The TOU project - Test 1

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


* 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


One of the bottlenecks for a wide-spreadpenetration of renewables is their intermittent production pattern.

Solar

Wind

Source : http://www.transparency.eex.com/

http://www.transparency.eex.com/

ORDECSYS


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

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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)

http://greentechmedia.com

ORDECSYS


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

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r

d

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–

C

h

ˆe

n

e

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g

–

S

w

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e

r

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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

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o

s

)

=

e

x

p

P

l2l

e

v

e

l

s

(

s

)

wl

!

P

t

2s

c

e

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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.


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


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


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



Appareil Lave-vaisselle




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



Appareil Sèche-linge




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}


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


Simulations obtained using a randomised first choice model. Acceptance here means the probability of adopting the given scenario rather than choosing “None”.

22


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



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



83.8% 86.4% 87.8%

0%

20%

40%

60%

80%

100%Gains annuels

CHF 100 CHF 300 CHF 700Propriété de la batterie




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.


22


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



Propriété de la batterie




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



83.8% 86.4% 87.8%

0%

20%

40%

60%

80%

100%Gains annuels

CHF 100 CHF 300 CHF 700Propriété de la batterie




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.


Results:

‣ ~84% acceptance ‣ low sensitivity to the

implementation details

ORDECSYS


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


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


General structure

CHPEV

Imported biomass

Electricity

Heat

CO2

Transport demand

Large-scale flow problem

ORDECSYS


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


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



2. Evolution of useful demands and of imported energy prices

Inputs

Future growth rate, SECO Population increase, OFS

ORDECSYS



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


Results

ORDECSYS


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


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

)



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


Global models of TOU pricing reveals how to price electricity based on measured elasticities

Supply Demand

ORDECSYS


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


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


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


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


0"

0.05"

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0.25"

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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


0"

0.05"

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WN! WP1! WM! WP2! SN! SP1! SM! SP2! IN! IP1 ! IM! IP2 !


Scenario based on 2012’s weather data

ORDECSYS


0"

0.05"

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WN! WP1! WM! WP2! SN! SP1! SM! SP2! IN! IP1! IM! IP2!


Scenario based on 2013’s weather data

ORDECSYS


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

[email protected]

ORDECSYS Christopher Andrey 2014

The TOU project - Test 1

Science

Transcript of The TOU project - Test 1