New energy base phase modifier contribution allocation method, device, equipment and medium

Abstract

The invention discloses a new energy base phase modifier contribution allocation method, device and equipment and a medium, and provides a rapid estimation method combining layered sampling, Neyman optimal allocation, sensitivity correction and a confidence interval drive stop mechanism on the premise of keeping Shapley value fairness and uniqueness. The calculation time is shortened, and the calculation complexity is reduced, so that unnecessary sample overhead is avoided while the precision is ensured; meanwhile, under the condition of considering engineering constraint conditions such as the short-circuit ratio and transient overvoltage of the new energy multi-station, the marginal contribution of the multiple phase modifiers in the new energy base generation increasing capacity can be efficiently estimated, and the obtained benefit function reflects the actual power generation capacity and stability of the power system, so that the accuracy and authenticity of the Shapley value are ensured.

Description

Technical Field

[0001] This application relates to the field of power system operation and optimized dispatching technology, and in particular, to a method, apparatus, equipment and medium for allocating the contribution of synchronous condensers in new energy bases. Background Technology

[0002] Under current environmental protection requirements, the installed capacity of new energy sources is rapidly increasing, while thermal power units are gradually being retired or converted into regulating power sources. This has led to a decrease in grid inertia and short-circuit capacity, posing challenges to voltage stability and the absorption of new energy sources.

[0003] Deploying synchronous condensers is an important means for new energy bases to improve the short-circuit ratio and enhance voltage support. However, when multiple synchronous condensers are present, how to fairly quantify the contribution of each device to the system's support capabilities has become a pressing issue for the ancillary services market.

[0004] The most commonly used method currently is contribution allocation based on Shapley values, as follows:

[0005] 1) Treat the camera adjuster as a participant in a cooperative game;

[0006] 2) Calculate the maximum simultaneous rate of renewable energy generation and the additional power generation that the system can achieve under different combinations (alliances);

[0007] 3) Allocate the marginal contribution of each camera according to the Shapley value formula.

[0008] The above contribution allocation method has the following drawbacks.

[0009] 1) High computational complexity: The Shapley value needs to consider all subset combinations, and the complexity increases exponentially with the number of participants n (2 n Or n!). It is difficult to apply in actual large-scale new energy bases (more than ten synchronous condensers).

[0010] 2) Insufficient accuracy of approximation methods: Although random sampling (Monte Carlo) can reduce complexity, its variance is too high when the size of the alliance varies greatly, resulting in significant estimation bias.

[0011] 3) Lack of scalability: Existing solutions struggle to balance fairness, accuracy, and large-scale applicability. Summary of the Invention

[0012] This application provides a method for allocating the contribution of synchronous condensers in new energy bases, which addresses the technical problems of existing synchronous condenser contribution allocation methods, such as high computational complexity, insufficient accuracy, and lack of scalability.

[0013] This application is achieved through the following solution:

[0014] A method for allocating the contribution of synchronous condensers in a new energy base includes the following steps:

[0015] S1. Data definition and parameter initialization, including basic sets and indexes, running and settlement parameters, Shapley and sampling parameters, and Shapley weight calculation formula;

[0016] S2. Calculate the baseline limiting simultaneity rate λ under the condition that the synchronous condenser is completely stopped. cr This serves as the benchmark for the increased benefits of all subsequent alliances;

[0017] S3. Engineering modeling of the alliance benefit function, and establishing the revenue from the increased power generation of the new energy system after the alliance is put into operation based on the obtained benchmark limit simultaneity rate;

[0018] S4. Stratified sampling and sample allocation: For each target player, based on the baseline limit of simultaneous rate, calculate the benefit for any alliance, and calculate the mean and standard deviation of the marginal contribution of samples within the stratum.

[0019] S5, Neyman Allocation and Sensitivity-Driven Correction: After allocating samples based on minimizing the variance of the estimated variance according to the standard deviation of the marginal contribution of samples within the layer under the total budget, the number of samples in each layer is corrected according to the power system sensitivity factor, which adopts MRSCR sensitivity or TOV sensitivity.

[0020] S6. Formal sampling and statistical estimation, wherein the statistical estimation includes in-layer sample marginal contribution estimation, in-layer marginal contribution mean estimation, in-layer sample standard deviation update estimation, and in-layer variance estimation;

[0021] S7. Shapley value estimation and confidence interval convergence, including synthetic Shapley value estimation, total variance estimation, and confidence interval estimation. When the confidence interval width or relative error is less than the corresponding set threshold, Shapley value estimation is stopped. Otherwise, the total sampling budget is expanded, and steps S5 to S7 are repeated to redistribute the number of samples for Shapley value estimation until the confidence interval width or relative error is less than the corresponding set threshold and the iteration is stopped.

[0022] S8. Output the Shapley value estimation results, including the synthetic Shapley estimate and the process indicators involved in the calculation.

[0023] Furthermore, in step S1,

[0024] The basic set and index include:

[0025] The set of participants (camera operators) is N = {1, 2, ..., n}, and the target player is i ∈ N;

[0026] New energy power station set: RE; monitoring bus set is

[0027] Power grid model: G = (B, L), containing bus set B and branch set L, per-unit base S base V base ;

[0028] Network admittance matrix: Y∈C |B|×|B| ;

[0029] The operating and settlement parameters include:

[0030] Predicted active power available for grid connection and rated active power for each station during the target time period: P gN,r (r∈RE);

[0031] Feed-in tariff weighting: B r >0;

[0032] Equipment upper and lower limits: P / Q upper and lower limits of each power generation / reactive power equipment, and bus voltage limits. Branch thermal stability / current limit;

[0033] Short-circuit ratio threshold for multiple stations: ζ min (e.g., 1.5);

[0034] Transient overvoltage threshold: (e.g., 1.30 pu);

[0035] Fault / Disturbance scenario set: Ω, including fault type, duration, and mitigation plan;

[0036] Transient simulation parameters Θ include model, controller, step size Δt, and simulation duration T;

[0037] Shapley and sampling parameters include:

[0038] Total sampling budget: M (adaptively grows); confidence level 1 - α (e.g., 0.95);

[0039] Error threshold: absolute ε or relative η;

[0040] Prior sampling samples for each layer:

[0041] Cache table (key is the federation bitmask, value is v(S));

[0042] The Shapley weight calculation formula includes:

[0043] Shapley weights:

[0044]

[0045] Where k is the layer size: k = 0, 1, ..., n-1, CK Number of combinations:

[0046] Furthermore, step S2 specifically includes the following steps:

[0047] S21. Establish a mathematical model, including the objective function, the value of active power injection from new energy sources as a function of λ, power flow balance and operational constraints, short-circuit ratio constraints, and transient voltage constraints under fault scenarios, wherein:

[0048] The objective function (the target quantity for monotonic search) is:

[0049]

[0050] Wherein, F(λ;S) represents the constraint system for the stability of static power flow, short-circuit ratio, and transient voltage when Alliance S is in operation and the output of new energy sources is scaled by λ;

[0051] The active power injection from new energy sources varies with the value of λ (per unit basis).

[0052] P g,r (λ)=λ·P gN,r ,r∈RE(3);

[0053] The power flow balance and operational constraints (typical AC power flow equations and upper and lower limits) are as follows:

[0054]

[0055] In the formula, for new energy unit i, the upper limit of active power is... The value is the predicted value for the calculation period.

[0056] Short-circuit ratio constraint is

[0057]

[0058] In the formula, ζ SCR,r (λ) Adopting the short-circuit ratio ζ of multiple new energy power stations MRSCR,r (λ), which is defined as

[0059]

[0060] Where S ac,r Let i be the short-circuit capacity of bus i. P r and P j Z represents the renewable energy injection power of bus r and bus j, respectively. rr Z is the multi-point Thevenin equivalent self-impedance of bus i. rjThe Thevenin equivalent mutual impedance at multiple points between bus r and bus j; Note: Equation (5) can be replaced according to the short-circuit ratio definition of the enterprise / standard, which may vary in different countries and different enterprises.

[0061] The transient voltage constraint under fault scenarios (based on electromechanical / electromagnetic simulation of each disturbance scenario and taking the peak value) is as follows:

[0062] Simulate V for each ω∈Ω r (t;λ,ω), t∈[0,T], the transient overvoltage (TOV) constraint is defined as

[0063]

[0064] The simulation platform, step size Δt, and model Θ of equation (6) are determined by the actual engineering practice. Equation (7) can be replaced according to the transient voltage constraint definition of the enterprise / standard. Different countries and different enterprises may have different definitions.

[0065] S21. Since the simultaneity rate of new energy sources ranges from [0,1], let the initial lower limit of the bisection method be λ. lo =0, upper limit λ hi =1.0, start the binary search main loop:

[0066] Pick

[0067] Static screening: Solve equation (4). If it does not converge or the constraints exceed the limits, it means that it is not feasible.

[0068] Short-circuit ratio constraint check: ζ is calculated according to equation (6). MRSCR If any monitoring bus fails to meet the requirements, it indicates that the procedure is not feasible.

[0069] Transient overvoltage simulation: According to equation (7), if the transient voltage exceeds the limit in a certain scenario, it indicates that it is not feasible;

[0070] If all are feasible: assign the value of λ to λ. lo Otherwise, assign the value of λ to λ. hi ;

[0071] Termination: When |λ hi -λ lo |≤τ λ (e.g. 10) -3 When ), λ cr =λ lo .

[0072] Furthermore, in step S3, the formula for calculating the revenue from increased renewable energy generation in the system after the alliance is put into operation is established based on the obtained benchmark limiting simultaneity rate:

[0073]

[0074] in: Let λ be the maximum simultaneous rate when Alliance S is put into operation. cr B is the baseline limiting simultaneous rate under conditions of complete camera shutdown; r As the weight of electricity price, P gN,r Rated power for new energy power plants.

[0075] Furthermore, step S4 specifically includes the following steps:

[0076] S41. Establish a stratification based on k = 0, 1, ..., n-1, and calculate the weights w. k :

[0077] k-th layer sample space: size

[0078] Computation layer Shapley weights w k ;

[0079] A subset is generated by uniform sampling without replacement, and bitmasks are used to avoid simulating duplicate samples (in conjunction with a global cache table).

[0080] S42. Prior Sampling: Estimating the Standard Deviation Within a Layer

[0081] First, randomly select from each layer. Subset

[0082] Calculate the sample marginal contribution:

[0083]

[0084] Calculate the mean and standard deviation of samples within the layer:

[0085]

[0086] Furthermore, step S5 specifically includes the following steps:

[0087] S51. Under the total budget M, minimize the sample allocation of the estimated variance:

[0088]

[0089] Where, m min For the minimum sample size in each layer (e.g., 11), K(·) is for rounding.

[0090] S52. Introduce a correction for the number of samples in each layer based on the power system sensitivity factor:

[0091] If the MRSCR or TOV sensitivity of a certain layer's corresponding alliance is higher than the set threshold, then increase m. k ;

[0092] If the contribution within a layer converges, then sampling is reduced and resources are transferred to layers with high uncertainty.

[0093] Furthermore, step S6 specifically includes the following steps:

[0094] S61, Define the limit of simultaneousity in a league.

[0095]

[0096] S62. Statistical estimation, including in-stratum sample marginal contribution estimation, stratum marginal contribution mean estimation, in-stratum sample standard deviation update estimation, and stratum variance estimation, incorporating prior sampling samples. Then continue sampling until the number of samples in each layer reaches m. k ,in:

[0097] The marginal contribution of samples within the layer is estimated using the formula for calculating the revenue from increased power generation of new energy sources in the system after the alliance is put into operation;

[0098] The formula for estimating the mean marginal contribution of a layer is:

[0099]

[0100] The formula for calculating the updated estimate of the standard deviation of samples within the stratum is: (combining prior samples and new samples):

[0101]

[0102] The formula for estimating the variance within a layer is (with finite population correction):

[0103]

[0104] Step S7 specifically includes the following steps:

[0105] S71. Estimation of synthetic Shapley value:

[0106]

[0107] S72. Total Variance Estimation:

[0108]

[0109] S73, Confidence Interval Estimation:

[0110] Under the normal approximation, the (1-α) confidence interval is:

[0111]

[0112] S74. Stopping rule: Stop when the CI width ≤ ε or the relative error ≤ η; otherwise, expand the total sampling budget M to M + ΔM, where ΔM is the expansion amount of the total sampling budget, and repeat steps S5 to S7 to redistribute the number of samples for Shapley value estimation until the confidence interval width or relative error is less than the corresponding set threshold, at which point the iteration stops.

[0113] This application also provides a new energy base synchronous condenser contribution sharing device, including:

[0114] The data definition and initialization module is used for data definition and parameter initialization, including basic sets and indexes, running and settlement parameters, Shapley and sampling parameters, and Shapley weight calculation formulas;

[0115] The additional reference calculation module is used to calculate the reference limiting simultaneity rate λ under the condition that all synchronous condensers are stopped. cr This serves as the benchmark for the increased benefits of all subsequent alliances;

[0116] The module for establishing the revenue model for increased power generation is used for engineering modeling of the alliance's benefit function. It establishes the revenue from increased power generation of new energy sources in the system after the alliance is put into operation based on the obtained benchmark limit simultaneity rate.

[0117] The stratified sampling and sample allocation module is used for stratified sampling and sample allocation. For each target player, based on the baseline limit of simultaneous rate, it calculates the benefit for any alliance and calculates the mean and standard deviation of the marginal contribution of samples within the stratum.

[0118] The sample size correction module is used for Neyman allocation and sensitivity-driven correction. After allocating samples based on minimizing the variance estimate by minimizing the standard deviation of the marginal contribution of samples within each layer under the total budget, the sample size for each layer is then corrected according to the power system sensitivity factor. The power system sensitivity factor uses...

[0119] MRSCR sensitivity or TOV sensitivity;

[0120] The formal sampling and statistical estimation module is used for formal sampling and statistical estimation. The statistical estimation includes in-layer sample marginal contribution estimation, in-layer marginal contribution mean estimation, in-layer sample standard deviation update estimation, and in-layer variance estimation.

[0121] The Shapley value iterative estimation module is used for Shapley value estimation and confidence interval convergence, including synthetic Shapley value estimation, total variance estimation, and confidence interval estimation. When the confidence interval width or relative error is less than the corresponding set threshold, Shapley value estimation stops; otherwise, the total sampling budget is expanded, the number of samples is redistributed, and Shapley value estimation is performed until the confidence interval width or relative error is less than the corresponding set threshold, at which point the iteration stops.

[0122] The Shapley value output module is used to output the Shapley value estimation results, including the synthetic Shapley estimate and the process indicators involved in the calculation.

[0123] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the new energy base synchronous condenser contribution sharing method.

[0124] This application also provides a storage medium that includes a stored program that, when the program is executed, controls the device where the storage medium is located to perform the steps of the new energy base synchronous condenser contribution sharing method.

[0125] Compared with the prior art, this application has the following advantages:

[0126] This application provides a method, apparatus, equipment, and medium for allocating the contribution of synchronous condensers (SCs) in renewable energy bases. The proposed method, while maintaining the fairness and uniqueness of the Shapley value, proposes a fast estimation method combining hierarchical sampling, Neyman optimal allocation, sensitivity correction, and a confidence interval-driven stopping mechanism. This shortens computation time, reduces computational complexity, and avoids unnecessary sample overhead while ensuring accuracy. Considering engineering constraints such as the multiple short-circuit ratio (MRSCR) and transient overvoltage (TOV) of renewable energy substations, this application can efficiently estimate the marginal contribution of multiple SCs to the increased generation capacity of renewable energy bases. The resulting benefit function reflects the actual power generation and stability of the power system, thus ensuring the accuracy and truthfulness of the Shapley value.

[0127] In addition to the purposes, features, and advantages described above, this application has other purposes, features, and advantages. A further detailed description of this application will be provided below with reference to the figures. Attached Figure Description

[0128] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0129] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

[0130] Figure 1This is a schematic flowchart of the contribution sharing method for synchronous condensers in new energy bases according to a preferred embodiment of this application;

[0131] Figure 2 This is a bar chart showing the contribution of each synchronous condenser to the annual increase in power generation revenue of the new energy power station according to a preferred embodiment of this application.

[0132] Figure 3 This is a schematic diagram of the contribution sharing device module of the new energy base synchronous condenser according to a preferred embodiment of this application;

[0133] Figure 4 This is a schematic block diagram of an electronic device according to a preferred embodiment of this application;

[0134] Figure 5 This is an internal structural diagram of a computer device according to a preferred embodiment of this application. Detailed Implementation

[0135] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0136] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0137] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, personal computer, mobile phone, etc., or a new energy base synchronous condenser contribution sharing device that can realize the above functions. The following uses the new energy base synchronous condenser contribution sharing device as the executing entity to describe this embodiment and the following embodiments.

[0138] Definitions of abbreviations and key terms:

[0139] Shapley Value: A method used in cooperative game theory to fairly distribute the benefits of alliances, satisfying axioms such as symmetry, efficiency, and uniqueness.

[0140] Coalition: A subset of participants, which in this context refers to a group of synchronous condensers that are in operation.

[0141] Marginal contribution: The incremental benefit that a participant brings to the alliance after joining the alliance.

[0142] Stratified sampling: A statistical method that divides the population into several sub-strata, samples from each stratum independently, and then weights them to synthesize a population estimate in order to reduce variance.

[0143] Neyman optimal allocation: a sample allocation strategy in stratified sampling that allocates the number of samples based on stratified weights and variance to minimize the overall variance.

[0144] MRSCR (Multi-Renewable Short Circuit Ratio): An indicator that measures the voltage support capability of the grid connection point of a new energy base.

[0145] TOV (Transient Over-Voltage): The instantaneous increase in bus voltage after a fault.

[0146] New energy base: a centralized construction of wind power and photovoltaic power plants and their supporting transmission systems.

[0147] Synchronous Condenser (SC): A device that provides reactive power support and short-circuit capacity, which can enhance grid stability.

[0148] like Figure 1 As shown, a preferred embodiment of this application provides a method for allocating the contribution of synchronous condensers in a new energy base, including the following steps:

[0149] S1. Data definition and parameter initialization, including basic sets and indexes, running and settlement parameters, Shapley and sampling parameters, and Shapley weight calculation formula;

[0150] S2. Calculate the baseline limiting simultaneity rate λ under the condition that the synchronous condenser is completely stopped. cr This serves as the benchmark for the increased benefits of all subsequent alliances;

[0151] S3. Engineering modeling of the alliance benefit function, and establishing the revenue from the increased power generation of the new energy system after the alliance is put into operation based on the obtained benchmark limit simultaneity rate;

[0152] S4. Stratified sampling and sample allocation: For each target player, based on the baseline limit of simultaneous rate, calculate the benefit for any alliance, and calculate the mean and standard deviation of the marginal contribution of samples within the stratum.

[0153] S5, Neyman Allocation and Sensitivity-Driven Correction: After allocating samples based on minimizing the variance of the estimated variance according to the standard deviation of the marginal contribution of samples within the layer under the total budget, the number of samples in each layer is corrected according to the power system sensitivity factor, which adopts MRSCR sensitivity or TOV sensitivity.

[0154] S6. Formal sampling and statistical estimation, wherein the statistical estimation includes in-layer sample marginal contribution estimation, in-layer marginal contribution mean estimation, in-layer sample standard deviation update estimation, and in-layer variance estimation;

[0155] S7. Shapley value estimation and confidence interval convergence, including synthetic Shapley value estimation, total variance estimation, and confidence interval estimation. When the confidence interval width or relative error is less than the corresponding set threshold, Shapley value estimation is stopped. Otherwise, the total sampling budget is expanded, and steps S5 to S7 are repeated to redistribute the number of samples for Shapley value estimation until the confidence interval width or relative error is less than the corresponding set threshold and the iteration is stopped.

[0156] S8. Output the Shapley value estimation results, including the synthetic Shapley estimate and the process indicators involved in the calculation.

[0157] This embodiment provides a method for allocating the contribution of synchronous condensers (SCs) in renewable energy bases. While maintaining the fairness and uniqueness of the Shapley value, this method proposes a fast estimation approach combining hierarchical sampling, Neyman optimal allocation, sensitivity correction, and a confidence interval-driven stopping mechanism. This shortens computation time, reduces computational complexity, and avoids unnecessary sample overhead while ensuring accuracy. Considering engineering constraints such as the multiple short-circuit ratio (MRSCR) and transient overvoltage (TOV) of renewable energy power plants, this embodiment can efficiently estimate the marginal contribution of multiple SCs to the increased generation capacity of renewable energy bases. The resulting benefit function reflects the actual power generation and stability of the power system, thus ensuring the accuracy and truthfulness of the Shapley value.

[0158] Preferably, in step S1,

[0159] The basic set and index include:

[0160] The set of participants (camera operators) is N = {1, 2, ..., n}, and the target player is i ∈ N;

[0161] New energy power station set: RE; monitoring bus set is

[0162] Power grid model: G = (B, L), containing bus set B and branch set L, per-unit base S base V base ;

[0163] Network admittance matrix: Y∈C |B|×|B| ;

[0164] The operating and settlement parameters include:

[0165] Predicted active power available for grid connection and rated active power for each station during the target time period: P gN,r (r∈RE);

[0166] Feed-in tariff weighting: B r>0;

[0167] Equipment upper and lower limits: P / Q upper and lower limits of each power generation / reactive power equipment, and bus voltage limits. Branch thermal stability / current limit;

[0168] Short-circuit ratio threshold for multiple stations: ζ min (e.g., 1.5, r∈R);

[0169] Transient overvoltage threshold: (e.g., 1.30 pu);

[0170] Fault / Disturbance scenario set: Ω, including fault type, duration, and mitigation plan;

[0171] Transient simulation parameters Θ include model, controller, step size Δt, and simulation duration T;

[0172] Shapley and sampling parameters include:

[0173] Total sampling budget: M (adaptively grows); confidence level 1 - α (e.g., 0.95);

[0174] Error threshold: absolute ε or relative η;

[0175] Prior sampling samples for each layer:

[0176] Cache table (key is the federation bitmask, value is v(S));

[0177] The Shapley weight calculation formula includes:

[0178] Shapley weights:

[0179]

[0180] Where k is the layer size: k = 0, 1, ..., n-1, C K Number of combinations:

[0181] This embodiment first defines the data and initializes the parameters. Its purpose and benefits include: the purpose is to establish a unified, reproducible, and constrained computing environment, and the benefits are that it can significantly improve the efficiency, robustness, transparency, and scalability of subsequent hierarchical sampling Shapley value calculation.

[0182] Preferably, step S2 specifically includes the following steps:

[0183] S21. Establish a mathematical model, including the objective function, the value of active power injection from new energy sources as a function of λ, power flow balance and operational constraints, short-circuit ratio constraints, and transient voltage constraints under fault scenarios, wherein:

[0184] The objective function (the target quantity for monotonic search) is:

[0185]

[0186] Wherein, F(λ;S) represents the constraint system for the stability of static power flow, short-circuit ratio, and transient voltage when Alliance S is in operation and the output of new energy sources is scaled by λ;

[0187] The active power injection from new energy sources varies with the value of λ (per unit basis).

[0188] P g,r (λ)=λ·P gN,r ,r∈RE (3);

[0189] The power flow balance and operational constraints (typical AC power flow equations and upper and lower limits) are as follows:

[0190]

[0191] In the formula, for new energy unit i, the upper limit of active power is... The value is the predicted value for the calculation period.

[0192] Short-circuit ratio constraint is

[0193]

[0194] In the formula, ζ SCR,r (λ) Adopting the short-circuit ratio ζ of multiple new energy power stations MRSCR,r (λ), which is defined as

[0195]

[0196] Where S ac,r Let i be the short-circuit capacity of bus i. P r and P j Z represents the renewable energy injection power of bus r and bus j, respectively. rr Z is the multi-point Thevenin equivalent self-impedance of bus i. rj The Thevenin equivalent mutual impedance at multiple points between bus r and bus j; Note: Equation (5) can be replaced according to the short-circuit ratio definition of the enterprise / standard, which may vary in different countries and different enterprises;

[0197] The transient voltage constraint under fault scenarios (based on electromechanical / electromagnetic simulation of each disturbance scenario and taking the peak value) is as follows:

[0198] Simulate V for each ω∈Ω r (t;λ,ω), t∈[0,T], the transient overvoltage (TOV) constraint is defined as

[0199]

[0200] The simulation platform, step size Δt, and model Θ of equation (6) are determined by the actual engineering practice. Equation (7) can be replaced according to the transient voltage constraint definition of the enterprise / standard. Different countries and different enterprises may have different definitions.

[0201] S21. Since the simultaneity rate of new energy sources ranges from [0,1], let the initial lower limit of the bisection method be λ. lo =0, upper limit λ hi =1.0, start the binary search main loop:

[0202] S211, take

[0203] S212, Static screening: Solve equation (4). If it does not converge or the constraints exceed the limits, it means that it is not feasible.

[0204] S213. Short-circuit ratio constraint check: ζ is calculated according to formula (6). MRSCR If any monitoring bus fails to meet the requirements, it indicates that the procedure is not feasible.

[0205] S214. Transient overvoltage simulation: According to equation (7), if the transient voltage exceeds the limit in a certain scenario, it is not feasible;

[0206] S215. If all are feasible: Assign λ to λ. lo Otherwise, assign the value of λ to λ. hi ;

[0207] S216, Termination: When |λ hi -λ lo |≤τ λ (e.g. 10) -3 When ), λ cr =λ lo .

[0208] Note: F(λ; S) is approximately monotonic with respect to λ (the larger λ is, the more difficult it is to achieve). It is effective by binary search. To speed up the process, a step-by-step screening sequence of "static constraint → short-circuit ratio constraint → transient overvoltage constraint" can be adopted.

[0209] In this embodiment, the purpose of performing the additional issuance benchmark calculation in this step is to obtain the result when no synchronous condenser is in operation. Maximum feasible simultaneous rate λ cr The benefits of all subsequent alliances are expressed in terms of λ. cr Using the issuance as a benchmark, calculate the maximum feasible concurrent rate λ. cr The benefits and objectives of using the issuance benchmark as the basis for all subsequent alliances include: calculating the maximum feasible concurrency rate λ without SC. crThis corresponds to the power level that can be safely connected to the grid without a synchronous condenser, and using it as a unified benchmark for power generation ensures that the benefit definitions of all alliances are consistent, the results are comparable, and the marginal contributions are clear. This not only conforms to the fairness axiom of the Shapley value, but also makes the allocation results more intuitive and transparent at the engineering and market levels.

[0210] Preferably, in step S3, the formula for calculating the revenue from increased renewable energy generation in the system after the alliance is put into operation, based on the obtained benchmark limiting simultaneity rate, is as follows:

[0211]

[0212] in: Let λ be the maximum simultaneous rate when Alliance S is put into operation. cr B is the baseline limiting simultaneous rate under conditions of complete camera shutdown; r As the weight of electricity price, P gN,r Rated power for new energy power plants.

[0213] In traditional Shapley values, the benefit function is usually an abstract revenue function. However, in this embodiment, the benefit function is defined as the revenue from the increased power generation of new energy sources after the alliance is put into operation. This definition introduces the power system stability criterion into the Shapley value benefit function for the first time, giving the alliance contribution allocation result an engineering and physical meaning, which is different from the abstract game model.

[0214] Preferably, step S4 specifically includes the following steps:

[0215] S41. Establish a stratification based on k = 0, 1, ..., n-1, and calculate the weights w. k :

[0216] S411, Sample space of the k-th layer: size

[0217] S412, Computation layer Shapley weights w k ;

[0218] S413. Use uniform sampling without replacement to generate subsets and use bitmasks to avoid simulating duplicate samples (in conjunction with a global cache table).

[0219] S42. Prior Sampling: Estimating the Standard Deviation Within a Layer

[0220] S421. Randomly select samples from each layer. Subset

[0221] S422. Calculate the sample marginal contribution:

[0222]

[0223] S423. Calculate the mean and standard deviation of samples within the layer:

[0224]

[0225] This embodiment calculates the benefit for each target player and any alliance based on the baseline simultaneous rate, and calculates the mean and standard deviation of the marginal contributions of samples within the stratum. Its advantages and purposes include: the mean provides an unbiased estimate for the stratum, making Shapley value synthesis more stable; the standard deviation characterizes intra-stratum variability and uncertainty, used for dynamically allocating samples and constructing confidence intervals, thereby ensuring estimation accuracy and fairness under limited budgets. Simultaneously, the mean reflects the average benefit of the adjusting mechanism, and the standard deviation reveals location sensitivity, possessing practical engineering value.

[0226] Preferably, step S5 specifically includes the following steps:

[0227] S51. Under the total budget M, minimize the sample allocation of the estimated variance:

[0228]

[0229] Where, m min For the minimum sample size in each layer (e.g., 11), K(·) is for rounding.

[0230] S52. Introduce a correction for the number of samples in each layer based on the power system sensitivity factor:

[0231] If the MRSCR or TOV sensitivity of a certain layer's corresponding alliance is higher than the set threshold, then increase m. k ;

[0232] If the contribution within a layer converges, then sampling is reduced and resources are transferred to layers with high uncertainty.

[0233] In the hierarchical sampling framework of this application, the sample size m of each layer k k The initial assignment was determined by Neyman. To further improve computational efficiency and accuracy, this invention incorporates power system sensitivity correction:

[0234] When the marginal contribution of a consortium layer is highly sensitive to changes in the short-circuit ratio (MRSCR) or transient overvoltage (TOV), indicating a large statistical variance in the sample of that layer or when the consortium is close to the stability constraint boundary, it exhibits high uncertainty. For such layers, the sample size m should be increased. kTo reduce estimation variance and improve result stability, a dynamic allocation mechanism based on both statistical variance and engineering sensitivity is used. Conversely, if the sample mean of a certain layer has converged and the confidence interval is small, it indicates that the marginal contribution estimation of that layer is relatively stable, and its sampling size can be appropriately reduced, shifting computational resources to layers with high uncertainty. This dynamic allocation mechanism allows sample resources to be concentrated on layers with the greatest uncertainty in contributing to the final Shapley value, significantly improving overall computational efficiency and estimation accuracy.

[0235] This embodiment, while minimizing the sample allocation of the estimation variance, also uses a dynamic sample allocation mechanism based on power system sensitivity factor (MRSCR or TOV) correction. Its purpose and benefits include: introducing MRSCR / TOV sensitivity correction on the basis of Neyman optimal allocation, aiming to guide limited sampling resources to layers that are critical to engineering, have large fluctuations, and are more sensitive to constraints; the benefit is that it significantly improves the estimation accuracy and robustness, making the Shapley value calculation not only statistically optimal, but also more reliable and efficient in the engineering sense of power systems.

[0236] Preferably, step S6 specifically includes the following steps:

[0237] S61, Define the limit of simultaneousity in a league.

[0238]

[0239] S62. Statistical estimation, including in-stratum sample marginal contribution estimation, stratum marginal contribution mean estimation, in-stratum sample standard deviation update estimation, and stratum variance estimation, incorporating prior sampling samples. Then continue sampling until the number of samples in each layer reaches m. k ,in:

[0240] The marginal contribution of samples within the layer is estimated using the formula for calculating the revenue from increased power generation of new energy sources in the system after the alliance is put into operation;

[0241] The formula for estimating the mean marginal contribution of a layer is:

[0242]

[0243] The formula for calculating the updated estimate of the standard deviation of samples within the stratum is: (combining prior samples and new samples):

[0244]

[0245] The formula for estimating the variance within a layer is (with finite population correction):

[0246]

[0247] This embodiment performs formal sampling and statistical estimation, including estimating the marginal contribution of samples within the layer, estimating the mean of the marginal contribution of the layer, updating the standard deviation of samples within the layer, and estimating the variance of the layer. Its purpose and benefits include: by quantifying the mean and fluctuation of the marginal contribution layer by layer, it ensures that the Shapley value calculation is fair, accurate, and controllable; the benefits are that it can explicitly control the error, construct confidence intervals, improve convergence efficiency, and make the results more transparent and interpretable.

[0248] Preferably, step S7 specifically includes the following steps:

[0249] S71. Estimation of synthetic Shapley value:

[0250]

[0251] S72. Total Variance Estimation:

[0252]

[0253] S73, Confidence Interval Estimation:

[0254] Under the normal approximation, the (1-α) confidence interval is:

[0255]

[0256] S74. Stopping rule: Stop when the CI width ≤ ε or the relative error ≤ η; otherwise, expand the total sampling budget M to M + ΔM, where ΔM is the expansion amount of the total sampling budget, and repeat steps S5 to S7 to redistribute the number of samples for Shapley value estimation until the confidence interval width or relative error is less than the corresponding set threshold, at which point the iteration stops.

[0257] This embodiment introduces a confidence interval-driven stopping criterion. Through confidence interval-driven iterative sampling, it stops early when the estimation accuracy meets the requirements, saving computation time and ensuring that the accuracy requirements are met within a limited budget. The relative error ≤ η specifically refers to whether the proportion of the uncertainty interval of the Shapley value estimation result to its own value is sufficiently small. It is defined as the relative half-width of the confidence interval not exceeding a specified proportion compared to the estimated Shapley value, i.e.:

[0258]

[0259] Preferably, for engineering implementation, this application is further optimized in the following ways:

[0260] Set up a cache table Cashe mechanism to avoid multiple simulations of the same alliance, reduce simulation costs, and improve simulation efficiency.

[0261] Configure parallel mechanisms: support parallelism between layers, between samples, and in fault scenarios; however, attention must be paid to the concurrency safety of Cashe.

[0262] Set robust measures: When the power flow does not converge / the simulation diverges, automatically reduce the step size or back off;

[0263] Set constraint replaceability: Supports different enterprise standards such as ESCR, multi-infeed SCR, transient voltage recovery time, etc.

[0264] As can be seen, this application proposes a cache table, parallel simulation, and robust rollback mechanism to ensure the feasibility of calculating the contribution allocation of synchronous condensers in large-scale new energy bases.

[0265] Preferably, the output Shapley value estimation result in step S8 mainly includes:

[0266] Main result: (Equation (16)), and give the confidence interval (1-α)CI (Equation (18));

[0267] Process metrics (with delivery output): m at each level k , Variance contribution Cache hit rate, statistics of transient voltage instability triggering scenarios, and frequency of the most "tight" constraints (short-circuit ratio MRSCR / transient overvoltage TOV / voltage / reactive power / thermal stability).

[0268] The validity and superiority of this application will be verified below:

[0269] To verify the effectiveness and superiority of the method of this invention, this embodiment selects measured data from the renewable energy power stations in the Yudao Kou area of ​​Chengde as a case study. This area is a typical large-scale wind and solar power integration region. The 500 kV Yudao Kou substation in the Saihanba area integrates 20 renewable energy power stations with a total installed capacity of 2,889 MW, including 15 wind farms with an installed capacity of 2,569 MW and 5 photovoltaic power stations with an installed capacity of 320 MW. Due to the low short-circuit ratio and prominent voltage stability issues in this area, it is suitable as a research object for the contribution sharing of synchronous condenser support.

[0270] Considering the transmission demand of 0.7 (approximately 2 million kilowatts) of new energy simultaneous rate, the optimal configuration scheme obtained by using the self-developed distributed synchronous condenser optimization configuration software is as follows: 5 50Mvar synchronous condensers (“Cheng Shangtou 21230.”, “Cheng Shanyuan 21115.”, “Cheng Jiaoding 3437.”, “Cheng Dianjiang 21230.”, “Cheng Binglang 21230.”), 1 20Mvar synchronous condenser (“Cheng Na Ri 3537.”), and 1 10Mvar synchronous condenser (“Cheng Changfeng 3437.”).

[0271] Based on annual wind and solar power output time-series data, the traditional empirical formula method was first used for configuration and allocation calculations. The results show that although this method can obtain a scheme that meets the requirements of short-circuit ratio and transient voltage recovery lower limit, the contribution allocation can only be approximately allocated according to capacity ratio or start-up and shutdown sequence, making it difficult to reflect the marginal effect of different synchronous condensers on system stability.

[0272] Subsequently, the contribution allocation was performed using the stratified sampling Shapley value method proposed in this application. Through stratified sampling and the Neyman dynamic allocation mechanism, rapid variance convergence is ensured within a limited sample budget, and the confidence interval width of the Shapley value for a single camera is controlled within 5%. The contribution proportion of each camera is as follows: Figure 2 As shown, the calculation results are as follows:

[0273] The 50Mvar camera in the "Chengbinglang" configuration contributed the most, accounting for 19.54%;

[0274] The 10Mvar synchronous condenser with the "Cheng Changfeng" configuration contributed the least, accounting for 4.49%;

[0275] The contribution of the 20Mvar synchronous condenser on “Chengna Day” was 8.29%.

[0276] Compared with the traditional capacity ratio method, this method reveals the difference in contribution per unit capacity: small-capacity synchronous condensers often exhibit higher unit benefits at voltage-weak nodes, with their contribution ratio exceeding that of linear capacity ratio. This result is consistent with engineering experience, validating the rationality of the benefit function (based on limiting simultaneity rate and MRSCR / TOV constraints).

[0277] Further comparisons show that, under the same sampling budget, the variance of the proposed method for allocating the contribution of synchronous condensers in new energy bases is reduced by approximately 40% compared to the random Monte Carlo sampling method, and the computation time is shortened by approximately 35%. This demonstrates that the proposed method can balance fairness and efficiency in large-scale new energy base scenarios, providing practical support for the settlement of ancillary services in the synchronous condenser market.

[0278] In conclusion, the practicality and engineering value of this technology in a multi-synchronous-conversion environment have been demonstrated through the case study of the Chengde Yudaokou New Energy Base.

[0279] As can be seen, the contribution sharing method of the new energy base synchronous condenser in this application mainly includes the following core points:

[0280] 1. A modeling method for alliance benefit functions based on the definition of limiting simultaneity rate, integrating power system engineering constraints such as power flow, short-circuit ratio, and transient voltage;

[0281] 2. A fast method for calculating Shapley values ​​based on hierarchical sampling and Neyman assignment;

[0282] 3. Dynamic sample allocation mechanism based on power system sensitivity factors (MRSCR, TOV);

[0283] 4. Sampling stopping criterion based on confidence interval driven;

[0284] 5. Application methods in the contribution sharing and ancillary service market of synchronous condensers in new energy bases.

[0285] like Figure 3 As shown, another preferred embodiment of this application also provides a new energy base synchronous condenser contribution sharing device, including:

[0286] The data definition and initialization module is used for data definition and parameter initialization, including basic sets and indexes, running and settlement parameters, Shapley and sampling parameters, and Shapley weight calculation formulas;

[0287] The additional reference calculation module is used to calculate the reference limiting simultaneity rate λ under the condition that all synchronous condensers are stopped. cr This serves as the benchmark for the increased benefits of all subsequent alliances;

[0288] The module for establishing the revenue model for increased power generation is used for engineering modeling of the alliance's benefit function. It establishes the revenue from increased power generation of new energy sources in the system after the alliance is put into operation based on the obtained benchmark limit simultaneity rate.

[0289] The stratified sampling and sample allocation module is used for stratified sampling and sample allocation. For each target player, based on the baseline limit of simultaneous rate, it calculates the benefit for any alliance and calculates the mean and standard deviation of the marginal contribution of samples within the stratum.

[0290] The sample quantity correction module is used for Neyman allocation and sensitivity-driven correction. After allocating the sample quantity by minimizing the variance estimate based on the standard deviation of the marginal contribution of the samples within the layer under the total budget, the sample quantity of each layer is corrected according to the power system sensitivity factor, which adopts MRSCR sensitivity or TOV sensitivity.

[0291] The formal sampling and statistical estimation module is used for formal sampling and statistical estimation. The statistical estimation includes in-layer sample marginal contribution estimation, in-layer marginal contribution mean estimation, in-layer sample standard deviation update estimation, and in-layer variance estimation.

[0292] The Shapley value iterative estimation module is used for Shapley value estimation and confidence interval convergence, including synthetic Shapley value estimation, total variance estimation, and confidence interval estimation. When the confidence interval width or relative error is less than the corresponding set threshold, Shapley value estimation stops; otherwise, the total sampling budget is expanded, the number of samples is redistributed, and Shapley value estimation is performed until the confidence interval width or relative error is less than the corresponding set threshold, at which point the iteration stops.

[0293] The Shapley value output module is used to output the Shapley value estimation results, including the synthetic Shapley estimate and the process indicators involved in the calculation.

[0294] A preferred embodiment of this application also provides a new energy base synchronous condenser contribution allocation device, which adopts the new energy base synchronous condenser contribution allocation method in the above embodiments, solving the technical problems of high computational complexity, insufficient accuracy, and lack of scalability in existing synchronous condenser contribution allocation methods. Compared with the prior art, the beneficial effects of the new energy base synchronous condenser contribution allocation device provided in this application are the same as those of the new energy base synchronous condenser contribution allocation method provided in the above embodiments, and other technical features in the new energy base synchronous condenser contribution allocation device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0295] like Figure 4 As shown, a preferred embodiment of this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the new energy base synchronous condenser contribution sharing method in the above embodiments.

[0296] The application provides an electronic device that employs the contribution allocation method for new energy base synchronous condensers in the above embodiments, solving the technical problems of existing synchronous condenser contribution allocation methods, such as high computational complexity, insufficient accuracy, and lack of scalability. Compared with the prior art, the beneficial effects of the electronic device provided in this application are the same as those of the new energy base synchronous condenser contribution allocation method provided in the above embodiments, and other technical features of the electronic device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0297] like Figure 5 As shown, a preferred embodiment of this application also provides a computer device, which may be a terminal or a liveness detection server, and its internal structure diagram may be as follows. Figure 5As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with other external computer devices via a network connection. When the computer program is executed by the processor, it implements the steps of the aforementioned method for allocating the contribution of synchronous condensers in new energy bases.

[0298] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0299] The computer equipment provided in this application employs the contribution allocation method for new energy base synchronous condensers in the above embodiments, solving the technical problems of high computational complexity, insufficient accuracy, and lack of scalability in existing synchronous condenser contribution allocation methods. Compared with the prior art, the beneficial effects of the computer equipment provided in this application are the same as those of the contribution allocation method for new energy base synchronous condensers provided in the above embodiments, and other technical features in the electronic equipment are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0300] A preferred embodiment of this application also provides a storage medium, the storage medium including a stored program, which, when the program is executed, controls the device where the storage medium is located to perform the steps of the new energy base synchronous condenser contribution sharing method in the above embodiments.

[0301] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0302] If the functions described in this embodiment are implemented as software functional units and sold or used as independent products, they can be stored in one or more computing device-readable storage media. Based on this understanding, the parts of this application's embodiments that contribute to the prior art or the technical solutions can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computing device (which may be a personal computer, server, mobile computing device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage media include: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0303] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented using various computer languages, such as the object-oriented programming language C++ and the embedded programming language C.

[0304] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0305] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0306] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0307] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for allocating the contribution of synchronous condensers in new energy bases.

[0308] The computer program product provided in this application can solve the technical problems of existing methods for allocating the contribution of synchronous condensers in new energy bases being complex, difficult to implement, computationally intensive, and requiring high computing power. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the method for allocating the contribution of synchronous condensers in new energy bases provided in the above embodiments, and will not be repeated here.

[0309] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0310] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for allocating the contribution of synchronous condensers in a new energy base, characterized in that, Including the following steps: S1. Data definition and parameter initialization, including basic sets and indexes, running and settlement parameters, Shapley and sampling parameters, and Shapley weight calculation formula; S2. Calculate the baseline limiting simultaneity rate λ under the condition that the synchronous condenser is completely stopped. cr This serves as the benchmark for the increased benefits of all subsequent alliances; S3. Engineering modeling of the alliance benefit function, and establishing the revenue from the increased power generation of the new energy system after the alliance is put into operation based on the obtained benchmark limit simultaneity rate; S4. Stratified sampling and sample allocation: For each target player, based on the baseline limit of simultaneous rate, calculate the benefit for any alliance, and calculate the mean and standard deviation of the marginal contribution of samples within the stratum. S5, Neyman Allocation and Sensitivity-Driven Correction: After allocating samples based on minimizing the variance of the estimated variance according to the standard deviation of the marginal contribution of samples within the layer under the total budget, the number of samples in each layer is corrected according to the power system sensitivity factor, which adopts MRSCR sensitivity or TOV sensitivity. S6. Formal sampling and statistical estimation, wherein the statistical estimation includes in-layer sample marginal contribution estimation, in-layer marginal contribution mean estimation, in-layer sample standard deviation update estimation, and in-layer variance estimation; S7. Shapley value estimation and confidence interval convergence, including synthetic Shapley value estimation, total variance estimation, and confidence interval estimation. When the confidence interval width or relative error is less than the corresponding set threshold, Shapley value estimation is stopped. Otherwise, the total sampling budget is expanded, and steps S5 to S7 are repeated to redistribute the number of samples for Shapley value estimation until the confidence interval width or relative error is less than the corresponding set threshold and the iteration is stopped. S8. Output the Shapley value estimation results, including the synthetic Shapley estimate and the process indicators involved in the calculation.

2. The method for allocating the contribution of synchronous condensers in new energy bases according to claim 1, characterized in that, In step S1, The basic set and index include: Participant set: N = {1, 2, ..., n}, target player i ∈ N; New energy power station set: RE; monitoring bus set is Power grid model: G = (B, L), containing bus set B and branch set L, per-unit base S base V base ; Network admittance matrix: Y∈C |B|×|B| ; The operating and settlement parameters include: Predicted active power available for grid connection and rated active power for each station during the target time period: Feed-in tariff weighting: B r >0; Equipment upper and lower limits: P / Q upper and lower limits of each power generation / reactive power equipment, and bus voltage limit V. i min ≤V i ≤V i max Branch thermal stability / current limit; Short-circuit ratio threshold for multiple stations: ζ min ; Transient overvoltage threshold: Fault / Disturbance scenario set: Ω, including fault type, duration, and mitigation plan; Transient simulation parameters Θ include model, controller, step size Δt, and simulation duration T; Shapley and sampling parameters include: Total sampling budget: M; confidence level 1-α; Error threshold: absolute ε or relative η; Prior sampling samples for each layer: The Shapley weight calculation formula includes: Shapley weights: Where k is the layer size: k = 0, 1, ..., n-1, C K Number of combinations:

3. The method for allocating the contribution of synchronous condensers in new energy bases according to claim 2, characterized in that, Step S2 specifically includes the following steps: S21. Establish a mathematical model, including the objective function, the value of active power injection from new energy sources as a function of λ, power flow balance and operational constraints, short-circuit ratio constraints, and transient voltage constraints under fault scenarios, wherein: The objective function is: Wherein, F(λ;S) represents the constraint system for the stability of static power flow, short-circuit ratio, and transient voltage when Alliance S is in operation and the output of new energy sources is scaled by λ; The active power injection from new energy sources varies with the value of λ. P g,r (λ)=λ·P gN,r , r∈RE (3); The power flow balance and operational constraints are as follows: In the formula, for new energy unit i, the upper limit of active power is... The value is the predicted value for the calculation period. Short-circuit ratio constraint is In the formula, ζ SCR,r (λ) Adopting the short-circuit ratio ζ of multiple new energy power stations MRSCR,r (λ), which is defined as Where S ac,r Let i be the short-circuit capacity of bus i. P r and P j Z represents the renewable energy injection power of bus r and bus j, respectively. rr Z is the multi-point Thevenin equivalent self-impedance of bus i. rj The Thevenin equivalent mutual impedance at multiple points between bus r and bus j; The transient voltage constraint under the fault scenario is: Simulate V for each ω∈Ω r (t;λ,ω), t∈[0,T], transient overvoltage constraint is defined as S21. Since the simultaneity rate of new energy sources ranges from [0,1], let the initial lower limit of the bisection method be λ. lo =0, upper limit λ hi =1.0, start the binary search main loop: Pick Static screening: Solve equation (4). If it does not converge or the constraints exceed the limits, it means that it is not feasible. Short-circuit ratio constraint check: ζ is calculated according to equation (6). MRSCR If any monitoring bus fails to meet the requirements, it indicates that the procedure is not feasible. Transient overvoltage simulation: According to equation (7), if the transient voltage exceeds the limit in a certain scenario, it indicates that it is not feasible; If all are feasible: assign the value of λ to λ. lo Otherwise, assign the value of λ to λ. hi ; Termination: When |λ hi -λ lo |≤τ λ (e.g. 10) -3 When ), λ cr =λ lo .

4. The method for allocating the contribution of synchronous condensers in new energy bases according to claim 3, characterized in that, In step S3, based on the obtained benchmark limiting simultaneity rate, the formula for calculating the revenue from increased renewable energy generation after the alliance is put into operation is as follows: in: Let λ be the maximum simultaneous rate when Alliance S is put into operation. cr B is the baseline limiting simultaneous rate under conditions of complete camera shutdown; r As the weight of electricity price, P gN,r Rated power for new energy power plants.

5. The method for allocating the contribution of synchronous condensers in new energy bases according to claim 4, characterized in that, Step S4 specifically includes the following steps: S41. Establish a stratification based on k = 0, 1, ..., n-1, and calculate the weights w. k : k-th layer sample space: size Computation layer Shapley weights w k ; A subset is generated by uniform sampling without replacement, and a bit mask is used to avoid simulating duplicate samples. S42. Prior Sampling: Estimating the Standard Deviation Within a Layer First, randomly select from each layer. Subset Calculate the sample marginal contribution: Calculate the mean and standard deviation of samples within the layer:

6. The method for allocating the contribution of synchronous condensers in new energy bases according to claim 5, characterized in that, Step S5 specifically includes the following steps: S51. Under the total budget M, minimize the sample allocation of the estimated variance: Where, m min K(·) represents the minimum sample size in each layer, and K(·) is used for rounding. S52. Introduce a correction for the number of samples in each layer based on the power system sensitivity factor: If the MRSCR or TOV sensitivity of a certain layer's corresponding alliance is higher than the set threshold, then increase m. k ; If the contribution within a layer converges, then sampling is reduced and resources are transferred to layers with high uncertainty.

7. The method for allocating the contribution of synchronous condensers in new energy bases according to claim 6, characterized in that, Step S6 specifically includes the following steps: S61, Define the limit of simultaneousity in a league. S62. Statistical estimation, including in-stratum sample marginal contribution estimation, stratum marginal contribution mean estimation, in-stratum sample standard deviation update estimation, and stratum variance estimation, incorporating prior sampling samples. Then continue sampling until the number of samples in each layer reaches m. k ,in: The marginal contribution of samples within the layer is estimated using the formula for calculating the revenue from increased power generation of new energy sources in the system after the alliance is put into operation; The formula for estimating the mean marginal contribution of a layer is: The formula for calculating the updated estimate of the standard deviation of samples within a layer is: The formula for estimating the variance within a layer is: Step S7 specifically includes the following steps: S71. Estimation of synthetic Shapley value: S72. Total Variance Estimation: S73, Confidence Interval Estimation: Under the normal approximation, the (1-α) confidence interval is: S74. Stopping rule: Stop when the CI width ≤ ε or the relative error ≤ η; otherwise, expand the total sampling budget M to M + ΔM, where ΔM is the expansion amount of the total sampling budget, and repeat steps S5 to S7 to redistribute the number of samples for Shapley value estimation until the confidence interval width or relative error is less than the corresponding set threshold, at which point the iteration stops.

8. A contribution sharing device for synchronous condensers in a new energy base, characterized in that, include: The data definition and initialization module is used for data definition and parameter initialization, including basic sets and indexes, running and settlement parameters, Shapley and sampling parameters, and Shapley weight calculation formulas; The additional reference calculation module is used to calculate the reference limiting simultaneity rate λ under the condition that all synchronous condensers are stopped. cr This serves as the benchmark for the increased benefits of all subsequent alliances; The module for establishing the revenue model for increased power generation is used for engineering modeling of the alliance's benefit function. It establishes the revenue from increased power generation of new energy sources in the system after the alliance is put into operation based on the obtained benchmark limit simultaneity rate. The stratified sampling and sample allocation module is used for stratified sampling and sample allocation. For each target player, based on the baseline limit of simultaneous rate, it calculates the benefit for any alliance and calculates the mean and standard deviation of the marginal contribution of samples within the stratum. The sample quantity correction module is used for Neyman allocation and sensitivity-driven correction. After allocating the sample quantity by minimizing the variance estimate based on the standard deviation of the marginal contribution of the samples within the layer under the total budget, the sample quantity of each layer is corrected according to the power system sensitivity factor, which adopts MRSCR sensitivity or TOV sensitivity. The formal sampling and statistical estimation module is used for formal sampling and statistical estimation. The statistical estimation includes in-layer sample marginal contribution estimation, in-layer marginal contribution mean estimation, in-layer sample standard deviation update estimation, and in-layer variance estimation. The Shapley value iterative estimation module is used for Shapley value estimation and confidence interval convergence, including synthetic Shapley value estimation, total variance estimation, and confidence interval estimation. When the confidence interval width or relative error is less than the corresponding set threshold, Shapley value estimation stops; otherwise, the total sampling budget is expanded, the number of samples is redistributed, and Shapley value estimation is performed until the confidence interval width or relative error is less than the corresponding set threshold, at which point the iteration stops. The Shapley value output module is used to output the Shapley value estimation results, including the synthetic Shapley estimate and the process indicators involved in the calculation.

9. An electronic 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 steps of the new energy base synchronous condenser contribution sharing method as described in any one of claims 1 to 7.

10. A storage medium comprising a stored program, characterized in that, When the program is running, it controls the device where the storage medium is located to perform the steps of the new energy base synchronous condenser contribution sharing method as described in any one of claims 1 to 7.

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