Method for predictive control of the orientation of a solar tracker

Abstract

A method for controlling the orientation of a single-axis solar tracker orientable about an axis of rotation, including observing the evolution over time of the cloud coverage above the solar tracker; determining the evolution over time of an optimum inclination angle of the solar tracker substantially corresponding to a maximum of solar radiation on the solar tracker, depending on the observed cloud coverage; predicting the future evolution of the cloud coverage based on the observed prior evolution of the cloud coverage; calculating the future evolution of the optimum inclination angle according to the prediction of the future evolution of the cloud coverage; servo-controlling the orientation of the solar tracker according to the prior evolution of the optimum inclination angle and depending on the future evolution of the optimum inclination angle.

Claims

1. A method for controlling a solar tracker comprising: observing cloud coverage above a solar tracker via satellite images of the sky over the solar tracker; comparing the observed cloud coverage with cloud coverage models stored in a database, each cloud coverage model being associated with an orientation setpoint for the solar tracker; matching the observed cloud coverage with a cloud coverage model; and controlling the orientation of the solar tracker to the orientation setpoint of the matched cloud coverage model, wherein the cloud coverage models include: a zero-cloud coverage model is associated with a direct inclination angle orientation set point; and a widespread cloud coverage model associated with a zero-inclination angle orientation set point.

2. The method according to claim 1, wherein each cloud coverage model is associated with an orientation setpoint value which depends on a composition of a cloud layer of the cloud coverage model, the composition depending on at least one of a number of clouds, coverage surface of the clouds, thickness of the clouds, location of the clouds, type of cloud(s).

3. The method according to claim 1, wherein, for each cloud coverage model, the corresponding orientation setpoint value is predefined according to at least one of the following parameters: an energy consumption necessary to modify the orientation of the solar tracker, and a displacement speed of the solar tracker during an orientation change.

4. The method according to claim 1, wherein the cloud coverage models comprise at least one fine cloud coverage model to which an orientation setpoint value is associated on a predefined intermediate angle between the direct inclination angle orientation set point and a zero inclination angle orientation set point.

5. The method according to claim 1, wherein the cloud coverage models comprise at least one irregular cloud coverage model to which an orientation setpoint value is associated with the direct inclination angle orientation set point.

6. The method according to claim 1, further comprising translating the observed cloud cover into a mapping of the solar luminance.

7. The method according to claim 6, wherein the cloud coverage models are based on different cartographic models and the comparison between the observed cloud coverage with the cloud coverage models is based on a comparison between the distribution of the solar luminance on the mapping with the distribution of the solar luminance in the different cartographic models.

Description

BRIEF DESCRIPTION OF DRAWINGS

(1) Other features and advantages of the present invention will appear upon reading the detailed description hereinafter, of non-limiting examples of implementation, made with reference to the appended figures in which:

(2) FIG. 1, already discussed, comprises four diagrams (1a), (1b), (1c) and (1d) each illustrating two solar trackers under different meteorological conditions;

(3) FIG. 2 is a schematic view of a single-axis solar tracker in accordance with the invention, where are illustrated the fixed structure and movable platform assembly and a system for observing the evolution over time of the cloud coverage;

(4) FIG. 3 is a schematic view of a first example of observation system;

(5) FIG. 4 is a flat down schematic representation of a first observation system equivalent to that of FIG. 3, and of a mapping of the solar luminance (to the right) derived from such an observation system;

(6) FIG. 5 is a schematic view of a second example of observation system;

(7) FIG. 6 is a schematic representation of an observation (at the top left) performed by an observation system equivalent to that of FIG. 5, and of a mapping of the solar luminance (at the bottom left) derived from such an observation, after several image processing steps, and of an equivalent matrix of luminance values (at the bottom right) derived from this mapping;

(8) FIG. 7a is a schematic side view of four columns of a mapping of the solar luminance, with the azimuth angles associated to the different columns, in order to illustrate the calculation implemented for calculating an equivalent luminance value serving to determine the optimum inclination angle;

(9) FIG. 7b is a schematic side view of four strips of a mapping of the solar luminance, with the elevation or inclination angles associated to the different strips, in order to illustrate the calculation implemented for calculating the perceived luminance value serving to determine the optimum inclination angle;

(10) FIG. 8 comprises four diagrams (8a), (8b), (8c) and (8d) each illustrating an image with the representation of a cloud observed at a past instant (t−2), of the same image observed at a past instant (t−1), of the same image observed at a present instant (t) and of the same image predicted by predictive calculation at a future instant (t+1);

(11) FIG. 9 shows three mappings of the solar luminance, to which are associated the corresponding optimum inclination angles, including a mapping at a present instant (t) and two predictions of mapping at future instants (t+1) and (t+n);

(12) FIG. 10 is a representation in the form of a functional diagram of the functional elements used for the implementation of a controlling method in accordance with the invention;

(13) FIG. 11 shows five predictive curves calculated for a first potential scenario defined during the servo-control step e), with from the top to the bottom, an evolution curve of the future (or predictive) optimum inclination angle calculated during step d), an evolution curve of the inclination angle of the solar tracker, an evolution curve of the energy consumption necessary to modify the orientation of the solar tracker, an evolution curve of the expected additional solar energy consumption, and an evolution curve of the expected energy yield; and

(14) FIG. 12 shows five predictive curves (similar to those of FIG. 11) calculated for a second potential scenario.

DETAILED DESCRIPTION

(15) Referring to FIG. 2, a single-axis solar tracker 1 orientable about an axis of rotation A, of the type comprising a fixed structure 11 for anchorage to the ground constituted by one or several pylon(s) anchored to the ground, for example by pile driving, screwing, bolting, ballasting, or any other equivalent means allowing fastening and stabilizing the fixed structure 11 to the ground.

(16) The solar tracker 1 further comprises a movable platform 12 rotatably mounted on the fixed structure 11 about the axis of rotation A, and more specifically rotatably mounted on the upper ends of the pylon(s). This platform 12 is specific to support at least one solar collector 13, and in particular one or several photovoltaic panel(s).

(17) Referring to FIG. 2 and to FIGS. 7a and 7b, the axis of rotation A is substantially horizontal and directed according to a longitudinal axis X according to the north-south direction. When the solar tracker 1 is flat down (as shown in FIGS. 2, 7a and 7b) with its platform 12 laying horizontally, the platform 12 extends according to a horizontal plane defined by the longitudinal axis X and by a transverse axis Y according to the east-west direction, orthogonally to a vertical axis Z.

(18) In the following description, the inclination angle of the solar tracker 1 (or inclination angle of the platform 12 and of the solar collector(s) 13) corresponds to the angle of the normal to the platform 12 with respect to the vertical axis Z considered in the plane (Y, Z).

(19) Thus, when the solar tracker 1 is flat down, this inclination angle is 0 degree.

(20) The solar tracker 1 also comprises an observation system 2 of the cloud coverage above the solar tracker 1 or, in other words for observing the sky above the solar tracker 1. This observation system 2 may be associated to one single solar tracker 1 or, for economic reasons, may be shared with several solar trackers.

(21) The observation system 2 is fixed, and may be raised with respect to the ground, for example by being mounted on a post 20.

(22) The solar tracker 1 further comprises an actuation system (not illustrated in FIG. 2 and bearing the reference number 3 in FIG. 10) which ensures rotating the platform 12 about the axis of rotation A.

(23) This actuation system 3 comprises an actuator, for example of the (electric, pneumatic or hydraulic) cylinder type or of the electric motor (for example rotary motor) type. This actuation system 3 further comprises a mechanical system for transmitting the movement at the output of the actuator (a rotational movement for a rotary motor, or a linear movement for a cylinder) into a rotational movement of the platform 12. As a non-limiting example, this mechanical transmission system may be a deformable-parallelogram system, a pulley system, a pinion system, a chain system, a belt system, a clutch system, a transmission shaft system, a connecting rod system, etc.

(24) It is possible to consider that the actuator is specific to the solar tracker 1, or is shared between several solar trackers. In the case where the actuator is shared, the platforms 12 of the different solar trackers are advantageously coupled in rotation, for a synchronous rotation under the effect of the common actuator.

(25) Referring to FIG. 10, the solar tracker 1 also comprises a control unit 4 such as an electronic board, which is connected to the observation system 2 to receive its observations (or observations data) and which is also connected to the actuation system 3 in order to control its operation and accordingly control the rotation of the platform 12, in other words the orientation of the solar tracker 1.

(26) This control unit 4 comprises several modules, namely: a cartographic module 40 provided to translate or convert each observation performed by the observation system 2 into a mapping of the solar luminance, by associating each mapping to a time instant; an archiving module 41 which archives each mapping generated by the cartographic module 40; a predictive calculation module 42 which calculates future evolution of the cloud coverage based on the prior observed evolution of the cloud coverage, and more precisely calculates predictive mappings of the solar luminance for future instants, this predictive calculation module 42 carrying out these calculations based on the mappings generated in real-time by the cartographic module 40 and based on the past mappings archived in the archiving module 41; an optimum inclination angle calculation module 43 which calculates the optimum inclination angle for each mapping generated in real-time by the cartographic module 40 (that is to say the optimum inclination angle at a present instant) and for each predictive mapping derived from the predictive calculation module 42 (in other words the optimum inclination angles for future instants); an optimum inclination angle evolution module 44 which recovers all optimum inclination angles derived from the optimum inclination angle calculation module 43 in order to establish the evolution of the optimum inclination angle, and therefore to predict and anticipate the changes of the optimum inclination angle; a parametrization module of the solar tracker 45 which comprises parameters relating to the speed of displacement of the actuation system 3 (and therefore to the speed necessary for an orientation change), parameters relating to the energy consumption necessary for the actuation system 3 for an orientation change, parameters relating to the solar energy production generated by the one or several solar collector(s) 13 depending on the received solar luminance, and parameters relating to a wear rate of the mechanical members of the solar tracker 1 loaded during an orientation change of the solar tracker 1, these parameters being in particular dependent on the angular deviation between the beginning and the end of an orientation change; an astronomical calculation module 46 which calculates in real-time the Sun's position, and therefore the direct inclination angle defined by the direct solar radiation at the solar tracker 1; a servo-control module 47 which calculates the servo-control of the orientation of the solar tracker 1, in other words the servo-control of its inclination angle depending on the evolution of the optimum inclination angle coming from the module 44, on the different parameters coming from the module 45 and on the direct inclination angle coming from module 46, where this servo-control module 47 outputs an angular setpoint to the actuation system 3 in order to control the orientation changes of the platform 12 of the solar tracker 1.

(27) It should be noted that this control unit 4 may be specific to the solar tracker 1, or shared between several solar trackers, and preferably between several solar trackers arranged in line (extending from north to south) within linear solar plants.

(28) In a first embodiment illustrated in FIG. 3, the observation system 2 comprises a support 21, in particular a full dome shaped support, supporting photosensitive cells 22.

(29) These photosensitive cells 22 are positioned along several strips (or lines) distributed at several angles called elevation angles θi which are measured with respect to the vertical axis Z in the plane (Y, Z), the reference frame (X, Y, Z) being centered on the center O of the full dome 21. Hence, the elevation angle θi is to be matched with the inclination angle of the solar tracker 1. In the example of FIG. 3, the photosensitive cells 22 are distributed according to six strips at elevation angles of 0, +θ1, +θ2, +θ3, −θ1, −θ2 and −θ3; for example with [θ1]=30 degrees, [θ2]=60 degrees and [θ3]=90 degrees. These elevation angles θi are also shown in FIG. 7b.

(30) On each strip are present one or even several photosensitive cells 22, In case of a strip with several photosensitive cells, the photosensitive cells 22 of the same strip are distributed according to several angles called azimuth angles Rj which are measured with respect to the vertical axis Z in the plane (X, Z). Thus, besides being distributed along the strips at different elevation angles θi, the photosensitive cells 22 are also distributed according to columns at different azimuth angles Rj. These azimuth angles Rj are shown in FIG. 7a.

(31) Generally, the more the first observation system 2 comprises photosensitive cells 22, and in particular the more the observation system 2 comprises strips of photosensitive cells 22, the better will be the resolution of the angular accuracy.

(32) These photosensitive cells 22 may be of the same technology as the photovoltaic panels 13 in order to enable the application of a weighting depending on the useful wavelength range of the photovoltaic panels 13. Preferably, these photosensitive cells 22 will undergo a prior calibration in order to obtain a better accuracy.

(33) Thus, with such an observation system 2, by recovering the measurements of the luminosity of each photosensitive cell 22 and by knowing the elevation angles θi (or associated inclination angles) of the different strips and the azimuth angles Rj of the different columns, the cartographic module 40 converts an observation performed by the observation system 2 into a mapping 5 of the solar luminance.

(34) This mapping 5 constitutes a bidimensional mapping in the sense that it forms a solar luminance map (or matrix) distributed according to: several strips 50(i) (i being an integer) established according to a first direction parallel to the axis of rotation A (therefore parallel to the axis X), and associated respectively to different elevation or inclination angles θi, so that each strip 50(i) corresponds to an inclination angle θi of the solar tracker 1 and several columns 51(j) (being an integer) established according to a second direction horizontal and orthogonal to the axis of rotation A (and therefore parallel to the axis Y) and associated respectively to different azimuth angles Rj.

(35) Thus, the mapping 5 comprises N boxes (where N corresponds to the number of photosensitive cells 22 and N=[i×j]) and an (absolute or relative) solar luminance value Lum(i, j) corresponds to each box.

(36) In FIG. 4 to the left, an example of the first observation system 2 is schematically illustrated flat down and comprises nine photosensitive cells 22 distributed according to three strips B1, B2, B3 which are associated to three elevation angles (or inclination angles), and according to three columns C1, C2, C3 which are associated to three azimuth angles. To this first observation system 2 corresponds a mapping 5 with three strips 50(1), 50(2), 50(3) and three columns 51(1), 51(2), 51(3), and where the solar luminance values are expressed in a relative manner as a percentage.

(37) In a second embodiment illustrated in FIG. 5, the observation system 2 comprises a camera, in particular a hemispherical camera, in order to extract images from the sky.

(38) Advantageously, the second observation system 2 (called camera in the following) is adapted to take images in a spectral band sufficient for the technology of the solar collectors 13, and in particular of the photovoltaic panel(s).

(39) Referring to FIG. 6, the camera 2 delivers a raw image IMB of the sky which is delivered afterwards to the cartographic module 40 to convert this raw image IMB (or observation) into a bidimensional mapping 5 of the solar luminance. A reference frame (X, Y) is associated to this bidimensional raw image IMB, these axes X and Y having already been defined hereinbefore.

(40) The cartographic module 40 implements a succession of image processing steps starting from the raw image IMB until the mapping 5.

(41) At a first step POND, the cartographic module 40 implements a frequency weighting applied on the recovered raw image IMB (or video signal), in order to obtain an image called weighted image IMP; this frequency weighting consisting in applying a frequency filter on the observation (whether the observation performed by the photosensitive cells 22 or performed by the camera) which depends on both the frequency response of the observation system 2 (whether the photosensitive cells 22 or the camera) and the useful frequency band (or spectral response) of the photovoltaic panels 13.

(42) At a second step TRAIT, the cartographic module 40 implements a processing of the weighted image IMP consisting in correcting the image from defects (noise suppression processing, blooming processing, saturation processing . . . ) in order to obtain an image called processed image IMT. Then, the cartographic module 40 implements a calculation (whether pixel by pixel, or area by area where each area comprises several pixels) of the distribution of the solar luminance on the processed image IMT in order to generate an initial mapping CI forming a map (or matrix) of solar luminance distributed according to several strips associated respectively to different elevation or inclination angles θ(i) and according to several columns associated respectively to different azimuth angles; such an initial mapping being equivalent to that already described hereinabove. In FIG. 6, the solar luminance values of the initial mapping CI are expressed in a relative manner as a percentage.

(43) At a third step SENS, the mapping module 40 applies on the initial mapping CI a coefficient depending on the variation of the sensitivity of the camera 2, in order to generate a mapping 5 of the same type as the mapping described hereinabove. Indeed, the magnitude (or luminosity) of the data delivered by the camera 2 is proportionally related to the value of the solar radiation, so that this coefficient takes into account this proportionality depending on the variation of the sensitivity of the camera 2.

(44) Thus, the mapping module 40 generates a mapping 5 forming a map (or matrix) of solar luminance distributed according to several strips 50(i) associated respectively to different elevation or inclination angles θi and according to several columns 51(j) associated respectively to different azimuth angles Rj. In the example of FIG. 6, the mapping 5 comprises five strips 50(1), . . . , 50(5) and seven columns 51(1), . . . , 51(7), and the solar luminance values are expressed in a relative manner as a percentage.

(45) The resolution of the mapping 5 (in other words the number of strips and columns) and therefore the angular accuracy depend on the fineness of the image processing implemented by the cartographic module 40, and also on the sensitivity and on the resolution of the observation system 2. For the first observation system 2 with photosensitive cells 22, this sensitivity depends on the sensitivity of the photosensitive cells 22, and this resolution depends on the number and on the distribution of the photosensitive cells 22. For the second observation system 2 of the camera type, this sensitivity and this resolution depend on the quality of the camera.

(46) Starting from such a mapping 5 (whether it is derived from either one of the observation systems 2 described hereinabove), the optimum inclination angle calculation module 43 implements a calculation based on this mapping 5 in order to extract an optimum inclination angle (θopt) which corresponds to the inclination angle (or elevation angle) to which is associated a maximum of solar luminance.

(47) For this calculation, and with reference to FIGS. 6 and 7, the optimum inclination angle calculation module 43 implements a succession of sub-steps. This succession of sub-steps constitutes an example of calculation or algorithm mode, and the invention would not of course be restricted to this example.

(48) In a first sub-step, the optimum inclination angle calculation module 43 calculates, for each strip 50(i) of the mapping 5, an equivalent luminance value Leq(i) from the set of luminance values L(i, j) taken in the strip 50(i). For each strip 50(i), the equivalent luminance value Leq(i) of the strip 50(i) is a function of the luminance values L(i, j) taken in the strip 50(i) and of the azimuth angles Rj of the different columns 51(j) according to the following formula (with reference to FIG. 7a):

(49) $\mathrm{Leq}\left(i\right)=\underset{j}{.Math.}\mathrm{Lum}\left(i,j\right)\times \cos \mathrm{Rj}$

(50) A matrix MLeq of the equivalent luminance values Leq(i) associated to the different strips 50(i) is thus obtained.

(51) In a second sub-step, the optimum inclination angle calculation module 43 calculates, for each strip 50(i) of the mapping 5, a perceived luminance value Lperc(i) by the tracker support 1 from equivalent luminance values Leq(i) calculated for all strips during the first sub-step, and from the inclination (or elevation) angles θi associated to the strips 50(i), according to the following formula (with reference to FIG. 7b):

(52) $\mathrm{Lperc}\left(i\right)=\underset{k}{.Math.}\mathrm{Leq}\left(k\right)\times \cos \left(\theta i-\theta k\right)\times p\left(i,k\right)$$\mathrm{where}p\left(i,k\right)=1\mathrm{if}\mathrm{abs}\left(\theta i-\theta k\right)<90\mathrm{degrees},\mathrm{and}p\left(i,k\right)=0\mathrm{else}.$

(53) The coefficient takes into account, beyond an angular deviation of 90 degrees, the radiation is not received by the plane solar collector(s).

(54) Thus, a matrix MLperc of the perceived luminance values Lperc(i) associated with the different strips 50(i) is obtained.

(55) In a last sub-step, the optimum inclination angle calculation module 43 retains the optimum inclination angle θopt as the inclination (or elevation) angle associated to the strip having the highest perceived luminance value Lperc(i).

(56) The predictive calculation module 42 calculates the predictive mappings 6 of the solar luminance for future instants (t+nP), where n is a non-zero integer and P the period of observation performed periodically and repeatedly by the observation system 2. These predictive mappings 6 are established based on the mapping 5 generated in real-time by the cartographic module 40 and based on the past mappings 5 archived in the archiving module 41.

(57) FIG. 8 illustrates four examples of situation of a cloud coverage evolving therein, with four diagrams 8a, 8b, 8c and 8d each representing an image with the representation of a cloud observed at a past instant (t−2), of the same cloud observed at a past instant (t−1), of the same cloud observed at a present instant (t) and of the same cloud predicted by predictive calculation at a future instant (t+1) (the period P is 1 in FIG. 8).

(58) The predictive calculation is based on a consideration of the past evolution of the solar luminance, between several past instants and the present instant, and in particular the evolution of the distribution of the solar luminance and the speed of evolution of the solar luminance.

(59) This predictive calculation can be based on a sliding time window, that is to say a window comprising a predefined number of the last past mappings.

(60) This predictive calculation is used to establish short-term predictive mappings 6 (or predictions of mapping). As a non-limiting example, the short-term concept covers calculations on a future horizon of a maximum of ten to thirty minutes, or even a maximum of one to two hours. It is of course possible to provide longer-term predictive calculations.

(61) The algorithm implemented for such a predictive calculation may possibly integrate improvements such as: the consideration of the prediction errors to improve the future predictions (indeed, it is possible to compare these mappings with the cartographic predictions performed earlier in order to draw lessons therefrom on the predictive calculation and to improve it); identifying the types of cloud depending on the mapping thanks to a database and/or thanks to analyses and plots performed in the past, so as to allow making long-term predictions depending on the types of clouds.

(62) The algorithm implemented for such a predictive calculation may also take into account the evolution of the Sun's position in the sky, in particular when the predictive calculation is performed at future instants sufficiently distant (for example beyond 30 minutes) so that the change of the Sun's position has an influence on the evolution of the solar luminance. This consideration of the Sun's position in the predictive calculation is illustrated by the linking arrow in dotted line in FIG. 10 between the predictive calculation module 42 and the astronomical calculation module 46.

(63) As shown in FIG. 9, the predictive calculation module 42 establishes predictive mappings 6, and to each predictive mapping 6 is associated a predictive optimum inclination angle θopt calculated by the optimum inclination angle calculation module 43 using the same calculation method described above.

(64) Thus, the optimum inclination angle evolution module 44 recovers all the optimum inclination angles (those of the past mappings, those of the present mapping, and those of the predictive mappings 6) and establishes a future evolution of the optimum inclination angle θopt and thus to predict and anticipate the changes of the optimum inclination angle

(65) Finally, the servo-control module 47 servo-controls the orientation of the solar tracker 1 depending on the past and future evolution of the optimum inclination angle θopt, and also depending on the energy consumption Cons necessary to modify the orientation of the solar tracker 1, on the speed of displacement in rotation of the solar tracker 1, and the production of additional solar energy Prod obtained with an orientation change.

(66) With reference to FIGS. 11 and 12, the servo-control module 47 is based on the future evolution of the optimum inclination angle θopt (first curve from the top).

(67) In the given example, the predictive optimum inclination angle θopt changes in value to reach a target value θc, for example due to a prediction of passage of a cloud in front of the Sun, from the future instant t1 to a future instant t2, before returning to its initial value.

(68) The servo-control module 47 establishes a potential scenario during which the inclination angle θ of the solar tracker 1 is modified starting from a present inclination angle θp until reaching the future or predictive optimum inclination angle, in this case following the prediction of evolution of the optimum inclination angle.

(69) In the given example, the scenario consists in servo-controlling the inclination angle θ on the first curve, and this servo-control depends on the speed of displacement in rotation of the solar tracker 1, in order to obtain a second evolution curve of the inclination angle θ of the solar tracker 1 during the orientation change of the scenario. Indeed, the solar tracker 1 has a displacement time necessary to get to reach the target optimum inclination angle θc.

(70) Thanks to the predictive calculation, the displacement of the solar tracker 1 is anticipated, in this case by starting earlier at the instant t10 (prior to t1) until reaching the target value θc at t11 (subsequent to t1), then by starting by anticipation the return to the instant t11 (prior to t2) until returning to the present inclination angle θp at the instant t13 (subsequent to t2).

(71) The servo-control module 47 determines the evolution of the energy consumption Cons necessary to modify the orientation of the solar tracker according to the second curve, in order to obtain a third evolution curve of this energy consumption Cons; the solar tracker 1 consuming during the orientation change phase, between the instants t10 and t11 then between the instants t12 and t13.

(72) The servo-control module 47 determines the evolution of the additional production Prod (or gain of production) expected following the second evolution curve of the inclination angle θ rather than remaining at the present inclination angle θp, in order to obtain a fourth curve of the evolution of this production Prod. Therefore, this additional production Prod corresponds to the gain of production expected if the scenario is followed rather than remaining in the initial or present situation at this angle θp.

(73) In the given example, the production Prod is negative between the instants t10 and t1 and between the instants t2 and t13 which correspond to periods where the inclination angle θ moves away from the inclination angle θopt, and the production Prod is positive between the instants t1 and t2 which correspond to a period where the inclination angle θ matches or even is equal to the inclination angle θopt.

(74) The servo-control module 47 determines the evolution of the energy yield Rend expected based on the difference between the production Prod and the energy consumption Cons, giving a fifth curve corresponding to the difference between the fourth curve and the third curve, in other words Rend=Prod−Cons.

(75) In the given example, the yield Rend is negative between the instants t10 and t1 and between the instants t2 and t13, and the yield Rend is positive between the instants t1 and t2.

(76) Finally, the servo-control module 47 follows the scenario (in other words servo-controls the solar tracker according to the second curve) if the energy yield is generally positive for the scenario, else the orientation of the solar tracker 1 is held at the inclination angle θp.

(77) The overall energy yield is established by studying the yield throughout the scenario period.

(78) In the example of FIG. 11, the overall yield is negative, because the sum of the surfaces Srn where the yield is negative (between t10 and t1 and between t2 and t13) is greater than the surface Srp where the yield is positive (between t1 and t2). The example in FIG. 11, corresponds for example to a situation where the predictive passage time (corresponding to the interval [t2−t1]) of a cloud in front of the Sun is too short compared to the time necessary for an orientation change (corresponding to the interval [t1−t10] or [t13−t2]).

(79) In the example of FIG. 12, the overall yield is positive, because the sum of the surfaces Srn where the yield is negative (between t10 and t1 and between t2 and t13) is lower than the surface Srp where the yield is positive (between t1 and t2). The example of FIG. 12 corresponds for example to a situation where the predictive passage time (corresponding to the interval [t2−t1]) of a cloud in front of the Sun is long compared to the time necessary for an orientation change (corresponding to the interval [t1−t10] or [t13−t2]).

(80) Thus, in the example of FIG. 11, the servo-control module 47 does not follow the scenario and maintains the orientation at the present value θp, while in the example of FIG. 12, the servo-control module 47 follows the scenario and ensures a servo-control of the inclination angle according to the second curve.

(81) Of course, the example of implementation mentioned hereinabove is not limiting and other improvements and details may be added to the solar tracker according to the invention, nevertheless without departing from the scope of the invention where other types of fixed structure or platform may be for example carried out.