Friday, April 5, 2013

Technical & Economical Potential of Wind Energy ,

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
Wind Atlas Methodology and application results

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.

A Geographical Information System For The
Assessment Of The Technically And Economically Exploitable RES

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
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
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
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
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
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
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


Wind farm
50 years

Buildings, Roads
50 years

Electrical networks
50 years

50 years

A2. Wind farm installation costs factors
Cost of land
€ 90 millions
€ 14 millions
€ 90 millions
electrical network
€ 17.4 millions
5% of cost
€ 700 millions
1% of cost
€ 14 millions
Architect, design
1% of cost
€ 14 millions
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
Wind turbines
1. 000,000,000 
Construction price
864 millions 
Average net cost
17.400 km 1,000 €/km
€ 17 millions
High network cost

€ 2,000 / km
technical. Specific 2.7%
€ 34.8 millions
A3. Operating and
Maintenance costs
Land planning

2 000. € / ha / year
Buildings cost

100 000. € / year
Payroll costs

7 500.350   € / year
1.9% of price
43 418 746,8
Insurance Rates
0.17% of production
€ 2.8 millions
Wind turbines
1 000P WT =
1,000,000 kW

14.6 km

800. ha
kW / year
8.760.000.000 000
Discount rate

B. Costs

B1. Expenses
1373719 066.8. €
Building cost

€ 14 millions
Turbine foundation

€ 866 millions
Land cost

€ 90 millions

€ 90 millions
Electrical network

€ 17.4 millions

509,719 066.8. €
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
2.47% / kWh
€ 8 millions
Land rent
0.24% / kWh
€ 0.8 millions
0.23% / kWh
€ 0.75 millions
Payroll cost
2.33% / kWh
€ 7.500.350.
6.17% / kWh
€ 20 millions
Insurance rate
0.17% / kWh
€ ***** millions
B3. Specific cost
In the cost of energy
Wind turbines
61.7% / kWh
€ 200 millions
0.9% / kWh
€ 3 millions
5.56% / kWh
€ 18 millions / kWh
5.56% / kWh
€ 18 millions / kWh
Electrical Network
0.216% / kWh
€ 0.7 millions / kWh
15.43% / kWh
€ 50 millions / kWh
 Equipment  2.32% / kWh
€ 7,5 millions / kWh
A, 047% / kWh
 0.152 millions €
Legal adviser
0.058% / kWh
0.187 millions €
0.07% / kWh /€
0.225 millions/ kWh
0.123% / kWh
€ 0.4 millions / kWh
production cost
2. Investment

A. Parameters

A1. Parameters

Economic project

10 years
The discount rate

Interest rate

Loan amortization

12 years
Grace period

2 years
€ 0.061. / kWh
Annual inflation

8. %
Energy value

€ 534.360 millions
Power warranty

A3. Investment
subsidy report.
0. %
Interest rate

Tax Reserve

2. %
Tax Accounting

20 millions
Annual rate

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
annual energy
8,760,000,000 Mwh
Capacity factor

90 %
Energy losses

Net energy

534.360 millions €
Net price sale

0.061. €  Mw
B. cost

Land cost

€ 90 millions

€ 42 millions
Electrical network.
1.74% of cost
€ 17.4 millions

1373719 066.8 €
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
Annual salary

7 500.350 €
2 800 053
Annual insurance.
0.2. %
Of production
Operation and
24 millions / year

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