FR3045194A1 - DESIGN AND CONTROL OF A MIXED ENERGY PRODUCTION SYSTEM - Google Patents

Abstract

A method of designing a mixed energy production system (1) comprising at least one intermittent energy generating device (2, 3) and possibly at least one energy storage device (5), comprising a first phase ( P1) defining a system of equations representing an optimization problem, characterized in that the system of equations uses sizing variables, state variables and exchange variables and that the method comprises a second phase (P2) of resolution of the system of equations, which comprises the following steps: (E22): first iterative step of calculating the state and exchange variables of the system of equations by considering the variables of sizing the equation system to a fixed value; (E24): second iterative step of calculating the sizing variables of the system of equations by considering the state and exchange variables at a value fixed at their value obtained by the first iterative step; (E26): end of iteration test step, and repetition of the two iterative steps (E22, E24) if the test is negative, setting for the first iterative step (E22) the values of the dimensioning variables to the values obtained by the second iterative step (E24), and consideration of the solution obtained at the end of the last two iterative steps (E22, E24) if the test is positive.

Description

The invention relates to a method for designing or sizing a mixed energy production system. It also relates to an associated design device implementing this method, as well as a computer program adapted for implementing all or part of the steps of this method. The invention also relates to a method of optimum operation of a mixed energy production system, with the associated device for the implementation of this method and the adapted computer program.

In certain particular territories, especially in isolated areas, it is known to install mixed, medium or small energy generation systems called micro-networks, and often independent, not linked to an energy distribution network of a certain size. wider territory. The aim of these systems is to try to combine the advantages of different and complementary energy production means, for example in the case of the production of electricity from so-called renewable energy production devices, such as a photovoltaic production device. and wind, supplemented by a generator and an electricity storage device. The production of photovoltaic or wind energy is advantageous but has the particularity of being intermittent, that is to say that it does not produce a constant energy over time. This intermittency complicates its use and the control of a production in correspondence with the demand and finally the cost of this produced energy. To mitigate this phenomenon of intermittency, it is known to associate an energy storage device and a generator, to form a global system of energy production, often called because of this hybrid or mixed system architecture. The storage device of a hybrid system then plays by its nature a buffer role to alternate storage phases of the energy produced by the intermittent energy source and the restitution phases of this energy, to reach a production in the time more stable and easier to operate. The generator also intervenes in case of high energy demand and / or low production by intermittent production sources.

However, the construction of such a system of mixed energy production requires defining the sizing of each of the means of production and their future use, that is to say their operation over time and their energy exchanges and mutual cooperation, otherwise says the operational management of the mixed energy system. This construction and operational management today rely on a very simplified approach, which leads to far from optimal solutions.

Thus, a general object of the invention is to propose an optimal definition solution of a mixed energy system, comprising its design, its dimensioning and its operational management. For this purpose, the invention is based on a method for designing a mixed energy production system comprising at least one intermittent energy production device and possibly at least one energy storage device, comprising a first phase of implementation. in equations determining a system of equations representing an optimization problem, characterized in that the system of equations uses sizing variables, state variables and exchange variables and that the method comprises a second resolution phase of the system of equations, which comprises the following steps: - iterative first step of calculating the state and exchange variables of the system of equations by considering the dimensioning variables of the system of equations at a fixed value ; second iterative step of calculating the sizing variables of the system of equations by considering the state and exchange variables at a value fixed at their value obtained by the first iterative step; - end of iteration test step, and repetition of the two iterative steps if the test is negative, by fixing for the first iterative step the values of the dimensioning variables to the values obtained by the iterative second step, and consideration of the solution obtained at the end of the last two iterative steps if the test is positive.

The method of designing a mixed energy production system may include a step of driving the mixed energy production system, using all or part of the system of equations of the first phase to determine the state and exchange values. forming the operating data of the system.

The method of designing a mixed power generation system may include an initialization step at the beginning of the second phase of assigning initial values to the sizing variables before the first execution of the two iterative steps.

The system of equations may use all or some of the following variables: at least one sizing variable corresponding to the installed power of an intermittent energy generating device; and / or - at least one dimensioning variable corresponding to the capacity of an energy storage device; and / or - at least one sizing variable corresponding to the installed power of a generator set; and / or - at least one state variable of an energy storage device consisting of a Boolean variable for respectively characterizing the charging state and the discharging state of said energy storage device; and / or - at least one state variable of a generator set consisting of a Boolean variable for characterizing respectively the state of said generator or not; and / or - at least one exchange variable per component of the energy production system consisting of an energy produced and / or stored and / or exchanged by each of said components. the system of equations can represent an optimization problem that integrates an optimization condition among: - the minimization of the overall cost of the mixed energy production system; or - the minimization of C02 emissions from the mixed energy production system; or - maximizing the energy production of the mixed energy production system by one or more intermittent energy generating device (s).

The second phase may include a robustness test step to determine if a solution obtained at the end of the second phase is robust, and may initiate the search for another solution if the test determines that the solution is not robust.

The second phase may comprise a reliability test step during the iterative steps, consisting in checking whether the values obtained between different iterations are sufficiently close to consider that there will be a convergence towards a solution, that is to say are not distant from each other beyond a predefined threshold.

The first phase of equations may include a step of equating which comprises reading and transmitting from an electronic memory a mathematical model for each component of the envisaged mixed power generation system.

The electronic memory may comprise a mathematical model for each of the following components: a device for producing photovoltaic energy; - a device for producing wind energy; - a generator; an energy storage device; one or more energy consuming components; - an external energy distribution network.

The first phase may comprise a step of acquiring digital data representing parameters of the system of equations, this step comprising transmissions of digital data from outside by remote communication devices and / or inputs by a human-machine interface , said digital data comprising: - digital data representing weather forecasts; - digital data representing geographical information of the territory concerned; - digital data representing future energy needs or wishes for future energy production. The invention also relates to a data storage medium readable by a computer on which is recorded a computer program comprising software means for implementing the steps of the method as described above. The invention also relates to a mixed energy production system comprising at least one intermittent energy generating device and possibly at least one energy storage device, comprising a management unit that implements all or part of the method such as previously described for the control of the mixed energy production system.

These objects, features and advantages of the present invention will be set forth in detail in the following description of a particular embodiment made in a non-limiting manner in relation to the appended figures among which:

FIG. 1 schematically represents a mixed energy system according to one embodiment of the invention.

FIG. 2 diagrammatically represents a flowchart showing the steps of the method of designing an energy production system according to one embodiment of the invention.

FIG. 3 shows in more detail the steps of an algorithm of the method for designing an energy production system according to the embodiment of the invention.

FIG. 4 represents a step of optimizing the robustness of the method of designing an energy production system according to the embodiment of the invention.

The technical problem posed above can also be posed in the following way: how to define simultaneously: - the dimensioning of the different components of a mixed energy production system, - as well as their future operation strategy, to reach a certain predefined objective?

This predefined objective may be to seek the provision of a desired (or estimated) amount of energy production in the least cost-effective manner, or by generating the minimum possible pollutant such as CO 2. Alternatively, the predefined objective may be to produce a greater amount of energy than necessary (desired and / or estimated) to sell a portion, while optimizing the overall economic aspect. In any case, the problem is a problem of optimization, and is based on the search for a double dimension, the sizing of the mixed energy production system and that of its future operation strategy, which induces its future management. .

FIG. 1 thus represents, by way of example, a mixed energy production system 1 designed and manufactured by the embodiment of the invention. It comprises devices for producing photovoltaic energy 2 and wind power 3, a generator 4, a power storage device 5, for example a battery. These various components forming the mixed energy production system 1 are interconnected by links 6, in particular with the storage device 5, which enable them to exchange energy. This energy production system 1 supplies energy to consumer components 11 of the energy produced by a distribution network 16. This energy production system 1 is also dependent on meteorological conditions, characterized by a set of meteorological data 17, including by example sunshine and wind.

A device for designing and / or controlling a mixed energy production system 1 comprises a management unit 20, which is in the form of at least one computer, on which software is installed. This management unit 20 implements a method of designing the mixed energy production system 1, which will be described in detail below. According to an alternative embodiment, the management unit 20 may have the function of defining a modification of the architecture of an existing energy production system 1 to improve it, which is very close to the construction method.

It can also implement a method of managing / controlling such a mixed energy production system 1 when it is designed, to drive it according to an optimized operating plan in order to achieve a predefined goal, for example enabling it to meet the needs of the consumer components 11 at a lower cost or less pollution. This control method can be implemented on a management unit different from that used for the design / dimensioning, but the models and algorithms used remain the same. In the case of control, it is connected to the various components of the system by communication devices 18 to be able to transmit commands and act on the operation of the energy production system 1, through actuators for example. In return, the management unit 20 can receive information from these same system components, for example on their electrical state such as the state of charge of the storage device, the voltage and / or the current at their terminals, the power and the output energy, etc., for example from sensors and via communication devices 18. As a remark, this management unit 20 can be physically close to the energy production system 1 or remotely, in which case she supervises and controls the system remotely. The management unit 20 determines in particular the energy that must be transmitted or restored by the energy storage device 5 and the startup or not of the generator 4.

Finally, optionally, a connection 8 of the energy production system 1 enables it to transmit all or part of its energy production on an external energy network, for example according to a previously established sales plan, and possibly to receive energy. energy from this external energy network.

Note that this energy production system 1 may include several intermittent energy production devices as shown and / or several storage devices, and these devices may be on the same site or remote from each other. Naturally, it may alternatively comprise other energy production components than those shown by way of non-limiting example. On the other hand, the illustrated power generation system 1 is intended for power generation. Alternatively, the invention may relate to a mixed energy production system 1 for producing heat.

According to one embodiment of the invention, the management unit 20 of the energy production system 1 thus implements a method of designing / sizing a mixed energy production system 1, including the improvement of a system existing energy production 1. This process is illustrated in Figures 2 to 4. Figure 2 shows schematically the whole process, Figure 3 shows in more detail the second phase of the process and Figure 4 a particular step of this second phase.

A first phase P1 of the process consists in equating the energy production system. For this, a future period T of operation of said system is determined by an operator of the system. As such a system is intended for operation over many years, this future period may be twenty years. Alternatively, to simplify the calculations to be performed, this period can be reduced to one year.

This first phase P1 comprises a first data acquisition step E11. For this, the management unit 20 receives in an electronic memory a set of digital data forming the parameters necessary for the system of equations. These digital data include in particular all or part of the following data: Numerical data representing future meteorological data, including for example sunlight and wind. These numerical data can be estimated from actual data measured over an equivalent historical period and / or from weather forecast models. They cover the entire future period T chosen; - Digital data representing a maximum dimensioning of each component of the system. For this, it is for example possible to indicate the maximum power output of each energy production component of the system. This parameter can be set according to the knowledge of the maximum future energy requirement and / or according to geographical constraints such as the area available in the territory concerned for installing photovoltaic panels or installing wind turbines. In addition, digital data representing a minimum dimensioning of each component of the system can be taken into account; - Digital data representing at least one parameter of a chosen optimization criterion. This parameter can be an optimization threshold, which can intervene in equations and / or conditions of the system of equations in order to reach one of the optimization criteria among, for example: - the minimum cost of energy production; - the minimum C02 release from energy production; - the minimum fuel usage for a generator; - the maximum use of solar production ...;

For this, the following numerical data can also be used: the maximum cost of energy production, the maximum C02 release of energy production, the maximum fuel consumption for a generator, the minimal use of solar generation, etc. - A need for future consumption by consumer components 11 and possibly by a network connected to the connection 8 of the system.

These digital data are determined and known on the future period T according to a certain time step. This time step can be of the order of one hour. It forms a compromise between precision and complexity of calculations. It can be chosen and predefined in a fixed manner in an electronic memory associated with the management unit 20, or be a calculation parameter entered by an operator and stored.

All these digital data forming the process parameters are finally advantageously entered by an operator via a human machine interface or transmitted by a communication means from a remote computer. For example, the meteorological digital data may be transmitted by a computer remote from a meteorological entity. All these digital data are stored on an electronic memory associated with the management unit 20.

Then, the sizing unit 20 realizes the equations of the system using a library of mathematical models for each component of the system. This library is available on an electronic memory accessible by the sizing unit.

Each mathematical model uses variables that correspond to the dimensioning of the considered component, called sizing variables, variables that concern its state, called state variables, as well as variables characterizing the energy flows of a component, called variable variables. 'trades. As examples, the dimensioning variables include the installed powers of each power generation component, the capacity of a storage device, the rated power of a generator, and so on. Some models are detailed below as examples. In all these models, the index k used represents a time step or instant, which varies over the whole period T considered according to the chosen time step. A model integrates at least one equation that characterizes the functioning of the modeled component, and an equation that makes it possible to consider the objective to be achieved in terms of optimization. The invention does not relate to these models as such, and other models, known from the state of the art or not, could alternatively be defined and used.

A photovoltaic power generation device 2 can for example be processed by a model formulated by the following equation:

Epv (k) = PVtimeserie (k) X PpvX Δΐ WHERE

Epv (k) is the energy produced by the photovoltaic power generation device for the time step k considered, which forms an exchange variable of said device,

Ppv is the power installed by the photovoltaic power generating device, which represents a dimensioning variable of the device, PVtimeserie (k) is the time series of power produced by an installed power unit. This production can be estimated via a known estimation method or by a series of measurements of a comparable real photovoltaic energy production device, Δΐ is the time step used. On the other hand, if the system is to be optimized according to the minimum cost criterion, the model also proposes the following Costpv cost equation of the photovoltaic energy production device 2:

Costpv = (CAPEX + OPEX) x Ppv where CAPEX represents the initial financial investment per installed photovoltaic power unit, and OPEX represents the annualized operating cost of the photovoltaic power generation device per installed photovoltaic power unit.

A wind energy generating device 3 may for example be treated analogously by a model formulated by the following equation:

or

Ewind (k) is the energy produced by the wind energy generating device for the time step k considered, which forms a variable for exchange of said device,

Pwind is the power installed by the wind power generating device, which represents a sizing variable of that device, Windtimeserie (k) is the time series of power produced by an installed power unit. This production can be estimated by a known estimation method or by a series of measurements of a comparable real wind energy generating device. On the other hand, if the system is to be optimized on the minimum cost criterion, the model also proposes the following cost equation CosUnd of the wind energy production device 3:

Costwind = (CAPEX + OPEX) x PWind where CAPEX represents the initial financial investment per unit of installed wind power, and OPEX represents the annualized cost of operation of the wind power generation device per unit of installed wind power.

A generator 4 can for example be processed by a model formulated by the following conditions:

or

Egen (k) is the energy produced at time step k by the generator, which forms a generator exchange variable,

Fgen is between 0 and 1 and represents the minimum operating power factor of the generator set, 5gen (k) is a binary variable which represents the on state (value 1) or off (0 value) of the generator set, and represents a state variable of the generator set,

Pgen represents the maximum power of the generator, which represents a variable of dimensioning of the generating set.

These conditions can be supplemented by taking into account additional constraints related to the minimum operating and stopping times of the generator, which can be written by the conditions:

for all k between 2 and T inclusive, and for any k1 between k and the minimum of k + minON-1 and T inclusive, and

for any k between 2 and T inclusive, and for any k2 between k and the minimum of k + minOFF-1 and T inclusive, where minON is the number of time steps corresponding to the minimum operating time of the generator, minOFF is the number of time steps corresponding to the minimum idle time of the generator. On the other hand, if the system is to be optimized on the minimum cost criterion, the model also proposes the following Costgen cost equation for generator 4:

Costgen = CAPEX X Pgen + OPEX where CAPEX represents the initial financial investment per unit of installed capacity, and OPEX represents the annualized operating cost of the generator, which can be detailed by the following formula:

Or

Mgen represents the maintenance cost per unit of installed power, and a, b, c are consumption factors that make it possible to estimate the operating cost of the generator related to fuel consumption.

The model of the storage device 5 applies to the entire storage system. When this storage system is based on electrochemical storage, it includes a battery, a converter and an auxiliary component. The storage model is technology independent; it is for example also valid for a storage device based on hydrogen, such as an electrolyzer, a fuel cell and auxiliaries. The model of the storage device 5 can be formulated through the following equation / conditions:

With

East (k) is the amount of energy stored in the system at the end of time step k, which forms a state variable of the storage device, Eess (k) is the charged energy (when negative) or discharged (when positive) during the time step k with the charging efficiency pc, which forms an exchange variable of said storage device, 5ess (k) is the binary variable which makes it possible to distinguish the charging phase and the discharge phase (5ess = 0 during the load, and 5ess = 1 during the discharge), which represents a state variable of the storage device; Cess represents the capacity of the storage system, and forms a dimensioning variable of the storage device,

Pmaxc is the maximum load power of the storage system; this value is positive,

Pmaxd is the maximum discharge power of the storage system; this value is positive.

As a remark, this system of equations is advantageous and new, especially since it is based on a binary variable 5ess (k), which represents a state variable.

As a variant, the preceding model can take into account: the variable efficiency as a function of the energy state and the storage power; - the maximum powers of charge and discharge according to the state of energy.

The Costess cost represented by the storage device 5 can be calculated as follows:

CAPEX is the initial investment of the storage device per unit capacity, OPEX is the annualized operating cost per unit capacity of the storage device,

Nremplace is the replacement cost per unit capacity of the storage device during the entire duration T considered.

As a remark, in the embodiment for which the energy production system 1 is linked to an external network via a connection 8, the model of the network is also taken into account. This model considers the energy Egridsource (k) that can be imported from the grid by the energy production system 1 during the time step k by the condition:

Where Pgridsourœmax (k) is the maximum draw off power from the network for time step k.

Moreover, the energy Egridioad (k) which can be transmitted to the grid by the energy production system 1 during the time step k is also characterized by the following condition: Pgridloadmax (k) X Δΐ 2 = Egridload (k) 2 = 0 Where Pgridioadmax (k) is the maximum injection power for time step k; this power is positive.

The Costgrid cost of this connection of the energy production system 1 to the network can be calculated as follows:

Or

Pgnd is the power of connection of the system of energy production 1 to the network. For any time k, we have:

Ci (k) and C2 (k) are respectively the energy costs imported and sold to the network for the time step k.

The method also takes into account a consumption model of the consumer components 11. This energy consumption E | 0ad (k) can be expressed simply:

Where PLoadtimes is the time series of power consumed.

The method therefore uses the different models stored for each component of the energy production system 1 and then considers the following energy balance equation: E | oad (k) + Egrjci | oac | (k) = Epv (k) + Ewjnd (k) + Egen (k) + Est0c (k) + Egrjcjsource (k) for all k between 1 and T inclusive.

The method furthermore considers a dimension enabling it to characterize the objective to be achieved. For example, in the case of cost minimization, the following quantity Obj is calculated:

Obj = CoStpv + CoStwind + CoStgen + CoStess + CoStgrid

The preceding explanations make it possible to understand how the method carries out a first equilibration phase P1, which comprises a second step E12 of meeting the equations and conditions of the individual components of the energy production system 1 and the equations and conditions interconnecting these components. . We simply call system of equations this set obtained. As a remark, as this system integrates a magnitude that must be optimized, that is to say mathematically minimize or maximize, its resolution is of the "optimization problem" type.

To fully understand the technical complexity of the technical problem of sizing an energy production system, it must be considered that the system of equations obtained is of the "optimization problem" type and includes a very large number of unknown variables to be determined. . By way of example, for a system of four components, each comprising a single sizing variable and two state variables, and considered over a period of one year with a time step of one hour, the number of variables to be determined is 4 + (2 * 4) x (365 * 24) = 70,084

This optimization problem naturally includes too many variables for manual resolution, and even too many variables for a conventional single-problem solution of the system of equations. For this reason, existing solutions of the state of the art do not allow to take into account an optimal strategy of operation of the system for sizing, as was recalled in the preamble.

The sizing method of an energy production system 1 according to the embodiment implements a second phase P2 of solving the system of equations established by the first phase P1 of the method. This second phase P2 implements two distinct and complementary calculation steps, which make it possible to split the optimization problem into two distinct problems. These two calculation steps are repeated iteratively, to converge towards a solution to the overall optimization problem, in a limited time and with an affordable computing resource.

For this, the second phase P2 comprises first an initialization step E20, which consists in giving an initial value to the sizing variables of the system of equations. According to one embodiment, these values are chosen by considering the average value of the minimum and maximum sizing for each of the components.

These minimum and maximum values are for example known from geographical data, such as the area available for the installation of photovoltaic panels and / or wind turbines in the territory under consideration. Alternatively or complement, they are set taking into account the maximum consumption desired by the consumer components. After this initialization step, the iterative steps are implemented.

The first iterative step E22 consists of setting the sizing variables to a known value and solving the system of equations by looking only for the other variables, ie the state and exchange variables. . During the first iteration, the values considered for the dimensioning variables are thus those fixed by the initialization step E20.

The second iterative step E24 consists of setting the state and exchange variables to their determined value, considered to be known, by the iterative first step E22, and to finding the sizing variables.

According to the embodiment, in each of these two steps E22, E24 solving the system of equations, the mixed linear programming method is used. The models proposed for the different components are compatible with this optimization method. At the end of the second iterative step E24, the method implements an end-of-iteration test step E26, which makes it possible to stop or continue the iterations. By way of example, this test step E26 can comprise the comparison between the different values of the variables of the equation system obtained and those obtained during the previous iteration: if their difference does not exceed a predefined threshold, for example 10 %, this threshold being able to be a parameter of the process established during the step E11, on the last two iterations, then the test is positive and can conclude that the convergence is sufficient and the process then retains these last values obtained as the solution of the system of equations and stops the iterations. Otherwise, the test is negative and steps E22, E24 are again implemented, using the results obtained by this last iteration as initial values of the next iteration. Alternatively, any other test condition may be implemented to determine whether convergence to a solution is considered sufficient or not.

The method may implement an additional reliability test step E27 at the end of the second iterative step E24, which also consists in comparing the values of the different variables of the equation system obtained and those obtained during the previous iteration. . If the values differ too much, it may be considered that continuation of the iterations may lead to a non-robust solution. In this case, the calculation can be resumed by modifying the initial values, during the initialization step E20, or by proposing another method of resolution.

At the end of the process, when the end-of-iteration test step E26 makes it possible to conclude that there is convergence towards a solution, the method may implement an additional, optional, robustness test step E28. This step consists of comparing the various configurations in the vicinity of the optimal solution found: if these neighboring configurations lead to a very degraded optimization criterion, for example a high cost, distant beyond a chosen threshold compared to that of the solution found, then the latter is considered as non-robust and another solution is sought. FIG. 4 particularly illustrates this robustness test step. It schematically represents the evolution of the cost associated with energy systems according to variables that vary over a space of possible solutions. The curve 30 of the cost obtained has two low values C * and C **, respectively for the solutions S * and S **: one realizes that if one moves very slightly away from the solution S *, the cost increases very quickly, which makes this solution S * not robust. On the contrary, the solution S ** is much more robust. This robustness test step E28 makes it possible to favor a solution of type S ** rather than S *.

Finally, the design / construction process may comprise a third phase P3 for manufacturing the energy production system 1. In the case of a new system, the management unit can then automatically transmit control commands of the various components of the system. by means of communication to a manufacturing entity of said components, naturally transmitting the dimensioning values obtained. It then participates in a step E31 of manufacturing an energy production system. In the case of improvement, modification of an existing installation, it can possibly act by actuators on the existing components, so as to automatically modify them to the new configuration obtained, for example by deactivating certain photovoltaic panels. As a variant or complement, the method of the invention can be used for controlling the hybrid system only. The sizing unit 20 then acts as a control unit, and acts on the production plane to modify the state variables of the energy production system to use it more optimally. In the latter case, it acts as a management unit of an energy production system and implements a control step E32 of said energy production system. Advantageously, it uses the same algorithms and models as those used for sizing, to calculate the operation data for controlling the system. The invention also relates to a device for manufacturing a mixed energy production system 1, comprising a management unit 20 comprising a software means and an electronic memory, and which implements the method of designing / building a system of mixed energy production 1 as described above. It also relates to a data recording medium readable by a computer on which is recorded a computer program comprising software means for implementing the steps of the method as described above. This control step corresponds to the activation of only part of the whole of the method used for the dimensioning: the dimensioning variables are fixed and the state and exchange variables are recalculated, forming data of operation of the system, this time on a shorter horizon than for sizing. The formatting of the data can be done periodically, whenever an optimization of the strategy is performed. In this calculation, parameters such as weather forecast data that cover the period relating to optimization for driving, for example between 12h and 48h, are used. The invention also relates to a mixed energy production system 1, for example as illustrated in FIG. 1, comprising, for example, at least one intermittent energy generating device 2, 3 and at least one storage device. energy 5, and integrating a management unit 20 which implements all or part of the method described above for controlling the mixed energy production system 1, in particular by calculating the state and / or exchange variables. This management unit 20 can also implement the method for a system optimization calculation, and propose a transformation of the architecture of the mixed energy production system 1 to improve its optimization. This modification can for example be implemented if the optimization criteria change, or if meteorological or geographical parameters change.

Claims (12)

claims A method of manufacturing a mixed energy production system (1) comprising at least one intermittent energy generating device (2, 3) and optionally at least one energy storage device (5), comprising a first phase (P1) of equations determining a system of equations representing an optimization problem, characterized in that the system of equations uses dimensioning variables, state variables and exchange variables and in that the method comprises a second phase (P2) solving the equation system, implemented by a management unit (20) in the form of at least one computer, on which is installed a computer program , which comprises the following steps: (E22): iterative first step of calculating the state and exchange variables of the system of equations by considering the sizing variables of the system of equations at a fixed value; (E24): second iterative step of calculating the sizing variables of the system of equations by considering the state and exchange variables at a value fixed at their value obtained by the first iterative step; (E26): end of iteration test step, and repetition of the two iterative steps (E22, E24) if the test is negative, setting for the first iterative step (E22) the values of the dimensioning variables to the values obtained by the second iterative step (E24), and consideration of the solution obtained at the end of the last two iterative steps (E22, E24) if the test is positive, and in that the method comprises a third phase (P3) of manufacturing the control system. mixed energy production (1) designed by the implementation of the first two phases (P1, P2) of the process. 2. A method of manufacturing a mixed energy production system (1) according to the preceding claim, characterized in that it comprises a control step (E32) of the mixed energy production system (1), using all or part of the system of equations of the first phase (P1) to determine the state and exchange values forming the operating data of the system. 3. A method for manufacturing a mixed energy production system (1) according to one of the preceding claims, characterized in that it comprises an initialization step (E20) at the beginning of the second phase (P2) of assigning initial values to the sizing variables before the first execution of the two iterative steps (E22, E24). 4. A method of manufacturing a mixed energy production system (1) according to one of the preceding claims, characterized in that the system of equations uses all or part of the following variables: - at least one dimensioning variable corresponding to the installed capacity of an intermittent energy generating device (2, 3); and / or - at least one dimensioning variable corresponding to the capacity of an energy storage device (5); and / or - at least one dimensioning variable corresponding to the installed power of a generator (4); and / or - at least one state variable of a power storage device (5) consisting of a Boolean variable for respectively characterizing the charging state and the discharging state of said energy storage device (5); and / or - at least one state variable of a generator set (4) consisting of a Boolean variable making it possible to respectively characterize the on state or not of said generator set (4); and / or - at least one exchange variable per component of the energy production system (1) consisting of an energy produced and / or stored and / or exchanged by each of said components. 5. A method of manufacturing a mixed energy production system (1) according to one of the preceding claims, characterized in that the system of equations represents an optimization problem which integrates an optimization condition among: - the minimizing the overall cost of the mixed energy production system (1); or - the minimization of C02 emissions from the mixed energy production system (1); or - maximizing the energy production of the mixed energy production system (1) by one or more intermittent energy generating devices (2, 3). 6. A method of manufacturing a mixed energy production system (1) according to one of the preceding claims, characterized in that the second phase (P2) comprises a robustness test step (E28) to determine if a solution obtained at the end of the second phase is robust, and in that it initiates the search for another solution if the test determines that the solution is not robust. 7. A method of manufacturing a mixed energy production system (1) according to one of the preceding claims, characterized in that the second phase (P2) comprises a reliability test step (E27) during the iterative steps, consisting of to check if the values obtained between different iterations are sufficiently close to consider that there will be a convergence towards a solution, that is to say are not distant from each other beyond a predefined threshold. 8. A method of manufacturing a mixed energy production system (1) according to one of the preceding claims, characterized in that the first phase (P1) of equations comprises a step (E12) of equations which comprises reading and transmission from an electronic memory of a mathematical model for each component of the envisaged mixed energy production system (1). 9. A method of manufacturing a mixed energy production system (1) according to the preceding claim, characterized in that the electronic memory comprises a mathematical model for each of the following components: - a photovoltaic power generation device (2) ; - a device for producing wind energy (3); - a generator (4); an energy storage device (5); one or more energy consuming components (11); - an external energy distribution network. 10. A method of manufacturing a mixed energy production system (1) according to one of the preceding claims, characterized in that the first phase (P1) comprises a step of acquisition (E11) of digital data representing parameters of the system of equations, this step comprising transmissions of digital data from outside by remote communication devices and / or seizures by a human machine interface, said digital data comprising: digital data representing meteorological forecasts; - digital data representing geographical information of the territory concerned; - digital data representing future energy needs or wishes for future energy production. 11. Data storage medium readable by a computer on which is recorded a computer program comprising software means for implementing the steps of the method according to one of claims 1 to 10. 12. Mixed energy production system (1) comprising at least one intermittent energy generating device (2, 3) and possibly at least one energy storage device (5), characterized in that it comprises a unit management system (20) which implements all or part of the method according to one of claims 1 to 10 for the design, manufacture and dimensioning of the mixed energy production system (1) on the one hand or the control of the production system energy (1) mixed on the other hand.