o which we subsc ibe in his documen . The ob-
jec i e is o apply he ime-managemen o each model elemen allowing hem o
be in di e en ime ins an s.
86
8.2 Simula ion pe o mance
8.2.1 Ta a asynch onous simula ion
This sec ion examines a new app oach o achie e asynch onous simula ions wi h
Ta a . This sec ion in oduces he concep s and cons uc ions ha Ta a a chi ec u e
includes o model powe g ids. Theses cons uc ions a e ocused on dependencies
be ween objec s ha a e massi e and e y ele an in a complex sys em simula ion.
In o de o p ope ly handle an asynch onous simula ion, i is impo an o unde -
s and he dynamics o coupled objec s. Fo he sake o cla i y, a aced execu ion
o objec s in e ac ion du ing an asynch onous simula ion is demons a ed.
8.2.2 Ta a sys em modelling
No mally, a Ta a beha iou is coupled wi h o he objec s, bo h o que ying hei
s a es o sending messages in o de o change hei s a es. In he Ta a model ep-
esen a ion, de ining beha iou which in e ac s wi h o he objec s is allowed.
This ep esen a ion app oach consis s o in e aces ha should be de ined in
he objec which could be ex e nally accessed. In Ta a , he e a e wo ypes o
in e aces:
1. e en in e aces ha handle messages and a e esponsible o modi ying he
objec in e nal a iables as eques ed, and
2. da a in e aces ha handle que ies and p o ide he alue o eques ed a ib-
u es
An example o hese ypes o in e aces is shown in he igu e 8.4. On he
one hand, he he mal beha iou wi hin a household has a da a dependence wi h
he empe a u e o he su ounding Ou doo . In his case, he Ou doo empe a u e
da a is eques ed by he associa ed objec h ough he ou doo da a in e ace. On
he o he hand, an sociological agen beha iou wan s o u n on he washing ma-
chine. Then, his sociological agen mus use he washing machine e en in e ace
o achie e his ask. The washing machine e en in e ace would change he wash-
ing machine mode o ``ON´´. The washing machine ope a ional beha iou would
calcula e he p ope powe consump ion based on his mode. La e on, when he
cycle ends, he ope a ional beha iou u ns o he washing machine.
87
8. SMART GRID MODELLING
Ou doo
Household
Washing
Machine
Ope a ional
Beha iou
Tempe a u e
Beha iou
Agen
Ac i i y
Beha iou
Ac i i y
Beha iou
u n on E en
In e ace
Da a
In e ace
ge (Tempe a u e)
Figu e 8.4: Dependencies examples be ween objec s.
8.2.3 A powe g id simula ion case
In o de o conside he main issues ha in ol e asynch onous simula ion a simula-
ion case is p oposed o show how objec s in e ac when wo king in di e en imes
(Figu e: 8.5).
The objec s wi hin his simula ion case a e an Ou doo , a Household, a Washing
Machine and a Radia o .
• The Ou doo is he objec ha ep esen s en i onmen al condi ions, in his
case, he empe a u e. The Ou doo empe a u e beha iou is esponsible o
se ing he empe a u e which can be loaded om an ex e nal da abase.
• The Household wo ks as a con aine o he appliances o a household, a
Washing Machine and Radia o in his case. The Household Beha iou is
conce ned wi h he he mal dynamics inside he household.
• The Elec ical de ices inside he Household a e a Radia o and a Washing
Machine. These de ices a e handled by an Agen .
88
8.2 Simula ion pe o mance
Ou doo
Household
Washing
Machine
Ope a ional
Beha iou
The mal
Beha iou
Agen
Ac i i y
Beha iou
Tempe a u e
Beha iou
u n on E en
In e ace
Da a
In e ace ge (Tempe a u e)
Radia o
Ope a ional
Beha iou
Da a
In e ace
Da a
In e ace ge (Tempe a u e)
ge (Powe )
Figu e 8.5: Model composi ion.
• Finally, he Agen ep esen s he people li ing in he Household and he as-
socia ed beha iou de ines he ac ions ha hese people a e pe o ming. Fo
example: a pe son u ning on he Washing Machine.
The coupling in his model is ep esen ed by he do ed lines in he igu e 8.5.
This coupling is always de ined om beha iou s o in e aces. The Agen depends
on he Washing Machine o change he ope a ion mode o his de ice. The Radi-
a o depends on he Household empe a u e, since he hea adia ion is calcula ed
based on he gap be ween he Radia o e e ence empe a u e and he Household
empe a u e. The Household has wo dependencies: wi h he Ou doo empe a u e
and wi h he Radia o powe , since he Household empe a u e is calcula ed by a
nume ical solu ion o a di e en ial equa ion which includes hese wo a iables.
No e ha , in his case, he e is a cyclic dependence be ween he Household and he
Radia o .
89
8. SMART GRID MODELLING
8.2.4 Asynch onous simula ion dynamics
A sys em simula ion equi es ime-managemen o ensu e ha empo al aspec s
a e co ec ly ep esen ed and emula ed. This empo al ep esen a ion only exis s
du ing he simula ion p ocess and is e e ed o as “Simula ion Time”. Simula ion
Time is ep esen ed as a imes amp, a long in ege whe e a uni co esponds o a
millisecond o eal ime.
The ime-managemen in a synch onous simula ion is cen alised while he
ime-managemen in an asynch onous simula ion is dis ibu ed. Tha is, an asyn-
ch onous simula ion in ol es ha e e y objec manages i s ime, so hey could ha e
di e en imes amps (Figu e: 8.6).
Ou doo
Household
Agen Washing
machine
Radia o
Simula ion Time
Ou doo
Household
Agen Washing
machine
Radia o
Simula ion Time
Simula ion Time
Simula ion Time
Simula ion Time
Simula ion Time
Figu e 8.6: Synch onous s Asynch onous simula ion.
In his simula ion pa adigm, when an objec is no coupled wi h o he objec s,
i s Simula ion Time de elops wi hou conside ing o he objec Simula ion Times.
In his simula ion case, he Ou doo is comple ely independen o o he objec s.
Howe e , when objec s a e coupled, he challenge consis s o co ec ly ep o-
ducing empo al ela ionships. The iden i ied empo al ela ionships a e as ollows:
1. Coupling wi h a da a in e ace
2. Cyclic coupling wi h da a in e aces
3. Coupling wi h an e en in e ace
90
8.2 Simula ion pe o mance
In he ollowing sec ions hese ela ionships a e discussed.
8.2.4.1 Coupling wi h a da a in e ace
Since an objec could access a a iable o an ex e nal objec which may be in a
di e en ime ins an , e e y objec mus keep he di e en s a es ha ha e been
calcula ed du ing he simula ion execu ion. So, when a a iable is modi ied, a s a e
snapsho is c ea ed in o de o keep he objec s a e in his ime ins an .
I an objec is que ying o a a iable alue in a ime ins an i, he e a e wo
cases: he objec Simula ion Time is delayed o ahead wi h espec o he ex e nal
objec Simula ion Time. In he i s case, he ex e nal objec is able o p o ide
he alue by e ie ing he las snapsho p e ious o his ime ins an ( i). In he
second case, he dependen objec mus wai un il he ex e nal objec eaches his
ime ins an ( i).
Figu e 8.7: The Household asks he Ou doo . Time is e ically ep esen ed.
In he igu e 8.7, he i s case is shown. The Household Simula ion Time is
iand he Ou doo Simula ion Time is j. Whene e iis lesse o equal han j,
he eques ed da a can be deli e ed since he da a has al eady been calcula ed and
s o ed.
Howe e , when he Household Simula ion Time ( i) is g ea e han he Ou doo
Simula ion Time ( j), he Household beha iou is blocked (Figu e: 8.8) un il jis
g ea e o equal han i(Figu e: 8.9) deli e ing he las Ou doo Tempe a u e alue
s o ed in he las calcula ed snapsho .
91
8. SMART GRID MODELLING
Figu e 8.8: The Household beha iou eques ge s blocked.
Figu e 8.9: The da a is deli e ed.
8.2.4.2 Cyclic coupling wi h da a in e aces
The cyclic dependence is a conc e e case o he da a dependence. Two objec s
depending on each o he whose Simula ion Times a e di e en , is handled wi h he
ollowing ules: he mos delayed one will always e ie e he equi ed da a while
he mos ad anced will be blocked un il he delayed eaches i s Simula ion Time
(Figu e: 8.10). The mu ual blocking is no possible since objec s e ie e he alue
o he cu en Simula ion Time o calcula e he nex Simula ion Time alue.
In he example shown in he igu e 8.10, he Household equi es he powe
consump ion o he Radia o in o de o calcula e he new empe a u e alue. On
he o he hand, he Radia o beha iou needs he Household empe a u e alue o
92
8.2 Simula ion pe o mance
Figu e 8.10: Radia o and Household cyclic dependence esolu ion.
modi y he Radia o s a e, since he e e ence empe a u e a he Radia o he mo-
s a se es as a con ol mechanism.
8.2.4.3 Coupling wi h an e en in e ace
The e en coupling means ha an objec ecei es ex e nal messages ha con ain
o de s o changing i s in e nal a iables. This is he case o objec s which a e
managed by people ha a e ep esen ed as Agen s in he model. The Agen in-
e ac s wi h hese objec s by sending a message using he objec e en in e ace.
When he message is ecei ed by he objec in e ace, he objec Simula ion Time
is de eloped and hen, a new snapsho s a e is c ea ed.
I could happen ha he Agen de elops i s Simula ion Time wi hou he in en-
ion o sending an o de o any objec . In his case, he Agen beha iou mus send
a ``No i ica ion Time Message´´ o he objec . In ac , when he Agen Simula ion
Time de elops, he Agen beha iou mus send a No i ica ion Time Message o all
objec s he Agen is con olling. This no i ica ion de e mines how long an objec
can de elop i s Simula ion Time. This ype o ela ionship means ha objec ’s Sim-
ula ion Time ha is con olled by an Agen , will ne e exceed he Agen Simula ion
Time.
Figu es 8.11-8.14 shows an e en ela ionship be ween a social Agen ha u ns
on he Washing Machine. In his example, he Washing Machine Simula ion Time
93
8. SMART GRID MODELLING
Figu e 8.11: The Agen sends a message o u n on he Washing Machine.
Figu e 8.12: The Washing Machine e en in e ace changes he objec mode o on.
is always behind he Agen Simula ion Time. In o he wo ds, he Agen Simula ion
Time se s a es ic ion o he Washing Machine Simula ion Time.
In he case o he Washing Machine, i s powe consump ion would be 0 a
he beginning o he simula ion as i ’s o . The e o e, a new snapsho is c ea ed
when he Agen u ns on he Washing Machine. F om ha momen , he Washing
Machine beha iou will calcula e he new powe consump ion wi h he es ic ion
ha he calcula ions de elopmen should no exceed he Agen Simula ion Time, in
case he Agen u ns o he Washing Machine.
94
CHAPTER
9
Simula ion esul s analysis
As said be o e, simula ions play a c ucial ole in he design o SG policies since
hey a e a way o es hem be o e hei launch. Howe e , he ou pu p o ided by
he simula ions mus be managed in a way ha allows he policy designe s o make
decisions. This sec ion explains he main conce ns when analysing esul s ob ained
in a SG simula ion. When acing a simula ion o SGs based on a complex sys em
app oach, he esul s analysis becomes a di icul s age since he amoun o en i ies
is la ge.
All sys ems con aining a la ge amoun o en i ies and ela ions in simula ion
p ocesses p o ide a la ge amoun o esul s. The way in which hese da a a e no -
mally expo ed is h ough da a iles. These da a iles a e usually designed acco ding
o he da a ha will be managed hus a oiding he possibili y o que ying his da a
beyond wha was decided o expo . The e o e, whene e we deem i con enien
o ex ac da a, which was no conside ed o expo a he design phase, a new
simula ion mus be con igu ed and execu ed.
In o de o exempli y his issue, a disagg ega ed model o a powe g id sys em
is used. This sys em only consis s o he demand side, which is disagg ega ed a
he de ice le el. I is p ecisely a his le el whe e we can ind a laye consis ing o
he e ogeneous elemen s, since he cha ac e is ics o ex ac om a adia o a e no
he same as he ones om a ele ision (TV). I we wan o p ese e all a iables ha
101
9. SIMULATION RESULTS ANALYSIS
Figu e 9.1: S uc u e example o expo simula ion esul s.
a e no common o e e y de ice, i will be necessa y o expo each de ice ype in o
a di e en da a shee (Figu e: 9.1). A his poin , once he da a expo a ion p ocess
has been de ined, we can s a hinking abou que ying i . The lis below s a es
some que y examples and how hey should be deal wi h acco ding o his da a
expo a ion s uc u e:
•Que ying he consump ion o all de ices. This que y is e y likely o be
equi ed. Acco ding o ou da a s uc u e, i s ly we calcula e he o al con-
sump ion a each de ice ype. This would in ol e opening as many iles as
de ice ypes and making he calcula ions o ob ain he o al consump ion pe
de ice ype. Secondly, hose columns which ha e he agg ega ed alue a
each de ice ype mus be mo ed in o a new shee whe e he inal calcula ion
would be pe o med ob aining he que y esul . The mo e de ice ypes he e
a e, he ickie his p ocess becomes.
•Que ying he consump ion o all de ices in a speci ic household. This
p ocess would consis in ga he ing he columns belonging o all he de ices
con ained in he household om he da a iles. Once hey a e all oge he in
a new shee , he que y esul can be ob ained by adding up.
•Que ying he consump ion o all de ices in a speci ic dis ic . The p o-
cess o ob ain his que y is eally icky. Fi s ly, all he de ices belonging
o a speci ic dis ic mus be lis ed. Nex , all he columns which e e o he
de ices consump ion mus be ga he ed om he de ice ype shee s ollowing
his lis . Finally, all ga he ed columns can be mo ed o a new shee whe e
he que y can be ob ained.
Taking hese examples in o accoun , i is possible o imagine how icky he
esul s managemen o mo e complica ed que ies can ge . P obably, some o hese
102
9.1 Business In elligence me hodologies o analysing da a
que ies a e easie o ob ain by ede ining he simula ion esul s o ma and unning
i again. Howe e , i would also be eally edious, and depending on he simula ion
kind, he esul s may di e om he p e ious simula ion and in he end i would be
necessa y o s a he esul analysis om he beginning.
All hese di icul ies in que ying he ou pu o a simula ion could in ol e ha
many o he que ies a e no made due o he ac ha hey in ol e a s ong and
ime consuming e o o pe o m hem. Un o una ely, his usually leads o ocus
on a small subse o a iables o he simula ion neglec ing much in o ma ion and
was ing oo much ime in pe o ming simple que ies.
The oo o he p oblem behind he esul analysis is ha such esul s ha e a
mul i-dimensional and a mul i-scale (namely empo al and spa ial) na u e which
canno be managed by using con en ional da a shee s. The example o he de-
mand disagg ega ion is mul i-dimensional and mul i-scale. Mul i-dimensional,
since each da ase ( o example, a powe measu e) is ela ed o a speci ic de ice,
loca ion (household, building, dis ic , e c.) and ime. Mul i-scale, since he in-
o ma ion can be agg ega ed a di e en ime scales (pe hou , pe day, pe mon h,
e c.) and a di e en spa ial le els (de ice, household, e c.).
9.1 Business In elligence me hodologies o analysing
da a
A amewo k, named Sumus, o applying BI me hodologies has been de eloped.
This amewo k o da a analysis is based on OLAP. OLAP is a solu ion used in
BI, he aim o which is o accele a e que ying la ge amoun o da a. OLAP is
based on cubes [CD97](Figu e: 9.2), a mul i-dimensional s uc u e whe e da a is
s o ed. These cubes enable he inse ion o da a, namely ac s, which a e e e ed
o se e al dimensions. Fo example, he measu e o powe aken om a washing
machine can be e e ed o he de ice, he household whe e he de ice is and he
ime. The e o e, in his case, he e would be h ee dimensions: de ices, households
and ime.
The s uc u e o a mul i-dimensional cube which add esses ou p oblem is
103
9. SIMULATION RESULTS ANALYSIS
Figu e 9.2: A mul i-dimensional cube o esiden ial consump ion.
Figu e 9.3: An OLAP Cube s uc u e.
p esen ed in he igu e 9.3. E e y cube consis s o dimensions, measu es and in-
dica o s. The lis below desc ibes e e y cube componen .
•Dimension: i es ablishes a way o access he da a inside he cube. E e y
single da a is ela ed o some elemen s such as when and whe e i happened.
Fo example, a da a o powe consump ion o a household would be ela ed
o he dimensions household and ime.
– Componen : i is an elemen which is ela ed o a dimension. Fo
example, a dimension which conce ns households would be illed by
componen s which a e households.
104
9.1 Business In elligence me hodologies o analysing da a
*Fea u e: i is a p ope y o he componen . In case he componen s
a e households, a possible ea u e could be he numbe o squa e
me e s he e is in each household.
– Taxonomy: i is a way o ca ego izing a dimension. The e a e di e en
ways o ca ego ize he componen s inside a dimension. Each o hese
ways is known as axonomy. In he example o he household dimen-
sion, a axonomy could be he size o he o ien a ion o he acade.
*Ca ego y: i is a se o componen s ha sa is y some speci ic con-
di ions. Fo ins ance, possible ca ego ies o he size axonomy
could be small, medium o big. The e o e, each o hese ca ego ies
would con ain a se o household componen s he ela ionship o
which is ha ing a simila size.
·Rule: i es ablishes he condi ion ha a componen mus mee
in o de o all in o he ca ego y ha owns he ule. In he
case o he small ca ego y, a possible ule could be: all he
household componen s he ea u e o which numbe o squa e
me e s is below 80m2
•Measu e: i p o ides a seman ic o he da a inse ed in he cube, e.g. he
powe o he household men ioned abo e is jus a numbe . Howe e , he
powe measu e is wha p o ides he seman ic o his numbe . A measu e is
usually ela ed o a me ic which enables he compa ison among measu es
ha a e in di e en cubes. In his case, he me ic o he powe measu e
would be Wa s.
•Indica o : i designa es he way in which a measu e o a se o measu es a e
agg ega ed. Fo example, he powe measu e could be agg ega ed using an
a e age unc ion. This way o agg ega ing measu es is known as indica o .
I is possible o ha e se e al indica o s o one measu e, i.e. he in eg al
ope a o o e he powe measu e would p o ide a second indica o o e his
measu e which could be designa ed as ene gy indica o .
•Fac : i ela es he measu es o a cube wi h he dimensions. A ac indica es
ha a ce ain combina ion o alues (measu es) ook place o a speci ic com-
bina ion o elemen s (componen s). In o he wo ds, a ac can be unde s ood
105
9. SIMULATION RESULTS ANALYSIS
Figu e 9.4: A ac consis s o con ex and s a e.
as a ela ion o a s a e o a con ex . The s a e is a se o measu es and he con-
ex consis s o componen s including ime. In igu e 9.4, he s a e con ains
20 (cen ig ades) and 135 (Wa s) as measu es. These measu es a e ela ed o
a con ex which indica es he ime and household whe e hose measu es we e
aken.
9.2 On-Line Analy ical P ocessing o Sma G ids
In his sec ion, all concep s exposed p e iously will be used in a p ac ical case.
Assuming ha a new SG policy is o be uned, se e al simula ions o powe g id
demand will be pe o med. To make decisions, hese simula ions mus ocus on he
powe demand and he empe a u e a he esiden ial sec o . The e o e, he scena io
o hose simula ions consis s o se e al dis ic s wi h households (Figu e: 9.5).
Each household con ains se e al de ices and calcula es he in e nal empe a u e.
To his end, se e al cubes ha e been designed so as o analyse he da a com-
ing om he simula ion: i s o all, he household cube which con ains he ac s
ega ding he empe a u e and, secondly, one cube pe de ice ype (TVs, Radia -
o s and Washing Machines, among o he s) which con ain ac s abou he de ices.
Since he e a e many kinds o de ices in a household, in his example we a e going
o ocus on wo o hem: TVs and adia o s.
The e a e wo dimensions in he household (HH) cube: one measu e and one
indica o (Figu e: 9.6). The Time dimension is common o all cubes and con igu es
a s anda d way o ca ego izing he imeline. Household dimension con ains he
106
9.2 On-Line Analy ical P ocessing o Sma G ids
Figu e 9.5: Scena io composi ion.
Figu e 9.6: Household cube.
households ans o med in o componen s which a e desc ibed by ea u es. The
empe a u e o he household is he only measu e ha his cube is going o s o e
and i will be agg ega ed using an a e age c i e ia acco ding o he designa ion o
he indica o .
The household dimension con ains a axonomy which conce ns he loca ions
(Figu e: 9.7). This axonomy is ca ego ized ollowing se e al le els: coun y, ci y
and dis ic . Fo ins ance, wo household componen s ha e been included, bo h o
which con ain a ea u e which is hei loca ion using UTM coo dina es. The e o e,
hese loca ion ea u es allow he dimension o iden i y which dis ic each house-
hold is loca ed in.
The TV and Radia o cases a e exposed in o de o demons a e why de ices
mus be disagg ega ed in o sepa a ed cubes. The main eason o his sepa a ion
is due o he ac ha bo h de ices do no sha e he same ea u es and, he e o e,
hei classi ica ion me hods a e di e en . This sepa a ion enhances he capaci y o
making que ies since i is possible o il e componen s by ea u es ha a e only
p esen in a speci ic kind o de ice.
107
9. SIMULATION RESULTS ANALYSIS
Figu e 9.7: Household dimension.
Figu e 9.8: TV cube.
The TV cube egis e s da a abou powe consump ion as well as he TV mode
(o , s andby and on) (Figu e: 9.8). E e y se o measu es (powe and mode) is
ela ed o h ee dimensions: ime, household and TV. Time and household dimen-
sions a e exac ly he same dimensions as he ones de ailed abo e. The TV di-
mension con ains in o ma ion abou he TVs in a componen o ma . Fu he mo e,
he e a e wo indica o s which a e esponsible o agg ega ing measu es: he mode
indica o , which pe o ms a calcula ion ha p o ides he pe cen age o TVs ha a e
u ned on, and he powe indica o , which agg ega es he powe measu es egis e ed
using an a e age o mula.
The TV dimension, like he household dimension, ocuses on speci ic ea u es
ela ed o TV componen s (Figu e: 9.9). In his case, he possibili y o il e ing
TVs using a echnological c i e ia is conside ed ele an . The e o e, wo ca ego ies
108
9.2 On-Line Analy ical P ocessing o Sma G ids
Figu e 9.9: TV dimension.
Figu e 9.10: Radia o cube.
ha e been c ea ed so as o sepa a e LED ele isions om LCD ele isions. This
in o ma ion will allow us o compa e he consump ion among he di e en TV
echnologies. Hence, TV componen s con ain he echnology ea u e which will be
used o calcula e whe he a TV belongs o he LED o LCD ca ego y by using he
ules ha a e ela ed o hese ca ego ies.
The adia o cube s o es measu es ela ed o bo h he powe consump ion and
he he mos a le el (Figu e: 9.10). These measu es a e ela ed o h ee dimensions,
as in he case o he TV cube. In his case, apa om ime and household dimen-
sions, a new dimension has been designed: adia o dimension. This dimension
con ains componen s ha ep esen adia o s and hei ea u es. In addi ion, he e
a e wo indica o s which agg ega e he measu es. On he one hand, he he mos a
indica o agg ega es he measu es s o ed using a g adien unc ion which shows
big changes in he he mos a le el in sho pe iods o ime. On he o he hand, he
powe indica o agg ega es he powe measu es using an a e age o mula like in
he TV cube.
109
9. SIMULATION RESULTS ANALYSIS
Figu e 9.11: Radia o dimension.
The adia o dimension ocuses on speci ic ea u es which conce n adia o
componen s (Figu e: 9.11). Since adia o s a e usually conside ed big consume s,
a axonomy o classi y hem in o wo g oups has been designed. Indeed, his ax-
onomy will allow us o ind ou he amoun o adia o componen s which a e in
wha we conside a small consume ca ego y (unde 1kW ins alled powe ) o a
big consume ca ego y (o e 1kW). Two componen s belong o his dimension and
con ain he ea u e ins alled powe which is used o pe o m he classi ica ion in
he ins alled powe axonomy.
9.2.1 N-Le el indica o s and Da a mining
So a , some mechanisms which allow us o ex ac in o ma ion based on he meas-
u es ha e been p esen ed: indica o s. These indica o s a e ega ded as i s le el
indica o s since hey a e jus based on measu es. Howe e , i is possible o de ine,
a second le el o indica o s which a e compu a ions ca ied ou based on p e ious
le el indica o s. This idea can be ex ended o he concep o N-Le el indica o s.
Th ough in oducing his concep , da a mining[RKU11] p ocedu es can be used in
o de o ind ou pa e ns.
An example o his is p esen ed in igu e 9.12. In his case, a da a mine has
been designed in o de o iden i y consump ion habi s which conce n adia o s. Us-
ing he he mos a indica o , which calcula es he g adien based on he he mos a
110
10.3 Mul i Objec i e Op imisa ion
Whe e id( )is he eloci y o he pa icle iin he dimension da ime .xid( )
is he posi ion o he pa icle iin he dimension da ime .c1and c2a e weigh
ac o s ha adjus he mo emen . pbes iis he bes posi ion achie ed by he pa icle
i.pbes gis he bes posi ion o he neighbou s o he pa icle i.u1and u2a e
andom ac o s in an in e al o [0,1] and wis he ine ial weigh .
This op imisa ion me hod is ini ialised wi h a andomly-gene a ed popula ion
o pa icles in he decision space which y o con e ge in o an op imal one by
ollowing he bes pa icles a e e y i e a ion. The algo i hm o his me hod is
p esen ed below:
I n i i a l i s e swa m .
I n i i a l i s e p b e s . Upda e g b e s . I n i i a l i s e e l o c i y .
i e a ionCoun e = 0
while (i e a ionCoun e <maxI e a ion ){
o ( each p a i c l e ) {
Pick andom u1 and u2
o ( each dimension ) {
Upda e p a i c l e e l o c i y and p o s i i o n
}
Upda e p b e s
}
Upda e g b e s
i e a ionCoun e ++
}
Repo e s u l s i n a c h i e
Lis ing 10.1: PSO code. Fo u he in o ma ion please check he bibliog aphy
10.3 Mul i Objec i e Op imisa ion
A mul i-objec i e op imisa ion p oblem can be exp essed as ollows:
min
x
~
F(x) = [F1(x), F2(x)...Fk(x)]T(10.3)
Subjec o:
gj(x)≤0, j = 1,2...m (10.4)
hl(x)=0, l = 1,2...e (10.5)
Whe e K is he numbe o objec i e unc ions and j and l a e, espec i ely, he
numbe o inequali ies and equali y cons ain s. x∈Enis he ec o o design
117
10. DEMAND SIDE MANAGEMENT POLICY DESIGN
a iables and ~
F(x)∈Ekis he ec o o objec i es, c i e ia, i ness o cos unc-
ions o be op imised. Any compa ison (≤,≥, e c.) among ec o s applies o ec-
o componen s [MA04]. O he p ima y concep s in mul i-objec i e op imisa ion
a e non-domina ed and domina ed poin s [S e89]. A ec o o objec i e unc ions
~
F(x∗)∈Zis non-domina ed i he e is no ano he ec o , ~
F(x)∈Z, such as
~
F(x)≤~
F(x∗)wi h a leas one Fi(x)< Fi(x∗). O he wise, ~
F(x∗)is domina ed.
[LTDZ02] p oposed a elaxed o m o dominance named -dominance. This
ac s as an a chi ing s a egy o ensu e bo h p ope ies o con e gence owa ds
he Pa e o-op imal se and p ope ies o di e si y among he solu ions ound. -
dominance is p oposed as an ex ension o he Pa e o-dominance ela ion so ha a
poin x∗no only domina es hose poin s xlowe o equal in all hei objec i es
~
F(x)≤~
F(x∗)and s ic ly lowe in a leas one objec i e Fi(x)< Fi(x∗), bu also
all poin s close enough o x∗(i.e., hose wi h a dis ance o x∗ ha is less han an
). This alue, , can be p o ided by he decision make o con ol he size o he
solu ion se [HDSQCCM07].
In mul i-objec i e op imisa ion p oblems, he op imum solu ion is, in gene al,
no as easy o o mula e as in single objec i e cases. Since ypically he e is no
single global solu ion, i is o en necessa y o de e mine a se o poin s ha all
ma ch a p ede e mined de ini ion o an op imum o e e y single p oblem [MA04].
In his sense, he p edominan concep in de ining an op imal poin is ha o Pa e o
op imali y [Pa 06].
A poin ~
x∗∈~
Xis Pa e o op imal i he e does no exis ano he poin , x∈X
such as ~
F(x)≤~
F(x∗)and Fi(x)< Fi(x∗) o a leas one unc ion. All Pa e o
op imal poin s lie in he bounda y o he easible c i e ion space Z[AP96]. A
Pa e o-op ima ha dly e e p o ides a single solu ion, bu a he a se o solu ions
called non-in e io o non-domina ed solu ions. The minima in he Pa e o sense a e
going o be in he bounda y o he objec i e egion, o in he locus o he angen
poin s o he objec i e unc ions, ha is in he egion in he space o alues o he
objec i e unc ions ec o [Coe98].
O en, algo i hms p o ide solu ions ha may no be Pa e o-op imal bu may
ul il o he c i e ia, which can be use ul o p ac ical applica ions. I is he case
o weakly Pa e o op imal. A poin ~
x∗∈~
Xis weakly Pa e o-op imal i he e is
no ano he poin , ~
x∗∈~
Xsuch as ~
F(x)≤~
F(x∗). Tha is, a poin is weakly
Pa e o op imal i he e is no o he poin ha imp o es all he objec i e unc ions
118
10.4 Mul i Objec i e Pa icle Swa m Op imisa ion
simul aneously.
10.4 Mul i Objec i e Pa icle Swa m Op imisa ion
The implemen a ion o he MOPSO used in hese s udies is desc ibed in [SC05].
This op imisa ion me hod is based on PSO. In his me hod, he i ness o a pa icle
is calcula ed conside ing se e al objec i es which a e de ined h ough i ness unc-
ions. This me hod uses he Pa e o-op imali y [SC05] app oach o add ess he
mul i-objec i e p oblem.
A se con aining he bes pa icles aised du ing he i e a ions is s o ed in wha
is named as a chi e, which e ie es he elemen s ha each he -dominance. This
dominance elaxes he weak-dominance cons ain s. The nex pseudo-code ex-
plains how he MOPSO wo ks:
I n i i a l i s e swa m .
I n i i a l i s e l e a d e s . Send l e a d e s o a ch i e . c owding ( l e a d e s ) .
i e a ionCoun e = 0
while (i e a ionCoun e <maxI e a ion ){
o ( each p a i c l e ) {
S e l e c l e a d e . F l i g h . Mu a ion . E a l u a i o n . Upda e p be s .
}
Upda e l e a de s , Send l e a d e s o a c h i e . c owding ( l e a d e s ) .
i e a ionCoun e ++
}
Repo e s u l s i n a c h i e
Lis ing 10.2: MOPSO code. Fo u he in o ma ion please check he bibliog aphy
Ini ially he swa m is popula ed h ough he c ea ion o he pa icles o indi-
iduals. A pa icle con ains a sys em con igu a ion which is se up andomly. This
con igu a ion is he pa icle gene ic code. Be o e s a ing he i e a i e p ocess,
he leade s a e calcula ed using he i ness unc ions which e alua e he op imali y
o he pa icles. Those ha each he -dominance c i e ia will be s o ed in he
a chi e. Tha is, he algo i hm includes a c owding p ocess ha is used o es ablish
a second disc imina ion c i e ion (addi ional o Pa e o dominance) [CL02, SC05].
Along he execu ion his a chi e may be illed up and, in hese cases, he a chi e
dele es pa icles based on he c owding ac o c i e ia. A e e y me hod i e a ion,
each pa icle is mo ed acco ding o i s p e ious posi ion and speed which a e cal-
cula ed as ollows:
119
10. DEMAND SIDE MANAGEMENT POLICY DESIGN
• The speed is calcula ed based on he p e ious pa icle speed, he dis ance o
i s bes posi ion and he dis ance o i s leade . This me hod spli s he sea ch
space in hype cubes, so ha he pa icle leade will be he one ha has he
bes i ness wi hin he hype cube.
• The posi ion is calcula ed adding he p e ious pa icle posi ion o he speed
calcula ed abo e.
A e he pa icle mo emen , he pa icle could be mu a ed wi h a p obabili y
calcula ed as 1 / gene ic code size (mu a ion a e). I a pa icle is going o be
mu a ed, his mu a ion may be uni o m o non-uni o m. The uni o m mu a ion
allows he pa icle o explo e he sea ch space. Howe e , a non-uni o m mu a ion
allows he pa icle o exploi he sea ch space whe e e i is. La e on, he pa icle
i ness is e alua ed and he bes posi ion o he pa icle is changed whene e he
new posi ion is be e han he bes achie ed in he pas .
Once all he pa icles ha e been p ocessed, he whole popula ion is e alua ed in
o de o upda e he leade s o he a chi e. I his a chi e eaches he size limi , some
o he pa icles inside i will be dele ed ollowing he same c i e ia as desc ibed
abo e. A e his s ep, he nex i e a ion will be execu ed. The numbe o i e a ions
mus be se be o ehand.
The eason why his me hod was selec ed ins ead o o he s such as Gene ics
Algo i hms is ha MOPSO has a good esul quali y and ime esponse ela ion.
The ime esponse is impo an since he o de s could a ise ega ding he cu en
p oduc ion-consump ion balance in a eal- ime sys em whe e he speediness o ap-
plying he measu emen s is c i ical.
120
Pa IV
Expe imen a ion
121
CHAPTER
11
Expe imen a ion
conside a ions
This pa o he documen is de o ed o show case s udies ha ha e been ca ied
ou in his esea ch. As p e iously said in chap e 7, he hypo heses ha ha e
been s a ed in his documen canno be e i ied, bu alida ed. Tha is, i is no
possible o e i y hem as o do so i would be necessa y o ca y ou all SG s udies.
Ne e heless, hey can be alida ed h ough he expe imen a ion wi h case s udies.
In his pa , hese case s udies a e p esen ed and explained, p o iding e idence ha
alida es he s a ed hypo heses.
In his chap e , some syn he ic case s udies a e p esen ed. The s udies ha e
been designed o show how he ools desc ibed in he hypo heses pa wo k. Tha
is, how a me amodel is de ined, how a simula ion model is de ined, how o c ea e
la ge-scale scena ios, e c. Each o he chap e s in his pa e e s o a speci ic case
s udy. These case s udies a e o ien ed o sol e eal p oblems and, he e o e, hey
can be conside ed as impo an e idences ha alida e he hypo heses.
123
11. EXPERIMENTATION CONSIDERATIONS
11.1 Modelling in Ta a
The wo k, published in [EHH13c] 1, p esen ed in his sec ion conce ns p ospec -
i e expe imen s ha we e de eloped o s udying he human lows on a shopping
cen e om he poin o iew o he amoun o people. In o de o un hese expe i-
men s, he p oposed amewo k will be cus omised. Fo his he de elopmen o he
me amodel, he beha io s and models in which he expe imen s will be desc ibed
is necessa y.
Howe e , as he aim o his case s udy is no o p o ide accu a e esul s, bu o
es he amewo k o complex sys em simula ion, a non-con as ed in o ma ion
has been used o simula ing he human lows. The in o ma ion we ha e designed
behind he human lows going shopping is exp essed in he lis below:
• F om 9.30 o 10.00, a ound 60-70 wo ke s a i e o he Shopping Cen e,
lea ing i be ween 16:00 and 16:50.
• F om 15.30 o 16.00, a ound 80-90 wo ke s a i e o he Shopping Cen e,
lea ing i be ween 22:00 and 22:50.
• F om 10.00 o 16.00, a ound 1000-2000 cus ome s will a i e o he Shop-
ping Cen e, lea ing i be ween 20 and 180 minu es la e .
• F om 16.00 o 22.00 a ound 2000-4000 cus ome s will a i e o he Shopping
Cen e, lea ing i be ween 20 and 180 minu es la e . I such lea ing ime
eaches he 22.00 limi , he lea ing ime will be 22.00
The nex sub-sec ions ocus on he s eps o de eloping a simula o om sc a ch
o unning his kind o expe imen . The i s s ep is he me amodel de elopmen
whe e he elemen s ha a e going o be used in he simula ion models a e desc ibed.
La e on, he beha iou s which add ess he way o ac ing o each model elemen
a e de eloped. Once he me amodel and beha iou s a e implemen ed, he P o ile
ool is used o gene a ing a huge scena io which con ains many agen s and wo
shopping cen es. Once he model is eady, he simula ion is execu ed and he
esul s coming om i a e analysed.
1People ha pa icipa ed in his expe imen : Jos´
e´
E o a, Jos´
e Juan He n´
andez and Ma io
He n´
andez.
124
11.1 Modelling in Ta a
Figu e 11.1: Case s udy’s me amodel.
11.1.1 Me amodel de elopmen
Fo his expe imen , he me amodel de elopmen is simple since i is only com-
posed by h ee elemen s. No e ha he me amodel design is one o he mos c i ical
p ocesses since i s good design will allow lexibili y o adding o modi ying ele-
men s wi hou a ec ing he whole s uc u e. In igu e 11.1, he me amodel o his
complex sys em is p esen ed.
F om he scene pa , he e is only one elemen : he Shopping cen e; he opo-
logy is emp y in his case and he popula ion has wo elemen s: selle and buye
agen s. F om he poin o iew o he expe imen con ex , bo h do he same: ge
in and ge ou om he Shopping cen e. Howe e , hey a e sepa a ed in o de o
p o ide concep ual cla i y. When unning expe imen s, i is impo an o keep he
concep s which exis in eali y in he me amodel. The nex XML codes ep esen
he desc ip ion o e e y me amodel elemen :
<class name=” Sho ppingCen e ” p a e n =” L oc a io n ”>
< ea u e name=” a d d e s s ” y pe =” s i n g ” />
< a i a b l e name=” pe sonCoun ” ype =” i n ” i n i i a l − a lue =”0” />
</class>
Lis ing 11.1: Desc ip ion o he Shopping Cen e me amodel class. This desc ip ion
con ains he add ess whe e he Shopping Cen e is loca ed. Fu he mo e, i con ains a
a iable which wo ks as a coun e o he numbe o people inside.
<class name=” S e l l e ” p a e n =” Bus ine ss ”>
< a i a b l e name=” s h o p p i n g C e n e I d ” yp e =” s i n g ” e q u i e d =” u e ”
>Id o he shopping c e n e whe e h e s e l l e wo ks </
a i a b l e>
<con ex name=” s ho p pi ngC en e ” yp e =” ShoppingCen e ”>Shopping
Ce n e whe e h e s e l l e wo ks </con ex >
125
11. EXPERIMENTATION CONSIDERATIONS
</class>
Lis ing 11.2: Desc ip ion o he Selle agen me amodel class. When he Selle is
ins an ia ed in he simula ion model, he shopping cen e id is equi ed in o de o
ela e he agen o whe e i wo ks. The ini /con igu e code will look o his id in o de
o se in he con ex a ibu e he shopping cen e p o ided.
<class name=” Buye ” p a e n =” B us in es s ”>
<con ex name=” s h o p p i n g C e n e L i s ” yp e =” Sho pp in gC en e ”
eplica ed=” ue”>A l i s c o n a i n i n g he Shopping C en e s
whe e he buye u s u a l l y go </con ex >
</class>
Lis ing 11.3: Desc ip ion o he Buye agen me amodel class. In his case, he buye
con ex is a lis o shopping cen es whe e he buye usually goes. Tha ’s he eason
why an id is no necessa y o be p o ided since his con ex lis will be illed by a
disco e y p ocess whe e he shopping cen es will be associa ed o his agen ollowing
a c i e ia (e.g. p oximi y, p ices, e c)
11.1.2 Simula ion de elopmen
The simula ion de elopmen is guided by he p esen ed Simula ion Li e Cycle
which conce ned ou main s eps. A his momen , all he ideas abou how he
expe imen should be de eloped mus be clea in o de o decide how o p oceed
wi h he da a, which elemen s a e necessa y o model, e c.
11.1.2.1 Da a p ocessing
Since he p oposed expe imen s in ol e he simula ion o many elemen s, he use
o a ool o au oma ing and gene a ing simula ion models is necessa y. In his case,
he P o ile is ed by a da a s o e whe e he s a is ical in o ma ion conce ning he
human lows on he shopping cen e is s o ed. Based on his in o ma ion, a model
ha ep esen s a conc e e scena io o he people lows is gene a ed. A his poin ,
and h ough he use o he P o ile ool, he scena io can be modi ied as needed
acco ding o he expe imen s design in o de o es how he people lows would
eac conce ning a iables a ia ion.
The able in o which he hypo heses we e ansla ed con ains he in o ma ion
abou he popula ion o he simula ion model (Table: 11.1). Ge ing in o he de ails
126
19.2 Case s udy
Table 19.1: Vehicles
B and Model Capaci y Range
Mega e-Ci y 9kWh 100km
Re a L-Ion 11kWh 120km
Think Ci y 25kWh 200km
Mi subishi i-Mie 16kWh 130km
Ci oen C-Ze o 16kWh 130km
Renaul Fluence-ZE 22kWh 160km
Nissan Lea 24kWh 160km
Tesla Roads e 42 42kWh 257km
Tesla Roads e 70 70kWh 483km
19.2 Case s udy
This sec ion is de o ed o explain how models ha e been de eloped. The EVs
ha e been modelled o ep esen any EV in he ma ke . I is easily pa ame e ised
by simply p o iding he ba e y capaci y (in kWh), he au onomy (in km) and he
s a ing s a e o cha ge ( om 0% o 100%). In his simula ion, ehicles in able
19.1 ha e been aken in o accoun .
These ehicles a e discha ged based on he mileage hey co e . This means
ha he ene gy ha has o be discha ged om he ba e y is calcula ed based on
he kilome es ha ha e been d i en and he au onomy o he ehicle. Vehicles a e
cha ged acco ding o he s anda d which is a 3,700 wa s.
This elec ical ehicle model needs o be commanded by an agen model in
o de o pe o m he ips (e.g. going o wo k). The e o e, an agen has been
de eloped o implemen he beha iou o he d i e who needs anspo a ion. This
agen has been implemen ed in acco dance o o dina y li es yles, since i was no
possible o ob ain in o ma ion o usage pa e ns o ehicles. These agen s execu e
ou ac i i ies which conce n he use o he ehicle: going o wo k, e u ning om
wo k, going o shops, e u ning om shops. An example o how hese agen s a e
scheduled is p esen ed below:
• Ac i i y 1. Id: go wo k. Time: 7:00. De ia ion: 500s. Pa e n: Monday,
Tuesday, Wednesday, Thu sday, F iday.
229
19. AGENT-BASED MODELLING FOR DESIGNING AN EV CHARGING
DISTRIBUTION SYSTEMS: A CASE STUDY IN SALVADOR OF BAHIA
• Ac i i y 2. Id: back om wo k. Time: 15:00. De ia ion: 500s. Pa e n:
Monday, Tuesday, Wednesday, Thu sday, F iday.
• Ac i i y 3. Id: go shop. Time: 18:00. De ia ion: 500s. Pa e n: Tuesday,
Thu sday, Sunday.
• Ac i i y 4. Id: back om shop. Time: 21:00. De ia ion: 500s. Pa e n:
Tuesday, Thu sday, Sunday.
In addi ion o his in o ma ion, agen s a e also p o ided wi h he dis ance o
wo k and o he shops. These dis ances a e calcula ed based on a no mal dis ibu-
ion cen ed in he a e age mileage d i en by people in Sal ado de Bahia. A e
each ac i i y, he agen s will plug in he EVs so ha hey can s a cha ging hei
ba e ies. Howe e , depending on he s a egy, he connec ion o he ehicles o he
g id will no necessa ily in ol e i s cha ging, as his will be decided by he s a egy.
This means ha in he simula ion in which he e is no a s a egy, EVs will s a o
cha ge as soon as hey a e plugged in. In he simula ion in which he e is a s a egy,
EV cha ging will s a in acco dance o he policies o he s a egy.
19.3 Resul s
Keeping he same dis ibu ion in as uc u es, he cha ging po en ial o he subs a-
ion o elec ical ehicles can be de ined as he di e ence be ween he maximum
deli e able powe o ehicles connec ed o cha ging s a ions and he cu en en-
e gy demand: Pp( ) = Pm( )−Pd( ). Ob iously, his po en ial is no he same
in all powe g id subs a ions, since bo h ac o s, Pm and Pd, may a y om one
subs a ion o ano he as well as seasonally.
In he p oposed cha ging model ha his pape explo es, one o mo e subs a ion
eede s will be enabled o allow he managemen o all ehicles based on hese
wo ac o s. Ideally, he ene gy used o hei cha ging should be he cheapes .
Mo eo e , sys em ope a ion should be acili a ed so ha gene a o s a e swi ched on
and o as li le as possible. To his end, he objec i e should pu sue he la ening
o he demand cu e.
230
19.3 Resul s
0
2
4
6
8
10
12
14
16
18
0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
MW
Figu e 19.1: EVs consump ion when hey s a cha ging as soon as hey a e plugged
19.3.1 No DSM policy
In his expe imen , he model p e iously p esen ed will be simula ed conside ing
ha EVs s a cha ging as soon as hey a e plugged in. In he igu e 19.1, he
consump ion o EVs o a weekday is p esen ed. The highes peak o his load
cu e can be ound a 7pm app oxima ely. This peak is due o he ac ha almos
e e yone is a home a his ime, wi h hei EV plugged in, wi h he esul ha ha
hei cha ging eaches he g ea es le el o o e lapping. This o e lapping e ec is
also high in he mo ning and a e noon, coinciding wi h commu e “ ush hou s”.
In he igu e 19.2, wo load cu es a e p esen ed: he o iginal load cu e o
2014 (g ey) and o 2030 in which EVs a e included (black). In his cha , i can be
seen he inc emen ha in ol es he EVs in he o al consump ion. As a emainde ,
his signi ican impac is caused by 5,253 EVs in a powe g id se ing an a ea o
142,000 (in 2014) and 149,000 (p edic ed o 2030) inhabi an s. I any o he h ee
p edic ions ha a e used in his s udy inc eases (popula ion, ehicle owne ship a e
o EVs pene a ion a e), would esul in he consump ion inc emen being highe .
I can be obse ed ha he impac o in oducing 5,253 EVs can each 8 MW,
which is signi ican conside ing ha he maximum consump ion o his a ea o
he analysed day is 103 MW. This app oxima ely means an 8% inc emen . Along
wi h his inc emen , we mus also conside how he es o he ene gy demand
no p o oked by he EVs in oduc ion has inc eased as well. This leads o a o al
inc emen o app oxima ely 15MW. This would mean ha his subs a ion would
ha e o be e-sized.
19.3.2 RTP-based DSM policy
Knowing he alley exis en in he ea ly mo ning, RTP policy has been p og ammed
o ou pu a e y cheap p ice o he ene gy ha is consumed om 1am o 8am. In
hese expe imen s, all ehicles ha e been uned o only accep his p ice. In he
231
19. AGENT-BASED MODELLING FOR DESIGNING AN EV CHARGING
DISTRIBUTION SYSTEMS: A CASE STUDY IN SALVADOR OF BAHIA
0
20
40
60
80
100
120
0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
MW
Figu e 19.2: Compa ison o he o iginal load cu e o 2014 (g ey) wi h he o al
consump ion including EVs cha ging o 2030 (black)
0
2
4
6
8
10
12
14
16
18
0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
MW
Figu e 19.3: EVs consump ion acco ding o he RTP based policy which makes
cheape cha ging om 1am o 8am
igu e 19.3, he consump ion o he EVs is p esen ed unde his new policy. As i
can be obse ed, since many ehicles ha e no been cha ged du ing he p e ious
days, mos o hem s a cha ging hei ba e ies a 1am, hus p oducing a high
demand peak o 16MW. This consump ion becomes 0 a 7am app oxima ely.
In he igu e 19.4, again, wo load cu es a e p esen ed: he o iginal load cu e
o 2014 (g ey) and o 2030 in which EVs a e included (black). This ime, he peak
ha al eady exis ed in he load cu e o 2014 has only inc eased as consequence
o he popula ion inc ease o 2030. This ime, EV cha ging has been shi ed o an
o -peak pe iod which is in he ea ly mo ning. In his new in e al, EVs cha ging
does no inc ease a all he o e all maximum peak o he demand allowing o a
g ea e in oduc ion o EVs i i was necessa y. No wi hs anding, he e is an ab up
inc ease o he consump ion a 1am since almos all EVs s a cha ging a ha ime
due o he low p ices. This is a non-desi ed e ec o he g id s abili y and should
be u he s udied in o de o smoo h hei cha ging s a ing p ocess.
232
19.4 Discussion
0
20
40
60
80
100
120
0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
MW
Figu e 19.4: Compa ison be ween o iginal load cu e o 2014 (g ey) wi h he o al
consump ion including EVs cha ging (black) using he RTP-based policy o 2030
19.4 Discussion
This wo k has been o ien ed o de elop he model o Sal ado o Bahia’s powe
g id using an Agen -based modelling app oach. This wo k has allowed o alida e
he abili y o his modelling app oach o:
1. Model indi idual decision making.
2. Conside local cons ain s.
3. Model powe g ids as social echnical sys em.
4. S udy eme gen synch onisa ion and coupling e ec s when many agen s co-
incide.
The majo ad an age is ha his model is able o simula e human decisions
and ac ions ha would a ec he unc ioning o he powe g id. In addi ion, such
changes in he g id would in u n in luence human decisions and ac ions. Once
his model is de eloped, u u e p ojec s will be o ien ed o design and assess DSM
policies ha would help educe in es men s on g id in as uc u e. On he o he
hand, he agen decision model could also be imp o ed. Fo example, agen s could
make decisions based on esponses o changes in he sys em, which will in u n
change he con ex o u u e decisions; o agen s could beha e in a he e ogeneous
way, hus maximising a ce ain p o i , ei he a ull cha ging o he ehicle o sa e
money.
233
Pa V
Conclusions
235
CHAPTER
20
Resul s
Resea ch wo k has been conduc ed o deal wi h he p oblems o designing and
e alua ing managemen policies ela ed o he SG pa adigm. These policies a e
aimed a di e en s akeholde s o bene i hei in e es s. Fo ins ance, inal cus-
ome ’s in e es s is he educ ion o he elec ici y cos s and he imp o emen o
he ene gy e iciency. The in e es s o ope a o s and e aile s is o imp o e hei
p o i by educing ope a ion and p oduc ion cos s. Globally, people wan o ha e a
mo e e icien powe g id in which CO2emissions a e educed wi h a highe RES
pene a ion.
The mo i a ion o conduc ing esea ch in he ield o Powe G ids is due o i s
p oposed e olu ion o SGs. In his e olu ion, IT seems o ha e an impo an ole.
This e olu ion is p oposed because he e is a signi ican need o he imp o emen
o powe g ids, such as: educ ion o he dependency on ossil- uel based ene gy
p oduc ion; ma ke conce ns such as uel p ices ola ili y; and educ ion o GHG
emissions.
SGs aim o ace he nex challenges: highe in oduc ion o RES; mo e e i-
ciency and lexibili y; and less dependency on ossil uels. Mo eo e , hey a e
cha ac e ised by he in oduc ion o au oma ion a dis ibu ion le el. I is expec-
ed ha his e olu ion will o e come he challenge o he massi e in oduc ion o
elec ical ehicles, dis ibu ed gene a ion and RES [HHM11]. The SG pa adigm
237
20. RESULTS
is an e en mo e impo an challenge o isola ed powe g ids as hey ha e limi ed
esou ces on he p oduc ion side o balance o e and demand.
This is he case o he Cana y Islands. In his e i o y, he e a e excellen
en i onmen al condi ions o inc easing he exploi a ion o RES. The Cana y Is-
lands ha e 2,500-3,000 hou s/yea o sola adia ion p oducing an a e age o 5-
6kWh/m2pe day (sou ce: Cana y Ins i u e o Technology). Fu he mo e, he e
a e 3,000-4,500 hou s/yea o wind wi h a speed a e age o 7-8m/s. This p oduces
625MWh pe day ha ing ins alled 75MW o wind ene gy. In his sense, he esul s
o his esea ch can be di ec ly applied o imp o ing powe g ids in he Cana y
Islands.
The s udy o hese sma managemen policies equi es models ep esen ing
he sys em a he lowes le el o de ail allowing o analyse side-e ec s. Agg ega ed
models a e no able o ep esen e en s on he demand side ha should be con-
side ed in designing hese policies. The e o e, Powe G id models should be dis-
agg ega ed o ep esen all loads ha can be managed. In hese models, complexi y
a ises since he e a e many he e ogeneous componen s which a e in e ac ing a di -
e en scales.
The eme gen beha iou o he sys em canno be in e ed wi h adi ional mod-
els, because complexi y canno be ep esen ed. SGs canno be s udied using adi-
ional app oaches since hese a e no able o cap u e all o he sys em’s p ope ies.
The bes way o s udy complex models is h ough So wa e ools. Howe e , i SGs
a e no suppo ed by he equi ed o malisms and me hodologies, so wa e ech-
nologies by hemsel es a e no su icien o pe o m hese s udies. This means,
he p oblem is no choosing he so wa e ool, bu applying he igh o malisms
and me hodologies. Fo hese easons, se e al au ho s ha e sugges ed he use o
complex sys em based o malisms o ep esen SGs. A Complex Sys ems app oach
acili a es he ep esen a ion o SGs a he lowes le el o de ail.
Unde he complex sys em app oach, only so wa e ools ha suppo his o m-
alism can be conside ed o s udy SGs. Ne e heless, he e a e s uc u al conce ns
ha mus be aken in o accoun o suppo he enginee ing o la ge-scale complex
sys ems. In he case o SGs, models may ha e millions o componen s. To model
hem, se e al complex sys em simula ion so wa e ools ha e been e iewed in his
documen . In all hese ools, a lack o a me hodological o ien a ion o ace he
modelling o la ge-scale complex sys ems was ound.
238
20.7 Publica ions
• Towa ds an in e disciplina y app oach o he simula ion o u u e SG a chi-
ec u es om a complex sys ems science poin o iew
–Au ho s: Viejo, P., K eme s, E., He n´
andez, M., He n´
andez, J., ´
E o a,
J., Langlois, P., Daude, E., Gonz´
alez de Du ana, J.M., & Ba ambones,
O.
–Cong ess: Eu opean Con e ence on Complex Sys ems 2011
–Place: Vienna, Aus ia
–Da e: Sep embe , 2011
• A la ge-scale elec ical g id simula ion o massi e in eg a ion o dis ibu ed
pho o ol aic ene gy sou ces
–Au ho s: ´
E o a, J., K eme s, E., He n´
andez, M., & He n´
andez, J. J.
–Cong ess: 6 h Eu opean Con e ence on PV-Hyb ids and Mini-G ids
(OTTI)
–Place: Chambe y, F ance
–Da e: Ap il, 2012
• Modelling li es yle aspec s in luencing he esiden ial load-cu e
–Au ho s: Hause , W., ´
E o a, J., & K eme s, E.
–Cong ess: 26 h Eu opean Con e ence on Modelling and simula ion
(ECMS 2012)
–Place: Koblenz, Ge many
–Da e: May, 2012
• Asynch onous Sma G id Simula ions
–Au ho s: ´
E o a, J., He n´
andez, J. J., & He n´
andez, M.
–Cong ess: UCNC’12 (Uncon en ional Compu a ion and Na u al Com-
pu a ion): CoSMoS - P oceedings o he 2012 Wo kshop on Complex
Sys ems Modelling and Simula ion
–Place: O leans, F ance
–Da e: Sep embe , 2012
• Decision suppo o Complex Sys ems: a Sma G id case
–Au ho s: ´
E o a, J., He n´
andez, J. J., & He n´
andez, M.
245
20. RESULTS
–Cong ess: UCNC’13 (Uncon en ional Compu a ion and Na u al Com-
pu a ion): CoSMoS - P oceedings o he 2013 Wo kshop on Complex
Sys ems Modelling and Simula ion
–Place: Milan, I aly
–Da e: July, 2013
• C i icali y in complex socio echnical sys ems: an empi ical app oach
–Au ho s: Viejo, P., K eme s, E., ´
E o a, J., He n´
andez, J. J., He n´
andez,
M., Ba ambones, O., & Gonz´
alez de Du ana, J. M.
–Cong ess: Eu opean Con e ence on Complex Sys ems 2013 (ECCS’13)
–Place: Ba celona, Spain
–Da e: Sep embe , 2013
• Asynch onous app oach o simula ions in Sma G id
–Au ho s: ´
E o a, J., He n´
andez, J. J., & He n´
andez, M.
–Cong ess: Eu opean Simula ion and Modelling Con e ence 2013 (ESM’13)
–Place: Lancas e , England
–Da e: Oc obe , 2013
• Ta a : A amewo k o de eloping simula o s based on Model D i en En-
ginee ing
–Au ho s: ´
E o a, J., He n´
andez, J. J., & He n´
andez, M.
–Cong ess: Eu opean Simula ion and Modelling Con e ence 2013 (ESM’13)
–Place: Lancas e , England
–Da e: Oc obe , 2013
• Model D i en Enginee ing o da a mine s simula ion
–Au ho s: ´
E o a, J., He n´
andez, J. J., & He n´
andez, M.
–Cong ess: IEEE In e na ional Con e ence on Da a Mining 2013 (ICDM’13)
- In e na ional Wo kshop on Domain D i en Da a Mining
–Place: Dallas, Uni ed S a es o Ame ica
–Da e: Decembe , 2013
• Agen -based modelling o designing an EV cha ging dis ibu ion sys ems: a
case s udy in Sal ado o Bahia
–Au ho s: ´
E o a, J., He n´
andez, J. J., Ba bosa, D. & Naza eno, P.
246
20.7 Publica ions
–Cong ess: Eu opean Simula ion and Modelling Con e ence 2014 (ESM’14)
–Place: Po o, Po ugal
–Da e: Oc obe , 2014
Nex lis con ains he publica ions eleased in jou nals:
• Ad an ages o Model D i en Enginee ing o s udying complex sys ems
–Au ho s: ´
E o a, J., He n´
andez, J. J., & He n´
andez, M.
–Jou nal: Na u al Compu ing
–DOI: 10.1007/s11047-014-9469-y
• C i icali y in complex socio echnical sys ems: an empi ical app oach o elec-
ical g ids 1
–Au ho s: Viejo, P., K eme s, E., ´
E o a, J., He n´
andez, J.J., He n´
andez,
M., Ba ambones, O, Gonz´
alez de Du ana, J.M.
–Jou nal: Applied ene gy
1This pape has been submi ed and now i is awai ing o he accep ance
247
CHAPTER
21
Discussion
In his chap e he scien i ic and echnical con ibu ions o his esea ch a e
desc ibed. “Scien i ic con ibu ions” e e o he c ea ion o knowledge whe eas
echnical con ibu ions e e o he c ea ion o new p ocesses o deal wi h speci ic
p oblems.
The pu pose o his esea ch is o explo e and desc ibe he applica ion o al eady
exis ing me hodologies o iden i y p oblems in SGs. This explo a ion has also
allowed an ou look on new esea ch a ge s ha could be s udied in u u e esea ch.
21.1 Con ibu ions
21.1.1 On Applying he complex sys em app oach in Sma G ids
Acco ding o he well-honou ed philosophe o science Thomas Kuhn, in he sci-
en i ic de elopmen o a discipline he e a e h ee main s ages [Kuh12]. These
h ee main s ages a e: “p escience”, “no mal science” and “ e olu iona y science”
(Figu e: 21.1). The “p escience” s age is cha ac e ised by ha ing nume ous incom-
pa ible and incomple e heo ies. The consensus o a p escien i ic communi y in
e ms o me hods, e minologies, expe imen s, e c. leads o he “no mal science”.
In his s age, new pa adigms can be concei ed o deal wi h anomalies eaching he
“ e olu iona y science” s age.
249
21. DISCUSSION
Figu e 21.1: Kuhn’s cycle.
In he case o he SG opic, i can be conside ed o be cu en ly in he second
s age: “no mal science”. Wi hin he s udy o his no mal science, se e al lim-
i a ions ha e been ound when SGs a e being modelled (appea ance o anom-
alies). The complex sys em app oach has been p oposed o o e come hem (new
pa adigms).
The e a e se e al s udies in he documen a ion ha asse ha powe g ids can-
no be s udied using adi ional o malisms. In hese s udies, he hypo hesis s a es
ha complex sys em app oach could o e come he limi a ion o he adi ional
o malisms. This hypo hesis canno be absolu ely e i ied since i canno be p o ed
o ca y ou all possible case s udies. Howe e , wha he hypo hesis s a es can be
ep oduced and e u ed.
In his esea ch, his hypo hesis has been ep oduced h ough he expe imen a-
ion o case s udies. These case s udies p o ide addi ional e idence ha his o m-
alism is alid [CGLP94, PKZK08, EKM+11]. Se e al case s udies ha e been ca -
ied ou o analyse SGs ollowing complex sys em app oach. I has been ound ha
his o malism is use ul o s udying SG policies wi h a maximum le el o disag-
g ega ion. When a new policy is being p oposed, he beha iou o a powe g id is
no known. In hese cases, he sys em can be modelled h ough i s componen s’
beha iou s and simula ed o ob ain he eme gen beha iou . A maximum le el o
disagg ega ion also allows o e-agg ega e he componen s’ beha iou s and analyse
250
21.1 Con ibu ions
hem a di e en le els (e.g. appliance, household, dis ic , coun y le el).
Ne e heless, in his esea ch, i has been ecognised ha complex sys em ap-
p oach by i sel may no be enough o deal wi h he s udy o SGs. O he issues
appea when using his o malism: complexi y o modelling sys ems, complexi y
o analysing da a and complexi y o designing s a egies. An impo an con ibu-
ion is he iden i ica ion o complemen a y me hodologies o o malisms ha could
be use ul o o e coming hese issues. In pa icula , he applica ion o MDE, BI
and SI should be conside ed.
The alidi y o his con ibu ion has been demons a ed h ough he execu ion o
case s udies. As in he case o complex sys em app oach, hese hypo heses canno
be e i ied. In o de o e i y hem absolu ely, hese hypo heses should be applied
o pe o m all SG case s udies, which is impossible. Ne e heless, in his esea ch,
se e al case s udies ha e been ca ied ou o p o ide e idence o i s alidi y.
21.1.2 On Modelling Sma G ids
This esea ch has ound ha he modelling o SGs equi es he coexis ence o di -
e en na u es in hei componen s. When SGs a e being modelled, i is necessa y
o desc ibe componen s ha may ha e di e en na u es such as: elec ical, he mo-
dynamical, me eo ological o sociological, among o he s.
Ob iously, elec ical componen s mus be ep esen ed since hei beha iou is
closely ela ed o he human beha iou s (e.g. when hese componen s a e swi ched
on and o ). Ac ually, he way in which he humans use hese elec ical compon-
en s will de e mine he sys em’s eme gen beha iou . The in e ac ion hey do o e
his componen s in ol e consump ions ha a ec how he sys em wo ks. Thus,
many elec ical models mus be modelled in o de o ep esen p oduc ion, dis i-
bu ion, demand, e c. wi h he deg ee o de ail ha is necessa y o es ing he SG
implemen a ion.
In addi ion, he modynamical componen s mus also be modelled since i is
necessa y o ep esen he he mal ans e ences be ween he mal elec ical com-
ponen s (e.g. e ige a o s, domes ic wa e hea e s, e c.). The modelling o hese
elec ical componen s mus include he modynamical beha iou ha calcula es he
he mal gains de i a i e om hei consump ions. Mo eo e , in as uc u e as
households may include a he mal beha iou as well. This beha iou would cal-
251
21. DISCUSSION
cula e he in as uc u e empe a u e based on he ex e nal empe a u e and he
he mal gains de i a i e om he he mal elec ical loads. In his manne , he mal
elec ical loads can calcula e hei in e nal empe a u e based on hei consump ion
and he household empe a u e.
The calcula ion o he in e nal empe a u e o a household depends on he ex-
e nal empe a u e, as p e iously s a ed. The e o e, i is necessa y o ep esen
he me eo ological condi ions o he en i onmen whe e he household is loca ed.
Thus, ano he kind o beha iou which is necessa y o model powe g ids is iden i-
ied. Fu he mo e, me eo ological condi ions mus be ep esen ed i he e a e RES
in he powe g id unde s udy. Renewable ene gy echnologies, such as PV cells
and wind u bines, equi e he ep esen a ion o en i onmen al condi ions. These
condi ions a e used by he models o hese ypes o echnologies o calcula e he
ene gy p oduced.
Fu he mo e, he ep esen a ion o SGs also use he modelling o human in e -
ac ions. Human beings a e use s o Powe G ids and he way hey use ene gy mus
be ep esen ed. This is a e y impo an conside a ion because o he signi ican
impac human in e ac ion has on he powe g id. As men ioned p e iously, in s a e
o he a , powe demand a ies geog aphically, seasonally, cul u ally, sociologic-
ally, e c. All hese a ia ions ha e o do wi h he way in which humans in e ac
wi h he powe g id.
In conclusion, he s udy o he applica ion o SG policies equi es he modelling
o many componen s wi h he e ogeneous na u es. The beha iou s ha ha e been
p e iously men ioned a e no only di e en because hey ha e di e en na u es:
elec ical, he mal, social, e c. bu also because hey equi e di e en execu ion
pa adigms: con inuous (e.g. he mal) o disc e e (e.g. washing machine).
21.1.3 On De eloping Complex Models
In his esea ch, MDE has been ound o be a complemen a y me hodology o
modelling SGs wi h a complex sys em app oach. When i is necessa y o in eg a e
di e en modelling echniques, s uc u al guidelines a e needed o deal wi h his
complexi y. In hese cases, MDE is use ul o managing he model de ini ion and
he simula o cons uc ion.
In his esea ch, he applica ion o MDE has been alida ed by ca ying ou he
252
21.1 Con ibu ions
case s udies. Mo eo e , his app oach is being used by o he esea che s. Cu -
en ly, EIFER and EDF a e de eloping hei own case s udies wi h his me hod-
ology. F om a scien i ic poin o iew, i is known ha o he labo a o ies a e e-
sea ching in he hypo hesis. To his day, he hypo hesis has no been e u ed.
The mos impo an implica ion o applying MDE in his ield is echnical:
MDE helps o build up and main ain la ge-scale simula ion models. Modelle s a e
assis ed in he cons uc ion p ocess by sepa a ing conce ns: he scena io desc ip-
ion (simula ion model) and he desc ip ion o indi idual componen s (beha iou s).
In his way, a se o s eps a e es ablished o guide he modelle o he goal:
• De eloping he simula ion model.
• Desc ibing a componen in he me amodel i i has no been p e iously de ined
he e.
• De eloping necessa y beha iou s no ye modelled.
• Building a simula o based on his simula ion model and simula e i .
Ano he MDE implica ion is i s adap abili y o changes. A single componen
o beha iou can be easily eplaced o added o an exis ing model scena io. Flex-
ibili y is an impo an ea u e o enginee ing SGs. In he design o a new policy,
i is necessa y o es i in di e en scena ios. The lexibili y p o ided by MDE
makes his possible. These scena ios may no only use di e en beha iou s o he
componen s bu may also o ganise componen s in a di e en way. In addi ion, he
same scena io can be used o es ing di e en policies.
Since MDE equi es he de ini ion o a me amodel, a echnical con ibu ion o
his esea ch is he de elopmen o a me amodel o SGs. In his sense, a me amodel
can be unde s ood as he c ea ion o an ag eemen con ac be ween many model-
le s in o de o make collabo a ion easie . The me amodel has se e al impo an
implica ions:
• Modelle s can sha e hei models whene e hey ha e been buil unde he
same me amodel.
• Modelle s can con ibu e o he me amodel by ex ending i s de ini ion o c e-
a ing new beha iou s ha can be used by o he s.
• Modelle s do no need o desc ibe he componen s o he eali y e e y ime
hey s a a new simula ion since hey ake ad an age o he al eady de ined
me amodel.
253
21. DISCUSSION
• Modelle s can use p e ious wo k de eloped by o he modelle s.
• Modelle s can de elop hei models as e hanks o all o hese abo e-men ioned
ac o s.
In his way, la ge simula ion models can be buil by de eloping he simula ion
model; and, in case i is needed, de eloping new componen s and beha iou s. In
his sense, modelle s a e guided comple ely so hey know wha hey ha e o do o
de elop simula ion models. The e o e, he complexi y o he scena io only a ec s
he simula ion model and no he o e all amewo k.
Fu he mo e, ools ha e been de eloped o deal wi h he complexi y o he ep-
esen a ion o la ge-scale sys em in his simula ion model. The ool named P o ile
was a esponse o he need o many modelle s had o he cons uc ion o la ge-
scale scena ios. In his sense, his ool handles he complexi y o building hese
simula ion models. Based on inpu da a p o ided by he modelle , his ool c e-
a es he scena io. Ano he impo an ool is he Simula o Builde which builds a
simula o based on a model ha desc ibes he scena io.
21.1.4 On Analysing Simula ion Resul s
This esea ch demons a es ha he applica ion o BI me hodologies is help ul.
These me hodologies ha e been used o analyse da a coming om he simula ion
o a complex sys em.
When SGs a e s udied as complex sys ems, he huge quan i y o esul s coming
om hese simula ions can be managed using BI me hodologies. BI me hodologies
can be used o disco e laws and egula i ies ha p o ide a be e knowledge o he
eme gen beha iou in SGs. These me hodologies allow science o be done o e
simula ed expe imen s. Based on he ou pu a ailable om he simula ions, i is
possible o ind ules, laws, egula i ies, e c. au oma ically.
In his esea ch, BI me hodologies a e used on he ou pu o he case s udies
p esen ed. The use o hese me hodologies is eally impo an in o de o know
how he eme gen beha iou in SGs is eached; and how i can be in luenced.
A e pe o ming all o he case s udies p esen ed in his documen , i has been
shown ha BI can be used in his con ex wi h na u alness. The mechanisms
p o ided by BI a e su icien o ep esen da a coming om SG simula ions and
allow i s exploi a ion.
254
21.3 Fu u e wo k
a e p og essi ely in oduced; o RES quo a inc eases. Fu he mo e, case s udies in
which bo h scena ios and policies can e ol e can be conside ed.
These kind o scena ios may be impo an o es ing he applicabili y o SG
policies in he long- e m. In o de o suppo hem, new mechanisms mus be de-
signed wi hin his ecosys em o me hodologies and o malisms.
21.3.3 On Disco e ing Pa e ns
As said be o e, BI me hodologies can be used o ind pa e ns in he simula ions o
SGs. The disco e y o pa e ns may ha e many di e en usages: ob aining a be e
knowledge o how eme gence is eached, simpli ying a complex model, eeding
back in o he design o SG policies, e c.
In his ield, much wo k can be ca ied ou as a con inua ion o his esea ch.
Da a mining echniques can be explo ed in o de o au oma ically de ec pa e ns in
se ies o da a. Howe e , his is no an easy ask, as he e is a main unde lying con-
ce n ha mus be conside ed when explo ing hese echniques: he huge quan i y
o da a.
As s a ed in he hypo heses pa o he documen , an impo an conce n o da a
managemen is he eloci y. Huge quan i y o da a is con adic o y o he eloci y.
This is especially icky when dealing wi h da a mining echniques ha equi e
a ho ough analysis o he da a. Fo his eason, his esea ch should consis in:
in es iga ing da a mining p ocedu es o ind pa e ns; and inding p ocedu es o
accele a e he p ocess.
One o he applica ions ha his esea ch may ha e is he simpli ica ion o com-
plex models. This would open ano he in e es ing u u e p ojec ha could consis
in au oma ing he model simpli ica ion p ocess. Tha means, he use o me hodolo-
gies, o malisms o echniques o iden i y beha iou al ea u es in complex models
ha can cha ac e ise simpli ied models.
To his end, esul s coming om he disco e y o pa e ns can be used in his
esea ch. A e , so wa e ools could be able o pa se da a coming om complex
models in o de o c ea e simpli ied models. The beha iou s p o ided by he sim-
pli ied model mus be ce i ied o be wo king a ce ain in e als ha ensu e meas-
u able e o s. Fo ins ance, hese simpli ied models could be used as submodels
wi hin bigge simula ions. The e o e, smalle complex simula ions can be used o
261
21. DISCUSSION
enginee bigge ones in which he compu a ional cos s can be educed.
21.3.4 On O he Fields
In he “hypo heses pa ” o his documen , he de ini ion o ex e nal alidi y in he
con ex o his esea ch was discussed. Howe e , a hypo hesis has ex e nal alidi y
i i applies o se e al case s udies o SG policies. This ex e nal alidi y has been
checked in his documen as hese me hodologies ha e been success ully applied
in all p esen ed case s udies.
Ne e heless, he second de ini ion p o ided o he ex e nal alidi y is no deal
wi hin his esea ch. This de ini ion conce ned he use o hese me hodologies in
o he complex sys ems apa om SGs. This is ac ually an ex ension o he scope
o he hypo heses de ined in his wo k.
I is easonable o belie e ha i hei applica ion in he ield o SGs has been
use ul, hei applica ion in o he ields can be help ul as well. Howe e , his should
be checked and is p oposed o u u e esea ch. In his sense, he hypo hesis o be
esea ched could be: “MDE, BI and SI can be help ul me hodologies o enginee -
ing complex sys ems o any na u e”. As in he p e ious hypo heses, his canno be
ully e i ied because i is no possible o pe o m all o he case s udies in all com-
plex sys ems. Howe e , a subse o hem can be ca ied ou , which could p o ide
new ideas o equi emen s.
21.3.5 On Expe imen ing wi h O e iew, Design concep s and
De ails p o ocol
Ano he line ha can be explo ed is he in eg a ion o he O e iew, Design con-
cep s and De ail (ODD) p o ocol in ou app oach o model agen -based models
wi h MDE. This p o ocol is a p oposed s anda d o desc ibing agen -based models
[GBB+06]. The e o e, ou app oach could include he desc ip ion o models ha
a e complian wi h ODD.
This p o ocol consis s o h ee blocks (O e iew, Design concep s and De ails),
which a e subdi ided in o se en elemen s: Pu pose, S a e Va iables and Scales,
P ocess O e iew and scheduling, Design concep s, Ini ializa ion, Inpu , and Sub-
models. The e o e, agen -based models can be desc ibed acco ding o hese se en
262
21.3 Fu u e wo k
elemen s. The nex lis summa ises he con en s o place a each o hese elemen s:
• Pu pose: desc ip ion o he scope o he model. Clea , concise and speci ic
o mula ion o he model’s pu pose. Explana ion o he need o building a
complex model.
• S a e a iables and scales: s a e a iables e e o low-le el a iables ha
cha ac e ise low-le el en i ies o he model. The scales e e o he simula ion
con igu a ion (e.g. leng h o ime s eps, size o habi a cells).
• P ocess O e iew and scheduling: he p ocess o e iew desc ibes en i on-
men al and indi idual p ocesses ha a e buil in o he model (e.g. ood p o-
duc ion, g ow h, mo emen ). The schedule has o do wi h how hese p o-
cesses occu and hei o de .
• Design concep s: hese concep s p o ide a common amewo k o designing
and communica ing indi idual-based models.
• Ini ializa ion: de ines how he en i onmen and he indi iduals ha e o be
c ea ed.
• Inpu : condi ions o he en i onmen ha change o e space and ime.
• Submodel: p esen a ion and explana ion o all he p ocesses lis ed abo e in
a de ailed manne , including he pa ame e isa ion o he model.
263
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