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ECONOMIC AND TECHNICAL ANALYSIS OF THE EUROPEAN SYSTEM WITH A HIGH RES SCENARIO

EDF R&D

Stockholm, 24 November 2015

Miguel Lopez-Botet Zulueta, Vera Silva

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Fuel Price

Coal 86 €/t

Gas 10 €/MMBtu

Oil 107 €/baril

CO2 35 €/t

Simulation of the EU Energy Roadmap « HiRES 2030 » scenario

60 % RES (generation)

40 % Wind & Solar

Thermal fossil fuel

Wind offshore

Biomass & Geothermal

Nuclear

Solar

Wind Onshore

Hydro power

High RES 2030 GW Load factor (h/yr)

Solar (PV) 220 1100

Onshore wind 280 1900

Offshore wind 205 3200

Hydro 120 3800

HiRES scenario - EU energy roadmapGeneration Mix 2030

All low carbon generation, including nuclear, is obtained from the roadmap scenario

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What is this study about?

Flexibility to handle variability Connecting RES and load

Keeping the lights on Balancing the economics

GW Demand

Net demand

PV Wind Hydro

0

20

40

60

80

100

120

140

0 2 4 6 8 10 12 14 16 18 20 22 24

Marginal prices

PV

Marginal price

h

Euro/MWh

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Location of VGHourly load factors (or

lower resolution)VG forecast errors

FlexAssessment

CONTINENTAL Model

Reserves and flexibility adequacy

Economicanalysis

Dynamic simulation platform

Market prices and generation costs

Generation loadfactors

Interconection loadfactors

Generation mix

Frequency stability

VG curtailment

Plant revenues

Investment / hourly dispatch

Investment loop

Representation of VG

Demand time seriesInvestment costs

Generation dynamic constraintsFuels costsCO2 price

Network transfer capacities

Input data

An integrated approach for the technical and economical analysis of High RES scenarios in Europe is required

M. Lopez-Botet, et all, ‘Methodology for the economic and technical analysis of the European power system with a largeshare of variable renewable generation’, presented at IEEE PES General Meeting, Washington, USA, 27-31 July, 2014.Langrene, N., van Ackooij, W., Breant, F., ‘Dynamic Constraints for Aggregated Units: Formulation and Application’, IEEETransactions on Power Systems, vol.26, no.3, Aug. 2011.

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Understanding the variability

-100

-

100

200

300

400

500

0 30 60 90 120 150 180 210 240 270 300 330 360

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Wind onshore generation for different geographical areas

Load

fact

or

Day

France Brittany Farm

-

50

100

150

200

250

0 30 60 90 120 150 180 210 240 270 300 330 360

Onshore wind daily average generation30 climatic years

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

30

%

15

%

Average difference of daily generation:

90 GW

Average load factor : 25%

Installed power : 280 GW

Summer

Winter

Geographical diversity does help, but there is still significant variability at European level

Source RTE

You can reduce the variability of wind at local level but the correlation in wind regimes acts as a limit at continental level

22%

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-

50

100

150

200

0 30 60 90 120 150 180 210 240 270 300 330 360

GW

Summer

Winter

PV dayly average load factor 13 % (25 GW) for 30 climatic years

(summer 20 %, winter 5 %)

InstalledCapacity220 GW

daysJan Fev Mar Apr Mai June Jully Aug Sep Oct Nov Dec

As for wind the geographical diversity reduces PV variability. This represents an

incentive for the aggregation of PV generation instead of self supply

The contribution of PV in Europe in winter is quite limited with an

average load factor of 5%

Source: RTE

The variability of PV between climate years is less pronounced than the one observed from wind

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Connecting RES and load

PV Wind Hydro

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Integrating a large share of variable RES requires a coordinated development of RES and networks

PV (GW)Offshore wind ( GW)Onshore wind (GW)

2 74

5 9

7228

67

71

347

2019 32

452 25

2 29 5

3 5 3

10

14 32

5 5

8 15 7

Interconnection reinforcement (GW) similar to TYNDP 2014

324

82

Network development scenarioRES geographical distribution

Interconnection reinforcement TYNDP 2010 (GW)

TYNDP 2014+47 GW en 16 ans

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Flexibility to handle variability

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Not only conventional generation, but also variable RES, will contribute to balancing and ancillary services

Penetration of RES > 100 % RES need to provide downward flexibility as

well as ancillary services

GW European Demand

Net demand(demand – variable RES)

400 GW ramp between Sunday and Monday

Middle of the day valley

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Variable RES are key to the decarbonisation of electricity generation but the system still needs backup capacity for

security of supply

Average CO2 with 60% RES = 125 g CO2 /kWh Average CO2 with additional coal/gas replacement = 73 g CO2 /kWh

(average CO2 today = 350 g CO2/kWh)

Full decarbonisation can only be achieved with a significant share of carbon free base load, such as nuclear

GW

-100

-

100

200

300

400

500

600

700

0 1000 2000 3000 4000 5000 6000 7000 8000

The need for baseload generation isreduced by 160 GW

With 40% wind and PV the need for backup generation increases 60 GW

European thermal installed capacity

No Wind & SolarTotal : 444 GW

With Wind & SolarTotal : 352 GW

GW

89GW

250GW

71GW

34GW

89GW78GW

86GW99GW

0

50

100

150

200

250

300

Nuclear Coal CCGT OCGT

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Storage and active demand may to a certain extent supplement generation to balance supply and demand

Storage and flexible demand contribute to the flexibility required for balancing but do not replace the need for backup generation

Net benefit interval as a function of storage cost and installed capacity

Net benefit of storage for different countries

1 GW 2 GW 4 GW

-200

0

200

400

600

800

1000

1 GW 2 GW 4 GW 1 GW 2 GW 4 GW

Weekly storage (40 h)M€/y

Gains coûts fixes

Fixed costsavings

Gains coûts variables

Variable costssavings

Net system benefitsinterval

Gains brutsGross benefit

Defaillance + Hydro + Pertes

Curtailementcosts + otherlosses

France Germany + Austria UK

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Keeping the lights on

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The exposure of the load-generation balance to weather uncertainties increases significantly

0 20 40 60 80 1000

3

6

9

12

% de cas

Mar

ge -

Hau

sse

(GW

)

1H2HJ-1

0 10 20 30 40 50 60 70 80 90 1005

10

15

20

25

30

35

% of hours

Up

ward

op

era

tio

n r

eserv

e 1

h (

GW

)

European level requirementSum of national requirements

The management of uncertainty will be facilitated by an increasing near real-time dimensioning of operating reserves

40% reduction

Intra-Day D+1 D+2 D+3

PV Site Wind Site

0

5

10

15

20

25

30

Intra-Day D+1 D+2 D+3 StatisticalM

AE (%

of i

nsta

lled

capa

city

) PV France Wind FranceObservability and forecasting are

essential to reduce the operation margins required to handle

load-generation balancing

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Balancing the economics

0

20

40

60

80

100

120

140

0 2 4 6 8 10 12 14 16 18 20 22 24

Marginal prices

PV

Marginal price

h

Euro/MWhSnapshot of the average annual short term system marginal costs

Cost

The marginal costs are obtained using a system level approach and considering a perfect market

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As the penetration of RES increases, Base Price falls and does not reflect the Complete Cost of base generation

anymore

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0

10

20

30

40

50

60

70

80

90

0% PV

+E

8% PV

+E

16% P

V+E

24% P

V+E

32% P

V+E

40% P

V+E

48% P

V+E

56% P

V+E

64% P

V+E

72% P

V+E

80% P

V+E

Num

ber o

f hou

rs in

the

year

€/M

Wh

Base Price in Germany

Number of hours where spot price is 0 in Germany

Drop in Base Price

Some hours with price at 0€/MWh

83€/MWh: Complete Cost of Coal (Base

Generation)

For a high penetration of RES, the notion of “base generation” disappears. Plants need to recover their costs on fewer hours but remain profitable as

long as the marginal price when operating is high enough.

Snapshot of the average of

the annual short term

system marginal

costs

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The market value of variable RES will decrease as their penetration levels increases

The decrease of market value depends on profile effects and load factor and is more pronounced for PV

RES market value in comparison to base price per country

-30-25-20-15-10-505

10

10% 20% 30% 40% 50% 60% 70%

Penetration rate in energy

Italy

GermanyFrance

Italy

Spain

France

Spain

Germany

Poland UK

Diff

eren

ce to

the

base

pric

e(€

/MW

h)

Wind

PV

With ~0% RES, the first MWs of RES have a market value close to the base price

UKBased on the average of the annual short term system

marginal costs

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0

50

100

150

200

250

Solar Onshore Wind Offshore Wind

Germany

Spain

France

GB

Italy

Poland

Costs IEA (WEO 2014)

RES

mar

ketr

even

ues

(€/k

W/y

ear)

* Includes: investment costs, O&M (labour force, maintenance …) and grid connection costs for offshore wind.

In a system with 60% RES penetration, wind and PV are not able to recover their costs

Onshore wind => economic value close to its costs. Offshore wind => penalised by high investment costs (around 350 €/kW/an)

PV => low revenues of PV due to a pronounced “cannibalisation” effect

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Sensitivity to variable RES penetration *

0%

20%

40%

60%

80%

100%

RES

Pen

etra

tion

V1 V2 V3 V4 V5 REF V6 V7 V8 V9 V10

The higher the penetration of RES the lower their value factor. This effect is more pronounced for PV

* None of the other parameters, such as the level of interconnection, were modified.

60%

Value Factor = RES revenue (€/MWh)Base Price(€/MWh)

0,0

0,2

0,4

0,6

0,8

1,0

1,2Iberian Peninsula

PVOnshoreOffshore

0,00,20,40,60,81,01,2

Great-BritainPVOnshoreOffshore

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Geographical diversity does help, but there is still significant variability at

European level

Variable RES are key to the decarbonisation of electricity

production but the system still needs backup capacity for security of supply

Storage and active demand may to a certain extent supplement generation

to balance supply and demand

Not only conventional generation, but also variable RES, will contribute to

balancing and existing ancillary services

Integrating a large share of variable RES requires a coordinated

development of RES and networks

Variable RES production should potentially provide new ancillary

services

The pace of deployment of RES should be optimised in order to limit costs of

storage or excessive curtailment

Key messages from this study

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Thank you for your attention!

Vera.silva@edf.frMiguel.lopez-botet-zulueta@edf.fr

Report available from :http://chercheurs.edf.com/fichiers/fckeditor/Commun/Innovation/departements/SummarystudyRES.pdf