Online evaluation method and system for multi-level coordinated frequency support capability of photovoltaic storage power stations

Abstract

The present invention provides an online evaluation method and system for the multi-level coordinated frequency support capability of a photovoltaic power station, comprising: obtaining operating boundary information of the photovoltaic power station before a load disturbance occurs and grid connection point voltage and current information after the load disturbance occurs; obtaining optimal support power fluctuation data based on the operating boundary information using a control trajectory solution model; calculating actual active output fluctuation data based on the grid connection point voltage and current information; performing similarity calculation on the optimal support power fluctuation data and the actual active output fluctuation data, and performing online evaluation of the frequency support capability of the photovoltaic power station based on the similarity score; the present invention can generate a theoretical optimal support power fluctuation process reflecting the comprehensive effect of the multi-level control logic of the photovoltaic power station under known operating boundary conditions by using a control trajectory solution model; and then, by calculating the similarity between the optimal support power fluctuation and the actual active output fluctuation, it can achieve online quantitative evaluation of the frequency support capability of the photovoltaic power station.

Description

Technical Field

[0001] The present invention relates to the field of new energy grid-connected technology, and in particular to a method and system for online evaluation of the multi-level coordinated frequency support capability of a photovoltaic power station. Background Art

[0002] Currently, the application of grid-type control technology in energy storage power stations can increase the system's inertia and short-circuit capacity, and improve the frequency and voltage stability in weak power grids. For photovoltaic power stations equipped with grid-type energy storage, the frequency support control of the system presents a multi-level feature, mainly including active frequency control at the unit level and additional frequency control at the station control level. Among them, the active frequency control at the unit level includes inertia and damping control links; the additional frequency control at the station control level includes virtual inertia and primary frequency regulation control. In addition, due to the influence of the command generation, transmission and execution links, the additional frequency control at the station control level has a certain time lag compared to the active frequency control at the unit level.

[0003] Renewable energy and energy storage power stations differ from traditional synchronous generators in that their output and energy release are limited, while multi-level control parameters and timing are flexible and controllable. Therefore, multi-level coordinated control technology, which takes into account the constraints of power station output, energy, and stability, has become a research hotspot. Consequently, for operating photovoltaic and energy storage power stations, the multi-level control architecture employed has different timing response characteristics when superimposed on each other during the frequency support process, making it difficult to clearly identify the effects of different levels from external observations. This makes online evaluation of the frequency support capabilities of current photovoltaic and energy storage power stations difficult. Summary of the Invention

[0004] In order to solve the problem that the multi-level control architecture adopted by current photovoltaic power stations is difficult to clearly identify the effects of different levels from external observation during the frequency support process, resulting in difficulty in online evaluation of the frequency support capability of current photovoltaic power stations, the present invention proposes an online evaluation method for the multi-level coordinated frequency support capability of photovoltaic power stations, including:

[0005] Obtain the operating boundary information of the photovoltaic power station before the load disturbance occurs and the voltage and current information of the grid connection point after the load disturbance occurs;

[0006] According to the operation boundary information, a pre-built control trajectory solution model is used to obtain optimal support power fluctuation data after the load disturbance occurs in the photovoltaic power station;

[0007] Calculating actual active power output fluctuation data of the photovoltaic power station after a load disturbance occurs based on the voltage and current information of the grid connection point;

[0008] Performing similarity calculation on the optimal supported power fluctuation data and the actual active power output fluctuation data to obtain a similarity score, and performing online evaluation on the frequency support capability of the photovoltaic power station based on the similarity score;

[0009] The control trajectory solution model is constructed with the goal of minimizing the sum of the maximum frequency change rate and the maximum frequency deviation after a load disturbance occurs in the photovoltaic power station.

[0010] Optionally, the control trajectory solution model includes the following construction process:

[0011] Simulating a frequency response trajectory of the photovoltaic power station based on a disturbance signal of a load disturbance occurring in the photovoltaic power station and preset operating boundary conditions;

[0012] Extracting the maximum frequency change rate and the maximum frequency deviation from the frequency response trajectory, and constructing an objective function with the goal of minimizing the sum of the maximum frequency change rate and the maximum frequency deviation;

[0013] Formulate constraints based on the objective function;

[0014] Based on the objective function and the constraint conditions, construct a control trajectory solution model;

[0015] The constraint conditions include one or more of the following: power constraint, energy constraint and stability constraint.

[0016] Optionally, the objective function is expressed as follows:

[0017] ;

[0018] Where,

[0019] ;

[0020] in, F represents the objective function; Indicates the inertia control parameters of the unit layer in the photovoltaic power station; represents the damping control parameter of the unit layer in the photovoltaic power station; Indicates the virtual inertia control parameters of the station control layer in the photovoltaic power station; Indicates the primary frequency regulation control parameters of the station control layer in the photovoltaic power station; Indicates control delay; B represents the weight amplification factor; Represents a photovoltaic power station The time in the window is Frequency deviation information when It represents the frequency response trajectory after the load disturbance occurs in the photovoltaic power station; Indicates the amplitude of the load disturbance; represents the natural frequency of the photovoltaic power station; represents the attenuation factor; represents the exponential function; represents the initial phase angle of the frequency response.

[0021] Optionally, the power constraint is expressed as follows:

[0022] ;

[0023] The expression of the energy constraint is as follows:

[0024] ;

[0025] The expression of the stability constraint is as follows:

[0026] ;

[0027] in, Indicates that after a load disturbance occurs in a photovoltaic power station Optimal support power fluctuation data at each moment; Indicates the power output limit of the photovoltaic power station; Indicates the energy release limit of the photovoltaic power station; represents the attenuation factor; represents the upper limit of the factor; Indicates the lower limit of the factor.

[0028] Optionally, obtaining optimal supported power fluctuation data after a load disturbance occurs in the photovoltaic power station by using a pre-built control trajectory solution model based on the operation boundary information includes:

[0029] According to the operation boundary information, a pre-built control trajectory solution model is used to solve the optimal frequency response trajectory of the photovoltaic power station after a load disturbance occurs;

[0030] Calculating optimal supporting power fluctuation data after a load disturbance occurs in the photovoltaic power station according to the optimal frequency response trajectory;

[0031] The operation boundary information includes: the power output limit and energy release limit of the photovoltaic power station.

[0032] Optionally, the calculation formula of the optimal support power fluctuation data is as follows:

[0033] ;

[0034] in, Indicates that after a load disturbance occurs in a photovoltaic power station Optimal support power fluctuation data at each moment; Indicates the inertia control parameters of the unit layer in the photovoltaic power station; represents the damping control parameter of the unit layer in the photovoltaic power station; represents the optimal frequency response trajectory after a load disturbance occurs in the photovoltaic power station; Indicates the virtual inertia control parameters of the station control layer in the photovoltaic power station; Indicates the primary frequency regulation control parameters of the station control layer in the photovoltaic power station; Indicates control delay; Indicates that after a load disturbance occurs in a photovoltaic power station Frequency deviation information at the moment.

[0035] Optionally, the calculation formula of the actual active output fluctuation data is as follows:

[0036] ;

[0037] in, express Actual active power output fluctuation data at each moment; represents real part extraction; express Voltage information of grid connection point at all times; express The complex conjugate of the grid-connected point current information at each moment; Indicates the stable output power of the PV-storage power station before load disturbance occurs.

[0038] Optionally, the similarity score is calculated as follows:

[0039] ;

[0040] in, C pp A similarity score representing the optimal support power fluctuation data and the actual active power output fluctuation data; Indicates the The optimal support power fluctuation data at each sampling point; i =1… m ; m Indicates the total number of sampling points; represents the mean value of the optimal support power fluctuation data; Indicates the Actual active output fluctuation data at each sampling point; Indicates the mean value of actual active power output fluctuation data.

[0041] Optionally, the online evaluation of the frequency support capability of the photovoltaic power station according to the similarity score includes:

[0042] When the similarity score is 1, it indicates that the frequency support capability of the photovoltaic power station is in an optimal state;

[0043] When the similarity score is 0, it indicates that the frequency support capability of the photovoltaic power station is in a mismatch state;

[0044] When the similarity score is -1, it indicates that the frequency support capability of the photovoltaic power station is in a reverse support state;

[0045] When the similarity score is greater than -1 and less than 0, it indicates that the frequency support capability of the photovoltaic power station is in a negative deviation state;

[0046] When the similarity score is greater than 0 and less than 1, it indicates that the frequency support capability of the photovoltaic power station is in a positive deviation state.

[0047] Based on the same inventive concept, the present invention also provides an online evaluation system for the multi-level coordinated frequency support capability of a photovoltaic power station, comprising:

[0048] An information acquisition module is used to obtain the operating boundary information of the photovoltaic power station before the load disturbance occurs and the voltage and current information of the grid connection point after the load disturbance occurs;

[0049] A model solving module is used to solve the model using a pre-built control trajectory according to the operation boundary information to obtain the optimal supporting power fluctuation data after the load disturbance occurs in the photovoltaic power station;

[0050] A disturbance output module, configured to calculate actual active power output fluctuation data of the photovoltaic power station after a load disturbance occurs based on the voltage and current information of the grid connection point;

[0051] an online evaluation module, configured to perform similarity calculation on the optimal supported power fluctuation data and the actual active power output fluctuation data to obtain a similarity score, and perform online evaluation on the frequency support capability of the PV power station based on the similarity score;

[0052] The control trajectory solution model is constructed with the goal of minimizing the sum of the maximum frequency change rate and the maximum frequency deviation after a load disturbance occurs in the photovoltaic power station.

[0053] Optionally, the online evaluation system further includes: a model building module, including:

[0054] A trajectory simulation submodule, configured to simulate the frequency response trajectory of the photovoltaic power station based on a disturbance signal of a load disturbance occurring in the photovoltaic power station and preset operating boundary conditions;

[0055] a target construction submodule, configured to extract a maximum frequency change rate and a maximum frequency deviation from the frequency response trajectory, and construct an objective function with the goal of minimizing the sum of the maximum frequency change rate and the maximum frequency deviation;

[0056] A constraint formulation submodule, used to formulate constraint conditions according to the objective function;

[0057] A model generation submodule, configured to construct a control trajectory solution model based on the objective function and the constraint conditions;

[0058] The constraint conditions include one or more of the following: power constraint, energy constraint and stability constraint.

[0059] Optionally, the objective function is expressed as follows:

[0060] ;

[0061] Where,

[0062] ;

[0063] in, F represents the objective function; Indicates the inertia control parameters of the unit layer in the photovoltaic power station; represents the damping control parameter of the unit layer in the photovoltaic power station; Indicates the virtual inertia control parameters of the station control layer in the photovoltaic power station; Indicates the primary frequency regulation control parameters of the station control layer in the photovoltaic power station; Indicates control delay; B represents the weight amplification factor; Represents a photovoltaic power station The time in the window is Frequency deviation information when It represents the frequency response trajectory after the load disturbance occurs in the photovoltaic power station; Indicates the amplitude of the load disturbance; represents the natural frequency of the photovoltaic power station; represents the attenuation factor; represents the exponential function; represents the initial phase angle of the frequency response.

[0064] Optionally, the power constraint is expressed as follows:

[0065] ;

[0066] The expression of the energy constraint is as follows:

[0067] ;

[0068] The expression of the stability constraint is as follows:

[0069] ;

[0070] in, Indicates that after a load disturbance occurs in a photovoltaic power station Optimal support power fluctuation data at each moment; Indicates the power output limit of the photovoltaic power station; Indicates the energy release limit of the photovoltaic power station; represents the attenuation factor; represents the upper limit of the factor; Indicates the lower limit of the factor.

[0071] Optionally, the model solving module includes:

[0072] A trajectory generation submodule is used to solve the optimal frequency response trajectory of the photovoltaic power station after a load disturbance occurs by using a pre-built control trajectory solution model based on the operation boundary information;

[0073] A fluctuation output submodule, configured to calculate optimal supporting power fluctuation data after a load disturbance occurs in the photovoltaic power station according to the optimal frequency response trajectory;

[0074] The operation boundary information includes: the power output limit and energy release limit of the photovoltaic power station.

[0075] Optionally, the calculation formula of the optimal support power fluctuation data is as follows:

[0076] ;

[0077] in, Indicates that after a load disturbance occurs in a photovoltaic power station Optimal support power fluctuation data at each moment; Indicates the inertia control parameters of the unit layer in the photovoltaic power station; represents the damping control parameter of the unit layer in the photovoltaic power station; represents the optimal frequency response trajectory after a load disturbance occurs in the photovoltaic power station; Indicates the virtual inertia control parameters of the station control layer in the photovoltaic power station; Indicates the primary frequency regulation control parameters of the station control layer in the photovoltaic power station; Indicates control delay; Indicates that after a load disturbance occurs in a photovoltaic power station Frequency deviation information at the moment.

[0078] Optionally, the calculation formula of the actual active output fluctuation data is as follows:

[0079] ;

[0080] in, express Actual active power output fluctuation data of the solar-storage power station at any moment; represents real part extraction; express Voltage information of grid connection point at all times; express The complex conjugate of the grid-connected point current information at each moment; Indicates the stable output power of the PV-storage power station before load disturbance occurs.

[0081] Optionally, the similarity score is calculated as follows:

[0082] ;

[0083] in, C pp A similarity score representing the optimal support power fluctuation data and the actual active power output fluctuation data; Indicates the The optimal support power fluctuation data at each sampling point; i =1… m ; m Indicates the total number of sampling points; represents the mean value of the optimal support power fluctuation data; Indicates the Actual active output fluctuation data at each sampling point; Indicates the mean value of actual active power output fluctuation data.

[0084] Optionally, the online evaluation module includes:

[0085] An optimal state evaluation submodule, configured to indicate that the frequency support capability of the photovoltaic power station is in an optimal state when the similarity score is 1;

[0086] An adaptation status evaluation submodule, configured to indicate that the frequency support capability of the photovoltaic power station is in a mismatch state when the similarity score is 0;

[0087] A reverse support evaluation submodule, configured to indicate that the frequency support capability of the photovoltaic power station is in a reverse support state when the similarity score is -1;

[0088] A negative deviation evaluation submodule, configured to indicate that the frequency support capability of the photovoltaic power station is in a negative deviation state when the similarity score is greater than -1 and less than 0;

[0089] The positive deviation evaluation submodule is used to indicate that the frequency support capability of the photovoltaic power station is in a positive deviation state when the similarity score is greater than 0 and less than 1.

[0090] In another aspect, the present invention further provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0091] The memory is used to store one or more programs;

[0092] When the one or more programs are executed by the at least one processor, the above-mentioned online evaluation method for the multi-level coordinated frequency support capability of a photovoltaic power station is implemented.

[0093] On the other hand, the present invention also provides a computer-readable storage medium having an execution program stored thereon. When the execution program is executed, the above-mentioned online evaluation method for the multi-level coordinated frequency support capability of a photovoltaic power station is implemented.

[0094] Compared with the prior art, the present invention has the following beneficial effects:

[0095] The present invention provides a method and system for online evaluation of the multi-level coordinated frequency support capability of a photovoltaic power station, comprising: respectively obtaining the operating boundary information of the photovoltaic power station before a load disturbance occurs and the voltage and current information of the grid connection point after the load disturbance occurs; according to the operating boundary information, using a pre-constructed control trajectory solution model, obtaining the optimal supporting power fluctuation data after the load disturbance occurs in the photovoltaic power station; according to the grid connection point voltage and current information, calculating the actual active power output fluctuation data after the load disturbance occurs in the photovoltaic power station; performing similarity calculation on the optimal supporting power fluctuation data and the actual active power output fluctuation data to obtain a similarity score, and evaluating the frequency support capability of the photovoltaic power station according to the similarity score. Online evaluation; wherein, the control trajectory solution model is constructed with the goal of minimizing the sum of the maximum frequency change rate and the maximum frequency deviation after the load disturbance occurs in the photovoltaic power station; the present invention, through the control trajectory solution model constructed with the goal of minimizing the sum of the maximum frequency change rate and the maximum frequency deviation, can generate a theoretical optimal support power fluctuation process reflecting the comprehensive effect of the multi-level control logic of the photovoltaic power station under known operating boundary conditions; and then, by performing a similarity analysis between the theoretical optimal support power fluctuation process and the actual measured active output fluctuation data, it can not only realize the online quantitative evaluation of the frequency support capability of the photovoltaic power station, but also assist in identifying the degree of matching between the control strategies of each level and the theoretical targets in the current actual control response. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 A schematic flow chart of an online evaluation method for multi-level coordinated frequency support capability of a photovoltaic power station provided by the present invention;

[0097] Figure 2This is a multi-level frequency support control block diagram of a photovoltaic power station in an online evaluation method for the multi-level coordinated frequency support capability of a photovoltaic power station provided by the present invention;

[0098] Figure 3 A multi-level frequency support control block diagram of a photovoltaic power station after Pade time lag equivalence is used in an online evaluation method for the multi-level coordinated frequency support capability of a photovoltaic power station provided by the present invention;

[0099] Figure 4 A test platform for an online evaluation method of multi-level coordinated frequency support capability of a photovoltaic power station provided in a specific embodiment;

[0100] Figure 5 The optimal frequency support control and measured power waveform in an online evaluation method for multi-level coordinated frequency support capability of a photovoltaic power station provided in a specific embodiment;

[0101] Figure 6 A schematic diagram of the structure of an online evaluation system for multi-level coordinated frequency support capability of a photovoltaic power station provided by the present invention;

[0102] Figure 7 This is a structural diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0103] The present invention proposes a method, system, device and medium for online evaluation of the multi-level coordinated frequency support capability of a photovoltaic power station. The specific implementation methods of the present invention are further described in detail below with reference to the accompanying drawings.

[0104] Example 1:

[0105] The present invention provides an online evaluation method for the multi-level coordinated frequency support capability of a photovoltaic power station. The flow chart is as follows: Figure 1 As shown, including:

[0106] Step 1: Obtain the operating boundary information of the photovoltaic power station before the load disturbance and the voltage and current information of the grid connection point after the load disturbance;

[0107] Step 2: Based on the operation boundary information, a pre-built control trajectory solving model is used to obtain optimal supporting power fluctuation data after a load disturbance occurs in the PV-storage power station;

[0108] Step 3: Calculate the actual active power output fluctuation data of the photovoltaic power station after the load disturbance occurs based on the voltage and current information of the grid connection point;

[0109] Step 4: Calculate the similarity between the optimal supported power fluctuation data and the actual active power output fluctuation data to obtain a similarity score, and perform an online evaluation of the frequency support capability of the PV power station based on the similarity score;

[0110] The control trajectory solution model is constructed with the goal of minimizing the sum of the maximum frequency change rate and the maximum frequency deviation after a load disturbance occurs in the photovoltaic power station.

[0111] Generally, the frequency support capability of a photovoltaic power station is usually judged offline using a single response indicator sampling analysis or a rule matching method based on static set values. For example, a fixed frequency deviation threshold, power response rate, or instantaneous output characteristics at a single moment is used as the evaluation basis, which fails to fully consider the dynamic changes and control superposition effects of the photovoltaic power station during the response process under a multi-level control structure. However, photovoltaic power stations usually adopt a multi-level collaborative control architecture, including inertia control and damping control at the unit level, virtual inertia control and primary frequency regulation control at the station control level, etc. There are differences in response timing and adjustment targets in each control link, and they are significantly affected by control delays and operating boundary restrictions. This complex response characteristic makes it difficult to accurately reflect the true level of frequency support capability by relying solely on static indicators or single-point response values, and it is also difficult to support scheduling optimization or control strategy adjustment. To solve the above problems, the present invention considers introducing the operating boundary information of the photovoltaic power station (for example, it may include: the power output limit and energy release limit of the photovoltaic power station) and the voltage and current information of the grid connection point as inputs, combining with the multi-level power response method, using the control trajectory solution model to output the theoretical optimal frequency response trajectory, and through similarity analysis with the measured power trajectory, to achieve dynamic, quantitative and online evaluation of the frequency support capability of the photovoltaic power station. Specifically:

[0112] In one implementation, the control trajectory solution model may include the following construction process:

[0113] Simulating a frequency response trajectory of the photovoltaic power station based on a disturbance signal of a load disturbance occurring in the photovoltaic power station and preset operating boundary conditions;

[0114] Extracting the maximum frequency change rate and the maximum frequency deviation from the frequency response trajectory, and constructing an objective function with the goal of minimizing the sum of the maximum frequency change rate and the maximum frequency deviation;

[0115] Formulate constraints based on the objective function;

[0116] Based on the objective function and the constraint conditions, construct a control trajectory solution model;

[0117] The constraint conditions may include one or more of the following: power constraint, energy constraint and stability constraint.

[0118] For a photovoltaic power station equipped with a grid-connected energy storage station, the frequency support control of the power grid in the grid-connected operation mode includes the inertia damping control at the unit layer and the additional primary frequency regulation and virtual inertia control at the station control layer. Considering that the power adjustment command from the station control layer to the unit layer needs to go through the steps of command generation, transmission and execution, there is a time lag. Based on this, the following can be obtained: Figure 2 The multi-level frequency support control block diagram of the photovoltaic power station is shown in the figure. g is the inertia control parameter of the unit layer in the photovoltaic power station, D g is the damping control parameter of the unit layer in the photovoltaic power station, M f is the virtual inertia control parameter of the station control layer in the photovoltaic power station, D f It is the primary frequency regulation control parameter of the station control layer in the photovoltaic power station. represents the time-delay link, s is the frequency domain variable in Laplace transform, represents the control delay, P L Indicates load power, P f Indicates the output power of the power station control layer; Δf indicates the frequency deviation, and f0 indicates the reference frequency. Since the time-delay link will show high-order nonlinearity under frequency and is difficult to analyze, it needs to be processed equivalently. Commonly used equivalent methods include Taylor equivalent and Pade equivalent. Considering that the evaluation of the multi-level coordinated frequency support capability of the photovoltaic power station involves the evaluation of the entire frequency dynamic process, the accuracy of the frequency dynamic characteristics obtained after adopting the Pade equivalent method is better than the Taylor equivalent method. Therefore, the present invention chooses to use the first-order Pade to perform the equivalent of the time-delay link. The control block diagram after the equivalent processing is as follows: Figure 3 As shown in the figure, when a small signal load disturbance occurs in the grid to which the photovoltaic power station is connected (for example, ΔP L ) when Figure 3 The frequency response expression of the solar-storage power station in the frequency domain can be derived:

[0119] ;

[0120] in, Indicates that a load disturbance occurs in the photovoltaic power station ΔP L The frequency domain variables s The frequency response trace below: Indicates control delay; Represents the inertia control parameters of the unit layer in the photovoltaic power station; Indicates the virtual inertia control parameters of the station control layer in the photovoltaic power station; represents the attenuation factor; represents the natural frequency of the photovoltaic power station; represents the damping control parameter of the unit layer in the photovoltaic power station; Indicates the primary frequency regulation control parameters of the station control layer in the photovoltaic power station;

[0121] When the small signal disturbance is in the form of A When there is a step disturbance, that is, ΔP L =A / s, substituting into the above formula, we can get the frequency domain and time domain expressions of the PV power station frequency response as follows:

[0122] ;

[0123] ;

[0124] in, Indicates the amplitude of the load disturbance; represents the initial phase angle of the frequency response;

[0125] Considering that after a load disturbance occurs in the power grid, excessive frequency changes or excessive frequency deviations will lead to the disconnection of new energy power stations and low-frequency load reduction operations, and the frequency change rate and frequency deviation can well measure the multi-level coordinated frequency support effect of photovoltaic power stations. To this end, the present invention considers minimizing the sum of the maximum frequency change rate and the maximum frequency deviation during the load disturbance as the objective function, taking into account the power constraints, energy constraints and stability constraints of the photovoltaic power station when performing frequency support, and constructing a control trajectory solution model for the multi-level frequency support photovoltaic power station. The multi-level frequency support control trajectory model of the photovoltaic power station is solved. The corresponding objective function expression can be as follows:

[0126] ;

[0127] in, F represents the objective function of the control trajectory solution model; Indicates the inertia control parameters of the unit layer in the photovoltaic power station; represents the damping control parameter of the unit layer in the photovoltaic power station; Indicates the virtual inertia control parameters of the station control layer in the photovoltaic power station; Indicates the primary frequency regulation control parameters of the station control layer in the photovoltaic power station; Indicates control delay; B represents the weight amplification factor; Represents a photovoltaic power station The time in the window is Frequency deviation information when represents the frequency response trajectory of a PV power station after a load disturbance (composed of frequency deviation information at different times). By constructing a control trajectory solution model with the goal of minimizing the sum of the maximum frequency change rate and the maximum frequency deviation, it can accurately reflect the optimal frequency support trajectory under the multi-level coordinated control of the PV power station. Specifically, in this objective function, considering that the calculation window length of the actual frequency change rate of the PV power station is generally 100ms, and the frequency of the PV power station generally reaches a steady state within 60s after a load fluctuation occurs, the following objective function can be formed:

[0128]

[0129] It should be noted that the time in the control trajectory solution model t The value range of can be [0,60]. In order to improve the calculation efficiency, the interval can be selected from the time when the load fluctuation starts to the time when the frequency recovers to the dead zone of the frequency modulation action.

[0130] Solar power stations are different from traditional synchronous power sources. Their power and energy release are limited. In addition, when performing frequency support, the frequency stability of the unit needs to be guaranteed. Therefore, the following constraints can be formulated:

[0131] For example, the power constraint above can be expressed as follows:

[0132] ;

[0133] The energy constraint can be expressed as follows:

[0134] ;

[0135] The expression of the stability constraint can be as follows:

[0136] ;

[0137] in, Indicates that after a load disturbance occurs in a photovoltaic power station Optimal support power fluctuation data at each moment; Indicates the power output limit of the photovoltaic power station; Indicates the energy release limit of the photovoltaic power station; represents the attenuation factor; represents the upper limit of the factor; Indicates the lower limit of the factor; preferably, =0.2; ;

[0138] In this implementation, by introducing disturbance signals and operating boundary conditions as input, a control trajectory solution model for frequency response trajectory simulation and performance optimization is constructed, and an objective function is clearly established in the model to quantitatively control the response performance by minimizing the weighted result of the maximum frequency deviation and the frequency deviation at a specified moment. Among them, the two key control indicators introduced in the objective function are the maximum frequency deviation and the terminal moment frequency deviation. (t=T) , respectively reflecting the dynamic response capability of the photovoltaic power station to the initial disturbance and the steady-state control capability at the tail of the disturbance process, combined with the weight amplification factor B The setting of can realize flexible regulation of performance requirements at different stages of the response process. In addition, the introduction of multiple sets of control parameters reflects that the model considers the frequency support mechanism of the photovoltaic power station at both the physical response and control logic levels. These control parameters are used to establish the frequency response trajectory, and then solve the power trajectory under boundary conditions. It can comprehensively consider the frequency behavior under the multi-level control structure in the process of dynamic modeling and optimization control, improve the response coordination and power regulation rationality, and achieve a dynamic balance between the theoretical optimal support trajectory and the actual operation constraints, thereby significantly improving the feasibility and stability of the frequency support effect in the actual system.

[0139] By solving the control trajectory model constructed by the above implementation method, the theoretical optimal supported power output behavior of the photovoltaic power station under given operating boundary conditions can be obtained. Specifically:

[0140] In one implementation, the process of obtaining the optimal supported power fluctuation data after a load disturbance occurs in the PV-storage power station by using a pre-built control trajectory solution model based on the operation boundary information in step 2 may include:

[0141] According to the operation boundary information, a pre-built control trajectory solution model is used to solve the optimal frequency response trajectory of the photovoltaic power station after a load disturbance occurs;

[0142] Calculating optimal supporting power fluctuation data after a load disturbance occurs in the photovoltaic power station according to the optimal frequency response trajectory;

[0143] In this implementation, operating boundary information is used as input to drive a pre-built control trajectory solution model to generate an optimal frequency response trajectory. The optimal supported power fluctuation data is then calculated, giving the frequency support performance evaluation model predictive capabilities and dynamic boundary adaptability. Specifically, the operating boundary information reflects the power regulation limit and energy release capacity of the current PV-storage power station, rather than a static setpoint. This input makes the solved frequency response trajectory physically executable and strategically rational in terms of control. Furthermore, the optimal frequency response trajectory is inferred from the control trajectory solution model, and then a theoretical supported power trajectory is calculated based on the frequency-power response relationship. This calculation path not only preserves the dynamic evolution of the frequency response but also avoids the bias caused by direct reliance on actual power measurement errors in the theoretical evaluation, thereby enhancing the accuracy and reference value of the support capability evaluation. The resulting optimal power fluctuation data, serving as a theoretical reference trajectory, provides a solid benchmark for subsequent comparisons of measured responses, improving the real-time, systematic, and engineering adaptability of online frequency support capability evaluation. This implementation method introduces the operating boundary information as the solution condition, combines the modeling of the frequency response trajectory with the theoretical power deduction based on the frequency-power coupling relationship, and realizes the continuous modeling logic from constraint drive to trajectory construction to response prediction. In practical applications, the frequency response of the photovoltaic power station is controlled by multiple parameter levels, including controller settings, energy storage SOC, grid-connected constraints, etc. By nesting the operating boundary information with the control trajectory solution model, not only can the frequency response trajectory within the operational range be generated, but also the frequency response trajectory within the operational range can be generated through precise control. and frequency change rate, so that the theoretical power trajectory of the output dynamically maps the real characteristics of the control hysteresis.

[0144] For example, the calculation formula for the above-mentioned optimal support power fluctuation data can be as follows:

[0145] ;

[0146] in, Indicates that after a load disturbance occurs in a photovoltaic power station Optimal support power fluctuation data at each moment; Indicates the inertia control parameters of the unit layer in the photovoltaic power station; represents the damping control parameter of the unit layer in the photovoltaic power station; represents the optimal frequency response trajectory after a load disturbance occurs in the photovoltaic power station; Indicates the virtual inertia control parameters of the station control layer in the photovoltaic power station; Indicates the primary frequency regulation control parameters of the station control layer in the photovoltaic power station; Indicates control delay; Indicates that after a load disturbance occurs in a photovoltaic power station Frequency deviation information at the moment; In this example, based on the two-layer control structure of the photovoltaic power station, the inertia response and frequency regulation behavior of the unit layer and the station control layer are modeled respectively, in which a control delay is specially set to reflect the time lag of the station control layer response relative to the unit layer, thereby more realistically reflecting the time-sharing response characteristics of each control link of the power station to the frequency disturbance. and The analytical expressions of these two different phase frequency deviations explicitly model the dynamic superposition process of the multi-level frequency support control mechanism of the photovoltaic power station, making the final output It not only includes the inertia support capability of the photovoltaic power station at the moment of disturbance, but also reflects the frequency compensation capability of the station control layer after time lag.

[0147] Based on the theoretical optimal support power fluctuation data obtained in the above implementation method, in order to further realize the online evaluation of the frequency support capability of the photovoltaic power station, after obtaining the theoretical optimal support power fluctuation data, it is also necessary to obtain the corresponding actual power response. For example, the calculation formula of the actual active output fluctuation data in the above step 3 can be as follows:

[0148] ;

[0149] in, express Actual active power output fluctuation data at each moment; represents real part extraction; express Grid connection point voltage information at the moment; express The complex conjugate of the grid-connected point current information at each moment; Indicates the stable output power of the PV-storage power station before load disturbance occurs.

[0150] The optimal frequency support control trajectory can be obtained by using the control trajectory solution model of the multi-level frequency support photovoltaic power station with energy storage. Combined with the actual active power output fluctuation data of the photovoltaic power station with energy storage, and based on the waveform similarity algorithm, the multi-level frequency support capability of the photovoltaic power station with energy storage can be evaluated online. For example, the calculation formula of the similarity score in the above step 4 can be as follows:

[0151] ;

[0152] in, C pp A similarity score representing the optimal support power fluctuation data and the actual active power output fluctuation data; Indicates the The optimal support power fluctuation data at each sampling point; i =1… m ; mIndicates the total number of sampling points; represents the mean value of the optimal support power fluctuation data; Indicates the Actual active output fluctuation data at each sampling point; Represents the mean of the actual active power output fluctuation data. In this example, calculating the similarity between the predicted optimal support power fluctuation and the actually measured active power output fluctuation helps to accurately quantify the actual performance of the current frequency support capability of the PV power station.

[0153] Based on the similarity score between the optimal supported power fluctuation data and the actual active power output fluctuation data of the PV-storage power station after a load disturbance obtained by the above calculation formula, the frequency support capability of the PV-storage power station can be evaluated online. Specifically:

[0154] In one implementation, the process of online evaluation of the frequency support capability of the photovoltaic power station based on the similarity score may include:

[0155] When the similarity score is 1, it indicates that the frequency support capability of the photovoltaic power station is in an optimal state;

[0156] When the similarity score is 0, it indicates that the frequency support capability of the photovoltaic power station is in a mismatch state;

[0157] When the similarity score is -1, it indicates that the frequency support capability of the photovoltaic power station is in a reverse support state;

[0158] When the similarity score is greater than -1 and less than 0, it indicates that the frequency support capability of the photovoltaic power station is in a negative deviation state;

[0159] When the similarity score is greater than 0 and less than 1, it indicates that the frequency support capability of the photovoltaic power station is in a positive deviation state;

[0160] In this implementation, the dynamic matching degree between the optimal supporting power fluctuation trajectory and the actual power response behavior of the photovoltaic power station is quantified by calculating the similarity between the optimal supporting power fluctuation data and the actual active output fluctuation data. Based on the calculation results, a multi-level frequency support capability evaluation criterion is set, thereby realizing an online, hierarchical, and interpretable capability assessment mechanism for the frequency support performance of the photovoltaic power station. This implementation adopts an evaluation expression in the form of a normalized mutual correlation coefficient, which makes the evaluation result dimensionless and has a unified numerical range (–1 to 1), facilitating comparative analysis across power stations and operating conditions. In the calculation, the theoretical power fluctuation value and the actual response value of each sampling point are compared point by point, and the interference of the absolute amplitude difference is eliminated by introducing a mean correction term, thereby further highlighting the matching degree between the trajectories in terms of trend direction, response rhythm, dynamic synergy, etc. This point-to-point matching method is different from the traditional single error calculation method and can more accurately capture the similarity of the two response curves in the dynamic process. Furthermore, the five-level support capability judgment interval (optimal state, positive deviation state, mismatch state, negative deviation state, and reverse support state) constructed on this basis not only covers the full range of response scenarios from complete matching to complete deviation, but also introduces the judgment dimension of "positive and negative direction deviation", so that whether the support behavior is conducive to frequency stability can be clearly expressed at the numerical level.

[0161] In summary, the present invention aims to address the problem that it is difficult to clearly identify the effects of different levels from external observations during the frequency support process of the multi-level collaborative control architecture adopted by the current photovoltaic power station, which makes it difficult to evaluate the frequency support capability of the current photovoltaic power station online. The present invention proposes an online evaluation method for the multi-level collaborative frequency support capability of a photovoltaic power station, clarifies the multi-level power response control architecture of the photovoltaic power station with active support capability, establishes a multi-level power response model of the photovoltaic power station, derives the analytical expressions of the frequency response in the frequency domain and the time domain, and takes the minimum sum of the maximum frequency change rate and the maximum frequency deviation as the goal, taking into account the power constraints, energy constraints and stability constraints of the photovoltaic power station when performing frequency support, constructs a control trajectory solution model for the multi-level frequency support photovoltaic power station; finally, the optimal frequency response trajectory is integrated with the actual active output fluctuation data, and based on the waveform similarity algorithm, the multi-level frequency support capability of the photovoltaic power station is evaluated online, which solves the problem that the multi-level frequency support performance of the photovoltaic power station in operation is difficult to evaluate online.

[0162] Example 2:

[0163] A specific embodiment is used to illustrate the online evaluation method of the multi-level coordinated frequency support capability of a photovoltaic power station proposed by the present invention. The composition of the test platform is as follows: Figure 4As shown, it includes: a converter controller (used to simulate the control logic of the energy storage converter in an actual photovoltaic power station, including power, current, frequency and other control links), an online monitoring device (used to collect voltage and current data of the grid-connected point of the simulated photovoltaic power station, and record the electrical change information before and after the load disturbance in real time), a real-time simulator (used to simulate the real grid disturbance scene, provide disturbance load change signal and frequency deviation input as the incentive condition of the controller and station system), a station power controller (used to dispatch photovoltaic and energy storage power response behavior, coordinate the frequency regulation and virtual inertia function of the station control layer, and the converter The system includes a power amplifier (used to amplify the low-level control signal output by the real-time simulator into a simulated power signal that can drive the physical device, providing realistic simulated electrical input conditions for the converter and control device), a simulation station monitoring system (used to configure operational boundary information such as power output limit and energy release limit before the disturbance, providing input parameters for the control trajectory solution model), and a portable oscilloscope (used to accurately record time series data of key quantities such as converter output, voltage, current, and frequency during the disturbance event, to verify the matching between the simulation results and the evaluation model).

[0164] First, the power output limit of the power station before the load disturbance occurs is obtained from the energy management system of the photovoltaic power station. a (For example, set to 0.2) and the energy release limit b (For example, set it to 1.5), and set the load fluctuation amplitude (for example, set it to 0.1), calculate the frequency response and power support expression in the time domain, input the calculated results into the control trajectory solution model of the multi-level frequency support, and choose to use particle swarm optimization, neural network and other optimization algorithms to solve it, and obtain the optimal frequency response trajectory of the photovoltaic power station. In order to improve the optimization efficiency, the current photovoltaic power station technical specifications can be referred to to give unknown parameters (M g 、D g 、M f 、D f 、 ), for example, the specific parameter range can be set as follows:

[0165] ;

[0166] in, Represents the inertia control parameters of the unit layer in the photovoltaic power station; represents the damping control parameter of the unit layer in the photovoltaic power station; Indicates the virtual inertia control parameters of the station control layer in the photovoltaic power station; Indicates the primary frequency regulation control parameters of the station control layer in the photovoltaic power station; Indicates control delay;

[0167] A 10% load is put into the simulated photovoltaic power station, and the voltage and current data of the photovoltaic power station at the grid connection point after the load fluctuation is collected using the online monitoring device. The actual frequency Δf of the photovoltaic power station at the grid connection point is calculated. actual and output power ΔP actual (that is, the actual active output fluctuation data) and the optimal frequency response trajectory are used to calculate the waveform similarity. The control parameters corresponding to the optimal frequency response trajectory and the actual power station control parameters are shown in Table 1. The obtained optimal frequency support control curve and the measured waveform are shown in Figure 5 As shown:

[0168] Table 1 Optimal control parameters and actual control parameters

[0169]

[0170] Finally, the theoretical data Δf, ΔP and the measured data Δf are solved according to the control trajectory solution model. actual , ΔP actual Perform similarity calculation, considering that after the load fluctuation of the photovoltaic power station occurs, it is difficult to know the fluctuation component of the measured photovoltaic power station, the frequency response and frequency support trajectory are related to the load disturbance amplitude. A There is a linear relationship, so the present invention uses the waveform similarity C pp Evaluate the multi-level coordinated frequency support performance of photovoltaic storage power stations, C pp The calculation formula is as follows:

[0171] ;

[0172] in, C pp Represents the similarity score between the optimal support power fluctuation data and the actual active output fluctuation data; Indicates the The optimal support power fluctuation data at each sampling point; i =1… m ; m Indicates the total number of sampling points; represents the mean value of the optimal support power fluctuation data; Indicates the Actual active output fluctuation data at each sampling point; Indicates the mean value of actual active power output fluctuation data. C pp The value range of is [-1,1], C pp =1 means the two waveforms are positively correlated. C pp =-1 means the two waveforms are negatively correlated. C pp =0 means the two waveforms are unrelated, that is,C pp The larger the value, the closer the multi-level measured active power support of the photovoltaic power station is to the optimal active power support. Figure 5 According to the above similarity calculation formula, the waveform similarity calculation result between the measured active power of the photovoltaic power station and the active power under optimal control is 0.285.

[0173] Therefore, this specific example illustrates the proposed online evaluation method for the multi-level coordinated frequency support capability of a photovoltaic power station. This method can accurately quantify the degree of deviation between the frequency support behavior of a photovoltaic power station and its ideal support target based on theoretical trajectory modeling and comparison with actual response data after a disturbance occurs. The similarity score calculated in this example is 0.285. This, combined with the significant differences between the actual power fluctuation trajectory and the optimal control trajectory, demonstrates the effectiveness of the proposed method in identifying issues such as delayed control response, insufficient support effectiveness, and suboptimal performance of the strategy.

[0174] Example 3:

[0175] Based on the same inventive concept, the present invention also provides an online evaluation system for the multi-level coordinated frequency support capability of a photovoltaic power station. The structural diagram is shown in FIG. Figure 6 As shown, including:

[0176] An information acquisition module is used to obtain the operating boundary information of the photovoltaic power station before the load disturbance occurs and the voltage and current information of the grid connection point after the load disturbance occurs;

[0177] A model solving module is used to solve the model using a pre-built control trajectory according to the operation boundary information to obtain the optimal supporting power fluctuation data after the load disturbance occurs in the photovoltaic power station;

[0178] A disturbance output module, configured to calculate actual active power output fluctuation data of the photovoltaic power station after a load disturbance occurs based on the voltage and current information of the grid connection point;

[0179] an online evaluation module, configured to perform similarity calculation on the optimal supported power fluctuation data and the actual active power output fluctuation data to obtain a similarity score, and perform online evaluation on the frequency support capability of the PV power station based on the similarity score;

[0180] The control trajectory solution model is constructed with the goal of minimizing the sum of the maximum frequency change rate and the maximum frequency deviation after a load disturbance occurs in the photovoltaic power station.

[0181] In one implementation, the online evaluation system may further include: a model building module, which may specifically include:

[0182] A trajectory simulation submodule, configured to simulate the frequency response trajectory of the photovoltaic power station based on a disturbance signal of a load disturbance occurring in the photovoltaic power station and preset operating boundary conditions;

[0183] a target construction submodule, configured to extract a maximum frequency change rate and a maximum frequency deviation from the frequency response trajectory, and construct an objective function with the goal of minimizing the sum of the maximum frequency change rate and the maximum frequency deviation;

[0184] A constraint formulation submodule, used to formulate constraint conditions according to the objective function;

[0185] A model generation submodule, configured to construct a control trajectory solution model based on the objective function and the constraint conditions;

[0186] The constraint conditions include one or more of the following: power constraint, energy constraint and stability constraint.

[0187] For example, the objective function above can be expressed as follows:

[0188] ;

[0189] Where,

[0190] ;

[0191] in, F represents the objective function; Indicates the inertia control parameters of the unit layer in the photovoltaic power station; represents the damping control parameter of the unit layer in the photovoltaic power station; Indicates the virtual inertia control parameters of the station control layer in the photovoltaic power station; Indicates the primary frequency regulation control parameters of the station control layer in the photovoltaic power station; Indicates control delay; B represents the weight amplification factor; Represents a photovoltaic power station The time in the window is Frequency deviation information when It represents the frequency response trajectory after the load disturbance occurs in the photovoltaic power station; Indicates the amplitude of the load disturbance; represents the natural frequency of the photovoltaic power station; represents the attenuation factor; represents the exponential function; represents the initial phase angle of the frequency response.

[0192] For example, the power constraint above can be expressed as follows:

[0193] ;

[0194] The energy constraint can be expressed as follows:

[0195] ;

[0196] The expression of the stability constraint can be as follows:

[0197] ;

[0198] in, Indicates that after a load disturbance occurs in a photovoltaic power station Optimal support power fluctuation data at each moment; Indicates the power output limit of the photovoltaic power station; Indicates the energy release limit of the photovoltaic power station; represents the attenuation factor; represents the upper limit of the factor; Indicates the lower limit of the factor.

[0199] In one implementation, the model solving module may include:

[0200] A trajectory generation submodule is used to solve the optimal frequency response trajectory of the photovoltaic power station after a load disturbance occurs by using a pre-built control trajectory solution model based on the operation boundary information;

[0201] A fluctuation output submodule, configured to calculate optimal supporting power fluctuation data after a load disturbance occurs in the photovoltaic power station according to the optimal frequency response trajectory;

[0202] The operation boundary information includes: the power output limit and energy release limit of the photovoltaic power station.

[0203] For example, the calculation formula for the above-mentioned optimal support power fluctuation data can be as follows:

[0204] ;

[0205] in, Indicates that after a load disturbance occurs in a photovoltaic power station Optimal support power fluctuation data at each moment; Indicates the inertia control parameters of the unit layer in the photovoltaic power station; represents the damping control parameter of the unit layer in the photovoltaic power station; represents the optimal frequency response trajectory after a load disturbance occurs in the photovoltaic power station; Indicates the virtual inertia control parameters of the station control layer in the photovoltaic power station; Indicates the primary frequency regulation control parameters of the station control layer in the photovoltaic power station; Indicates control delay; Indicates that after a load disturbance occurs in a photovoltaic power station Frequency deviation information at the moment.

[0206] For example, the calculation formula for the above actual active power output fluctuation data can be as follows:

[0207] ;

[0208] in, express Actual active power output fluctuation data of the solar-storage power station at any moment; represents real part extraction; express Voltage information of grid connection point at all times; express The complex conjugate of the grid-connected point current information at each moment; Indicates the stable output power of the PV-storage power station before load disturbance occurs.

[0209] For example, the calculation formula of the similarity score can be as follows:

[0210] ;

[0211] in, C pp A similarity score representing the optimal support power fluctuation data and the actual active power output fluctuation data; Indicates the The optimal support power fluctuation data at each sampling point; i =1… m ; m Indicates the total number of sampling points; represents the mean value of the optimal support power fluctuation data; Indicates the Actual active output fluctuation data at each sampling point; Indicates the mean value of actual active power output fluctuation data.

[0212] In one implementation, the online evaluation module may include:

[0213] An optimal state evaluation submodule, configured to indicate that the frequency support capability of the photovoltaic power station is in an optimal state when the similarity score is 1;

[0214] An adaptation status evaluation submodule, configured to indicate that the frequency support capability of the photovoltaic power station is in a mismatch state when the similarity score is 0;

[0215] A reverse support evaluation submodule, configured to indicate that the frequency support capability of the photovoltaic power station is in a reverse support state when the similarity score is -1;

[0216] A negative deviation evaluation submodule, configured to indicate that the frequency support capability of the photovoltaic power station is in a negative deviation state when the similarity score is greater than -1 and less than 0;

[0217] The positive deviation evaluation submodule is used to indicate that the frequency support capability of the photovoltaic power station is in a positive deviation state when the similarity score is greater than 0 and less than 1.

[0218] Example 4:

[0219] like Figure 7 As shown, the present invention also provides an electronic device, which may be a computer, a single-chip microcomputer, a smart mobile device, or the like. The electronic device in this embodiment may include a processor, a memory, a transceiver component, and the like. The memory, processor, and transceiver component are connected via a bus; the memory may be used to store an execution program, which may include instructions; and the processor may be used to execute the instructions stored in the memory. The memory may also be used to store data, which may be accessed and / or modified during the execution of the instructions.

[0220] The processor may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to realize the steps of the online evaluation method of the multi-level collaborative frequency support capability of a photovoltaic power station in the above embodiment.

[0221] Example 5:

[0222] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device-readable storage medium (Memory). The electronic device-readable storage medium is a memory device within the electronic device, used to store programs and data. It is understood that the storage medium herein may include both built-in storage media within the electronic device and, of course, extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for being loaded and executed by a processor. These instructions may be one or more executable programs (including program code). It should be noted that the storage medium herein may be high-speed RAM memory or non-volatile memory, such as at least one disk drive. The processor loading and executing the one or more instructions stored in the storage medium implements the steps of the online evaluation method for the multi-level coordinated frequency support capability of a photovoltaic power station with energy storage, as described in the above-mentioned embodiment.

[0223] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0224] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0225] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1The function specified in one or more boxes.

[0226] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0227] Finally, 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 its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art may still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims.

Claims

1. An online evaluation method for the multi-level coordinated frequency support capability of a photovoltaic power station, characterized in that: include: Obtain the operating boundary information of the photovoltaic power station before the load disturbance occurs and the voltage and current information of the grid connection point after the load disturbance occurs; According to the operation boundary information, a pre-built control trajectory solution model is used to obtain optimal support power fluctuation data after the load disturbance occurs in the photovoltaic power station; Calculating actual active power output fluctuation data of the photovoltaic power station after a load disturbance occurs based on the voltage and current information of the grid connection point; Performing similarity calculation on the optimal supported power fluctuation data and the actual active power output fluctuation data to obtain a similarity score, and performing online evaluation on the frequency support capability of the photovoltaic power station based on the similarity score; The control trajectory solution model includes the following construction process: Simulating a frequency response trajectory of the photovoltaic power station based on a disturbance signal of a load disturbance occurring in the photovoltaic power station and preset operating boundary conditions; Extracting the maximum frequency change rate and the maximum frequency deviation from the frequency response trajectory, and constructing an objective function with the goal of minimizing the sum of the maximum frequency change rate and the maximum frequency deviation; Formulate constraints based on the objective function; Based on the objective function and the constraint conditions, construct a control trajectory solution model; The constraint conditions include one or more of the following: power constraint, energy constraint and stability constraint; The objective function is expressed as follows: ； Where, ； ； ； in, represents the objective function; Indicates the inertia control parameters of the unit layer in the photovoltaic power station; represents the damping control parameter of the unit layer in the photovoltaic power station; Indicates the virtual inertia control parameters of the station control layer in the photovoltaic power station; Indicates the primary frequency regulation control parameters of the station control layer in the photovoltaic power station; Indicates control delay; represents the weight amplification factor; Represents a photovoltaic power station The time in the window is Frequency deviation information when It represents the frequency response trajectory after the load disturbance occurs in the photovoltaic power station; Indicates the amplitude of the load disturbance; represents the natural frequency of the photovoltaic power station; represents the attenuation factor; represents the exponential function; represents the initial phase angle of the frequency response.

2. The method according to claim 1, wherein The power constraint is expressed as follows: ； The expression of the energy constraint is as follows: ； The expression of the stability constraint is as follows: ； in, Indicates that after a load disturbance occurs in a photovoltaic power station Optimal support power fluctuation data at each moment; Indicates the power output limit of the photovoltaic power station; Indicates the energy release limit of the photovoltaic power station; represents the attenuation factor; represents the upper limit of the factor; Indicates the lower limit of the factor.

3. The method according to claim 1 or 2, wherein: The method of obtaining optimal supporting power fluctuation data after a load disturbance occurs in the photovoltaic power station by using a pre-built control trajectory solving model based on the operating boundary information includes: According to the operation boundary information, a pre-built control trajectory solution model is used to solve the optimal frequency response trajectory of the photovoltaic power station after a load disturbance occurs; Calculating optimal supporting power fluctuation data after a load disturbance occurs in the photovoltaic power station according to the optimal frequency response trajectory; The operation boundary information includes: the power output limit and energy release limit of the photovoltaic power station.

4. The method according to claim 3, wherein The calculation formula of the optimal support power fluctuation data is as follows: ； in, Indicates that after a load disturbance occurs in a photovoltaic power station Optimal support power fluctuation data at each moment; Indicates the inertia control parameters of the unit layer in the photovoltaic power station; represents the damping control parameter of the unit layer in the photovoltaic power station; represents the optimal frequency response trajectory after a load disturbance occurs in the photovoltaic power station; Indicates the virtual inertia control parameters of the station control layer in the photovoltaic power station; Indicates the primary frequency regulation control parameters of the station control layer in the photovoltaic power station; Indicates control delay; Indicates that after a load disturbance occurs in a photovoltaic power station Frequency deviation information at the moment.

5. The method according to claim 1, wherein The calculation formula of the actual active output fluctuation data is as follows: ； in, express Actual active power output fluctuation data at each moment; represents real part extraction; express Voltage information of grid connection point at all times; express The complex conjugate of the grid-connected point current information at each moment; Indicates the stable output power of the PV-storage power station before load disturbance occurs.

6. The method according to claim 1, wherein The similarity score is calculated as follows: ； in, A similarity score representing the optimal support power fluctuation data and the actual active power output fluctuation data; Indicates the The optimal support power fluctuation data at each sampling point; ; Indicates the total number of sampling points; represents the mean value of the optimal support power fluctuation data; Indicates the Actual active output fluctuation data at each sampling point; Indicates the mean value of actual active power output fluctuation data.

7. The method according to claim 1 or 6, wherein: The online evaluation of the frequency support capability of the photovoltaic power station according to the similarity score includes: When the similarity score is 1, it indicates that the frequency support capability of the photovoltaic power station is in an optimal state; When the similarity score is 0, it indicates that the frequency support capability of the photovoltaic power station is in a mismatch state; When the similarity score is -1, it indicates that the frequency support capability of the photovoltaic power station is in a reverse support state; When the similarity score is greater than -1 and less than 0, it indicates that the frequency support capability of the photovoltaic power station is in a negative deviation state; When the similarity score is greater than 0 and less than 1, it indicates that the frequency support capability of the photovoltaic power station is in a positive deviation state.

8. An online evaluation system for multi-level coordinated frequency support capability of a photovoltaic power station, characterized by: include: An information acquisition module is used to obtain the operating boundary information of the photovoltaic power station before the load disturbance occurs and the voltage and current information of the grid connection point after the load disturbance occurs; A model solving module is used to solve the model using a pre-built control trajectory according to the operation boundary information to obtain the optimal supporting power fluctuation data after the load disturbance occurs in the photovoltaic power station; A disturbance output module, configured to calculate actual active power output fluctuation data of the photovoltaic power station after a load disturbance occurs based on the voltage and current information of the grid connection point; an online evaluation module, configured to perform similarity calculation on the optimal supported power fluctuation data and the actual active power output fluctuation data to obtain a similarity score, and perform online evaluation on the frequency support capability of the PV power station based on the similarity score; The online evaluation system further includes a model building module, including: A trajectory simulation submodule, configured to simulate the frequency response trajectory of the photovoltaic power station based on a disturbance signal of a load disturbance occurring in the photovoltaic power station and preset operating boundary conditions; a target construction submodule, configured to extract a maximum frequency change rate and a maximum frequency deviation from the frequency response trajectory, and construct an objective function with the goal of minimizing the sum of the maximum frequency change rate and the maximum frequency deviation; A constraint formulation submodule, used to formulate constraint conditions according to the objective function; A model generation submodule, configured to construct a control trajectory solution model based on the objective function and the constraint conditions; The constraint conditions include one or more of the following: power constraint, energy constraint and stability constraint; The objective function is expressed as follows: ； Where, ； ； ;in, represents the objective function; Indicates the inertia control parameters of the unit layer in the photovoltaic power station; represents the damping control parameter of the unit layer in the photovoltaic power station; Indicates the virtual inertia control parameters of the station control layer in the photovoltaic power station; Indicates the primary frequency regulation control parameters of the station control layer in the photovoltaic power station; Indicates control delay; represents the weight amplification factor; Represents a photovoltaic power station The time in the window is Frequency deviation information when It represents the frequency response trajectory after the load disturbance occurs in the photovoltaic power station; Indicates the amplitude of the load disturbance; represents the natural frequency of the photovoltaic power station; represents the attenuation factor; represents the exponential function; represents the initial phase angle of the frequency response.

9. The system according to claim 8, wherein The power constraint is expressed as follows: ； The expression of the energy constraint is as follows: ； The expression of the stability constraint is as follows: ； in, Indicates that after a load disturbance occurs in a photovoltaic power station Optimal support power fluctuation data at each moment; Indicates the power output limit of the photovoltaic power station; Indicates the energy release limit of the photovoltaic power station; represents the attenuation factor; represents the upper limit of the factor; Indicates the lower limit of the factor.

10. The system according to claim 8 or 9, characterized in that The model solving module includes: A trajectory generation submodule is used to solve the optimal frequency response trajectory of the photovoltaic power station after a load disturbance occurs by using a pre-built control trajectory solution model based on the operation boundary information; A fluctuation output submodule, configured to calculate optimal supporting power fluctuation data after a load disturbance occurs in the photovoltaic power station according to the optimal frequency response trajectory; The operation boundary information includes: the power output limit and energy release limit of the photovoltaic power station.

11. The system according to claim 10, wherein: The calculation formula of the optimal support power fluctuation data is as follows: ； in, Indicates that after a load disturbance occurs in a photovoltaic power station Optimal support power fluctuation data at each moment; Indicates the inertia control parameters of the unit layer in the photovoltaic power station; represents the damping control parameter of the unit layer in the photovoltaic power station; represents the optimal frequency response trajectory after a load disturbance occurs in the photovoltaic power station; Indicates the virtual inertia control parameters of the station control layer in the photovoltaic power station; Indicates the primary frequency regulation control parameters of the station control layer in the photovoltaic power station; Indicates control delay; Indicates that after a load disturbance occurs in a photovoltaic power station Frequency deviation information at the moment.

12. The system according to claim 8, wherein The calculation formula of the actual active output fluctuation data is as follows: ； in, express Actual active power output fluctuation data of the solar-storage power station at any moment; represents real part extraction; express Voltage information of grid connection point at all times; express The complex conjugate of the grid-connected point current information at each moment; Indicates the stable output power of the PV-storage power station before load disturbance occurs.

13. The system according to claim 8, wherein The similarity score is calculated as follows: ； in, A similarity score representing the optimal support power fluctuation data and the actual active power output fluctuation data; Indicates the The optimal support power fluctuation data at each sampling point; ; Indicates the total number of sampling points; represents the mean value of the optimal support power fluctuation data; Indicates the Actual active output fluctuation data at each sampling point; Indicates the mean value of actual active power output fluctuation data.

14. The system according to claim 8 or 13, wherein: The online evaluation module includes: An optimal state evaluation submodule, configured to indicate that the frequency support capability of the photovoltaic power station is in an optimal state when the similarity score is 1; An adaptation status evaluation submodule, configured to indicate that the frequency support capability of the photovoltaic power station is in a mismatch state when the similarity score is 0; A reverse support evaluation submodule, configured to indicate that the frequency support capability of the photovoltaic power station is in a reverse support state when the similarity score is -1; A negative deviation evaluation submodule, configured to indicate that the frequency support capability of the photovoltaic power station is in a negative deviation state when the similarity score is greater than -1 and less than 0; The positive deviation evaluation submodule is used to indicate that the frequency support capability of the photovoltaic power station is in a positive deviation state when the similarity score is greater than 0 and less than 1.

15. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, an online evaluation method for multi-level coordinated frequency support capability of a photovoltaic power station according to any one of claims 1 to 7 is implemented.

16. A computing device readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, an online evaluation method for multi-level coordinated frequency support capability of a photovoltaic power station as described in any one of claims 1 to 7 is implemented.

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