Offshore wind power grid-connected type unit and network-constructing type unit two-stage robust optimization configuration method, system, device and medium

Abstract

The present application belongs to the technical field of offshore wind power, and discloses a two-stage robust optimization configuration method, system, equipment and medium for offshore wind power grid-following type units and grid-constructing type units, to solve the problem that the prior art cannot simultaneously meet the dual requirements of technical performance and economy. The method comprises the following steps: constructing a dynamic mathematical model, designing multi-dimensional evaluation indexes based on the dynamic mathematical model; obtaining a plurality of typical operating scenarios and their probability distributions considering the uncertainty of offshore wind power output and multiple source uncertainty factors; based on the multi-dimensional evaluation indexes, the typical scenario set and the probability, using an improved multi-objective optimization algorithm to perform static stage optimization, calculating and outputting a recommended ratio range of the capacity ratio of the grid-constructing type units and the grid-following type units; obtaining a preset operating stage dynamic optimization triggering condition, when the condition is met, based on the recommended ratio range and the prediction data, using a model predictive control algorithm to perform rolling optimization, and outputting a dynamic adjustment strategy.

Description

Technical Field

[0001] This invention belongs to the field of offshore wind power technology, specifically relating to a two-stage robust optimization configuration method, system, equipment, and medium for offshore wind power grid-connected units and grid-connected units. Background Technology

[0002] In offshore wind power systems, grid-connected (GFL) and grid-connected (GFM) turbines are two main grid-connected operation modes. Traditional GFL turbines rely on grid voltage and frequency support, essentially acting as "passive response" devices. In scenarios with a high proportion of renewable energy, their equivalent inertia approaches zero, causing the system rate of change of frequency (RoCoF) to easily exceed safety thresholds (e.g., 1.5 Hz / s, far exceeding the safety limit of 0.5 Hz / s). Furthermore, their voltage recovery capability after a fault is weak, potentially leading to transient instability. In contrast, GFM turbines, through virtual synchronous machine (VSG) technology, simulate the characteristics of a synchronous machine, actively providing inertia support and voltage regulation, significantly improving system stability. For example, they can reduce the system rate of change of frequency to below 0.4 Hz / s and accelerate voltage recovery. However, GFM turbines have higher control equipment and maintenance costs, resulting in a life-cycle cost (LCC) 8%-12% higher than the all-GFL solution.

[0003] Therefore, the ratio of GFL (Ground Fluid) to GFM (Ground Float) turbines becomes a critical issue in the design of offshore wind farms, requiring a balance between technical performance (such as inertia support, voltage stability, and black start capability) and economic efficiency (such as investment costs, operation and maintenance expenses, and market returns). A reasonable ratio can not only improve the stability of the power system but also effectively control costs and maximize economic benefits. However, existing optimization methods mainly focus on the static planning stage, such as deterministic optimization based on annual average wind speed, neglecting the impact of real-time operating condition changes such as sudden wind speed drops, extreme weather, and grid failures on system stability during offshore wind power operation. In addition, existing technologies lack dynamic control mechanisms during the operation phase, making it impossible to dynamically adjust the number and parameters of GFM turbines based on real-time data. For example, when a sudden drop in wind speed leads to an increase in system inertia demand, the number of GFM turbines under the static ratio is fixed, requiring manual intervention to switch modes, resulting in long response delays, and the system frequency fluctuations during this period may exceed the safe range.

[0004] In summary, existing technologies for optimizing the ratio of GFL and GFM units suffer from problems such as a disconnect between "static planning and dynamic operation", incomplete scenario coverage, and weak real-time control capabilities, making it difficult for the optimization results to simultaneously meet the dual requirements of technical performance and economy. Summary of the Invention

[0005] Based on the aforementioned shortcomings and deficiencies in the existing technology, one of the objectives of this invention is to at least solve one or more of the aforementioned problems in the existing technology. In other words, one of the objectives of this invention is to provide a two-stage robust optimization configuration method, system, equipment, and medium for offshore wind power grid-connected and grid-connected units that meets one or more of the aforementioned requirements. Through multi-index fusion evaluation and a two-stage optimization mechanism, this invention aims to solve the problems of disconnect between static planning and dynamic operation and weak real-time control capability in traditional methods, thereby achieving synergistic optimization of technical performance and economic benefits.

[0006] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a two-stage robust optimization configuration method for offshore wind power grid-connected and grid-connected units, comprising the following steps: S1, constructing a dynamic mathematical model to characterize the dynamic electrical characteristics of the units and their impact on system frequency and voltage response; S2, designing multi-dimensional evaluation indicators based on the dynamic mathematical model, wherein the multi-dimensional evaluation indicator system includes a technical performance evaluation indicator system and an economic evaluation indicator system; S3, obtaining multiple typical operating scenarios and their probability distributions considering the uncertainty of offshore wind power output and multiple sources of uncertainty factors, thereby obtaining a set of typical scenarios and their probabilities; S4, based on the multi-dimensional evaluation indicators and the set of typical scenarios and their probabilities, performing static stage optimization using an improved multi-objective optimization algorithm, calculating and outputting a recommended ratio range for the capacity matching of grid-connected and grid-connected units; S5, obtaining preset dynamic optimization trigger conditions for the operating stage, and when the conditions are met, performing rolling optimization using a model predictive control algorithm based on the recommended ratio range and predicted data, and outputting a dynamic adjustment strategy.

[0008] As a preferred option, the construction of the dynamic mathematical model in step S1 includes: constructing a frequency response model for the grid-type generating unit based on the active reference power, the output active power, and the virtual inertia constant;

[0009] The expression for the frequency response model of the network-type generating unit is as follows:

[0010] ,

[0011] In the formula, for time, In order to be in The system frequency at any given time. In order to be in The rate of change of the system frequency at time t. In order to be in Active reference power at any given time, In order to be in Output active power at any time In order to be in The virtual inertia constant at time t.

[0012] As a preferred embodiment, 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 total life cycle cost, potential revenue from the ancillary services market, and system-level economic benefits; the inertia support capability index, by calculating the equivalent system inertia constant and the virtual inertia contribution factor, achieves a quantitative assessment of the system's frequency change rate; 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 evaluate the system's ability to achieve a black start within a limited time; and the comprehensive stability index integrates multiple technical performance evaluation indices using a weighted or fuzzy comprehensive method.

[0013] As a preferred approach, step S4 employs an improved multi-objective optimization algorithm for static stage optimization, specifically as follows:

[0014] An improved non-dominated sorting genetic algorithm II is adopted as the optimization model, and the expected value integration, robust integration, and worst-case integration are used as the objective function set of the optimization model.

[0015] The expected value integration is obtained by weighted averaging of the probability distributions of typical operating scenarios;

[0016] The robust integration introduces a cross-scenario volatility penalty factor based on the expected value;

[0017] The worst-case scenario integration reflects the feasibility under the most unfavorable scenario.

[0018] As a preferred embodiment, the preset dynamic optimization triggering conditions for the operation phase include wind speed fluctuation exceeding a threshold and abnormal system frequency change rate;

[0019] The formula for calculating the wind speed fluctuation rate is as follows:

[0020] ,

[0021] In the formula, For at any time t Wind speed fluctuation rate, For at any time The wind speed value, In the time window T The average wind speed within the area, The threshold for wind speed fluctuation rate;

[0022] The formula for judging the abnormality of the system frequency change rate is:

[0023] ,

[0024] In the formula, for The rate of change of system frequency at any given time This is the threshold for the rate of change of the system frequency.

[0025] As a preferred embodiment, step S5 employs a model predictive control algorithm for rolling optimization, specifically: based on real-time or ultra-short-term predicted wind speed and system state data, the model predictive control algorithm dynamically adjusts the actual operating number of grid-connected and grid-following units, and dynamically adjusts the control parameters of the grid-connected units; the optimization objective of the model predictive control algorithm within each rolling window is...

[0026] In the formula, To control the sequence of variables in the rolling window The values ​​within, Optimize the time step for the current scrolling. The system frequency change rate, This refers to the system bus voltage. This is the voltage reference value. This represents the change in the number of grid-connected generating units participating in the current time step. Provides virtual inertia changes for grid-type units in the current time step. The adjustment amount of the frequency droop coefficient of the grid-type unit in the current time step. This represents the adjustment amount of the voltage droop coefficient for grid-type generating units in the current time step. These are the weighting factors corresponding to the respective indicators.

[0027] As a preferred embodiment, the control parameters of the grid-type unit include virtual inertia, voltage droop coefficient, and frequency droop coefficient.

[0028] Secondly, the present invention provides a two-stage robust optimization configuration system for offshore wind power grid-connected units and grid-connected units, used to implement the optimization configuration method as described in the first aspect.

[0029] Thirdly, the present invention provides an electronic device, the computer device including a memory, a processor and a computer program, wherein the computer program, when executed by the processor, implements the optimization configuration method as described in the first aspect.

[0030] Fourthly, 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.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. Full-cycle collaborative optimization: This invention employs a two-stage optimization model, integrating static stage optimization and operational stage dynamic optimization. During the planning stage, the improved NSGA-II algorithm outputs the basic configuration ranges for grid-connected and grid-following units; while during the operational stage, the MPC algorithm dynamically adjusts the number of units and control parameters based on real-time or ultra-short-term forecast data. This design achieves full-cycle collaborative optimization from project planning to actual operation, improving the overall system efficiency.

[0033] 2. Multi-objective trade-off optimization: This invention constructs a multi-dimensional evaluation index system from the perspectives of technical performance and economic efficiency, and uses an expert weighting method to synthesize technical indicators. Combined with the improved NSGA-II algorithm, this invention can output a Pareto front solution set, providing decision-makers with a trade-off solution for multiple technical and economic objectives. This method effectively avoids the drawbacks of traditional methods that focus only on a single indicator, resulting in "technical compliance but poor economic efficiency" or "low cost but insufficient stability," thus achieving a balance and optimization between technical performance and economic benefits.

[0034] Further or more detailed beneficial effects will be described in conjunction with specific embodiments in the detailed implementation. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart illustrating the optimized configuration method provided in an embodiment of the present invention.

[0037] Figure 2 This is a structural diagram of the electronic device provided in the embodiment of the present invention.

[0038] Icon labels:

[0039] 200. Electronic devices;

[0040] 201. Processor; 202. Communication bus; 203. User interface; 204. Network interface; 205. Memory. Detailed Implementation

[0041] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0042] In the following description, several embodiments of the present invention are provided. Different embodiments can be substituted or combined. Therefore, the present invention can also 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 containing one or more other possible combinations of A, B, C, and D, even if such embodiments are not explicitly described in the following text.

[0043] 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 function and arrangement of the described elements without departing from the scope of the invention. Various processes or components may be appropriately omitted, substituted, or added to the various examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0044] To facilitate a better understanding of the embodiments of the present invention, its application scenarios will be explained before providing a detailed explanation of the specific implementation methods.

[0045] The two-stage robust optimization configuration method for grid-connected and grid-connected offshore wind turbines described in the embodiments of this specification is applied to the entire lifecycle 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 and operational phases.

[0046] Example 1:

[0047] like Figure 1 As shown in the figure, this embodiment provides a two-stage robust optimization configuration method for offshore wind power grid-connected units and grid-connected units, including the following steps:

[0048] The first step is to establish dynamic mathematical models for offshore wind turbines connected to the grid and grid-connected turbines. These dynamic mathematical models can accurately characterize the dynamic electrical characteristics of the turbines and their impact on system frequency.

[0049] Specifically, the dynamic mathematical model is a network-type unit frequency response model, and its expression is:

[0050] ,

[0051] In the formula, for time, In order to be in The system frequency at any given time. In order to be in The rate of change of the system frequency at time t. In order to be in Active reference power at any given time, In order to be in Output active power at any time In order to be in The virtual inertia constant at time t.

[0052] The second step involves designing a multi-dimensional evaluation index system based on the dynamic mathematical model. This system includes both technical performance evaluation indexes and economic evaluation indexes. It is understood that the dynamic mathematical model describes how the physical characteristics of a wind turbine dynamically respond when it receives grid commands or disturbances. Simulations using the dynamic mathematical model yield a series of dynamic data, such as the system frequency curve. Voltage curve V(t) Then, the technical performance evaluation indicators are calculated using the data generated from these simulations. The multi-dimensional evaluation indicator system follows the design logic of "mathematical model → simulation → generation of dynamic data → calculation of technical performance evaluation indicator values".

[0053] Specifically, the technical performance evaluation index system includes at least the inertia support capability index, transient voltage safety margin index, black start contribution potential index, and comprehensive stability index. The economic evaluation index system includes total life cycle cost, potential revenue from the ancillary services market, and system-level economic benefits. It is understood that the values ​​of the indicators covered by the economic evaluation index system depend on the optimization decision itself, i.e., the capacity ratio of grid-connected (GFM) and grid-joined (GFL) units. For example, the higher the proportion of grid-connected units, the higher their initial investment cost. The inertia support capability index quantifies the system frequency change rate by calculating the equivalent system inertia constant and virtual inertia contribution factor. The transient voltage safety margin index quantifies the safety margin of the voltage recovery process after a fault. The black start contribution potential index assesses the system's ability to achieve a black start within a limited time. The comprehensive stability index integrates multiple technical performance evaluation indicators using a weighted or fuzzy comprehensive method.

[0054] More specifically, the equivalent system inertia constant is calculated using the following formula:

[0055] ,

[0056] In the formula, For the system's equivalent inertia, The number of synchronizers in the system. For the first i The inertia constant of a synchronous generator. For the first iRated capacity (baseline capacity) of the synchronous machine. The number of grid-forming wind turbines, For the first j The virtual inertia constant provided by the GFM unit. For the first j The capacity of the GFM unit.

[0057] More specifically, based on the equivalent system inertia constant, this embodiment defines a "virtual inertia contribution factor" to more precisely measure the contribution of a single grid-connected (GFM) unit to the system inertia. The virtual inertia contribution factor is dynamically calculated based on the system configuration, and its calculation formula is as follows:

[0058] ,

[0059] In the formula, Contribution factor to virtual inertia This represents the total system capacity.

[0060] More specifically, the system rate of change of frequency (RoCoF) is calculated using the following formula:

[0061] ,

[0062] In the formula, The system frequency change rate, The system's rated frequency, The rate of change of disturbance power

[0063] More specifically, the transient voltage safety margin index is calculated using the following formula:

[0064] ,

[0065] In the formula, For transient voltage safety margin, For the moment after the fault t voltage amplitude, As the lower limit of voltage safety, This is the voltage recovery time window.

[0066] More specifically, the black start contribution potential index is calculated using the following formula:

[0067] ,

[0068] In the formula, This represents the probability of a successful system boot from a black screen. Let be the successful start-up probability of a single GFM unit, which follows a Bernoulli distribution.

[0069] More specifically, the comprehensive stability index uses an expert weighting method to integrate multiple technical indicators into a unified stability assessment index, which is calculated using the following formula:

[0070] ,

[0071] In the formula, As a comprehensive stability evaluation index, For the first k One technical evaluation indicator, For the first k The weighting factor of each indicator reflects its relative importance.

[0072] More specifically, the Life Cycle Cost (LCC) metric is calculated using the following formula:

[0073] ,

[0074] In the formula, For total lifecycle cost, For initial investment costs, For the first t Annual maintenance costs For the first t Annual failure cost Due to demolition and environmental protection costs, The discount rate is... This refers to the lifespan in years.

[0075] More specifically, the potential revenue metric for the ancillary services market is calculated using the following formula:

[0076] ,

[0077] In the formula, To generate potential revenue from the ancillary services market, To support the number of service market types, For the unit in the m The service capacity provided in the market For service hours, The unit price for market services.

[0078] More specifically, system-level economic benefit indicators are calculated using the following formula:

[0079] ,

[0080] In the formula, To reduce the investment cost of stabilization equipment, To reduce the investment cost of stabilization equipment, To reduce the cost of load shedding losses, Additional costs for grid-connected units.

[0081] The third step involves acquiring multiple typical operating scenarios and their probabilities, taking into account the uncertainty of offshore wind power output and other multi-source uncertainties (such as load fluctuations, grid failures, and extreme weather). Specifically, this embodiment collects raw data on wind, load, and failures from multiple public or specific channels, such as meteorological agencies and power grid companies. Then, using existing technologies such as Monte Carlo simulation or machine learning, these data are transformed into a massive amount of preliminary operating scenarios containing multiple uncertainties. Finally, through dimensionality reduction algorithms such as clustering, a set of typical operating scenarios with a controllable number, representing the main future risks, and with clear probabilities of occurrence is obtained.

[0082] The fourth step is to establish a two-stage optimization configuration model, including:

[0083] 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 matching between grid-connected units and grid-linked units.

[0084] Specifically, the multi-objective optimization algorithm includes the improved Non-dominated Sorting Genetic Algorithm II (NSGA-II). NSGA-II is built on a genetic evolution mechanism and features fast non-dominated sorting, crowding distance preservation, and elite retention. It can output Pareto front solution sets and provide various compromise ratio suggestions for power grid operation.

[0085] More specifically, the decision variables in the optimization model of this embodiment include the capacity matching vector. This represents a specific configuration scheme, namely the capacity ratio of grid-connected units (GFM) and grid-linked units (GFL), and its expression is: It needs to meet the following requirements. ,therefore It can be represented as ,in This represents the proportion of grid-connected turbines (GFM) to the total capacity of the wind farm.

[0086] This embodiment aims to achieve multiple objectives simultaneously; however, these objectives often conflict. For example, increasing the proportion of grid-connected units can enhance system stability, but it also significantly increases the total lifecycle cost. The ultimate goal of this embodiment is to minimize the total lifecycle cost while maximizing system stability, ancillary service benefits, and system-level efficiency. Since optimization algorithms are typically used to solve minimization problems, this embodiment transforms the original maximization problem into a minimization problem by taking the inverse. Furthermore, the objective function values ​​are obtained through simulation calculations under corresponding typical scenarios. The objective function set of this embodiment includes multiple performance evaluation metrics:

[0087]

[0088] Each objective function All in typical scenarios The results were obtained through simulation evaluation.

[0089] To ensure consistent and stable system performance across all typical scenarios, this embodiment introduces the following three integrated forms of objective functions (which can be used in combination):

[0090] (1) Expected value integration (generalized optimality)

[0091] ,

[0092] In the formula, For expected value-based integration results, For the first The probability of each scenario occurring.

[0093] This method utilizes probability distributions from typical scenarios. A weighted average is calculated to reflect the average performance of the current capacity allocation under all representative operating conditions.

[0094] (2) Robust integration (expectation + variance)

[0095] ,

[0096] In the formula, For robust integration results, To find the expected value of the objective function in all scenarios. For the objective function j The scenario volatility penalty factor is used to adjust the degree of robustness. This represents the variance of the objective function across all scenarios.

[0097] This structure introduces a cross-scenario volatility penalty factor on top of the expected performance. This improves the adaptability and stability of the optimization results to extreme operating conditions, and is particularly suitable for situations with extreme wind speed variations in offshore wind power environments.

[0098] (3) Worst-case scenario integration (conservative safeguards)

[0099] ,

[0100] In the formula, This is the worst-case scenario integration result.

[0101] This is used to ensure that the optimization results remain feasible and secure even in the most unfavorable scenarios.

[0102] After comprehensively considering the above integration methods, the multi-objective robust optimization problem in this embodiment can be uniformly expressed as:

[0103] ,

[0104] The optimal solution set for the capacity ratio of the network structure and the root network structure can be obtained by genetic crossover (such as simulated binary crossover SBX), polynomial mutation, and non-dominated sorting selection.

[0105] Specifically, the triggering conditions for dynamic adjustment include wind speed fluctuation exceeding a threshold and abnormal system frequency change rate. When either condition is met, the dynamic optimization process begins.

[0106] More specifically, the formula for calculating wind speed fluctuation rate is:

[0107] ,

[0108] In the formula, For at any time t Wind speed fluctuation rate, For at any time The wind speed value, Represents a point in the past ( t - T Up to the current time ( t Each discrete point in time within this time window, i.e., the current rolling optimization time step, In the time window T The average wind speed within the area, This is the threshold for wind speed fluctuation rate.

[0109] More specifically, the expression for the system rate of change of frequency (RoCoF) anomaly is:

[0110] ,

[0111] In the formula, This is the threshold for the rate of change of the system frequency.

[0112] 2. Dynamic optimization during operation: Based on real-time or ultra-short-term predicted wind speed and system status data, the actual number of units in operation and control parameters are dynamically adjusted using the model predictive control (MPC) algorithm. This includes at least the virtual inertia, voltage droop coefficient, and frequency droop coefficient, in order to meet preset technical performance constraints and optimize economic benefits. The dynamic adjustment method is based on the model predictive control (MPC) algorithm.

[0113] In this embodiment, a second-stage "dynamic optimization model for operation" is introduced during the actual operation of the wind farm. Within the determined recommended capacity ratio range, the number of grid-connected (GFM) and grid-fed (GFL) units, control modes, and parameter settings are dynamically adjusted based on real-time or short-term predicted operating conditions to adapt to fluctuations such as wind speed disturbances and grid anomalies, thereby improving system stability and reducing operating losses.

[0114] Specifically, system state variables Including system frequency Wind speed fluctuation rate and the current GFM unit participation capacity .

[0115] Specifically, control variables Including adjustments to the number of GFM units Adjustment amount of the frequency droop coefficient in the control parameters Adjustment amount of voltage droop coefficient .

[0116] More specifically, the optimization objective of the Model Predictive Control (MPC) algorithm within each rolling window is:

[0117] In the formula, To control the sequence of variables (such as GFM quantity, inertia) in a scrolling window The values ​​within, Optimize the time step for the current scrolling. The system frequency change rate, This refers to the system bus voltage. This is the voltage reference value. This represents the change in the number of grid-connected generating units participating in the current time step. Provides virtual inertia changes for grid-type units in the current time step. The adjustment amount of the frequency droop coefficient of the grid-type unit in the current time step. This represents the adjustment amount of the voltage droop coefficient for grid-type generating units in the current time step. These are the weighting factors corresponding to the respective indicators.

[0118] Specifically, dynamically adjusting the control parameters of grid-connected units includes adjusting the virtual inertia and voltage / frequency droop coefficients to adapt to different system operating conditions.

[0119] More specifically, the frequency droop control model is as follows:

[0120] ,

[0121] Understandable The larger the voltage, the stronger the voltage regulation capability; when a fault voltage drop occurs, it can temporarily increase the voltage. To achieve voltage support.

[0122] More specifically, the voltage droop control model is as follows:

[0123] ,

[0124] Understandable The larger the voltage, the stronger the voltage regulation capability; when a fault voltage drop occurs, it can temporarily increase the voltage. To achieve voltage support.

[0125] Finally, based on the optimization results of step four, the optimal capacity ratio and dynamic adjustment strategy for grid-connected and grid-connected units are determined.

[0126] Example 2:

[0127] This embodiment provides a two-stage robust optimization configuration system for offshore wind power grid-connected units and grid-connected units, used to implement the optimization configuration method as described in Embodiment 1.

[0128] Example 3:

[0129] like Figure 2 As shown, this embodiment provides an electronic device, which may include: at least one processor, at least one network interface, a user interface, a memory, and at least one communication bus.

[0130] The communication bus can be used to enable communication between the various components mentioned above.

[0131] The user interface may include buttons, and optional user interfaces may also include standard wired interfaces and wireless interfaces.

[0132] The network interface may include, but is not limited to, Bluetooth modules, NFC modules, Wi-Fi modules, etc.

[0133] The processor may include one or more processing cores. It connects various parts of the electronic device via various interfaces and lines, executing instructions, programs, code sets, or instruction sets stored in memory, and accessing data stored in memory to perform various functions and process data. Optionally, the processor can be implemented using at least one hardware form of DSP, FPGA, or PLA. The processor may integrate one or more of the following: CPU, GPU, and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor.

[0134] The memory may include RAM or ROM. Optionally, the memory may include a non-transitory computer-readable medium. The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor. The memory, as a computer storage medium, may 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 execute the optimization configuration steps mentioned in the foregoing embodiments.

[0135] Example 4:

[0136] This embodiment provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform the above-described instructions. Figure 1 One or more steps in the illustrated embodiment. If the constituent modules of the above-described electronic device are implemented as software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0137] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as 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 this specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a 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 via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs (DVDs)), or semiconductor media (e.g., Solid State Disks (SSDs)).

[0138] Those skilled in the art will understand that all or part of the processes in the method of Embodiment 1 described above can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks. Unless otherwise specified, the technical features of this embodiment and the implementation scheme can be combined arbitrarily.

[0139] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0140] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0141] The above description is merely an exemplary embodiment of the present invention and should not be construed as limiting the scope of the invention. Any equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of embodiments of the invention upon considering the specification and practicing the disclosure herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of the invention are defined by the claims.

Claims

1. A two-stage robust optimization configuration method for offshore wind power grid-connected units and grid-connected units, characterized in that, Including the following 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; Step S1, which involves constructing a dynamic mathematical model, includes: A frequency response model for grid-type generating units is constructed based on active reference power, output active power, and virtual inertia constant. The expression for the frequency response model of the network-type generating unit is as follows: ， In the formula, for time, for The system frequency at any given time, for The rate of change of system frequency at any given time for Always-on active power reference. for Constantly outputting active power. for The virtual inertia constant at any given moment; S2. Based on the dynamic mathematical model, design multi-dimensional evaluation indicators. The multi-dimensional evaluation indicator system includes a technical performance evaluation indicator system and an economic evaluation indicator system. S3. Obtain multiple typical operating scenarios and their probability distributions that take into account the uncertainty of offshore wind power output and multiple sources of uncertainty factors, and obtain a set of typical scenarios and probabilities. 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 stage optimization, calculate and output the recommended ratio range of capacity matching between grid-connected units and grid-connected units; Step S4 describes the static stage optimization using an improved multi-objective optimization algorithm, specifically as follows: An improved non-dominated sorting genetic algorithm II is adopted as the optimization model, and the expected value integration, robust integration, and worst-case integration are used as the objective function set of the optimization model. The expected value integration is obtained by weighted averaging of the probability distributions of typical operating scenarios; The robust integration introduces a cross-scenario volatility penalty factor based on the expected value; The worst-case integration reflects the feasibility under the most unfavorable scenario; S5. Obtain the preset dynamic optimization trigger conditions for the running stage. When the conditions are met, based on the recommended ratio range and prediction data, use the model prediction control algorithm to perform rolling optimization and output a dynamic adjustment strategy. The preset dynamic optimization trigger conditions for the operation phase include wind speed fluctuation rate exceeding the threshold and abnormal system frequency change rate. Step S5, which involves using a model predictive control algorithm for rolling optimization, specifically includes: Based on real-time or ultra-short-term wind speed and system status data, model predictive control algorithms are used to dynamically adjust the actual number of grid-connected and grid-following units in operation, as well as the control parameters of grid-connected units. The control parameters of the grid-type generator unit include virtual inertia, voltage droop coefficient, and frequency droop coefficient.

2. The two-stage robust optimization configuration method for offshore wind power grid-connected units and grid-connected units according to claim 1, characterized in that: The technical performance evaluation index system includes inertia support capability index, transient voltage safety margin index, black start contribution potential index, and comprehensive stability index; The economic evaluation indicator system includes total life cycle cost, potential revenue from the ancillary services 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 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 evaluate the system's ability to achieve a black start within a limited time. The comprehensive stability index is obtained by integrating multiple technical performance evaluation indicators using a weighted or fuzzy comprehensive method.

3. The two-stage robust optimization configuration method for offshore wind power grid-connected units and grid-connected units according to claim 1, characterized in that: The formula for calculating the wind speed fluctuation rate is as follows: ， In the formula, For at any time t Wind speed fluctuation rate, For at any time The wind speed value, In the time window T The average wind speed within the area, The threshold for wind speed fluctuation rate; The formula for judging the abnormality of the system frequency change rate is: ， In the formula, for The rate of change of system frequency at any given time This is the threshold for the rate of change of the system frequency.

4. The two-stage robust optimization configuration method for offshore wind power grid-connected units and grid-connected units according to claim 3, characterized in that: The optimization objective of the model predictive control algorithm within each rolling window is: In the formula, To control the sequence of variables in the rolling window The values ​​within, Optimize the time step for the current scrolling. The system frequency change rate, This refers to the system bus voltage. This is the voltage reference value. This represents the change in the number of grid-connected generating units participating in the current time step. Provides virtual inertia changes for grid-type units in the current time step. The adjustment amount of the frequency droop coefficient of the grid-type unit in the current time step. This represents the adjustment amount of the voltage droop coefficient for grid-type generating units in the current time step. These are the weighting factors corresponding to the respective indicators.

5. A two-stage robust optimization configuration system for offshore wind power grid-connected units and grid-connected units, characterized in that, Used to implement the optimized configuration method as described in any one of claims 1 to 4.

6. A computer device, the computer device comprising a memory, a processor, and a computer program, characterized in that, When the computer program is executed by the processor, it implements the optimized configuration method as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the optimized configuration method as described in any one of claims 1 to 4.

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