Provisional agenda and presentation for the global atlas training workshop in lima, peru, EN

Seminar Schedule Spatial Planning Techniques for Renewable Power Generation

2 – 3 February 2015, Lima, Peru

Monday, 2 February Tuesday, 3 February

09:00-10:45 FROM 8:30: REGISTRATION Opening/Welcome Address Solar power spatial planning techniques, L. Koerner

Strategies: From the technical potential to the realizable potential; Dr. D. Jacobs

Opening remarks, Edwin Quintanilla, Vice Minister of Energy

Introduction of participants

Overview on the seminar, L. Koerner

09:45 – 10:45 Introduction to IRENA’s Global Atlas and hot spot identification, A. Jain

The availability of resources and setting of deployment targets based on resource assessments

The availability of flexibility in the power sector

Case study: Resource assessment and target setting in Saudi Arabia

The availability of grid infrastructure o Using the existing grid, expanding the grid or developing

renewables off-grid o Grid expansion planning and stakeholder involvement o Grid connection charging

10:45-11:00 Coffee break Coffee break

11:00-12:45 Solar and wind power spatial planning techniques; L. Koerner Strategies (continued) and Finance mechanisms; Dr. D. Jacobs

Wind power spatial planning techniques

Overview on wind energy estimation and formation of wind

Spatial setup of wind farms

Estimating wind electricity yield

Worked example: Estimating wind capacity and yield at a given site

Solar power spatial planning techniques

Solar resource

Spatial setup of large-scale PV plants

The availability of space (spatial planning) o From technical potential to the realizable potential: o Spatial planning and RES deployment – the German

framework Finance Mechanisms

Designing finance mechanisms for different market segments

Net Metering policies for small-scale installations?

12:45-13:45 Lunch Lunch

13:45-15:15 Solar power spatial planning techniques (continued) Hands-on exercise part 1: Hot spot analysis Economic assessment of PV and wind for energy planning L. Koerner

Finance Mechanisms Dr. D. Jacobs

Spatial power spatial planning techniques (continued)

Estimating PV electricity yield

Worked example: Estimating PV capacity and yield at a given site

CSP: Direct normal irradiance and spatial requirements Hands-on exercise part 1 (ca. 20-30 minutes):

Delegates use Global Atlas and identify hot spot areas in their country for wind and solar energy deployment.

Economic assessment of PV and wind for energy planning:

Levelised cost of electricity (LCOE)

Worked example: LCOE sensitivity of PV projects

FIT design and locational signals Hands-on exercise part 3 (ca. 40-50 minutes):

Delegates use RENAC’s financial analysis tool for wind and solar feed-in-tariff estimation and present their tariffs.

Finance Mechanisms (continued):

Auction design and spatial planning

Case study South Africa, China and Brazil

15:15-15:30 Coffee break Coffee break

15:30 -17:00 Economic assessment of PV and wind for energy planning (continued) Hands-on exercise part 2: LCOE estimation; L. Koerner

Finance Mechanisms (continued)

Project Development; Dr. D. Jacobs

Worked example: LCOE sensitivity of wind projects

Worked example: Effects of data uncertainty on the LCOE of PV

Finance Mechanisms (continued):

Combining FITs and auctions?

Options for mini-grid finance

Monday, 2 February Tuesday, 3 February

Hands-on exercise part 2 (ca. 60 minutes):

Delegates estimate the LCOE for two solar and wind hot spots in their country and present their findings.

Financing support mechanisms: Design options and international experience

Project Development:

Reducing administrative barriers

The importance of resource mapping for investors and project developers

Assessment and revising of existing policies and frameworks 16:30 -17:00: Panel Discussion and closing remarks

Introduction

IRENA Global AtlasSpatial planning techniques 2-day seminar

About Renewables Academy (RENAC)

• RENAC is a berlin-based training specialist for Renewable Energy and Energy Efficiency.

• RENAC trained more than 4,000 persons from over 130 countries.

• RENAC’s clients are from public and private sectors.

• RENAC offers

� short-term trainings and

� academic education (MBA-Renewables, GPE-New Energy)

� Capacity Building Services (RENAC supports third parties to build up their own

capacities for trainings)

• RENAC is a private sector company with 27 employees.

• RENAC is independent.

2

About the tutor

Lars Koerner coordinates training programs at Renewables Academy

(RENAC) AG mainly in the field of solar energy. He holds a Diploma

in Environmental Engineering / Renewable Energies. Before joining

RENAC in 2014 he gained several years of experience as project

engineer and senior product manager at SolarWorld AG where he

also managed several PV-Diesel-Hybrid rural electrification projects.

His experience in the area of solar energy spans further through his

work at the German Aerospace Center (DLR) in Almeria/Spain and

Fraunhofer ISE in Freiburg/Germany. He is an expert in sizing and

simulation of solar energy systems and the co-author on off-grid and

hybrid systems in Earthscan’s 3rd edition of “Planning and Installing

Photovoltaic Systems”.

3

SETTING THE FRAME

4

5

Resource Mapping

Scenarios

RE Markets

Once we know resource and zones: How do we get to realistic and

feasible scenarios?

What needs to be done to create the right framework for low-risk

scenario deployment?

Instruments for scenario

development

Political, regulatory & financial

instruments

6

Resource Mapping

Scenarios

Energy planning instrumentsD

ay 1

1. National capacity and electricity yield estimationResult: Technical potential for identified areas

2. Finding economically most viable applications and areas Result: Overview on RE generation cost

3. Define priority areas for various RE technologies

7

Scenarios

RE Market

Strategies:

1. Target setting 2. The availability of flexibility in the

power sector? 3. The availability of grid

infrastructure? 4. The availability of space (spatial

planning)?

Instruments:

5. Designing finance mechanisms for different market segments

6. Financing support mechanisms7. Reducing administrative barriers

Project development:

8. Resource mapping for investors and project developers

9. Monitoring and reviewing (target achievement)

Day

2

Thank you very much for your attention!

Lars KoernerRenewables Academy (RENAC)Phone +49 30 52 689 [email protected]

Global Atlas Training on Planning the

Renewable Energy Transition Solar and

Wind MapsLima, Peru, Feb. 2-3th 2015

Current Status of Capacity building

• Why capacity building?

Countries Renewable targets are

• 20% by 2020, 30% by 2030

• Detailed feasibility studies are not conducted to derive these targets

• Mismatch between Renewable Resource and Renewable potential

• Who is funding?

The module is financed by Flemish government, Germany, and the Brussels

Region.

• Who is attending?

The training module is specialised for policy and decision makers. It therefore

focuses on the strategic aspects of planning methods rather than on technical

aspects:

2

Current Status of Capacity building (contd.)

• Where is the capacity module delivered

The module is being deployed in 3 countries

• November 12th – 13th . First session – African Clean Energy Corridor. Arusha,

Tanzania

• December 17th -18th . Second session – MENA. Cairo, Egypt

• February 2nd – 3rd. Third session – Latin American. Lima, Peru

• What are the outcomes?

It presents the different approaches to evaluation of technical potentials, and in

particular emphasizes the sensitivity of the results to the selection of constraints, the

approach, which is chosen, and the way the calculations are performed.

Using the results of previous geospatial analysis performed by IRENA, the training

session builds capacity of the policy and decision makers to identify high-potential

developable renewable energy.

3

RENEWABLE RESOURCES

RENEWABLE POTENTIALS

4

Global Atlas

5

What share of my energy mix canbe supplied by renewable energy?

Where are the resources located?

What is the most cost-effective combination of technologies?

What amount of investments does it represent? How many jobs ?

Is there a large enough market for sustaining a supply chain?

6

Conceptual diagram of Renewable Energy Potentials (from NREL, 2012)

How competitive is it?

How much can it cost?

Where can it be

harvested? How much

power?

Where is the resource?

Complexity StandardsPrivate sector

interest Risks

• COUNTRY-DRIVEN

• LONG TERM PLANNING PROCESS

• COMMITMENT REQUIRED

8

Geospatial information. Resource, infrastructures, population density.. What next?

Energy modelers, general public,

lobbyists

Project developers, grid simulation, rural

electrification agencies, energy agencies

Need: number of MW that can be installed

for a given technology.

Outcome is in MW.

Often presented as tables with MW per

region / country.

Follow-up: high level discussions with policy

makers, broad grid simulations (power).

Need: locations of suitable areas for future

developments.

Outcome is a suitability map.

Follow-up: consultation process with policy

makers, zoom on a few select areas,

dynamic grid simulation using time series

(power).

On such areas, limited analysis on technical

potential into more detail.

Numbers are best guest, depend on

model. High disparity despite apparent

precision.

Outcome is a map and a consultation

process leading to spatial planning. MW are

closer to project reality.

IRENA: Estimating the renewable energy

potential in Africa.

IRENA: Global Atlas, ECOWAS zoning,

Africa Clean Energy Corridor

Winds in Africa. Mesoscale 5km basemap from 3TIER. Average annual wind speeds at 80 m high.

The values can not be usedwithout validation, but the windpatterns appear clearly, and areconsistent with other mesoscalesources. The boxes attempt tohighlight areas with possiblystrong annual average windspeeds.

This rough approximation doesnot exclude the possibility of goodwind sites outside the red squares,due to local effects not capturedby the mesoscale model.

10

Data

bankability

Investor’s

interest

PUBLICSECTOR EFFORT

Local measurements

PRIVATESECTOR EFFORT

Existing local measurements

Data quality

Zoning

NOT ‘BANKABLE’

‘BANKABLE’

Demonstration on ECOWAS within GEOSS AIP-6

Presented at the GEO-X Ministerial Summit

Geneva, Jan. 14-17th, 2014

11

http://irena.masdar.ac.ae/?map=507

http://globalatlas.irena.org/

http://globalatlas.irena.org/

http://irena.masdar.ac.ae/?map=507

GLOBAL ATLAS – A UNIQUE

DATA INFRASTRUCTURE

12

Bridge the gap between nations having access to the

necessary funding, technologies, and expertise to evaluate

their national potentials, and those deprived of those

elements.

13

Bridge the gap between nations having access to the necessary funding,

technologies, and expertise to evaluate their national potentials, and those

deprived of those elements.

Access to data and methods

Building capacities on strategic planning

Mobilizing technical assistance

14

15

Albania, Australia, Austria, Belgium, Colombia, Denmark, Egypt, Ethiopia, Fiji island,France, Gambia, Germany, Greece, Grenada, Honduras, India, Iraq, Iran, Israel, Italy,Kazakhstan, Kenya, Kiribati, Kuwait, Lithuania, Luxembourg, Maldives, Mali,Mauritania, Mauritius, Mexico, Mongolia, Montenegro, Morocco, Mozambique,Namibia, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Peru,Philippines, Poland, Portugal, Qatar, Saudi Arabia, Senegal, Seychelles, South Africa,Spain, Sudan, Swaziland, Switzerland, Tonga, Tunisia, Turkey, UAE, Uganda, UK, UnitedRepublic of Tanzania, Uruguay, USA, Vanuatu, Yemen, Zimbabwe.

1,000 datasets. 45+ national atlases.

17

Map gallery – information accessed easily

Hot spot-LCOE-FIT/ Auction approach

20

Potential Collaboration opportunities?

• Integrate capacity module in existing programs

Freely available open source tool with webinars, online videos, presentations and

experts

E.g. UN-ESCAP and IRENA planning for resource mapping trainings

IRENA can works with other development partners to deliver this module

• Potential funding for two capacity sessions in Asia-Pacific

21

22

www.irena.org/GlobalAtlas

[email protected]

IRENA Global Atlas

@GlobalREAtlas

GlobalAtlasSolarandWind

Session 2: Wind power spatial planning techniques


Central questions we want to answer

• After having identified those areas which are potentially available for renewables, we

want to estimate…

� what the potential wind capacity per km² and in total is (W/km²), and,

� how much electricity (Wh/km²/a) can be generated in areas with different wind

regimes.

• We also need to know which parameters are the most sensitive ones in order to identify

the most important input parameters.

2

3

© R

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201

4

Wind speed at hub height (m/s)

Energy generation costs at specific site (€/Wh)

Wind speed extrapolation to turbine hub height

Roughness length or wind shear exponent

Hub height (m)

Energy output calculation

Power curve, wind turbine density (W/km2), air density

Weibull distribution (k, A)

Electrical losses (%)

CAPEX

OPEX

WACC

Life time

Economic parameters

(wind farm and grid connection)

Annual energy prod. (Wh/a/km2)Wind capacity per area (W/km2) C

AP

EX

= C

apita

l exp

endi

ture

, OP

EX

= O

pera

tion

expe

nditu

re, W

AC

C =

Wei

ghte

d av

erag

e co

st o

f ca

pita

l (de

bt, e

quity

)

Areas potentially suitable for wind farms (km2) Site assessment (wind atlas data, wind speed (m/s) for certain height (m))

Exclusion of non-suitable land areas and adding of buffer zones

Nature protected area

Urban area (buffer zone: 8–10 hub height)

Transport, supply and communication infrastructure

Areas technically not suitable (high slope and above certain altitude, etc.)

Landscape, historic area, other non-usable land (glaciers, rivers, etc.)

Areas potentially suitable for wind farms (km2)

Priority areas for wind power (km2), potentially installed capacity (W), potentially

generated energy (Wh/a) and costs

Energy policy analysis

Economic assessment

done

pend

ing

Agenda

1. Formation of wind

2. Technical aspects we need to know

3. Spatial setup of wind farms

4. Estimating wind electricity yield

5. Worked example: Estimating wind capacity and yield at a given site

4

1. FORMATION OF WIND

5

High and low pressure area

• High pressure area occurs when air becomes colder (winter high pressure areas can be quite strong and lasting). The air becomes heavier and sinks towards the earth. Skies are usually clear. The airflow is clockwise (northern hemi). The air flows towards the low pressure area over the ground.

Source: http://www.experimentalaircraft.info/weather/weather-info-1.phpar

Isobars

• Low pressure occurs when air becomes warmer. The air becomes lighter and rises. The pressure lowers towards the center and air flow is counterclockwise (northern hemi). Clouds will appear due to rising of the moist warm air and the weather will deteriorate. Air will flow back to the high pressure area at higher altitudes in the atmosphere.

6

Mountain valley breeze

7

Sea-land breeze

8

2. TECHNICAL ASPECTSWE NEED TO KNOW

9

Vertical wind shear profile and roughness of surface

Profile above area with low roughness (sea, low grass)

Hei

ght

Hei

ght

Profile above area with high roughness (forest, town) 10

Roughness classes and roughness lengths (European wind atlas)

Rough-ness class

Roughnesslength Z 0 [m] Landscape type

0 0.0002 Water surface

0.5 0.0024 Completely open terrain with a smooth surface, e.g. concrete runways in airports, mowed grass, etc.

1 0.03 Open agricultural area without fences and hedgerows and very scattered buildings. Only softly rounded hills

1.5 0.055 Agricultural land with some houses and 8 meters tall sheltering hedgerows with a distance of approx. 1250 meters

2 0.1 Agricultural land with some houses and 8 meters tall sheltering hedgerows with a distance of approx. 500 meters

2.5 0.2 Agricultural land with many houses, shrubs and plants, or 8 metre tall sheltering hedgerows with a distance of approx. 250 meters

3 0.4 Villages, small towns, agricultural land with many or tall sheltering hedgerows, forests and very rough and uneven terrain

3.5 0.8 Larger cities with tall buildings 4 1.6 Very large cities with tall buildings and skyscrapers 11

Calculating wind speed at different heights

h2

h1

Where:

h1 : height [m]

h2 : height [m]

v1 : wind speed at h1 [m/s]


z0 : roughness length [m]

�2 = �1 ∗ln(

ℎ2

�0)

ln(ℎ1

�0)

12

Schematic wind shear for different roughness classes - wind speed measured at the same height

13

J.lie

rsch

; Key

Win

dEne

rgy,

200

9

Site specific wind resource assessment for wind farm planning• To calculate the annual energy production of

a wind turbine the distribution of wind speeds

is needed. It can be approximated by a

Weibull equation with parameters A and K

• The distribution of wind directions is important

for the siting of wind turbines in a wind farm.

The wind rose shows probability of a wind

from a certain sector.

• Wind speed distributions are measured for

different wind direction sectors.

14

h w(v

)

Weibull equation factors for different regions

• For regions with similar topography the k factors are also similar

� 1.2 < k < 1.7 Mountains

� 1.8 < k < 2.5 Typical North America and Europe

� 2.5 < k < 3.0 Where topography increases wind speeds

� 3.0 < k < 4.0 Winds in e.g. monsoon regions

• Scaling factor A is related to mean wind speed ( vavg ~ 0,8…0,9 · A)

• Relation of mean wind vavg, k und A (mean wind vavg, calculation)

• Warning: Only rough values! – On site monitoring is necessary !

Source: J.liersch; KeyWindEnergy, 2009

15

Wind Atlas based on modelling

• A suitable number of high quality

measurements is characterized for its local

effects

• The measurements are combined into an

atlas

• Sample: 3TIER’s Global Wind Dataset 5km

onshore wind speed at 80m height units in

m/s

• Limitations for complex terrain and costal

zones

16

Map: IRENA Global Atlas; Data: 3TIER’s Global WindDataset

Power of wind

17

P = ½ x ρρρρ x A x v3

� P = power of wind (Watt)

� ρ = air density (kg/m3; kilogram per cubic meter)

� A = area (m2; square meter)

� v = wind speed (m/s; meter per second)

Quick exercise: doubling of wind speed

• Let's double the wind speed and calculate what happens to the power of the swept rotor

area. Assume length of rotor blades (radius) 25 m and air density 1.225 kg/m^3).

• wind speed = 5 m wind speed = 10 m

18

3. SPATIAL SETUP OFWIND FARMS

19

Wake effect

� Clouds form in the wake of the front row of wind turbines at the Horns Rev offshore wind farm in the North Sea

� Back-row wind turbines losing power relative to the front row Source: www.popsci.com/technology/article/2010-01/wind-turbines-leave-clouds-and-energy-inefficiency-their-wake

20

Legend:

Predominant wind direction

Position of wind turbine to beinstalled

One rotor diameter in order todetermine best position toinstall the desired wind turbines

5 rotordiameters

7 rotor diameters

Distance between turbines to reduce wake effects

21

4. ESTIMATING WIND ELECTRICITY YIELD

22

What needs to be done

1. Define a representative mix of suitable turbines (potentially site-specific).

2. Get power curve information for all turbine types.

3. Extrapollate average wind speeds to applicable hub heights.

4. Choose the wind speed distribution curve which is most likely at given site(s).

5. Calculate wind speed distributions for given hub heights.

6. Use wind speed distributions and power curves to calclulate representative wind energy

yield(s).

23

Wind energy yield calculation

• vi = wind speed class i [m/s]

• hi = relative frequency of wind

speed class in %

• Pi = power output of wind

turbine at wind speed class vi

[kW]

• Ei= energy yield of wind speed

class i [kWh] vi in m/s

Ei in kWh

vi in m/s

hi in % vi in m/s

Pi in kW

Power curve of a specific wind turbine

Wind speed distribution for a specific site

© R

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201

4

Annual energy production of a wind turbine

25

Ei = Pi x tiEi = energy yield of wind class, i = 1, 2, 3 …n[Wh, watthours]

ti = duration of wind speeds at wind class [h/a, hours/year]

Pi = power of wind class vi of wind turbine power curve [Watt, joule per second]

EΣ = E1 + E2 +…+ EnEΣ = energy yield over one year [Wh/a, watthours / year]

Shape of different wind speed distributions

• Weibull distribution: shape factor k=1,25 andA= 8 m/s

26

• Weibull distribution: shape factor k=3 and A= 8 m/s

Sample power curves of wind turbines(82 m rotor diameter, 2 and 3 MW)

Sou

rce:

Ene

rcon

pro

duct

info

rmat

ion

2014

27

5. ESTIMATING WIND CAPACITY AND YIELD AT A GIVEN SITE

Worked example

28

Wind energy yield estimation south-west of Cairo

• Steps performed:

1) Retrieve average wind speed data from

Global Atlas

2) Estimate electricity yield of one wind

turbine

3) Estimate wind power capacity and

potential wind energy per km² at given

location

29

Pen and paper exercise (start)

30

• Average wind speed = ??? at 80 m height

Retrieving average wind speed

31

Extrapolation to hub height

• Wind data provided for height: h1 = 80 m

• Let‘s choose hub height: h2 = 90 m

• Roughness length: z0 = 0.1m

32

h2

h1

Where:

h1 : height [m]

h2 : height [m]




�2 = �1 ∗ln(

ℎ2

�0)

ln(ℎ1

�0)

Estimating wind speed distribution

• Deriving Weibull distribution

� Average wind speed: v2 = vavg = 7.3 m/s

� Assumption (based on accessible data) � k = 3.5

� Scaling factor: vavg = 0.9 * A � A = vavg / 0.9

A = (vavg / 0.9) = (7.3 m/s) / 0.9 = 8.11 m/s

33

Resulting wind distribution

34

vi (m/s)Weibull probability(%)

number of hours at v i m/s per year

0.0 0 0.01.0 0.002301447 20.22.0 0.012930901 113.33.0 0.03481178 305.04.0 0.067742212 593.45.0 0.107112259 938.36.0 0.14337442 1,256.07.0 0.164325824 1,439.58.0 0.160762789 1,408.39.0 0.132719153 1,162.6

10.0 0.090914034 796.411.0 0.05061706 443.412.0 0.022370894 196.013.0 0.007647482 67.014.0 0.001966378 17.215.0 0.000369182 3.216.0 4.90543E-05 0.417.0 4.46477E-06 0.0

Choosing the wind turbine

• We choose enercon E82-2000

35

E82-2000

vi (m/s)

Output powerof E82-2000, (kW)

0.01.0 0

2.0 3

3.0 25

4.0 82

5.0 174

6.0 321

7.0 532

8.0 815

9.0 1180

10.0 1612

11.0 1890

12.0 2000

13.0 2050

14.0 2050

15.0 2050

16.0 2050

17.0 2050

�� Pen and paper exercise• Annual energy output of wind turbine at vi = 6 m/s = ???

• Annual energy output of wind turbine at vi = 7 m/s = ???

36

vi (m/s)Weibull probability(%)

number of hours at v i m/s per year

0.0 0 0.01.0 0.002301447 20.22.0 0.012930901 113.33.0 0.03481178 305.04.0 0.067742212 593.45.0 0.107112259 938.36.0 0.14337442 1,256.07.0 0.164325824 1,439.58.0 0.160762789 1,408.39.0 0.132719153 1,162.6

10.0 0.090914034 796.411.0 0.05061706 443.412.0 0.022370894 196.013.0 0.007647482 67.014.0 0.001966378 17.215.0 0.000369182 3.216.0 4.90543E-05 0.417.0 4.46477E-06 0.0

vi (m/s)

Output powerof E82-2000, (kW)

0.01.0 0

2.0 3

3.0 25

4.0 82

5.0 174

6.0 321

7.0 532

8.0 815

9.0 1180

10.0 1612

11.0 1890

12.0 2000

13.0 2050

14.0 2050

15.0 2050

16.0 2050

17.0 2050

Calculate power output per wind speed class

vi (m/s)

number of hours at v im/s per year

Outputpower ofE82-2000, (kW)

E82-2000, annual energy yield, (kWh/a)

0.0 0.01.0 20.2 0 02.0 113.3 3 3403.0 305.0 25 7,6244.0 593.4 82 48,6615.0 938.3 174 163,2656.0 1,256.0 321 403,1637.0 1,439.5 532 765,8118.0 1,408.3 815 1,147,7509.0 1,162.6 1180 1,371,891

10.0 796.4 1612 1,283,80811.0 443.4 1890 838,03612.0 196.0 2000 391,93813.0 67.0 2050 137,33314.0 17.2 2050 35,31215.0 3.2 2050 6,63016.0 0.4 2050 88117.0 0.0 2050 80

37

Example:@ v=7.0 m/s:1,439.5 h/a * 532 kW = 765,811 kWh/a

Total energy:Summation overall wind classes= 6.603 MWh/a

Estimating capacity per km²

• Rotor diameter d=82 m

• Distance d1 primary wind direction:

7 rotor diameters = 7 * 82 m = 574 m

• Distance d2 secondary wind direction:

5 rotor diameters = 5 * 82 m = 410 m

• Area needed for one turbine:

574 m * 410 m = 235,340 m² = 0.24 km²

• Capacity per km²:

2 MW/0.24 km² = 8.3 MW/km²

38

Estimating energy per km² and capacity factor

• Capacity per km²:

2 MW/0.24 km² = 8.3 MW/km²

• Energy generation per wind turbine:

6,603 MWh per turbine (E82-2000) with 2 MW rated capacity,

OR: 6,603 MWh / 2 MW � 3,302 MWh / 1 MW

• Energy generated per km²:

3,302 MWh/MW * 8.3 MW/km² = 27,4 GWh/km²/a

• Capacity Factor: 3,302 MWh / 1 MW = 3,302 h

3,302 h / 8,760 h = 37.7%

39

Please remember

• The previous worked example is only a rough estimate and results are only true for the

given assumptions (specific site, one turbine type, wind distribution assumptions, etc.)

• The calculated energy yield should be considered as ideal result. In real-life power output

is likely to be slightly below these values due to downtimes (maintenance, grid outages),

cabling and transformation losses, deviation from ideal distribution of wind turbines on

the given site, etc.

40

41

© R

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201

4

Wind speed at hub height (m/s)


Wind speed extrapolation to turbine hub height

Roughness length or wind shear exponent

Hub height (m)


Power curve, wind turbine density (W/km2), air density

Weibull distribution (k, A)

Electrical losses (%)

CAPEX

OPEX

WACC

Life time

Economic parameters

(wind farm and grid connection)

Annual energy prod. (Wh/a/km2)Wind capacity per area (W/km2) C

AP

EX

= C

apita

l exp

endi

ture

, OP

EX

= O

pera

tion

expe

nditu

re, W

AC

C =

Wei

ghte

d av

erag

e co

st o

f ca

pita

l (de

bt, e

quity

)

Areas potentially suitable for wind farms (km2) Site assessment (wind atlas data, wind speed (m/s) for certain height (m))

Exclusion of non-suitable land areas and adding of buffer zones

Nature protected area

Urban area (buffer zone: 8–10 hub height)

Transport, supply and communication infrastructure


Landscape, historic area, other non-usable land (glaciers, rivers, etc.)

Areas potentially suitable for wind farms (km2)

Priority areas for wind power (km2), potentially installed capacity (W), potentially



Economic assessment

done

pend

ing

done

done



Solutions

43

Solution: doubling of wind speed

• Power of swept rotor calculated with 25 m rotor radius and 1.225 kg/m^3 air density

• wind speed = 5 m/s wind speed = 10 m/s

power = 150 kW power = 1200 kW

• Doubling of wind speed increases power by factor 8.

• Calculation:

Power =0,5 * air density * (wind speed)^3 * blade length^2 * 3.1415

Power = 0,5 * 1,225 kg/m^3 * 5^3 m^3/s^3 * 25^2 m^2 * 3.1415 = 150 kW

Power = 0,5 * 1,225 kg/m^3 * 10^3 m^3/s^3 * 25^2 m^2 * 3.1415 = 1202.6 kW

Units:[kg/m^3 * ^3 m^3/s^3 * m^2 = Joule/s = W] 44

Retrieving average wind speed

45

• Average wind speed 7.2 m/s at 80 m height

Extrapolation to hub height

• Wind data provided for height: h1 = 80 m

• Let‘s choose hub height: h2 = 90 m

• Roughness length: z0 = 0.1m

• Result: v 2 = 7.3 m/s

46

h2

h1

Where:

h1 : height [m]

h2 : height [m]




�2 = �1 ∗ln(

ℎ2

�0)

ln(ℎ1

�0)

Session 3: Solar power spatial planning techniques



• After having identified those areas which are potentially available for renewables, we

want to estimate…

� what the potential solar PV capacity per km² and in total is (W/km²), and,

� how much electricity (Wh/km²/a) can be generated in areas with different solar

resource availability.

• We also need to know which parameters are the most sensitive ones in order to identify

the most important input parameters.

• In this section, we will focus on grid-tied PV but also provide useful numbers for CSP.

2

Contents

1. Solar resource

2. Spatial setup of large-scale PV plants

3. Estimating PV electricity yield

4. Worked example: Estimating PV capacity and yield at a given site

5. A few words on CSP

3

4

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Irradiation on tilted plane (Wh/m²/a)


Conversion horizontal solar radiation to optimally tilted plane

Optimal tilt angle


Pre-conversion losses

Conversion losses

System losses (%)

CAPEX

OPEX

WACC

Life time

Economic parameters (PV plant and grid connection)

Annual energy prod. (Wh/km2/a)PV capacity per area (W/km2) C

AP

EX

= C

apita

l exp

endi

ture

, OP

EX

= O

pera

tion

expe

nditu

re, W

AC

C =

Wei

ghte

d av

erag

e co

st o

f ca

pita

l (de

bt, e

quity

) Areas potentially suitable for PV systems (km2) Site assessment (solar atlas data, solar radiation

(kWh/m²/a); open-land and settlements (roofs)

Exclusion of non-suitable areas

Nature conservation areas

Exclusion of non-suitable built-up areas (i.e. non-suitable roofs)

Transport, supply and communication infrastructure; very remote areas


Landscape, historic area, other non-usable land (glaciers, rivers, roads etc.)

Areas potentially suitable for PV systems (km2)

Priority areas for PV (km2), potentially installed capacity (W), potentially



Economic assessment

Perform

ance Ratio

5

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Optimal tilt angle



Conversion losses

System losses (%)

CAPEX

OPEX

WACC

Life time



AP

EX

= C

apita

l exp

endi

ture

, OP

EX

= O

pera

tion

expe

nditu

re, W

AC

C =

Wei

ghte

d av

erag

e co

st o

f ca

pita

l (de

bt, e

quity

)

Areas potentially suitable for PV systems (km2) Site assessment (solar atlas data, solar radiation (kWh/m²/a); open-land and settlements (roofs)











Economic assessment

Perform

ance Ratio

done

pend

ing

1. SOLAR RESOURCE

6

Solar radiation variation

� The sun’s power density when

its rays reach the earth’s

atmosphere is known as the

solar constant and equals

1366 ±7 W/m2

Graph: RENAC

7

Three component radiation model

• Global radiation is composed of

� direct radiation (coming

directly from sun, casting

shadows)

� diffuse radiation (scattered,

without clear direction),

and,

� reflected radiation (albedo).

8

Solar irradiation – Lima, Peru

9

Sou

rce:

Dat

a fr

om M

eteo

norm

7

kWh/

(m²/

day)

Diffuse horizontal irradiation Global horizontal irradiation (GHI)

Global horizontal irradiation and irradiation on the tilted plane• Irradiation data is usually provided as global

horizontal irradiation (GHI)

• If moving away from the equator, more

irradiation can be received by tilting solar

modules

Rules of thumb:

1. Tilt angle against the horizontal = Latitude

of the PV installation site*

2. Minimum angle of 10°…15°to avoid

settlement of dust and dirt.

10

*In regions with latitudes >30°the tilt angle is

usually between 5°and 20°less than the

latitude. The greater the latitude the higher the

subtracted value.

2. SPATIAL SETUP OFLARGE-SCALE PV PLANTS

11

How much power (MWp) can we fit in one km²…

Source: Albrecht Tiedemann 12

…and limit excessive shading?• Self-shading occurs when the rows of PV modules in arrays partially shade the PV

modules in the rows behind.

• The only unaffected row is the one in the front.

Source: RENAC (Simulation made using PV*SOL premium 7.0)

13

Which space between rows is needed?

14

?

Which space between rows is needed?• Space between rows depends on:

� Latitude (sun path)

� Inclination of solar panels

� Setup of solar panels on mounting structure

� Minimum space needed for O&M (car/small truck should fit through)

15

Solar panel inclination and inter-row spacing

16

Tilt angle should alwaysbe higher than 15°(toavoid settlement of dirt

and humidity)Minimum space between

module rows (accessability)

Power density of large-scale PV plants

17

c-Si

CdTe

Majority of Latin America:ca. 80 MWp/km² c-Sica. 60 MWp/km² CdTe

3. ESTIMATING PVELECTRICITY YIELD

18

Yield of a solar PV system• The fundamental question to answer is how well the system performs and how much electricity does

the solar PV system deliver to the grid

• Energy losses occur at every step of the conversion between solar energy and AC electricity fed into

the grid

• Pre-PV generator losses

• PV generator losses (module and thermal losses)

• System losses

• The task of the design engineers is to optimize the plant maximizing energy yield by reducing losses

19

� Shading losses

� Temperature losses� Soiling losses� Wiring losses

� Inverter losses � Energy delivered to the grid

Performance ratio as a measure of the quality of a PV plant• The performance ratio PR defines the overall solar PV plant performance

• It is calculated as the relation between the energy yield that has actually been generated

(Yreal) and the theoretical energy yield (Yideal):

PR = Yreal / Yideal

• How to calculate the ideal yield Yideal ?

� Peak-sun hour method!

20Source (diagram): http://pvcdrom.pveducation.org/index.html

Estimating PV plant electricity yield using expecte d Performance Ratios• Note: Only for rough estimations!

• Electricity yield of a PV system:

• ‘h’ is Peak Sun Hours, unit: hrs (do not confuse with sunshine hours!)

Peak Sun Hours = Annual irradiation in kWh/(m²*a) / 1000 W/m²

21

h Peak Sun Hoursnpre Pre-conversion efficiencynsys System efficiencynrel Relative efficiencyPnom Nominal power at STC

4. ESTIMATING PV CAPACITY AND YIELD AT A GIVEN SITE

Worked example:

22

PV energy yield estimation in Lima

• Steps performed:

1) Retrieve global

horizontal irradiation

data from Global Atlas

2) Estimate specific

electricity yield

(kWh/kWp)

3) Estimate PV capacity

and potential solar

energy yield per km² at

given location

23

Source: IRENA Global Atlas

�� Pen and paper exercise (start)

24

Retrieving global horizontal irradiation

• Hourly average global horizontal irradiance of ??? W/m²

Annual global horizontal irradiation? = ??? kWh/m²/a

25

Sou

rce:

IRE

NA

Glo

bal A

tlas

Adjusting horizontal irradiation to irradiation on tilted plane• Coordinates of the chosen site in Lima: 12.05°S and 77.05°W.

• Tilt angle of PV modules at this location should be about 15°.

• GHI at this location : 1,600 kWh/m²/a global horizontal irradiation. At this latitude,

irradiation on the tilted plane approximately equals GHI. However, the monthly

distribution of energy will change (see next slide).

• For other locations, online tools or professional databases such as Meteonorm produce

can be used to find the optimum tilt angle and its resulting irradiation value.

• Irradiation in the optimally inclined modules plane : = ??? kWh/m²/a

26

Monthly distribution of solar irradiation (in Lima)

27

GHI

on tilted plane

Sou

rce:

Dat

a fr

om M

eteo

norm

7

Estimating the specific PV electricity yield

• Assumptions*:

� Free-standing arrays

� PR of c-Si modules = 75%

� PR of CdTe modules = 78% (mainly due to lower temperature sensitivity)

• Annual Peak Sun Hours = ???

• Annual electricity yield estimation:

� c-Si: = ??? kWh/kWp/a

� CdTe: = ??? kWh/kWp/a

28*PR: own estimates

Power density of large-scale PV plants

29

c-Si

CdTe


• c-Si:

�

= ??? GWh/km²/a

• CdTe:

�

= ??? GWh/km²/a

• Capacity factor:

= ???%

30

Please remember

• The previous worked example is only a rough estimate and results are only true for the

given assumptions (open-land installation, module types, solar resource data,

Performance Ratio assumptions, etc.)

• Factors which might influence electricity output, which have not been considered in detail

here are for instance: heavy soiling of modules, shading from other objects, additional

temperature losses if ventilation is lower than in the case of free-standing arrays (e.g.

roof-parallel installation), etc.

31

32

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Optimal tilt angle



Conversion losses

System losses (%)

CAPEX

OPEX

WACC

Life time



AP

EX

= C

apac

ity e

xpen

ditu

re, O

PE

X =

Ope

ratio

n ex

pend

iture

, WA

CC

= W

eigh

ted

aver

age

cost

of

capi

tal (

dept

h, e

quity

) Areas potentially suitable for PV systems (km2) Site assessment (solar atlas data, solar radiation

(kWh/m²/a); open-land and settlements (roofs)











Economic assessment

Perform

ance Ratio

donedone

pend

ing

5. A FEW WORDS ON CSP

33

Geographical and irradiation requirements for CSP

• Map shows annual

Direct Normal

Irradiation (DNI) in

kWh/m²/day

• CSP needs not only

high levels of DNI (>

2,000 kWh/m²/year

considered

economically viable)

but also flat ground and

sufficient water supply

34

Map

: IR

EN

A G

loba

l Atla

s; N

AS

A d

ata

Parabolic trough collector - principle

▪ Parabolic mirror tracks the sun in one axis and reflects Direct Normal Irradiation (DNI) on

Heat Collecting Element (HCE)

35

Gra

ph: R

EN

AC

Parabolic trough power plant

• Operating temperature: 300°C to 500°C

• Concentration Factor 70 - 90

• Heat transfer fluid: thermal oil, direct steam, molten salt

• Typical power size: 50 to 400 MWel (for a solar field for 50 MWel over

500,000 m² of aperture area)

• High manufacturing quality requirements: System will have to be aligned to track the

sun with 0.1°precision!

36

Solar tower

• Solar radiation is reflected from heliostats (large steel reflectors) onto a receiver (heat

exchanger) at the top of the solar tower.

• Here the heat is transferred to water to produce steam to drive a steam generator to

generate electricity.

37

Gra

ph: R

EN

AC

CSP Plants – Costs and cost trends

• The LCOE of CSP plants varies considerably depending on –

� the technology

� the location of the plant, i.e. irradiation levels

� the level of thermal storage, i.e. capacity factors

• Potential further reduction in LCOE of 45-60% predicted by 2025 by IRENA in 2012.

38

Sou

rces

: 1)

Fra

unho

fer

Inst

itute

for

Sol

ar E

nerg

y S

yste

ms

ISE

: Lev

eliz

ed

cost

of e

lect

ricity

-re

new

able

ene

rgy

tech

nolo

gies

, Nov

embe

r 20

13;

2) IR

EN

A_C

SP

Cos

t Ana

lysi

s, J

une

2012

; 2)

Technology Estimated LCOE

Parabolic Trough1)(DNI: 2,000 – 2,500 kWh/m²*a;

PR=90%) 0.15 – 0.20 EUR2013

Solar Tower2) 0.12 – 0.21 EUR2011/kWh

PV1)(utility scale; 2,000 kWh/m²*a; PR=85%) average: 0.08 EUR2013/kWh



�� Solutions

40

Retrieving global horizontal irradiation• Hourly average global horizontal irradiance of 206 W/m²

Annual GHI = 206 W/m² * 8760 h/a = 1800 kWh/m²/a

41

Sou

rce:

IRE

NA

Glo

bal A

tlas

Adjusting horizontal irradiation to irradiation on tilted plane• Not applicable for our site in Lima for the annual values.

• For other latitudes, please consult online tools/softwares/databases to transform GHI ito

values for the tilted plane.

42

Estimating the specific PV electricity yield

• Assumptions*:

� Free-standing arrays

� PR of c-Si modules = 75%

� PR of CdTe modules = 78% (mainly due to lower temperature sensitivity)

• Annual Peak Sun Hours = (1,800 kWh/m²/a) / (1,000 W/m²) = 1,800 h/a

• Electricity yield estimation:

� c-Si: 1kWp * 75% * 2,330 h/a ≈ 1,350 kWh/kWp/a

� CdTe: 1kWp * 78% * 2,330 h/a ≈ 1,400 kWh/kWp/a

43*PR: own estimates


• c-Si:

� 80 MWp/km² * 1,350

MWh/MWp/a

= 108 GWh/km²/a

• CdTe:

� 60 MWp/km² * 1,400

MWh/MWp/a

= 84 GWh/km²/a

44

Peru:ca. 80 MWp/km² c-Sica. 62 MWp/km² CdTe

Session 4: Economic assessment of PV and wind for energy planning



1. Once we know how much electricity can be produced in our country with given resources

(technical potential), we will be able to estimate their generation costs

2. As all available data comes with uncertainties, we should know

a. how sensitive results react on changing input parameters, and,

b. what socio-economic effect highly uncertain input data could have.

2

3

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Optimal tilt angle



Conversion losses

System losses (%)

CAPEX

OPEX

WACC

Life time


Annual energy prod. (Wh/km2/a)PV capacity per area (W/km2)












Economic assessment

Perform

ance Ratio

donedone

CA

PE

X=

Cap

ital e

xpen

ditu

re, O

PE

X =

Ope

ratio

n ex

pend

iture

, WA

CC

= W

eigh

ted

aver

age

cost

of

capi

tal (

debt

, equ

ity)

Contents

1. Levelized cost of electricity (LCOE)

2. Worked example: LCOE sensitivity of PV projects

3. Worked example: LCOE sensitivity of wind projects

4. Worked example: Effects of data uncertainty on the LCOE of PV

4

1. LEVELIZED COST OFELECTRICITY (LCOE)

5

Levelized Cost of Electricity (LCOE)

• Calculates the average cost per unit electricity. LCOE takes into account the time value

of money (i.e. capital costs).

Where:

• LCOE: Average Cost of Electricity generation in $/unit electricity

• I0: Investment costs in $

• At: Annual total costs in $ in each year t

• Qel: Amount of electricity generated

• i: Discount interest rate in %

• n: useful economic life

• t: year during the useful life (1, 2, …n)6

2. LCOE SENSITIVITY OFPV PROJECTS

Worked example:

7

Worked example – Grid-tied PV in Pucallpa, Peru

• Project type: Grid-tied

• Location at latitude: 10°South

• Reference irradiation (GHI): 2,050 kWh/m²/a

• Reference specific yield (P50): 1,580 MWh/MWp

• System size: 10 MWp

• Specific project CAPEX: 2.000.000 USD/MWp

• Project annual OPEX: 1.5% of project CAPEX

• Discount rate (WACC): 8%

• Project duration: 30 years

• Inverter replacements: 2

• Solar panel degradation: 0,7% p.a. (linear)

8

LCOE sensitivity (absolute)

9

Baseline LCOE: 146 USD/MWh

LCOE sensitivity (relative)

10

Baseline LCOE: 146 USD/MWh

3. LCOE SENSITIVITY OFWIND PROJECTS

Worked example:

11

Worked example – Grid-tied wind project Egypt (variation A)• Project type: Grid-tied wind

• Location: Peru / South of Lima

• Average wind speed @ 80m: 7.3 m/s

• Wind distribution, shape parameter: 3.5

• Wind distr., scale parameter: 8.11

• Technical availability: 97%

• Reference specific yield (P50): 3,202 MWh/MW (techn. availability considered)

• Capacity factor: 36.6%

• System size: 8 MW (4 turbines)

• Specific project CAPEX: 4.000.000 USD per turbine



• Project duration: 20 years12

LCOE sensitivity (absolute) – Wind speed only

13

Baseline LCOE: 87.6 USD/MWh

LCOE sensitivity (absolute) – other parameters

14


Worked example – variation B: lower wind speed & lower shape parameter• Project type: Grid-tied wind

• Location: Peru / south of Lima

• Average wind speed @ 80m: 7.3 m/s 5.5 m/s

• Wind distribution, shape parameter: 3.5 m/s 1.5 m/s

• Wind distr., scale parameter: 6.11

• Technical availability: 97%

• Reference specific yield (P50): 2,054 MWh/MW (techn. Availability considered)

• Capacity factor: 23.5%

• System size: 8 MWp (4 turbines)

• Specific project CAPEX: 4.000.000 USD per turbine



• Project duration: 20 years15

LCOE sensitivity (absolute) – Wind speed only

16


LCOE sensitivity (absolute) – other parameters

17


Shape parameter more sensitive!!!

Conclusions on sensitivities and for scenario development• Variations of the shape factor of the Weibull distribution of wind can have very different

effects depending on the chosen scenario

� In variation A (high wind, high shape factor), varying of the shape factor only had a

very little effect on the LCOE.

� In variation B (lower wind, lower shape factor), varying of the shape factor had a

considerable effect on the LCOE.

� Reason : The chosen wind turbine for the scenario has a power curve which

operates better under weaker winds.

� It is crucial for wind scenario developments, to chose appropriate turbines for sites

with different wind speeds and wind speed distributions.

18

Comparison of Weibull curves for variations A (left ) + B (right)

19

4. EFFECTS OF DATAUNCERTAINTY ON THE LCOE OF PV

Worked example:

20

Why data quality is so important

• All data comes with uncertainties :

� Measurements are always subject to deviations, and ,

� models used for predictions can never simulate what happens in reality.

• It is obvious that the lower uncertainty is the more accurate predictions will be. This, in

turn, will enable us to make better estimates .

• In the following, we will demonstrate how good data (i.e. data with low uncertainties) will

potentially help saving funds for PV Power Purchase Agreements.

21

Uncertainty assumptions

• Low resolution NASA SSE data: +/- 13,7%

• Average Meteonorm 7 data: +/- 7,5%

• Best ground measurement at site: +/- 3,0%

• Important note : Besides uncertainty of irradiation data, there is also uncertainty within

the simulation model and nameplate capacity. However, the latter are comparably small

so that we will, to keep the example simple, only look at resource uncertainty. In real-life,

when it comes to detailed project development, one should always ask the project

developer to provide information about his uncertainty assumptions.

22

Worked example – Grid-tied PV in Pucallpa, Peru

• Project type: Grid-tied

• Location at latitude: 20°North

• Reference irradiation: 2050 kWh/m²/a

• Reference specific yield (P50): 1580 MWh/MWp

• System size: 10 MWp

• Specific project CAPEX: 2.000.000 USD/MWp



• Project duration: 30 years

• Inverter replacements: 2

• Solar panel degradation: 0,7% p.a. (linear)

23

Exceedance probability

24

P50: 1580 MWh/MWp

P90

LCOE depends on quality of meteo data

25

LCOE is key factor for PPA tariff calculation

• Assuming a 10% premium on the LCOE as margin for IPP

� Best case: 152 USD/MWh +10% = 167 USD/MWh

� Worst case: 177 USD/MWh +10% = 195 USD/MWh

� Delta: 28 USD/MWh (incl. 10% premium)

26

Country sets a 5% PV goal by 2020

• Sample: Peru

• Total electricity demand 2010: 37 TWh (Source: Google Public Data)

• 5% of total: 1.85 TWh

• PPA tariff difference: 28 USD/MWh

• „Unnecessary“ payments in 2020: 1,850,000 MWh * 28 USD/MWh =51.8 Mio USD

• PV power needed: 1,200 MWp (with best P90 value)

27

„Unnecessary“ payments due to inaccurate data• PV power needed by 2020: 1,200 MWp (with best P90 value)

• Avoidable payments: 155 Mio USD

28

29

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4




Optimal tilt angle



Conversion losses

System losses (%)

CAPEX

OPEX

WACC

Life time



AP

EX

= C

apita

l exp

endi

ture

, OP

EX

= O

pera

tion

expe

nditu

re, W

AC

C =

Wei

ghte

d av

erag

e co

st o

f ca

pita

l (de

bt, e

quity

)












Economic assessment

Perform

ance Ratio

donedone done



Dr. David Jacobs – IET (International Energy Transit ion)

Session 5/6: From scenarios to policy and market development


Dr. David Jacobs – IET (International Energy Transit ion) 2

Scenarios

RE Market

Strategies:




planning)?

Instruments:







Resource assessment and target setting


The relation between resource mapping and target setting • Mapping results into availability of information on amount of available

resource and suitable areas

• Policymakers are enabled to set targets based on available resources

• HOWEVER: Resource mapping is only the first step:

� Limiting factors need to be taken into consideration to elaborate the

the economic potential

4


From technical potential economic potential

5

Source: http://www.wbgu.de/fileadmin/templates/dateien/veroeffentlichungen/hauptgutachten/jg2003/wbgu_jg2003_engl.pdf


From technical potential economic potential

6Source: Desertec Foundation 2009, http://www.desertec.org/fileadmin/downloads/DESERTEC-WhiteBook_en_small.pdf


Questions

7

How did you set targets for

renewables in your country?

Did you analyse the available

resources first?


Questions

8

How did you set targets for

renewables in your country?

Did you analyse the available

resources first? What were the reasons

objectives/reasons for setting

renewable energy targets in

your country?


Objectives for setting renewable energy targets

• Make use of existing, national resources (Increasing energy security)

• Diversifying the fuel mix

• Reducing fossil fuel consumption (for both importers and exporters)

• Improving energy access

• Mitigating climate change and other environmental risks (fuel spills)

• Macro-economic benefits (i.e., job creation)

• Increasing private sector investment

9

Source: E3 Analytics, Toby Couture


How to integrate target setting for renewables into integrated resource planning?

• What is the target function in your country for determining the optimal

electricity mix?

� least cost planning?

� Industry policy?

� Security of supply?

� Energy access?

� Climate policy?

10


Renewable energy targets

11

• More countries are setting policy targets for renewable energy:

� 144 countries with targets as of 2013

• Countries are also enacting support policies to ensure fulfillment of

the target:

� 138 countries as of 2013

Source: REN21 Global Status Report (GSR) 2014


Target characteristics

12

• Decision parameters for setting RE targets:

Option 1: Technology Neutral (generic RE target) vs. Technology

Differentiated (wind, solar, biomass, etc.)

Option 2: Short-term targets versus long-term target (harvest the low

hangging fruits first?)

Option 3: National targets versus regional planning (locational signals for

harvesting renewables in different “hot spots”?)


How to Set Targets after Resource Assessment

Establishing targets requires a few essential components:

1. Identify resources – theoretical/technical potential

2. Identifying constraints (e.g. grid capacity, available land, financial

resources, etc.) – derive the economic potential

3. Substract areas dedicated to natural protection – ecological

potential

4. Model the current and future electricity mix – feasible level of

system integration of wind and PV? Cost effects?

Come up with the realizable potential and translate this into targets!


Experience from emerging markets:

The rationale for target setting in Saudi Arabia


Renewable energy programs in Saudi Arabia – identify ing the best locations

• The Kingdom of Saudi Arabia targets a newly installed renewable energy

capacity of 54 GW by 2032

• Rationale:

� cost savings (oil)

� technological leadership

� climate protection

� energy access

15

Source: KA-Care, https://www.irena.org/DocumentDownloads/masdar/Abdulrahman%20Al%20Ghabban%20Presentation.pdf


Renewable energy programs in Saudi Arabia –Target setting approach

• Technology specific targets (for better system integration and

industrial policy)

� PV: 16 GW

� CSP: 25 GW

� Wind: 9 GW

� Waste-to-Energy: 3 GW

� Geothermal: 1 GW

16

Source: KA-Care, https://www.irena.org/DocumentDownloads/masdar/Abdulrahman%20Al%20Ghabban%20Presentation.pdf


Assessing resource availability – KSA solar map

• Renewable energy atlas was

launched in Dec 2013:

• Existing resource maps are

important elements for Statement of

Opportunities (SOO) for project

developers

• Onsite measurement required for

financing

• Available ONLINE:

http://rratlas.kacare.gov.sa/RRMMP

ublicPortal/

17

Source: http://rratlas.kacare.gov.sa/RRMMPublicPortal/


Limiting factors for the actually realizable potential:

Available grids, available space (spatial planning),system flexibility


The relation between resource mapping limiting factors (grid, space, flexibility)

• To derive the actually realizable potential from the theoretical/technical

potential requires an analysis of all limiting factors

� The availability of grid infrastructure

� The availability of space (spatial planning and protected areas)

� The technical potential of the electricity system to absorb

fluctuating renewables (wind and solar)

19


Availability of grid infrastructure?

Using the existing grid, expanding the grid or developing renewables off-grid


Least cost grid expansion plan in Rwanda

• Grid expansion is a crucial component for rural electrification

• However, costs of transmission, distribution, and oil have gone up; costs of off-grid

solutions have come down

�

21

Source: World Bank http://siteresources.worldbank.org/EXTAFRREGTOPENERGY/Resources/717305-1327690230600/8397692-1327691237767/DAKARHVI_AEI_Practitioner_WorkshopNov14-15_2011_Nov7.pdf


Rule of thumb for rural electrification and technology choice

� Due to dramatic reductions in PV costs

in the past years, PV mini-grids are a

viable alternatives to grid extension and

diesel mini-grids.

� The LCOE will generally be competitive

with that of grid extension when the

extension would imply less than 10

connections/km.

� Obstacles: the need for upfront

financing, ensuring proper maintenance,

etc.

22

Source: Norplan 2012


Rule of thumb for rural electrification and technology choice

� Several factors influence the viability of off-grid solutions, including mini-

grids, solar-home-systems and hybrid systems, e.g. the level of market

penetration, transport cost for equipment, etc.

� The rules-of-thumb are fairly sensitive to the assumed consumption per

household (50kWh /HH/month).

• If lower, the number of connections would have to be higher to

justify grid extension.

• If higher, grid connection might already make sense with less

connections

23

Source: Norplan 2012


Questions

24

What decision parameters do

you apply in your country for

grid expansion of off-grid

solutions?


The availability of grid infrastructure

Anticipating required grid expansion to reach ambitious long-term targets (lessons learned from Germany)


Insufficient grid capacity

• Insufficient grid capacity for new projects due to underdeveloped

grid infrastructure?

• Originally designed for conventional, centralized power system –

no grid at best locations for renewables?

• National grid extension plans has to be prepared (well in

advance!)


Grid extension plans in Germany

� Transport renewable electricity from the

North (onshore and offshore wind) to the

load centers in the South

� Distribution grid upgrade:

• Most renewable energy projects in

Germany are connected to the

distribution grid

• High shares of renewables (PV) in

Bavarian distribution grids

• Bi-directional transformer stations

NEP 2013, Stand: Juli 2013 www.netzentwicklungsplan.de


Grid expansion for the German Energiewende

• Part of European grid integration

process (TEN-E)

• Grid development plan for new

electricity lines from 2013

� 2,800 km of new transmission

lines

� 2,900 km of grid upgrades

28


• 10-year network development plan

from ENTSO-e

• The latest report pinpoints about 100

spots on the European grid where

bottlenecks exist or may develop in

the future

• Transmission adequacy by 2030?

• Full market coupling with European

neigbours (e.g. one merit order for

Germany and Austria).

The expansion of the European transmission grid

Source: ENTSO-e 2014


Stakeholder engagement

30

In how far are citizens and other

concerned actors involved in the

planning and siting process for

energy infrastructure in your

country? Is there a trade-off between quick

planning (and execution) of projects

and stakeholder engagement?


Reasons for opposition from citizens and communities

• Visual impact (noise in the case of wind energy)

• Lack of information about the required grid infrastructure for the

energy transition (“we want to produce electricity decentrally, no

offshore wind!)

• Lack of information about the need for the existing project (why

through my village and not the neighbouring village?).

• Lack of direct financial advantages for communities and citizens

31


Financial compensation for exposure to new electric ity grid • Amendment to German law

(NABEG):

� Effected villages can receive

one-off payment of 40.000 € per

km of new transmission line in

their territory

� Much critizised!

32

• German deployment of renewable energy sources large grass-rout driven

• Denmark: Project developers need to involve local citizens in financing renewable

energy power plants


New transmission technologies: underground cable

• Underground solutions are being discussed in more densely

populated areas

• more expensive than above-ground options (factor 3-10)

� more costly insulation is used

� more complex equipment

� larger cables are needed

33


The availability of grid infrastructure

Which grid connection charging approach fits with your grid expansion plan?


General best practise for grid connection

• Fair and transparent grid connection procedures required

• Data (grid availability, costs, technical) need to be verifiable and

disclosed by grid operator/utility

• Clear rules about grid connection point and step in grid connection

application


Cost sharing methodologies for grid connection

Who pays for grid connection

(nearest connection point)?

Who pays for grid reinforcement

(because of existing grid capacity

restrictions)?


Grid connection costs for different renewable energ y technologies

Source: Auer et al. 2007, http://greennet.i-generation.at/files/Report%20on%20Synthesis%20of%20Results%20on%20RES-E%20Grid%20Integration%20%28D11%20GreenNet-EU27%29.pdf


Distribution and transmission grid reinforcement



Shallow vs. deep connection charging


• Who pays for the connection to the nearest connection point?

• Who pays for distribution and transmission network upgrades?

• Who pays for substation, etc.


Super shallow connection charging for solar in India• ADB financed renewable energy development in Rajasthan, India

• ADB provided $500 m for transmission grid expansion

� construction of grid substations at project location

� construction of associated automation and control infrastructure

� Objective:

� Decrease grid-related costs for project developers;

� Access locations with high solar radiation

40

Source: ADB - Rajasthan Renewable Energy Transmission Investment Program


The availability of space

Spatial planning and the deployment of renewables


Spatial planning: Introductory questions

42

Who is responsible for spatial

planning (national, regional,

local)?

How are (renewable) energy

projects integrated into spatial

planning legislation?

Is there competition for limited

space?


General approach:

• Clarify responsibilities for spatial planning (interplay between regional level and

national planning programs)

• Identify areas which are definitely excluded from building renewable energy

projects (matrimonial heritage sites, natural parks, etc.).

• Identify areas which might be potentially excluded (environmentally and

culturally sensitive areas) or where there is potential competition with other

infrastructure development

43


General approach – densely populated countries

• In densely populated countries:

� Determine the minimum distance of renewable energy projects from cities,

villages, houses, industrial complexes

� Reserve space for the future development of renewable energy projects

(especially in areas with high resource potential).

� Define the role of renewables in spatial planning (see case study

Germany)

44


Spatial planning in Germany

45

• Complex interplay of planning legislation at national (Raumordnungsgesetz),

regional (Landesplanungsgesetze) and community level (Baugesetzbuch).

• Typical planning process:

� Spatial development plan from local government

� First planning draft from local planning authority

� In parallel: First draft for environmental impact assessment

� Start of first participation phase (written comments from citizens on

planned project)

http://www.kommunal-erneuerbar.de/fileadmin/content/PDF/62_Renews_Spezial_Planungsrecht_online.pdf


Spatial planning in Germany

46

• Typical planning process (continued):

� Comments from citizens and other actors are included an first planning

draft is presented

� In parallel: Environmental Impact Assessment from responsible authority

� Start of second participation phase (at least one month for further

comments)

� Followed by weighting whether stakeholder statements should be

incorporated (if yes, another round of stakeholder participation is

necessary).

� Next step: crucial phase of approval process (approval from a higher

ranking planning level, e.g. regional or national).



Spatial planning in Germany – Land use plans

47

• Land use plans include:

� Determination of general spatial structure (settlements, free zones,

infrastructure such as streets, energy, industrial areas).

� Optional implementation of so-called special area classes

(Sondergebietsklassen)



Spatial planning in Germany – Indicated Areas

48

• Implementation of “Indicated Areas” (Sondergebietsklasse) for wind energy in

1995 accelerated market development

Indicated Area, here it is allowed to build wind energy

Here it is forbidden to build wind energy

Source: http://www.unendlich-viel-energie.de/mediathek/hintergrundpapiere/planungsrecht-und-erneuerbare-energien


Spatial planning in Germany – Priority Areas

49

• Priority areas lead to exclusion from other spatial planning focuses. In this

areas, only wind energy projects can be realized

Priority Areas, here wind energy need to be build

Theoretically wind can also be build in these areas



Spatial planning in Germany – The role of communities

50

• Communities have to comply with planning processes at the next higher political level

(regional). However, communities can determine details such as the maximum hight of

wind power plants and the distance to the next settlement



Spatial planning in Germany – Compensation measures

51

• Re-create and ecological equilibrium via compensation measures



The availability of system flexibility

Measures to integrate increasing shares of fluctuating renewables


The relation between resource mapping and system flexibility • The technical potential for renewable energy sources in a give country

is not only limited by the availability of grid infrastructure and space

• The integration of fluctuating renewables might also be limited due to

technical issues (volatility, ramping capability, etc.)

• Therefore, an assessment of various flexibility options in the electricity

system is essential in order to assess the actually realizable potential

53


• Options for integrating high shares of wind and solar PV

• Grid expansion/integration; smart grid

• Dispatch of conventional power plants • Dispatch and curtailment from renewable energy

sources

• Demand response

• Storage

Creating a flexible power market


Electricity demand and renewable power generation in Germany in 2022

The electricity market is determined by wind and so lar PV

Source: Agora Energiewende 2012


• High upfront investment

(capital costs)

• Almost zero marginal costs

• Fluctuating supply (depending

on the weather)

Important features of wind and solar

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

CCGT Coal Nuclear Wind PV

OPEX

CAPEX

Share of fixed versus variable costs of selected power generation technologies


• High upfront investment (capital costs) –INVESTMENT SECURITY is crucial!

• Almost zero marginal costs – they come FIRST in the MERIT ORDER!

• Fluctuating supply (depending on the weather) – backup needs to be provided by other flexibility options

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

CCGT Coal Nuclear Wind PV

OPEX

CAPEX

Share of fixed versus variable costs of selected power generation technologies

Important features of wind and solar


• Base load power plants disappear (fossil fuel power plants need to

become more flexible)

• Reduce must-run requirements of conventional power plants

• Reduced full-load hours for coal and gas-fired power plants

• changing economics and additional revenue requirements via

capacity markets?

• Upgrade existing power plant in order to allow for better ramping

capabilities

Conventional power plants need to become more flexi ble


Making best use of the existing grid infrastructure:

Net Metering Policy Design


Simplistic grid parity and “self-consumption”

60

Source: Eclareon 2013


Grid parity in Sydney, Australia (residential)

61



“Grid parity” in Sao Paulo, Brazil (residential)

62



Electricity tariff structure and incentives for sel f-consumption

• Contrary to European countries and the US, electricity prices in

developing countries/African countries are generally low for domestic

consumers and high for commercial consumers/industry

• Example: Kenya

63

Source: Hille et al. 2011


Net metering programs world-wide

Europe Americas Asia Middle East Africa

Belgium

Czech Republic

Denmark

Greece

Italy

Malta

Switzerland

Portugal

Spain

Guatemala

Canada (regional)

Mexico

USA (43 States)

Peru

DominicanRepublic

Panama

Japan

Philippines

Singapore

South Korea

Jordan

Palestine

Uruguay

Tunesien

Cap Verde

64Source: REN21 2013


Net Metering Design Features: Eligible technologies and sectors

Features Design Options

Eligible Renewable/Other Technologies:

Photovoltaics (but also Solar Thermal Electric, Landfill Gas, Wind, Biomass, Hydroelectric, Geothermal Electric, Municipal Solid Waste, Hydrokinetic, Anaerobic Digestion, Small Hydroelectric, Tidal Energy, Wave Energy, Ocean Thermal)

Applicable Sectors:

Residential (limitation to certain system size?)Commercial, Industrial, Schools, Local Government, State Government, Federal Government, Agricultural, Institutional


Net Metering Design Options


Program size • Defined as a percentage of total peak demand• Defined as a capacity limit • Unlimited

System size: • Limit on installed capacity per unit (e.g. 10 kW)• Limitation in relation to the average, annual electricity

demand in a region/country (e.g. average electricity demandof 300 kWh/a; 1% of 300 kWh = maximum size of 3 kw)

• Local electricity generation may not exceed local electricitydemand (household with 300 kWh consumption may not produce/net meter more than 300 kWh of generation).


Roll-over provisions for excess electricity


Program size • Indefinate• Yearly• Monthly• Hourly

The value of the role over:

• retail price• wholesale price• combinations


Auto consumptions and the “solidarity”-based electri city system

• Are there major exemptions/privileges for electricity auto-consumption

in your country?

� Grid usage fees?

� Other taxes or levies?

• If industry subsidizes household electricity prices in Africa countries, do

you want them to auto-produce/consume electricity (and no longer pay

the higher industrial/commercial rate?

68


Investment (in)security in the case of net metering

• Changes in Net Metering regulations will effect new power plants AND

existing power plants

• Changes in electricity pricing (moving from monopolised markets to

liberalized markets in the coming 20 years?)

• Changes in electricity rate structure (costumer classes)

69



Dr. David JacobsIET – International Energy TransitionPhone +49 163 2339046Fax: +49 30 [email protected]@InterEnerTrans


Session 7/8: From scenarios to policy and market development



Scenarios

RE Market

Strategies:




planning)?

Instruments:







Establishing political and financial instruments:

Designing finance mechanisms for different market segments


Overview of support mechanisms for RES-e

SUPPORT MECHANISMS

Price-based support Quantity basedsupport

Investment focussed Investment subsidies

Tax incentives

Generation focused Feed-in tariffs

Net metering

Tax incentives

Tender scheme

Quota obligation (TGC / RPS)


Custom taxes

• Are there custom taxes for renewable energy equipment?

• If yes, what is the rational?

Pilot projects

• In emerging RE markets:

� Have you started with pilot projects in order to make actors

familiar with renewables (fluctuations, permitting, grid access,

etc.)?


Local content requirement

• Several countries have introduced local content requirements in

national support mechanisms, i.e. obligations to produce a certain

share of renewable energy equipment locally/nationally (e.g. Spain,

China, India, Argentina - Chubut, Ontario - Canada, Malaysia, Italy)

• These requirements can be implemented in national feed-in tariff

mechanisms� Establish a national renewable energy industry

� Take advantage of positive macro-economic effects

Source: Mendonca et al. 2009

• Problem: potential confliction with international trade rules (WTO)

• Malaysia: Adder for nationally produced equipment:


From scenarios to instruments:

FIT design and locational signals


Basic feed-in tariff design

� Purchase obligation

� “Independent” from power demand

� Fixed tariff payment based on the actual power generation costs

� Price setting will be discussed later

� Long duration of tariff payment


Tariff calculation methodology

� Tariff calculation based on technology specific generation

costs + “reasonable” rates of return

� Don’t use “avoided costs” as point of reference

� Cost factors:

� Investment costs (material and capital costs); Grid-related

and administrative costs (including grid connection, costs for

licensing procedure; Operation and maintenance costs; Fuels

costs (biomass and biogas)


Tariff calculation methodology

� Targeted IRR (Internal rate of return)

� In the EU, feed-in tariffs target at an internal rate of

return of 5-9 percent (certain jurisdictions use return

on equity)

� In developing countries, the targeted IRR usually needs

to be higher (10-20 percent)

� Public investment (monopolist, often without profit

interest); or private IPPs (profitability important)?

� Similar profitability for renewable energy projects

needed as for convention energy market


Equity IRR expectation in developing countries

Figure 4: Equity IRR expectation in developing coun tries:

0%

5%

10%

15%

20%

25%

Infrastructure

investment

(developed

world)

Technology

risk (missing

track record)

Political risk Reg. Risk, soft

political risk,

transparency,

legal

framework

Counterparty

risk

Currency

safety cushion

Infrastructure

investment

(developing

world)

Source. Fulton et al. 2011


Debt-equity ratio: • International benchmarking

• South Africa, Nersa: 70:30

• Ruanda FIT: 75:25

• Nigeria: 60:40

• Germany: 90:10; 70:30

• Netherlands: 80:20 (biomass); 90:10 wind


Hands-on exercise: How to calculate FIT levels for your country?


Important FIT design features (continued)

� Payment duration

� Eligibility

� Technology-specific tariffs

� Feed-in tariff calculation

� FIT degression

� Capacity caps


Locational signals for new power generation- Location-specific tariff payment

15

• Mostly applied for wind energy (Germany and France)

• Reduce accumulation of wind power plants in coastal areas (increases public

acceptance); visual impact; grid integration

• Location specific tariffs in Germany depend on wind speed at a given location

(measured during the first 10 years of operation)

• First 10 years: flat rate

• Final 5 years: depending on “quality” of site


Location specific tariffs - Germany

Source: Klein et al. 2008



Source: Klein et al. 2008




Location specific tariffs • French FIT for solar also includes location specific tariffs

Source: http://re.jrc.ec.europa.eu/pvgis/countries/europe.htm


Additional measures for locational incentives

20

• Nodal pricing

• Using differentiated grid-usage fees

• Define areas with good, medium and no grid connection capability



Auction design and spatial planning


Increasing use of auctions in emerging markets

Source: IRENA 2013


Tender/auctioning mechanism

� Government issues call for tender

� Generally: bids for cost per unit of electricity (generation focused)

� Sometimes: bids for upfront investment cost of one project

(investment focused)

� For example: 100 MW wind energy onshore

� Bidder with the lowest price “wins” contract and has the exclusive right

for renewable electricity generation


• Basic price finding mechanism:

• English (or Ascending)• Price for item is increased until only one bidder if left and the item

is sold to that bidder

• Dutch (or Descending clock) Multi-round bid

• Auctioner starts with a high price and then calls out successively

lower prices until quantity offered and quantity required match!

Auctions design: How to determine prices?


• Sealed-bid auction

• Each bidder writes down a single bid which is not disclosed to

other bidders and the most competitive bidders win (“pay as

bid”).

• Other selection criteria than the price?

• Local content

• job creation

• ownership

• socioeconomic development

• Resource securitization in the case of biomass

• Locational incentives

Auctions design: How to determine prices?


• Prequalification requirements for auctions – important for project

realization rate!

• Material pre-qualifications

• Project development experience

• Securitization of land, grid access

• Contracts for equipment

• Etc.

• Financial prequalification

• Bid bonds

• Etc.

Auction design: Who can participate? (Prequalifica tion)


Auction design and site determination

• Option 1: Allow project developers to freely select sites (within the

existing spatial planning arrangement)

• Option 2: Package pre-selected sites in order to have better control

over land use (and help to shorten bidding process).

27


• Which authority should be in charge of procurement?

• Technology neutral versus technology-specific aucti ons?

• How often will procurement take place (frequency)?

• Size of each procurement round? Technology-specific?

• Upper or lower limit on project size?

• Upper or lower limit on prices?

Auction design: Other important design decisions


Pros and cons of auction mechanisms

Advantages Disadvantages

Cost efficiency and price competition in emerging markets

High administrative costs (complexity)

High investor security (PPA) Discontinuous market de velopment (stop-and-go cycles)

Volume and budget control risks of not winning proje ct increases finance costs

Predictability of RE-based electricity supply (sector growth)

Risk of underbidding (lack of deployment and target achievement)

Combination with local content, etc.



Case study South Africa


• In 2009, the government began exploring feed-in tar iffs (FITs)

• later rejected in favor of competitive tenders:

• Insecurity about “right tariff levels” (2009, 2011)

• FITs prohibited by the government’s public finance and

procurement regulations?

• Move back to FITs after several auction rounds?

South Africa: Moving from FITs to auctions

Source: Eberhard et al. 2014


• Auction design and results:

• Department of Energy in charge of auction (not Esko m!)

• Strict pre-qualification (EIA; resource measurement )

• Bids needed to be fully underwritten with debt and equity (avoid

under-bidding)

• Selection of 28 projects with 1416 MW (investment of US$6 billion)

• Reasons for high prices:

• Most bids close to the maximum price (previously ca lculated FITs) -

Lack of competition

• significant upfront administrative requirements

• high bid costs

South Africa: First bidding round in 2011



• Second round in November 2011

• Tighter procurement process and increase competition

• Seventy-nine bids for 3233 MW – 19 projects selected

• Third round started in May 2013

• 93 bids for 6023 MW – 73 projects with 1456 MW selected

• Prices fell further in round three

• Increased local content

• wide variety of domestic and international project developers,

sponsors and equity shareholders

South Africa: Second and third round in 2011 and 20 13


• Decline of submitted bids over time:

• Lack of competition in the 1st round – right benchmark?

• General cost decline of PV and wind in the past 3 years!

• How many projects will eventually be realized?

South Africa: Successful auctions?




Case study China


• Policy framework:

• 2005 Renewable Energy Law – clear roadmap and target s (15

percent of primary energy supply by 2020)

• Initially passed to support FITs but no consensus o f tariff level

based on experience with previous concession loans

China: Moving from auctions to FITs

Source: https://openknowledge.worldbank.org/handle/10986/18676


• Policy framework:

• First auction for onshore wind started in 2003

• Sealed bid, single round determined prices

• Early auction rounds: bids below cost of production – projects were not

completed

• Loose prequalification requirements

• Large state-owned enterprises wanted to enter the market and could

cross subsidize their low bids with coal-generation business

• Effects:

• slow expansion of wind power sector

• insecurity for investors

China: Auction design features and effects


• Adjustment of auction design:

• Minimum price

• Stricter pre-qualifications

• Local content requirement

• Further adjustment in 2007:

• Winner was no longer the lowest price but the bidder that was

closest to the average price resulting from all bids, after

excluding the highest and lowest bids

• Further adjustment:

• Move back to “lowest bid” design

China: Auction design adjustments


• China used auction round as a price-discovery mecha nism for FIT program

(attract international investors)

• 2009: Establishment of location-differentiated feed -in tariffs for wind energy

• 2011: FITs for solar PV

• 2014: Offshore wind tariffs

• Emerging technologies such as CSP and offshore wind energy continue to

use bidding for contracts

China: Successful auctions?



Combining FITs and auctions?


• Do you have experience in setting prices administratively?

• Is there sufficient interest in investing in renewables in your

country (competition in Least Developed Countries)?

• Is the market big enough to create competition (size of auction)?

• Which type of actors should invest (small vs. big)?

Auctions or FITs: No easy answer…


• Use auctions to determine FIT prices (China)?

• Use auctions for emerging technologies and FITs for mature

technologies (Denmark, China)?

• Use auctions for large projects and FITs for small projects

(France, Taiwan)?

Auctions and FITs?


Financing support mechanisms:

Design options and international experience


Financing support programs in developing countries

� low electricity costs

� little acceptance of electricity price increases


Combined financing – Taiwan

Source: David Jacobs

� Add additional financing to the national RES fund (levy on producers from

conventional electricity)

� Increase the retail electricity price by a certain share (after general elections

next year)

Conventional electricity producers

Retail price increase

Renewable Energy Fund (FIT Fund)

Payment for producers under the feed-in tariff

scheme

Money

Money

Money


RES financing in Malaysia – limited electricity pric e increase (limited scope of FIT program)

Source: Kettha 2010


RES Fund in Malaysia


International RES financing? – The future of interna tional climate talks?



Reducing administrative barriers


High number of institutions involved in planning and permitting process

• Lengthy and complicated application process

• High number of rejections

• High administrative costs

Institution A

Institution C

Institution D

Institution B

Institution G

Institution E

Institution F

RES-e developer


High number of institutions involved in planning and permitting process

• Solution: One-stop-shop institution

Institution A

Institution C

Institution D

Institution B

Institution G

Institution E

Institution F

RES-e developer

One-stop institution


Long lead times

• Long lead times to obtain necessary permits

• Spain and Portugal: 12 year for small hydro

• France: 5 years for wind energy

• Approval rates (France - wind energy) = less than 30%


Long lead times

• Exact length of procedure not known up-front: clear guidelines and

obligatory response periods for authorities needed

• Clear attribution of responsibilities

• Especially spatial planning related permits can take many years

(wind, biomass)


From instruments to market deployment:

The importance of resource mapping for investors and project developers


From resource mapping to the actual deployment of renewables

55


Resource mapping and project development

• Purpose of resource mapping:

� Helping governments and utilities plan and guide investment

through improved understanding of resource availability and

constraints

� Providing commercial developers with information on

resource location

� Shortening project development times and access to finance by

providing ground-based datasets for resource validation purposes

56


Resource mapping and project development

• The first step on a long process until project operation

57

Source: http://www1.eere.energy.gov/femp/pdfs/large-scalereguide.pdf


Renewable energy project planning

• Site selection based on:

� Resource availability (maps)

� Grid availability

� Planning and support framework

• Feasibility Analysis (Site-specific assessment)

� identify physical and spatial issues

� determine technical performance potential

(onsite measurement) and economic viability

� identify environmental, social or other

constraints

58Source: http://www.epa.gov/oswercpa/docs/handbook_siting_repowering_projects.pdf


Renewable energy project planning

• Design and development

� Design and planning of the physical aspects

of the project (negotiation of financial,

regulatory, contractual, and other

nonphysical aspects)

• Construction and Commissioning

• Performance Period

� Operation and Maintenance

• Decommissioning

� equipment replacement, permit revision, and

new financing; negotiating a new lease

agreement

59Source: http://www.epa.gov/oswercpa/docs/handbook_siting_repowering_projects.pdf


Longer term weather trends?

• Measurements usually have to take place for a longer period of time in order to

convince investors of the quality of the site:

� Wind projects may require 12-18 months of direct readings from a

mounted met mast on each potential site. 12 months is possible but

requires correlation with geographically close meteorological information

from an airport or other measuring stations

� Because CSP projects tend to be very large scale and depend on direct

versus diffused irradiation, 12 months of data appears to be a minimum for

CSP if correlated with 15 years of satellite data

� Solar PV usually required one year of

measurements

60Source: David Renne, NREL


Longer term weather trends?

• Long-term fluctuations?

� Effects from climate change and

other environmental impacts

� German average solar radiation

5% higher than expected

(increase since mid-80s)

� Opposite development in Chinese

cities due to smog

� “global dimming and brightening” -

only the 10 most recent years as

benchmark!

61

Source: Fraunhofer ISE (Müller et al. 2014)


Short term variability due to whether events

• Not crucial for project finance

• However, crucial for predictability of

electricity output and therefore for

system (and market) integration

• Important improvements in

• Example: cloud shading for solar PV

62


Assessment and revising of existing policies and frameworks?


Review and assessment

• Assess target achievement (annually, bi-annually)

• Identify bottle-necks and barriers (finance, grid access, administrative

barriers, etc.).

• Adjust policies and framework conditions

64



Dr. David JacobsIET – International Energy TransitionPhone +49 163 2339046Fax: +49 30 [email protected]@InterEnerTrans

Provisional agenda and presentation for the global atlas training workshop in lima, peru, EN

Presentations & Public Speaking

Transcript of Provisional agenda and presentation for the global atlas training workshop in lima, peru, EN