Method for controlling an agrivoltaic device

Abstract

The invention relates to a method for controlling an agrivoltaic system comprising: - a shading structure comprising mobile photovoltaic panels arranged above a set of cultivated plants, - a system for control and real-time data acquisition in order to control the horizontal, vertical or angular position of the panels, and - an optimisation module for determining the optimal position of the panels on the basis at least of the phenological stage of said plants, by reference to a model that comprises, for each plant: a set of phenological stages and a correlation between said stage and at least one of: - a set of physiological needs; - a set of agro-climatic sensitivities; - a set of agro-climatic indicators; in order to have the plants in microclimatic conditions which are favourable for obtaining a desired agricultural result while seeking to achieve optimum photoelectric energy production compared to a reference without plant cultivation.

Description

Method for controlling an agrivoltaic system.

[0001] The present invention relates to the field of agrivoltaics, that is to say, the production of electrical energy using photovoltaic cells coupled with agricultural production, the aim of which is to protect crops from climatic hazards and to modulate the microclimate in the face of climate change, in order to ensure a viable, sustainable agricultural production and solar-based electricity production on the same surface.

[0002] Agricultural production is ensured by a set of cultivated plants. The plants may be the same or different.

[0003] Electricity is produced by a set of photovoltaic panels mounted on a supporting structure, covering all or part of the cultivated plants. As described later, the photovoltaic panels are movable, for example to track the sun's path.

[0004] Optimizing electricity production generally involves orienting photovoltaic panels perpendicular to the sun's rays. However, this optimization can negatively impact agricultural production, for example, due to shadows cast by the photovoltaic panels on cultivated plants.

[0005] There are solutions that seek to resolve this technological challenge, notably through the example given in the prior art document FR3019274.

[0006] The present invention aims to further improve existing solutions.

[0007] For the purposes of the present invention, "variety" and "cultivar" are used interchangeably.

[0008] The concept of "variety" is not limited to taxonomic rank. For the purposes of this invention, it should be understood as the cultivated plant or its type. Therefore, one can speak of the agricultural production of a "variety" of fruit trees, as well as the agricultural production of a "variety" of strawberries, and so on.

[0009] By modifying microclimatic conditions under an agrivoltaic device, the present invention makes it possible to reduce abiotic stresses (water, heat and radiation) for plants, ensure their physiological needs, protect them against climatic hazards, and make it possible to find a synergy between agricultural production and photovoltaic electricity production.

[0010]

[0011] More specifically, according to one of its objects, the invention relates to a method for controlling an agrivoltaic device, said agrivoltaic device comprising:

[0012] - a set of photovoltaic panels arranged above an area containing a set of cultivated plants,

[0013] - a set of actuators, configured to modify the position of said set of panels,

[0014] - a supporting structure that supports the photovoltaic panels, said panels being movable in at least one of the following ways: rotation about a horizontal axis; translation in a horizontal plane; translation along a vertical axis, so as to be able to adopt several horizontal, vertical or angular positions thanks to said set of actuators, and arranged at a height less than a predetermined value of a set of cultivated plants, the shadow projected on the plants by the photovoltaic panels being modified by the change of position or angle of said panels, thus modifying the microclimatic conditions of the area including the set of cultivated plants;

[0015] - a real-time control and data acquisition system, configured to control the horizontal, vertical, or angular position of the entire set of photovoltaic panels,

[0016] - a server, configured to receive and store agro-climatic data and including an optimization module comprising lines of code defining its operation and configured to issue command instructions to the control system for the position of the photovoltaic panels;

[0017] the process comprising steps consisting of:

[0018] - determine the phenological stage at which the said plants are found;

[0019] - receive, via an optimization module, a set of agro-climatic data relating to the area including a set of cultivated plants;

[0020] - calculate, using the optimization module, a set of agro-climatic indicators based on said agro-climatic data.

[0021] It is essentially characterized by the fact that it also includes steps consisting of:

[0022] - determine the potential yield of the entire crop,

[0023] - determine the impact of each calculated agro-climatic indicator on the potential yield of all cultivated plants according to their determined phenological stage,

[0024] - to send, via the optimization module to the control system, a control instruction, including the horizontal, vertical or angular position that at least one photovoltaic panel must adopt for a given period, the control instruction being a function of the set of agro-climatic indicators and the impact of each calculated agro-climatic indicator on the potential yield of the set of cultivated plants.

[0025] Thanks to the present invention, it is possible to influence the yield of cultivated plants by modifying the microclimate through the control of the position of the panels of the agrivoltaic device, using calculated agro-climatic indicators, with the yield being calculated by phenological stage.

[0026] In particular, the optimization module can calculate a set of agro-climatic indicators using models that take into account the impact of photovoltaic panels on the microclimate. These indicators can be linked to the conditions for optimal crop development at each phenological stage, allowing for the calculation of yield and quality of agricultural production, and the calculation of electricity production using a model. delimitation of electrical output for any position of the photovoltaic panels.

[0027]

[0028] It can be predicted that the potential yield is calculated with reference to a model including the impact of each agro-climatic indicator, by phenological stage, on an average Olympic yield, the potential yield being calculated preferably at time t.

[0029]

[0030] It can be predicted that the command instruction is determined by an optimization algorithm, having been previously trained on a set of agro-climatic data composed of data constructed using data from local climate histories and climate evolution models, allowing relevant decisions to be made in view of the variety of climate scenarios that may occur.

[0031]

[0032] The set of agro-climatic indicators can take into account the microclimatic impact of the agrivoltaic system.

[0033]

[0034] In particular, it can be predicted that the microclimatic impact of the agrivoltaic system is taken into account through models of the microclimate induced by the agrivoltaic system, such as shade cast, air temperature, soil temperature, potential evapotranspiration, humidity, wind, soil moisture, etc., using data from the dimensional calculation module of the shade structure.

[0035] Electricity production can take into account all the positions of the photovoltaic panels, thanks to an electricity production model which can notably calculate the loss of electricity production when the panels are not in an angular position corresponding to maximum production.

[0036]

[0037] The optimization module can calculate the microclimate modulation and plant protection requirements based on relevant agro-climatic indicators specific to the cultivated plants, as well as the impact of these indicators on the potential yield of the cultivated plants for each phenological stage determined separately. Potential yield can be determined by at least one of the following methods, used individually or in combination: theoretically, through calculation, through modeling, or through observation.

[0038]

[0039] We can also foresee a step consisting of:

[0040] - assign a weight to each calculated agro-climatic indicator; the weight assigned being a function of the determined phenological stage.

[0041]

[0042] We can also plan to: - accumulate all the points awarded, and - control the position of the panels according to the difference between the sum of all the points awarded and a predetermined threshold value.

[0043]

[0044] More specifically, a weight can be assigned to each calculated agro-climatic indicator and to electricity production; the assigned weight being a function of its impact on the potential crop yield, which depends on the determined phenological stage and can be positive or negative depending on its impact on agricultural and electricity production. These weights allow the optimization algorithm to determine the placement of the panels, ensuring optimal crop development while guaranteeing significant electricity production.

[0045]

[0046] It can be expected that agro-climatic data will be sent by at least one of the following:

[0047] - local agro-climatic sensors, placed under the set of photovoltaic panels;

[0048] - remote agro-climatic sensors, placed at a distance less than a predetermined value from the area comprising said set of cultivated plants, but not under the set of photovoltaic panels;

[0049] - satellite sensors;

[0050] Agro-climatic data may also include:

[0051] - historical data stored in memory; and

[0052] - weather forecast data.

[0053]

[0054] We can predict at least one of the following steps:

[0055] - a determination of the physiological needs of said plants by phenological stage;

[0056] - a model of the electrical production of said photovoltaic panels at least by time step and by the relative position of said photovoltaic panels and the sun;

[0057] - a dimensional calculation of the supporting structure that supports the photovoltaic panels;

[0058] - a determination of the microclimatic conditions of the area including all the cultivated plants of the agrivoltaic system;

[0059] - a model of the desired agricultural result of said plants, based on the agro-climatic indicators of said plants, for each phenological stage of said plants;

[0060]

[0061] the command instruction being a function of at least one of the said steps.

[0062]

[0063] We can also anticipate steps consisting of:

[0064] - to detect or predict a predetermined climatic or meteorological event; and

[0065] - issue a command instruction for the horizontal, vertical or angular of at least one photovoltaic panel, depending on the time at which said climatic or meteorological event is predicted or detected.

[0066]

[0067] We can also foresee a step consisting of simulating the electrical production of the agrivoltaic system as a function at least of its latitude and the relative position of the photovoltaic panels with respect to the sun, said photovoltaic panels being bifacial.

[0068]

[0069] A machine learning stage can be planned, configured to simultaneously optimize the production of electricity from photovoltaic sources and the agricultural production of said plants.

[0070] learning being based on said agro-climatic data.

[0071]

[0072] Preferably, the training of the algorithm for optimizing the control instructions of photovoltaic panels uses historical agro-climatic data, and allows learning through varied and diverse climatic scenarios in order to generate control instructions for the position of the panels of the agrivoltaic device maximizing agricultural and electrical production.

[0073]

[0074] It can be anticipated that the step of determining the phenological stage at which said plants are found is implemented by declaration, by calendar date, by image processing of said plants, or by accumulation of degrees / day, possibly including a shading effect.

[0075]

[0076] It can be anticipated that the agrivoltaic system is also connected to a set of at least one meteorological station, possibly in a network;

[0077] the agro-climatic data received by the optimization module being sourced at least by said set from at least one meteorological station.

[0078]

[0079] According to another of its objects, the invention also relates to a computer program comprising program code instructions for the execution of the steps of the process according to the invention, when said program is executed on a server, configured to receive and store agro-climatic data, and comprising an optimization module including lines of code defining its operation and configured to issue command instructions to the control system for the position of the photovoltaic panels.

[0080] The present invention offers the following 3 advantageous criteria for controlling an agrivoltaic system: • Efficiency, thanks to a microclimate simulation module which allows the calculation of the microclimate induced by the panels of the agrivoltaic device, which is linked to crop yield (and which is a yield model, not a growth model), thanks to a semi-empirical model; • Simplicity, thanks to the agro-climatic indicators of the invention which are much simpler than existing ecophysiological models which, comprising a huge number of parameters, are very complicated; • Robustness, thanks to a climate data set to train the control algorithm.

[0081]

[0082] Other features and advantages of the present invention will become more apparent from the following description, given by way of illustrative and non-limiting example and made with reference to the accompanying figures.

[0083] [Fig. 1] illustrates an agrivoltaic system according to the prior art;

[0084] [Fig. 2] illustrates a functional diagram for the implementation of an embodiment of the process according to the invention;

[0085] [Fig. 3] illustrates the sending of agro-climatic data to a server according to the invention. Detailed description

[0086] The present invention is based on determining the phenological stage of cultivated plants, and on controlling the position of photovoltaic panels, particularly at the determined phenological stage.

[0087] Preferably, the phenological stages are those of the BBCH scale.

[0088] We can predict a set of phenological stages per plant or per group of plants (for example pome fruits, especially the vine; stone fruits, etc.).

[0089]

[0090] * Agrivoltaic system *

[0091]

[0092] An agrivoltaic system, also called a "shade structure", is illustrated in Figure 1.

[0093] It includes a load-bearing structure that supports a set of photovoltaic panels.

[0094] Cultivated plants can be of any type and for example include vegetable crops, vines, trees - especially fruit trees, shrubs, etc., including a plurality of different plants.

[0095] The plan is to determine the phenological, developmental or growth stage at which the said plants are found, for example, emergence from dormancy, budding, flowering, maturation, etc.

[0096] For example, the phenological stage can be declared, particularly via a graphical interface. It can also be determined by calendar date, more precisely by the difference between today's date and the sowing date, or even by degree days.

[0097] It can also be determined by image processing of the plants. For example, a series of images of the cultivated plants is taken manually, with a camera, or with a drone, and image processing software is used to determine the phenological stage. Preferably, for this purpose, one puts in It employs artificial intelligence software that includes learning tailored to each cultivated plant. Finally, the phenological stage can also be determined by calculating the cumulative degree-days, a calculation that may potentially include the effect of the agrivoltaic installation on the microclimate.

[0098] The phenological stage can be determined by several of the preceding means.

[0099] Therefore, there is a sharing of solar light radiation between photovoltaic panels and cultivated plants, indiscriminately called "crops".

[0100] An optimization module, described later, allows for the optimization of both agricultural and photovoltaic production, that is, the optimization of light sharing. For example, during meiosis, it is possible to prioritize sunlight exposure for cultivated plants at the expense of photovoltaic electricity production.

[0101] By placing photovoltaic panels above part of the cultivated plants, the shade structure has an impact on them because it creates shade, therefore it has an impact on radiation, on local wind, on soil moisture (evapotranspiration is then limited), on temperature, etc.

[0102] Photovoltaic panels can also serve as physical protection (for example against hail), and have a microclimatic impact (temperature, radiation, etc.) on the cultivated area.

[0103] The supporting structure includes uprights that support a framework onto which the photovoltaic panels are hinged.

[0104] Photovoltaic panels are mobile.

[0105] Preferably, they rotate from east to west to follow the sun's path. Their angular position can therefore be modified. In this case, they rotate about a horizontal axis and translate in a horizontal or vertical plane, as illustrated by the double arrows in Figure 1, thanks to a set of actuators.

[0106] It is expected that the photovoltaic panels will be retractable, or even stackable.

[0107] They can therefore adopt: - several horizontal positions, for example to move the shadow cast by photovoltaic panels onto cultivated plants, - several vertical positions, for example by adjusting the height of the photovoltaic panels on the supporting structure, which also allows modification of the shadow cast by the photovoltaic panels on cultivated plants, and - several angular positions, for example to adapt the angular position of the photovoltaic panels to the position of the sun.

[0108] The position or orientation of the photovoltaic panels modifies the microclimatic conditions of the cultivated plants in the agrivoltaic system, for example depending on the amount of shade cast (or not), which impacts the irradiance received by the crops, leaf temperature, humidity, and soil temperature. soil, evapotranspiration from the soil or wind passing between the photovoltaic panels and cultivated plants.

[0109] The present invention makes it possible to adapt the position or orientation of photovoltaic panels according to the physiological needs of crops or their needs for protection against climatic hazards.

[0110] [OR I] * SCADA *

[0112]

[0113] The agrivoltaic system is expected to include a real-time control and data acquisition system, known by the acronym SCADA for Supervisory Control And Data Acquisition, which is a large-scale remote management system enabling remote control of technical installations, and which is configured to control the horizontal, vertical or angular position of all the photovoltaic panels.

[0114]

[0115] * Agro-climatic sensors *

[0116]

[0117] The agrivoltaic system is expected to include a set of at least one agro-climatic sensor.

[0118] Agro-climatic sensors can be local, i.e., placed under the set of photovoltaic panels.

[0119] Agro-climatic sensors can be remote, i.e. placed at a distance less than a predetermined value from the area comprising said set of cultivated plants, but not under the set of photovoltaic panels.

[0120] Typically, remote agro-climatic sensors are located away from the area containing the cultivated plants. Preferably, these sensors are located in a geographical area likely to have a meteorological or climatic impact on the cultivated area, for example, upstream of prevailing winds.

[0121] Satellite sensors can also be used to measure or calculate the meteorological or climatic impact on the area encompassing all cultivated plants.

[0122] Agro-climatic data can come from local, remote or satellite agro-climatic sensors, as illustrated in Figure 3.

[0123] They can also come from a server's memory that records historical data, for example, historical data from local or remote agro-climatic sensors.

[0124] They can also originate from the memory of a server that calculates weather forecasts.

[0125] Agro-climatic data may come from all or some of the sources mentioned above; that is to say, agro-climatic data may be combined, for example satellite data are combined with local agro-climatic data, forecasts are combined with local agro-climatic data, etc.

[0126] For example, a low-pressure system is detected by a satellite. A weather model predicts thunderstorms over the area encompassing all the cultivated plants within a predetermined timeframe. Local agro-climatic sensors do not yet detect variations in pressure, temperature, or humidity, but remote agro-climatic sensors detect a sudden change in at least one of these values.

[0127] Agro-climatic data, from local or remote agro-climatic sensors, or from weather forecasts, make it possible, for example, to detect or predict a predetermined climatic or meteorological event.

[0128] The agro-climatic data is sent to a remote server.

[0129]

[0130] * Server *

[0131]

[0132] A remote server is planned which is configured to receive agro-climatic data, and to issue command instructions for the position of all the panels to the SCADA, as illustrated in Figure 2.

[0133] The server includes lines of code, governing the operation of a plurality of modules described below, which allow the position of the photovoltaic panels to be determined in order to maximize agricultural and electrical production, based at least on the calculated agro-climatic indicators, and then to send to the SCADA the position control instructions for all the panels of the agrivoltaic device.

[0134]

[0135] * Agro-climatic indicators *

[0136]

[0137] From agro-climatic data, preferably local agro-climatic data (measured or transposed), it is possible to calculate a set of agro-climatic indicators over different periods of the year or phenological stages.

[0138] Preferably, agro-climatic indicators are based on the cultivated plants.

[0139] Agro-climatic indicators can be a known index, for example a drought index, a night coolness index, Huglin index, rainfall distribution, etc., which are calculated or known from a database.

[0140] Agro-climatic indicators can be a combination or ratio of agro-climatic data, for example an incident radiation / temperature index, which has a particular influence on photosynthesis, etc.

[0141] Agro-climatic indicators can also be a cumulative collection of agro-climatic data, for example, cumulative sunshine, cumulative temperature; cumulative irradiance received, particularly at a certain height above the ground predetermined over a predetermined period, a cumulative amount of radiation received for photosynthetically active radiation exceeding 800 pmol / nr 2 .s' 1, a cumulative rainfall, a number of dry days, a number of rainy days (preferably with a daily rainfall amount > 1 mm), a number of annual frost days, a number of frost days in spring, a maximum duration of frost days, a sum of frost temperatures, a number of scorching days where the temperature is above 27°C, a number of very scorching days where the temperature is above 30°C, a cumulative average temperature over a period, a cumulative average temperature base 6°C over a period, a cumulative average temperature base 0°C, a water deficit, in particular with a cumulative Rainfall evapotranspiration calculation over a period, a number of days with a water deficit of less than -5 mm over a predetermined period, etc.

[0142]

[0143] We can plan to classify agro-climatic indicators by phenological stage, that is to say, to calculate only the indicators of the phenological stage at which the cultivated plants are located.

[0144] Advantageously according to the invention, since agro-climatic indicators are constructed according to phenological stages, agro-climatic indicators can be considered to be pheno-climatic indicators.

[0145]

[0146] * microclimate module *

[0147] Preferably, a microclimate simulation module is provided. This module allows the microclimate of the shade structure to be calculated, that is to say the influence that this structure has on the cultivated plants, in particular due to the shade cast, or the protection against rain, etc.

[0148] The input data for the microclimate module includes, for example, historical data and / or measurements, and comparative data between historical data and local measurements under shade structures. This allows, in particular, the construction of an agroclimatic radiation indicator (cumulative irradiance received during a phenological stage), or even a humidity indicator.

[0149]

[0150] * Description of cultivated plants *

[0151]

[0152] Crops are described by sizing according to their stage of development (for example by measuring canopy height, stem length, etc.).

[0153] Crops are described according to a set of physiological characteristics, such as water, heat or radiation stress limits and according to each phenological stage.

[0154] Crops are described by their relevant agro-climatic indicators, and their impact on future development, yield, and the quality of agricultural production.

[0155] Crop sensitivities are defined here as the impact of relevant agro-climatic indicators on their development for the construction of agricultural yield, for each phenological stage, for example the cumulative scalding temperature will be relevant for wheat and will have a strongly negative impact during the stem elongation period.

[0156] They are assigned values, negative or positive, relative to reference values ​​of agro-climatic indicators, representing the impact on crop development or yield building or the quality of agricultural production;

[0157] * Weighting *

[0158]

[0159] A weighting module can be provided, preferably by phenological stage, configured to assign a set of points to the indicators, and accumulate them according to the following criteria:

[0160] A set of points is assigned to electricity production, for example by increments of quantity produced or proportionally to the quantity produced;

[0161] A set of points is awarded for stress avoided;

[0162] A set of points is assigned to the amount of radiation or if the amount of radiation is greater than a predetermined threshold value;

[0163] Etc.

[0164] We can then accumulate all the points awarded and control the position of the panels according to the difference between the sum of all the points awarded and a predetermined threshold value.

[0165] For example, for each criterion, we calculate the difference between the sum of all the points awarded for said criterion and a predetermined threshold value for said criterion.

[0166] This allows us to control the position of the photovoltaic panels, for example according to priority, starting with the criterion that presents the greatest difference.

[0167] For example, the measured cumulative radiation amount is below a threshold value and shows the largest deviation. This suggests that the photovoltaic panels are casting too much shade and that their position or orientation should be adjusted to allow more incident radiation to pass through.

[0168] The advantage of a weighting module by phenological stage is that, for two different phenological stages, the same sensors, even if they resulted in the same measurement values, will not give the same weightings, which makes it possible to control the position of the photovoltaic panels as close as possible to the real needs of the cultivated plants, that is to say a control of the position of the photovoltaic panels, therefore of the production of electrical energy and the production of the cultivated plants, by phenological stage.

[0169]

[0170] * Modules *

[0171] An optimization module is planned, and at least one of the other modules described below, in communication with the optimization module, and in this case all on the remote server.

[0172]

[0173] * Heuristic Module *

[0174] We can foresee a heuristic module, which includes a set of rules, which correspond to a course of action to be taken, that is to say the position which the photovoltaic panels must adopt in the event of predetermined events, for example bad weather, or in the event of singular events, for example rainfall below or above a threshold value, wind speed above a threshold value, an episode of hail, frost, air or soil temperature below or above a threshold value, etc.

[0175] It is therefore possible to protect the shade structure or the plants, by calculating the risk or by connecting to a weather station.

[0176] Preferably the heuristic module, if it is activated, short-circuits all other modules.

[0177]

[0178] * Optimization module *

[0179]

[0180] The remote server is expected to include an optimization module, configured to optimize both photovoltaic energy production and crop production.

[0181] To this end, it aims to determine the optimal position of all photovoltaic panels based at least on the relative position of said photovoltaic panels and the sun, and to place the plants in microclimatic conditions favorable to obtaining a desired agricultural result while seeking to achieve an optimum reducing the production of photoelectric energy as little as possible.

[0182] It allows you to determine the optimal position of the photovoltaic panels based on: - agro-climatic data, which can be historical data, real-time data, or predictions, and in this case from agro-climatic indicators, - information relating to cultivated plants (in particular the phenological stage), - information on the structure of the agrivoltaic system.

[0183] The optimization module includes a computer and a set of at least one memory, as well as wired or wireless communication means.

[0184] The optimization module is configured to receive data and issue command instructions.

[0185] In this case, the optimization module is configured to receive agro-climatic data or agro-climatic indicators.

[0186] Agro-climatic data include at least one of the following: - pedoclimatic data, which relate to all conditions external climatic factors to which the soil of cultivated plants is subjected, and for example temperature, waterlogging, humidity, aeration, partial pressure of CO2, rainfall, wind, etc.; and - meteorological data, the processing of which aims to assist in the management of agricultural operations and agricultural forecasting.

[0187] Agro-climatic data is sent by at least one of the agro-climatic sensors.

[0188] Upon output from the optimization module, it sends command instructions to the SCADA to optimally position the photovoltaic panels.

[0189]

[0190] It can be predicted that the optimization module includes a machine learning algorithm, therefore artificial intelligence.

[0191] In this case, the machine learning algorithm is configured to simultaneously optimize photovoltaic electricity production and agricultural plant production.

[0192] Preferably, a training phase is planned based on at least one of the following datasets:

[0193] - local and / or remote agro-climatic data, i.e. from local and / or remote agro-climatic sensors respectively, in real time, and described later;

[0194] - historical data stored in memory; and

[0195] - weather forecast data.

[0196] A predictive weather model can be used to forecast local and global conditions, both temporally and spatially. For example, the weather model can calculate a predetermined risk for a predetermined geographical area within a predetermined time frame, such as the risk of rain on a plot of land in less than 3 hours, a heatwave in the region for the next 3 weeks, and so on. The model may include the calculation of a confidence index or a probability rate.

[0197] This allows us to anticipate the operation of the shade structure over a time horizon of a few hours to a few days.

[0198] We can plan a training step on local data, for example radiation, temperature, wind, humidity etc.

[0199] We can plan a decision step regarding the position (vertical, horizontal, angular) of the photovoltaic panels according to a predetermined time step, a predetermined sequence, for example controlled by the time of year.

[0200] For example, we can plan a decision step regarding the position (vertical, horizontal, angular) of the photovoltaic panels every hour in winter; every 10 minutes in summer, etc., or whose time step is controlled by the sunshine curve.

[0201] Preferably, a review stage is planned at the end of the year, consisting of determining at least the production of photovoltaic electrical energy.

[0202]

[0203] * Module for determining physiological needs *

[0204]

[0205] We can plan for a module to determine the physiological needs of plants.

[0206] Indeed, plants are sensitive; for example, they exhibit sensitivity to cold, heat, frost, hail, drought, etc., and this sensitivity is a function of the phenological stage but sometimes also of the humidity.

[0207] Because of this sensitivity, agro-climatic conditions can put cultivated plants into a state of stress.

[0208] Although stressful situations are generally considered to be something to avoid, some stresses are nevertheless sought after; for example, the vine needs controlled water stress to produce quality grapes for winemaking.

[0209] This module allows the physiological needs of plants to be determined by phenological stage, for example in the form of a correspondence table.

[0210] Typically, each phenological stage corresponds to a set of needs, for example, an average temperature, an average daily amount of sunshine, etc.

[0211] The physiological needs of plants can include, for example, the content of macro-elements (carbon, oxygen, hydrogen, nitrogen, phosphorus, sulfur, potassium, calcium, magnesium); and trace elements (iron, manganese, copper, zinc, boron, molybdenum); the values ​​of which are obtained by chemical measurements of the soil but on which the shade structure has a priori little or no influence.

[0212] The physiological needs of plants include, in particular, water content; the amount of light, temperature, etc., which are agroclimatic data.

[0213] Physiological needs may also include agro-climatic indicators to be achieved, for example a cumulative amount of sunshine, a cumulative amount of temperature; etc. over a given period, the period being a predefined duration, for example a number of days, or the phenological stage as a whole.

[0214] This module allows you to define, in particular, light and water requirements, or stress limits, to ensure a yield of a given plant crop.

[0215] This module also calculates the period of time during which plants are subjected to stress, in relation to their phenological stage. The optimization module can then control the position of the photovoltaic panels based on this temporal weighting.

[0216] For example, exposing cultivated plants to heat stress (e.g., a heatwave) over a one-day period does not have the same impact as exposing them to heat stress over several weeks. The present invention makes it possible to take these elements into consideration by the optimization module.

[0217] Typically, stress limits are known by threshold values ​​stored in memory. By comparing the values ​​of agro-climatic indicators to the corresponding threshold values, it is possible to determine whether crops are experiencing water stress (soil moisture above or below a predetermined threshold value), heat stress (air or soil temperature above or below a predetermined threshold value), or radiative stress (lightness above a predetermined threshold value).

[0218] Preferably, the module for determining the physiological needs of plants includes the optimal values ​​of the physiological needs of cultivated plants for each phenological stage.

[0219] For example, temperature is very important for the budding stage but less so for the ripening stage.

[0220]

[0221] * Electricity production modeling module *

[0222]

[0223] A module for modeling the electrical production of photovoltaic panels can be planned.

[0224] This module allows modeling the amount of photovoltaic energy produced by the agrivoltaic system, at least by time step and by the relative position of the photovoltaic panels and the sun. The relative position can be determined, for example, based on the date in the calendar or based on sensors, such as photodiodes.

[0225] It allows us to calculate, and possibly predict, the amount of photovoltaic electricity produced even in photovoltaic panel positions that are not optimal for photovoltaic electricity production.

[0226] Preferably, the electrical production of the agrivoltaic system is simulated as a function of at least its latitude and the relative position of the photovoltaic panels with respect to the sun.

[0227] Preferably, the monofacial or bifacial nature of the photovoltaic panels is taken into account in the electricity production simulation. Indeed, bifacial photovoltaic panels also produce electricity through reflection off the ground, i.e., diffuse and reflected radiation.

[0228] This module advantageously takes into account the bifaciality coefficient; the network incidence loss parameters, or "IAM" or "array incidence loss" by anglicism, as described for example on the page https: / / www.pvsyst.com / help / iam_loss.htm.

[0229] This module is connected to the dimensional calculation module for the load-bearing structure.

[0230]

[0231] * Shade structure dimensional calculation module *

[0232] We can plan a dimensional calculation module for the supporting structure that supports the photovoltaic panels. This module allows you to calculate or record at least one of the following: - the distance between the posts of the supporting structure that holds the photovoltaic panels, - the height of the photovoltaic panels, that is to say the height of the axis of rotation of the photovoltaic panels, - the minimum and maximum values ​​of the rotation angle of the photovoltaic panels, - the minimum and maximum translation position of the photovoltaic panels, both vertically and in a horizontal plane; - the potential transparency of photovoltaic panels, - the azimuth of the photovoltaic panels, - the GPS coordinates of the photovoltaic panels, - the tilt of the photovoltaic panels, - the dimensions of the photovoltaic panels, their transparency, - the model, the electrical characteristics of the photovoltaic panels, - etc.

[0233]

[0234] * Transposition module *

[0235]

[0236] This module calculates the micro-climatic impact of the shade structure.

[0237] Remote agro-climatic data can be used instead of local agro-climatic data, but this can introduce bias since remote microclimatic conditions are generally different from local microclimatic conditions.

[0238] To this end, a transposition module for the value of agro-climatic data can be provided, which makes it possible to determine the influence of the shade structure on the microclimate, by comparison with a control area without shade structures.

[0239] In this case, we plan an agro-climatic data transposition module which transposes at least some distant agro-climatic data (outside shade structure) into local agro-climatic data (under shade structure), for example incident radiation, temperature, wind, etc.

[0240] The transposition module is connected to the dimensional calculation module of the load-bearing structure.

[0241] It is configured to transpose the value of distant agro-climatic data into the value of local agro-climatic data, with reference to a mathematical model that takes into account shading, for example a lookup table, which depends in particular on the supporting structure and the latitude.

[0242] Preferably, remote agro-climatic data are measured in a three-dimensional coordinate system, and according to a predetermined step, in this case 10 cm x 10 cm in a horizontal plane.

[0243] Preferably, the transposition module is also configured to calculate the potential evapotranspiration (ETP) of the soil under the shade structure, the limo- nature The soil type (clay or limestone) is known, for example through a preliminary assessment. The available water capacity of the soil under the shade structure is also known, for example through a preliminary assessment. A water balance is also calculated, taking into account rainfall (measured or averaged). The shading of the photovoltaic panels can then be managed so that, with the potential evapotranspiration calculated, the water balance remains positive.

[0244] The transposition module is also configured to calculate stress risk (water, radiation, or heat), with stress being dependent on the phenological stage. Stress is an agro-climatic sensitivity. For example, plants are particularly sensitive to water stress at a certain phenological stage and less sensitive to water stress at other phenological stages.

[0245] For example, water stress is detected if the water balance is below a predetermined threshold value. The risk of water stress can be correlated with the difference between the water balance value and the predetermined threshold value.

[0246]

[0247] * Agricultural output modeling module *

[0248] We can plan for a modelling module to determine the desired agricultural outcome of cultivated plants.

[0249] This module allows for the modeling of agricultural production according to the physiological needs and agro-climatic sensitivities of said plants, for each phenological stage of said plants.

[0250] It can be predicted that agricultural production will take precedence over photovoltaic production, so that the position of a photovoltaic panel, which could for example be perpendicular to the sun's rays to optimize electricity production, may have its position rotated so as not to be perpendicular to the sun's rays, to optimize agricultural production, or vice versa depending on local climatic conditions.

[0251] But more generally, the present invention aims to optimize both agricultural production and photovoltaic energy production.

[0252] Typically, this module is configured to calculate the desired agricultural result by summing the agricultural results by phenological stage, under shade.

[0253]

[0254] It is possible to issue a command instruction for the horizontal, vertical or angular position of the photovoltaic panels based on the result of the calculation of at least one of said modules, thanks to an optimization algorithm of the optimization module.

[0255] The optimization module can thus issue a command instruction to the SCADA control system, for example to anticipate a climatic event approaching the area of ​​cultivated plants, and for example unfold the photovoltaic panels to the maximum so that they protect all the cultivated plants from rain, snow, hail, etc.

[0256] In contrast to the case above, for example after a long period of wet weather, or without sunshine, the command instruction to the SCADA control system may be to fold the photovoltaic panels as far as possible in order to leave maximum sunlight for the cultivated plants.

[0257] The command instruction sent to the SCADA control system includes the horizontal, vertical or angular position that at least one photovoltaic panel must adopt for a given period, i.e. a determined time range.

[0258] According to the invention, the horizontal, vertical or angular position is a function at least of said determined phenological stage and of the relative position of said photovoltaic panel and the sun, which is linked to the day of the year and the time of day; as well as of the relative position of said photovoltaic panel and said plants, that is to say in particular of the shadow projected by a photovoltaic panel on the plants, as a function of its horizontal and vertical position, as well as its angular position.

[0259] Thanks to the present invention, for example depending on the season, the sowing date and possibly other parameters, the SCADA can automatically and regularly control the position and orientation of the photovoltaic panels in order to meet the sunlight, temperature, humidity, and rainfall requirements of the crops over a given period of time.

[0260] For example, photovoltaic panels are oriented for a portion of the day in a first position to allow as much light as possible to pass through, at the expense of electricity production. Then, once the plants' sunlight requirements are met, the photovoltaic panels are oriented for another portion of the day in a second position to maximize electricity production.

[0261] However, for example if an agro-climatic data, for example the local temperature measured at the level of cultivated plants is excessive, or greater than a predetermined value, the orientation of the photovoltaic panels can be modified to shelter the cultivated plants from the sun by the shadow projected from the photovoltaic panels and thus avoid excessive heating of them.

[0262] The invention is not limited to the embodiments previously described.

[0263] It can be anticipated that the agrivoltaic system is also connected to a set of at least one meteorological station, i.e. to remote agro-climatic sensors.

[0264] For example, one can foresee a plurality of networked weather stations.

[0265] The agro-climatic data emitted by the set of meteorological stations is received by the optimization module.

[0266] For example, a drought is observed for 15 days in March. Thanks to the present invention, it is possible to calculate a set of agro-climatic indicators allowing, in particular, the determination of stress water, heat stress, shading, temperature, etc. of cultivated plants, and the influence that this event will have on yield, by phenological stage.

[0267] Thanks to the present invention, it is possible to calculate, in addition, using artificial intelligence, the impact of each agro-climatic indicator, by phenological stage, on the overall yield.

[0268] Typically, historical data is public, outside of the shadow box.

[0269] It is therefore possible to calculate the impact of the shade structure on the overall yield, and therefore the yield under the shade structure.

[0270] For example, it is possible thanks to the invention to predict that the yield of plants grown with shade in a given area, having a local climate, for example that of the city of Agen, is equivalent to a climate without shade in an area located at a higher latitude (in the northern hemisphere), for example that of the city of Tours.

[0271] The optimization module allows for the optimization of cultivated plant yields, using agro-climatic indicators, while minimizing photovoltaic production losses.

[0272]

[0273] Nomenclature

[0274] 10 photovoltaic panels

[0275] 20 load-bearing structure

[0276] 21 post

[0277] 30 cultivated plants

[0278] 100 agrivoltaic devices

[0279] 200 server

[0280] 210 optimization module

[0281] 220 heuristic module

[0282] 230 module for determining physiological needs

[0283] 240 module for modeling electricity production

[0284] 250 transposition module

[0285] 260 agricultural outcome modeling module

[0286] 300 Real-time command and data acquisition (SCADA) system

[0287] 400 local agro-climatic sensors

[0288] 410 remote agro-climatic sensors

[0289] 420 satellite

Claims

Demands

1. A method for controlling an agrivoltaic system, said agrivoltaic system comprising: - a set of photovoltaic panels arranged above an area containing a set of cultivated plants, - a set of actuators, configured to modify the position of said set of panels, - a supporting structure that supports the photovoltaic panels, said panels being mobile in at least one of the following ways: rotation about a horizontal axis, translation in a horizontal plane, translation along a vertical axis, so as to be able to adopt several horizontal, vertical or angular positions thanks to said set of actuators, and arranged at a height less than a predetermined value of a set of cultivated plants, the shadow cast on the plants by the photovoltaic panels being modified by the change in position or angle of said panels, thus modifying the microclimatic conditions of the area comprising the set of cultivated plants, - a real-time control and data acquisition system, configured to control the horizontal, vertical, or angular position of the entire set of photovoltaic panels, - a server, configured to receive and store agroclimatic data, and comprising an optimization module including lines of code defining its operation and configured to issue command instructions to the control system for the position of the photovoltaic panels; the process comprising steps consisting of: - determine the phenological stage at which the said plants are found; - receive, via an optimization module, a set of agro-climatic data relating to the area including a set of cultivated plants; - to calculate, using the optimization module, a set of agro-climatic indicators based on said agro-climatic data; characterized in that it further comprises steps consisting of: - determine the potential yield of the entire crop, - determine the impact of each calculated agro-climatic indicator on the potential yield of all cultivated plants according to their determined phenological stage, - to send, via the optimization module to the control system, a control instruction including the horizontal, vertical or angular position that at least one photovoltaic panel must adopt for a given period, the control instruction being a function of the set of agro- indicators climatic factors, and the impact of each calculated agro-climatic indicator on the potential yield of all cultivated plants.

2. A method according to claim 1, further comprising steps consisting of: - assign a weight to each calculated agro-climatic indicator; the assigned weight being a function of the determined phenological stage, - accumulate all the points awarded, and - control the position of the panels according to the difference between the sum of all the points awarded and a predetermined threshold value.

3. A method according to claim 1 or 2, wherein the agro-climatic data are sent by at least one of the following: - local agro-climatic sensors, placed under the set of photovoltaic panels; - remote agro-climatic sensors, placed at a distance less than a predetermined value from the area comprising said set of cultivated plants, but not under the set of photovoltaic panels; - satellite sensors; agro-climatic data can also be: - historical data stored in memory; and - weather forecast data.

4. A method according to any one of the preceding claims, comprising at least one of the following steps: - a determination of the physiological needs of said plants by phenological stage; - a model of the electrical production of said photovoltaic panels at least by time step and by the relative position of said photovoltaic panels and the sun; - a dimensional calculation of the supporting structure that supports the photovoltaic panels; - a determination of the microclimatic conditions of the area including all the cultivated plants of the agrivoltaic system; - a model of the desired agricultural result of said plants, based on the agro-climatic indicators of said plants, for each phenological stage of said plants; the command instruction being a function of at least one of said stages.

5. A method according to any one of the preceding claims, further comprising steps consisting of: - to detect or predict a predetermined climatic or meteorological event; and - issue a command instruction for the horizontal, vertical or angular position of at least one photovoltaic panel, depending on the time at which said climatic or meteorological event is predicted or detected.

6. A method according to any one of the preceding claims, further comprising a step of simulating the electrical production of the agrivoltaic device as a function at least of its latitude and the relative position of the photovoltaic panels with respect to the sun, said photovoltaic panels being able to be bifacial.

7. A method according to any one of the preceding claims, comprising a machine learning step, configured to simultaneously optimize the production of electricity from photovoltaic sources and the agricultural production of said plants, the learning being based on said agro-climatic data.

8. A method according to any one of the preceding claims, wherein the step of determining the phenological stage at which said plants are found is implemented by declaration, by calendar date, by image processing of said plants, or by accumulation of degrees / day, possibly including a shading effect.

9. A method according to any one of the preceding claims, wherein the agrivoltaic device is further connected to a set of at least one meteorological station, optionally networked; the agro-climatic data received by the optimization module being provided by at least one meteorological station from said set.

10. A computer program, comprising program code instructions for carrying out the steps of the process according to any one of the preceding claims, where said The program is run on a server, configured to receive and store agro-climatic data, and includes an optimization module comprising lines of code defining its operation and configured to issue command instructions to the control system for the position of the photovoltaic panels.

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