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
threedimensional boundary correction method is introduced.
The whole calculation procedure is a twostep one.
First, the threedimensional 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 multiblock
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 pointbypoint 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 twostep 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
timeaveraging 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
PPrreefefeaassibibiliiltiyty
Models
Screening
Criteria
Energy
calculation models
•Network analysis
•Cost analysis
The analysis is focused on a specific geographical area . For each
RES in a preselected
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 preselected 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 REStoPower 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
abovementioned 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 preprocessing 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 technoeconomic 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:
A 2
is provided
in the right part
ones, in [W m2]. 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
m3.
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


Our team reads every day materials from renowned universities and from manufacturers. We select for our blog subscribers that seems interesting. We like to be always in touch with news, to know how to choose what is most cost effective for our clients. But we're more happy when we have our own studies to show, which we present with great love. We thank our subscribers in the U.S., Canada, UAE, KSA, Brazil, Lebanon, India, Indochina, Germany, Greece, Chile, Kuwait and Romania for giving us constantly follow.
ReplyDelete