Two-stage robust optimization configuration method, system, equipment and medium for offshore wind power grid-following type unit and grid-constructing type unit

Abstract

The invention belongs to the technical field of offshore wind power, and discloses a two-stage robust optimization configuration method, system, equipment and medium for an offshore wind power grid-following type unit and a grid-constructing type unit, so as to solve the problem that the dual requirements of technical performance and economical efficiency are difficult to meet at the same time in the prior art. The method comprises the following steps: constructing a dynamic mathematical model, and designing a multi-dimensional evaluation index based on the dynamic mathematical model; obtaining a plurality of typical operation scenes and probability distribution of the typical operation scenes considering factors of offshore wind power output uncertainty and multi-source uncertainty; based on the multi-dimensional evaluation index, the typical scene set and the probability, an improved multi-objective optimization algorithm is adopted to carry out static stage optimization, and a recommended ratio range of the capacity ratio of the network construction type unit to the network following type unit is calculated and output; and obtaining a preset operation stage dynamic optimization trigger condition, and when the condition is met, performing rolling optimization by adopting a model prediction control algorithm based on the recommended ratio range and the prediction data, and outputting a dynamic adjustment strategy.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of offshore wind power, and particularly relates to a two-stage robust optimization configuration method, system, device and medium for offshore wind power grid-following type units and grid-forming type units. BACKGROUND

[0002] In an offshore wind power system, grid-following type (GFL) units and grid-forming type (GFM) units are two main grid-connected operation modes. The traditional grid-following type unit relies on grid voltage and frequency support, and is essentially a "passive response" device. In a high proportion of new energy scenarios, the equivalent inertia of the grid-following type unit tends to zero, which leads to the system frequency rate of change (RoCoF) easily exceeding the safety threshold (such as 1.5 Hz / s, far exceeding the safety limit of 0.5 Hz / s), and the voltage recovery ability after a fault is weak, which may cause transient instability. In contrast, the grid-forming type unit can actively provide inertia support and voltage regulation through virtual synchronous machine (VSG) technology to simulate the characteristics of a synchronous machine, which significantly improves the stability of the system, for example, reduces the system frequency rate of change to below 0.4 Hz / s, and speeds up the voltage recovery speed. However, the control equipment and operation and maintenance cost of the grid-forming type unit is higher, and the life cycle cost (LCC) is 8%-12% higher than that of the full GFL scheme.

[0003] Therefore, in the design of an offshore wind farm, the ratio of GFL and GFM units becomes a key problem, and both technical performance (such as inertia support, voltage stability, black start capability) and economy (such as investment cost, operation and maintenance cost, market benefit) need to be considered. A reasonable ratio not only can improve the stability of the power system, but also can effectively control the cost and maximize the economic benefit. However, existing optimization methods mainly focus on the static planning stage, such as deterministic optimization based on annual average wind speed, which ignores the influence of real-time working condition changes such as wind speed sudden drop, extreme weather, and grid fault on the stability of the system in the operation of offshore wind power. In addition, the existing technology lacks a dynamic regulation mechanism in the operation stage, and cannot dynamically adjust the number and parameters of the GFM unit according to real-time data. For example, when the system inertia demand increases due to wind speed sudden drop, the number of GFM units under static ratio is fixed, and manual intervention is required to switch the mode, which has a long response delay, and the system frequency fluctuation during this period may exceed the safety range.

[0004] In summary, the existing technology has problems such as "static planning-dynamic operation" disconnection, incomplete scene coverage, and weak real-time regulation capability in the optimization of the ratio of GFL and GFM units, which makes it difficult to meet the dual requirements of technical performance and economy. SUMMARY

[0005] Based on the above-mentioned shortcomings and deficiencies existing in the prior art, one of the purposes of the present application is to at least solve one or more of the above-mentioned problems existing in the prior art, in other words, one of the purposes of the present application is to provide a two-stage robust optimization configuration method, system, device and medium for offshore wind power grid-connected type units and grid-constructing type units, which meets one or more of the aforementioned needs, solves the problem of disconnection between static planning and dynamic operation in traditional methods, and weak real-time control capability, and further realizes the synergistic optimization of technical performance and economic benefit through multi-index fusion evaluation and two-stage optimization mechanism.

[0006] In order to achieve the above-mentioned purposes of the application, the following technical solutions are adopted in the present application: In a first aspect, the present application provides a two-stage robust optimization configuration method for offshore wind power grid-connected type units and grid-constructing type units, comprising the steps of: S1, constructing a dynamic mathematical model to represent the dynamic electrical characteristics of the units and their influence on system frequency and voltage response; S2, designing multi-dimensional evaluation indexes based on the dynamic mathematical model, wherein the multi-dimensional evaluation index system includes a technical performance evaluation index system and an economic evaluation index system; S3, obtaining a plurality of typical operating scenarios considering offshore wind power output uncertainty and multi-source uncertainty factors and their probability distribution, to obtain a typical scenario set and probability; S4, based on the multi-dimensional evaluation indexes and the typical scenario set and probability, using an improved multi-objective optimization algorithm for static stage optimization, calculating and outputting a recommended capacity ratio range of grid-constructing type units and grid-connected type units; S5, obtaining a preset operating stage dynamic optimization trigger condition, when the condition is met, based on the recommended capacity ratio range and prediction data, using a model predictive control algorithm for rolling optimization, and outputting a dynamic adjustment strategy.

[0007] As a preferred scheme, the step S1 of constructing a dynamic mathematical model comprises: constructing a grid-constructing type unit frequency response model based on active reference power, output active power and virtual inertia constant. The expression of the grid-constructing type unit frequency response model is , In the formula, is the moment, is the system frequency at the moment, is the system frequency change rate at the moment, is the active reference power at the moment, is the output active power at the moment, is the virtual inertia constant at the moment.

[0008] ​​​​​As a preferred solution, the technical performance evaluation index system includes an inertia support capability index, a transient voltage safety margin index, a black start contribution potential index, and a comprehensive stability index; the economic evaluation index system includes full life cycle cost, potential revenue from the ancillary service market, and system-level economic benefits; the inertia support capability index achieves a quantitative assessment of the system frequency change rate by calculating the equivalent system inertia constant and the virtual inertia contribution factor; the transient voltage safety margin index is used to quantify the safety margin of the voltage recovery process after a fault; the black start contribution potential index is used to assess the system's ability to achieve a black start within a limited time; and the comprehensive stability index uses a weighted or fuzzy comprehensive approach to integrate multiple technical performance evaluation indicators.

[0009] As a preferred solution, the improved multi-objective optimization algorithm is used in step S4 to perform static phase optimization, specifically: An improved non-dominated sorting genetic algorithm II is used as an optimization model, and expected value integration, robustness integration and worst-case integration are used as objective function sets of the optimization model; The expected value integration is obtained by weighted averaging the probability distribution of typical operating scenarios; The robust integration introduces a cross-scenario volatility penalty factor based on the expected value; The worst-case integration reflects feasibility under the most unfavorable scenario.

[0010] As a preferred solution, the preset dynamic optimization triggering conditions in the operation phase include wind speed fluctuation rate exceeding a threshold and abnormal system frequency change rate; The calculation formula for the wind speed fluctuation rate is: , Where, For the moment t The wind speed fluctuation rate, For the moment The wind speed value, For the time window T The average wind speed within is the threshold value of wind speed fluctuation rate; The judgment formula for abnormal system frequency change rate is: , Where, for The rate of change of system frequency at a given moment, is the threshold of the system frequency change rate.

[0011] As a preferred solution, the rolling optimization using the model predictive control algorithm in step S5 is specifically as follows: based on the real-time or ultra-short-term predicted wind speed and system status data, the model predictive control algorithm is used to dynamically adjust the actual operating number of the grid-type and grid-following type units, as well as the control parameters of the grid-type units; the optimization target of the model predictive control algorithm in each rolling window is Where, To control the amount of sequence in the rolling window The value inside, Optimize the time step for the current scroll, is the system frequency change rate, is the system bus voltage, is the voltage reference value, is the change in the number of participating network units in the current time step, Provides virtual inertia change for the meshed unit in the current time step, The adjustment amount of the frequency droop coefficient of the grid-type unit in the current time step, is the adjustment value of the voltage droop coefficient of the grid-type unit in the current time step, is the weight factor corresponding to the corresponding indicator.

[0012] As a preferred solution, the control parameters of the grid-type unit include virtual inertia, voltage droop coefficient and frequency droop coefficient.

[0013] In a second aspect, the present invention provides a two-stage robust optimization configuration system for offshore wind power grid-following type units and grid-forming type units, which is used to implement the optimization configuration method as described in the first aspect.

[0014] In a third aspect, the present invention provides an electronic device, wherein the computer device includes a memory, a processor, and a computer program, and when the computer program is executed by the processor, the optimization configuration method as described in the first aspect is implemented.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the optimization configuration method as described in the first aspect.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. Whole cycle collaborative optimization: The application adopts a two-stage optimization model, which integrates static stage optimization and dynamic optimization in the running stage. In the planning stage, the improved NSGA-II algorithm is used to output the basic matching range of the network type and the network type unit; and in the running stage, the MPC algorithm is used to dynamically adjust the number of units and control parameters according to real-time or short-term prediction data. This design realizes the whole cycle collaborative optimization from project planning to actual operation, and improves the overall efficiency of the system.

[0017] 2. Multi-objective compromise optimization: The application constructs a multi-dimensional evaluation index system from the two dimensions of technical performance and economy, and uses the expert weighting method to integrate the technical indicators. Combined with the improved NSGA-II algorithm, the application can output the Pareto frontier solution set, providing a technical-economical multi-objective compromise scheme for decision makers. This method effectively avoids the drawbacks of traditional methods that only focus on a single indicator, such as "technical compliance but poor economy" or "low cost but insufficient stability", and realizes the balance and optimization of technical performance and economic benefits.

[0018] Further or more detailed beneficial effects will be described in the specific embodiments in conjunction with specific examples. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0020] Figure 1 is a flowchart of the optimization configuration method provided by the embodiment of the application.

[0021] Figure 2 is a structural diagram of the electronic device provided by the embodiment of the application.

[0022] Reference numerals: 200, electronic device; 201, processor; 202, communication bus; 203, user interface; 204, network interface; 205, memory. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the application will be described clearly and completely below in conjunction with the drawings in the embodiments of the application.

[0024] In the following description, multiple embodiments of the present invention are provided. Different embodiments may be replaced or combined, and therefore the present invention may be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present invention should also be considered to include embodiments that include one or more of all other possible combinations of A, B, C, and D, even if such embodiments may not be explicitly described in the following text.

[0025] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of the present invention. Various examples may appropriately omit, replace, or add various processes or components. For example, the described method may be performed in an order different from the order described, and various steps may be added, omitted, or combined. Furthermore, features described in some examples may be combined in other examples.

[0026] In order to facilitate a better understanding of the embodiments of the present invention, before explaining the specific implementation methods of the present invention in detail, its application scenarios are first described.

[0027] The two-stage robust optimization configuration method for offshore wind power grid-following units and grid-building units described in the embodiments of this specification is applied to the full-cycle management process of offshore wind farm planning and operation. In these scenarios, the application of the optimization configuration method aims to achieve a balanced improvement in technical performance and economic benefits through coordinated optimization of the static stage and the operating stage.

[0028] Example 1: like Figure 1 As shown, this embodiment provides a two-stage robust optimization configuration method for offshore wind power grid-following type units and grid-forming type units, including the following steps: The first step is to establish a dynamic mathematical model of offshore wind power grid-following units and grid-forming units. The dynamic mathematical model can accurately characterize the dynamic electrical characteristics of the units and their impact on the system frequency.

[0029] Specifically, the dynamic mathematical model is a grid-type unit frequency response model, and its expression is: , Where, for time, For The system frequency at the moment, For The system frequency change rate at time For Active reference power at the moment, is the active power output at the time t, is the active power output at the time t, is the virtual inertia constant at the time t. is the virtual inertia constant at the time t.

[0030] Secondly, based on the dynamic mathematical model, a multi-dimension evaluation index system is designed, which includes a technical performance evaluation index system and an economic evaluation index system. It can be understood that the dynamic mathematical model describes how the physical characteristics of the wind turbine will dynamically respond when receiving grid instructions or disturbances. Using the dynamic mathematical model for simulation, a series of dynamic data, such as system frequency curve , voltage curve V(t) , etc. are obtained. Then, the technical performance evaluation index is calculated using the simulation data. The multi-dimension evaluation index system follows the design logic of “mathematical model → simulation → generate dynamic data → calculate technical performance evaluation index value”.

[0031] Specifically, the technical performance evaluation index system includes at least inertia support capability index, transient voltage safety margin index, black start contribution potential index and comprehensive stability index. The economic evaluation index system includes life cycle cost, auxiliary service market potential income and system level economic benefit. It can be understood that the values of the indexes covered by the economic evaluation index system depend on the optimization decision itself, i.e. the capacity ratio of grid-forming (GFM) and grid-following (GFL) units. For example, the higher the proportion of grid-forming units, the higher the initial investment cost. The inertia support capability index quantitatively evaluates the system frequency change rate by calculating the equivalent system inertia constant and the virtual inertia contribution factor. The transient voltage safety margin index is used to quantify the safety margin of the voltage recovery process after the fault. The black start contribution potential index is used to evaluate the ability of the system to achieve black start within a limited time. The comprehensive stability index integrates multiple technical performance evaluation indexes in a weighted or fuzzy comprehensive manner.

[0032] More specifically, the equivalent system inertia constant is calculated by the following formula: , wherein, is the system equivalent inertia, is the number of synchronous machines in the system, is the inertia constant of the i-th synchronous generator, i is the rated capacity (reference capacity) of the i-th synchronous machine, is the number of grid-forming (GFM) wind turbine generators, i is the rated capacity (reference capacity) of the i-th synchronous machine, is the number of grid-forming (GFM) wind turbine generators, is the rated capacity (reference capacity) of the i-th synchronous machine, jvirtual inertia constant provided by the GFM unit, is the j capacity of the GFM unit.

[0033] More specifically, on the basis of the equivalent system inertia constant, the embodiment defines a "virtual inertia contribution factor" to more finely measure the contribution degree of a single grid-forming (GFM) unit to the system inertia. The virtual inertia contribution factor is dynamically calculated according to the system configuration, and the calculation formula is: , wherein, is the virtual inertia contribution factor, is the total capacity of the system.

[0034] More specifically, the system rate of change of frequency (RoCoF) is calculated by the following formula: , wherein, is the system rate of change of frequency, is the system rated frequency, is the rate of change of disturbance power More specifically, the transient voltage safety margin index is calculated by the following formula: , wherein, is the transient voltage safety margin, is the voltage amplitude at the time t after the fault, is the lower limit of voltage safety, is the voltage recovery time window.

[0035] More specifically, the black start contribution potential index is calculated by the following formula: , wherein, is the system black start success probability, is the success start probability of a single GFM unit, subject to Bernoulli distribution.

[0036] More specifically, the comprehensive stability index index is fused into a unified stability evaluation index by expert weighting method, and is calculated by the following formula: , wherein, is the comprehensive stability evaluation index, is the k technical evaluation index, is the weighting factor of the k index, reflecting its relative importance.

[0037] More specifically, the life cycle cost (LCC) indicator is calculated using the following formula: , Where, For the full life cycle cost, is the initial investment cost, For the t Annual operation and maintenance costs, For the t Annual failure cost, For demolition and environmental protection costs, is the discount rate, The life cycle years.

[0038] More specifically, the potential revenue index of the ancillary services market is calculated using the following formula: , Where, For the potential revenue of the ancillary services market, is the number of auxiliary service market types, For the unit in m The service capacity provided in the market, For service time, Unit price for market services.

[0039] More specifically, the system-level economic benefit index is calculated using the following formula: , Where, To reduce the investment cost of stabilization equipment, To reduce the investment cost of stabilization equipment, To reduce the load shedding loss cost, Additional cost for grid-type units.

[0040] The third step is to obtain multiple typical operating scenarios and their probabilities, taking into account the uncertainty of offshore wind power output and other sources of uncertainty (such as load fluctuations, grid failures, and extreme weather). Specifically, this embodiment collects raw data on wind, load, and faults from multiple public or private channels, such as meteorological agencies and power grid companies. Next, using existing technical methods such as Monte Carlo simulation or machine learning, this data is converted into a large number of preliminary operating scenarios incorporating multiple uncertainties. Finally, using dimensionality reduction algorithms such as clustering, these scenarios are filtered and refined, ultimately resulting in a manageable set of typical operating scenarios that represent the main future risks and have clear probability of occurrence.

[0041] The fourth step is to establish a two-stage optimization configuration model, including: 1. Static Phase Optimization: Based on the technical and economic indicators designed in the second step and the typical operating scenarios and their probabilities obtained in the third step, an improved multi-objective optimization algorithm (Improved NSGA-II) is used to calculate and output the recommended range or basic ratio of the capacity ratio between grid-connected units and grid-following units.

[0042] Specifically, the multi-objective optimization algorithm includes an improved non-dominated sorting genetic algorithm II (NSGA-II). The NSGA-II is built based on a genetic evolution mechanism and has the characteristics of fast non-dominated sorting, congestion distance preservation, and elite retention. It can output a Pareto front solution set and provide a variety of compromise ratio suggestions for power grid operation.

[0043] More specifically, the decision variables in the optimization model of this embodiment include the capacity ratio vector , represents a specific configuration scheme, that is, the capacity ratio of grid-forming units (GFM) and grid-following units (GFL), and its expression is , need to meet ,therefore It can be expressed as ,in It represents the ratio of the capacity of grid-connected turbines (GFM) to the total capacity of the wind farm.

[0044] This embodiment expects to achieve multiple goals at the same time, but there are often conflicts between these goals. For example, increasing the proportion of grid-type units can enhance system stability, but at the same time it will significantly increase the life cycle cost. The ultimate goal of this embodiment is to minimize the life cycle cost while maximizing system stability, ancillary service revenue and system-level benefits. Since optimization algorithms are usually used to solve minimum value problems, this embodiment converts the original maximization problem into a minimization problem by taking the opposite number. In addition, the objective function value is obtained by simulation calculation under the corresponding typical scenario. The objective function set of this embodiment includes multiple performance evaluation indicators:

[0045] Each objective function In typical scenarios The following simulation evaluation is obtained.

[0046] To ensure both consistency and stability of system performance in all typical scenarios, this embodiment introduces the following three objective function integration forms (which can be used in combination): (1) Expected value integration (generalized optimality) , Where, is the expected value integration result, For the The probability of a scenario occurring.

[0047] This way reflects the average performance of the current capacity mix under all representative operating conditions through the probability distribution of typical scenarios The weighted average reflects the average performance of the current capacity mix under all representative operating conditions.

[0048] (2) Robust integration (expectation + variance) , where, is the robust integration result, is the expected value of the objective function under all scenarios, is the scenario volatility penalty factor of the objective function j , used to adjust the degree of robustness, is the variance value of the objective function under all scenarios.

[0049] This structure introduces a cross-scenario volatility penalty factor on the basis of expected performance, improving the adaptability and stability of the optimization result to extreme conditions, especially in the case of extreme wind speed fluctuations in offshore wind power environments.

[0050] (3) Worst-case integration (conservative guarantee) , where, is the worst-case integration result.

[0051] It is used to ensure that the optimization result is feasible and safe under the most unfavorable scenario.

[0052] After considering the above integration methods, the multi-objective robust optimization problem of the embodiment can be expressed as: , Through genetic crossover (such as simulated binary crossover SBX), polynomial mutation, and non-dominated sorting selection, the optimal solution set of the network type and network type capacity mix is obtained.

[0053] Specifically, the triggering conditions for dynamic adjustment include wind speed fluctuation rate exceeding the threshold and system frequency change rate being abnormal. When either condition is met, the dynamic optimization process is entered.

[0054] More specifically, the formula for calculating the wind speed fluctuation rate is: , where, is the wind speed fluctuation rate at time t , is the wind speed value at time , represents a certain time in the past ( t -T ) to the current moment( t ) Each discrete time point within this time window is the current rolling optimization time step, For the time window T The average wind speed within is the threshold of wind speed fluctuation rate.

[0055] More specifically, the expression for the system rate of change of frequency (RoCoF) anomaly is: , Where, is the threshold of the system frequency change rate.

[0056] 2. Dynamic optimization during the operation phase: Based on real-time or ultra-short-term predicted wind speed and system status data, the model predictive control (MPC) algorithm is used to dynamically adjust the actual operating number of units and control parameters, including at least the virtual inertia size, voltage droop coefficient, and frequency droop coefficient, to meet the preset technical performance constraints and optimize economic benefits. The dynamic adjustment method is implemented based on the model predictive control (MPC) algorithm.

[0057] During the actual operation of the wind farm, this embodiment introduces a second-stage "dynamic optimization model during operation" to dynamically adjust the operating number, control mode, and parameter settings of the grid-forming (GFM) and grid-following (GFL) units within the determined recommended capacity ratio range based on real-time or short-term predicted operating conditions. This allows the model to adapt to fluctuating conditions such as wind speed disturbances and grid anomalies, thereby improving system stability and reducing operating losses.

[0058] Specifically, the system state variables Including system frequency , wind speed fluctuation rate and current GFM unit participation capacity .

[0059] Specifically, control variables Including adjustment of the number of GFM units , Control parameter adjustment frequency droop coefficient adjustment amount and voltage droop coefficient adjustment .

[0060] More specifically, the optimization objective of the model predictive control MPC algorithm within each rolling window is: Where, For the control quantity sequence (such as GFM number, inertia) in the rolling window The value inside, Optimize the time step for the current scroll, is the system frequency change rate, is a system bus voltage, is a voltage reference value, is a change in the number of grid-forming units participating in the current time step, is a change in the virtual inertia provided by the grid-forming unit in the current time step, is an adjustment amount of the frequency droop coefficient of the grid-forming unit in the current time step, is an adjustment amount of the voltage droop coefficient of the grid-forming unit in the current time step, is a corresponding weight factor for the corresponding index.

[0061] Specifically, the dynamic adjustment of the control parameters of the grid-forming unit includes adjusting the virtual inertia size and the voltage / frequency droop coefficient to adapt to different system operating states.

[0062] More specifically, the frequency droop control model is as follows: , It can be understood that, The larger the voltage regulation capability is enhanced, and when a fault voltage drop occurs, the voltage support can be temporarily increased.

[0063] More specifically, the voltage droop control model is as follows: , It can be understood that, The larger the voltage regulation capability is enhanced, and when a fault voltage drop occurs, the voltage support can be temporarily increased.

[0064] Finally, according to the optimization result of the fourth step, the optimal capacity ratio of the grid-following unit and the grid-forming unit and the dynamic adjustment strategy are determined.

[0065] Embodiment Two: The embodiment provides a two-stage robust optimization configuration system for offshore wind power grid-following units and grid-forming units, which is used to realize the optimization configuration method as described in Embodiment One.

[0066] Embodiment Three: As Figure 2 shown, the embodiment provides an electronic device, which can include at least one processor, at least one network interface, a user interface, a memory, and at least one communication bus.

[0067] The communication bus can be used to realize the connection and communication of the above-mentioned components.

[0068] The user interface can include a key, and the optional user interface can further include a standard wired interface and a wireless interface.

[0069] The network interface can include, but is not limited to, a Bluetooth module, an NFC module, a Wi-Fi module, and the like.

[0070] The processor can include one or more processing cores. The processor connects various parts in the entire electronic device through various interfaces and lines, and performs various functions of the electronic device and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory. Optionally, the processor can be implemented in at least one of the hardware forms of DSP, FPGA, and PLA. The processor can integrate one or a combination of CPU, GPU, and modem, and the like. Among them, the CPU mainly processes operating systems, user interfaces, and application programs, and the like; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor, but can be realized by a separate chip.

[0071] The memory can include RAM and can also include ROM. Optionally, the memory includes a non-transitory computer readable medium. The memory can be used to store instructions, programs, codes, code sets or instruction sets. The memory can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, and the like), instructions for implementing the above-mentioned various method embodiments, and the like; the data storage area can store data involved in the above-mentioned various method embodiments, and the like. The memory can also be at least one storage device located away from the above-mentioned processor. The memory as a computer storage medium can include an operating system, a network communication module, a user interface module, and an optimization configuration application. The processor can be used to call the optimization configuration application stored in the memory, and perform the steps of optimization configuration mentioned in the above-mentioned embodiments.

[0072] Embodiment Four: The embodiment provides a computer readable storage medium, which stores instructions. When the instructions are run on a computer or a processor, the computer or the processor executes the steps of one or more of the above-mentioned embodiments. When each component module of the above-mentioned electronic device is realized in the form of a software function unit and sold or used as an independent product, it can be stored in the computer readable storage medium. Figure 1

[0073] ​In the above embodiments, all or part of the methods can be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the methods can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted by the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, data center, etc. that includes one or more available media sets. The available media can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a digital versatile disc (DVD)), or a semiconductor medium (for example, a solid state disk (SSD)) and the like.

[0074] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned method embodiments can be implemented by a computer program to instruct the relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned method embodiments. The storage medium mentioned above includes ROM, RAM, magnetic or optical discs, and various media that can store program codes. In the case of no conflict, the technical features in the embodiments and the implementation schemes can be combined arbitrarily.

[0075] It should be noted that for the above-mentioned method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, some steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily necessary for the present application.

[0076] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0077] The above merely illustrates the embodiments of the present application, and cannot be used to limit the scope of the present application. Any equivalent changes and modifications made according to the teachings of the present application shall fall within the scope of the present application. Any further embodiments of the present application will be readily apparent to those skilled in the art in view of the disclosure herein. The present application is intended to cover any variations, uses or adaptive changes of the present application falling within the generic principles of the present application and including common knowledge or conventional technical means in the art not recited in the present application. The scope of the present application is defined by the claims and their equivalents, and the embodiments and examples are merely illustrative and not restrictive.

Claims

1. A two-stage robust optimization configuration method for offshore wind power grid-following type units and grid-building type units, characterized by: Including steps: S1. Construct a dynamic mathematical model to characterize the dynamic electrical characteristics of the unit and its impact on the system frequency and voltage response; S2. Designing a multi-dimensional evaluation index system based on the dynamic mathematical model, wherein the multi-dimensional evaluation index system includes a technical performance evaluation index system and an economic evaluation index system; S3. Obtain multiple typical operation scenarios and their probability distributions considering uncertainty in offshore wind power output and multi-source uncertainty factors, and obtain a typical scenario set and probability; S4. Based on the multi-dimensional evaluation indicators and the typical scenario set and probability, an improved multi-objective optimization algorithm is used to perform static phase optimization, and a recommended capacity ratio range of the grid-forming units and the grid-following units is calculated and output; S5. Obtain preset dynamic optimization trigger conditions for the operation phase. When the conditions are met, perform rolling optimization using a model predictive control algorithm based on the recommended ratio range and predicted data, and output a dynamic adjustment strategy.

2. A two-stage robust optimization configuration method for offshore wind power grid-following type units and grid-forming type units according to claim 1, characterized in that: The step S1 of constructing a dynamic mathematical model includes: Based on active reference power, output active power and virtual inertia constant, a frequency response model of grid-type units is constructed; The expression of the frequency response model of the grid-type unit is: ， Where, for time, for System frequency at the moment, for The rate of change of system frequency at a given moment, for Active reference power at all times, for Output active power at all times, for Virtual inertia constant at time.

3. The two-stage robust optimization configuration method for offshore wind power grid-following and grid-forming units according to claim 1 is characterized by: The technical performance evaluation index system includes inertia support capacity index, transient voltage safety margin index, black start contribution potential index and comprehensive stability index; The economic evaluation index system includes the whole life cycle cost, potential revenue from the ancillary service market and system-level economic benefits; The inertia support capability index is used to quantitatively evaluate the system frequency change rate by calculating the equivalent system inertia constant and the virtual inertia contribution factor. The transient voltage safety margin indicator is used to quantify the safety margin of the voltage recovery process after a fault; The black start contribution potential indicator is used to evaluate the system's ability to achieve a black start within a limited time; The comprehensive stability index adopts a weighted or fuzzy comprehensive approach to integrate multiple technical performance evaluation indicators.

4. A two-stage robust optimization configuration method for offshore wind power grid-following type units and grid-forming type units according to claim 1, characterized in that: Step S4 uses an improved multi-objective optimization algorithm to perform static phase optimization, specifically: An improved non-dominated sorting genetic algorithm II is used as an optimization model, and expected value integration, robustness integration and worst-case integration are used as objective function sets of the optimization model; The expected value integration is obtained by weighted averaging the probability distribution of typical operating scenarios; The robust integration introduces a cross-scenario volatility penalty factor based on the expected value; The worst-case integration reflects feasibility under the most unfavorable scenario.

5. The two-stage robust optimization configuration method for offshore wind power grid-following and grid-forming units according to claim 1 is characterized by: The preset dynamic optimization triggering conditions in the operation phase include wind speed fluctuation rate exceeding a threshold and abnormal system frequency change rate; The calculation formula of the wind speed fluctuation rate is: ， Where, For the moment t The wind speed fluctuation rate, For the moment The wind speed value, For the time window T The average wind speed within is the threshold value of wind speed fluctuation rate; The judgment formula for abnormal system frequency change rate is: ， Where, for The rate of change of system frequency at a given moment, is the threshold value of the system frequency change rate.

6. A two-stage robust optimization configuration method for offshore wind power grid-following type units and grid-forming type units according to claim 5, characterized in that: Step S5 uses the model predictive control algorithm to perform rolling optimization, specifically: Based on real-time or ultra-short-term predicted wind speed and system status data, the model predictive control algorithm is used to dynamically adjust the actual operating number of grid-type and grid-following units, as well as the control parameters of the grid-type units; The optimization objective of the model predictive control algorithm in each rolling window is , where To control the amount of sequence in the rolling window The value inside, Optimize the time step for the current scroll, is the system frequency change rate, is the system bus voltage, is the voltage reference value, is the change in the number of participating network units in the current time step, Provides virtual inertia change for the meshed unit in the current time step, The adjustment amount of the frequency droop coefficient of the grid-type unit in the current time step, is the adjustment value of the voltage droop coefficient of the grid-type unit in the current time step, is the weight factor corresponding to the corresponding indicator.

7. A two-stage robust optimization configuration method for offshore wind power grid-following units and grid-forming units according to claim 6, characterized in that: The control parameters of the grid-type unit include virtual inertia, voltage droop coefficient and frequency droop coefficient.

8. A two-stage robust optimization configuration system for offshore wind power grid-following units and grid-building units, characterized by: Used to implement the optimization configuration method as described in any one of claims 1 to 7.

9. A computer device comprising a memory, a processor, and a computer program, wherein: When the computer program is executed by a processor, the optimization configuration method according to any one of claims 1 to 7 is 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 optimization configuration method according to any one of claims 1 to 7 is implemented.

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