Distributed photovoltaic active power grid frequency supporting method and system

Abstract

The invention discloses a distributed photovoltaic active power grid frequency supporting method and system. The method comprises the following steps: acquiring distributed photovoltaic key parameters; according to the distributed photovoltaic key parameters, a distributed photovoltaic fuzzy adaptive control model is established, the distributed photovoltaic fuzzy adaptive control model comprises four fuzzy controllers, and membership functions and fuzzy rules of the four fuzzy controllers are set based on different scenes; and inputting the real-time distributed photovoltaic key parameters into the distributed photovoltaic fuzzy adaptive control model to obtain the maximum output value of the distributed photovoltaic, and processing the maximum output value of the distributed photovoltaic to obtain the output reference value of the distributed photovoltaic. According to the method, fine control over extremely-high-proportion distributed photovoltaic is achieved, the illumination factor and the frequency adjusting capacity are considered, safety and stability of the power grid during rapid illumination and load change are ensured, the frequency of the power grid can be actively supported, and the frequency adjusting capacity and transient stability of the power grid are improved.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method and system for distributed photovoltaic active support of power grid frequency. Background Art

[0002] At a time when global climate change and environmental protection are becoming increasingly urgent, the proposal of the "carbon peak" and "carbon neutrality" goals has pointed out the direction for the development of clean energy. Among them, photovoltaic power generation, with its clean and renewable advantages, is gradually becoming an important part of the power system. With the advancement of technology and the reduction of costs, the penetration rate of photovoltaic power generation has increased rapidly in the power grid, and it is expected to reach or even exceed half of the total load in the future, marking that the transformation of the energy structure has entered a critical stage. However, this trend has also brought unprecedented challenges. The output of photovoltaic power generation is highly dependent on natural conditions, especially the intensity of light. Its volatility and uncertainty have brought tremendous pressure to the safe and stable operation of the power grid. How to efficiently utilize photovoltaic power generation resources while ensuring the safety of the power grid has become a major issue that needs to be urgently solved in the power industry.

[0003] Faced with the high penetration rate of photovoltaic power generation and its inherent volatility, the existing photovoltaic control strategies have their own advantages, but it is difficult to deal with them perfectly. Traditional droop control and virtual inertia control methods mainly focus on improving the stability of the power grid under disturbances, but they do not fully consider the direct impact of light changes on photovoltaic output during design. Therefore, in practical applications, they are often limited by the adequacy of the system's spare capacity and it is difficult to provide timely and effective support when the light changes sharply. On the other hand, although the control strategy based on maximum power point tracking (MPPT) can maximize the power generation efficiency of the photovoltaic system, it only focuses on power generation optimization under light conditions and ignores the regulation requirements of the grid frequency, resulting in the inability to provide necessary support when the grid frequency fluctuates. Therefore, the current market lacks a comprehensive photovoltaic control strategy that can fully adapt to light changes and effectively participate in grid frequency regulation. This has, to a certain extent, limited the process of safe and stable access to the grid for extremely high-proportion distributed photovoltaics. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a method for distributed photovoltaics to actively support the grid frequency to solve the problem that the prior art lacks a control strategy that can simultaneously consider the light factor and the frequency regulation capability to achieve a high proportion of distributed photovoltaics to be safely and stably connected to the grid.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for distributed photovoltaic active support of grid frequency, comprising:

[0008] Obtain key parameters of distributed photovoltaics;

[0009] According to the key parameters of the distributed photovoltaic, a distributed photovoltaic fuzzy adaptive control model is established, wherein the distributed photovoltaic fuzzy adaptive control model includes four fuzzy controllers, and the membership functions and fuzzy rules of the four fuzzy controllers are respectively set based on different scenarios;

[0010] The key parameters of real-time distributed photovoltaics are input into the distributed photovoltaic fuzzy adaptive control model to obtain the maximum output value of the distributed photovoltaics, and the maximum output value of the distributed photovoltaics is processed to obtain the output reference value of the distributed photovoltaics.

[0011] As a preferred solution of the method for distributed photovoltaic active support of grid frequency described in the present invention, different scenarios include:

[0012] Scenario 1 is that the real-time power system frequency is less than the first frequency threshold, and the real-time light intensity is less than the second light intensity threshold;

[0013] Scenario 2 is that the absolute value of the real-time light intensity change rate is greater than the third threshold value of the light intensity change rate;

[0014] Scenario three is that the real-time power system frequency change rate is greater than the fourth frequency change rate threshold.

[0015] As a preferred solution of the method for distributed photovoltaic active support of grid frequency described in the present invention, wherein: the membership functions and fuzzy rules of the four fuzzy controllers are respectively set based on different scenarios, including:

[0016] The first fuzzy controller sets a first membership function and a fuzzy rule according to scenario one, the second fuzzy controller sets a second membership function and a fuzzy rule according to scenario two, and the third fuzzy controller sets a third membership function and a fuzzy rule according to scenario three;

[0017] According to the second membership function and the fuzzy rule and the third membership function and the fuzzy rule, a fourth membership function and a fuzzy rule of the fourth fuzzy controller are set.

[0018] As a preferred solution of the method for distributed photovoltaic active support of grid frequency described in the present invention, it also includes:

[0019] The first membership function and fuzzy rule include: when the real-time power system frequency is less than the first frequency threshold, the distributed photovoltaic output is increased; when the real-time light intensity is less than the second light intensity threshold, the distributed photovoltaic output is reduced;

[0020] The second membership function and fuzzy rule include: when the absolute value of the real-time light intensity change rate is greater than the third threshold value of the light intensity change rate, increasing the distributed photovoltaic output;

[0021] The third membership function and fuzzy rule include: when the real-time power system frequency change rate is greater than the fourth frequency change rate threshold, the distributed photovoltaic output is rapidly increased;

[0022] The fourth membership function and fuzzy rule are further supplements to the second membership function and fuzzy rule and the third membership function and fuzzy rule.

[0023] As a preferred solution of the method for distributed photovoltaic active support of grid frequency described in the present invention, obtaining the maximum output value of distributed photovoltaic includes:

[0024] The key parameters of real-time distributed photovoltaics are input into the distributed photovoltaic fuzzy adaptive control model to obtain the maximum output value of discrete distributed photovoltaics. The maximum output values ​​of discrete distributed photovoltaics are smoothly connected to obtain the maximum output value of distributed photovoltaics at each moment.

[0025] As a preferred solution of the method for distributed photovoltaic active support of grid frequency described in the present invention, wherein: the maximum output value of the distributed photovoltaic is processed to obtain the output reference value of the distributed photovoltaic, including:

[0026] The maximum output value of the distributed photovoltaic is reduced to obtain a reference output value of the distributed photovoltaic, which is expressed as:

[0027] P pv (t) = ρ·P rated γ * (t)

[0028] Among them, ρ is the reserve coefficient, P rated is the active power reference value of the power system, γ * (t) is the maximum output value of distributed photovoltaic, P PV (t) is the output reference value of distributed photovoltaic, and t is time.

[0029] As a preferred scheme of the method of distributed photovoltaic active supporting grid frequency described in the present invention, the key parameters of distributed photovoltaic include the light intensity of each distributed photovoltaic at the current moment, the light intensity change rate, the power system frequency deviation and the power system frequency change rate.

[0030] In a second aspect, the present invention provides a distributed photovoltaic system for actively supporting grid frequency, comprising:

[0031] Data acquisition module, used to obtain key parameters of distributed photovoltaics;

[0032] A rule setting module, used for establishing a distributed photovoltaic fuzzy adaptive control model according to the key parameters of the distributed photovoltaic, wherein the distributed photovoltaic fuzzy adaptive control model includes four fuzzy controllers, and the membership functions and fuzzy rules of the four fuzzy controllers are respectively set based on different scenarios;

[0033] The fuzzy adaptive control module is used to input the key parameters of real-time distributed photovoltaics into the distributed photovoltaic fuzzy adaptive control model, obtain the maximum output value of distributed photovoltaics, and process the maximum output value of distributed photovoltaics to obtain the output reference value of distributed photovoltaics.

[0034] In a third aspect, the present invention provides a computing device, comprising:

[0035] Memory and processor;

[0036] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method for distributed photovoltaic active support of grid frequency are implemented.

[0037] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for distributed photovoltaic active support of grid frequency.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides an innovative fuzzy adaptive control strategy, which not only comprehensively considers the volatility and uncertainty of photovoltaic power generation, but also realizes fine control of extremely high-proportion distributed photovoltaics by introducing key parameters such as light intensity and frequency deviation, light intensity change rate and frequency change rate. At the same time, the strategy takes into account both light factors and frequency regulation capabilities, ensuring the safety and stability of the power grid when light and load change rapidly. By establishing a multi-input fuzzy controller and setting reasonable membership functions and rules, the present invention can intelligently adjust the output of distributed photovoltaics to actively support the frequency of the power grid, thereby improving the frequency regulation capability and transient stability of the power grid. In addition, by smoothly connecting discrete output points and reserving standby power, the continuity of photovoltaic output power and the reliability of the system are guaranteed, providing an effective solution for the safe and stable access of extremely high-proportion distributed photovoltaics to the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0040] Figure 1 This is a schematic diagram of the overall process of a method for distributed photovoltaic active support of grid frequency according to an embodiment of the present invention;

[0041] Figure 2 A schematic diagram of a distributed photovoltaic fuzzy adaptive control model of a method for distributed photovoltaic active support of grid frequency according to an embodiment of the present invention;

[0042] Figure 3 A schematic diagram of fuzzy rules of a first fuzzy controller of a method for distributed photovoltaic active support of grid frequency according to an embodiment of the present invention;

[0043] Figure 4 The fuzzy rule intention of the second fuzzy controller of the method for actively supporting grid frequency with distributed photovoltaic power generation according to an embodiment of the present invention;

[0044] Figure 5 A schematic diagram of fuzzy rules of a third fuzzy controller of a method for distributed photovoltaic active support of grid frequency according to an embodiment of the present invention;

[0045] Figure 6 A schematic diagram of fuzzy rules of a fourth fuzzy controller of a method for distributed photovoltaic active support of grid frequency according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0049] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0050] At the same time, in the description of the present invention, it should be noted that the orientations or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the system or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0051] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0052] Example 1

[0053] Reference Figure 1-2, as an embodiment of the present invention, provides a method for distributed photovoltaic active support of grid frequency, comprising:

[0054] S100: Obtain key parameters of distributed photovoltaics;

[0055] Preferably, the key parameters of distributed photovoltaics include the light intensity, light intensity change rate, power system frequency deviation and power system frequency change rate of each distributed photovoltaic at the current moment;

[0056] In the embodiment of the present application, after obtaining these key parameters, these parameters are preprocessed, and the preprocessing steps include but are not limited to: data cleaning, data smoothing, data standardization or normalization, and time alignment to ensure the accuracy and reliability of the subsequent fuzzy adaptive control system;

[0057] Specifically, real-time sampling is performed with Δt as the sampling interval to obtain the light intensity of the i-th distributed photovoltaic at time t Light intensity change rate and the power system frequency deviation Δf(t), the power system frequency change rate df(t);

[0058] Light intensity change rate It is expressed as:

[0059]

[0060] Among them, Δt is the time interval for sampling the light intensity change rate, is the rate of change of light intensity, is the light intensity at the current time t, is the light intensity at time t-1;

[0061] The power system frequency deviation Δf(t) is expressed as:

[0062] Δf(t)=f pll (t)-f N

[0063] Among them, f pll is the tracking frequency of the distributed photovoltaic phase-locked loop at time t, f N is the system rated frequency, Δf(t) is the power system frequency deviation;

[0064] The power system frequency change rate df(t) is expressed as:

[0065] df(t)=(Δf(t)-Δf(t-Δt)) / Δt

[0066] Among them, df(t) is the rate of change of power system frequency, Δf(t) is the frequency deviation of power system, and Δt is the time interval for sampling the rate of change of light intensity.

[0067] It should be noted that the present invention selects light intensity, light intensity change rate, power system frequency and power system frequency change rate as key parameters of distributed photovoltaics based on the following reasons:

[0068] ① Light intensity directly determines the power generation capacity of the photovoltaic system and is the main source of volatility in photovoltaic power generation. By real-time monitoring of light intensity and its rate of change, the present invention can accurately capture the potential changes in photovoltaic output power and provide basic data support for subsequent frequency regulation.

[0069] ② The power system frequency is an important indicator of the stable operation of the power grid, and its fluctuation directly reflects the supply and demand balance of the power grid. Monitoring the power system frequency and its rate of change helps the present invention to respond to the frequency fluctuation of the power grid in a timely manner, actively support the power grid frequency by adjusting the output of distributed photovoltaics, and enhance the stability and reliability of the power grid.

[0070] It should also be noted that, by preprocessing these key parameters, the present invention can eliminate noise and outliers in the data, unify the dimension of the data, ensure the accuracy and consistency of the data, thereby improving the processing efficiency and accuracy of the subsequent fuzzy adaptive control system, and enhancing the robustness and reliability of the system.

[0071] S102: According to the key parameters of distributed photovoltaics, a distributed photovoltaic fuzzy adaptive control model is established. The distributed photovoltaic fuzzy adaptive control model includes four fuzzy controllers. The membership functions and fuzzy rules of the four fuzzy controllers are set based on different scenarios. Figure 2 As shown, it represents the distributed photovoltaic fuzzy adaptive control model of the present invention. Figure 2 middle, is the light intensity, is the rate of change of light intensity, Δf is the frequency deviation of the power system within a period of time, df is the rate of change of the power system frequency, Fuzzy I is the first fuzzy controller, Fuzzy II is the second fuzzy controller, Fuzzy III is the third fuzzy controller, Fuzzy IV is the fourth fuzzy controller, γ 1 is the distributed photovoltaic output value obtained by the first fuzzy controller, γ 2 is the distributed photovoltaic output value obtained by the second fuzzy controller, γ 3 is the distributed photovoltaic output value obtained by the third fuzzy controller, γ 4 is the distributed photovoltaic output value obtained by the fourth fuzzy controller, ReserveCapacity represents the reserve for load shedding, P PV It is the output reference value of distributed photovoltaic;

[0072] It should be noted that in the distributed photovoltaic fuzzy adaptive control model, membership functions (MFs) and fuzzy rules (Fuzzy Rules) are two key elements for building a fuzzy controller, which together determine the control strategy of the system under different working conditions.

[0073] Membership function is a basic concept in fuzzy set theory, which defines the mapping relationship between input variables, such as power system frequency, light intensity, light intensity change rate, frequency change rate, etc. in the present invention and fuzzy sets. In short, the membership function is used to convert precise input values, such as specific frequency values ​​or light intensity values, into the membership degree of a fuzzy set. This membership degree is a numerical value between 0 and 1, indicating the degree to which the input value belongs to a fuzzy set. For example, in the fuzzy set of light intensity, there may be fuzzy sets such as "low", "medium", and "high", and the membership function determines the degree to which a given light intensity value belongs to these fuzzy sets.

[0074] Fuzzy rules are control strategies based on expert knowledge or system characteristics, and they are expressed in the form of "if-then". The premise part of the fuzzy rule describes the fuzzy state of the input variable, that is, the fuzzy set to which the input variable belongs after being transformed by the membership function, while the conclusion part specifies the control actions that the system should take under these fuzzy states, such as increasing or decreasing the output of distributed photovoltaics. The number and specific content of fuzzy rules depend on the complexity of the control system and the control objectives.

[0075] The membership function converts the precise input value into fuzzy quantity, so that the fuzzy rules can be inferred based on these fuzzy quantities; secondly, the fuzzy rules determine the control action according to the fuzzy state of the input variable, and this fuzzy state is provided by the membership function; finally, the execution effect of the control action will affect the input variable of the system through the feedback mechanism, thus forming a closed-loop control system. By constantly adjusting the membership function and fuzzy rules, the control system can be more adaptable to the complex and changing operating environment and improve the stability and robustness of the system.

[0076] Preferably, scenario one is that the real-time power system frequency is less than the first frequency threshold, and the real-time light intensity is less than the second light intensity threshold; scenario two is that the absolute value of the real-time light intensity change rate is greater than the third light intensity change rate threshold; scenario three is that the real-time power system frequency change rate is greater than the fourth frequency change rate threshold;

[0077] It should be noted that in the present invention, the first threshold, the second threshold, the third threshold and the fourth threshold mentioned are all key parameter limit values ​​set according to the actual operation of the power system and the characteristics of distributed photovoltaics. The specific values ​​are not limited, but need to be flexibly set according to multiple factors such as actual application scenarios, grid requirements, photovoltaic system performance and historical data;

[0078] Taking the power system frequency and light intensity as an example, if the normal system frequency is 50Hz, the first threshold can be set to 50Hz. When the real-time power system frequency is lower than this value, the system may face the risk of insufficient power supply and corresponding adjustment measures need to be taken; for light intensity, the ideal value when the light is sufficient can reach 1000W / (m2). The second threshold can be set to 1000W / (m2) as a basis for judging whether the light is significantly weakened.

[0079] Preferably, the first fuzzy controller sets the first membership function and the fuzzy rule according to scenario one, the second fuzzy controller sets the second membership function and the fuzzy rule according to scenario two, and the third fuzzy controller sets the third membership function and the fuzzy rule according to scenario three; the fourth membership function and the fuzzy rule of the fourth fuzzy controller are set according to the second membership function and the fuzzy rule and the third membership function and the fuzzy rule;

[0080] Preferably, the first membership function and the fuzzy rule include: when the real-time power system frequency is less than a first frequency threshold, increasing the distributed photovoltaic output, and when the real-time light intensity is less than a second light intensity threshold, reducing the distributed photovoltaic output;

[0081] Preferably, the second membership function and the fuzzy rule include: when the absolute value of the real-time light intensity change rate is greater than the third threshold value of the light intensity change rate, increasing the distributed photovoltaic output;

[0082] Preferably, the third membership function and the fuzzy rule include: when the real-time power system frequency change rate is greater than a fourth frequency change rate threshold, rapidly increasing the distributed photovoltaic output;

[0083] Preferably, the fourth membership function and the fuzzy rule are further supplementary to the second membership function and the fuzzy rule and the third membership function and the fuzzy rule;

[0084] Specifically, the corresponding measures for different scenarios are:

[0085] ① When the frequency of the power system becomes smaller, it means that the grid load exceeds the generator output, and the output of distributed photovoltaics should be increased; when the light intensity becomes smaller, the output upper limit of distributed photovoltaics becomes smaller, and the output of distributed photovoltaics should be reduced accordingly.

[0086] ② If the clouds above the distributed photovoltaics are suddenly blocked and the rate of change of light intensity is a large negative value, the light intensity will suddenly drop. According to the first membership function and fuzzy rules, the output of distributed photovoltaics will also drop sharply, causing power fluctuations in the power grid. In order to maintain the power balance of the system, it is necessary to add a second membership function and fuzzy rules to increase the output of distributed photovoltaics, avoiding sudden changes in photovoltaic output due to sudden changes in light intensity, which is similar to adding "inertia" to the change in the influence of light intensity on photovoltaic output;

[0087] ③ If the grid load drops suddenly, the frequency change rate of the power system is a large positive value. If only based on the first membership function and fuzzy rules and the second membership function and fuzzy rules, the output of distributed photovoltaics will also drop after a period of time, but the response time is slow, which will cause a sudden increase in the grid frequency. In order to maintain the transient frequency stability of the grid and maintain power balance, it is necessary to add fuzzy rule III to quickly increase the output of distributed photovoltaics and suppress the unbalanced power caused by load changes, simulating the "inertia" of virtual inertia control;

[0088] ④ If the light intensity drops sharply, if the "inertia" compensation mechanism of the second membership function and fuzzy rules is not enough to completely offset the sudden drop in photovoltaic output, the fourth membership function and fuzzy rules will intervene to provide enhanced compensation measures, and even combine the current trend of grid load changes to comprehensively adjust photovoltaic output to more finely control power output; if the grid load drops significantly, the rapid response of the third membership function and fuzzy rules can initially alleviate the problem of excess power, but it is not enough to completely prevent the sudden increase in grid frequency. The fourth membership function and fuzzy rules will further optimize the adjustment strategy of photovoltaic output according to the real-time changes in light intensity to achieve more accurate power balance; if in extreme scenarios where both light intensity and grid load change significantly at the same time, the second membership function and fuzzy rules and the third membership function and fuzzy rules may be difficult to work alone due to the mutual constraints between the control targets. At this time, the fourth membership function and fuzzy rules will play their core advantages of comprehensive evaluation and dynamic adjustment, and flexibly and accurately adjust photovoltaic output based on the dual change rates of light intensity and grid frequency to ensure that the system can still maintain a high degree of stability and power balance when facing a complex and changing operating environment.

[0089] It should be noted that the present invention realizes intelligent monitoring and dynamic response to key parameters such as power system frequency and light intensity through the construction of the above-mentioned distributed photovoltaic fuzzy adaptive control model; the model uses four carefully designed fuzzy controllers to flexibly adjust the output of distributed photovoltaics for different scenarios such as power system frequency reduction, light intensity reduction, sharp changes in light intensity, and sudden changes in grid load. By converting the precise input value into a fuzzy quantity through the membership function, and reasoning based on the fuzzy rules formulated based on expert knowledge, the system can quickly and accurately judge the current working conditions and take corresponding control actions. This fuzzy adaptive control strategy not only effectively improves the adaptability of distributed photovoltaics in complex and changeable environments, but also enhances the frequency stability and power balancing ability of the power system. When the grid load fluctuates or the light conditions change, the model can respond quickly, suppress power imbalance by increasing or reducing photovoltaic output, simulate the "inertia" of virtual inertia control, thereby avoiding the sharp fluctuation of grid frequency and ensuring the safe and stable operation of the power system.

[0090] S104: inputting the key parameters of the real-time distributed photovoltaic into the distributed photovoltaic fuzzy adaptive control model, obtaining the maximum output value of the distributed photovoltaic, and processing the maximum output value of the distributed photovoltaic to obtain the output reference value of the distributed photovoltaic;

[0091] Preferably, the key parameters of real-time distributed photovoltaics are input into the distributed photovoltaic fuzzy adaptive control model to obtain the maximum output value of discrete distributed photovoltaics, and the maximum output values ​​of discrete distributed photovoltaics are smoothly connected to obtain the maximum output value of distributed photovoltaics at each moment;

[0092] In the embodiment of the present application, the expression for obtaining the maximum output value of distributed photovoltaic at each moment is:

[0093]

[0094] Among them, γ * (t) is the maximum output value of distributed photovoltaic, t j is the time corresponding to the jth sampling with Δt as the time interval;

[0095] Preferably, the maximum output value of the distributed photovoltaic is reduced to obtain a reference output value of the distributed photovoltaic, which is expressed as:

[0096] P pv (t) = ρ·P rated γ * (t)

[0097] Among them, ρ is the reserve coefficient, P rated is the active power reference value of the power system, γ *(t) is the maximum output value of distributed photovoltaic, P PV (t) is the output reference value of distributed photovoltaic, t is time;

[0098] It should be noted that by smoothly connecting the discrete maximum output values ​​of distributed photovoltaics, the photovoltaic output power is ensured to change continuously over time, which improves the stability and control accuracy of the system; at the same time, a certain proportion of spare capacity is reserved, and the output reference value of distributed photovoltaics is obtained through load reduction processing, which not only ensures the safe operation of the power system, but also improves the economy and reliability of the photovoltaic system. This series of measures jointly realizes the fuzzy adaptive control of distributed photovoltaics and optimizes its power generation efficiency and use efficiency.

[0099] The above is a schematic scheme of a method for distributed photovoltaic active support of grid frequency in this embodiment. It should be noted that the technical scheme of the distributed photovoltaic active support grid frequency system and the technical scheme of the above-mentioned distributed photovoltaic active support grid frequency method belong to the same concept. For details not described in detail in the technical scheme of the distributed photovoltaic active support grid frequency system in this embodiment, please refer to the description of the technical scheme of the above-mentioned distributed photovoltaic active support grid frequency method.

[0100] In this embodiment, the distributed photovoltaic system actively supporting the grid frequency includes:

[0101] Data acquisition module, used to obtain key parameters of distributed photovoltaics;

[0102] A rule setting module is used to establish a distributed photovoltaic fuzzy adaptive control model according to key parameters of distributed photovoltaics. The distributed photovoltaic fuzzy adaptive control model includes four fuzzy controllers. The membership functions and fuzzy rules of the four fuzzy controllers are set based on different scenarios.

[0103] The fuzzy adaptive control module is used to input the key parameters of real-time distributed photovoltaics into the distributed photovoltaic fuzzy adaptive control model, obtain the maximum output value of distributed photovoltaics, and process the maximum output value of distributed photovoltaics to obtain the output reference value of distributed photovoltaics.

[0104] This embodiment further provides a computing device, which is applicable to the case where distributed photovoltaics actively support the grid frequency, and includes:

[0105] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the method of realizing distributed photovoltaic active support of grid frequency as proposed in the above embodiment.

[0106] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing distributed photovoltaic active support of grid frequency as proposed in the above embodiment is implemented.

[0107] The storage medium proposed in this embodiment and the method for realizing distributed photovoltaic active support of grid frequency proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0108] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0109] Example 2

[0110] Reference Figure 3-6 Tables 1-4 are an embodiment of the present invention, which provides a method for distributed photovoltaic active support of grid frequency. In order to verify its beneficial effects, some parameter settings of the present invention are provided.

[0111] According to the method of the present invention, a fuzzy adaptive control model including four fuzzy controllers is designed to realize refined control of the distributed photovoltaic system. Each fuzzy controller maps the fuzzy set of input variables to the fuzzy set of output variables through its specific membership function and fuzzy rules, thereby realizing the regulation of system behavior.

[0112] The membership functions of the first fuzzy controller are shown in Table 1. The fuzzy rules of the first fuzzy controller are as follows: Figure 3 As shown;

[0113] Table 1

[0114]

[0115] In Table 1, is the light intensity, Δf(t) is the frequency deviation of the power system; NB (Negative Big): means "negative big", that is, the value of the variable is in its larger negative range; NM (Negative Medium): means "negative medium", that is, the value of the variable is in its medium negative range; NS (Negative Small): means "negative small", that is, the value of the variable is in its smaller negative range; ZO (Zero): means "zero", that is, the value of the variable is close to or equal to zero; PS (Positive Small): means "positive small", that is, the value of the variable is in its smaller positive range; PM (Positive Medium): means "positive medium", that is, the value of the variable is in its medium positive range; PB (Positive Big): means "positive big", that is, the value of the variable is in its larger positive range. It should be noted that Figure 3 The characters that appear have the same meaning as those in Table 1.

[0116] The membership functions of the second fuzzy controller are shown in Table 1, and the fuzzy rules of the second fuzzy controller are as follows: Figure 4 As shown;

[0117] Table 2

[0118]

[0119] In Table 2, is the rate of change of light intensity, Δf(t) is the frequency deviation of the power system; NB (Negative Big): means "negative big", that is, the value of the variable is in its larger negative range; NM (Negative Medium): means "negative medium", that is, the value of the variable is in its medium negative range; NS (Negative Small): means "negative small", that is, the value of the variable is in its smaller negative range; ZO (Zero): means "zero", that is, the value of the variable is close to or equal to zero; PS (Positive Small): means "positive small", that is, the value of the variable is in its smaller positive range; PM (Positive Medium): means "positive medium", that is, the value of the variable is in its medium positive range; PB (Positive Big): means "positive big", that is, the value of the variable is in its larger positive range. It should be noted that Figure 4 The characters that appear have the same meaning as those in Table 2.

[0120] The membership functions of the third fuzzy controller are shown in Table 3, and the fuzzy rules of the third fuzzy controller are as follows: Figure 5 As shown;

[0121] Table 3

[0122]

[0123] In Table 3, is the light intensity, df(t) is the rate of change of power system frequency, NB (Negative Big): means "negative big", that is, the value of the variable is in its larger negative range; NM (Negative Medium): means "negative medium", that is, the value of the variable is in its medium negative range; NS (Negative Small): means "negative small", that is, the value of the variable is in its smaller negative range; ZO (Zero): means "zero", that is, the value of the variable is close to or equal to zero; PS (Positive Small): means "positive small", that is, the value of the variable is in its smaller positive range; PM (Positive Medium): means "positive medium", that is, the value of the variable is in its medium positive range; PB (Positive Big): means "positive big", that is, the value of the variable is in its larger positive range. It should be noted that Figure 5 The characters that appear have the same meaning as those in Table 3.

[0124] The membership function of the fourth fuzzy controller is shown in Table 4, and the fuzzy rules of the fourth fuzzy controller are as follows: Figure 6 As shown;

[0125] Table 4

[0126]

[0127] In Table 4, is the rate of change of light intensity, df(t) is the rate of change of power system frequency, NB (Negative Big): means "negative big", that is, the value of the variable is in its larger negative range; NM (Negative Medium): means "negative medium", that is, the value of the variable is in its medium negative range; NS (Negative Small): means "negative small", that is, the value of the variable is in its smaller negative range; ZO (Zero): means "zero", that is, the value of the variable is close to or equal to zero; PS (Positive Small): means "positive small", that is, the value of the variable is in its smaller positive range; PM (Positive Medium): means "positive medium", that is, the value of the variable is in its medium positive range; PB (Positive Big): means "positive big", that is, the value of the variable is in its larger positive range. It should be noted that Figure 6 The characters that appear have the same meaning as those in Table 4.

[0128] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for distributed photovoltaic active support of grid frequency, characterized in that: include: Obtain key parameters of distributed photovoltaics; According to the key parameters of the distributed photovoltaic, a distributed photovoltaic fuzzy adaptive control model is established, wherein the distributed photovoltaic fuzzy adaptive control model includes four fuzzy controllers, and the membership functions and fuzzy rules of the four fuzzy controllers are respectively set based on different scenarios; The key parameters of real-time distributed photovoltaics are input into the distributed photovoltaic fuzzy adaptive control model to obtain the maximum output value of the distributed photovoltaics, and the maximum output value of the distributed photovoltaics is processed to obtain the output reference value of the distributed photovoltaics.

2. The method for distributed photovoltaic active support of grid frequency as claimed in claim 1, characterized in that: Different scenarios include: Scenario 1 is that the real-time power system frequency is less than the first frequency threshold, and the real-time light intensity is less than the second light intensity threshold; Scenario 2 is that the absolute value of the real-time light intensity change rate is greater than the third threshold value of the light intensity change rate; Scenario three is that the real-time power system frequency change rate is greater than the fourth frequency change rate threshold.

3. The method for distributed photovoltaic active support of grid frequency as claimed in claim 1 or 2, characterized in that: The membership functions and fuzzy rules of the four fuzzy controllers are set based on different scenarios, including: The first fuzzy controller sets a first membership function and a fuzzy rule according to scenario one, the second fuzzy controller sets a second membership function and a fuzzy rule according to scenario two, and the third fuzzy controller sets a third membership function and a fuzzy rule according to scenario three; According to the second membership function and the fuzzy rule and the third membership function and the fuzzy rule, a fourth membership function and a fuzzy rule of the fourth fuzzy controller are set.

4. The method for distributed photovoltaic active support of grid frequency as claimed in claim 3, characterized in that: Also includes, The first membership function and fuzzy rule include: when the real-time power system frequency is less than the first frequency threshold, the distributed photovoltaic output is increased; when the real-time light intensity is less than the second light intensity threshold, the distributed photovoltaic output is reduced; The second membership function and fuzzy rule include: when the absolute value of the real-time light intensity change rate is greater than the third threshold value of the light intensity change rate, increasing the distributed photovoltaic output; The third membership function and fuzzy rule include: when the real-time power system frequency change rate is greater than the fourth frequency change rate threshold, the distributed photovoltaic output is rapidly increased; The fourth membership function and fuzzy rule are further supplements to the second membership function and fuzzy rule and the third membership function and fuzzy rule.

5. The method for distributed photovoltaic active support of grid frequency as claimed in claim 4, characterized in that: Obtaining the maximum output value of distributed photovoltaics includes: The key parameters of real-time distributed photovoltaics are input into the distributed photovoltaic fuzzy adaptive control model to obtain the maximum output value of discrete distributed photovoltaics. The maximum output values ​​of discrete distributed photovoltaics are smoothly connected to obtain the maximum output value of distributed photovoltaics at each moment.

6. The method for distributed photovoltaic active support of grid frequency as claimed in claim 5, characterized in that: The maximum output value of the distributed photovoltaic is processed to obtain a distributed photovoltaic output reference value, including: The maximum output value of the distributed photovoltaic is reduced to obtain a reference output value of the distributed photovoltaic, which is expressed as: P pv (t)=ρ·P ated ·c * (t) Among them, ρ is the reserve coefficient, P rated is the active power reference value of the power system, γ * (t) is the maximum output value of distributed photovoltaic, P PV (t) is the output reference value of distributed photovoltaic, and t is time.

7. The method for distributed photovoltaic active support of grid frequency as claimed in claim 1 or 6, characterized in that: The key parameters of distributed photovoltaics include the light intensity of each distributed photovoltaic at the current moment, the light intensity change rate, the power system frequency deviation and the power system frequency change rate.

8. A distributed photovoltaic system that actively supports grid frequency, characterized in that: include, Data acquisition module, used to obtain key parameters of distributed photovoltaics; A rule setting module, used for establishing a distributed photovoltaic fuzzy adaptive control model according to the key parameters of the distributed photovoltaic, wherein the distributed photovoltaic fuzzy adaptive control model includes four fuzzy controllers, and the membership functions and fuzzy rules of the four fuzzy controllers are respectively set based on different scenarios; The fuzzy adaptive control module is used to input the key parameters of real-time distributed photovoltaics into the distributed photovoltaic fuzzy adaptive control model, obtain the maximum output value of distributed photovoltaics, and process the maximum output value of distributed photovoltaics to obtain the output reference value of distributed photovoltaics.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method for distributed photovoltaic active support of grid frequency as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for distributed photovoltaic active support of grid frequency as described in any one of claims 1 to 7.

Images