WO2024133789A1

WO2024133789A1 - A method for determining dynamic time series for each wind turbine of a wind farm

Info

Publication number: WO2024133789A1
Application number: PCT/EP2023/087441
Authority: WO
Inventors: Romain SAINT LEGER; Pierre CUNY
Original assignee: Totalenergies Onetech
Priority date: 2022-12-23
Filing date: 2023-12-21
Publication date: 2024-06-27

Abstract

The present invention relates to a method for determining dynamic time series for each wind turbine of a wind farm, the method comprising the following steps: - obtaining turbine position data and reference position data, - obtaining a steady state time series for each wind turbine, - obtaining a reference time series for each reference point of the wind farm, - determining shifted time series corresponding to the steady state time series of each turbine whose values of each timestep are shifted from a timeshift, and - determining a dynamic time series for each turbine, corresponding to the shifted time series of said turbine which have been resampled so as to associate an updated wind speed to each timestep.

Description

A method for determining dynamic time series for each wind turbine of a wind farm

TECHNICAL FIELD OF THE INVENTION

The present invention concerns a method for determining dynamic time series for each wind turbine of a wind farm. The present invention also concerns an associated computer program product. The present invention also relates to an associated readable information carrier.

BACKGROUND OF THE INVENTION

Wind resource estimation is a very crucial task in the wind energy sector.

To enable quick computations, the commercialized wind resource assessment software only considers steady state wind flows and not dynamic wind flows. That means that wind has the same temporality everywhere in the field at each timestep.

Consequently, the global power estimation is correct, but not the power generated at each timestep. In particular, such software cannot model the aggregation effect, which is the fact that the random fluctuations in the production of renewable electrical production systems (such as wind turbines or photovoltaic panels) are statistically reduced when these productions are injected into the same electrical grid.

SUMMARY OF THE INVENTION

Hence, there exists a need for a more accurate method enabling the estimation of wind resource received by wind turbines of a windfarm so as to be able to model the aggregation effect.

To this end, the invention relates to a method for determining dynamic time series for each wind turbine of a wind farm, the method comprising the following steps which are computer-implemented:

- obtaining turbine position data which are data relative to the position of each wind turbine of the wind farm,

- obtaining reference position data which are data relative to the position of at least one reference point of the wind farm, each reference point being a point in the environment of the wind farm,

- obtaining a steady state time series for each wind turbine, each steady state time series extending over a given time period divided in timesteps, each steady state time series comprising at least a wind speed at the wind turbine for each timestep of the given time period, - obtaining a reference time series for the or each reference point of the wind farm, each reference time series extending over the given time period divided in timesteps, each reference time series comprising at least a wind speed and a wind direction at the reference point for each timestep of the given time period,

- determining at least one shifted time series for each turbine, the shifted time series corresponding to the steady state time series of said turbine whose values of each timestep are shifted from a timeshift, the timeshift being specific to the timestep, to the turbine and to a reference point, each timeshift being determined as a function of the turbine position data, the reference position data and the reference time series of the corresponding reference point, each timeshift corresponding to a time difference for a wind front to reach the turbine starting from the corresponding reference point and propagating according to the wind speed and the wind direction at said reference point for the considered timestep, and

- determining a dynamic time series for each turbine, the dynamic time series corresponding to the shifted time series of said turbine which have been resampled so as to associate an updated wind speed to each timestep.

The method according to the invention may comprise one or more of the following features considered alone or in any combination that is technically possible:

- for each turbine and each timestep, the determination of each timeshift comprises:

- determining a distance, called delay distance, between the reference point corresponding to the timeshift and the projection of the position of the turbine on the wind direction for said reference point, and

- dividing the determined delay distance by the wind speed at said reference point, so as to obtain the timeshift specific to said turbine, said timestep and said reference point;

- each delay distance is determined with the following formula:

Where:

• (X^Mi Y^Mi) are the Cartesian coordinates of the turbine

• (X^Rk Y^Rk) are the Cartesian coordinates of the reference point R_k, and

• d^Rk is the wind direction for the reference point R_k

- the timeshift is obtained by dividing the determined delay distance by the wind speed which has been multiplied by a correction factor, the correction factor being specific to each wind turbine, to each reference point, and to each timestep, the correction factor being an average speed-up factor on the path of the wind between the wind turbine and the considered reference point at the considered timestep;

- each correction factor is obtained through a look-up table specific to the considered wind turbine and to the considered reference point, each look-up table associating a correction factor to several couples of wind speed and wind direction, the correction factor determined for each timestep corresponding to the couple of the look-up table the closest to the wind speed and the wind direction of the reference point at said timestep;

- for each turbine, the updated wind speed of each timestep is determined by interpolating the value of the wind speed between the shifted wind speed before the timestep and the shifted wind speed after the timestep of the corresponding shifted time series;

- when several reference points are considered, for each turbine, a shifted time series is determined for each reference point, the determination of the dynamic time series for each turbine comprising:

- determining an intermediate dynamic time series for each shifted time series by resampling the shifted time series so as to associate an updated wind speed to each timestep, and

- determining a mean wind speed for each timestep as a function of the updated wind speed of each intermediate dynamic time series, the dynamic time series for said turbine associating the mean wind speed to each timestep;

- for each turbine, the mean wind speed determined for each timestep is a mean of the updated wind speeds of each intermediate dynamic time series at the corresponding timestep, which have been weighted by the inverse of the square of the delay distances between said turbine and each reference point;

- for each turbine, when the mean of the wind speeds of all the timesteps of the dynamic time series, called total dynamic wind speed, is different from the mean of the wind speed of all the timesteps of the corresponding steady state time series, called total steady state wind speed, the method comprises a step of scaling the dynamic time series of said turbine so that the total dynamic wind speed matches the total steady state wind speed;

- the method comprises a step of determining a power for each timestep of each dynamic time series, the power being determined on the basis of a function linking the power to the wind speed;

- the method comprises a cleaning step comprising:

- the determination of a global trend for the power generated by the turbines at each timestep, and - for each turbine, the adaptation of the power(s) determined at each timestep which do(es) not fulfill the global trend, so as to fulfill the global trend;

- for each turbine, the steady state time series take into account the wake effect induced by the turbines surrounding said turbine.

The invention also relates to a computer program product comprising a readable information carrier having stored thereon a computer program comprising program instructions, the computer program being loadable onto a data processing unit and causing a method as previously described to be carried out when the computer program is carried out on the data processing unit.

The invention also relates to a readable information carrier on which is stored a computer program product as previously described.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be easier to understand in view of the following description, provided solely as an example and with reference to the appended drawings in which:

Figure 1 is a schematic view of an example of a wind farm with a wind front propagating on the wind turbines,

Figure 2 is a schematic view of an example of a computer for implementing a method for determining dynamic time series for each wind turbine of a wind farm, Figure 3 is a flowchart of an example of implementation of a method for determining dynamic time series for each wind turbine of a wind farm,

Figure 4 is a schematic representation illustrating an example of different time series for a turbine among a steady state time series, a shifted time series and a (intermediate) dynamic time series,

Figure 5 is a schematic representation of the geometrical configuration between a turbine and a reference point, enabling to determine a time shift for each timestep, and

Figure 6 is a schematic representation of the geometrical configuration between a turbine and a reference point, enabling to determine the coordinates of the projection of the position of the turbine on the wind front line at the reference point.

DETAILED DESCRIPTION OF SOME EMBODIMENTS

An example of a wind farm 10 is illustrated on figure 1 . A wind farm or wind park, also called a wind power station or wind power plant, is a group of connected wind turbines in the same location used to produce electricity. A wind farm also comprises a power station and the cables connecting the turbines to the power station. Wind farms vary in size from a small number of turbines to several hundred wind turbines covering an extensive area. Wind farms can be either onshore or offshore (bottom-fixed or floating).

The wind farm 10 comprises a plurality of wind turbines Mi. In the example of figure 1 , the wind farm 10 comprises nine turbines M1 to M9. However, the invention also applies to wind farms having less (at least two) or more turbines Mi, even to large wind farms which are wind farms having at least 20 turbines.

As illustrated on figure 1 , a wind front propagates on the wind turbines Mi. In particular, on this figure 1 , the wind front WF propagates from a reference point R1 and at a wind speed S^R1. Such a wind front WF reaches the different wind turbines Mi of the wind farm at different instants. The determination method that will be described in the following enables to take into account the delays for the wind front to propagate on each turbine Mi of the wind farm.

An example of a calculator 20 and a computer program product 22 are illustrated on figure 2.

The calculator 20 is preferably a computer.

More generally, the calculator 20 is a computer or computing system, or similar electronic computing device adapted to manipulate and/or transform data represented as physical, such as electronic, quantities within the computing system's registers and/or memories into other data similarly represented as physical quantities within the computing system's memories, registers or other such information storage, transmission or display devices.

The calculator 20 interacts with the computer program product 22.

As illustrated on figure 2, the calculator 20 comprises a processor 24 comprising a data processing unit 26, memories 28 and a reader 30 for information media. In the example illustrated on figure 2, the calculator 20 comprises a human machine interface 32, such as a keyboard, and a display 34.

The computer program product 22 comprises an information medium 36.

The information medium 36 is a medium readable by the calculator 20, usually by the data processing unit 26. The readable information medium 36 is a medium suitable for storing electronic instructions and capable of being coupled to a computer system bus.

By way of example, the information medium 36 is a USB key, a floppy disk or flexible disk (of the English name "Floppy disc"), an optical disk, a CD-ROM, a magneto-optical disk, a ROM memory, a memory RAM, EPROM memory, EEPROM memory, magnetic card or optical card. On the information medium 36 is stored the computer program 22 comprising program instructions.

The computer program 22 is loadable on the data processing unit 26 and is adapted to entail the implementation of a method for determining dynamic time series for each wind turbine of a wind farm, when the computer program 22 is loaded on the processing unit 26 of the calculator 20.

A method for determining dynamic time series for each wind turbine of a wind farm, will now be described with reference to the organigram of figure 3, and to figures 4 and 5.

The determination method comprises a step 100 of obtaining turbine position data which are data relative to the position of each wind turbine Mi of the wind farm. In an example, the turbine position data have been obtained through measurements performed by sensors or global positioning system or geographical information. The obtaining step 100 is, for example, implemented by the calculator 20 interacting with the computer program product 22, that is to say is computer-implemented.

The determination method comprises a step 110 of obtaining reference position data which are data relative to the position of at least one reference point Rk of the wind farm.

Each reference point Rk corresponds to a position in the environment of the wind farm for which measurement data are obtained.

In an example, the reference position data have been obtained through measurements performed by sensors or global positioning system or geographical information. The obtaining step 1 10 is, for example, implemented by the calculator 20 interacting with the computer program product 22, that is to say is computer-implemented.

The determination method comprises a step 120 of obtaining a steady state time series for each wind turbine Mi. The obtaining step 120 is, for example, implemented by the calculator 20 interacting with the computer program product 22, that is to say is computer- implemented.

Each steady state time series extends over a given time period divided in timesteps T_n. Each steady state time series comprises at least a wind speed s^Mi(T_n) at the wind turbine Mi for each timestep T_n of the given time period.

Preferably, for each turbine M_i; the steady state time series takes into account the wake effect induced by the turbines Mi surrounding said turbine Mi. The wake effect is the fact that, as wind flows through a wind turbine, wind speed decreases and turbulence increases. This phenomenon reduces the total produced energy. The wake effect is, for example, taken into account in the wind speeds s^Mi(T_n) of each timestep T_n and in a corresponding value of wind power. For example, the steady state time series are obtained using a commercial wind software such as WindPro, OpenWind or Meteodyn, for example on the basis of data relative to the configuration of the wind farm and past wind data for the area of the windfarm.

Preferably, each steady state time series also comprises the wind power corresponding to the wind speed s^Mi(T_n) at each timestep T_n.

The determination method comprises a step 130 of obtaining a reference time series for the or each reference point Rk of the wind farm. The obtaining step 130 is, for example, implemented by the calculator 20 interacting with the computer program product 22, that is to say is computer-implemented.

Each reference time series extends over the given time period divided in timesteps T_n. Each reference time series comprises at least a wind speed s^Rk(T_n) and a wind direction 0^Rk(T_n) at the reference point Rk for each timestep T_n of the given time period.

For example, the reference time series are obtained through measurements performed by sensors (real data).

The determination method comprises a step 140 of determining at least one shifted time series for each turbine Mi. The determination step 140 is, for example, implemented by the calculator 20 interacting with the computer program product 22, that is to say is computer-implemented.

The shifted time series corresponds to the steady state time series of said turbine Mi whose values of each timestep T_n are shifted from a timeshift At_n ^Rk (delay). The timeshift At_n ^Rk is specific to the timestep T_n, to the turbine Mi and to a reference point Rk. For example, as illustrated on figure 4, for the steady state time series, the value at timestep T_n-i corresponds to a timestep equal to 16:20, the value at timestep T_n corresponds to a timestep equal to 16:30 and the value at timestep T_n+i corresponds to a timestep equal to 16:40. With the shifted time series, the value at timestep T_n-i is shifted to 16:24 (timeshift = 4 minutes), the value at timestep T_n is shifted to 16:36 (timeshift = 6 minutes) and the value at timestep T_n+i is shifted to 16:42 (timeshift = 2 minutes).

Each timeshift At_n ^Rk is determined as a function of the turbine position data, the reference position data and the reference time series of the corresponding reference point Rk.

Each timeshift At_n ^Rk corresponds to a time difference for a wind front to reach the turbine Mi starting from the corresponding reference point Rk and propagating according to the wind speed s^Rk(T_n) and the wind direction 0^Rk(T_n) at said reference point Rk for the considered timestep T_n.

In a preferred embodiment, for each turbine Mi and each timestep T_n, the determination of each timeshift At_n ^Rk comprises the determination of a distance, called delay distance d^MiRk, between the reference point Rk corresponding to the timeshift At_n ^Rk and the projection of the position of the turbine Mi on the wind direction 0^Rk(T_n) for said reference point Rk.

In particular, on the example of figure 5, the wave front WF is assumed to be straight. The wind vector at the reference point Rk is noted S^Rk. The projection of the position of the turbine Mi along the line of this wind vector S^Rk is noted M’. The projection angle of R_kM_t on the line (RkM’) is noted 0_m. The delay distance d^MiRk corresponds to the distance ||/?M'||.

Preferably, each delay distance d^MiRk is determined with the following formula:

Where:

• (X^Mi y^Mi) are the Cartesian coordinates of the turbine M

• (X^Rk; Y^Rk) are the Cartesian coordinates of the reference point R_k, and

• d^Rk is the wind direction for the reference point R_k.

Then, the determined delay distance d^MiRk is divided by the wind speed s^Rk(T_n) at the considered reference point Rk, so as to obtain the timeshift At_n ^Rk. Such a timeshift At_n ^Rk is specific to said turbine M_i; to said timestep T_n and to said reference point Rk.

In a preferred embodiment, the determined delay distance d^MiRk is divided by the wind speed s^Rk(T_n) which has been multiplied by a correction factor SUF. The correction factor SUF is calculated at each time steps T_n for every wind turbine so as to correct the reference wind speed s^Rk(T_n) used to calculate the timeshifts At_n ^Rk. The correction factor SUF is specific to each wind turbine Mi and each reference point Rk. The correction factor SUF is an average speed-up factor on the path of the wind between the wind turbine Mi and the considered reference point Rk.

The correction factor SUF enables to take into account local acceleration and deceleration of wind linked to topography and/or roughness and/or wind speed deficit linked to wake losses.

In an example of embodiment, the correction factor SUF is obtained through several look-up tables. Each look-up table is specific to a wind turbine Mi and to a reference point Rk. Each look-up table associates a correction factor SUF to several couples of wind speed and wind direction. For each timestep T_n, each turbine Mi and each reference point Rk, the correction factor SUF is the correction factor SUF corresponding to the couple of the lookup table the closest to the wind speed s^Rk(T_n) and the wind direction e^Rk of the reference point Rk. In an example of embodiment, the look-up tables are obtained with different mapping of the speed-up factor and of the wake deficit according to wind direction for the considered environment of the wind farm. Such mappings are for example obtained with classic commercial tools (like WindPro, Meteodyn or Openwind).

For example, for a certain number of wind vectors and wind speeds, we build look-up tables of the correction factor to apply to the reference wind speed for every wind turbines and reference points.

As illustrated on figure 6, the correction factor SUF is the average value of speed-up factor along the path followed by the wind propagating along the wind direction (between point H and M). H is the projection of M (the position of the turbine) on the “virtual” wind front line at the reference point Rk. The position of H is for example obtained with the following formula:

H = P~ H'

Where :

• H is the origin of the wind front WF of coordinates (X^H , Y^H),

• Rk is the position of the reference point of coordinates (X^Rk, Y^Rk),

• Mt is the position of the turbine of coordinates (X^Ml,- Y^Mi),

• s^Rk is the speed of the wind at the reference point Rk,

• 0^Rk is the direction of the wind at the reference point Rk (± 180°),

• H'is the point of coordinates (X^Rk', Y^Mi' with : o M’ is the point of coordinates

Y^Mi') with M' = PM, o R’ is the point of coordinates (X^Rk', y^Rfc'),with R^! = PR, and

The determination method comprises a step 150 of determining a dynamic time series for each turbine Mi. The determination step 150 is, for example, implemented by the calculator 20 interacting with the computer program product 22, that is to say is computer- implemented.

The dynamic time series corresponds to the shifted time series of said turbine Mi which has been resampled so as to associate an updated wind speed s_u ^Mi(T_n) to each timestep T_n. The resampling enables to keep the original timestep T_n (those of the steady state time series) uniform.

For example, figure 4 illustrates the fact that the dynamic time series (or if applicable the intermediate dynamic time series) is resampled with:

- for the value at timestep equal to 16:30, the value at timestep T_n-i which has been shifted to 16:24 and the value at timestep T_n which has been shifted to 16:36, and - for the value at timestep equal to 16:40, the value at timestep T_n which has been shifted to 16:36 and the value at timestep T_n+i which has been shifted to 16:42.

Preferably, for each turbine M_i; the updated wind speed s_u ^Mi(T_n) of each timestep T_n is determined by interpolating the value of the wind speed between the shifted wind speed before the timestep T_n and the shifted wind speed after the timestep T_n of the corresponding shifted time series. The interpolation is preferably a linear interpolation.

In an example of embodiment, when several reference points Rk are considered, for each turbine M_i; a shifted time series is determined for each reference point Rk (as each timeshift is specific to a reference point Rk, the number of shifted time series is equal to the number of reference points Rk). The determination of the dynamic time series for each turbine Mi comprises:

- determining an intermediate dynamic time series for each shifted time series by resampling the shifted time series so as to associate an updated wind speed s_u ^Mi(T_n) to each timestep T_n,

- determining a mean wind speed s_m ^Mi(Tn) for each timestep T_n as a function of the updated wind speed of each intermediate dynamic time series.

Preferably, for each turbine M_i; the mean wind speed s_m ^Mi(Tn) determined for each timestep T_n is a mean of the updated wind speeds s_u ^Mi(T_n) of each intermediate dynamic time series at the corresponding timestep T_n, which have been weighted by the inverse of the square of the delay distances d^MiRk between said turbine Mi and each reference point Rk.

For example, the mean wind speed s_m ^Mi(Tn) at the timestep T_n for the turbine Mi is given by the following formula:

Where:

N is the number of reference point Rk,

- is the delay distance for the turbine Mi and the reference point Rk, and

‘ ^s (Rk ^Tn) '^s

wind speed for the turbine Mi at the timestep T_n obtained on the basis of the reference point Rk.

In this embodiment, the dynamic time series for each turbine Mi is obtained on the basis of the corresponding intermediate dynamic series, by associating the determined mean wind speed s_m ^Mi(Tn) to each timestep T_n of the dynamic time series.

Optionally, the determination method comprises a step 160 of scaling the dynamic time series of said turbine Mi. The scaling step 160 is, for example, implemented by the calculator 20 interacting with the computer program product 22, that is to say is computer- implemented. This enables to eliminate averaging bias.

In particular, the scaling step 160 occurs when, for each turbine M_i; the mean of the wind speeds s_m ^Mi(Tn) of all the timesteps T_n of the dynamic time series, called total dynamic wind speed, is different from the mean of the wind speed s^Mi(T_n) of all the timesteps T_n of the steady state time series, called total steady state wind speed. The scaling step 160 consists in adapting the quantity of wind speed of the dynamic time series, proportionally for each timestep T_n, so that the total dynamic wind speed matches the total steady state wind speed.

Optionally, the determination method comprises a step 170 of determining a power for each timestep T_n of each dynamic time series. The determination step 170 is, for example, implemented by the calculator 20 interacting with the computer program product 22, that is to say is computer-implemented.

In an example, the power is determined on the basis of a function linking the power to the wind speed. The function or curve is, for example, obtained with the steady state time series. That way, wake effect originally computed by commercial tools is integrated in the post-processed time series.

Optionally, the determination method comprises a step 180 of cleaning the determined powers. The cleaning step 180 is, for example, implemented by the calculator 20 interacting with the computer program product 22, that is to say is computer-implemented. Hence, the cleaning step 180 enables to avoid biases linked to averaging and aberrant points, for example, by flagging aberrant points and reconstructing them following global trend.

Preferably, the cleaning step 180 comprises the determination of a global trend for the power generated by the turbines Mi at each timestep T_n (power range). Then, for each turbine M_i; the power determined at each timestep T_n is adapted when it does not fulfill the global trend, so as to fulfill the global trend.

Hence, the present invention enables to obtain dynamic time series for each turbine, taking into account the fact that the temporality is not the same everywhere in the field at each timestep. This enables a more precise temporal estimation of wind resources received by each turbine and also to quantify the benefit of the aggregation effect in the following sizing of the downstream processes. In addition, the obtained dynamic time series are for example used to adapt the design of the wind farm (sizing, positioning) so as to improve the production, or more preferably to adapt the balance of the grid strategy and optionally the downstream process control.

It should be noted that by shifting wind speed instead of power in the shifted time series, we avoid biases linked to non-linearity of power according to wind speed. In addition, the present invention applies for wind farms that can be either onshore or offshore (bottom-fixed or floating).

The person skilled in the art will understand that the embodiments and variants described above can be combined provided that they are technically compatible

Claims

1 A method for determining dynamic time series for each wind turbine (Mi) of a wind farm, the method comprising the following steps which are computer-implemented:

- obtaining turbine position data which are data relative to the position of each wind turbine (Mi) of the wind farm,

- obtaining reference position data which are data relative to the position of at least one reference point (R_k) of the wind farm, each reference point (R_k) being a point in the environment of the wind farm,

- obtaining a steady state time series for each wind turbine (Mi), each steady state time series extending over a given time period divided in timesteps (T_n), each steady state time series comprising at least a wind speed (s^Mi(T_n)) at the wind turbine (Mi) for each timestep (T_n) of the given time period,

- obtaining a reference time series for the or each reference point (R_k) of the wind farm, each reference time series extending over the given time period divided in timesteps (T_n), each reference time series comprising at least a wind speed (s^Rk(T_n)) and a wind direction (0^Rk(T_n)) at the reference point (R_k) for each timestep (T_n) of the given time period,

- determining at least one shifted time series for each turbine (Mi), the shifted time series corresponding to the steady state time series of said turbine (Mi) whose values of each timestep (T_n) are shifted from a timeshift (At_n ^Rk), the timeshift (At_n ^Rk) being specific to the timestep (T_n), to the turbine (Mi) and to a reference point (R_k), each timeshift (At_n ^Rk) being determined as a function of the turbine position data, the reference position data and the reference time series of the corresponding reference point (R_k), each timeshift (At_n ^Rk) corresponding to a time difference for a wind front to reach the turbine (Mi) starting from the corresponding reference point (R_k) and propagating according to the wind speed (s^Rk(T_n)) and the wind direction (0^Rk(T_n)) at said reference point (R_k) for the considered timestep (T_n), and

- determining a dynamic time series for each turbine (Mi), the dynamic time series corresponding to the shifted time series of said turbine (Mi) which have been resampled so as to associate an updated wind speed (s_u ^Mi(T_n)) to each timestep (T_n).

2.- A method according to claim 1 , wherein for each turbine (Mi) and each timestep (T_n), the determination of each timeshift (At_n ^Rk) comprises:

- determining a distance, called delay distance (d^MiR_k), between the reference point (R_k) corresponding to the timeshift (At_n ^Rk) and the projection of the position of the turbine (Mi) on the wind direction (0^Rk(T_n)) for said reference point (R_k), and - dividing the determined delay distance (d^MiRk) by the wind speed (s^Rk(T_n)) at said reference point (Rk), so as to obtain the timeshift (At_n ^Rk) specific to said turbine (Mi), said timestep (T_n) and said reference point (Rk).

3.- A method according to claim 2, wherein each delay distance (d^MiRk) is determined with the following formula:

Where:

• (X^Mi Y^Mi) are the Cartesian coordinates of the turbine

• (X^Rk; Y^Rk) are the Cartesian coordinates of the reference point R_k, and

• d^Rk is the wind direction for the reference point R_k.

4.- A method according to claim 2 or 3, wherein the timeshift (At_n ^Rk) is obtained by dividing the determined delay distance (d^MiRk) by the wind speed (s^Rk(T_n)) which has been multiplied by a correction factor (SUF), the correction factor being specific to each wind turbine (Mi), to each reference point (Rk), and to each timestep (T_n), the correction factor (SUF) being an average speed-up factor on the path of the wind between the wind turbine (Mi) and the considered reference point (Rk) at the considered timestep (T_n).

5.- A method according to claim 4, wherein each correction factor (SUF) is obtained through a look-up table specific to the considered wind turbine (Mi) and to the considered reference point (Rk), each look-up table associating a correction factor (SUF) to several couples of wind speed and wind direction, the correction factor (SUF) determined for each timestep (T_n) corresponding to the couple of the look-up table the closest to the wind speed (s^Rk(T_n)) and the wind direction (e^Rk) of the reference point (Rk) at said timestep (T_n).

6.- A method according to any one of claims 1 to 5, wherein, for each turbine (Mi), the updated wind speed (s_u ^Mi(T_n)) of each timestep (T_n) is determined by interpolating the value of the wind speed between the shifted wind speed before the timestep (T_n) and the shifted wind speed after the timestep (T_n) of the corresponding shifted time series.

7.- A method according to claim 6, wherein when several reference points (Rk) are considered, for each turbine (Mi), a shifted time series is determined for each reference point (Rk), the determination of the dynamic time series for each turbine (Mi) comprising: - determining an intermediate dynamic time series for each shifted time series by resampling the shifted time series so as to associate an updated wind speed (s_u ^Mi(T_n)) to each timestep (T_n), and

- determining a mean wind speed (s_m ^Mi(Tn)) for each timestep (T_n) as a function of the updated wind speed of each intermediate dynamic time series, the dynamic time series for said turbine (Mi) associating the mean wind speed (Sm^Mi(T_n)) to each timestep (T_n).

8.- A method according to any one of claims 2 to 5 and claim 7, wherein, for each turbine (Mi), the mean wind speed (s_m ^Mi(Tn)) determined for each timestep (T_n) is a mean of the updated wind speeds (s_u ^Mi(T_n)) of each intermediate dynamic time series at the corresponding timestep (T_n), which have been weighted by the inverse of the square of the delay distances (d^MiRk) between said turbine (Mi) and each reference point (R_k).

9.- A method according to claim 7 or 8, wherein, for each turbine (Mi), when the mean of the wind speeds (s_m ^Mi(Tn)) of all the timesteps (T_n) of the dynamic time series, called total dynamic wind speed, is different from the mean of the wind speed (s^Mi(T_n)) of all the timesteps (T_n) of the corresponding steady state time series, called total steady state wind speed, the method comprises a step of scaling the dynamic time series of said turbine (Mi) so that the total dynamic wind speed matches the total steady state wind speed.

10.- A method according to any one of claims 1 to 9, wherein the method comprises a step of determining a power for each timestep (T_n) of each dynamic time series, the power being determined on the basis of a function linking the power to the wind speed.

1 1.- A method according to claim 10, wherein the method comprises a cleaning step comprising:

- the determination of a global trend for the power generated by the turbines (Mi) at each timestep (T_n), and

- for each turbine (Mi), the adaptation of the power(s) determined at each timestep (T_n) which do(es) not fulfill the global trend, so as to fulfill the global trend.

12.- A method according to any one of claims 1 to 1 1 , wherein, for each turbine (Mi), the steady state time series take into account the wake effect induced by the turbines (Mi) surrounding said turbine (Mi).

13.- A computer program product comprising a readable information carrier having stored thereon a computer program comprising program instructions, the computer program being loadable onto a data processing unit and causing a method according to any one of claims 1 to 12 to be carried out when the computer program is carried out on the data processing unit.

14.- A readable information carrier on which a computer program product according to claim 13 is stored.