the tou project - test 1
TRANSCRIPT
Adaptive and TOU Pricing Schemes for Smart Technology Integration
ORDECSYS Christopher Andrey 2014
An overview of the results
ORDECSYS
» The TOU Project - An overview
Funded by: Consortium:
(Forschungsprogramm Energie - Wirtschaft - Gesellschaft)
Aim of the project:
Assess the influence of smart grid technologies (decentralised storage and demand-response) on the
long-term planning of a regional energy system
ORDECSYSY
ORDECSYS
» The TOU Project - An overview
* Statistique suisse de l’électricité 2013, SFOE ** Presentation by Pascal Previdoli, SFOE
{Higher energy efficiency
+
More renewables
2012 Production*
Swiss Target 2050** Factor
PV 320 GWh 11.4 TWh x35
Wind 88 GWh 4 TWh x45
+
Nuclear Phase-Out GHG Emission Reduction
In particular, the Swiss Energy Strategy 2050 massively relies on
investments in intermittent renewables
ORDECSYS
» The TOU Project - An overview
One of the bottlenecks for a wide-spreadpenetration of renewables is their intermittent production pattern.
Solar
Wind
Source : http://www.transparency.eex.com/
ORDECSYS
» The TOU Project - An overview
Seite 8
Leitstudie 2009 national ohne zusätzliche Verbraucher –2050 (meteorologisches Basisjahr 2007)
German load curve in 2050Dr. Kurt Rohrig, Fraunhofer-Institut, Kassel
Seite 8
Leitstudie 2009 national ohne zusätzliche Verbraucher –2050 (meteorologisches Basisjahr 2007)
PV Hydro Biomass Geothermal Wind Others
Storage
Demand-Response
ORDECSYS
» The TOU Project - An overview
Both storage and demand-response may be achieved through time-dependent financial incentives.
Load reduction vs LMP in PJM (USA)greentechmedia.com (peak ~ 160 GW)
ORDECSYS
» The TOU Project - An overview
Measure of the attractiveness of demand-response and decentralised storage in electric vehicles
10
Scenario 1 Scenario 2 Scenario 3
ApplianceDishwasher
DryerFreezer
Control Method Own Computer ManualNetwork Operator
Delay6 hours
10 min 2 hours
Yearly Incentive 50 CHF10 CHF
0 CHF
! ChoiceX
Table 2: Demand-Response Evaluation - Example {Table:DR-Ex}
2.5 Storage in Electric Vehicles
The aim of the third and final part of the survey is to understand under which cir-
cumstances respondents would agree to put their electric car at the disposal of the
network operator so that the latter could use the cars’ batteries astemporary storage
units. In particular, we are interested in estimating the role of financial incentives, of
the ownership model of the battery, of the guaranteed autonomy after participating to
such a service, and of the minimum duration the car has to be connected to the electric
network per day.
The respondents haveagain been introduced to the subject via a short home-made
factual animation, embedded in the survey environment by LINK. Clicking on Figure
2 will open the animation on YouTube. In this case too, the respondents were asked
(i) to imagine themselves living in 2030 and (ii) to imagine owning an electric car.
Figure 2: Storage in Electric Vehicles - Animation {Fig:YouTube2}
Table 3 gives the list of attributesand the levels these attributes can take.
R
u
e
d
u
G
o
t
h
a
r
d
5
–
C
h
ˆe
n
e
-
B
o
u
r
g
–
S
w
i
t
z
e
r
l
a
n
d
T
e
l
.
+
4
1
2
2
9
4
0
3
0
2
0
–
w
w
w
.
o
r
d
e
c
s
y
s
.
c
o
m
p
r
o
b
(
s
c
e
n
a
r
i
o
s
)
=
e
x
p
P
l2l
e
v
e
l
s
(
s
)
wl
!
P
t
2s
c
e
n
a
r
i
o
s
e
x
p
P
k2l
e
v
e
l
s
(
t
)
wk
!
through conjoint analysis techniques
-5 0 5 10
0.2
0.4
0.6
0.8
1.0
16
Quite surprisingly, the amount of time by which the consumption is shifted hasalmost no influence on the probability of a given scenario.
3.2.4 Yearly Incentive
Figure 8: Part-worths of the yearly incentive attribute. Source: Annex D. {DR-U4}
Finally, the impact of yearly incentive on the choices reveals that o↵ering only amodest amount of 20 CHF per year seems to dramatically increases the probability ofadoption.
3.3 Storage in Electric Vehicles
The methodology adopted in the second part of the survey has allowed us to measurethe part-worths of each of the levels of the attributes. Let us consider each of theattributes in turn:
Rue du Gothard 5 – Chene-Bourg – Switzerland Tel. +41 22 940 30 20 – www.ordecsys.com
ORDECSYS
» The TOU Project - An overview
Sample:
A total of 1045 respondents from‣ Canton of Geneva (373)‣ Canton of Vaud (367)‣ Cantons of Neuchâtel, Fribourg, Jura (305)
Ages ranging from 15 to 74
Online survey (internet users)
Survey rolled out between Nov. 4th and Nov. 18th, 2013
Introduction to the ES2050, DR and storage in two short animations
ORDECSYS
» The TOU Project - An overview
Conjoint SimulationsLave-vaisselle 1/2
TOU Pricing Ordecsys10.02.2014 | 13 |
N = 1048 respondence
Appareil Lave-vaisselle
Contrôle Par votreordinateur
Amplitude de déplacement 10 minutes
Impact sur la facture annuelle CHF 0
80.1%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Acc
epta
nce
73.7% 80.1% 76.7%
0%
20%
40%
60%
80%
100%Contrôle
Manuel Par votre ordinateur Par votre distributeurd’électricité
80.1% 79.6% 79.3% 78.5% 81.2%
0%
20%
40%
60%
80%
100%Amplitude de déplacement
10 minutes 30 minutes 1 heure 2 heures 6 heures
21Conjoint SimulationsLave-vaisselle 2/2
TOU Pricing Ordecsys10.02.2014 | 14 |
N = 1048 respondence
Appareil Lave-vaisselle
Contrôle Par votreordinateur
Amplitude de déplacement 10 minutes
Impact sur la facture annuelle CHF 0
80.1% 84.2% 85.1% 86.8%
0%
20%
40%
60%
80%
100%Impact sur la facture annuelle
CHF 0 CHF 10 CHF 20 CHF 50
80.1%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Acc
epta
nceFigure 14: Utilities of the yearly incentive attribute. Source: Annex E. {DR-S2}
Another interesting simulation is the one related to the flexibility of dryers’ use. Thescenario Dryer, Own Computer, 10 minutes, CHF 0 has a rather low acceptance of72.7% as shown on the LHS of Figure 15. Remember that the dryer had the lowestutility, see Figure 5. However, o↵ering 50 CHF per year can increase the acceptanceby almost 10 percentage points, as can be noticed on the RHS of Figure 15.
Conjoint SimulationsSèche-linge 2/2
TOU Pricing Ordecsys10.02.2014 | 18 |
N = 1048 respondence
Appareil Sèche-linge
Contrôle Par votreordinateur
Amplitude de déplacement 10 minutes
Impact sur la facture annuelle CHF 0
72.7%80.7% 81.5% 83.3%
0%
20%
40%
60%
80%
100%Impact sur la facture annuelle
CHF 0 CHF 10 CHF 20 CHF 50
72.7%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Acc
epta
nce
Figure 15: Utilities of the yearly incentive attribute. Source: Annex E. {DR-S3}
Rue du Gothard 5 – Chene-Bourg – Switzerland Tel. +41 22 940 30 20 – www.ordecsys.com
Simulations obtained using a randomised first choice model. Acceptance here means the probability of adopting the given scenario rather than choosing “None”.
Results:
‣ ~80% acceptance ‣ low sensitivity to the
implementation details
ORDECSYS
» The TOU Project - An overview
Simulations obtained using a randomised first choice model. Acceptance here means the probability of adopting the given scenario rather than choosing “None”.
22
4.2 Storage in Electric Vehicles
Generally speaking, the acceptance of temporary storage in electric cars is very wellaccepted as can be seen on the simulations presented in Figure 16 and 17.
Conjoint SimulationsPropriété du ménage 1/2
TOU Pricing Ordecsys10.02.2014 | 32 |
N = 1048 respondence
Propriété de la batterie
Propriété du ménage
Autonomie garantie 400 kilomètres
Durée de la mise à disposition par jour 1 heure
Gains annuels 100
83.8%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Acc
epta
nce
80.3% 80.9% 83.2% 83.8%
0%
20%
40%
60%
80%
100%Autonomie garantie
100 kilomètres 150 kilomètres 250 kilomètres 400 kilomètres
83.8% 83.5% 83.7% 82.4%
0%
20%
40%
60%
80%
100%Mise à disposition par jour
1 heure 2 heures 6 heures 12 heures
Figure 16: Utilities of the yearly incentive attribute. Source: Annex E. {EV-S1}Conjoint SimulationsPropriété du ménage 2/2
TOU Pricing Ordecsys10.02.2014 | 33 |
N = 1048 respondence
83.8% 86.4% 87.8%
0%
20%
40%
60%
80%
100%Gains annuels
CHF 100 CHF 300 CHF 700Propriété de la batterie
Propriété du ménage
Autonomie garantie 400 kilomètres
Durée de la mise à disposition par jour 1 heure
Gains annuels 100
83.8%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Acc
epta
nce
Figure 17: Utilities of the yearly incentive attribute. Source: Annex E. {EV-S2}
All combinations of levels give rise to acceptabilities that are in the 80% range.
Rue du Gothard 5 – Chene-Bourg – Switzerland Tel. +41 22 940 30 20 – www.ordecsys.com
22
4.2 Storage in Electric Vehicles
Generally speaking, the acceptance of temporary storage in electric cars is very wellaccepted as can be seen on the simulations presented in Figure 16 and 17.
Conjoint SimulationsPropriété du ménage 1/2
TOU Pricing Ordecsys10.02.2014 | 32 |
N = 1048 respondence
Propriété de la batterie
Propriété du ménage
Autonomie garantie 400 kilomètres
Durée de la mise à disposition par jour 1 heure
Gains annuels 100
83.8%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Acc
epta
nce
80.3% 80.9% 83.2% 83.8%
0%
20%
40%
60%
80%
100%Autonomie garantie
100 kilomètres 150 kilomètres 250 kilomètres 400 kilomètres
83.8% 83.5% 83.7% 82.4%
0%
20%
40%
60%
80%
100%Mise à disposition par jour
1 heure 2 heures 6 heures 12 heures
Figure 16: Utilities of the yearly incentive attribute. Source: Annex E. {EV-S1}Conjoint SimulationsPropriété du ménage 2/2
TOU Pricing Ordecsys10.02.2014 | 33 |
N = 1048 respondence
83.8% 86.4% 87.8%
0%
20%
40%
60%
80%
100%Gains annuels
CHF 100 CHF 300 CHF 700Propriété de la batterie
Propriété du ménage
Autonomie garantie 400 kilomètres
Durée de la mise à disposition par jour 1 heure
Gains annuels 100
83.8%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Acc
epta
nceFigure 17: Utilities of the yearly incentive attribute. Source: Annex E. {EV-S2}
All combinations of levels give rise to acceptabilities that are in the 80% range.
Rue du Gothard 5 – Chene-Bourg – Switzerland Tel. +41 22 940 30 20 – www.ordecsys.com
Results:
‣ ~84% acceptance ‣ low sensitivity to the
implementation details
ORDECSYS
» The TOU Project - An overview
ETEM-SG is a long-term energy planning (LTEP) model:
‣ used to assess the impact of regional energy/climate policies
‣ represents the entire energy system of a region
‣ embeds a detailed representation of
‣ technologies (investment costs, O&M costs, efficiency, etc.)
‣ demands for energy services in all sectors (residential, industry, etc.)
‣ dynamics of the demands
ORDECSYS
» The TOU Project - An overview
Horizon: typically 10 to 50 years
Period 1 Period i Period Ntypically 1 to 5 years
Investment iCalibration
Comparaison simulation - courbes de charges réellesRésidentiel collectif - Printemps
0
10
20
30
40
50
60
70
80
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240
500
1000
1500
2000
2500
3000
3500
4000
Autre électroménag
Clim
Chauff appoint
Chauff principa
Veilles
Plaques de cuisson
Fours micro-ondes
Fours traditionnel
E.C.S.
Informatique
Congél
Combinés
Réfrig
Eclairage
Sèche-linge
Lave-vaisselle
Lave-linge
Téléviseur
Total réel coll
568LES BOUDINES;28/05/2002862AVANCHET PARC RTE DE MEYRIN; 12/07/2002675LIGNON EST; printemps 2002
Détente-SIG-c_zone_hab.xls
demand allocation(dynamics)
General time-structure
ORDECSYS
» The TOU Project - An overview
General structure
CHPEV
Imported biomass
Electricity
Heat
CO2
Transport demand
Large-scale flow problem
ORDECSYS
» The TOU Project - An overview
Annexes
1 Modèle déterministe
Soit i et j les indices des commodités, k l’indice des technologies et t l’indice des périodes, la
formulation mathématique simplifiée du modèle ETEM s’écrit:
min f(X,C, I, E) (1a)
Iit
+X
k
Xout
ikt
= Eit
+X
k
Xin
ikt
+ dit
, 8i 8t (1b)
X
j
�ijkt
Xin
jkt
= Xout
ikt
, 8i 8k 8t (1c)
X
i
Xout
ikt
↵kt
�kt
(ckt
+X
lt
Ckl
), 8k 8t (1d)
gm
(X,C, I, E) 0, 8m (1e)
avec X = (Xin, Xout), les variables représentant les flots de commodités entrant et sortant
des technologies, C les variables d’investissement dans les capacités de technologies et I et Eles variables d’import et d’export. La fonction objectif f(X,C, I, E) représente l’ensemble des
coûts et profits annualisés fixes et variables associés aux technologies et à leur utilisation, aux
investissements, aux imports et aux exports. Les contraintes (1b) garantissent la satisfaction
des demandes et la conservation des flots, les contraintes (1c) lient les inputs aux outputs
des technologies et les contraintes (1c) sont des contraintes de capacité sur l’utilisation des
technologies. Enfin, les contraintes gm
(X,C, I, E) représentent l’ensemble des contraintes
de bornes sur l’activité des technologies, les investissements, les capacités, les exports et les
imports. Les principaux paramètres du modèle sont les suivants:
• ↵: facteur de disponibilité des technologies.
• �: facteur d’efficacité des technologie reliant les inputs aux outputs.
• �: facteur de conversion de la capacité en énergie.
• d: vecteur de demandes.
2 Modèle stochastique
Nous donnons ici la formulation stochastique du modèle ETEM pour laquelle les scénarios
indexés par ! ont un tronc commun avant la période t et se séparent ensuite avec des demandes
d!
différentes. A chaque scénario !, on associe une probabilité ⇡!
de réalisation. Ainsi, le
1
General mathematical structureX = flows, C = capacity increase, I = imports, E = exports i,j = commodity index, t = time index, k = technology index
minimise total costflow conservation
technology description
activity bounded by capacity
other constraints (e.g. CO2)
ORDECSYS
» The TOU Project - An overview{Current energy system
(capacities)
Evolution of useful demands and of imported energy prices
(drivers)
Catalogue of existing and future technologies
ETEMSmartGrid
Sources of uncertainties
‣ Capacity expansion (technology portfolio) ‣ Activities (operation) ‣ GHG and pollutants emissions ‣ Imports and exports ‣ Marginal costs (electricity, GHG, etc.)
1
2
3
4
ORDECSYS
» The TOU Project - An overview
Cantons of Vaud & Geneva 2005-2050
1. Current energy system
Inputs
Hydro VD Hydro GE PV Cheneviers Tridel Pierre de Plan Chatillon Veytaux
Electricity production 2005 Load curve 2005
0"
0.1"
0.2"
0.3"
0.4"
0.5"
0.6"
0.7"
0.8"
0.9"
""""""WN"" """"""WP1""""""""WM""""""""WP2"" """"""SN"" """"""SP1"" """"""SM"" """"""SP2"" """"""IN"" """"""IP1"" """"""IM"" """"""IP2""
Transport Heat & Warm Water Industry Residential Electricity
Food/Textile/Paper Chemistry/Metallurgy Machines
Construction
Tertiary
Others
Industry consumption by sector 2005
ORDECSYS
» The TOU Project - An overview
Cantons of Vaud & Geneva 2005-2050
2. Evolution of useful demands and of imported energy prices
Inputs
Future growth rate, SECO Population increase, OFS
ORDECSYS
» The TOU Project - An overview
Cantons of Vaud & Geneva 2005-2050
3. Catalogue of existing and future technologies
Inputs
Investment cost : 1500 MCHF/GWO&M costs : 40 MCHF/GW/yearLifetime : 30 yearsEmissions : 0 tCO2/PJUpper-bound : Suisse.Eole
ORDECSYS
» The TOU Project - An overview
Results
ORDECSYS
» The TOU Project - An overview
12:00 16:00 20:00 0:00 4:00 8:00 12:005
5.5
6
6.5
7
7.5
8
8.5
9
9.5
10
August 15 − 16, 2007
No
rmal
ized
po
wer
(k
W)
Fig. 5. Simulation results with δ = 0.007. Although the tracking parameterdoes not satisfy the conditions of Theorem 3.2, convergence still occurs.
12:00 16:00 20:00 0:00 4:00 8:00 12:005
5.5
6
6.5
7
7.5
8
8.5
9
9.5
10
August 15 − 16, 2007
No
rmal
ized
po
wer
(k
W)
Fig. 6. Simulation results with δ = 0.003. At this point the trackingparameter is small enough that the negotiation process does not converge.
PEVs charge for less time than others. As a consequence,total demand ramps down at the beginning of the charginginterval, and ramps up at the end.
V. CONCLUSIONS
In this paper, decentralized charging control of largepopulations of PEVs is formulated as a class of finite-horizondynamic games. The decentralized approach works by solv-ing a relatively simple local problem and iterates quickly toa global Nash equilibrium. This strategy does not requiresignificant central computing resources or communicationsinfrastructure.
The paper establishes, under certain mild conditions, ex-istence, uniqueness and social optimality of the Nash equi-librium attained through decentralized control. A negotiationprocedure is proposed that converges to a charging strategythat is nearly optimal. In fact, for a homogeneous PEV pop-ulation, the charging strategy degenerates to a purely socialoptimal ‘valley-filling’ strategy. The results are illustratedwith various numerical examples.
12:00 16:00 20:00 0:00 4:00 8:00 12:005
5.5
6
6.5
7
7.5
8
8.5
9
9.5
10
August 15 − 16, 2007
No
rmal
ized
po
wer
(k
W)
best charging strategy of PEV 2
best charging strategy of PEV 1
non−PEV base demand
average charging strategy
Fig. 7. Converged Nash equilibrium for a heterogeneous population ofPEVs with δ = 0.015.
APPENDIX
The proof of Theorem 3.3 proceeds by considering, with-out loss of generality, adjacent time instants t and s = t+1.Local charging controls (!un
t , !unt+1), that are optimal with
respect to u and xnt , can be decomposed as !un
t = bn,∗−an,∗
and !unt+1 = bn,∗ + an,∗ respectively. It is possible to show
that
an,∗ = arginfan∈Sbn,∗
"#an − 1
2(ut+1 − ut)
+1
4δ
$p(dt+1 + ut+1)− p(dt + ut)
%&2'
with Sbn,∗ ! {an;−bn,∗ ≤ an ≤ bn,∗}.Relationship (11a) can be established by contradiction.
If (11a) were not true, then it can be shown that an,∗ <12 (ut+1 − ut), implying that !un
t+1 − !unt < ut+1 − ut for all
n, and hence that
avg(!ut+1)− avg(!ut) < ut+1 − ut
where !u ≡(!un; 1 ≤ n < ∞
). This, however, conflicts with
the fact that {!un;n < ∞} is a Nash equilibrium with respectto u, see Theorem 2.1. Hence a contradiction.
Relationship (11b) is also proved by contradiction, andfollows a similar argument as the proof of (11a). In this casethough, it is determined that
avg(!ut+1)− avg(!ut) > ut+1 − ut,
which conflicts with {!un;n < ∞} being a Nash equilibriumwith respect to u.
Proof by contradiction is also used to establish (11c).Assume there are adjacent times t, t+ 1 ∈ [*t0,*ts], such that
dt+1 + ut+1 = dt + ut +B, (14)
where B > 0 without loss of generality. Then there will alsoexist an n and C ≥ B such that
dt+1 + !unt+1 = dt + !un
t + C.
The theorem states that !uns > 0 for all n and all s ∈ [*t0,*ts],
so there always exists a sufficiently small ε > 0 such that
12:00 16:00 20:00 0:00 4:00 8:00 12:005
5.5
6
6.5
7
7.5
8
8.5
9
9.5
10
August 15 − 16, 2007
Norm
aliz
ed p
ow
er (
kW
)
Fig. 5. Simulation results with δ = 0.007. Although the tracking parameterdoes not satisfy the conditions of Theorem 3.2, convergence still occurs.
12:00 16:00 20:00 0:00 4:00 8:00 12:005
5.5
6
6.5
7
7.5
8
8.5
9
9.5
10
August 15 − 16, 2007
Norm
aliz
ed p
ow
er (
kW
)
Fig. 6. Simulation results with δ = 0.003. At this point the trackingparameter is small enough that the negotiation process does not converge.
PEVs charge for less time than others. As a consequence,total demand ramps down at the beginning of the charginginterval, and ramps up at the end.
V. CONCLUSIONS
In this paper, decentralized charging control of largepopulations of PEVs is formulated as a class of finite-horizondynamic games. The decentralized approach works by solv-ing a relatively simple local problem and iterates quickly toa global Nash equilibrium. This strategy does not requiresignificant central computing resources or communicationsinfrastructure.
The paper establishes, under certain mild conditions, ex-istence, uniqueness and social optimality of the Nash equi-librium attained through decentralized control. A negotiationprocedure is proposed that converges to a charging strategythat is nearly optimal. In fact, for a homogeneous PEV pop-ulation, the charging strategy degenerates to a purely socialoptimal ‘valley-filling’ strategy. The results are illustratedwith various numerical examples.
12:00 16:00 20:00 0:00 4:00 8:00 12:005
5.5
6
6.5
7
7.5
8
8.5
9
9.5
10
August 15 − 16, 2007
Norm
aliz
ed p
ow
er (
kW
)
best charging strategy of PEV 2
best charging strategy of PEV 1
non−PEV base demand
average charging strategy
Fig. 7. Converged Nash equilibrium for a heterogeneous population ofPEVs with δ = 0.015.
APPENDIX
The proof of Theorem 3.3 proceeds by considering, with-out loss of generality, adjacent time instants t and s = t+1.Local charging controls (!un
t , !unt+1), that are optimal with
respect to u and xnt , can be decomposed as !un
t = bn,∗−an,∗
and !unt+1 = bn,∗ + an,∗ respectively. It is possible to show
that
an,∗ = arginfan∈Sbn,∗
"#an − 1
2(ut+1 − ut)
+1
4δ
$p(dt+1 + ut+1)− p(dt + ut)
%&2'
with Sbn,∗ ! {an;−bn,∗ ≤ an ≤ bn,∗}.Relationship (11a) can be established by contradiction.
If (11a) were not true, then it can be shown that an,∗ <12 (ut+1 − ut), implying that !un
t+1 − !unt < ut+1 − ut for all
n, and hence that
avg(!ut+1)− avg(!ut) < ut+1 − ut
where !u ≡(!un; 1 ≤ n < ∞
). This, however, conflicts with
the fact that {!un;n < ∞} is a Nash equilibrium with respectto u, see Theorem 2.1. Hence a contradiction.
Relationship (11b) is also proved by contradiction, andfollows a similar argument as the proof of (11a). In this casethough, it is determined that
avg(!ut+1)− avg(!ut) > ut+1 − ut,
which conflicts with {!un;n < ∞} being a Nash equilibriumwith respect to u.
Proof by contradiction is also used to establish (11c).Assume there are adjacent times t, t+ 1 ∈ [*t0,*ts], such that
dt+1 + ut+1 = dt + ut +B, (14)
where B > 0 without loss of generality. Then there will alsoexist an n and C ≥ B such that
dt+1 + !unt+1 = dt + !un
t + C.
The theorem states that !uns > 0 for all n and all s ∈ [*t0,*ts],
so there always exists a sufficiently small ε > 0 such that
Demand response can flatten the load curve through iterative negotiation processes (modelled via mean field games)
Ma, Callaway & Hiskens, 2007
ORDECSYS
» The TOU Project - An overview
Global models of TOU pricing reveals how to price electricity based on measured elasticities
Supply Demand
ORDECSYS
» The TOU Project - An overview
0"
0.1"
0.2"
0.3"
0.4"
0.5"
0.6"
2005" 2010" 2015" 2020" 2025" 2030" 2035" 2040" 2045" 2050"
NEP"
NEP"-"CO2"
NEP"+"DR"
NEP"+"V2G"
NEP"+"DR"+"V2G"
0"
0.1"
0.2"
0.3"
0.4"
0.5"
0.6"
0.7"
2005" 2010" 2015" 2020" 2025" 2030" 2035" 2040" 2045" 2050"
NEP"
NEP"."CO2"
NEP"+"DR"
NEP"+"V2G"
NEP"+"DR"+"V2G"
Photovoltaics
Wind turbines
Demand response tends to delay investments in renewables by allowing
demand to better match existing production facilities’ constraints.
ORDECSYS
» The TOU Project - An overview
0"
0.1"
0.2"
0.3"
0.4"
0.5"
0.6"
2005" 2010" 2015" 2020" 2025" 2030" 2035" 2040" 2045" 2050"
NEP"
NEP"-"CO2"
NEP"+"DR"
NEP"+"V2G"
NEP"+"DR"+"V2G"
0"
0.1"
0.2"
0.3"
0.4"
0.5"
0.6"
0.7"
2005" 2010" 2015" 2020" 2025" 2030" 2035" 2040" 2045" 2050"
NEP"
NEP"."CO2"
NEP"+"DR"
NEP"+"V2G"
NEP"+"DR"+"V2G"
Photovoltaics
Wind turbines
Demand response tends to delay investments in renewables by allowing
demand to better match existing production facilities’ constraints.
However, when combining DR with V2G possibilities*, investments in
intermittent renewables are encouraged.
*Dual use of electric vehicles batteries: Vehicle to Grid.
ORDECSYS
» The TOU Project - An overview
12#
14#
16#
18#
20#
22#
24#
2010# 2015# 2020# 2025# 2030# 2035# 2040# 2045# 2050#
NEP#
NEP#-#CO2#
NEP#+#DR#
NEP#+#V2G#
NEP#+#DR#+#V2G#
Demand response tends decrease the need for imports, by allowing assets
to be more efficiently managed.
ORDECSYS
» The TOU Project - An overview
12#
14#
16#
18#
20#
22#
24#
2010# 2015# 2020# 2025# 2030# 2035# 2040# 2045# 2050#
NEP#
NEP#-#CO2#
NEP#+#DR#
NEP#+#V2G#
NEP#+#DR#+#V2G#
Demand response tends decrease the need for imports, by allowing assets
to be more efficiently managed.
However, when combined with V2G possibilities, imports raise due to the electricity demand stemming from
electric vehicles.
ORDECSYS
» The TOU Project - An overview
0"
0.05"
0.1"
0.15"
0.2"
0.25"
0.3"
WN! WP1! WM! WP2! SN! SP1! SM! SP2! IN! IP1 ! IM! IP2 !
Demand-response allows the energy system to dynamically adapt to changing weather conditions
Scenario based on 2011's weather data
ORDECSYS
» The TOU Project - An overview
0"
0.05"
0.1"
0.15"
0.2"
0.25"
0.3"
WN! WP1! WM! WP2! SN! SP1! SM! SP2! IN! IP1 ! IM! IP2 !
Demand-response allows the energy system to dynamically adapt to changing weather conditions
Scenario based on 2012’s weather data
ORDECSYS
» The TOU Project - An overview
0"
0.05"
0.1"
0.15"
0.2"
0.25"
0.3"
WN! WP1! WM! WP2! SN! SP1! SM! SP2! IN! IP1! IM! IP2!
Demand-response allows the energy system to dynamically adapt to changing weather conditions
Scenario based on 2013’s weather data
ORDECSYS
» The TOU Project - An overview
1. Integration of electricity network contraints, e.g. to define zonal pricing schemes (in progress) 2. Load shedding 3. Evaluation of the repercussion of an energy/climate policy on the value chain
Conclusions
Perspectives
1. The effects of demand-response and storage can be assessed through ETEMSmartGrid 2. Suisse-Romande’s households have a positive view of EVs and of DR mechanisms 3. EVs and DR can be exploited for a faster integration of renewables 4. Stochastic weather scenarios’ impact on DR and renewables has been studied
ORDECSYS Christopher Andrey 2014