Distributed photovoltaic power extraction method and system, storage medium and electronic equipment

Abstract

The invention provides a distributed photovoltaic power extraction method and system, a storage medium and electronic equipment. The method comprises the following steps: establishing a transformer area hybrid power model; determining an evaluation function based on the joint negentropy equation and the transformer area hybrid power model; and performing iterative calculation on the evaluation function based on the whale algorithm, and determining the optimal multiplying power between the generated power of the low-voltage distributed power supply and the low-voltage distributed photovoltaic output profile curve. According to the distributed photovoltaic power extraction method, the evaluation function is obtained by establishing the transformer area hybrid power model and combining the joint negentropy equation with the transformer area hybrid power model, the evaluation function is iterated through the whale algorithm to obtain the optimal solution, and the optimal multiplying power is obtained, so that rapid and real-time photovoltaic output extraction can be realized; the obtained optimal multiplying power can be used for accurate and efficient output prediction, and reference is provided for power voltage control of the power distribution network.

Description

Technical Field

[0001] The present disclosure relates to the field of distribution network operation perception technology, and in particular, to a distributed photovoltaic power extraction method, system, storage medium and electronic device. Background Art

[0002] At present, distributed photovoltaics are developing rapidly and are connected to low-voltage distribution networks in large quantities. As the number and installed capacity of distributed photovoltaics increase, problems such as power reverse transmission and voltage over-limit are caused. The output of distributed photovoltaics is greatly affected by environmental factors, and the randomness of local climate is very strong, resulting in large fluctuations in the output of distributed photovoltaics, which has a great impact on the safe and stable operation of distribution networks and even large power grids.

[0003] At present, machine learning methods are generally used to predict the output of distributed photovoltaics based on meteorological information and historical data. However, on the one hand, this method has a large error. On the other hand, the output data obtained in this way is of reference significance for day-ahead scheduling, but it is difficult to use for real-time control. In terms of power control, especially voltage control at the distribution network and substation level, due to the lack of real-time and accurate output data of distributed photovoltaics, it is difficult to achieve high real-time and high-precision control, resulting in voltage exceeding the limit and reduced power supply reliability. Summary of the invention

[0004] The purpose of the embodiments of the present disclosure is to provide a distributed photovoltaic power extraction method, system, storage medium and electronic device to solve at least one of the technical problems in the prior art that the distributed photovoltaic output prediction has large errors and is not suitable for real-time control.

[0005] In order to solve the above technical problems, the first aspect of the present invention provides a distributed photovoltaic power extraction method, including: establishing a substation hybrid power model; determining an evaluation function based on a joint negative entropy equation and the substation hybrid power model; iteratively calculating the evaluation function based on a whale algorithm to determine the optimal ratio between the low-voltage distributed power generation power and the low-voltage distributed photovoltaic output contour curve.

[0006] Furthermore, the hybrid power model of the substation area includes:

[0007]

[0008] A T (t) = A L (t)+A pv (t);

[0009] In the formula, A T (t) is the power of transformer at the transformer substation, A pv (t), A L (t) are the power generation power and load power of distributed photovoltaic, is the profile curve of the observable low-voltage distributed photovoltaic power generation curve, λ * is the actual ratio between the generated power and the contour curve.

[0010] Furthermore, the joint negative entropy equation includes:

[0011]

[0012] Where J(X,Y) is the joint negative entropy between random variables X and Y, p(x,y) is the joint probability distribution function between X and Y, χ is the range of variable x, and v is the range of Y;

[0013] The evaluation function includes:

[0014]

[0015] Where λ is the solution ratio between the low-voltage distributed power generation power and the low-voltage distributed photovoltaic output contour curve.

[0016] Furthermore, the iterative calculation of the evaluation function based on the whale algorithm to determine the optimal ratio between the low-voltage distributed power generation power and the low-voltage distributed photovoltaic output contour curve includes: presetting the number of whales, the maximum number of iterations, and initializing the whale position and speed; calculating the whale fitness based on the whale position and the evaluation function, and recording the whale position of the optimal whale; updating the random number p, the first coefficient vector A, the second coefficient vector C, and the range [-a, a] of the first coefficient vector A; determining whether the random number p is less than a preset threshold; if so, the whale surrounds the prey; if not, or if |A|≥1, the whale performs a global random search; iterating until the maximum number of iterations is reached, and outputting the optimal solution as the optimal ratio.

[0017] Furthermore, it also includes: acquiring the gateway power of the transformer in the substation by collecting the gateway meter data of the transformer in the substation, or acquiring the AC sampling data of the distribution transformer terminal.

[0018] Furthermore, the method further includes: determining the contour curve based on the light intensity curve of the stage area.

[0019] Furthermore, after iteratively calculating the evaluation function based on the whale algorithm to determine the optimal ratio between the low-voltage distributed power generation power and the low-voltage distributed photovoltaic output contour curve, it includes: filtering the noise of the optimal ratio based on a filtering algorithm.

[0020] The second aspect of the present invention provides a distributed photovoltaic power extraction device, including: a model building module, used to establish a substation hybrid power model; a function determination module, used to determine an evaluation function based on a joint negative entropy equation and the substation hybrid power model; a solution calculation module, used to iteratively calculate the evaluation function based on a whale algorithm to determine the optimal ratio between the low-voltage distributed power generation power and the low-voltage distributed photovoltaic output contour curve.

[0021] A third aspect of the present invention provides a storage medium storing a computer program, wherein the computer program implements the steps of the method when executed by a processor.

[0022] A fourth aspect of the present invention provides an electronic device, comprising at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method when executing the computer program on the memory.

[0023] The distributed photovoltaic power extraction method of the present invention establishes a substation hybrid power model, establishes the relationship between the substation transformer threshold power, load and distributed photovoltaic output, and uses the known typical photovoltaic output of the substation as the photovoltaic characteristic curve to combine the joint negative entropy equation with the substation hybrid power model to obtain an evaluation function for solving the photovoltaic output, and iterates the evaluation function through the whale algorithm to obtain the optimal solution. The obtained optimal ratio can realize fast and real-time photovoltaic output extraction, and the obtained optimal ratio can be used for accurate and efficient output prediction, which provides a reference for power and voltage control of the distribution network.

[0024] The distributed photovoltaic power extraction device, storage medium and electronic device in the present invention have all the beneficial effects of the above-mentioned distributed photovoltaic power extraction method, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0026] Figure 1 A flow chart of a distributed photovoltaic power extraction method provided by an embodiment of the present invention;

[0027] Figure 2 A flow chart of applying the whale algorithm provided in an embodiment of the present invention to photovoltaic power (photovoltaic output) extraction;

[0028] Figure 3The calculation result of photovoltaic power (photovoltaic output) extraction based on 5 days of photovoltaic data provided in the embodiment of the present invention;

[0029] Figure 4 The calculation result of photovoltaic power (photovoltaic output) extraction based on 10 days of photovoltaic data provided in the embodiment of the present invention;

[0030] Figure 5 The calculation result of photovoltaic power (photovoltaic output) extraction based on 25 days of photovoltaic data provided in the embodiment of the present invention. DETAILED DESCRIPTION

[0031] It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but only as an example of an embodiment. Other modifications within the scope and spirit of the present disclosure will occur to those skilled in the art.

[0032] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the general description of the present disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the present disclosure.

[0033] These and other characteristics of the present disclosure will become apparent from the following description of a preferred form of embodiment given as a non-limiting example, with reference to the attached drawings.

[0034] It should also be understood that, although the present disclosure has been described with reference to some specific examples, those skilled in the art will be able to realize many other equivalent forms of the present disclosure that have the features described in the claims and are therefore within the scope of protection defined thereby.

[0035] The above and other aspects, features and advantages of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0036] Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments claimed are merely examples of the present disclosure, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid obscuring the present disclosure with unnecessary or redundant details. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present disclosure in a variety of ways with substantially any suitable detailed structure.

[0037] This specification may use the phrases "in one embodiment," "in another embodiment," "in a further embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present disclosure.

[0038] The first aspect of the present invention provides a distributed photovoltaic power extraction method, combining Figure 1 As shown, including:

[0039] S100, establishing a hybrid power model for the substation area;

[0040] S200, determining an evaluation function based on a joint negative entropy equation and the substation hybrid power model;

[0041] S300, iteratively calculating the evaluation function based on the whale algorithm to determine the optimal ratio between the low-voltage distributed power generation power and the low-voltage distributed photovoltaic output profile curve.

[0042] In step S100, a hybrid power model of the substation is established. The hybrid power model of the substation is constructed to represent the relationship between the substation transformer threshold power, load and distributed photovoltaic output, as well as the relationship between the typical photovoltaic output and the total photovoltaic output of the substation, wherein the photovoltaic characteristic curve is generated according to the known typical photovoltaic output of the substation; the contour curves of the substation transformer threshold power and the low-voltage distributed photovoltaic power generation power curve are known quantities.

[0043] Furthermore, the hybrid power model of the substation area includes:

[0044]

[0045] A T (t) = A L (t)+A pv (t);

[0046] In the formula, A T (t) is the transformer threshold power in the substation, A pv (t), A L (t) are the power generation power and load power of distributed photovoltaic, is the profile curve of the observable low-voltage distributed photovoltaic power generation curve, λ * is the actual ratio between the power generation and the profile curve. T (t) is a known quantity.

[0047] Preferably, the gateway power of the transformer in the substation is obtained by collecting gateway meter data of the transformer in the substation, or by AC sampling data of the distribution transformer terminal.

[0048] For example, A T (t) is obtained by collecting the gateway meter data of the substation transformer, or by obtaining the AC sampling data of the distribution transformer terminal.

[0049] Preferably, the contour curve is determined based on a light intensity curve of the stage area.

[0050] For example The acquisition method is to connect a low-voltage distributed power supply with communication or generate it from local meteorological information. Considering the accuracy of the method, the low-voltage distributed power supply contour curve can be simplified to the light intensity curve of the substation. The average value of the light intensity within the sampling interval can be equivalent to the power contour curve of the low-voltage distributed power supply in the substation.

[0051] In step S200, an evaluation function is determined based on the joint negative entropy equation and the substation hybrid power model. Negative entropy can evaluate the dependency between two or more random variables. If the joint negative entropy of two random variables is greater than the sum of their respective negative entropies, then there is a certain dependency between the two variables; conversely, if the joint negative entropy is equal to the sum of their respective negative entropies, then the two variables are independent. Negative entropy is combined with photovoltaic output, and the gateway measurement data of the distribution substation is added to establish a photovoltaic evaluation function.

[0052] For random variables X and Y, the calculation formulas for their negative entropy are as follows:

[0053]

[0054] Where J(X) is the negative entropy of random variable X, J(Y) is the negative entropy of random variable Y, χ is the range of variable x, p(x) is the probability distribution function of random variable X, and p(y) is the probability distribution function of random variable Y.

[0055] Furthermore, the joint negative entropy equation includes:

[0056]

[0057] Where J(X,Y) is the joint negative entropy between random variables X and Y, p(x,y) is the joint probability distribution function between X and Y, χ is the range of variable x, and v is the range of Y;

[0058] The evaluation function includes:

[0059]

[0060] Where λ is the solution ratio between the low-voltage distributed power generation power and the low-voltage distributed photovoltaic output contour curve.

[0061] It can be seen from the hybrid power model of the substation that as long as λ is determined * The decomposition between low-voltage distributed power and non-low-voltage distributed power loads can be completed. Let λ be the approximate true value λ to be solved *When λ is not optimal, the load curve decomposed by the formula will contain a certain proportion of low-voltage distributed power information. When the separation effect is better, the load information will contain less low-voltage distributed power information, that is, the negative entropy between the two is the smallest. In the field of signal processing, negative entropy is often used to measure the correlation between signals, targeting discrete scenarios of applications.

[0062] In step S300, the evaluation function is iteratively calculated based on the whale algorithm to determine the optimal ratio between the power generation of the low-voltage distributed power supply and the low-voltage distributed photovoltaic output profile curve. The whale algorithm (abbreviated as WOA, namely Whale Optimization Algorithm) is an optimization algorithm based on the foraging behavior of whales in nature. The algorithm simulates the predation behavior of whales in the ocean to find the optimal solution to the optimization problem. The process of the whale algorithm usually includes the following steps: initialization: randomly generate parameters such as the position and speed of the whale population; evaluate fitness: calculate the fitness value of each whale individual, usually determined according to the objective function of the problem; update the optimal solution: record the optimal whale individual in the current population; shrink surround and bubble net attack: according to the algorithm rules, update the position of the whale individual to simulate the predation behavior of the whale; random search: execute the random search phase with a certain probability to increase the diversity of the population; iterative termination: when the preset number of iterations is reached or other termination conditions are met, the algorithm ends.

[0063] The evaluation function of photovoltaic decomposition is added to the whale algorithm, and the characteristics of the whale optimization algorithm are used to iterate the rate parameters. Figure 2 As shown, including:

[0064] S1, preset the number of whales, the maximum number of iterations, and initialize the whale position and speed;

[0065] S2. Calculating the whale fitness based on the whale position and the evaluation function, and recording the whale position of the optimal whale;

[0066] S3, update the range of the random number p, the first coefficient vector A, the second coefficient vector C, and the first coefficient vector A to [-a, a];

[0067] S4. Determine whether the random number p is less than a preset threshold; if so, the whale surrounds the prey; if not, or if |A|≥1, the whale performs a global random search;

[0068] S5. Iterate until the maximum number of iterations is reached, and output the optimal solution as the optimal ratio.

[0069] In step S1, the number of whales N and the maximum number of iterations t are preset. max , and initialize (randomly generate) the whale's position and speed.

[0070] In step S2, based on the whale position and the evaluation function, the whale fitness is calculated, and the whale position of the optimal whale is recorded.

[0071] In step S3, the random number p, the first coefficient vector A, the second coefficient vector C, and the range [-a, a] of the first coefficient vector A are updated. Where a gradually decreases with the increase in the number of iterations, p is a random number used to determine the whale's behavior selection, and A and C are coefficient vectors used for position update.

[0072] In step S4, it is determined whether the random number p is less than a preset threshold; if so, the whale surrounds the prey; if not, or if |A|≥1, the whale performs a global random search. For example, when p<0.5 (preset threshold, which can be preset as needed), the whale surrounds the prey. In order to simulate the spiral process of the humpback whale, that is, the foaming net attack process, a spiral equation is established between the whale and the prey to simulate the spiral movement of the humpback whale, and the position of the whale is updated accordingly. When |A|≥1, or p≥0.5, the whale performs a global random search to explore other areas in the solution space.

[0073] In step S5, the iteration is performed until the maximum number of iterations is reached, and the optimal solution is output as the optimal ratio. That is, the above steps S2 to S4 are repeated until the maximum number of iterations t is reached. max , and then output the optimal solution.

[0074] Further, after the step S300, the evaluation function is iteratively calculated based on the whale algorithm to determine the optimal ratio between the low-voltage distributed power generation power and the low-voltage distributed photovoltaic output profile curve, it includes:

[0075] S400: filtering out noise from the optimal ratio based on a filtering algorithm.

[0076] Specifically, a filtering algorithm such as a Kalman filtering algorithm or a particle filtering algorithm may be used to remove noise and improve data accuracy.

[0077] Further, after the step S400, the optimal magnification is subjected to noise filtering based on a filtering algorithm, the method further includes:

[0078] Based on the optimal magnification and the actual magnification, the accuracy (ie, error) of the optimal magnification is determined.

[0079] In this embodiment, the Whale Algorithm is used to analyze the negative entropy solution of distributed photovoltaics: Taking a typical substation as an example, 5 days, 10 days, 25 days, and 50 days of continuous data are selected, and the negative entropy separation algorithm is used to obtain the photovoltaic output decomposition results. Taking the error of 10% as the threshold, considering the requirements of error and calculation amount, the 10-day data set is selected to meet the requirements of engineering implementation.

[0080] like Figure 3 As shown, the photovoltaic parameters with a time scale of 5 days were selected for sampling, and the calculated results showed an error of 14.01%.

[0081] like Figure 4 As shown, the photovoltaic parameters with a time scale of 10 days were selected for sampling, and the calculated results showed an error of 10.05%.

[0082] like Figure 5 As shown, the photovoltaic parameters with a time scale of 25 days were selected for sampling, and the calculated results showed an error of 8.44%.

[0083] The photovoltaic processing decomposition is performed on the sampling data. The photovoltaic decomposition experiments are performed on the 5-day sampling data, 10-day sampling data, and 25-day sampling data. The experimental data show that the photovoltaic processing results based on the 5-day sampling data have an error of 14%. The photovoltaic processing results based on the 10-day sampling data have an error of 10%. The photovoltaic processing results based on the 25-day sampling data have an error of 8.5%. In engineering projects, the 10% error is used as the threshold, which can be obtained from the experimental results. The photovoltaic processing decomposition error based on the ten-day sampling data meets the engineering requirements. Therefore, in practical applications, the 10-day sampling data is also selected as the data set for solution.

[0084] After determining the error threshold (i.e., accuracy threshold), the whale algorithm is used to accelerate the data solving process and complete the iteration of the photovoltaic factor value λ at a faster speed to find the optimal solution to ensure the real-time decomposition of photovoltaic power output.

[0085] The distributed photovoltaic power extraction method of the present invention establishes a substation hybrid power model, establishes the relationship between the substation transformer threshold power, load and distributed photovoltaic output, and uses the known typical photovoltaic output of the substation as the photovoltaic characteristic curve to combine the joint negative entropy equation with the substation hybrid power model to obtain an evaluation function for solving the photovoltaic output, and iterates the evaluation function through the whale algorithm to obtain the optimal solution. The obtained optimal ratio can realize fast and real-time photovoltaic output extraction, and the obtained optimal ratio can be used for accurate and efficient output prediction, which provides a reference for power and voltage control of the distribution network.

[0086] A second aspect of the present invention provides a distributed photovoltaic power extraction device, comprising:

[0087] Model building module, used to build a hybrid power model of the substation;

[0088] A function determination module, used for determining an evaluation function based on a joint negative entropy equation and the substation hybrid power model;

[0089] The solution calculation module is used to iteratively calculate the evaluation function based on the whale algorithm to determine the optimal ratio between the low-voltage distributed power generation power and the low-voltage distributed photovoltaic output contour curve.

[0090] The distributed photovoltaic power extraction device in the present invention establishes a substation hybrid power model, establishes the relationship between the substation transformer threshold power, load and distributed photovoltaic output, and uses the known typical photovoltaic output of the substation as the photovoltaic characteristic curve to combine the joint negative entropy equation with the substation hybrid power model to obtain an evaluation function for solving the photovoltaic output, and iterates the evaluation function through the whale algorithm to obtain the optimal solution. The obtained optimal ratio can realize fast and real-time photovoltaic output extraction, and the obtained optimal ratio can be used for accurate and efficient output prediction, which provides a reference for power and voltage control of the distribution network.

[0091] A third aspect of the present invention provides a storage medium storing a computer program, wherein when the computer program is executed by a processor, the steps of implementing the method include:

[0092] S100, establishing a hybrid power model for the substation area;

[0093] S200, determining an evaluation function based on a joint negative entropy equation and the substation hybrid power model;

[0094] S300, iteratively calculating the evaluation function based on the whale algorithm to determine the optimal ratio between the low-voltage distributed power generation power and the low-voltage distributed photovoltaic output profile curve.

[0095] The storage medium in the present invention, when the computer program therein is executed by the processor, establishes a substation hybrid power model, establishes the relationship between the substation transformer threshold power, load and distributed photovoltaic output, and uses the known typical photovoltaic output of the substation as the photovoltaic characteristic curve, combines the joint negative entropy equation with the substation hybrid power model to obtain an evaluation function for solving the photovoltaic output, and iterates the evaluation function through the whale algorithm to obtain the optimal solution. The obtained optimal multiple can realize fast and real-time photovoltaic output extraction, and the obtained optimal multiple can be used for accurate and efficient output prediction, which provides a reference for power and voltage control of the distribution network.

[0096] A fourth aspect of the present invention provides an electronic device, comprising at least a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of the method when executing the computer program in the memory, including:

[0097] S100, establishing a hybrid power model for the substation area;

[0098] S200, determining an evaluation function based on a joint negative entropy equation and the substation hybrid power model;

[0099] S300, iteratively calculating the evaluation function based on the whale algorithm to determine the optimal ratio between the low-voltage distributed power generation power and the low-voltage distributed photovoltaic output profile curve.

[0100] The electronic device in the present invention, when the computer program in its memory is executed by the processor, establishes a substation hybrid power model, establishes the relationship between the substation transformer threshold power, load and distributed photovoltaic output, and uses the known typical photovoltaic output of the substation as the photovoltaic characteristic curve, combines the joint negative entropy equation with the substation hybrid power model to obtain an evaluation function for solving the photovoltaic output, and iterates the evaluation function through the whale algorithm to obtain the optimal solution. The obtained optimal ratio can realize fast and real-time photovoltaic output extraction, and the obtained optimal ratio can be used for accurate and efficient output prediction, providing a reference for power and voltage control of the distribution network.

[0101] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by 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 embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0102] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0103] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0104] In the embodiments provided in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0105] If the integrated module is implemented in the form of 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, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned motor torque control method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0106] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A distributed photovoltaic power extraction method, characterized in that: include: Establish a hybrid power model for the substation area; Determining an evaluation function based on the joint negative entropy equation and the substation hybrid power model; The evaluation function is iteratively calculated based on the whale algorithm to determine the optimal ratio between the low-voltage distributed power generation power and the low-voltage distributed photovoltaic output contour curve.

2. The method according to claim 1, characterized in that The hybrid power model of the substation area includes: A T (t)=A L (t)+A pv (t); In the formula, A T (t) is the transformer threshold power in the substation, A pv (t), A L (t) are the power generation power and load power of distributed photovoltaic, is the profile curve of the observable low-voltage distributed photovoltaic power generation curve, λ * is the actual ratio between the generated power and the contour curve.

3. The method according to claim 2, characterized in that The joint negative entropy equation includes: Where J(X,Y) is the joint negative entropy between random variables X and Y, p(x,y) is the joint probability distribution function between X and Y, χ is the range of variable x, and v is the range of Y; The evaluation function includes: Where λ is the solution ratio between the low-voltage distributed power generation power and the low-voltage distributed photovoltaic output contour curve.

4. The method according to claim 3, characterized in that: The iterative calculation of the evaluation function based on the whale algorithm to determine the optimal ratio between the low-voltage distributed power generation power and the low-voltage distributed photovoltaic output contour curve includes: Preset the number of whales, the maximum number of iterations, and initialize the whale positions and speeds; Based on the whale position and the evaluation function, the whale fitness is calculated, and the whale position of the optimal whale is recorded; Update the random number p, the first coefficient vector A, the second coefficient vector C, and the range of the first coefficient vector A to [-a, a]; Determine whether the random number p is less than a preset threshold; if so, the whale surrounds the prey; if not, or if |A|≥1, the whale performs a global random search; Iterate until the maximum number of iterations is reached, and output the optimal solution as the optimal ratio.

5. The method according to claim 2, characterized in that: Also includes: The gateway power of the transformer in the substation is obtained by collecting the gateway meter data of the transformer in the substation, or by the AC sampling data of the distribution transformer terminal.

6. The method according to claim 2, characterized in that Also includes: The contour curve is determined based on a light intensity curve of the stage area.

7. The method according to claim 1, characterized in that After the evaluation function is iteratively calculated based on the whale algorithm to determine the optimal ratio between the low-voltage distributed power generation power and the low-voltage distributed photovoltaic output profile curve, it includes: The optimal ratio is subjected to noise filtering based on a filtering algorithm.

8. A distributed photovoltaic power extraction device, characterized in that: include: Model building module, used to build a hybrid power model of the substation; A function determination module, used for determining an evaluation function based on a joint negative entropy equation and the substation hybrid power model; The solution calculation module is used to iteratively calculate the evaluation function based on the whale algorithm to determine the optimal ratio between the low-voltage distributed power generation power and the low-voltage distributed photovoltaic output contour curve.

9. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. An electronic device, comprising at least a memory and a processor, wherein a computer program is stored in the memory, wherein: The processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program on the memory.

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