New energy SVG integration method and system based on multi-scene adaptive regulation and control

Abstract

The invention relates to the technical field of power system automation and control, in particular to a new energy SVG integration method and system based on multi-scene adaptive regulation and control. Determining a current control scene through a signal sent by a control center; dynamically adjusting the operation parameters of the SVG according to different scene classification results; and regulating and controlling the active power and reactive power of the SVG through a multi-objective optimization algorithm, and adjusting the strategy according to the SVG state classification result. According to the method, the operation parameters of the SVG are intelligently adjusted through real-time signal analysis of the control center, so that the system can optimize reactive power and voltage regulation in different scenes, and it is ensured that the overall performance of a power grid is remarkably improved. Through a multi-objective optimization algorithm, the power and voltage deviation can be controlled while multiple operation objectives are met, so that the robustness and the response speed of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system automation and control, and in particular to a new energy SVG integration method and system based on multi-scenario adaptive regulation. Background Art

[0002] With the transformation of the global energy structure, the proportion of new energy, especially photovoltaic and wind power, is increasing, resulting in an increasingly complex operating environment for power systems. Traditional power grids can maintain high stability in the face of large disturbances due to their strong grid structure. However, in weak power grids with a high proportion of new energy access, the stability and voltage support capabilities of the system face severe challenges. This is mainly due to the fact that a large number of new energy units are connected to the grid through power electronic equipment, which significantly changes the dynamic characteristics of the power grid, especially after the short-circuit fault is cleared, which easily causes overvoltage problems. To address this problem, static VAR generators (SVGs) are widely used in new energy power grids to provide reactive power compensation and voltage support. However, the existing SVG technology does not perform well when a high proportion of new energy is connected, especially when the system is subject to large disturbances, and often cannot meet the voltage support requirements.

[0003] The present invention proposes a new energy SVG integration method and system based on multi-scenario adaptive control. By introducing advanced scene recognition and classification algorithms, multiple operation scenarios in the power grid (such as photovoltaic priority, wind power priority, load balance, etc.) are identified in real time, and the operation parameters of SVG are dynamically adjusted according to different scenarios. At the same time, a multi-objective optimization algorithm is used to control the active and reactive power of SVG to ensure the stability and reliability of the system under various complex operating conditions. This technology aims to improve the voltage support capacity of the new energy power grid and ensure efficient operation under different operation scenarios. Summary of the invention

[0004] In view of the above problems existing in the prior art, the present invention is proposed.

[0005] Therefore, the present invention provides a new energy SVG integration method based on multi-scenario adaptive control, which can solve the problem of insufficient voltage support in weak power grids with a high proportion of new energy access in traditional solutions. Existing SVG technology often has poor effects when dealing with large disturbances. The present invention achieves more accurate reactive power control and voltage stability through multi-scenario recognition and adaptive control, and improves the reliability of the system under complex operating conditions.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a new energy SVG integration method based on multi-scenario adaptive control, including: determining the current control scenario through a signal sent by a control center; dynamically adjusting the operating parameters of the SVG according to the classification results of different scenarios; regulating the active and reactive power of the SVG through a multi-objective optimization algorithm, and making adjustment strategies according to the SVG state classification results.

[0007] As a preferred solution of the new energy SVG integration method based on multi-scenario adaptive control described in the present invention, wherein: the signal sent by the control center to determine the current control scenario includes identifying the control information sent by the control center, parsing the signal sent by the control center, and determining the current control scenario. The control scenarios include photovoltaic priority scenarios, wind power priority scenarios and load balancing scenarios, and the operating parameters of the SVG are dynamically adjusted based on the scenario classification results.

[0008] As a preferred solution of the new energy SVG integration method based on multi-scenario adaptive control described in the present invention, the photovoltaic priority scenario includes calculating the reactive power required to be provided by the SVG according to the volatility of photovoltaic power generation and the current grid demand, which is expressed as:

[0009]

[0010] Among them, t is the time, Q SVG,PV is the reactive power required by SVG in the photovoltaic priority scenario, L is the total load demand, P PV is the PV power generation, V is the voltage output of SVG, α and β are the adjustment coefficients of different PV priority scenarios;

[0011] Based on the deviation between the current voltage and the target voltage, the output voltage V of the SVG in the PV priority scenario is adjusted. SVG,PV It is expressed as,

[0012] V SVG,PV (t) = V ref +A·(V ref -V(t))·(1-e -b·t )

[0013] Among them, V ref is the target voltage reference value, A is the voltage adjustment coefficient of the PV priority scenario, and b is the voltage recovery time constant;

[0014] Combining reactive power compensation and voltage regulation, the SVG control strategy S for the photovoltaic priority scenario is generated. SVG,PV , expressed as,

[0015]

[0016] Among them, θ is the response sensitivity parameter of the photovoltaic priority scenario control strategy.

[0017] As a preferred solution of the new energy SVG integration method based on multi-scenario adaptive control described in the present invention, the wind power priority scenario includes combining the current wind power generation and the fluctuation characteristics of wind power generation to calculate the reactive power demand required by SVG, expressed as:

[0018]

[0019] Among them, Q SVG,w The reactive power required by SVG in the PV priority scenario, P w is the photovoltaic power generation, α w is the basic compensation coefficient for wind power priority scenario, β w is the wind power fluctuation adjustment coefficient, ω w is the wind power fluctuation frequency, sin(·) is the sine function;

[0020] Considering the volatility of wind power, SVG is required to adjust the voltage dynamically. Adaptive voltage adjustment items are introduced to adjust the output voltage V of SVG in the photovoltaic priority scenario. SVG,w It is expressed as,

[0021]

[0022] Among them, γ w is the voltage adjustment coefficient of the wind power priority scenario, tanh(·) is the hyperbolic tangent function, σ V is the voltage adjustment parameter;

[0023] During the operation of the power grid, the voltage deviation V is monitored in real time. ref -V changes, if the voltage fluctuation exceeds ±6%, an exponential decay algorithm is used to reduce σ V , improve the response speed of voltage regulation. If the voltage fluctuation is not detected to exceed ±6%, there is no need to change σ V ;

[0024] The exponential decay algorithm is expressed as,

[0025]

[0026] Where, δ is the attenuation coefficient;

[0027] Combining reactive power demand and voltage regulation, the control strategy S of SVG for wind power priority scenario is generated SVG,w It is expressed as,

[0028] S SVG,w (t) = Q SVG,w (t)+V SVG,w(t)·(1+η w ·cos(ω w ·t))

[0029] Among them, cos(·) is the cosine function, η w It is the additional adjustment coefficient in the wind power priority scenario.

[0030] As a preferred solution of the new energy SVG integration method based on multi-scenario adaptive control described in the present invention, the load balancing scenario includes considering photovoltaic and wind power generation at the same time, and calculating the reactive power Q required to be provided by SVG in the load balancing scenario according to the current total load demand. SVG,b It is expressed as,

[0031] Q SVG,b (t) = c·(L(t)-P gen (t))

[0032] Where c is the reactive power compensation coefficient in the load balancing scenario, P gen is the total power generation;

[0033] Adjust the SVG output voltage V for load balancing scenarios SVG,b It is expressed as,

[0034] V SVG,b (t) = V ref +d·(V ref -V(t)

[0035] Where d is the voltage adjustment factor for the load balancing scenario.

[0036] As a preferred solution of the new energy SVG integration method based on multi-scenario adaptive control described in the present invention, the multi-objective optimization algorithm includes considering the control requirements of SVG for active power and reactive power in different operating scenarios, ensuring that the difference between the output power of SVG and the target power is minimized, while controlling the voltage deviation to maintain the voltage stability of the power grid, and introducing the power change rate and reactive power change rate as the control objects. The multi-objective optimization algorithm is expressed as:

[0037]

[0038] Where Z is the SVG state indicator value, P is the total active power output of SVG, N is the total number of scenarios, and P i is the target power of the i-th scenario, γ is the weight of the voltage deviation, α is the exponential decay coefficient of the voltage deviation, δ is the weight of the active power rate, η is the weight of the reactive power rate, R is the active power change rate, Q is the reactive power change rate, t0 and t1 are the start and end times of the optimization respectively;

[0039] When 0≤Z<1, it is judged that the power deviation, reactive power and voltage fluctuation of SVG after scene adjustment are well controlled, and the overall performance reaches the stable optimization state B1;

[0040] When 1≤Z<2, it is determined that the SVG has power deviation or voltage deviation after scene adjustment, and the overall performance is in deviation adjustment state B2;

[0041] When Z≥3, it is determined that the SVG has a large power or voltage deviation after the scene adjustment. The power change during the adjustment process leads to a decrease in the SVG performance, and the overall performance is in the deep correction state B3.

[0042] As a preferred solution of the new energy SVG integration method based on multi-scenario adaptive control described in the present invention, the adjustment strategy according to the SVG state classification result includes that when the SVG is in the B1 state, the power and voltage are adjusted to maintain stability on the basis of maintaining the current level. The adjustment formula of the B1 state is expressed as:

[0043] P new (t) = P old (t)·(1+∈ p ·sin(ω p ·t))

[0044] Q new (t) = Q old (t)·(1+∈ q ·cos(ω q t))

[0045] Among them, P new is the adjusted active power output, P old is the active power output before adjustment, Q new is the adjusted reactive power output, Q old is the reactive power output before adjustment, ∈ p ,∈ q are the amplitude coefficients of active power and reactive power, ω p ,ω q are the adjustment frequencies of active power and reactive power respectively;

[0046] When the SVG is in the B2 state, the control of reactive power and voltage fluctuations is gradually strengthened to reduce the deviation and make the SVG close to the B1 state. The adjustment formula for the B2 state is:

[0047]

[0048]

[0049] Among them, λp is the time attenuation coefficient of reactive power, κ is the time response coefficient of active power;

[0050] When SVG is in the B3 state, it is necessary to increase the reactive power compensation and voltage regulation, and at the same time consider accelerating the response speed. The adjustment formula for the B3 state is:

[0051]

[0052] Among them, k p , k q are the adjustment coefficients of reactive power and active power, λ′ p , q are the different time attenuation coefficients adjusted in B3 state, ω′ p ,ω′ q Different correction frequencies are adjusted for B3 status, μ p , μ q are different dynamic adjustment items of B3 state adjustment, ln(·) is the natural logarithmic function;

[0053] After completing the adjustment to the B3 state, continue to monitor key indicators, calculate new SVG state indicator values, compare the differences between the indicator values ​​of the secondary assessment and the indicator values ​​of the initial assessment, and judge the effectiveness of the adjustment measures. If the overall performance is adjusted to the B2 or B1 state, the adjustment is considered successful. If the SVG state indicator value decreases but is still B3 or the SVG state indicator value increases, an alarm is triggered to notify the operator to perform manual intervention.

[0054] Another object of the present invention is to provide a new energy SVG integrated system based on multi-scenario adaptive control. Through the multi-scenario adaptive control mechanism, it is ensured that the SVG system can achieve the best power regulation effect under different operating environments. By identifying and classifying typical scenarios of photovoltaic priority, wind power priority and load balance, an algorithm based on multi-objective optimization is used to dynamically adjust the active and reactive power output of SVG to maintain the stability of the power grid. Through a closed-loop feedback mechanism, the operating state of the system is monitored and corrected in real time, so that SVG can respond quickly to changes in the power grid and achieve a smooth transition from a deep correction state to a stable optimization state. The present invention not only improves the utilization efficiency of new energy power generation, but also enhances the adaptability of the power grid to load fluctuations, ultimately achieving the purpose of improving the overall performance and safety of the power grid.

[0055] As a preferred solution of the new energy SVG integrated system based on multi-scenario adaptive control described in the present invention, it includes: a scene recognition and classification module, a scene adjustment module and a multi-target adjustment module;

[0056] The scene recognition and classification module identifies the current control scene by analyzing the signal sent by the control center and provides control scene classification;

[0057] The scene adjustment module dynamically adjusts the operating parameters of the SVG according to the identified scene categories to ensure efficient operation of the system in different scenarios;

[0058] The multi-objective adjustment module uses a multi-objective optimization algorithm to regulate the active and reactive power of the SVG, and performs an adjustment strategy according to the overall performance classification result to optimize the overall performance and maintain voltage stability.

[0059] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of any one of the methods of integrating new energy SVG based on multi-scenario adaptive control are implemented.

[0060] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of any one of the methods described in a new energy SVG integration method based on multi-scenario adaptive control are implemented.

[0061] Beneficial effects of the present invention: By introducing a multi-scenario adaptive control strategy, the uncertainty and volatility problems in renewable energy power generation are effectively addressed, especially the accurate identification and dynamic control of the three major scenarios of photovoltaic, wind power and load balancing, so that SVG can always maintain a stable operating state in a complex power grid environment. The method intelligently adjusts the operating parameters of SVG through real-time signal analysis of the control center, so that the system can optimize reactive power and voltage regulation in different scenarios, ensuring that the overall performance of the power grid is significantly improved. Through a multi-objective optimization algorithm, the method can control the deviation of power and voltage while meeting multiple operating goals, thereby improving the robustness and response speed of the system. Unlike traditional single control scenarios, the present invention realizes adaptive control of power grids in a changing environment, greatly improves the utilization efficiency of new energy, reduces the risk and cost of power grid operation, and realizes efficient integration and optimization of renewable energy power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0063] Figure 1A schematic flow chart of a new energy SVG integration method based on multi-scenario adaptive control is provided as an embodiment of the present invention.

[0064] Figure 2 A module diagram of a new energy SVG integrated system based on multi-scenario adaptive control provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0065] 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.

[0066] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a new energy SVG integration method based on multi-scenario adaptive control, including:

[0067] S1: Determine the current control scenario through the signal sent by the control center.

[0068] It should be noted that relevant data is collected from the power grid in real time and standardized to ensure that all data is at the same scale for easy model processing. A multi-scene recognition model is trained using a machine learning model (such as a support vector machine (SVM), a neural network, or a decision tree). The input of the model is the standardized data, and the output is the recognized scene label.

[0069] Scene labels in the training dataset can include:

[0070] PV priority scenario: When the proportion of PV power generation to total power generation is high and the load demand is relatively stable.

[0071] Wind power priority scenario: when the proportion of wind power generation to total power generation is high and the load demand is relatively stable.

[0072] Load balancing scenario: When load demand fluctuates greatly, it is necessary to comprehensively utilize photovoltaic and wind power generation to maintain grid balance.

[0073] In real-time operation, after receiving the power grid data, the features are classified through the multi-scenario recognition model. The specific classification logic is as follows:

[0074] Photovoltaic priority scene recognition:

[0075] Condition 1: The photovoltaic power generation power is greater than the set threshold value.

[0076] Condition 2: Environmental conditions (such as light intensity) indicate that photovoltaic power generation capacity is strong.

[0077] Model output: If the above conditions are met, the model classifies the data into the PV priority scenario.

[0078] Wind power priority scenario identification:

[0079] Condition 1: The wind power generation capacity is greater than the set threshold value.

[0080] Condition 2: Environmental conditions (such as wind speed) indicate that wind power generation capacity is strong.

[0081] Model output: If the above conditions are met, the model classifies the data into a wind power priority scenario.

[0082] Load balancing scenario identification:

[0083] Condition 1: The load demand fluctuates greatly, and the power generation of photovoltaic and wind power is at a medium level and cannot meet the current load demand alone.

[0084] Condition 2: The grid frequency and voltage may become unstable, requiring comprehensive regulation of photovoltaic and wind power to maintain grid balance.

[0085] Model Output: If the above conditions are met, the model classifies the data into a load balancing scenario.

[0086] The model outputs the current control scenario signal according to the input real-time data and dynamically adjusts the operating parameters of the SVG based on the scenario classification results.

[0087] S2: Dynamically adjust the operating parameters of SVG according to the classification results of different scenarios.

[0088] It should be noted that the photovoltaic priority scenario includes calculating the reactive power that SVG needs to provide based on the volatility of photovoltaic power generation and the current grid demand, expressed as:

[0089]

[0090] Among them, t is the time, Q SVG,PV is the reactive power required by SVG in the photovoltaic priority scenario, L is the total load demand, P PV is the PV power generation, V is the voltage output of SVG, α and β are the adjustment coefficients of different PV priority scenarios;

[0091] Based on the deviation between the current voltage and the target voltage, the output voltage V of the SVG in the PV priority scenario is adjusted. SVG,PV It is expressed as,

[0092] V SVG,PV (t) = Vref +A·(V ref -V(t))·(1-e -b·t )

[0093] Among them, V ref is the target voltage reference value, A is the voltage adjustment coefficient of the PV priority scenario, and b is the voltage recovery time constant;

[0094] Combining reactive power compensation and voltage regulation, the SVG control strategy S for the photovoltaic priority scenario is generated. SVG,PV , expressed as,

[0095]

[0096] Among them, θ is the response sensitivity parameter of the photovoltaic priority scenario control strategy.

[0097] The photovoltaic priority scenario adjusts the reactive power output of the SVG based on the volatility of photovoltaic power generation and the current grid demand. By calculating the difference between photovoltaic power generation and total load demand, the output voltage and reactive power of the SVG are dynamically adjusted to ensure the stable operation of the grid. The core of this scenario is to make adjustments based on real-time voltage deviations to minimize the impact of photovoltaic power generation on the grid, while optimizing voltage stability and system performance.

[0098] It should be noted that the wind power priority scenario includes calculating the reactive power demand that SVG needs to provide in combination with the current wind power generation and the fluctuation characteristics of wind power generation, which is expressed as:

[0099]

[0100] Among them, Q SVG,w The reactive power required by SVG in the PV priority scenario, P w is the photovoltaic power generation, α w is the basic compensation coefficient for wind power priority scenario, β w is the wind power fluctuation adjustment coefficient, ω w is the wind power fluctuation frequency, sin(·) is the sine function;

[0101] Considering the volatility of wind power, SVG is required to adjust the voltage dynamically. Adaptive voltage adjustment items are introduced to adjust the output voltage V of SVG in the photovoltaic priority scenario. SVG,w It is expressed as,

[0102]

[0103] Among them, γ w is the voltage adjustment coefficient of the wind power priority scenario, tanh(·) is the hyperbolic tangent function, σ V is the voltage adjustment parameter;

[0104] During the operation of the power grid, the voltage deviation V is monitored in real time. ref -V changes, if the voltage fluctuation exceeds ±6%, an exponential decay algorithm is used to reduce σ V , improve the response speed of voltage regulation. If the voltage fluctuation is not detected to exceed ±6%, there is no need to change σ V ;

[0105] The exponential decay algorithm is expressed as,

[0106]

[0107] Where, δ is the attenuation coefficient;

[0108] Combining reactive power demand and voltage regulation, the control strategy S of SVG for wind power priority scenario is generated SVG,w It is expressed as,

[0109] S SVG,w (t) = Q SVG,w (t)+V SVG,w (t)·(1+η w ·cos(ω w ·t))

[0110] Among them, cos(·) is the cosine function, η w It is the additional adjustment coefficient in the wind power priority scenario.

[0111] The wind power priority scenario dynamically adjusts the reactive power output and voltage control of SVG based on the volatility of wind power generation and current grid demand. By combining wind power generation and its fluctuation characteristics, the reactive power required by SVG is calculated, and an adaptive adjustment mechanism is introduced to adjust the output voltage to ensure that voltage deviation and reactive power are effectively controlled. The core of this scenario is the rapid response and adjustment to voltage fluctuations to maintain the stability of the grid.

[0112] It should be noted that the load balancing scenario includes considering both photovoltaic and wind power generation, and calculating the reactive power Q required to be provided by SVG in the load balancing scenario according to the current total load demand. SVG,b It is expressed as,

[0113] Q SVG,b (t) = c·(L(t)-P gen (t))

[0114] Where c is the reactive power compensation coefficient in the load balancing scenario, P gen is the total power generation;

[0115] Adjust the SVG output voltage V for load balancing scenarios SVG,b It is expressed as,

[0116] V SVG,b (t) = V ref +d·(V ref -V(t)

[0117] Where d is the voltage adjustment factor for the load balancing scenario.

[0118] The load balancing scenario considers the power generation of both photovoltaic and wind power, and dynamically adjusts the reactive power output and voltage control of the SVG according to the current total load demand.

[0119] S3: Through the multi-objective optimization algorithm, the active and reactive power of the SVG is regulated, and the adjustment strategy is made according to the SVG status classification results.

[0120] It should be noted that the multi-objective optimization algorithm includes considering the control requirements of SVG for active power and reactive power in different operation scenarios, ensuring that the difference between the output power of SVG and the target power is minimized, while controlling the voltage deviation to maintain the voltage stability of the power grid, and introducing the power change rate and reactive power change rate as control objects. The multi-objective optimization algorithm is expressed as:

[0121]

[0122] Where Z is the SVG state indicator value, P is the total active power output of SVG, N is the total number of scenarios, and P i is the target power of the i-th scenario, γ is the weight of the voltage deviation, α is the exponential decay coefficient of the voltage deviation, δ is the weight of the active power rate, η is the weight of the reactive power rate, R is the active power change rate, Q is the reactive power change rate, t0 and t1 are the start and end times of the optimization respectively;

[0123] When 0≤Z<1, it is judged that the power deviation, reactive power and voltage fluctuation of SVG after scene adjustment are well controlled, and the overall performance reaches the stable optimization state B1;

[0124] When 1≤Z<2, it is determined that the SVG has power deviation or voltage deviation after scene adjustment, and the overall performance is in deviation adjustment state B2;

[0125] When Z≥3, it is determined that the SVG has a large power or voltage deviation after the scene adjustment. The power change during the adjustment process leads to a decrease in the SVG performance, and the overall performance is in the deep correction state B3.

[0126] Furthermore, the adjustment strategy according to the SVG state classification result includes that when the SVG is in the B1 state, the power and voltage are adjusted to maintain stability on the basis of maintaining the current level. The adjustment formula of the B1 state is expressed as:

[0127] P new (t) = P old (t)·(1+∈ p ·sin(ω p ·t))

[0128] Q new (t) = Q old (t)·(1+∈ q ·cos(ω q ·t))

[0129] Among them, P new is the adjusted active power output, P old is the active power output before adjustment, Q new is the adjusted reactive power output, Q old is the reactive power output before adjustment, ∈ p ,∈ q are the amplitude coefficients of active power and reactive power, ω p ,ω q are the adjustment frequencies of active power and reactive power respectively;

[0130] When the SVG is in the B2 state, the control of reactive power and voltage fluctuations is gradually strengthened to reduce the deviation and make the SVG close to the B1 state. The adjustment formula for the B2 state is:

[0131]

[0132]

[0133] Among them, λ p is the time attenuation coefficient of reactive power, κ is the time response coefficient of active power;

[0134] When SVG is in the B3 state, it is necessary to increase the reactive power compensation and voltage regulation, and at the same time consider accelerating the response speed. The adjustment formula for the B3 state is:

[0135]

[0136] Among them, k p , k q are the adjustment coefficients of reactive power and active power, λ′ p , q are the different time attenuation coefficients adjusted in B3 state, ω′ p ,ω′ q Different correction frequencies are adjusted for B3 status, μ p , μ qare different dynamic adjustment items of B3 state adjustment, ln(·) is the natural logarithmic function;

[0137] After completing the adjustment to the B3 state, continue to monitor key indicators, calculate new SVG state indicator values, compare the differences between the indicator values ​​of the secondary assessment and the indicator values ​​of the initial assessment, and judge the effectiveness of the adjustment measures. If the overall performance is adjusted to the B2 or B1 state, the adjustment is considered successful. If the SVG state indicator value decreases but is still B3 or the SVG state indicator value increases, an alarm is triggered to notify the operator to perform manual intervention.

[0138] The above is a schematic scheme of a new energy SVG integration method based on multi-scenario adaptive regulation in this embodiment. It should be noted that the technical scheme of the new energy SVG integration system based on multi-scenario adaptive regulation and the technical scheme of the new energy SVG integration method based on multi-scenario adaptive regulation belong to the same concept. The details not described in detail in the technical scheme of the new energy SVG integration system based on multi-scenario adaptive regulation in this embodiment can be found in the description of the technical scheme of the new energy SVG integration method based on multi-scenario adaptive regulation.

[0139] like Figure 2 As shown, the new energy SVG integrated system based on multi-scenario adaptive control in this embodiment includes: a scene recognition and classification module, a scene adjustment module and a multi-target adjustment module;

[0140] The scene recognition and classification module identifies the current control scene by analyzing the signal sent by the control center and provides control scene classification;

[0141] The scene adjustment module dynamically adjusts the operating parameters of the SVG according to the identified scene categories to ensure efficient operation of the system in different scenarios;

[0142] The multi-objective adjustment module uses a multi-objective optimization algorithm to regulate the active and reactive power of the SVG, and performs an adjustment strategy according to the overall performance classification result to optimize the overall performance and maintain voltage stability.

[0143] This embodiment further provides a computing device, which is applicable to the case of a new energy SVG integration method based on multi-scenario adaptive control, including:

[0144] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0145] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0146] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0147] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0148] Embodiment 2 is the second embodiment of the present invention, which provides a new energy SVG integration method based on multi-scenario adaptive control. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0149] The experimental group adopted the SVG integration method of the present invention, and dynamically adjusted the active and reactive power of SVG to adapt to different grid operation scenarios. The control group adopted the traditional SVG regulation method, and did not introduce the strategy of multi-scenario adaptive regulation. In the specific implementation process, the experiment was carried out on an SVG device with a rated power of 500Mvar. The simulation scenario included daytime peak load (photovoltaic priority scenario), nighttime wind power generation peak (wind power priority scenario) and random load fluctuation scenario (load balancing scenario). The experimental group and the control group were tested under the same environmental conditions, respectively, and the output power, voltage stability and response time of SVG in each scenario were measured. The test data was recorded by real-time monitoring equipment, and the various indicators were compared in detail, as shown in Figure 1.

[0150] Table 1. Data comparison table

[0151]

[0152] By analyzing the above experimental data, we can draw the following conclusions:

[0153] Comparison of output reactive power and active power: The reactive power output of the experimental group in the photovoltaic priority scenario and the wind power priority scenario was significantly higher than that of the control group (450Mvar and 470Mvar, respectively, in the experimental group, and 430Mvar and 440Mvar, respectively, in the control group), which shows that the method of the present invention can more accurately respond to the grid demand in different scenarios. In terms of active power, the experimental group also showed better output capacity, which shows that through dynamic adjustment, the experimental group can better adapt to the load demand of the grid.

[0154] Voltage deviation and voltage stability: The voltage deviation of the experimental group in each scenario is lower than that of the control group, especially in the wind power priority scenario, the voltage deviation of the experimental group is only 1.0kV, while that of the control group is 2.2kV. This shows that the method of the present invention has obvious advantages in dealing with voltage fluctuations. The voltage stability data also shows that the performance of the experimental group (98% in the photovoltaic priority scenario) is better than that of the control group (92% in the photovoltaic priority scenario), which verifies the effectiveness of the method of the present invention in maintaining grid stability.

[0155] Response time and power fluctuation rate: The response time of the experimental group was significantly shorter than that of the control group, with an average reduction of about 100ms, which proves that the adaptive control mechanism of the method of the present invention can respond to grid changes more quickly and reduce regulation delays. In terms of power fluctuation rate, the fluctuation rate of the experimental group is generally lower than that of the control group, especially in the wind power priority scenario, the fluctuation rate of the experimental group is 4.8%, while that of the control group is 7.5%, indicating that the method of the present invention has significant advantages in reducing power fluctuations.

[0156] In summary, the data of this embodiment fully proves that the method of the present invention shows higher voltage stability, response speed and power output efficiency in multi-scenario adaptive regulation compared with the traditional SVG regulation method. These advantages can effectively overcome the problems of large voltage fluctuations, long response time and inaccurate power regulation in the prior art, and have significant innovation and novelty, providing a more reliable and efficient solution for actual power grid operation.

[0157] 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 new energy SVG integration method based on multi-scenario adaptive control, characterized by: include, Determine the current control scenario through the signal sent by the control center; Dynamically adjust SVG operating parameters based on different scene classification results; Through the multi-objective optimization algorithm, the active and reactive power of SVG is regulated, and the adjustment strategy is made according to the SVG status classification results.

2. A new energy SVG integration method based on multi-scenario adaptive control as claimed in claim 1, characterized in that: The signal sent by the control center to determine the current control scenario includes identifying and parsing the control information sent by the control center, determining the current control scenario, the control scenarios include photovoltaic priority scenarios, wind power priority scenarios and load balancing scenarios, and dynamically adjusting the operating parameters of the SVG based on the scenario classification results.

3. A new energy SVG integration method based on multi-scenario adaptive control as claimed in claim 2, characterized in that: The photovoltaic priority scenario includes calculating the reactive power that the SVG needs to provide based on the volatility of photovoltaic power generation and the current grid demand, expressed as: Among them, t is the time, Q SVG,PV is the reactive power required by SVG in the photovoltaic priority scenario, L is the total load demand, P PV is the photovoltaic power generation, V is the voltage output of SVG, α and β are the adjustment coefficients of different photovoltaic priority scenarios; Based on the deviation between the current voltage and the target voltage, the output voltage V of the SVG in the PV priority scenario is adjusted. SVG,PV It is expressed as, V SVG,PV (t)=V ref +A·(V ref -V(t))·(1-e -b·t ) Among them, V ref is the target voltage reference value, A is the voltage adjustment coefficient of the PV priority scenario, and b is the voltage recovery time constant; Combining reactive power compensation and voltage regulation, the SVG control strategy S for the photovoltaic priority scenario is generated. SVG,PV , expressed as, Among them, θ is the response sensitivity parameter of the photovoltaic priority scenario control strategy.

4. The new energy SVG integration method based on multi-scenario adaptive control as claimed in claim 3, characterized in that: The wind power priority scenario includes calculating the reactive power demand that SVG needs to provide by combining the current wind power generation and the fluctuation characteristics of wind power generation, which is expressed as: Among them, Q SVG,w The reactive power required by SVG in the PV priority scenario, P w is the photovoltaic power generation, α w is the basic compensation coefficient for wind power priority scenario, β w is the wind power fluctuation adjustment coefficient, ω w is the wind power fluctuation frequency, sin(·) is the sine function; Considering the volatility of wind power, SVG is required to adjust the voltage dynamically. Adaptive voltage adjustment items are introduced to adjust the output voltage V of SVG in the photovoltaic priority scenario. SVG,w It is expressed as, Among them, γ w is the voltage adjustment coefficient of the wind power priority scenario, tanh(·) is the hyperbolic tangent function, σ V is the voltage adjustment parameter; During the operation of the power grid, the voltage deviation V is monitored in real time. ref -V changes, if the voltage fluctuation exceeds ±6%, an exponential decay algorithm is used to reduce σ V , improve the response speed of voltage regulation. If the voltage fluctuation is not detected to exceed ±6%, there is no need to change σ V ; The exponential decay algorithm is expressed as, Where δ is the attenuation coefficient; Combining reactive power demand and voltage regulation, the control strategy S of SVG for wind power priority scenario is generated SVG,w It is expressed as, S SVG,w (t)=Q SVG,w (t)+V SVG,w (t)·(1+η w ·cos(ω w ·t)) Among them, cos(·) is the cosine function, η w It is the additional adjustment coefficient in the wind power priority scenario.

5. The new energy SVG integration method based on multi-scenario adaptive control as claimed in claim 4, characterized in that: The load balancing scenario includes considering both photovoltaic and wind power generation, and calculating the reactive power Q required to be provided by SVG in the load balancing scenario according to the current total load demand. SVG,b It is expressed as, Q SVG,b (t)=c·(L(t)-P gen (t)) Where c is the reactive power compensation coefficient in the load balancing scenario, P gen is the total power generation; Adjust the SVG output voltage V for load balancing scenarios SVG,b It is expressed as, V SVG,b (t)=V ref +d·(V ref -V(t)) Where d is the voltage adjustment factor for the load balancing scenario.

6. A new energy SVG integration method based on multi-scenario adaptive control as claimed in claim 5, characterized in that: The multi-objective optimization algorithm includes considering the control requirements of SVG for active power and reactive power in different operation scenarios, ensuring the minimum difference between the output power of SVG and the target power, and controlling the voltage deviation to maintain the voltage stability of the power grid, and introducing the power change rate and reactive power change rate as control objects. The multi-objective optimization algorithm is expressed as: Where Z is the SVG state indicator value, P is the total active power output of SVG, N is the total number of scenarios, and P i is the target power of the i-th scenario, γ is the weight of the voltage deviation, α is the exponential decay coefficient of the voltage deviation, δ is the weight of the active power rate, η is the weight of the reactive power rate, R is the active power change rate, Q is the reactive power change rate, t0 and t1 are the start and end times of the optimization respectively; When 0≤Z<1, it is judged that the power deviation, reactive power and voltage fluctuation of SVG after scene adjustment are well controlled, and the overall performance reaches the stable optimization state B1; When 1≤Z<2, it is determined that the SVG has power deviation or voltage deviation after scene adjustment, and the overall performance is in deviation adjustment state B2; When Z≥3, it is determined that the SVG has a large power or voltage deviation after the scene adjustment. The power change during the adjustment process leads to a decrease in the SVG performance, and the overall performance is in the deep correction state B3.

7. A new energy SVG integration method based on multi-scenario adaptive control as claimed in claim 6, characterized in that: The adjustment strategy according to the SVG state classification result includes that when the SVG is in the B1 state, the power and voltage are adjusted to maintain stability on the basis of maintaining the current level. The adjustment formula of the B1 state is expressed as: P new (t)=P old (t)·(1+∈ p ·sin(ω p ·t)) Q new (t)=Q old (t)·(1+∈ q ·cos(ω q ·t)) Among them, P new is the adjusted active power output, P old is the active power output before adjustment, Q new is the adjusted reactive power output, Q old is the reactive power output before adjustment, ∈ p ,∈ q are the amplitude coefficients of active power and reactive power, ω p ,ω q are the adjustment frequencies of active power and reactive power respectively; When the SVG is in the B2 state, the control of reactive power and voltage fluctuations is gradually strengthened to reduce the deviation and make the SVG close to the B1 state. The adjustment formula for the B2 state is: Among them, λ p is the time attenuation coefficient of reactive power, κ is the time response coefficient of active power; When SVG is in the B3 state, it is necessary to increase the reactive power compensation and voltage regulation, and at the same time consider accelerating the response speed. The adjustment formula for the B3 state is: Among them, k p , k q are the adjustment coefficients of reactive power and active power, λ′ p , q are the different time attenuation coefficients adjusted in B3 state, ω′ p ,ω′ q Different correction frequencies are adjusted for B3 status, μ p , μ q are different dynamic adjustment items of B3 state adjustment, ln(·) is the natural logarithmic function; After completing the adjustment to the B3 state, continue to monitor key indicators, calculate new SVG state indicator values, compare the differences between the indicator values ​​of the secondary assessment and the indicator values ​​of the initial assessment, and judge the effectiveness of the adjustment measures. If the overall performance is adjusted to the B2 or B1 state, the adjustment is considered successful. If the SVG state indicator value decreases and is still B3 or the SVG state indicator value increases, an alarm is triggered to notify the operator to perform manual intervention.

8. A system based on the new energy SVG integration method based on multi-scenario adaptive control according to any one of claims 1 to 7, characterized in that: It includes scene recognition and classification module, scene adjustment module and multi-objective adjustment module; The scene recognition and classification module identifies the current control scene by analyzing the signal sent by the control center and provides control scene classification; The scene adjustment module dynamically adjusts the operating parameters of the SVG according to the identified scene categories to ensure efficient operation of the system in different scenarios; The multi-objective adjustment module uses a multi-objective optimization algorithm to regulate the active and reactive power of the SVG, and performs an adjustment strategy according to the overall performance classification result to optimize the overall performance and maintain voltage stability.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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