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

Abstract

The invention relates to the technical field of new energy power systems, in particular to an offshore wind power integration aid decision-making method, system and device considering power grid strength coupling constraints and a medium. Calculating a power grid mutual impedance matrix of the offshore wind power cluster and the three-phase short-circuit capacity of each wind power plant grid-connected point, and generating a power grid strength evaluation index according to a calculation result; sorting the node wind power plants according to the power grid strength, and screening out other wind power plants having a strong coupling relationship with weak nodes based on a sorting result and an interaction influence factor between the weak node wind power plants to form a to-be-optimized wind power plant set; establishing a multi-objective optimization model, taking the power grid strength evaluation index as a constraint condition into the multi-objective optimization model, and executing output scheduling optimization calculation; the method can provide technical support for weak node power grid strength improvement and regulation and control decision after large-scale offshore wind power is connected into the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy power systems, and in particular to an offshore wind power grid connection auxiliary decision-making method, system, equipment and medium taking into account grid strength coupling constraints. Background Art

[0002] Offshore wind power is usually a centralized access to the grid after multiple wind farms are converged through AC or flexible DC, forming a wind power grid-connected system that includes wind power, SVG multi-type converters, and dynamic components such as AC and DC cables. In large-scale offshore wind power development areas, multiple AC and DC wind power grid-connected systems are connected to the grid through different access points with similar electrical distances. Especially during periods when wind power generation capacity is strong and conventional units are running at a low capacity, the support capacity of the grid itself is relatively weak, which significantly reduces the strength of the main grid and increases the risk of oscillation instability of offshore wind power. The grid strength index is not only related to the short-circuit capacity of the grid, but also affected by multiple factors such as the output level of the offshore wind power new energy cluster, the operating status of the reactive compensation equipment, and the impedance angle. Therefore, adding the grid strength index constraint as an inequality constraint to the operation optimization model of the offshore wind power new energy cluster can help reduce the risk of stable operation of the grid, improve the stability margin of the grid, and prevent oscillation accidents that may occur when large-scale offshore wind power is connected.

[0003] However, there are many problems with the current offshore wind power grid-connected auxiliary decision-making methods. Traditional offshore wind power cluster operation optimization solutions mainly focus on how to improve the cluster's absorption capacity under the constraints of grid operation, and rarely conduct systematic 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 optimization models of grid operation modes, and are unable to form a comprehensive decision-making system with multiple constraint objectives. This limitation not only reduces the safety of offshore wind power access, but also restricts the comprehensive evaluation and optimization of the grid operation status. It is urgent to deepen the research and application of related technologies.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0005] The present invention provides an offshore wind power grid connection auxiliary decision method, system, device and medium taking into account grid strength coupling constraints, thereby effectively solving the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: an offshore wind power grid connection auxiliary decision method considering grid strength coupling constraints, comprising the following steps:

[0007] S10: Acquire real-time state estimation data of the offshore wind power cluster, wherein the data includes active output, reactive output and grid topology information of each wind farm;

[0008] S20: Based on the real-time state estimation data, 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 are calculated, and a grid strength assessment index is generated according to the calculation results;

[0009] S30: Analyze the offshore wind power cluster using the grid strength assessment index, identify weak node wind farms whose grid strength is lower than a preset threshold, and sort these node wind farms according to the grid strength to obtain a sorting result;

[0010] S40: Based on the ranking results and the interaction factors between the weak node wind farms, other wind farms having a strong coupling relationship with the weak nodes are screened out to form a set of wind farms to be optimized;

[0011] S50: establishing a multi-objective optimization model based on the set of wind farms to be optimized, incorporating the grid strength assessment index as a constraint into the multi-objective optimization model, and performing output dispatch optimization calculation;

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

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

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

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

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

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

[0018]

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

[0020] Further, in step S20, a power grid strength evaluation index is generated according to the calculation result, and the power grid strength evaluation index is a new energy multi-station short circuit ratio MESCR index, and the new energy multi-station short circuit ratio MESCR index includes:

[0021] Generate the new energy multi-station short circuit ratio MESCR index as the grid strength assessment index:

[0022]

[0023] In the formula, K MESCR,i It is the MESCR index of the short-circuit ratio of new energy multiple stations. is the apparent power of offshore wind farm i, is the complex interaction coefficient between offshore wind farm j and offshore wind farm i, S ki It is the three-phase short-circuit capacity of each wind farm grid connection point.

[0024] Further, in step S30, the grid strength assessment index is used to analyze the offshore wind power cluster, identify weak node wind farms whose grid strength is lower than a preset threshold, and sort these node wind farms according to the grid strength to obtain sorting results, including:

[0025] Set the warning extreme value of power grid strength to K MESCRmin , screen out offshore wind farms whose grid strength is lower than the warning extreme value, and form a set of wind farms with weak grid strength S E , the model includes:

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

[0027] In the formula, S E is the wind farm set with weak grid strength, Si For the set S E The i-th station, K MESCR,i K is the MESCR index of the new energy multi-station short-circuit ratio, MESCRmin To set the warning extreme value of power grid strength.

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

[0029] The wind farm ensemble model to be optimized is expressed as:

[0030]

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

[0032] Furthermore, it also includes:

[0033] After step S40, judging whether to trigger optimization calculation according to the set of wind farms to be optimized includes:

[0034] If the set of wind farms to be optimized is an empty set, it is determined that there is no need to trigger the optimization calculation, and the next calculation cycle is entered;

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

[0036] Furthermore, in step S50, a multi-objective optimization model is established based on the set of wind farms to be optimized, and the grid strength assessment index is incorporated into the multi-objective optimization model as a constraint condition to perform output scheduling optimization calculation, including:

[0037] Calculate the active power optimization regulation quantity, the model includes:

[0038]

[0039] Where ΔP i To optimize the active power regulation of offshore wind farms, To optimize the grid strength of wind farm i in round n with short-circuit ratio MESCR as the evaluation index, KMESCRmin To set the grid strength warning extreme value for the risk of voltage oscillation instability in offshore wind power clusters, γ is the adjustment factor, P c，i To have active power regulation capability with offshore wind farms.

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

[0041] A data acquisition module is used to acquire real-time state estimation data of the offshore wind power cluster, wherein the data includes active output, reactive output and grid topology information of each wind farm;

[0042] A state estimation and calculation module, 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 a grid strength assessment index according to the calculation results;

[0043] An analysis and identification module is used to analyze the offshore wind power cluster using the grid strength assessment index, identify weak node wind farms whose grid strength is lower than a preset threshold, and sort these node wind farms according to the grid strength to obtain a sorting result;

[0044] A screening and optimization module, for screening out other wind farms having a strong coupling relationship with the weak nodes based on the sorting results and the interaction influence factors between the weak node wind farms, to form a set of wind farms to be optimized;

[0045] A multi-objective optimization module, 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 condition into the multi-objective optimization model to perform output scheduling optimization calculation;

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

[0047] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0048] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0049] The beneficial effects of the present invention are:

[0050] It can effectively solve the practical engineering problem of how to quickly identify the output characteristics under large-scale offshore wind power grid connection and accurately make auxiliary decisions on offshore wind power dispatching and operation based on the grid strength index. It comprehensively considers the impact of the operating status of each wind power generation unit and SVG reactive compensation key power generation and transmission equipment in the offshore wind power cluster on the grid strength, establishes a rapid optimization solution model, and online forms an optimized set of operating working points of adjustable equipment such as wind power generation and SVG reactive compensation, provides auxiliary decision-making suggestions for improving the grid strength of weak nodes when offshore wind farms are connected to the grid, and improves the grid voltage stability margin and wind power consumption level; in addition, this method can be easily implemented and applied in engineering based on the dispatching automation system platform, and has a certain dispatching production guidance value. The feasibility of the method has been verified through practice. The method can provide technical support for improving the grid strength of weak nodes and regulating decisions after large-scale offshore wind power is connected to the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

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

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

[0054] Figure 3 Construct a flow chart for the set of stations to be optimized;

[0055] Figure 4 A classification diagram of offshore wind farm output operation scenarios;

[0056] Figure 5 The schematic diagram of the structure of the offshore wind power grid connection auxiliary decision system considering the grid strength coupling constraint;

[0057] Figure 6 A schematic diagram of the structure of a computer device. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0059] like Figures 1 to 4 As shown: An offshore wind power grid connection auxiliary decision method considering grid strength coupling constraints includes the following steps:

[0060] S10: obtaining real-time state estimation data of the offshore wind power cluster, including active output, reactive output and grid topology information of each wind farm;

[0061] S20: Based on the real-time state estimation data, 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 are calculated, and a grid strength assessment index is generated according to the calculation results;

[0062] S30: Analyze the offshore wind power cluster using the grid strength assessment index, identify weak node wind farms whose grid strength is lower than a preset threshold, and sort these node wind farms according to the grid strength to obtain a sorting result;

[0063] S40: Based on the sorting results and the interaction factors between the weak node wind farms, other wind farms with strong coupling relationships with the weak nodes are screened out 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 grid strength assessment index is incorporated into the multi-objective optimization model as a constraint condition to perform output dispatch optimization calculation;

[0065] S60: Generate auxiliary decision information based on the solution results of the multi-objective optimization model to guide the output dispatch of each wind farm, thereby improving the operational stability of the power grid and reducing the risk of oscillation.

[0066] It can effectively solve the practical engineering problem of how to quickly identify the output characteristics under large-scale offshore wind power grid connection and accurately make auxiliary decisions on offshore wind power dispatching and operation based on the grid strength index. It comprehensively considers the impact of the operating status of each wind power generation unit and SVG reactive compensation key power generation and transmission equipment in the offshore wind power cluster on the grid strength, establishes a rapid optimization solution model, and online forms an optimized set of operating working points for adjustable equipment such as wind power generation and SVG reactive compensation, provides auxiliary decision-making suggestions for improving the grid strength of weak nodes when offshore wind farms are connected to the grid, and improves the grid voltage stability margin and wind power consumption level; in addition, this method can be easily implemented and applied in engineering based on the dispatching automation system platform, and has a certain dispatching production guidance value. The feasibility of the method has been verified through practice. This method can provide technical support for improving the grid strength of weak nodes and regulating decisions after large-scale offshore wind power is connected to the grid.

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

[0068] By analyzing wind farms at nodes with weaker grid strength and sorting and prioritizing them, we ensure that weak nodes are optimized in a timely manner, which helps maintain the operational stability of the grid when large-scale wind power is connected.

[0069] By calculating the interaction factors between weak nodes and screening wind farms with strong coupling relationships with weak nodes, precise optimization scheduling is achieved. This method can prevent the risk of local instability from increasing due to the neglect of the coupling relationship 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, provide real-time scheduling optimization solutions, and ensure the effectiveness of scheduling decisions.

[0070] The grid strength and wind power output are comprehensively optimized as multiple objectives to avoid focusing only on output improvement while ignoring the limitations of grid safety. Multi-objective optimization enables the system to both improve wind power absorption capacity and maintain grid stability. Through multiple rounds of optimization calculations and decision outputs, this method can gradually improve the weak nodes of the grid and reduce the risk of oscillation accidents that may occur after large-scale offshore wind power access.

[0071] S20: Based on the real-time state estimation data, 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 are calculated, and a grid strength assessment index is generated according to the calculation results;

[0072] In order to improve the grid-connected voltage performance of offshore wind power clusters and enhance the strength of the weak power grid system of offshore wind power clusters, the common reactive power compensation methods currently include installing SVC, SVG, and using the reactive power generated by the wind turbines themselves for reactive power compensation. Each reactive power compensation method has different mechanisms for improving the strength of the system power grid. Among them, the investment of SVC mainly affects the short-circuit capacity of each node of the power grid by changing the admittance matrix of the power grid. SVC is regarded as the admittance connected in parallel with the busbar and included in the admittance matrix, while the reactive power of SVG and the wind farm itself mainly increases the reactive power of the system node power supply. The access of various reactive power compensation methods will affect the short-circuit ratio calculation of each grid-connected point of the offshore wind power cluster, thereby affecting the calculation of the output limit of the wind farm at each grid-connected point, and further affecting the absorption capacity of each new energy station.

[0073] Therefore, in order to carry out refined online calculation of offshore wind power cluster power grid strength based on the MESCR index of new energy multi-station short-circuit ratio, it is necessary to monitor the operation of different reactive sources such as SVC, SVG, and wind turbines online, analyze the system structure (lines, transformers, etc.) according to the state estimation data, and establish a system admittance matrix, in which the conventional units of the power grid take the second transient reactance into the admittance matrix, and ignore the impedance of the inverter interface of the wind turbine generator set in the offshore wind farm. Then, the admittance matrix is ​​inverted to obtain the system impedance matrix. According to the principle of symmetrical short-circuit calculation, the inverse of the self-impedance of the system node is the per-unit value of the node short-circuit current, thereby calculating the short-circuit capacity of each node in the system, and then measuring the system strength of each grid connection point. Specifically:

[0074] First, the real-time state estimation data of the offshore wind power cluster at time t is obtained. The state estimation data can generally be obtained through the dispatching automation system. The data file includes information such as the active output, reactive output, key equipment parameters, and network topology connection relationship of each power source. Based on the state estimation data, the grid mutual impedance matrix Z reflecting the current operating state of the offshore wind power cluster and the three-phase short-circuit capacity S of each wind farm grid connection point can be generated online. ki .

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

[0076] The grid mutual impedance matrix Z is obtained by inverting the node admittance matrix Y, where 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, and the inverse matrix of the admittance matrix Y is the mutual impedance matrix Z=Y -1 ;

[0077] The diagonal element Y of the node 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 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) is the mutual admittance, which means the current injected into the network through node j when unit voltage is applied to node i and other nodes are grounded, and the value of the non-diagonal element is the negative value of the branch admittance connecting nodes i and j.

[0079] According to the characteristics of offshore wind power clusters, a scheme using mutual impedance matrix and node admittance matrix is ​​proposed. This method adapts to the complex situation of multiple wind farms interconnected and improves the response and management capabilities of the power system to offshore wind power access.

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

[0081]

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

[0083] By calculating the three-phase short-circuit capacity S of each wind farm grid connection point ki , can accurately evaluate the system's carrying capacity in the event of a short circuit, provide the necessary technical basis for the design and operation of the power grid, and help prevent system overload and failure.

[0084] Then, the new energy multi-station short-circuit ratio MESCR indicator proposed in the State Grid Enterprise Standard "New Energy Multi-Station Short-Circuit Ratio Calculation Specification Q / GDW12290-2023" is used as the basis for judging the strength of the power grid. Compared with the traditional DC transmission multi-feed short-circuit ratio indicator, this indicator can consider the voltage interaction between different new energy stations, and can fully consider the impact of SVG output and submarine cable charging power on the stability of new energy units. It can more accurately reflect the stable operation risks of offshore wind power clusters when they are concentratedly connected to the power grid.

[0085] As a preference of the above embodiment, in step S20, generating a power grid strength assessment index according to the calculation result includes:

[0086] The grid strength assessment index is the MESCR index of new energy multi-station short-circuit ratio, which includes:

[0087]

[0088] In the formula, K MESCR,i It is the MESCR index of the short-circuit ratio of new energy multiple stations. is the apparent power of offshore wind farm i, is the complex interaction coefficient between offshore wind farm j and offshore wind farm i, S ki It is the three-phase short-circuit capacity of each wind farm grid connection point.

[0089] The MESCR indicator 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 station, this multi-station 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, is the mutual impedance between offshore wind farm j and offshore wind farm i; is the self-impedance of offshore wind farm i; and are the voltages of offshore wind farm j and offshore wind farm i respectively.

[0093] When the electrical distance between offshore wind farms is close and the phase angle difference is small, r ji The voltage phase angle difference can be ignored in the calculation, that is:

[0094]

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

[0096] S30: Analyze the offshore wind power cluster using the grid strength assessment index, identify weak node wind farms whose grid strength is lower than a preset threshold, and sort these node wind farms according to the grid strength to obtain a sorting result;

[0097] The mutual impedance of an offshore wind farm cluster reflects the relationship between the current fluctuation of wind farm j and the node voltage fluctuation of wind farm i caused by it, and is suitable as a basis for measuring the distance of electrical power. The control systems of actual wind power generation units have a range of voltage and current measurement accuracy, and the wind farm cannot perceive the node voltage disturbance within the measurement accuracy. According to research, the voltage and current measurement accuracy of wind turbines is generally 0.5% to 1%. Therefore, when the mutual impedance Z in an offshore wind farm cluster is ji When it is less than 0.5%, when the power of wind farm j changes, wind farm i cannot sense the voltage fluctuation and the control system will not act, 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, and the wind farms are further sorted by the interactive influence factor, and the wind farms with larger influence weights are adjusted first. Specifically:

[0098] First, based on the grid strength of each wind farm connection point in the offshore wind farm cluster obtained in step S20, the offshore wind farms are sorted from weak to strong; the grid strength warning extreme value for the risk of voltage oscillation instability in the offshore wind farm cluster is set to K MESCRmin (Generally K MESCRmin The typical value is 1.5). The warning extreme value is used as the screening condition to screen the offshore wind farms whose grid strength is less than the warning minimum value, forming a set of offshore wind farms with weak grid strength S E ;

[0099] In this embodiment, in step S30, the offshore wind power cluster is analyzed using the grid strength assessment index to identify weak node wind farms whose grid strength is lower than a preset threshold, and these node wind farms are sorted according to the grid strength to obtain a sorting result, including:

[0100] Set the warning extreme value of power grid strength to K MESCRmin (Generally K MESCRmin The typical value is 1.5), and the offshore wind farms with grid strength lower than the warning extreme value are screened out to form a set of wind farms with weak grid strength S E , the model includes:

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

[0102] In the formula, S E is the wind farm set with weak grid strength, S i For the set S E The i-th station, K MESCR,i K is the MESCR index of the new energy multi-station short-circuit ratio, MESCRmin To set the warning extreme value of power grid strength.

[0103] By identifying weak node wind farms where the grid strength is below the preset threshold, the potential vulnerable links in the grid can be grasped in a timely manner, thereby providing data support for subsequent scheduling and optimization and improving the stability of the grid.

[0104] S40: Based on the sorting results and the interaction factors between the weak node wind farms, other wind farms with strong coupling relationships with the weak nodes are screened out to form a set of wind farms to be optimized;

[0105] Then, the interaction factor is used as the criterion for the degree of grid strength coupling between two wind farms. The smaller the interaction factor, the lower the coupling degree. The set S of offshore wind farms with weak grid strength is traversed. E Each station in the set S E The i-th station S iWith the jth station S j Interaction Factors and threshold r min (Usually take r min The typical value is 0.005) as the screening condition, and the screening is formed with the i-th site S i The wind farm set S with strong coupling relationship between power grid strength R，i Each element in the set is composed of the station number and the interaction factor value. All the strongly coupled weak grid strength station sets are further processed into a union to form S R , models, including:

[0106]

[0107] S R ={S R,i |S i ∈S E};

[0108] In the formula, S R，i is the i-th station S i A collection of wind farms with strong coupling relationship between grid strength, is the jth offshore wind farm that has a strong coupling relationship with offshore wind farm i, is the complex interaction coefficient between offshore wind farm j and offshore wind farm i.

[0109] Finally, traverse the strongly coupled weak grid strength station set S R For each station in, for the i-th station S i , all interactive factors associated with the station The larger the accumulated value is, the more it reflects the influence weight of the station in the entire wind power cluster.

[0110] As a preference of the above embodiment, in step S40, based on the sorting results and the interaction factors between the weak node wind farms, other wind farms having a strong coupling relationship with the weak node are screened out to form a set of wind farms to be optimized, including:

[0111] The wind farm ensemble model to be optimized is expressed as:

[0112]

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

[0114] By identifying wind farms that have a strong coupling relationship with the grid strength of weak nodes, the allocation and use of resources can be optimized more effectively, ensuring that when the grid load is high, those wind farms that have a greater impact on grid stability can be dispatched first.

[0115] In this embodiment, it also includes:

[0116] After step S40, according to the set of wind farms to be optimized S λ , determine whether to trigger optimization calculation, including:

[0117] If the wind farm set S to be optimized λ If it is an empty set, it is determined that there is no need to trigger the optimization calculation and enter the next calculation cycle;

[0118] If the wind farm set S to be optimized λ If it is not empty, the process goes 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 , a method for constructing a set of wind farms to be optimized, comprising the steps of:

[0121] (1) The stations are sorted according to the grid strength of each grid connection point of the offshore wind power cluster, and the stations with grid strength below the warning extreme value K are screened out. MESCRmin stations, forming a station set S with weak grid strength E ;

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

[0123] (3) Based on the set S E The interaction factor r of the i-th station ji , extract the station set S that is strongly coupled with its power grid R，i ;

[0124] (4) Determine the set S E Whether the station satisfies the traversal, if so, proceed to the next step; if not, continue traversing;

[0125] (5) Form a set of sites to be optimized S with the cumulative value of the impact factor 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 grid strength assessment index is incorporated into the multi-objective optimization model as a constraint condition to perform output dispatch optimization calculation;

[0127] The offshore wind power cluster optimization of the present invention refers to the situation where the installation positions of various reactive compensation devices are known, by adjusting the output of SVG and wind turbines, to meet multiple constraints such as qualified system voltage deviation and grid strength being higher than the warning value. The operating states of different reactive compensation devices will affect the grid strength of the offshore wind power cluster. By adjusting the output of reactive compensation devices, the system operating short-circuit ratio can be improved to a certain extent, and the grid strength can be improved. Considering the regulating effect of reactive compensation devices on system voltage and grid strength, a collection of offshore wind power stations to be optimized that takes into account grid strength requirements and voltage fluctuations is formed. Specifically:

[0128] First, the optimization mode of each wind power station to be optimized is determined based on the operation scenario, and the current active output value P of the wind farm is 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 scene Z2 (between 40%P N With 60%P N between) or high output scenario Z3 (greater than 60% P N ), according to the current grid connection point voltage U of the wind farm pcc It is determined that the operating condition of the grid access is the 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 operation 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 deviation voltage operating condition V3, the reactive power priority mode is adopted, because only when the grid node voltage is positively offset (that is, the voltage at the wind farm grid connection point is higher than 1pu), the short circuit ratio K of the wind farm grid connection point is MESCR The reactive power required in the regulation process can match the reactive power direction required in the reactive-voltage regulation process. At this time, the reactive power of the reactive source SVG and the wind turbine itself in the wind farm can be reduced first, and the AVC system of the station can be controlled to reduce the voltage at the wind farm grid connection point to the normal voltage operating condition V2, while also taking into account the improvement of the grid strength. In other scenarios and working conditions, the active power priority mode can be adopted.

[0130] Then, the available active regulation capacity of each wind farm to be optimized is calculated, and the optimization round of each wind farm is determined. Figure 4 As shown, for the offshore wind farm station set S to be optimized λ The wind farm S of the i-th wind farm is obtained through the wind farm energy management system. i The active output P of the current operating point w g，i , calculate the active power regulation capability as the real-time output P of the current operating point w g，i Minimum distance technical output P min，i The difference between the minimum technical output P min，i The typical value is usually 10%P N .

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

[0132] Where P c，i To adjust the active power of offshore wind farms, P g，i is the current active power output of wind farm i, P min，i Provides the minimum technical output for wind farm i.

[0133] The offshore wind farm station set S to be optimized obtained in step S40 λ , which contains the accumulated value of the station number and the interaction factor for each element, and sorts them from large to small according to the accumulated value to form the optimization order of the wind farm. Assuming that the total number of optimization rounds is N (usually N is 5), the wind farm station set S λ Divide into N groups according to the optimization order, and the regulation amount of the ith station in the nth round, where γ is the regulation factor (generally 0.5). It can be seen that the stronger the active regulation ability of the station and the weaker the grid strength, the greater the optimization regulation amount; the greater the cumulative value of the station's interactive influence factor, the higher the priority of participating in the regulation and the greater the regulatory effect.

[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 grid strength assessment index is incorporated into the multi-objective optimization model as a constraint condition to perform output scheduling optimization calculation, including:

[0135] Calculate the active power optimization regulation quantity, the model includes:

[0136]

[0137] Where ΔP i To optimize the active power regulation of offshore wind farms, To optimize the grid strength of wind farm i in round n with short-circuit ratio MESCR as the evaluation index, KMESCRmin To set the grid strength warning extreme value for the risk of voltage oscillation instability in offshore wind power clusters, γ is the adjustment factor (usually 0.5), P c，i To have active power regulation capability with offshore wind farms.

[0138] Incorporating grid strength assessment indicators as constraints into the optimization model ensures that the grid always operates safely during the optimization process, helps prevent oscillation and instability risks caused by insufficient grid strength, and improves the overall safety of the grid. When calculating the active optimization regulation, the regulation capacity of the wind farm can be effectively evaluated and utilized to ensure that the resources of each wind farm in the grid are reasonably 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 optimization adjustment amount of each station is used as an auxiliary decision to guide the wind farm output arrangement of the offshore wind power cluster, thereby improving the grid strength of weak nodes when the offshore wind farm is connected to the power grid, and improving the grid voltage stability margin and wind power consumption level.

[0140] The present invention also includes an offshore wind power grid connection auxiliary decision system considering grid strength coupling constraints, using the above method, such as Figure 5 As shown, including:

[0141] The data acquisition module is used to obtain the real-time state estimation data of the offshore wind power cluster, including the active output, reactive 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 the offshore wind power cluster 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] An analysis and identification module is used to analyze the offshore wind power cluster using the grid strength assessment index, identify weak node wind farms whose grid strength is lower than a preset threshold, and sort these node wind farms according to the grid strength to obtain a sorting result;

[0144] The screening and optimization module is used to screen out other wind farms with strong coupling relationships with weak nodes based on the sorting results and the interaction factors between weak node wind farms to form 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 grid strength assessment index as a constraint into the multi-objective optimization model to perform output dispatch optimization calculations;

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

[0147] See also Figure 6 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0148] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0149] Among them, 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, disk or optical disk.

[0150] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0151] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0152] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations 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 one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0153] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

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

[0155] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0156] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0157] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An offshore wind power grid connection auxiliary decision-making method considering grid strength coupling constraints, characterized in that: The steps include: S10: Acquire real-time state estimation data of the offshore wind power cluster, wherein the data includes active output, reactive output and grid topology information of each wind farm; S20: Based on the real-time state estimation data, 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 are calculated, and a grid strength assessment index is generated according to the calculation results; S30: Analyze the offshore wind power cluster using the grid strength assessment index, identify weak node wind farms whose grid strength is lower than a preset threshold, and sort these node wind farms according to the grid strength to obtain a sorting result; S40: Based on the ranking results and the interaction factors between the weak node wind farms, other wind farms having a strong coupling relationship with the weak nodes are screened out to form a set of wind farms to be optimized; S50: establishing a multi-objective optimization model based on the set of wind farms to be optimized, incorporating the grid strength assessment index as a constraint into the multi-objective optimization model, and performing output dispatch optimization calculation; S60: Generate auxiliary decision information based on the solution results of the multi-objective optimization model to guide the output dispatch 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 is characterized in that: In step S20, the mutual impedance matrix of the power grid of the offshore wind power cluster and the three-phase short-circuit capacity of each wind farm grid connection point are calculated, including: The grid mutual impedance matrix Z is obtained by inverting the node admittance matrix Y, where the node admittance matrix Y is an N×N order sparse matrix. If there is a grounding branch in the offshore wind power cluster, the admittance matrix Y is a non-singular matrix, and the inverse matrix of the admittance matrix Y is the mutual impedance matrix Z=Y -1 ; The diagonal elements Y of the node admittance matrix ii is the self-admittance, which is numerically equal to the current injected into the network through node i when unit voltage is applied to node i and all other nodes are grounded; Off-diagonal element Y ji is the mutual admittance, which represents the current injected into the network through node j when unit voltage is applied to node i and other nodes are grounded, and the value of the off-diagonal element is the negative value of the branch admittance connecting nodes i and j.

3. The offshore wind power grid connection auxiliary decision-making method considering grid strength coupling constraints according to claim 1 is characterized in that: In step S20, the mutual impedance matrix of the power grid of the offshore wind power cluster and the three-phase short-circuit capacity of each wind farm grid connection point are calculated, including: The three-phase short-circuit capacity of each wind farm grid connection point is calculated by the following formula: In the formula, S ki is the three-phase short-circuit capacity of each wind farm grid connection point, I ki is the maximum short-circuit current when the grid connection point of wind farm i is short-circuited, U Bi is the rated voltage of the grid-connected point of wind farm i, Z ii is the self-impedance of the node where the grid connection point of wind farm i is located.

4. The offshore wind power grid connection auxiliary decision-making method considering grid strength coupling constraints according to claim 1 is characterized in that: In step S20, a power grid strength assessment index is generated according to the calculation result, including: Generate the new energy multi-station short circuit ratio MESCR index as the grid strength assessment index: In the formula, K MESCR,i It is the MESCR index of the short-circuit ratio of new energy multiple stations. is the apparent power of offshore wind farm i, is the complex interaction coefficient between offshore wind farm j and offshore wind farm i, S ki It is the three-phase short-circuit capacity of each wind farm grid connection point.

5. The offshore wind power grid connection auxiliary decision-making method considering grid strength coupling constraints according to claim 1 is characterized in that: In step S30, the grid strength assessment index is used to analyze the offshore wind power cluster, identify weak node wind farms whose grid strength is lower than a preset threshold, and sort these node wind farms according to the grid strength to obtain sorting results, including: Set the warning extreme value of power grid strength to K MESCRmin , screen out offshore wind farms whose grid strength is lower than the warning extreme value, and form a set of wind farms with weak grid strength S E , the model includes: S E ={S i |K MESCR,i <K MESCRmin }; In the formula, S E is the wind farm set with weak grid strength, S i For the set S E The i-th station, K MESCR,i K is the MESCR index of the new energy multi-station short-circuit ratio, MESCRmin To set the warning extreme value of power grid strength.

6. The offshore wind power grid connection auxiliary decision-making method considering grid strength coupling constraints according to claim 1 is characterized in that: In step S40, based on the ranking results and the interaction factors between the weak node wind farms, 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: The wind farm ensemble model to be optimized is expressed as: In the formula, S λ is the set of wind farms to be optimized, S R，i is the i-th station S i A collection of wind farms with strong coupling relationship between grid strength, is the complex interaction coefficient between offshore wind farm j and offshore wind farm i, S i is the i-th station in the set, S R Traverse the set of strongly coupled weak grid strength stations.

7. The offshore wind power grid connection auxiliary decision-making method considering grid strength coupling constraints according to claim 1 is characterized in that: Also includes: After step S40, judging whether to trigger optimization calculation according to the set of wind farms to be optimized includes: If the set of wind farms to be optimized is an empty set, it is determined that there is no need to trigger the optimization calculation, and the next calculation cycle is entered; If the set of wind farms to be optimized is not empty, the process proceeds to step S50 to perform output scheduling optimization calculation.

8. The offshore wind power grid connection auxiliary decision-making method considering grid strength coupling constraints according to claim 1 is characterized in that: In step S50, a multi-objective optimization model is established based on the set of wind farms to be optimized, and the grid strength assessment index is incorporated into the multi-objective optimization model as a constraint condition to perform output scheduling optimization calculation, including: Calculate the active power optimization regulation quantity, the model includes: Where ΔP i To optimize the active power regulation of offshore wind farms, To optimize the grid strength of wind farm i in round n with short-circuit ratio MESCR as the evaluation index, K MESCRmin To set the grid strength warning extreme value for the risk of voltage oscillation instability in offshore wind power clusters, γ is the adjustment factor, P c，i To have active power regulation capability with offshore wind farms.

9. An offshore wind power grid connection auxiliary decision system considering grid strength coupling constraints, characterized in that: Use of the method according to any one of claims 1 to 8, comprising: A data acquisition module is used to acquire real-time state estimation data of the offshore wind power cluster, wherein the data includes active output, reactive output and grid topology information of each wind farm; A state estimation and calculation module, 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 a grid strength assessment index according to the calculation results; An analysis and identification module is used to analyze the offshore wind power cluster using the grid strength assessment index, identify weak node wind farms whose grid strength is lower than a preset threshold, and sort these node wind farms according to the grid strength to obtain a sorting result; A screening and optimization module, for screening out other wind farms having a strong coupling relationship with the weak nodes based on the sorting results and the interaction influence factors between the weak node wind farms, to form a set of wind farms to be optimized; A multi-objective optimization module, 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 condition into the multi-objective optimization model to perform output scheduling optimization calculation; The decision generation module is used to generate auxiliary decision information through the solution results of the multi-objective optimization model, so as to guide the output scheduling of each wind farm, thereby improving the operation stability of the power grid and reducing the risk of oscillation.

10. 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, the method according to any one of claims 1 to 8 is implemented.

11. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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