Technical & Economical Potential of Wind Energy ,

Introduction

Decision makers and investors for our projects need the appropriate information in order to decide if the potential our projects are economically and technically viable

The integrated information tool developed by as provides this information and gives a complete view with estimates the energy production costs and performs parametric analysis of this investments .

Moreover the results of the analysis give to potential investors a clear picture of the wind investing environment in a  selectid  area.

            This project is subject to negotiation between

                                partners and investors

PART I

Wind Atlas Methodology and application results

Introduction

When a number of measurements covering an area are available then it might be of

interest to exploit these discrete data for an assessment of the wind potential, treating

the area as a continuum instead of a grid of points. The methodology described in the

following results in the assessment of the wind potential of a large area, without any

limitations regarding its size.

The computational method developed by as achieves to establish an interpolation

procedure that receives as input a substantial number of measurements and produces a prediction at an arbitrary point inside the area of interest.

The procedure undertaken for the measurements is beyond the scope of the interpolation method, as long as there are sufficient points where wind data are available to describe the mesoscale effect.

Description of the methodology

The methodology is derived from the assumption that the wind flow at high altimeters is inviscid, free from the influence of the surface boundary layer, governed strictly by meteorological mechanisms. On the other hand, the boundary layer phenomena are predominant close to the surface. There, the combined action of the topography and the boundary layer is enough to determine the wind speed and direction at any given point. In essence, a three-dimensional boundary correction method is introduced.

The whole calculation procedure is a two-step one. First, the three-dimensional space, which is defined by the surface and reaches up to a few kilometres in altitude, is analysed employing a potential flow code (mass conservation). The code works using

normalized variables, imposing a unity velocity boundary condition at the upper side

of the mesh. Because of the need to cover very large geographical areas, a multi-block

approach is followed.

The area is divided into a large number of blocks, each of which is independently

handled. Another set of blocks is generated from the first, defining a mesh of

staggered with respect to the original series blocks. These two sets of blocks are used

together to generate the final results, through extension, averaging and interpolation.

Taking advantage from the fact that potential flow results far from the boundaries are

insensitive to perturbations of boundary conditions, the above procedure yields a

smooth and continuous solution at the block interfaces. Individual calculations are

performed for each one of the chosen wind directions.

In a second step, a boundary layer correction is applied in order to introduce the

viscous phenomena to the calculation. A simplified approach is followed for the

viscous correction, derived from flat terrain boundary layer theory and the assumption

of constant roughness (as long as roughness maps are not available). Correction is

performed on a point-by-point basis. This simple method presents the significant

advantage that mass rate is maintained. However, it is possible to substitute this by

any boundary layer correction procedure.

At the end of this two-step procedure the flow field is completely defined, although

still normalized by the wind speed at the upper bound. The normalization assumption

that the wind speed at high altimeter is equal to unity is not equivalent to suggesting

that it is constant too. On the contrary, it is known to exhibit significant variation. At

this stage of the methodology, the intention is to calculate the wind speed at the upper

bound. To this end, the available measurements are used.For each point in the geographical area were measurements exist, it is possible to calculate the average wind speed for each direction of interest. Using this value, and the respective value at the computational grid node, the wind speed at the upper bound of the specific point can be predicted. This way, the measurements are used to predict the wind speed at a grid point at the upper bound. Interpolation of these values yields the wind speed at every point of the upper bound. The normalized values in the complete geographical area and at every height can then be converted to actual wind speeds.

The results attained up to this point still cover independently each direction. Using

time-averaging information (probability density function of the wind direction), also

yielded through the measurements, the average wind speed may be calculated at each

point. Using this procedure the Weibull distribution shape factors can also be derived,

which might be of interest for a better prediction of the electrical energy production

by wind turbines in an area.

PART II

A Geographical Information System For The

Assessment Of The Technically And Economically Exploitable RES

Potential

Aim of the methodology is to provide valid information for the availability, exploitability and

economical efficiency of the electricity production from RES by using geographical

information systems and computer models.

The information system is able to:

• Estimate the prevailing (theoretical) potential for each RES within the selected area

• Estimate the technically exploitable RES potential within the area

• Estimate the most economically attractive RET.s (Renewable Energy Technologies) in

each location

• Estimate the infrastructure required (electrical network, roads etc) for the RET.s

installation and the associated costs

• Analyse the impact of the existing legislative framework (mainly incentives, fiscal

measures etc.) on the economic viability of private investments in RET.s

• Evaluate the RES penetration in extended geographical areas for further use in strategic

energy planning.

Where the required information includes

• The theoretical potential of each RES

• Geographical data

• Electricity network data

• Technical and economic data concerning existing RET.s

• Legislative framework parameters

• Information for the financial environment for private RES investments

Section 1: Measured RES potential Data Geography Technology

Characteristics

RET costs, existing networks

Financial environment, regulations

Potential assessment

Potential assessment

Available potential for installations

Available potential for installations

Energy calculations

Energy calculations

Economic assessment

Economic assessment

PPrree-f-efeaassibibiliiltiyty

Models

Screening

Criteria

Energy calculation models

•Network analysis

•Cost analysis

The analysis is focused on a specific geographical area . For each RES in a pre-selected

region, the s/w tools report a variety of information which is either organised into a regional

RES database or calculated by computer models. This information have been classified in the

following five sections

1. Theoretical Potential

2. Available Potential

3. Technically Exploitable Potential

4. Economically Exploitable Potential

5. Prefeasibility Analysis of RES investments

The system is based on the idea that the portion of the energy content of renewables, which

can be transformed into electricity, is constrained (hierarchically) by:

• The available potential (especially for wind energy installations when a number of issues

regarding the use of land for wind energy development are usually under consideration).

• The mature technologies for RES exploitation and their efficiency (including their costs)

• The economic feasibility of RES investment, which is influenced by various factors such

as the cost of RET.s installation the requirement for development of the electricity network

infrastructure etc).

• The legal and financial framework regulating the RES sector, which has been proved to

be a crucial factor for the RET.s penetration into the energy system..

A short description of the above sections is presented here.

Section 1: Theoretical Potential: RES in nature

The primary energy of each RES is defined hereby as the Theoretical Potential. The objective

of this module is the analysis and evaluation of the theoretical potential of each R.E.S., as

well as the presentation of this potential on suitably defined maps.

For each RES, the following options are available:

A. Wind

o Isospeed curves

o Colour representation of different wind speed classes

B. Solar

Isometric curves in pre-selected solar radiation classes and Colour presentation of

different solar radiation classes. The information of the solar potential can be retrieved as

mean yearly, or as monthly data.

Section 2: Available Potential: RES potential in relation with land planning and technoeconomic constraints

The available potential demonstrates the energy by each RES, which is available for

exploitation.

A. Wind

• Legal and environmental constraints (land planning constraints, inhabited areas, protected

areas, distance from airports, etc)

• General technical rules (i.e. maximum altitude, maximum land slope, etc)

• Other rough techno economic rules (i.e. minimum annual mean wind velocity, maximum

distance from the grid and the road network, etc)

• Other rules imposed

B. Solar

• Land use

• Upper and lower limit of solar radiation

• Altitude & orientation rules

Section 3: Technically Exploitable Potential: RES to Power and Energy

The Technically Exploitable Potential is defined hereby as the capacity and expected energy

production from a RES-to-Power stations (wind turbine, water turbine, PV etc.).

A. Wind

• Type of Wind generator to be used (technology)

• General rules for the sitting of wind turbines.

The information system uses as input the dominating wind direction at the selected area, the

turbine.s power curve, and the mean wind speed at all available cells, and calculates:

• Total number of wind turbines in the area

• Total installed capacity (MW)

• Estimation of the energy produced, i.e.

o Total annual energy production

o Monthly variation of energy production

o Energy density (MWh/km2)

o Utilisation Factor of the Wind parks (in monthly and annual basis)

Data from meteorological stations and measuring sites

For each of the six stations (four Romanian and two Bulgarian) where data were made

available by the local project partners, the following information was provided:

! The geodetic coordinates of the site (latitude / longitude), and the elevation above

sea level (a.s.l.).

! A brief description of the surroundings.

! The measuring and analysis of measurements procedures.

! The two Weibull distribution function parameters (as derived from the Wind Atlas

standard procedure . WAsP Atlas LIB files), namely the scale parameter A, and

the shape parameter k.

From the above-mentioned two Weibull function parameters, the mean wind velocity

can be calculated as:

                                   

The above procedure was followed for all 12 sectors (of 30o each) that data existed,

for five levels above ground (10, 25, 50, 100 and 200 m a.g.l.) and for four standard

roughness classes (0., 0.03, 0.1 and 0.4 m).

The flow field for the area under investigation has been analyzed using the CRES

Wind Atlas Methodology (3D Boundary Layer correction model) for the four main

wind directions (north to south, east to west, south to north, west to east), and then an

averaging procedure was followed. A pre-processing was necessary in order to derive

from the data provided for each site the information required for the four primary

directions. More specifically, the data used were those for the z0=0.03m standard

roughness class, at the standard level of 10 meters a.g.l. (except from two of the cases

where the sites were located in the sea, for which z0=0m).

Database description

The RES database information could be categorised into the following five groups:

• Prevailing (theoretical) potential information describing the geographical distribution of

the RES potential.

• Digital Elevation Models, describing the essential earth surface parameters, such as earth.s

elevation, slope and aspect.

• Cartographic information describing the existing infrastructures and the natural

environment.

• Electricity network information describing the geographical distribution, topology and

attributes of the high and medium voltage electricity network.

• Renewable Energy Technologies techno-economic data.

Group A. RES potential information

The RES potential is measured using a different method, for every RE source.

In the case of wind energy the potential database consists of

− Geographically located measurements (time series of wind velocity per direction)

− Normalised distribution of calculated wind velocity data (mean annual wind speed at

specific heights) using a geographical matrix (grid).

− Time series and wind rose files.

In the case of small hydroelectric potential the database consists of measurements at specific

river sites including

− Mean annual flow.

− Flow time series.

− Net head.

In the case of biomass potential the database consists of

− Agriculture land use maps.

− Estimations of agriculture residues per region.

− Forestry and energy cultivation maps.

In the case of solar energy potential the database consists of

− Geographically located measurements (time series of mean monthly total irradiation and

air temperature)

− Normalised distribution of solar irradiation data (mean insulation per month, clearness

index using a geographical matrix (grid).

Group B. Digital Elevation models

The data of this group consist of Digital Elevation models (DEM’s) presenting earth’s surface

information such as

− Elevation

− Slope

− Aspect

Group C. Cartographic Information

The data belonging to this group present basic cartographic and environmental information

and consist of

− Road network.

− Urban centres

− Administrative boundaries

− Land use maps classified into land use classes.

− Protected areas.

Group D. Electricity Network Information

Electricity Network Information describes the attributes as well as the topology of high and

medium voltage electricity network, including

− Generation units data

− Circuit data

− Bus data

Group E. RES technologies information

The data belonging to this group present information on RE technologies equipment such as

− Wind turbines power curves and related technical attributes

− PV systems attributes

− Biomass plants attributes

Scenarios for the assessment of the profitability of Wind

Energy investments

The following tables demonstrate the methodology for the assessment of the profitability

of wind energy investments by evaluating the economical parameters of an hypothetical

installation.

In the first part, an analysis of the associated to the installation as well as operation and

maintenance costs is performed in order to calculate the cost of energy in the form of

€/kWh. For this reason, a typical wind park comprising 10 wind turbines of 650 kW each

one with a capacity factor of 35% (corresponding to approximately 7.5 m/sec mean wind

velocity at hub height) is selected as a case.

In the second part, taking as input, standard parameters financial environment for a typical

for the European Union are taken as a case in order to calculate the profitability indicators

for the selected investment (Internal Rate of Return- IRR, Pay Back Period - PBP, Net

Present Value - NPV).

It must be emphasized that the scenario selected includes a 30% subsidy to the capital cost

while the discount rate is 5%.

These two heights were selected as representative of the hub height of  two typical types of wind turbines, namely the 500 kW and 1 MW rated machines respectively.

The mean speed is shown at the left part of these two figures, while the power density,

calculated as:

P  = 1  ρ v 3  

A      2

is provided in the right  part ones, in [W m-2]. The wind speed of  each simulation is

weighted  by  the  frequency  of  occurrence  of  the  corresponding  primary  wind directions. The power density, for which the cube of the  calculated wind speed was used and not the third  moment of the  wind speed (due to lack of adequate data), is  calculated with a constant standard air density of 1.225 kg m-3. 

Costing of products
A. Parameters
A1. LIFE
Wind farm	50 years
Buildings, Roads	50 years
Electrical networks	50 years
Infrastructure	50 years
A2. Wind farm installation costs factors
Cost of land	6.5%	€ 90 millions
Buildings	1%	€ 14 millions
Roads	6.5%	€ 90 millions
electrical network	1.74%	€ 17.4 millions
Workshops	5% of cost	€ 700 millions
Financial	1% of cost	€ 14 millions
Architect, design	1% of cost	€ 14 millions
Builder	1% of cost	€ 14 millions
Technical Assistance	1% of cost	€ 14 millions
Legal Consultant	1% of cost	€ 14 millions
Technical Adviser	1% of cost	€ 14 millions
Personal Training	0.3% of cost	€ 4.2 millions
Price	Wind turbines	1. 000,000,000  €
Construction price	Foundation	864 millions  €
Average net cost	17.400 km 1,000 €/km	€ 17 millions
High network cost		€ 2,000 / km
Equipment	technical. Specific 2.7%	€ 34.8 millions
A3. Operating and	Maintenance costs	FACTORS
Land planning		2 000. € / ha / year
Buildings cost		100 000. € / year
Payroll costs		7 500.350   € / year
Fees	1.9% of price	43 418 746,8 €
Insurance Rates	0.17% of production	€ 2.8 millions
A4. OTHER PARAMETERS
Wind turbines	1 000P WT =	1,000,000 kW
Roads		14.6 km
Usable		800. ha
Electricity	kW / year	8.760.000.000 000
Factor	Capacity	100%
Discount rate		10%
B. Costs
B1. Expenses	installation.	1373719 066.8. €
Building cost		€ 14 millions
Turbine foundation		€ 866 millions
Land cost		€ 90 millions
Roads		€ 90 millions
Electrical network		€ 17.4 millions
Infrastructure		509,719 066.8. €
Installation	Workshops  and maintenance	€ 70 millions
Legal adviser		€ 14 millions
Technical Equipment		€ 37 millions
Training expenses		€ 0.42 millions
B2. Breakdown of	annual operation and	maintenance costs
Exploitation	2.47% / kWh	€ 8 millions
Land rent	0.24% / kWh	€ 0.8 millions
Buildings.	0.23% / kWh	€ 0.75 millions
Payroll cost	2.33% / kWh	€ 7.500.350.
maintenance	6.17% / kWh	€ 20 millions
Insurance rate	0.17% / kWh	€ ***** millions
B3. Specific cost	In the cost of energy	Production
Wind turbines	61.7% / kWh	€ 200 millions
Buildings.	0.9% / kWh	€ 3 millions
Roads	5.56% / kWh	€ 18 millions / kWh
Land	5.56% / kWh	€ 18 millions / kWh
Electrical Network	0.216% / kWh	€ 0.7 millions / kWh
Infrastructure	15.43% / kWh	€ 50 millions / kWh
Technical	Equipment  2.32% / kWh	€ 7,5 millions / kWh
Consultant	A, 047% / kWh	0.152 millions €
Legal adviser	0.058% / kWh	0.187 millions €
Education	0.07% / kWh /€	0.225 millions/ kWh
Directors	0.123% / kWh	€ 0.4 millions / kWh
Electricity	production cost	€ 0.037
2. Investment	Analysis
A. Parameters
A1. Parameters	Investment
Economic project		10 years
The discount rate	60%
Interest rate	5.%
Loan amortization		12 years
Grace period		2 years
A2. ELECTRICITY	PRICE	€ 0.061. / kWh
Annual inflation		8. %
Energy value		€ 534.360 millions
Power warranty	100%
A3. Investment	subsidy report.	0. %
Interest rate	5%
Tax Reserve		2. %
Tax Accounting		€ 20 millions
Annual rate	10%
A4. Parameters	From  the previous analysis
Wind turbines	1 000. P	1,000,000 Mw / h
Cost of land		90 millions €
Road length		14.6. km
Aria park		3000. ha
Production	annual energy	8,760,000,000 Mwh
Capacity factor		90 %
Energy losses		10%
Net energy		534.360 millions €
Net price sale		0.061. €  Mw
B. cost
Land cost		€ 90 millions
Roads		€ 42 millions
Electrical network.	1.74% of cost	€ 17.4 millions
Infrastructure		1373719 066.8 €
Consulting	1.00% of cost	€ 14 millions
Legal adviser	1.00% of cost	€ 14 millions
technical equipment	2.70% of cost	€ 34.8 millions
Training costs	0.03. %	€ 0.42 millions
Turbines cost		€ 1000 millions
Installation Cost		1 373 719 066.8 €
Wind farms	operating and	maintenance
Annual salary		7 500.350 €
Annual	Maintenance	€ 2 800 053
Annual insurance.	0.2. %	Of production
Operation and	Maintenance	€ 24 millions / year