Photovoltaic power generation system optimization method based on dynamic reactive power margin management

Abstract

The invention discloses a photovoltaic power generation system optimization method based on dynamic reactive power margin management, and relates to the technical field of new energy power generation, and the method comprises the steps: obtaining first parameters of a photovoltaic power generation system, the first parameters comprising photovoltaic array output power, inverter operation parameters and power grid operation parameters; calculating the current reactive power margin of the system based on the first parameter to obtain a reactive power margin parameter; according to the reactive power margin parameter, setting a dynamic threshold identification system reactive power optimization space to obtain a second parameter which represents the adjustable reactive power capacity of the system; and inputting the reactive power margin parameter and the adjusted reactive power into a particle swarm optimization algorithm, and searching an optimal reactive power distribution scheme which enables the system voltage stability margin to be maximum through the particle swarm optimization algorithm. And the robustness and reliability of the system are improved.

Description

Technical Field

[0001] The invention relates to the technical field of renewable energy power generation, and in particular to a photovoltaic power generation system optimization method based on dynamic reactive power margin management. Background Art

[0002] With the rapid development of renewable energy, the proportion of photovoltaic power generation systems in the power system continues to increase. However, due to the intermittent and fluctuating characteristics of photovoltaic power generation, its large-scale grid connection has brought challenges to the safe and stable operation of the power grid. Especially in terms of reactive power management, the traditional static reactive power compensation method is difficult to adapt to the dynamic characteristics of photovoltaic power generation systems, which may lead to problems such as insufficient or wasteful reactive power margin in the system.

[0003] At present, the research on reactive power management of photovoltaic power generation systems mainly focuses on the following aspects: fixed power factor control, constant reactive power control, voltage droop control, etc. Although these methods have improved the reactive power regulation capability of the system to a certain extent, there are still the following problems: the reactive power margin cannot be dynamically adjusted according to the real-time operating status of the system, resulting in low utilization of reactive resources in the system; the fluctuation characteristics of photovoltaic power generation are not considered, and the reactive power demand changes of the system cannot be responded to quickly; the overall reactive power optimization of the system is not considered, and it is difficult to achieve global optimal control. Therefore, there is an urgent need for a reactive power control method for photovoltaic power generation systems that can dynamically manage reactive power margin, quickly respond to system changes, and achieve global optimization. Summary of the invention

[0004] In view of the problems existing in the existing photovoltaic power generation system, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to achieve dynamic management of the reactive power margin of the photovoltaic power generation system, how to quickly respond to changes in the reactive power demand of the system, and how to achieve global optimization of the reactive power resources of the entire system.

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

[0007] In a first aspect, an embodiment of the present invention provides a control big data early warning analysis method based on power grid monitoring data, which includes obtaining a first parameter of a photovoltaic power generation system, wherein the first parameter includes a photovoltaic array output power, an inverter operating parameter, and a power grid operating parameter;

[0008] Based on the first parameter, calculating the current reactive power margin of the system to obtain a reactive power margin parameter;

[0009] According to the reactive margin parameter, a dynamic threshold is set to identify the reactive optimization space of the system, and a second parameter is obtained, wherein the second parameter represents the adjustable reactive capacity of the system;

[0010] Combining the reactive power margin parameter and the second parameter, dynamically optimizing the reactive power of the system;

[0011] The reactive power margin parameter and the adjusted reactive power are input into a particle swarm optimization algorithm, and the particle swarm optimization algorithm is used to find an optimal reactive power allocation scheme that maximizes the system voltage stability margin.

[0012] As a preferred solution of the control big data early warning analysis method based on power grid monitoring data described in the present invention, wherein: the first parameter also includes environmental parameters, and the environmental parameters include temperature, radiation intensity and wind speed.

[0013] As a preferred solution of the control big data early warning analysis method based on power grid monitoring data described in the present invention, wherein: based on the first parameter, the current reactive power margin of the system is calculated to obtain a reactive power margin parameter, which is expressed as:

[0014] QM(t)=f(P1,P2,…,P n ;θ)

[0015] Where Pn represents n collected system parameters, f is the function for evaluating reactive margin, and θ represents the corresponding model parameters.

[0016] As a preferred solution of the control big data early warning analysis method based on power grid monitoring data described in the present invention, wherein: according to the reactive margin parameter, a dynamic threshold is set to identify the reactive optimization space of the system, including:

[0017] When the reactive power margin QM(t) of the system is higher than the dynamic threshold, it indicates that there is room for reactive power optimization in the system.

[0018] The dynamic threshold acquisition includes:

[0019] According to the system operation record in the historical data, historical reactive power margin data is obtained, wherein the historical reactive power margin data is the reactive power margin QM(t) when the system operates normally;

[0020] The dynamic threshold is set at 60% of the historical reactive margin data;

[0021] The second parameter is obtained by calculating the adjustable reactive capacity in the first parameter.

[0022] As a preferred solution of the control big data early warning analysis method based on power grid monitoring data described in the present invention, wherein: combining the reactive margin parameter and the second parameter to dynamically optimize the reactive power of the system includes:

[0023]

[0024] Where Q(t+1) is the predicted reactive power of the system at time t+1, Q(t) is the reactive power of the system at time t, α is the adjustment factor, ranging from 0.1 to 0.5, QM max is the maximum reactive power margin of the system; where Q(t+1) is the predicted reactive power of the system at time t+1, Q(t) is the reactive power of the system at time t, α is the adjustment factor, ranging from 0.1 to 0.5, QM max is the maximum reactive power margin of the system.

[0025] As a preferred solution of the control big data early warning analysis method based on power grid monitoring data described in the present invention, the reactive margin parameter and the adjusted reactive power are input into the particle swarm optimization algorithm, and the reactive distribution of the system is optimized by finding the reactive margin parameter and the adjusted reactive power that maximize the system voltage stability margin, which specifically includes:

[0026] Calculate the velocity v of particle i moving from its current position x to its new position y at time t, expressed as,

[0027] v(i,t+1)=wv(i,t)+c1r1*(pbest(i)-x(i,t))+c2r2*(gbest-x(i,t))

[0028] Among them, w is the inertia weight, ranging from 0.4 to 0.9, c1 and c2 are acceleration constants, ranging from 1.5 to 2.5, r1 and r2 are random numbers between (0,1), pbest(i) is the individual optimal position of particle i, and gbest is the global optimal position;

[0029] The particle updates its position according to its velocity, expressed as:

[0030] x(i,t+1)=x(i,t)+v(i,t+1)

[0031] Among them, x(i,t+1) represents the new position of particle i at time t+1;

[0032] The system voltage stability margin is obtained by using a reactive margin parameter and adjusted reactive power.

[0033] As a preferred solution of the control big data early warning analysis method based on power grid monitoring data described in the present invention, wherein: the optimization system reactive power distribution also includes:

[0034] Update the fitness value of the particle swarm and adjust the particle distribution accordingly, expressed as:

[0035]

[0036] Where fit is the fitness value, fit(i,t) represents the fitness value of particle i at time t, fit(i,t+1) represents the fitness value of the particle at time t+1, β is the adjustment coefficient, ranging from 0.1 to 0.3, VSM is the voltage stability margin, VSM max It is the maximum voltage stability margin allowed by the system.

[0037] In a second aspect, an embodiment of the present invention provides a photovoltaic power generation system optimization system based on dynamic reactive power margin management, which includes an acquisition module for acquiring a first parameter of the photovoltaic power generation system, wherein the first parameter includes a photovoltaic array output power, an inverter operating parameter, and a grid operating parameter;

[0038] A calculation module, configured to calculate a current reactive power margin of the system based on the first parameter to obtain a reactive power margin parameter;

[0039] An identification module, used to set a dynamic threshold to identify the reactive optimization space of the system according to the reactive margin parameter, and obtain a second parameter, wherein the second parameter represents the adjustable reactive capacity of the system;

[0040] A regulating module, used for combining the reactive power margin parameter and the second parameter to perform dynamic reactive power optimization regulation on the system;

[0041] The optimization module is used to input the reactive power margin parameter and the adjusted reactive power into a particle swarm optimization algorithm, and to find an optimal reactive power allocation scheme that maximizes the system voltage stability margin through the particle swarm optimization algorithm.

[0042] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the photovoltaic power generation system optimization method based on dynamic reactive power margin management as described in the first aspect of the present invention are implemented.

[0043] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the photovoltaic power generation system optimization method based on dynamic reactive power margin management as described in the first aspect of the present invention are implemented.

[0044] The beneficial effects of the present invention are as follows: the present invention can monitor and adjust the reactive resources of the system in real time through dynamic reactive margin management, quickly respond to changes in the reactive demand of the system, and locate the optimization space; based on this, the system reactive power can be quickly optimized and adjusted to adapt to the fluctuation characteristics of photovoltaic power generation; the above two types of information are combined, and the overall reactive power optimization of the system can be achieved through the particle swarm optimization algorithm, thereby improving the voltage stability and reactive resource utilization of the system; the method of the present invention has strong adaptability, and can dynamically adjust the control parameters according to the actual operating conditions of the system, thereby improving the robustness and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of 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 work.

[0046] Figure 1 The present invention is a schematic diagram of the overall process of a photovoltaic power generation system optimization method based on dynamic reactive power margin management according to an embodiment of the present invention.

[0047] Figure 2 Schematic diagram of the structure of a dynamic reactive power margin management module in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the above-mentioned objects, 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 accompanying drawings.

[0049] 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.

[0050] 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.

[0051] Example 1

[0052] Reference Figure 1-2 , which is the first embodiment of the present invention, and provides a photovoltaic power generation system optimization method based on dynamic reactive power margin management, comprising:

[0053] S1: obtaining first parameters of the photovoltaic power generation system, the first parameters including photovoltaic array output power, inverter operating parameters and grid operating parameters;

[0054] Furthermore, the first parameter also includes environmental parameters, and the environmental parameters include temperature, radiation intensity and wind speed.

[0055] It should be noted that the parameters are optimally selected, among which the photovoltaic array output power, inverter operating parameters and grid operating parameters can reflect the reactive power demand and adjustable capacity of the system to the greatest extent, while environmental parameters such as temperature, irradiation intensity and wind speed have an important influence on the output characteristics of the photovoltaic power generation system, which helps to more accurately predict the changes in the reactive power demand of the system.

[0056] S2: Based on the first parameter, calculate the current reactive power margin of the system to obtain a reactive power margin parameter;

[0057] Furthermore, based on the first parameter, the current reactive power margin of the system is calculated to obtain a reactive power margin parameter, which is expressed as:

[0058] QM(t)=f(P1,P2,…,P n ;θ)

[0059] Among them, P n represents n collected system parameters, f is the function for evaluating reactive margin, and θ represents the corresponding model parameters.

[0060] It should be noted that the f function needs to be determined based on the actual system characteristics and operating environment. Different systems and environments will form different functional relationships.

[0061] Preferably, the specific form of the function f can be determined by machine learning methods, where θ represents the parameters of the model, which can be learned by training a large amount of historical data and is not a fixed value.

[0062] Specifically, for example, the f function is constructed by a multi-layer perceptron (MLP) neural network, which can be expressed as:

[0063] QM(t)=f(P1,P2,…,P n ;θ)=g(W2·g(W1·P+b1)+b2)

[0064] Among them, P = [P1, P2, ..., Pn] is the input system parameter, W1 and W2 are the weight parameters of the neural network, b1 and b2 are bias parameters, which are all included in θ, and g is the activation function, such as the ReLU function.

[0065] It should also be noted that the reactive margin parameter is an indicator to measure the current reactive power regulation capability of the system. This reactive margin can be used in the following steps to determine whether there is room for reactive power optimization in the system.

[0066] S3: According to the reactive margin parameter, a dynamic threshold is set to identify the reactive optimization space of the system, and a second parameter is obtained, where the second parameter represents the adjustable reactive capacity of the system;

[0067] Furthermore, according to the reactive margin parameters, dynamic thresholds are set to identify the reactive power optimization space of the system, including:

[0068] When the reactive power margin QM(t) of the system is higher than the dynamic threshold, it indicates that there is reactive power optimization space in the system.

[0069] Dynamic threshold acquisition includes,

[0070] According to the system operation record in the historical data, historical reactive power margin data is obtained, wherein the historical reactive power margin data is the reactive power margin QM(t) when the system is operating normally;

[0071] The dynamic threshold is set at 60% of the historical reactive margin data;

[0072] Wherein, the second parameter is obtained by calculating the adjustable reactive capacity in the first parameter.

[0073] It should be noted that the setting of the dynamic threshold at 60% of the historical reactive margin data is an optimized choice. If the threshold is set too high, some potential optimization opportunities may be missed; if it is set too low, it may lead to frequent optimization operations and increase system instability. Through actual testing and analysis, it is found that setting the threshold at around 60% can achieve a better balance between optimization effect and system stability.

[0074] S4: Combining the reactive power margin parameter and the second parameter to dynamically optimize the reactive power of the system;

[0075] Furthermore, the system is dynamically optimized for reactive power by combining the reactive power margin parameter and the second parameter, including:

[0076]

[0077] Among them, Q(t+1) is the predicted reactive power of the system at time t+1, Q(t) is the reactive power of the system at time t, α is the adjustment factor, and the value range is 0.1 to 0.5, QM max is the maximum reactive power margin of the system.

[0078] It should be noted that the selection of α in this step has an important influence on the dynamic response characteristics of the system. The larger the α value, the faster the system responds to the change of reactive margin, but it may cause system oscillation; the smaller the α value, the smoother the system response, but it may affect the optimization effect. In practical applications, the α value can be adjusted according to system characteristics and operating requirements. Through a large number of experiments, we found that when the α value is between 0.1 and 0.5, the system can achieve a good balance between response speed and stability.

[0079] S5: Input the reactive power margin parameters and the adjusted reactive power into the particle swarm optimization algorithm, and use the particle swarm optimization algorithm to find the optimal reactive power allocation scheme that maximizes the system voltage stability margin.

[0080] Furthermore, the reactive margin parameter and the adjusted reactive power are input into a particle swarm optimization algorithm, and the reactive distribution of the system is optimized by finding the reactive margin parameter and the adjusted reactive power that maximize the system voltage stability margin, specifically including:

[0081] Calculate the velocity v of particle i moving from its current position x to its new position y at time t, expressed as,

[0082] v(i,t+1)=wv(i,t)+c1r1*(pbest(i)-x(i,t))+c2r2*(gbest-x(i,t))

[0083] Where w is the inertia weight, ranging from 0.4 to 0.9, c1 and c2 are acceleration constants, ranging from 1.5 to 2.5, r1 and r2 are random numbers between (0, 1), pbest(i) is the individual optimal position of particle i, and gbest is the global optimal position;

[0084] The particle updates its position according to the velocity, expressed as:

[0085] x(i,t+1)=x(i,t)+v(i,t+1)

[0086] Among them, x(i,t+1) represents the new position of particle i at time t+1;

[0087] The system voltage stability margin is obtained by using a reactive power margin parameter and adjusted reactive power.

[0088] It should be noted that the parameter selection in the particle swarm optimization algorithm has an important influence on the optimization effect. The inertia weight w affects the global search ability of the particles, and the acceleration constants c1 and c2 affect the tendency of the particles to move to the individual optimal position and the global optimal position respectively. In practical applications, the convergence speed and optimization effect of the algorithm can be improved by tuning the parameters. After a large number of experiments, we found that when the value of w is between 0.4 and 0.9, and the values ​​of c1 and c2 are between 1.5 and 2.5, the algorithm can achieve a good balance between global search ability and local refined search.

[0089] Furthermore, optimizing the reactive power distribution of the system also includes:

[0090] Update the fitness value of the particle swarm and adjust the particle distribution accordingly, expressed as:

[0091]

[0092] Where fit is the fitness value, fit(i,t) represents the fitness value of particle i at time t, fit(i,t+1) represents the fitness value of the particle at time t+1, β is the adjustment coefficient, ranging from 0.1 to 0.3, VSM is the voltage stability margin, VSM max It is the maximum voltage stability margin allowed by the system.

[0093] It should be noted that by adjusting the fitness value of the particles, the particle swarm can be guided to search in a direction that is more conducive to improving the system voltage stability margin. The choice of β affects the adjustment range of the fitness value. The larger the β value, the more obvious the adjustment, but it may affect the convergence of the algorithm; the smaller the β value, the smoother the adjustment, but it may reduce the optimization efficiency. Through experiments, we found that when the β value is between 0.1 and 0.3, a good balance can be achieved between the optimization effect and the stability of the algorithm.

[0094] Furthermore, the present embodiment also provides a photovoltaic power generation system optimization system based on dynamic reactive power margin management, including: an acquisition module for acquiring a first parameter of the photovoltaic power generation system, wherein the first parameter includes a photovoltaic array output power, an inverter operating parameter, and a grid operating parameter;

[0095] A calculation module, used for calculating the current reactive power margin of the system based on the first parameter to obtain a reactive power margin parameter;

[0096] An identification module, used to set a dynamic threshold to identify the reactive optimization space of the system according to the reactive margin parameter, and obtain a second parameter, wherein the second parameter represents the adjustable reactive capacity of the system;

[0097] A regulating module, used for combining the reactive power margin parameter and the second parameter to perform dynamic reactive power optimization regulation on the system;

[0098] An optimization module is used to input the reactive power margin parameter and the adjusted reactive power into a particle swarm optimization algorithm, and to find the optimal reactive power allocation scheme that maximizes the system voltage stability margin through the particle swarm optimization algorithm.

[0099] This embodiment also provides a computer device, which is suitable for the case of a photovoltaic power generation system optimization method based on dynamic reactive power margin management, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the photovoltaic power generation system optimization method based on dynamic reactive power margin management as proposed in the above embodiment.

[0100] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0101] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the photovoltaic power generation system optimization method based on dynamic reactive power margin management as proposed in the above embodiment is implemented.

[0102] In summary, the present invention can monitor and adjust the system reactive resources in real time through dynamic reactive margin management, quickly respond to changes in the system reactive demand, and locate optimization space; based on this, the system reactive power can be quickly optimized and adjusted to adapt to the fluctuation characteristics of photovoltaic power generation; the above two types of information are combined to achieve overall reactive power optimization of the system through the particle swarm optimization algorithm, thereby improving the voltage stability of the system and the utilization rate of reactive resources; the method of the present invention has strong adaptability and can dynamically adjust the control parameters according to the actual operating conditions of the system, thereby improving the robustness and reliability of the system.

[0103] Example 2

[0104] Referring to Tables 1 to 3, an embodiment of the present invention provides a photovoltaic power generation system optimization method based on dynamic reactive power margin management. In order to verify its beneficial effects, an exemplary application scenario and comparison scheme are provided for scientific demonstration.

[0105] S1: Obtain the first parameter of the photovoltaic power generation system, S2: Based on the first parameter, calculate the current reactive power margin of the system, obtain the reactive power margin parameter, and obtain it through the (ReLU) function; S3: According to the reactive power margin parameter, set a dynamic threshold to identify the reactive power optimization space of the system, and obtain the second parameter, which characterizes the adjustable reactive power capacity of the system; S4: Combine the reactive power margin parameter and the second parameter to dynamically optimize the reactive power of the system; S5: Input the reactive power margin parameter and the adjusted reactive power into the particle swarm optimization algorithm, and use the particle swarm optimization algorithm to find the optimal reactive power allocation scheme that maximizes the system voltage stability margin;

[0106] According to the above scheme, some specific values ​​of the photovoltaic power generation system in the whole process are shown. The reactive margin parameters of the system and the data applied to the particle swarm optimization algorithm are shown in Table 1.

[0107] Table 1 Changes of some system parameters

[0108]

[0109] As can be seen from Table 1, after the reactive power optimization adjustment is performed according to the reactive margin parameters and dynamic thresholds, the reactive power output of the system is adjusted to varying degrees, so that it can better adapt to the overall operation requirements of the system. The voltage stability margin of the system is the optimal state information obtained by the particle swarm optimization algorithm. The stability of the photovoltaic power generation system can be quickly judged from the index, thus providing a basis for subsequent maintenance and optimization.

[0110] Among them, step S3 involves the optimization of dynamic thresholds. When determining the optimal value of the dynamic threshold, historical data is used for detection. This part is only used as an example to slightly simplify the setting process of the dynamic threshold. The reactive margin parameter of the system can be defined as between 0 (the system has no reactive regulation capability) and 1 (the system has maximum reactive regulation capability), and the normal operation threshold of the system is set to 0.55. By setting different thresholds, detection is performed, as shown in Table 2.

[0111] Table 2 Dynamic threshold test table

[0112]

[0113] In the above situation, we can see that system No. 001 is actually running normally, but because the threshold is set too low, the system mistakenly judges that there is a large room for optimization, which may lead to unnecessary adjustments; although the reactive margin of No. 002 is low, the threshold is set too low, and the system still judges that there is room for optimization, which may cause the system to be over-adjusted; although there may be some problems with system No. 003, because the reactive margin has changed from the normal 0.55 to 0.55, the system does not recognize the optimization space because the threshold is set too high; even if the reactive margin of system No. 004 is high, indicating that the system is in good operating condition, the threshold is too high and the system may miss the optimization opportunity.

[0114] At the same time, we provide a set of simulation data for comparison with the traditional adjustment model that does not involve reactive power adjustment through optimal parameters and does not use the particle swarm optimization algorithm, see Table 3

[0115] Table 3 Parameter comparison table

[0116]

[0117] Table 3 shows the difference between our solution and the traditional model under actual system conditions, assuming that the system value at a certain moment is the baseline of the reactive margin parameter and reactive adjustment, that is, the value of the traditional model, and compares the changes in these values ​​when using our model. It can be seen that by using our model, the reactive margin of the system can be evaluated more accurately, and the operating efficiency of the system can be improved through dynamic reactive optimization and regulation, which ultimately improves the voltage stability margin and reduces system losses, verifying the effectiveness of the optimization solution.

[0118] In order to further verify the adaptability of the method of the present invention under different lighting conditions, we selected the operating data of a typical day for analysis.

[0119] During periods of time when light intensity varies greatly (such as 10:00-11:00 and 14:00-15:00), the method of the present invention can adjust the reactive output of the system more quickly and accurately, making the reactive output curve smoother and effectively reducing voltage fluctuations.

[0120] Compared with the traditional method, the fluctuation amplitude of the system voltage under the method of the present invention is significantly reduced, especially in the period of drastic changes in light intensity, the stability of the voltage is significantly improved. During the entire observation period, the method of the present invention keeps the system voltage in the range of 34.5kV to 35.5kV, while the voltage fluctuation range under the traditional method is 34.0kV to 36.0kV.

[0121] In order to quantitatively evaluate the advantages of the method of the present invention, we calculated the average voltage deviation and maximum voltage deviation of the system in one day, and the results are shown in Table 4:

[0122] Table 4 Statistical comparison of voltage deviation

[0123]

[0124] As can be seen from Table 4, compared with the traditional method, the average voltage deviation and the maximum voltage deviation of the method of the present invention are reduced by 50%, and the number of voltage over-limit is reduced by 86.7%. These data fully prove the significant advantages of the method of the present invention in improving the voltage stability of photovoltaic power generation systems.

[0125] In addition, we also analyzed the reactive resource utilization of the system. Under the traditional method, the average reactive resource utilization of the system was 75%, while after adopting the method of the present invention, the average reactive resource utilization increased to 92%, an increase of 22.7%. This shows that the method of the present invention can make fuller use of the reactive regulation capacity of the system and effectively improve the use efficiency of the equipment.

[0126] Finally, we tested the dynamic response capability of the system. We simulated a large grid disturbance (a voltage drop of 0.1 pu) and recorded the time required for the system to return to a stable state. The results showed that when the traditional method was used, the system took 5 seconds to recover stability; while with the method of the present invention, it only took 2 seconds to recover stability, and the dynamic response time was shortened by 60%. This result shows that the method of the present invention can significantly improve the system's anti-disturbance capability and recovery speed.

[0127] In summary, through actual system testing and data analysis, we have verified the significant advantages of the method of the present invention in improving the voltage stability of photovoltaic power generation systems, increasing reactive resource utilization, reducing system losses, and enhancing dynamic response capabilities. These advantages enable the method of the present invention to better cope with the intermittent and fluctuating characteristics of photovoltaic power generation, and provide strong technical support for the grid-connected operation of large-scale photovoltaic power generation systems.

[0128] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. 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 photovoltaic power generation system optimization method based on dynamic reactive power margin management, characterized in that: include: Acquire first parameters of the photovoltaic power generation system, wherein the first parameters include photovoltaic array output power, inverter operating parameters, and grid operating parameters; Based on the first parameter, calculating the current reactive power margin of the system to obtain a reactive power margin parameter; According to the reactive margin parameter, a dynamic threshold is set to identify the reactive optimization space of the system, and a second parameter is obtained, wherein the second parameter represents the adjustable reactive capacity of the system; Combining the reactive power margin parameter and the second parameter, dynamically optimizing the reactive power of the system; The reactive power margin parameter and the adjusted reactive power are input into a particle swarm optimization algorithm, and the particle swarm optimization algorithm is used to find an optimal reactive power allocation scheme that maximizes the system voltage stability margin.

2. The photovoltaic power generation system optimization method based on dynamic reactive power margin management according to claim 1, characterized in that: The first parameters also include environmental parameters, which include temperature, radiation intensity and wind speed.

3. The photovoltaic power generation system optimization method based on dynamic reactive power margin management according to claim 1 or 2, characterized in that: Based on the first parameter, the current reactive power margin of the system is calculated to obtain a reactive power margin parameter, which is expressed as: QM(t)=f(P1,P2,...,P n ;0) Among them, P n represents n acquired system parameters, f is the function for evaluating reactive margin, and θ represents the corresponding model parameters.

4. The photovoltaic power generation system optimization method based on dynamic reactive power margin management according to claim 3, characterized in that: According to the reactive margin parameters, dynamic thresholds are set to identify the reactive optimization space of the system. include, When the reactive power margin QM(t) of the system is higher than the dynamic threshold, it indicates that there is room for reactive power optimization in the system. The dynamic threshold acquisition includes: According to the system operation record in the historical data, historical reactive power margin data is obtained, wherein the historical reactive power margin data is the reactive power margin QM(t) when the system operates normally; The dynamic threshold is set at 60% of the historical reactive margin data; The second parameter is obtained by calculating the adjustable reactive capacity in the first parameter.

5. The photovoltaic power generation system optimization method based on dynamic reactive power margin management according to claim 4, characterized in that: Combine the reactive margin parameter and the second parameter to adjust the system dynamically for reactive power optimization, including: Where Q(t+1) is the predicted reactive power of the system at time t+1, Q(t) is the reactive power of the system at time t, α is the adjustment factor, ranging from 0.1 to 0.5, QM max is the maximum reactive power margin of the system; where Q(t+1) is the predicted reactive power of the system at time t+1, Q(t) is the reactive power of the system at time t, α is the adjustment factor, ranging from 0.1 to 0.5, QM max is the maximum reactive power margin of the system.

6. The photovoltaic power generation system optimization method based on dynamic reactive power margin management according to claim 5, characterized in that: The reactive margin parameter and the adjusted reactive power are input into the particle swarm optimization algorithm, and the reactive distribution of the system is optimized by finding the reactive margin parameter and the adjusted reactive power that maximize the system voltage stability margin, specifically including: Calculate the velocity v of particle i moving from its current position x to its new position y at time t, expressed as, v(i,t+1)=wv(i,t)+c1r1*(pbest(i)-x(i,t))+c2r2*(gbest-x(i,t)) Among them, w is the inertia weight, ranging from 0.4 to 0.9, c1 and c2 are acceleration constants, ranging from 1.5 to 2.5, r1 and r2 are random numbers between (0,1), pbest(i) is the individual optimal position of particle i, and gbest is the global optimal position; The particle updates its position according to its velocity, expressed as: x(i,t+1)=x(i,t)+v(i,t+1) Among them, x(i,t+1) represents the new position of particle i at time t+1; The system voltage stability margin is obtained by using a reactive power margin parameter and adjusted reactive power.

7. The photovoltaic power generation system optimization method based on dynamic reactive power margin management according to claim 5 or 6, characterized in that: The optimization system reactive power distribution also includes: Update the fitness value of the particle swarm and adjust the particle distribution accordingly, which can be expressed as: Where, fit is the fitness value, fit(i, t) represents the fitness value of particle i at time t, fit(i, t+1) represents the fitness value of the particle at time t+1, β is the adjustment coefficient, ranging from 0.1 to 0.3, VSM is the voltage stability margin, VSM max It is the maximum voltage stability margin allowed by the system.

8. A photovoltaic power generation system optimization system based on dynamic reactive power margin management, characterized in that: include, An acquisition module, used to acquire first parameters of the photovoltaic power generation system, wherein the first parameters include photovoltaic array output power, inverter operating parameters and grid operating parameters; A calculation module, configured to calculate a current reactive power margin of the system based on the first parameter to obtain a reactive power margin parameter; An identification module, used to set a dynamic threshold to identify the reactive optimization space of the system according to the reactive margin parameter, and obtain a second parameter, wherein the second parameter represents the adjustable reactive capacity of the system; A regulating module, used for combining the reactive power margin parameter and the second parameter to perform dynamic reactive power optimization regulation on the system; The optimization module is used to input the reactive power margin parameter and the adjusted reactive power into a particle swarm optimization algorithm, and to find an optimal reactive power allocation scheme that maximizes the system voltage stability margin through the particle swarm optimization algorithm.

9. A computing 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 photovoltaic power generation system optimization method based on dynamic reactive power margin management 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 photovoltaic power generation system optimization method based on dynamic reactive power margin management as described in any one of claims 1 to 7.

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