Power system optimal power flow scheduling method and device based on incremental particle swarm optimization
By introducing incremental particle swarm optimization mechanism and social learning rules into the optimal power flow scheduling of the power system, the problem of fast local convergence but insufficient accuracy of existing methods under wind power conditions is solved, and more efficient and stable power system optimization is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- STATE GRID JIANGSU ECONOMIC RES INST
- Filing Date
- 2025-11-27
- Publication Date
- 2026-04-21
AI Technical Summary
Existing optimal power flow scheduling methods for power systems based on particle swarm optimization are prone to getting trapped in local optima, have insufficient convergence speed and accuracy, long computation time, and poor adaptability to wind power fluctuations when dealing with uncertainties in wind power, making it difficult to achieve fast and efficient multi-objective optimization.
An incremental particle swarm optimization mechanism is introduced, which dynamically introduces new particles in each iteration and adjusts their positions through social learning rules to maintain population diversity and global search capability. This is combined with a multi-objective function optimization model that considers power generation cost and network loss.
It improves the economy and stability of the power system, reduces total generation costs and network losses, enhances computational efficiency, adapts to the uncertainty of wind power output, avoids local optima traps, and achieves rapid and efficient dispatch optimization.
Smart Images
Figure CN121906458A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of lidar, and particularly relates to a method and apparatus for optimal power flow scheduling of power systems based on incremental particle swarm optimization. Background Technology
[0002] Optimal Power Flow (OPF) is a mathematical optimization method applied to power systems. Its goal is to find the optimal operating mode while satisfying various operational constraints, thereby achieving both economic efficiency and security of the power system. By optimizing generator output, transformer taps, and other adjustable parameters, OPF can reduce system operating costs or achieve other operational objectives while ensuring stable grid operation. Its fundamental function is to optimize the generation, transmission, and consumption of electricity to minimize operating costs.
[0003] With the large-scale integration of renewable energy sources (RES), especially wind and solar power, power systems face more complex uncertainties and volatility issues in operation and dispatch. Wind power output is directly affected by meteorological conditions, exhibiting significant randomness and intermittency, which places higher demands on OPF (Optimization-Oriented Power Optimization). In recent years, numerous studies have attempted to incorporate wind power grid integration into OPF optimization models to improve renewable energy utilization and maintain grid stability. In this process, various metaheuristic intelligent optimization methods, such as the Creeper Fish Swarm Optimization (KHA), Artificial Bee Colony Optimization (ABC), Firefly and Jaya Hybrid Algorithm, Differential Evolutionary Particle Swarm Optimization (DE-PSO), and Cuckoo Search (ECSA), have been applied to OPF problems involving wind power. These methods improve the adaptability of traditional mathematical programming methods to nonlinear and nonconvex problems by simulating natural group behavior or evolutionary patterns.
[0004] However, each of the aforementioned algorithms has its shortcomings. For example, the KHA and ABC algorithms are relatively simple to implement, but lack a solid theoretical foundation, and their convergence characteristics and performance are difficult to predict. DE-PSO combines the global search of differential evolution with the local exploitation of particle swarm optimization, but its computational complexity is high. Some studies have shown that although the Barnacles Mating Optimizer (BMO) performs well in wind and solar optimization, its applicability still needs further verification. Overall, particle swarm optimization (PSO) remains the most commonly used method in OPF due to its simplicity, ease of implementation, and fast convergence speed. However, its drawbacks are also quite obvious: excessively fast convergence may lead to a decrease in population diversity, thus getting trapped in local optima and making it difficult to escape the local solution domain.
[0005] While some progress has been made in existing OPF solution schemes based on intelligent optimization algorithms, there are still many shortcomings, making it difficult to balance optimization accuracy and computational efficiency under conditions containing wind power uncertainties.
[0006] First, it is prone to getting trapped in local optima. Particle swarm optimization (PSO) and its improved methods often show fast convergence speed in the early iterations, but due to insufficient global search capabilities, they tend to converge to local solutions too early, especially in scenarios with wind power uncertainties, where the complex and variable optimal solution region makes it difficult for traditional PSO to escape the local solution domain.
[0007] Secondly, convergence speed and accuracy are limited. While some improved algorithms enhance global search capabilities, they often sacrifice convergence speed, leading to a significant increase in computation time. In scenarios where power system scheduling requires rapid solutions, this insufficient convergence efficiency severely restricts their practical application value.
[0008] Third, the ability to handle uncertainty is insufficient. Wind power output is significantly affected by weather conditions, exhibiting volatility and randomness. Existing methods mostly approximate wind power output through static scenario simulations or probabilistic models, lacking dynamic adaptability and making it difficult to maintain stable optimization performance under different wind power levels.
[0009] Fourth, insufficient population diversity. During the iteration process, most intelligent algorithms experience a gradual shrinking of the population's distribution range, leading to reduced search space diversity. If the early distribution of individuals becomes too concentrated, the ability to explore subsequent new solutions becomes insufficient, further exacerbating the problem of local optima.
[0010] In summary, existing OPF solution methods based on PSO and other metaheuristic algorithms generally suffer from problems such as fast local convergence but insufficient accuracy, a significant trade-off between computation time and convergence performance, and poor adaptability to wind power fluctuations when dealing with problems involving wind power uncertainties. Therefore, it is urgent to propose a new optimization method that can improve global search capabilities and uncertainty handling performance while ensuring fast convergence, thereby achieving economical, efficient, safe, and reliable operation of the power system.
[0011] No effective solutions have yet been proposed to address the aforementioned problems with the relevant technologies. Summary of the Invention
[0012] The purpose of this invention is to address the technical problem that various algorithms are insufficient when dealing with the more complex uncertainties and fluctuations faced by power systems in operation and scheduling.
[0013] To achieve the above objectives, this invention provides a power system optimal power flow scheduling method based on incremental particle swarm optimization, which includes the following steps:
[0014] S1: Construct the optimal power flow model for the power system;
[0015] S2: Initialize the particle population, including randomly generating the position and velocity of each particle in the initial particle population, calculating the fitness of each particle in the initial particle population, and updating the individual historical best position of each particle and the global historical best position of the initial particle population based on the fitness.
[0016] S3: Update the velocity and position of all particles in the current particle population;
[0017] S4: In each iteration, at least one new particle is dynamically introduced, and the new particle is revived and corrected. The new particle also participates in the fitness evaluation process of the current round and updates the fitness value of the target particle population after the introduction of the new particle. After multiple iterations, the target particle population is generated.
[0018] S5: Based on the target particle population, determine the optimal output solution, and implement the optimal power flow scheduling of the power system according to the optimal solution.
[0019] In an optional embodiment, the optimal solution is determined based on the target particle population, including: updating the individual historical best position corresponding to each particle in the target particle population and the global historical best position corresponding to the target particle population; determining whether the individual historical best position and the global historical best position satisfy a preset convergence criterion; if they satisfy the criterion, the global historical best position is output as the optimal solution; otherwise, the process returns to step S3.
[0020] In one optional embodiment, constructing an optimal power flow model for the power system includes: constructing an optimal power flow model for the power system based at least on a multi-objective function corresponding to minimizing generation costs and minimizing network active power losses. The optimal power flow model for the power system further includes at least power balance equality constraints and system operation security inequality constraints.
[0021] In one alternative embodiment, at least one new particle is dynamically introduced during each iteration, and the method further includes introducing new particles at a fixed frequency or a fixed iteration period during each iteration.
[0022] In one optional embodiment, during each iteration, at least one new particle is dynamically introduced, and the new particle is revived and corrected. The method further includes: after dynamically introducing the new particle, the new particle learns from model particles in the particle population whose performance falls within a preset ranking range according to a predetermined learning mechanism, and its position is adjusted based on a preset update formula; the preset update formula is:
[0023]
[0024] For learning rate, The position before the new particle update. This represents the position of the model particle.
[0025] In one alternative embodiment, the control variables include at least one of the following: generator active power output, generator terminal voltage, transformer turns ratio, and reactive power compensation device output.
[0026] In an optional embodiment, in steps S2 and S4, when calculating the fitness of a particle, a penalty function method is used to handle particles that violate the system operation safety inequality constraints.
[0027] To achieve the above objectives, the present invention also provides a power system optimal power flow scheduling device based on incremental particle swarm optimization, which is used to perform the following steps:
[0028] S1: Construct the optimal power flow model for the power system;
[0029] S2: Initialize the particle population, including randomly generating the position and velocity of each particle in the initial particle population, calculating the fitness of each particle in the initial particle population, and updating the individual historical best position of each particle and the global historical best position of the initial particle population based on the fitness.
[0030] S3: Update the velocity and position of all particles in the current particle population;
[0031] S4: In each iteration, at least one new particle is dynamically introduced, and the new particle is revived and corrected. The new particle also participates in the fitness evaluation process of the current round and updates the fitness value of the target particle population after the introduction of the new particle. After multiple iterations, the target particle population is generated.
[0032] S5: Based on the target particle population, determine the optimal output solution, and implement the optimal power flow scheduling of the power system according to the optimal solution.
[0033] To achieve the above objectives, the present invention also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs; wherein when the one or more programs are executed by the one or more processors, the one or more processors implement an optimal power flow scheduling method for a power system based on incremental particle swarm optimization.
[0034] To achieve the above objectives, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an optimal power flow scheduling method for a power system based on incremental particle swarm optimization. Attached Figure Description
[0035] Figure 1 A flowchart of an optimal power flow scheduling method for power systems based on incremental particle swarm optimization is provided for the implementation of this invention.
[0036] Figure 2 A schematic diagram of an optimal power flow scheduling device for a power system based on incremental particle swarm optimization is provided for this application. Detailed Implementation
[0037] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0038] like Figure 1 As shown, Figure 1 This application provides a flowchart of an optimal power flow scheduling method for power systems based on incremental particle swarm optimization. The method includes the following steps:
[0039] S1: Construct the optimal power flow model for the power system; S2: Initialize the particle population, including randomly generating the position and velocity of each particle in the initial particle population, calculating the fitness of each particle in the initial particle population, and updating the individual historical best position of each particle and the global historical best position of the initial particle population based on the fitness.
[0040] S3: Update the velocity and position of all particles in the current particle population;
[0041] S4: In each iteration, at least one new particle is dynamically introduced, and the new particle is revived and corrected. The new particle also participates in the fitness evaluation process of the current round and updates the fitness value of the target particle population after the introduction of the new particle. After multiple iterations, the target particle population is generated.
[0042] S5: Based on the target particle population, determine the optimal output solution, and implement the optimal power flow scheduling of the power system according to the optimal solution.
[0043] To overcome the limitations of existing OPF optimization methods in scenarios involving wind power integration, the method presented in this application introduces an incremental mechanism based on traditional PSO. This involves adding new particles in each iteration and using a "social learning" rule to guide these new particles toward the optimal solution, thereby maintaining population diversity and improving global search capabilities. This method effectively enhances the optimization performance of OPF under wind power grid integration conditions, reducing power generation costs and network losses while improving grid stability and computational efficiency.
[0044] This method introduces an incremental update mechanism and social learning rules on the basis of traditional particle swarm optimization (PSO), achieving efficient optimization of power systems containing wind power.
[0045] In an optional embodiment, the optimal solution is determined based on the target particle population, including: updating the individual historical best position corresponding to each particle in the target particle population and the global historical best position corresponding to the target particle population; determining whether the individual historical best position and the global historical best position satisfy a preset convergence criterion; if they satisfy the criterion, the global historical best position is output as the optimal solution; otherwise, the process returns to step S3.
[0046] In one optional embodiment, constructing an optimal power flow model for the power system includes: constructing an optimal power flow model for the power system based at least on a multi-objective function corresponding to minimizing generation costs and minimizing network active power losses. The optimal power flow model for the power system further includes at least power balance equality constraints and system operation security inequality constraints.
[0047] As stated above, this application aims to minimize both power generation cost and network active power loss by constructing an optimal power flow model. In this application, by reading network parameters, load, and wind farm output (or other scenarios), the following objective function and constraints are established. Furthermore, in this model, weighted or... The constraints are transformed into a single-objective solution.
[0048] The sub-objectives are expressed as follows:
[0049] The function expressing the cost of electricity generation is as follows:
[0050]
[0051] in, , For the reactive / reactive load of the i-th bus; , , Let be the fuel cost coefficient for the i-th generating unit.
[0052] The power generation cost function and network loss function are expressed as follows:
[0053]
[0054] in, Number of lines; Let K be the conductance of the k-th loop. , These are the voltage amplitudes at both ends of the line bus; and It is the phase angle.
[0055] The optimal power flow model of this power system also includes equality constraints and inequality constraints.
[0056] The following are the balance formulas for power loss, power generation, load power, and active and reactive power:
[0057] In the optimal power flow model, to ensure the safety and stability of the power system operation, the following constraints must be met: First, the active and reactive power outputs of generator units need to be limited to their respective rated upper and lower limits, while the voltage amplitude at the generator terminals must also be kept within the allowable range. Second, the voltage amplitude of the load bus needs to be controlled to ensure the stability of the system voltage level. Third, the adjustment range of transformer tap changes should be limited to the physically feasible upper and lower limits to avoid operational risks caused by over-adjustment. Furthermore, the output capacity of reactive power compensation devices needs to be maintained within their rated range to ensure the flexibility of voltage support. Finally, the power flow of transmission lines should be less than the thermal stability limit to avoid line overload and safety accidents. These constraints ensure that the system optimization results meet both economic objectives and the safety requirements of engineering operation.
[0058] The optimal power flow model for this power system provides the following inequality constraints:
[0059]
[0060]
[0061]
[0062]
[0063]
[0064] In this optimal power flow model of the power system, randomly generated particles With speed Fitness is calculated based on the weighted single objective. Particle Swarm Optimization (PSO) is a food search optimization method based on swarm activity, where each individual is called a particle. Each particle has a position and a velocity in the search space, representing its state. In an NNN-dimensional search space, the position and velocity of the i-th particle can be expressed as:
[0065]
[0066] Particles search for the optimal solution in the search space based on their own perception and historical experience. By continuously updating their position and velocity, particles can gradually approach the global optimum. The update rule is as follows:
[0067]
[0068]
[0069] in, This represents the individual historical best of the i-th particle. Indicates the globally optimal position; and These are cognitive factors and social factors, respectively. and A random number between 0 and 1;
[0070] In each iteration, new particles are introduced at a fixed frequency or a fixed iteration period. In each iteration, at least one new particle is dynamically introduced and the new particle is revived and corrected. It also includes: after the new particle is dynamically introduced, the new particle learns from model particles in the particle population whose performance ability is within a preset ranking range according to a predetermined learning mechanism, and the position of the new particle is adjusted based on a preset update formula.
[0071] The default update formula is:
[0072]
[0073] For learning rate, -U(0,1) The position before the new particle update. This represents the position of the model particle.
[0074] At least one new particle is introduced in each iteration, and revival correction is performed using equation (12) before participating in the fitness evaluation of the current round. When calculating the fitness of a particle each time, the penalty function method is used to handle particles that violate the system operation safety inequality constraints.
[0075] Each time a new particle is introduced, the fitness is calculated and the individual / global optimum is refreshed; if the convergence criterion is met, the optimal solution is output; otherwise, return to step 3.
[0076] It should be noted that the control variables include at least one of the following: generator active power output, generator terminal voltage, transformer turns ratio, and reactive power compensation device output.
[0077] Compared with existing technologies, the power system optimal power flow scheduling method based on incremental particle swarm optimization proposed in this invention has the following significant advantages and technical effects:
[0078] First, this invention introduces an incremental update mechanism into traditional particle swarm optimization, allowing new particles to be dynamically added in each iteration. Combined with social learning rules for position correction, this significantly enhances population diversity and global search capability. Compared to existing technologies, this method effectively avoids prematurely getting trapped in local optima, improving optimization accuracy and stability.
[0079] Secondly, this invention considers both power generation costs and system grid losses in its optimization model, and incorporates the uncertainty of wind power output into the modeling. This ensures that the optimization results not only guarantee the economic efficiency of the power system but also take into account the safety and reliability of grid operation. In contrast, traditional methods often focus only on a single objective, making it difficult to achieve coordinated optimization of multiple objectives under fluctuating wind power conditions.
[0080] Furthermore, the IPSO OPF algorithm proposed in this invention has significant advantages in convergence speed and computational efficiency. Numerical examples demonstrate that, compared to the traditional PSO OPF, this method achieves better scheduling results in most scenarios with fewer iterations and shorter computation time. This is particularly important for intraday scheduling scenarios requiring rapid solutions.
[0081] Through the above technical solution, this invention can significantly improve the operational performance of power systems containing wind power, specifically by: significantly reducing total power generation costs, effectively suppressing system network losses, maintaining bus voltage within a stable range, and achieving higher convergence speed and computational efficiency in the optimization process. Therefore, this invention significantly enhances the economy, stability, and adaptability to uncertainties of power systems containing wind power, and has broad prospects for engineering applications.
[0082] Corresponding to the methods given in the above method embodiments, this application also provides a corresponding apparatus, which includes a module for executing the corresponding methods in the above method embodiments. This module can be software, hardware, or a combination of software and hardware. It is understood that the technical features described in the above method embodiments are also applicable to the following apparatus embodiments. Therefore, details not described in detail can be found in the above method embodiments, and for brevity, will not be repeated here.
[0083] Figure 2 This diagram illustrates an optimal power flow scheduling device for a power system based on incremental particle swarm optimization, provided for the purposes of this application. For ease of explanation, only the parts relevant to the embodiments of this application are shown. (Refer to...) Figure 2 The device may specifically include the following units:
[0084] The construction unit 201 is used to construct the optimal power flow model of the power system; the calculation unit 202 is used to initialize the particle population, including randomly generating the position and velocity of each particle in the initial particle population, calculating the fitness of each particle in the initial particle population, and updating the individual historical best position corresponding to each particle and the global historical best position corresponding to the initial particle population based on the fitness.
[0085] Update unit 203 is used to update the velocity and position of all particles in the current particle population.
[0086] The correction unit 204 is used to dynamically introduce at least one new particle in each iteration, revive and correct the new particle, and have the new particle participate in the fitness evaluation process of the current round, and update the fitness value of the target particle population after the introduction of the new particle, and generate the target particle population after multiple iterations.
[0087] The determination unit 205 is used to determine the optimal solution based on the target particle population, and to realize the optimal power flow scheduling of the power system based on the optimal solution.
[0088] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method and system embodiments section, and they will not be repeated here.
[0089] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0090] This application provides an electronic device, including: one or more processors; a storage device for storing one or more programs; when one or more programs are executed by one or more processors, the one or more processors implement an optimal power flow scheduling method for a power system based on incremental particle swarm optimization.
[0091] This application provides a computer-readable storage medium, characterized in that it stores a computer program thereon, wherein the program, when executed by a processor, implements an optimal power flow scheduling method for a power system based on incremental particle swarm optimization.
[0092] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate form. A computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographic device / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals. In the above embodiments, the descriptions of each embodiment have different focuses; parts not described or recorded in a certain embodiment can be referred to in the relevant descriptions of other embodiments.
[0093] The above embodiments are merely illustrative examples and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.
Claims
1. A power system optimal power flow scheduling method based on incremental particle swarm optimization, characterized in that, The method includes the following steps: S1: Construct the optimal power flow model for the power system; S2: Initialize the particle population, including randomly generating the position and velocity of each particle in the initial particle population, calculating the fitness of each particle in the initial particle population, and updating the individual historical best position corresponding to each particle and the global historical best position corresponding to the initial particle population based on the fitness. S3: Update the velocity and position of all particles in the current particle population; S4: In each iteration, at least one new particle is dynamically introduced, and the new particle is revived and corrected. The new particle also participates in the fitness evaluation process of the current round and updates the fitness value of the target particle population after the introduction of the new particle. After multiple iterations, the target particle population is generated. S5: Based on the target particle population, determine the optimal solution for output, and implement the optimal power flow scheduling of the power system according to the optimal solution.
2. The method according to claim 1, characterized in that, Based on the target particle population, the optimal output solution is determined, including: Update the individual historical best position of each particle in the target particle population and the global historical best position of the target particle population; Determine whether the individual historical best position and the global historical best position satisfy a preset convergence criterion. If they satisfy the criterion, output the global historical best position as the optimal solution; otherwise, return to step S3.
3. The method according to claim 1, characterized in that, Constructing an optimal power flow model for the power system includes: The optimal power flow model of the power system is constructed based on at least the multi-objective function corresponding to the minimum generation cost and the minimum network active power loss. The optimal power flow model of the power system also includes at least the power balance equation constraint and the system operation safety inequality constraint.
4. The method according to claim 1, characterized in that, In each iteration, at least one new particle is dynamically introduced, and the process also includes: In each iteration, the new particles are introduced at a fixed frequency or a fixed iteration period.
5. The method according to claim 1, characterized in that, In each iteration, at least one new particle is dynamically introduced, and the new particle is revived and corrected. The process also includes: After the new particle is dynamically introduced, the new particle learns from model particles in the particle population whose performance is within a preset ranking range according to a predetermined learning mechanism, and the position of the new particle is adjusted based on a preset update formula. The preset update formula is: ； For learning rate, The position of the new particle before the update. The position of the model particle.
6. The method according to claim 1, characterized in that, The control variables include at least one of the following: generator active power output, generator terminal voltage, transformer turns ratio, and reactive power compensation device output.
7. The method according to claim 3, characterized in that, In steps S2 and S4, when calculating the fitness of a particle, a penalty function method is used to handle particles that violate the system operation safety inequality constraints.
8. A power system optimal power flow scheduling device based on incremental particle swarm optimization, characterized in that, The device is used to perform the following steps: Construction units are used to build optimal power flow models for power systems. The computing unit is used to initialize a particle swarm, including randomly generating the position and velocity of each particle in the initial particle swarm, calculating the fitness of each particle in the initial particle swarm, and updating the individual historical best position corresponding to each particle and the global historical best position corresponding to the initial particle swarm based on the fitness. The update unit is used to update the velocity and position of all particles in the current particle population. The correction unit is used to dynamically introduce at least one new particle in each iteration, revive and correct the new particle, and have the new particle participate in the fitness evaluation process of the current round, and update the fitness value of the target particle population after the introduction of the new particle, and generate the target particle population after multiple iterations. The determining unit is used to determine the optimal solution based on the target particle population, and to implement the optimal power flow scheduling of the power system based on the optimal solution.
9. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, It stores a computer program, characterized in that the program, when executed by a processor, implements the method as described in any one of claims 1-7.