Offshore wind power grid connection auxiliary decision method, system, equipment and medium considering power grid strength coupling constraint

Abstract

This invention relates to the field of new energy power system technology, and in particular to a method, system, equipment, and medium for auxiliary decision-making regarding offshore wind power grid connection considering grid strength coupling constraints. The method includes acquiring real-time state estimation data of an offshore wind power cluster; calculating the grid mutual impedance matrix of the offshore wind power cluster and the three-phase short-circuit capacity of each wind farm's grid connection point; generating grid strength assessment indices based on the calculation results; ranking these node wind farms according to grid strength; and, based on the ranking results and the interaction factors between weak node wind farms, selecting other wind farms with strong coupling relationships with weak nodes to form a set of wind farms to be optimized; establishing a multi-objective optimization model, and incorporating the grid strength assessment indices as constraints into the multi-objective optimization model to perform output scheduling optimization calculations. This method can provide technical support for improving grid strength at weak nodes and for control decisions after large-scale offshore wind power grid connection.

Description

Technical Field

[0001] This invention relates to the field of new energy power system technology, and in particular to a method, system, equipment and medium for auxiliary decision-making of offshore wind power grid connection considering grid strength coupling constraints. Background Technology

[0002] Offshore wind power typically involves multiple wind farms converging and connecting to the grid via AC or flexible DC connections, forming a grid-connected system that includes wind turbines, various types of SVG converters, and AC / DC cables. In large-scale offshore wind power development areas, multiple AC / DC wind power grid-connected systems are connected to the grid through different access points with similar electrical distances. Especially during periods of high wind power generation capacity but low conventional turbine operating rates, the grid's own support capacity is relatively weak, significantly reducing the strength of the main grid and increasing the risk of oscillations and instability in offshore wind power. Grid strength indicators are not only related to the grid's short-circuit capacity but are also influenced by factors such as the output level of the offshore wind power cluster, the operating status of reactive power compensation equipment, and impedance angle. Therefore, incorporating grid strength indicator constraints as inequality constraints into the operational optimization model of offshore wind power clusters helps reduce the risk of grid instability, improve grid stability margins, and prevent oscillation accidents that may occur during large-scale offshore wind power integration.

[0003] However, current offshore wind power grid connection auxiliary decision-making methods have many problems. Traditional offshore wind power cluster operation optimization schemes mainly focus on how to improve the cluster's absorption capacity under grid operation constraints, rarely conducting system analysis from the perspective of grid strength. This leads to the failure to fully consider the relationship between grid strength constraints and potential maximum output capacity when designing optimization models. In addition, existing decision-making systems often lack the ability to couple grid strength indicators with grid operation mode optimization models, failing to form a comprehensive decision-making system with multiple constraints. This limitation not only reduces the security of offshore wind power access but also restricts the comprehensive assessment and optimization of grid operation status, necessitating in-depth research and application of related technologies.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the general background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a method, system, device, and medium for auxiliary decision-making regarding offshore wind power grid connection that takes into account grid strength coupling constraints, thereby effectively solving the problems in the background art.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a method for auxiliary decision-making regarding offshore wind power grid connection considering grid strength coupling constraints, comprising the following steps:

[0007] S10: Obtain real-time status estimation data of offshore wind power clusters, including active power output, reactive power output and grid topology information of each wind farm;

[0008] S20: Based on the real-time state estimation data, calculate the grid mutual impedance matrix of the offshore wind power cluster and the three-phase short-circuit capacity of each wind farm grid connection point, and generate grid strength assessment index based on the calculation results.

[0009] S30: The power grid strength assessment index is used to analyze the offshore wind power cluster, identify weak node wind farms with power grid strength below a preset threshold, and sort these node wind farms according to the power grid strength to obtain the sorting results;

[0010] S40: Based on the ranking results and the interaction factors between wind farms with weak nodes, other wind farms with strong coupling relationships with weak nodes are selected to form a set of wind farms to be optimized.

[0011] S50: Based on the set of wind farms to be optimized, establish a multi-objective optimization model, and incorporate the power grid intensity assessment index as a constraint into the multi-objective optimization model to perform power output scheduling optimization calculation;

[0012] S60: Based on the solution results of the multi-objective optimization model, auxiliary decision-making information is generated to guide the power output scheduling of each wind farm, thereby improving the operational stability of the power grid and reducing the risk of oscillation.

[0013] Further, in step S20, the grid mutual impedance matrix of the offshore wind power cluster and the three-phase short-circuit capacity of each wind farm's grid connection point are calculated, including:

[0014] The grid mutual impedance matrix Z is obtained by inverting the node admittance matrix Y. The node admittance matrix Y is an N×N sparse matrix. If there is a grounding branch in the offshore wind power cluster, then the admittance matrix Y is a non-singular matrix. The inverse of the admittance matrix Y is the mutual impedance matrix Z = Y. -1 ;

[0015] The diagonal element Y of the node admittance matrix ii Self-admittance is numerically equal to the current injected into the network through node i when a unit voltage is applied to node i and all other nodes are grounded;

[0016] Off-diagonal element Y ji Mutual admittance represents the current injected into the network through node j when a unit voltage is applied to node i and other nodes are grounded, and the value of the off-diagonal element is the negative of the branch admittance connecting nodes i and j.

[0017] Further, in step S20, the grid mutual impedance matrix of the offshore wind power cluster and the three-phase short-circuit capacity of each wind farm's grid connection point are calculated, wherein the three-phase short-circuit capacity S of each wind farm's grid connection point is... ki The model includes:

[0018]

[0019] In the formula, S ki For the three-phase short-circuit capacity of each wind farm's grid connection point, I ki U is the maximum short-circuit current at the grid connection point of wind farm i when it is short-circuited. Bi Z is the rated voltage at the grid connection point of wind farm i. ii is the self-impedance of the node where the wind farm i is connected to the grid.

[0020] Further, in step S20, a power grid strength assessment index is generated based on the calculation results. This index is the short-circuit ratio (MESCR) index for multiple renewable energy power plants, which includes:

[0021] The short-circuit ratio (MESCR) index for multiple renewable energy power plants is generated and used as the grid strength assessment index.

[0022]

[0023] In the formula, K MESCR,i The MESCR (Medium-Short Circuit Ratio) index is used for multiple power stations in the new energy sector. Let i be the apparent power of the offshore wind farm. S is the complex interaction coefficient between offshore wind farm j and offshore wind farm i. ki This refers to the three-phase short-circuit capacity of each wind farm's grid connection point.

[0024] Further, in step S30, the offshore wind power cluster is analyzed using the aforementioned grid strength assessment index to identify weak node wind farms with grid strength below a preset threshold, and these node wind farms are ranked according to grid strength to obtain a ranking result, including:

[0025] The early warning extreme value for power grid strength is set to K. MESCRmin Offshore wind farms with grid strength below the warning extreme value are selected to form a set S of wind farms with weak grid strength. E The model includes:

[0026] S E ={S i |K MESCR,i <K MESCRmin};

[0027] In the formula, S E S is a collection of wind farms with weak grid strength.i Let set S E The i-th station, K MESCR,i For the MESCR index of short-circuit ratio in multiple new energy power stations, K MESCRmin To set early warning extreme values ​​for power grid strength.

[0028] Further, in step S40, based on the ranking results and the interaction factors between wind farms with weak nodes, other wind farms with strong coupling relationships with the weak nodes are selected to form a set of wind farms to be optimized, including:

[0029] The ensemble model of the wind farms to be optimized is represented as follows:

[0030]

[0031] In the formula, S λ For the wind farm aggregate to be optimized, S R，i To connect with the i-th station S i A collection of wind farms with a strong coupling relationship to the grid intensity. S is the complex interaction coefficient between offshore wind farm j and offshore wind farm i. i Let S be the i-th station in the set. R Traverse the set of strongly coupled weak power grid strength stations.

[0032] Furthermore, it also includes:

[0033] After step S40, based on the set of wind farms to be optimized, it is determined whether to trigger optimization calculation, including:

[0034] If the set of wind farms to be optimized is an empty set, it is determined that no optimization calculation needs to be triggered, and the next calculation cycle will begin.

[0035] If the set of wind farms to be optimized is not empty, proceed to step S50 and perform power output scheduling optimization calculation.

[0036] Further, in step S50, based on the set of wind farms to be optimized, a multi-objective optimization model is established, and the grid intensity assessment index is incorporated as a constraint into the multi-objective optimization model. Output scheduling optimization calculations are then performed, including:

[0037] The model for calculating active power optimization adjustment includes:

[0038]

[0039] In the formula, ΔP i To optimize the active power regulation of offshore wind farms. To optimize the grid strength of wind farm i in the nth round, using the short-circuit ratio (MESCR) as the evaluation index, KMESCRmin To set the grid strength early warning extreme value for the risk of voltage oscillation instability in offshore wind power clusters, γ is an adjustment factor, and P c，i To improve the active power regulation capability of offshore wind farms.

[0040] The present invention also includes an offshore wind power grid connection auxiliary decision-making system considering grid strength coupling constraints, using the method described above, comprising:

[0041] The data acquisition module is used to acquire real-time status estimation data of offshore wind power clusters. The data includes the active power output, reactive power output and grid topology information of each wind farm.

[0042] The state estimation and calculation module is used to calculate the grid mutual impedance matrix of the offshore wind power cluster and the three-phase short-circuit capacity of each wind farm grid connection point based on the real-time state estimation data, and generate grid strength assessment indicators based on the calculation results.

[0043] The analysis and identification module is used to analyze the offshore wind power cluster using the power grid strength assessment index, identify weak node wind farms with power grid strength below a preset threshold, and sort these node wind farms according to power grid strength to obtain the sorting results.

[0044] The screening and optimization module is used to screen out other wind farms that have a strong coupling relationship with the weak nodes based on the ranking results and the interaction influence factors between the wind farms with weak nodes, forming a set of wind farms to be optimized.

[0045] The multi-objective optimization module is used to establish a multi-objective optimization model based on the set of wind farms to be optimized, and to incorporate the power grid intensity assessment index as a constraint into the multi-objective optimization model to perform power output scheduling optimization calculations.

[0046] The decision generation module is used to generate auxiliary decision information based on the solution results of the multi-objective optimization model, which is used to guide the power output scheduling of each wind farm, thereby improving the operational stability of the power grid and reducing the risk of oscillation.

[0047] The present invention also includes a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described above.

[0048] The present invention also includes a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described above.

[0049] The beneficial effects of this invention are as follows:

[0050] This method effectively addresses the practical engineering problem of rapidly identifying power output characteristics and accurately making auxiliary decisions for offshore wind power dispatch and operation based on grid strength indicators under large-scale offshore wind power grid connection. It comprehensively considers the impact of the operating status of each wind power generation unit and key power transmission and generation equipment (SVG reactive power compensation) of the offshore wind power cluster on grid strength, establishes a rapid optimization solution model, and generates an optimized set of operating points for adjustable equipment such as wind power generation and SVG reactive power compensation online. This provides auxiliary decision-making suggestions for improving grid strength at weak nodes when offshore wind farms are connected to the grid, thereby enhancing grid voltage stability margin and wind power absorption. Furthermore, this method can be easily implemented and applied on a dispatch automation system platform, possessing certain dispatch production guidance value. Practical verification has validated the feasibility of the method, which can provide technical support for improving grid strength at weak nodes and making control decisions after large-scale offshore wind power grid connection. Attached Figure Description

[0051] 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 recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart of an auxiliary decision-making method for offshore wind power grid connection considering grid strength coupling constraints;

[0053] Figure 2 A flowchart illustrating the grid connection auxiliary decision-making method for offshore wind power considering grid strength coupling constraints;

[0054] Figure 3 Build a flowchart for the set of sites to be optimized;

[0055] Figure 4 Classification diagram of power output operation scenarios for offshore wind farms;

[0056] Figure 5 A schematic diagram of the structure of an offshore wind power grid connection auxiliary decision-making system considering grid strength coupling constraints;

[0057] Figure 6 This is a schematic diagram of the structure of a computer device. Detailed Implementation

[0058] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0059] like Figures 1 to 4 As shown: A method for grid-connected auxiliary decision-making for offshore wind power considering grid strength coupling constraints, comprising the following steps:

[0060] S10: Obtain real-time status estimation data of offshore wind power clusters, including active power output, reactive power output and grid topology information of each wind farm;

[0061] S20: Based on real-time state estimation data, calculate the grid mutual impedance matrix of the offshore wind power cluster and the three-phase short-circuit capacity of each wind farm grid connection point, and generate grid strength assessment indicators based on the calculation results.

[0062] S30: The power grid strength assessment index is used to analyze the offshore wind power cluster, identify the weak node wind farms with power grid strength below the preset threshold, and sort these node wind farms according to the power grid strength to obtain the sorting results;

[0063] S40: Based on the ranking results and the interaction factors between wind farms with weak nodes, other wind farms with strong coupling relationships with weak nodes are selected to form a set of wind farms to be optimized.

[0064] S50: Based on the set of wind farms to be optimized, a multi-objective optimization model is established, and the power grid intensity assessment index is incorporated as a constraint into the multi-objective optimization model to perform power dispatch optimization calculation;

[0065] S60: By solving the multi-objective optimization model, auxiliary decision-making information is generated to guide the power output scheduling of each wind farm, thereby improving the operational stability of the power grid and reducing the risk of oscillation.

[0066] This method effectively addresses the practical engineering problem of rapidly identifying power output characteristics and accurately making auxiliary decisions for offshore wind power dispatch and operation based on grid strength indicators under large-scale offshore wind power grid connection. It comprehensively considers the impact of the operating status of each wind power generation unit and key power transmission and generation equipment (SVG reactive power compensation) of the offshore wind power cluster on grid strength, establishes a rapid optimization solution model, and generates an optimized set of operating points for adjustable equipment such as wind power generation and SVG reactive power compensation online. This provides auxiliary decision-making suggestions for improving grid strength at weak nodes when offshore wind farms are connected to the grid, thereby enhancing grid voltage stability margin and wind power absorption. Furthermore, this method can be easily implemented and applied on a dispatch automation system platform, possessing certain dispatch production guidance value. Practical verification has validated the feasibility of the method, which can provide technical support for improving grid strength at weak nodes and making control decisions after large-scale offshore wind power grid connection.

[0067] By introducing a grid strength assessment index and incorporating grid strength as a constraint in power output scheduling optimization, the problem of insufficient consideration of grid strength in traditional offshore wind power grid connection auxiliary decision-making schemes is effectively solved, thereby avoiding the risk of oscillation and instability caused by weak grid nodes.

[0068] By analyzing wind farms with weak grid strength and prioritizing them, we ensured that weak nodes were optimized in a timely manner, which helps maintain the stability of the power grid under large-scale wind power integration.

[0069] By calculating the interaction factors between weak nodes, wind farms with strong coupling relationships with weak nodes are screened, achieving precise optimization scheduling. This approach can prevent the risk of local instability from escalating due to the neglect of coupling relationships between wind farms. The dynamic optimization capability based on real-time state estimation data enables the system to adapt to the continuous changes in the power grid and wind power clusters, providing real-time scheduling optimization schemes and ensuring the effectiveness of scheduling decisions.

[0070] By comprehensively optimizing grid strength and wind power output as multiple objectives, this approach avoids the limitation of focusing solely on output improvement while neglecting grid security. Multi-objective optimization enables the system to both enhance wind power absorption capacity and maintain grid stability. Through multiple rounds of optimization calculations and decision outputs, this method can gradually improve weak nodes in the grid and reduce the risk of oscillation accidents that may occur after large-scale offshore wind power integration.

[0071] S20: Based on real-time state estimation data, calculate the grid mutual impedance matrix of the offshore wind power cluster and the three-phase short-circuit capacity of each wind farm grid connection point, and generate grid strength assessment indicators based on the calculation results.

[0072] To improve the grid connection voltage performance of offshore wind power clusters and enhance the strength of the weak grid system, common reactive power compensation methods include installing SVCs, SVGs, and utilizing the reactive power generated by the wind turbines themselves. Each method improves the grid strength through different mechanisms. SVCs primarily affect the short-circuit capacity of each node by altering the grid's admittance matrix, with SVCs considered as having their admittance in parallel with the busbar included in the admittance matrix. SVGs, along with the reactive power of the wind farm itself, mainly increase the reactive power of the system's node power sources. The integration of various reactive power compensation methods affects the calculation of the short-circuit ratio at each grid connection point of the offshore wind power cluster, thus influencing the calculation of the wind farm output limit at each grid connection point, and consequently affecting the absorption capacity of each renewable energy plant.

[0073] Therefore, to conduct refined online calculations of the grid strength of offshore wind power clusters based on the MESCR (Mean Exchange Stability Ratio) index for multiple new energy sites, it is necessary to monitor the operation of different reactive power sources such as SVC (Self-Controlled Ventilation), SVG (Self-Controlled Ventilation), and wind turbines online. Based on state estimation data, the system structure (lines, transformers, etc.) needs to be analyzed to establish a system admittance matrix. The secondary transient reactance of conventional units in the grid is included in the admittance matrix, while the impedance of the inverter interfaces of offshore wind turbines is ignored. Then, the admittance matrix is ​​inverted to obtain the system impedance matrix. According to the principle of symmetrical short-circuit calculation, the reciprocal of the self-impedance of a system node is the per-unit value of the node's short-circuit current. This allows for the calculation of the short-circuit capacity of each node in the system, thereby measuring the system strength at each grid connection point. Specifically:

[0074] First, real-time grid state estimation data for the offshore wind farm cluster at time t is obtained. This state estimation data can generally be acquired through a dispatch automation system. The data file includes information such as the active and reactive power outputs of each power source, key equipment parameters, and network topology connections. Based on the state estimation data, the grid mutual impedance matrix Z, reflecting the current operating state of the offshore wind farm cluster, and the three-phase short-circuit capacity S at each wind farm's grid connection point can be generated online. ki .

[0075] In this embodiment, step S20 involves calculating the grid mutual impedance matrix of the offshore wind power cluster and the three-phase short-circuit capacity of each wind farm's grid connection point, including:

[0076] The grid mutual impedance matrix Z is obtained by inverting the node admittance matrix Y. The node admittance matrix Y is an N×N sparse matrix. If there are grounding branches in the offshore wind farm, then the admittance matrix Y is a non-singular matrix, and the inverse of the admittance matrix Y is the mutual impedance matrix Z = Y. -1 ;

[0077] The diagonal element Y of the nodal admittance matrix ii (i = 1, 2, ..., n) is the self-admittance, which is numerically equal to the current injected into the network through node i when a unit voltage is applied to node i and all other nodes are grounded;

[0078] Off-diagonal element Y ji (j = 1, 2, ..., n, i = 1, 2, ..., n; j ≠ i) represents the mutual admittance, which indicates the current injected into the network through node j when a unit voltage is applied to node i and other nodes are grounded. The values ​​of the off-diagonal elements are the negative values ​​of the branch admittance connecting nodes i and j.

[0079] To address the characteristics of offshore wind power clusters, a scheme utilizing mutual impedance matrices and node admittance matrices is proposed. This method adapts to the complex situation of interconnected multiple wind farms and improves the power system's response and management capabilities for offshore wind power integration.

[0080] In step S20, the grid mutual impedance matrix of the offshore wind power cluster and the three-phase short-circuit capacity of each wind farm's grid connection point are calculated. The three-phase short-circuit capacity S of each wind farm's grid connection point is... ki The model includes:

[0081]

[0082] In the formula, S ki For the three-phase short-circuit capacity of each wind farm's grid connection point, I ki U is the maximum short-circuit current at the grid connection point of wind farm i when it is short-circuited. Bi Z is the rated voltage at the grid connection point of wind farm i. ii is the self-impedance of the node where the wind farm i is connected to the grid.

[0083] The three-phase short-circuit capacity S of each wind farm's grid connection point was calculated. ki It can accurately assess the system's carrying capacity under short-circuit conditions, providing necessary technical basis for the design and operation of the power grid and helping to prevent system overload and failure.

[0084] Then, the short-circuit ratio (MESCR) index for new energy multi-stations proposed in the State Grid enterprise standard "Calculation Specification for Short-Circuit Ratio of New Energy Multi-Station" (Q / GDW12290-2023) is adopted as the basis for judging grid strength. Compared with the traditional DC transmission multi-infeed short-circuit ratio index, this index can consider the voltage interaction between different new energy stations, fully consider the impact of SVG output and submarine cable charging power on the stability of new energy units, and more accurately reflect the stable operation risk when offshore wind farms are centrally connected to the grid.

[0085] As a preferred embodiment of the above, in step S20, generating power grid strength assessment indicators based on the calculation results includes:

[0086] The power grid strength assessment index is the short-circuit ratio (MESCR) of multiple renewable energy power plants. The MESCR index includes:

[0087]

[0088] In the formula, K MESCR,i The MESCR (Medium-Short Circuit Ratio) index is used for multiple power stations in the new energy sector. Let i be the apparent power of the offshore wind farm. S is the complex interaction coefficient between offshore wind farm j and offshore wind farm i. ki This refers to the three-phase short-circuit capacity of each wind farm's grid connection point.

[0089] The MESCR index can comprehensively consider the mutual influence of multiple wind farms and reflect the overall strength of the power grid. Compared with the short-circuit ratio of a single wind farm, this multi-site evaluation method can more accurately evaluate the carrying capacity of the power grid under different operating conditions.

[0090] The complex interaction coefficient between offshore wind farm j and offshore wind farm i The computational model includes:

[0091]

[0092] In the formula, Let J be the mutual impedance between offshore wind farm j and offshore wind farm i; The self-impedance of offshore wind farm i; and These are the voltages of offshore wind farm j and offshore wind farm i, respectively.

[0093] When the electrical distance between offshore wind farms is relatively short and the phase angle difference is small, r ji The calculation can ignore the voltage phase angle difference, that is:

[0094]

[0095] In the formula, r ji It represents the absolute value of the complex interaction coefficient between offshore wind farm j and offshore wind farm i.

[0096] S30: The power grid strength assessment index is used to analyze the offshore wind power cluster, identify the weak node wind farms with power grid strength below the preset threshold, and sort these node wind farms according to the power grid strength to obtain the sorting results;

[0097] The mutual impedance of an offshore wind farm cluster reflects the relationship between the current fluctuations in wind farm j and the resulting node voltage fluctuations in wind farm i, making it suitable as a basis for measuring electrical distance. However, the control systems of actual wind power generation units all have a limited range of voltage and current measurement accuracy. The wind farm cannot perceive node voltage disturbances within this measurement accuracy range. Surveys show that the voltage and current measurement accuracy of wind turbine units is generally 0.5% to 1%. Therefore, when the mutual impedance Z within the offshore wind farm cluster... ji When the voltage fluctuation is less than 0.5%, when the power of wind farm j changes, wind farm i will not detect the voltage fluctuation, and the control system will not operate; that is, wind farm i is not affected by wind farm j. Therefore, the calculation range can be determined by setting the mutual impedance threshold to 0.005pu, that is, only the influence of wind farms with mutual impedance not less than 0.005pu is considered. Furthermore, the wind farms can be ranked using interactive influence factors, prioritizing the adjustment of wind farms with higher influence weights. Specifically:

[0098] First, based on the grid strength of each wind farm's grid connection point within the offshore wind power cluster obtained in step S20, the offshore wind farms are sorted from weakest to strongest; the grid strength warning extreme value for the risk of voltage oscillation instability in the offshore wind power cluster is set as K. MESCRmin (K is usually taken) MESCRmin With a typical value of 1.5, the warning extreme value is used as a screening condition to select offshore wind farms with grid strength less than the warning minimum value, forming a set S of offshore wind farms with weak grid strength. E ;

[0099] In this embodiment, in step S30, the offshore wind power cluster is analyzed using a power grid strength assessment index to identify weak node wind farms with power grid strength below a preset threshold. These node wind farms are then ranked according to their power grid strength to obtain a ranking result, including:

[0100] The early warning extreme value for power grid strength is set to K. MESCRmin (K is usually taken) MESCRmin With a typical value of 1.5, offshore wind farms with grid strength below the warning extreme value are selected to form a set S of wind farms with weak grid strength. E The model includes:

[0101] S E ={S i |K MESCR,i <K MESCRmin};

[0102] In the formula, S E S is a collection of wind farms with weak grid strength. i Let set S E The i-th station, K MESCR,i For the MESCR index of short-circuit ratio in multiple new energy power stations, K MESCRmin To set early warning extreme values ​​for power grid strength.

[0103] By identifying wind farms with weak grid strength below a preset threshold, potential vulnerabilities in the power grid can be identified in a timely manner, thereby providing data support for subsequent scheduling and optimization and improving grid stability.

[0104] S40: Based on the ranking results and the interaction factors between wind farms with weak nodes, other wind farms with strong coupling relationships with weak nodes are selected to form a set of wind farms to be optimized.

[0105] Then, the interaction factor is used as a criterion for the degree of grid strength coupling between two wind farms. The smaller the interaction factor, the lower the degree of coupling. This is applied to the set S of offshore wind farms with weak grid strength. E Each station in the group, in the form of S E The i-th station S iWith the j-th station S j Interaction factors and threshold r min (usually r is taken) min Using a typical value of 0.005 as the filtering condition, the selection criteria are used to identify sites S with the i-th station. i A set of wind farms S with strong coupling relationship to grid strength R，i Each element in this set consists of a site number and an interaction factor value. Further, all strongly coupled weak grid intensity site sets are combined to form S. R The model includes:

[0106]

[0107] S R ={S R,i |S i ∈S E};

[0108] In the formula, S R，i To connect with the i-th station S i A collection of wind farms with a strong coupling relationship to the grid intensity. Let j be the offshore wind farm that has a strong coupling relationship with offshore wind farm i. Let be the complex interaction coefficient between offshore wind farm j and offshore wind farm i.

[0109] Finally, the set S of strongly coupled weak grid strength power stations is traversed. R For each station in the network, for the i-th station S i All interactive influencing factors associated with this station The larger the sum, the more it reflects the influence weight of the wind farm in the entire wind power cluster.

[0110] As a preferred embodiment of the above, in step S40, based on the ranking results and the interaction influence factors between wind farms with weak nodes, other wind farms with strong coupling relationships with the weak nodes are screened out to form a set of wind farms to be optimized, including:

[0111] The ensemble model of the wind farm to be optimized is represented as follows:

[0112]

[0113] In the formula, S λ For the wind farm aggregate to be optimized, S R，i To connect with the i-th station S i A collection of wind farms with a strong coupling relationship to the grid intensity. S is the complex interaction coefficient between offshore wind farm j and offshore wind farm i.i Let S be the i-th station in the set. R Traverse the set of strongly coupled weak power grid strength stations.

[0114] By identifying wind farms that are strongly coupled with the grid strength of weak nodes, resource allocation and use can be optimized more effectively, ensuring that wind farms that have a greater impact on grid stability can be prioritized when the grid load is high.

[0115] In this embodiment, it also includes:

[0116] After step S40, based on the set S of wind farms to be optimized λ Determine whether to trigger optimization calculations, including:

[0117] If the set of wind farms to be optimized is S λ If the set is empty, it is determined that no optimization calculation needs to be triggered, and the next calculation cycle begins;

[0118] If the set of wind farms to be optimized is S λ If it is not empty, proceed to step S50 to perform output scheduling optimization calculation.

[0119] By treating the optimized wind farm set S λ Effective judgment can quickly determine whether further optimization calculations are needed, avoiding unnecessary waste of computing resources and improving overall decision-making efficiency;

[0120] refer to Figure 3 The method for constructing wind farm ensembles to be optimized includes the following steps:

[0121] (1) Based on the grid strength of each grid connection point of the offshore wind power cluster, the stations are sorted and those with grid strength lower than the warning extreme value K are selected. MESCRmin The power stations form a set of power stations with weak grid strength, S. E ;

[0122] (2) Traverse set S E The i-th station S i ;

[0123] (3) Based on set S E The interaction influence factor r of the i-th station ji Extract the set S of power stations that are strongly coupled with the power grid strength. R，i ;

[0124] (4) Determine set S E If the station has been traversed, proceed to the next step; otherwise, continue traversing.

[0125] (5) Form a set S of sites to be optimized, with the cumulative value of the impact factors as the weight. λ.

[0126] S50: Based on the set of wind farms to be optimized, a multi-objective optimization model is established, and the power grid intensity assessment index is incorporated as a constraint into the multi-objective optimization model to perform power dispatch optimization calculation;

[0127] The offshore wind power cluster optimization of this invention refers to, under the condition that the installation locations of each reactive power compensation device are known, adjusting the SVG and wind turbine output to meet multiple constraints such as acceptable system voltage deviation and grid strength exceeding the warning value. Different operating states of reactive power compensation devices will affect the grid strength of the offshore wind power cluster. Adjusting the output of reactive power compensation devices can improve the system's short-circuit ratio and increase grid strength to a certain extent. Considering the regulating effect of reactive power compensation devices on system voltage and grid strength, a set of offshore wind power sites to be optimized is formed that takes into account both grid strength requirements and voltage fluctuations. Specifically:

[0128] First, the optimization mode for each wind farm to be optimized is determined based on the operating scenario, according to the current active power output P of the wind farm. g According to 40% P N 60% P N The boundary determines that its operating scenario is a low-output scenario Z1 (less than 40% P). N ), medium output scenario Z2 (between 40% P) N With 60% P N (between) or high-output scenarios Z3 (greater than 60% P) N According to the current grid connection voltage U of the wind farm pcc Its operating condition when connected to the power grid is determined to be negative deviation voltage operating condition V1 (less than U). N ), normal voltage operating condition V2 (between U N with 1.02%U N (between) or positive deviation voltage operating condition V3 (greater than 1.02% U N ).

[0129] When the wind farm is in high-output scenario Z3 and the grid connection point is in positive voltage deviation operating condition V3, the reactive power priority mode is adopted because the short-circuit ratio K of the wind farm's grid connection point is only used when the grid node voltage deviates positively (i.e., the voltage at the wind farm's grid connection point is higher than 1pu). MESCR The reactive power required for the adjustment process must match the reactive power direction required for the reactive power-voltage adjustment process. At this time, the reactive power of the reactive power source SVG and the reactive power of the wind turbine itself can be reduced first. The AVC system of the wind farm can control the voltage at the grid connection point of the wind farm to be reduced to the normal voltage operating condition V2. At the same time, the grid strength can be improved. In other scenarios and operating conditions, the active power priority mode can be adopted.

[0130] Then, the active power available regulation capacity of each wind farm to be optimized is calculated, and the optimization round for each wind farm is determined. Available regulation capacity, such as... Figure 4 As shown, for the set S of offshore wind farms to be optimized λ The i-th wind farm in the system obtains the S of the wind farm through the wind farm energy management system. i The active power output P at the current operating point w g，i The calculated active power regulation capacity is the real-time output P at the current operating point w. g，i Minimum Distance Technical Output P min，i The difference, where the minimum technical output P min，i A typical value is taken as 10% P. N .

[0131] P c,i =P g,i -P min,i ;

[0132] In the formula, P c，i To improve the active power regulation capability of offshore wind farms, P g，i For the current active power output of wind farm i, P min，i Minimize technical output for wind farm i.

[0133] Based on the set of offshore wind farms to be optimized S obtained in step S40 λ This includes each element consisting of a wind farm number and the cumulative value of its interaction factors, sorted from largest to smallest by cumulative value to form the optimization order of the wind farms. Assuming a total of N optimization rounds (generally N is taken as 5), then the wind farm set S... λ Divided into N groups according to the optimization order, the regulation amount of the i-th power station in the n-th round is given, where γ is the regulation factor (generally taken as 0.5). It can be seen that the stronger the active power regulation capability of the power station and the weaker the grid strength, the larger the optimized regulation amount; the larger the cumulative value of the interaction influence factor of the power station, the more priority it will participate in regulation and play a greater regulation role.

[0134] As a preferred embodiment of the above, in step S50, a multi-objective optimization model is established based on the set of wind farms to be optimized, and the power grid intensity assessment index is incorporated as a constraint into the multi-objective optimization model. Output scheduling optimization calculations are then performed, including:

[0135] The model for calculating active power optimization adjustment includes:

[0136]

[0137] In the formula, ΔP i To optimize the active power regulation of offshore wind farms. To optimize the grid strength of wind farm i in the nth round, using the short-circuit ratio (MESCR) as the evaluation index, KMESCRmin To set the grid strength early warning extreme value for the risk of voltage oscillation instability in offshore wind power clusters, γ is an adjustment factor (generally taken as 0.5), P c，i To improve the active power regulation capability of offshore wind farms.

[0138] Incorporating grid strength assessment indicators as constraints into the optimization model ensures the safe operation of the grid throughout the optimization process, helps prevent oscillations and instability risks caused by insufficient grid strength, and improves the overall security of the grid. When calculating the active power optimization regulation, it can effectively assess and utilize the regulation capacity of wind farms, ensuring that the resources of each wind farm in the grid are rationally allocated, thereby improving the overall power generation efficiency.

[0139] After each round of optimization and adjustment, the grid strength of the power grid is recalculated. When the grid strength of all stations is greater than the warning extreme value or reaches the maximum round, the optimization calculation is completed. The final optimized adjustment amount of each station is used as an auxiliary decision to guide the power output arrangement of offshore wind power clusters, thereby improving the grid strength of weak nodes when offshore wind farms are connected to the grid, and enhancing the grid voltage stability margin and wind power absorption level.

[0140] This invention also includes an offshore wind power grid connection auxiliary decision-making system considering grid strength coupling constraints, using the method described above, such as... Figure 5 As shown, it includes:

[0141] The data acquisition module is used to acquire real-time status estimation data of offshore wind power clusters. The data includes the active power output, reactive power output and grid topology information of each wind farm.

[0142] The state estimation and calculation module is used to calculate the grid mutual impedance matrix of offshore wind power clusters and the three-phase short-circuit capacity of each wind farm grid connection point based on real-time state estimation data, and generate grid strength assessment indicators based on the calculation results.

[0143] The analysis and identification module is used to analyze offshore wind power clusters using grid strength assessment indicators, identify weak node wind farms with grid strength below a preset threshold, and sort these node wind farms according to grid strength to obtain the sorting results.

[0144] The screening and optimization module is used to screen out other wind farms that have a strong coupling relationship with the weak nodes based on the ranking results and the interaction factors between wind farms with weak nodes, forming a set of wind farms to be optimized.

[0145] The multi-objective optimization module is used to establish a multi-objective optimization model based on the set of wind farms to be optimized, and incorporate the power grid strength assessment index as a constraint into the multi-objective optimization model to perform power dispatch optimization calculations.

[0146] The decision generation module is used to generate auxiliary decision information based on the solution results of the multi-objective optimization model. This information is used to guide the power output scheduling of each wind farm, thereby improving the operational stability of the power grid and reducing the risk of oscillations.

[0147] Please see Figure 6 The diagram shows a structural schematic of a computer device provided in an embodiment of this application. An embodiment of this application provides a computer device 400, including a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, it performs the method described above.

[0148] This application embodiment also provides a storage medium 430, on which a computer program is stored, and the computer program is executed by a processor 410 to perform the above method.

[0149] The storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0150] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0151] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0152] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0153] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0154] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0155] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0156] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0157] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for assisting decision-making of offshore wind power grid connection considering grid strength coupling constraints, characterized in that, Includes the following steps: S10: Obtain real-time status estimation data of offshore wind power clusters, including active power output, reactive power output and grid topology information of each wind farm; S20: Based on the real-time state estimation data, calculate the grid mutual impedance matrix of the offshore wind power cluster and the three-phase short-circuit capacity of each wind farm grid connection point, and generate grid strength assessment index based on the calculation results. S30: The power grid strength assessment index is used to analyze the offshore wind power cluster, identify weak node wind farms with power grid strength below a preset threshold, and sort these node wind farms according to the power grid strength to obtain the sorting results; S40: Based on the ranking results and the interaction factors between wind farms with weak nodes, other wind farms with strong coupling relationships with weak nodes are selected to form a set of wind farms to be optimized. The method for constructing the wind farm set to be optimized includes the following steps: The offshore wind farms were ranked according to the grid strength at each grid connection point, and those with grid strength below the warning threshold were selected. K MESCRmin These power stations form a cluster of power stations with weak grid strength. ; Traverse the set of power stations with the aforementioned weak power grid strength The first in i Stations ; The set of power stations based on the weak power grid strength The Middle i Interaction factors of individual stations Extract the set of power stations that are strongly coupled with the power grid strength. ; The set of substations for determining the strength of the weak power grid S E If the station has been traversed, proceed to the next step; otherwise, continue traversing. The accumulated value of the interaction influence factor corresponding to each station is taken as a weight to form a set of wind power plants to be optimized ; S50: Based on the set of wind farms to be optimized, establish a multi-objective optimization model, and incorporate the power grid intensity assessment index as a constraint into the multi-objective optimization model to perform power output scheduling optimization calculation; The model for calculating active power optimization adjustment includes: ； In the formula, In order to cooperate with offshore wind farms i Active power optimization adjustment amount To optimize the first n Wind farm in each cycle i The power grid strength evaluated using the short-circuit ratio (MESCR) as the evaluation index. K MESCRmin To set the grid strength early warning extreme value for the risk of voltage oscillation instability in offshore wind power clusters, γ is an adjustment factor. To improve the active power regulation capability of offshore wind farms; S60: Based on the solution results of the multi-objective optimization model, auxiliary decision-making information is generated to guide the power output scheduling of each wind farm, thereby improving the operational stability of the power grid and reducing the risk of oscillation.

2. The offshore wind power grid connection auxiliary decision-making method considering grid strength coupling constraints according to claim 1, characterized in that, In step S20, the grid mutual impedance matrix of the offshore wind power cluster and the three-phase short-circuit capacity of each wind farm's grid connection point are calculated, including: The grid mutual impedance matrix Z is obtained by inverting the node admittance matrix Y. The node admittance matrix Y is an N×N sparse matrix. If there is a grounding branch in the offshore wind power cluster, the admittance matrix Y is a non-singular matrix. The inverse of the admittance matrix Y is the mutual impedance matrix. Z = Y -1 ; The diagonal elements of the node admittance matrix Y ii Self-admittance is numerically equal to the current injected into the network through node i when a unit voltage is applied to node i and all other nodes are grounded; off-diagonal elements Y ji Mutual admittance represents the current injected into the network through node j when a unit voltage is applied to node i and other nodes are grounded, and the value of the off-diagonal element is the negative of the branch admittance connecting nodes i and j.

3. The method of claim 1, wherein the method further comprises: In step S20, the grid mutual impedance matrix of the offshore wind power cluster and the three-phase short-circuit capacity of each wind farm's grid connection point are calculated, including: The three-phase short-circuit capacity of each wind farm's grid connection point is calculated using the following formula: ； In the formula, S ki The three-phase short-circuit capacity of each wind farm's grid connection point, I ki For wind farm i Maximum short-circuit current when grid connection point is short-circuited U Bi For wind farm i Rated voltage at grid connection point Z ii For wind farm i The self-impedance of the node where the grid connection point is located.

4. The method of claim 1, wherein, In step S20, power grid strength assessment indicators are generated based on the calculation results, including: The short-circuit ratio (MESCR) index for multiple renewable energy power plants is generated and used as the grid strength assessment index. ； In the formula, The MESCR (Medium-Short Circuit Ratio) index is used for multiple power stations in the new energy sector. For offshore wind farms i Apparent power For offshore wind farms j and offshore wind farms i The complex interaction coefficient between them S ki This refers to the three-phase short-circuit capacity of each wind farm's grid connection point.

5. The offshore wind power grid connection auxiliary decision-making method considering grid strength coupling constraints according to claim 1, characterized in that, In step S30, the offshore wind power cluster is analyzed using the aforementioned grid strength assessment index to identify weak node wind farms with grid strength below a preset threshold. These node wind farms are then ranked according to their grid strength to obtain the ranking results, including: The warning extreme value for power grid strength is set to K MESCRmin Offshore wind farms with grid strength below the warning extreme value were selected to form a set of wind farms with weak grid strength. S E The model includes: ； In the formula, S E A collection of wind farms with weak grid strength. S i For set S E The Middle i Each station The MESCR (Medium-Short Circuit Ratio) index is used for multiple new energy power plants. K MESCRmin To set early warning extreme values ​​for power grid strength.

6. The method of claim 1, wherein, In step S40, based on the ranking results and the interaction factors between wind farms with weak nodes, other wind farms with strong coupling relationships with the weak nodes are selected to form a set of wind farms to be optimized, including: The ensemble model of the wind farms to be optimized is represented as follows: ； In the formula, For wind farm aggregation to be optimized, In order to be with the first i Stations A collection of wind farms with a strong coupling relationship to the grid intensity. For offshore wind farms j and offshore wind farms i The complex interaction coefficient between them For the set of i Each station Traverse the set of strongly coupled weak power grid strength stations.

7. The method of claim 1, wherein the method further comprises: Also includes: After step S40, based on the set of wind farms to be optimized, it is determined whether to trigger optimization calculation, including: If the set of wind farms to be optimized is an empty set, it is determined that no optimization calculation needs to be triggered, and the next calculation cycle will begin. If the set of wind farms to be optimized is not empty, proceed to step S50 and perform power output scheduling optimization calculation.

8. An offshore wind power grid connection auxiliary decision system considering grid strength coupling constraints, characterized in that, Using the method as described in any one of claims 1 to 7, comprising: The data acquisition module is used to acquire real-time status estimation data of offshore wind power clusters. The data includes the active power output, reactive power output and grid topology information of each wind farm. The state estimation and calculation module is used to calculate the grid mutual impedance matrix of the offshore wind power cluster and the three-phase short-circuit capacity of each wind farm grid connection point based on the real-time state estimation data, and generate grid strength assessment indicators based on the calculation results. The analysis and identification module is used to analyze the offshore wind power cluster using the power grid strength assessment index, identify weak node wind farms with power grid strength below a preset threshold, and sort these node wind farms according to power grid strength to obtain the sorting results. The screening and optimization module is used to screen out other wind farms that have a strong coupling relationship with the weak nodes based on the ranking results and the interaction influence factors between the wind farms with weak nodes, forming a set of wind farms to be optimized. The multi-objective optimization module is used to establish a multi-objective optimization model based on the set of wind farms to be optimized, and to incorporate the power grid intensity assessment index as a constraint into the multi-objective optimization model to perform power output scheduling optimization calculations. The decision generation module is used to generate auxiliary decision information based on the solution results of the multi-objective optimization model, which is used to guide the power output scheduling of each wind farm, thereby improving the operational stability of the power grid and reducing the risk of oscillation.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-7.

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

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