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Renewable Energy for Electricity

Simon Müller

Analyst, Renewable Energy Division

5 April 2013

© OECD/IEA 2011

The source of all RE Energy

© OECD/IEA 2011

Forms of Primary RE Energy

Solar

Wind

Hydro

Ocean

Bioenergy

Geothermal

• Radiation

• Movement

• Movement

• Movement

• Heat

• Heat

© OECD/IEA 2011

How can we “make” electricity?

There are only two ways that we use mass scale!

1. Movement (often via heat)

Direct: Wind, Hydro, Ocean

Heat: Bioenergy, geothermal, CSP

© OECD/IEA 2011

How can we “make” electricity?

2. Photoelectric effect

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What is a Watt? What is a Watt-hour? A Watt-hour measures energy!

Running your mobile phone one hour: 0.1 Wh

Running your computer one hour: 10 Wh

Running a water boiler one hour: 1 000 Wh = 1kWh

Running the IEA for one hour: 250 000 Wh = 0.25 MWh

Running Paris for 1 hour: 2 000 000 000 Wh = 2 GWh

Running the World for one hour 3 000 000 000 000 = 3 000 GWh = 3 TWh

A Watt measures energy per time!

A human: 100 W

A solar panel: 200 W

A wind turbine: 2 000 000 W = 2 MW

Large offshore windpark 1 000 000 000 W = 1 GW

© OECD/IEA 2011

Hydro power Sub-types

Reservoir

Run of river

Pumped

Itaipu Dam, Brazil, 14GW

Goldistal Pumped , Germany, 1 GW

Size

Large: 100 MW – 15 GW

Small: 100 kW – 300 MW

Stats (2011)

3644 TWh

Approx 1000 GW

16.2% of global el (2009)

Cost

18 – 100 USD/MWh

1 - 2 mio USD/MW

1-15 bn USD for a large plant

© OECD/IEA 2011

Geothermal Sub-types

Flash

Binary

Itaipu Dam, Brazil, 14GW

Flash geothermal plant

Size

Flash: 10 MW – 250 MW

Binary: 12 MW –20 MW

Stats (2009)

66 TWh

0.33% of global electricity

Cost

Flash: 50 – 80 USD/MWh

Binary: 60-200 USD/MW

2-4 mio USD/MWh Flash

2.5-6 mio USD/MWh Binary

© OECD/IEA 2011

Wind power Sub-types

Onshore

Offshore

Xinjiang, China

Size

Turbine: 1 – 6 MW

Farm: 100 MW – 1000 MW

Stats (2011)

420 TWh

240 GW (2011)

Cost for onshore

40 – 160 USD/MWh

1.1 – 2.3 mio USD/MW

1 bn USD for a large farm

Cost for offshore

140 – 300 USD/MWh

3 – 6? mio USD/MW

5 bn USD for a large farm

© OECD/IEA 2011

© OECD/IEA 2011

© OECD/IEA 2011

Bioenergy Sub-types

Biomass (cofiring)

Biogas

Biogas digester

Biomass plant, Pfaffenhofen, Germany, 6 MW

Size

Large: 100 kW – 100 MW

Cofiring: 20 – 100s MW

Stats (2011)

307 TWh

1.3% of global electricity

Cost

70 – 150 USD/MWh

20 – 70 USD/MWh (cofiring)

2.6 – 4.1 mio USD/MW

300 mio USD for a large plant

© OECD/IEA 2011

Solar CSP

Sub-types

Trough

Tower

Dish

Tower system

Size

1 - 250 MW

Stats (2011)

3.77 TWh

Cost

180 – 300 USD/MWh

4.2 – 8.2 mio USD/MW

500 mio USD for a large system

Kramer Junction, Nevada

© OECD/IEA 2011

Solar PV

Sub-types

Silicone

Thin film

PV rooftop system

Size

Panel: abt 200 W

System: 1 kW – 250 MW

Stats (2011)

65 TWh (100 TWh)

70 GW (100 GW 2012)

0.1% of global electricity

Cost

100 – 300 USD/MWh

1.x – 5 mio USD/MW

100 mio USD for a large park

40 MW solar farm, Germany

© OECD/IEA 2011

Cost of Electricity generation

Source: Bloomberg New Energy Finance

© OECD/IEA 2013

Grid Integration of Variable Renewables

Phase III

April 2013

© OECD/IEA 2013

Grid integration of variable renewables 2011: IEA published technical

assessment of integration

Key points:

Case-study approach

No principle technical ceiling

Feasible share of variable RE depends on system flexibility Flexible supply

Storage

Interconnections

Demand side response

New phase:

Refined definition and assessment of flexibility economics

© OECD/IEA 2013

GIVAR III Structure

Market design and policy recommendations

FAST 2

Variable RE Impact Analysis

IMRES

Flexible Resources Analysis

Case Studies • Market design review • Data questionnaire • Stakeholder interviews

Desk research • Cost of flex options • Technical characteristics of flex options • Integration studies

© OECD/IEA 2013

FAST 2

Video preview of FAST2

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Economics of flexibility

Systems are different – impacts will vary too

But common groups of effects

Uncertainty

Reserves

Distant resources

Transmission grid

Modularity

Distribution grid

Variability

Cycling / start-ups

Abundance Scarcity Capital

utilisation

Grid costs Higher

operational costs

Curtail-ment

Cost of complementary /

back-up generation

Cost of holding

reserves

Cost effective integration strategies needed to minimise these costs: Interconnection, demand side response,

flexible generation and storage

Optimised operations and variable renewables deployment

© OECD/IEA 2013

Better operational tactics - Germany

Despite the increase in variable renewables, cooperation between four TSOs has led to cost savings

Source: Hirth, Ziegenhagen, 2013

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Spain – geospread of wind generation

Wind power capacity in Spain is broadly distributed across the country

Installed wind capacity and network structure (Jan 2012)

Total power flows (2011)

Source; REE – 2011 Annual Report and Presentations: ”Impact of RES on the ancillary services”, 24 May 2011; “Integrating renewable energy into the Spanish energy networks –Technology challenges”, 15 September 2010

\

© OECD/IEA 2013

Modelling benefit of flexibility

Generic power system to study flexibility options:

Designed to capture different properties of real systems

12 GW of nuclear; fairly inflexible coal (70% minimum output); other plants decided by capacity expansion model at 0% var RE

German load and variable RE generation profile

Two baseline scenarios: 30% and 45% wind and solar (2/3 to 1/3)

Add a flexibility option to the system

Value calculated as difference in total costs

Baseline case

with flex option

Co

sts

Benefit of flexibility

Reduced operational costs

Reduced curtailment

Less costly complementary plant mix

© OECD/IEA 2013

First results – costs of thermal system

Cost per MWh from thermal system under different penetration levels and flexibility options

Relevant assumptions: WACC 7%, CO2 30USD/tn, GAS 8 USD/MBTU, COAL 2.7 USD/MBTU

Up to 35% reduced increase

© OECD/IEA 2013

Key adaptation factor: utilisation of assets

Non-varRE system costs increase if fuel savings outweigh capital utilisation effect

Cost effective adaptation means dealing with changed capital utilisation!

0

5000

10000

15000

20000

25000

30000

35000

Baseline 0%

Baseline 45%

Adapted 45%

mill

ion

USD

pe

r ye

ar

MWh costs in baseline driven by two factors:

Saved fuel (-)

Less MWh to recover CAPEX (+)

© OECD/IEA 2013

So what are these solutions?

Find assets that have a low utilisation anyway and make them provide flexibility!

Electric water heaters

Electric cars (if they are around …)

Water pumps etc.

Take all measures that increase utilisation of existing and new assets

Last option: deploying high CAPEX solutions with low utilisation (battery storage)

© OECD/IEA 2013

Conclusions

Wind and solar generation can cause additional system level costs

These costs are a result of the interaction with the system, they are caused by varRE as well as by the existing system

Smart approaches exist to mitigate these costs

1. First priority: Better operational tactics

2. Second priority: Grid friendly varRE deployment and integrated planning

3. Third priority: Deploy new assets to help integration (low capex is key!)

© OECD/IEA 2013

Thanks

Simon.mueller@IEA.org