Power grid access simulation method and system for high-proportion new energy

Abstract

The invention relates to the technical field of power grid access simulation, in particular to a power grid access simulation method and system for high-proportion new energy. The method comprises the following steps of collecting operation parameters of a thermal power generating unit and a new energy generating unit, constructing a feature tensor of a combined generating unit, deducing self constraints and network topology constraints of the generating security constraint information on the basis of the feature tensor, performing collaborative fusion on the constraints to obtain a collaborative constraint spectrum, and establishing a generating unit combination model on the basis of the spectrum. Grid-connected simulation data are obtained through long-period grid-connected simulation, the combined load capacity is measured and calculated, qualified combination screening is carried out according to the load capacity, cost optimization is carried out on the qualified combination screening, and finally an optimized low-cost unit is generated. According to the invention, the reliability and adaptability of new energy access to the power grid are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid access simulation, and in particular to a power grid access simulation method and system for high-proportion renewable energy. Background Art

[0002] Due to the intermittent and random nature of renewable energy, the challenges of stability and security brought by its grid connection are becoming increasingly prominent. Traditional thermal power units assume the role of peak load regulation and base load in the power grid, but the volatility of renewable energy units puts greater pressure on the operation and regulation of thermal power units. In addition, the differences in operating characteristics between renewable energy and thermal power units, as well as the complexity of interconnection between different units in the power grid, make it extremely difficult to build a grid access model that is compatible with a high proportion of renewable energy. At present, in grid access simulation, the characteristics of thermal power units and renewable energy units are usually modeled separately, and the overall characteristics of their joint operation are not effectively considered. Traditional modeling methods often ignore the synergistic relationship between thermal power units and renewable energy units and their comprehensive impact on grid security and load capacity. In addition, the existing methods for studying the topological constraints of the power grid network are mostly limited to static analysis, lacking accurate modeling of the deep correlation between the dynamic characteristics of the complex network and the joint operation characteristics of the units, resulting in insufficient accuracy of the power grid simulation results, making it difficult to provide reliable support for actual operation. Summary of the invention

[0003] Based on this, it is necessary to provide a grid access simulation method and system for a high proportion of renewable energy to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a grid access simulation method for a high proportion of renewable energy includes the following steps:

[0005] Step S1: collecting the operating parameters of the thermal power unit and the operating parameters of the new energy unit; constructing a combined tensor of the operating parameters of the thermal power unit and the operating parameters of the new energy unit to generate a combined unit feature tensor;

[0006] Step S2: deriving the unit's own constraints on the combined unit's characteristic tensor to obtain its own safety constraints; performing network topology constraint analysis on the combined unit's characteristic tensor to generate network security constraints;

[0007] Step S3: Perform collaborative fusion processing on the self-security constraints and network security constraints to obtain a collaborative constraint spectrum; perform combined enumeration modeling on the combined unit characteristic tensors according to the collaborative constraint spectrum to generate a unit combination model;

[0008] Step S4: performing a long-period grid-connected simulation on the unit combination model based on the collaborative constraint spectrum to obtain grid-connected simulation data; performing load capacity calculation on the unit combination model according to the grid-connected simulation data to obtain a combined load capacity;

[0009] Step S5: Screen qualified combinations based on combined load capacity to obtain qualified combinations; perform cost optimization deduction on the qualified combinations to generate optimized low-cost units.

[0010] The present invention collects the operating parameters of thermal power units and new energy units, constructs the characteristic tensor of the combined units, and provides a comprehensive data basis for subsequent analysis. The derivation of the unit's own constraints is combined with the network topology constraint analysis to ensure the safety of the unit operation and the stability of the network. The constraint spectrum after collaborative fusion processing provides a scientific basis for the establishment of the unit combination model. The long-term grid-connected simulation can simulate the actual operating conditions and obtain more reliable grid-connected simulation data. The load capacity calculation provides a basis for the reasonable configuration of the units. The qualified combination screening based on load capacity improves the effectiveness of the unit combination. The cost optimization derivation significantly reduces the operating cost, optimizes the economy and reliability of the grid access as a whole, and improves the adaptability and flexibility of the power system to a high proportion of new energy.

[0011] Preferably, step S1 comprises the following steps:

[0012] Step S11: Collecting the operating parameters of thermal power units and new energy units;

[0013] Step S12: performing time sequence alignment on the operating parameters of the thermal power unit and the operating parameters of the new energy unit to generate synchronous operating parameters;

[0014] Step S13: performing principal component analysis on the synchronous operation parameters to obtain dimension-reduced operation features;

[0015] Step S14: Perform three-boundary tensor transformation on the dimension-reduced operation characteristics to generate a combined unit characteristic tensor.

[0016] The present invention establishes a comprehensive data foundation by collecting the operating parameters of thermal power units and new energy units. The timing alignment technology ensures the synchronization of operating data of different units and provides consistency for subsequent analysis. Principal component analysis improves the interpretability of data through dimensionality reduction processing, reduces redundant information, and makes key features more prominent. The three-boundary tensor transformation realizes the integration of multi-dimensional data. The generated combined unit feature tensor provides rich feature representation for subsequent constraint analysis and modeling, which improves the overall understanding of unit performance in complex power grid environments, enhances the system's responsiveness and adaptability to the access of a high proportion of new energy, optimizes the scheduling and operation efficiency of the power system, and promotes the effective use of new energy and the stability of the power grid.

[0017] Preferably, step S2 comprises the following steps:

[0018] Step S21: Deconstruct the dynamic characteristics of the combined unit characteristic tensor to obtain the thermal power unit constraints; analyze the new energy output characteristics of the combined unit characteristic tensor to generate the new energy unit constraints;

[0019] Step S22: performing group constraint connection on the thermal power unit constraints and the new energy unit constraints to obtain their own safety constraints;

[0020] Step S23: quantifying stability based on the constraints of thermal power units and new energy units, and generating stability constraints;

[0021] Step S24: performing a balanced power tensor operation on the characteristic tensor of the combined unit according to the stability constraint to obtain a node constraint; performing a phase angle sensitivity matrix decomposition on the node constraint to obtain a phase angle constraint;

[0022] Step S25: Calculate the power flow safety region for the phase angle constraint to generate a power flow constraint; perform topological integration processing on the node constraint, phase angle constraint and power flow constraint to generate a network topology constraint;

[0023] Step S26: Expand and map the network topology constraints based on the stability constraints to generate network security constraints.

[0024] The present invention extracts the constraint characteristics of thermal power units and new energy units by dynamically deconstructing the characteristic tensor of the combined unit, ensuring the safety of the units under different operating conditions. The group constraint connection provides a comprehensive evaluation of the overall safety of the units. Stability quantification provides a quantitative basis for the safe operation of the power system under load fluctuations. The balanced power tensor operation realizes the effective management of node power and reflects the real-time status of the power grid operation. The phase angle sensitivity matrix decomposition enhances the understanding of the impact of phase angle changes on the system. The flow safety domain calculation ensures the flow safety of the power grid under different operating conditions. The topological integration processing effectively combines node constraints, phase angle constraints and flow constraints to form a comprehensive network topology constraint. The network security constraints generated by the extended mapping provide a guarantee for the stable operation of the power grid under the condition of a high proportion of new energy access, which improves the reliability and flexibility of the power system as a whole and promotes the efficient use of new energy and the safety of the power grid.

[0025] Preferably, step S21 includes the following steps:

[0026] Extract power balance features from the combined unit feature tensor to obtain a power balance constraint set; calculate the power boundary of the thermal power unit on the power balance constraint set to obtain a power boundary constraint set;

[0027] The thermal power unit ramp rate analysis is performed on the power boundary constraint set to obtain the ramp constraint set; the start and stop time modeling is performed on the ramp constraint set to obtain the time constraint set;

[0028] The power balance constraint set, power boundary constraint set, ramp constraint set and time constraint set are dynamically integrated to obtain the constraints of the thermal power unit;

[0029] Analyze the new energy output characteristics of the combined unit characteristic tensor to obtain the output characteristic set;

[0030] The wind power prediction model is performed on the output characteristic set to obtain the wind power constraint set; the photovoltaic power generation characteristics of the wind power constraint set are analyzed to obtain the photovoltaic constraint set;

[0031] Based on the wind power constraint set and the photovoltaic constraint set, the absorption capacity is predicted to obtain the predicted absorption capacity; the absorption constraint mapping is performed on the predicted absorption capacity to obtain the absorption constraint set;

[0032] The output characteristics of the output feature set, wind power constraint set, photovoltaic constraint set and consumption constraint set are integrated to generate constraints for new energy units.

[0033] The present invention establishes a power balance constraint set through power balance feature extraction, which provides a basis for the stable operation of the power grid. The power boundary calculation of the thermal power unit ensures that the unit operates within a safe range. The climbing rate analysis quantifies the dynamic response capability of the thermal power unit. The start and stop time modeling provides time constraints for unit scheduling. The dynamic characteristic fusion improves the comprehensiveness and accuracy of the constraints of the thermal power unit. The analysis of new energy output characteristics lays a solid foundation for subsequent modeling. Wind power prediction modeling improves the prediction accuracy of wind power output. The analysis of photovoltaic power generation characteristics further enriches the dimension of new energy constraints. The absorption capacity prediction provides a basis for the load management of the power grid. The absorption constraint mapping ensures the flexible scheduling of the power grid under the condition of a high proportion of new energy access. The output characteristic integration realizes the comprehensive consideration of the constraints of various new energy units, which improves the adaptability and stability of the power system to a high proportion of new energy access as a whole, and promotes the optimal allocation and utilization efficiency of power resources.

[0034] Preferably, step S24 comprises the following steps:

[0035] Perform unbalanced power analysis on the characteristic tensor of the combined unit to obtain the power deviation;

[0036] According to the power deviation, the characteristic tensor of the combined unit is distributed and mapped to generate the unit power distribution;

[0037] According to the stability constraint, the power distribution of the unit is subjected to balance correction processing to obtain the balanced power tensor;

[0038] Based on the balanced power tensor, the node power of the combined unit characteristic tensor is readjusted to generate node constraints;

[0039] The phase angle sensitivity of the node constraint is calculated to obtain the phase angle sensitivity parameter; the matrix eigenvalue decomposition of the phase angle sensitivity parameter is performed to obtain the phase angle constraint.

[0040] The present invention accurately obtains the power deviation through unbalanced power analysis, reflecting the imbalance in the operation of the power grid. The unit power distribution generated by the distribution mapping technology provides an intuitive basis for the subsequent power scheduling. The balance correction processing ensures the power balance under the stability constraint. The generated balanced power tensor provides important support for the dynamic regulation of the power grid. The node power readjustment improves the load adaptability of each node in the power grid. The generated node constraints provide more accurate constraints for the operation of the power grid. The phase angle sensitivity calculation enhances the understanding of the impact of phase angle changes on the system. The matrix eigenvalue decomposition of the sensitivity parameter provides a theoretical basis for the establishment of phase angle constraints. The overall operation safety and stability of the power system under the condition of a high proportion of new energy access are improved, and the effective management and scheduling of fluctuating loads by the power grid are promoted.

[0041] Preferably, step S3 comprises the following steps:

[0042] Step S31: Based on the stability constraint, correlation mining is performed on the self-security constraint and the network security constraint to obtain constraint correlation; conflict resolution is performed on the constraint correlation to generate constraint association data;

[0043] Step S32: construct a constraint spectrum for the self-security constraint and the network security constraint according to the constraint association data to obtain a collaborative constraint spectrum;

[0044] Step S33: enumerating combination schemes for the characteristic tensors of the combined units according to the collaborative constraint spectrum to obtain an enumerated combination scheme;

[0045] Step S34: Perform combined mathematical modeling based on the enumerated combination scheme to generate a unit combination model.

[0046] The present invention reveals the intrinsic connection between different constraints by mining the correlation between its own safety constraints and network security constraints, thus providing important data support for system optimization. Conflict resolution ensures the coordination between constraints. The generated constraint association data lays the foundation for subsequent analysis. The construction of collaborative constraint spectrum realizes the comprehensive consideration of multiple constraints and improves the overall stability of system operation. The enumeration of combination schemes provides diversified choices for the flexible scheduling of units and enhances the resilience of power systems in complex environments. Combinatorial mathematical modeling based on enumerated combination schemes provides a scientific basis for unit scheduling and operation. The generated unit combination model provides effective support for the access and optimized scheduling of a high proportion of new energy, which improves the overall operation efficiency and reliability of the power system and promotes more efficient utilization of renewable energy.

[0047] Preferably, step S4 comprises the following steps:

[0048] Step S41: construct a 36-hour rolling time window for the unit combination model to obtain a unit rolling time window; set boundary conditions for the unit rolling time window to generate an initial time window boundary;

[0049] Step S42: performing steady-state grid-connected simulation on the initial time window boundary based on the cooperative constraint spectrum to obtain steady-state characteristic data; performing transient grid-connected simulation on the steady-state characteristic data based on the cooperative constraint spectrum to generate transient response parameters;

[0050] Step S43: Perform rolling time domain advancement on the transient response parameters to obtain time domain advancement data; perform unit coordination simulation on the time domain advancement data to generate simulated output coordination information;

[0051] Step S44: performing new energy fluctuation response processing according to the simulation processing coordination information to generate a fluctuation response signal; performing long-period dynamic characteristic evaluation based on the fluctuation response signal to generate grid-connected simulation data;

[0052] Step S45: Based on the grid-connected simulation data, load tracking rating is performed on the unit combination model to obtain tracking capability information; load capacity evaluation is performed on the tracking capability information to obtain the combined load capacity.

[0053] The present invention improves the timeliness and flexibility of unit scheduling by constructing a 36-hour rolling time window. The boundary condition setting ensures the authenticity and operability of the simulation data. The steady-state characteristic data generated by the steady-state grid-connected simulation provides a basis for the unit performance evaluation. The transient grid-connected simulation further reveals the response capability of the unit under dynamic changes. The rolling time domain advancement realizes the real-time tracking of transient response and improves the adaptability of the system. The simulated output coordination information generated by the unit coordination simulation provides a basis for the coordinated scheduling between units. The new energy fluctuation response processing improves the system's ability to cope with fluctuations. The long-period dynamic characteristic evaluation provides a reference for the long-term stable operation of the power grid. The load tracking rating provides a scientific basis for the load management of the power system. The load capacity evaluation ensures the safe operation of the unit combination under high load conditions, which improves the dispatching efficiency and reliability of the power system in an environment with a high proportion of new energy access as a whole, and promotes the stable access and utilization of renewable energy.

[0054] Preferably, step S42 includes the following steps:

[0055] Based on the collaborative constraint spectrum, the power balance adjustment is performed on the initial time window boundary to obtain the 36-hour power parameters; the network power flow analysis is performed on the 36-hour power parameters to generate the steady-state power flow parameters;

[0056] The steady-state power flow parameters are adjudicated by the data of the first 24 hours to obtain the 24-hour steady-state data; the state characteristics of the 24-hour steady-state data are summarized to generate steady-state characteristic data;

[0057] Based on the collaborative constraint spectrum, the steady-state characteristic data is dynamically reconstructed to obtain a dynamic topological model; the dynamic topological model is sectioned at 24 moments to generate the initial state value of the sectioned section;

[0058] The transient response calculation is performed on the initial value of the cut section state to obtain the transient calculation value; the characteristic parameters are extracted from the transient calculation value to obtain the transient response parameters.

[0059] The present invention ensures the power balance of the power grid in different time periods by performing power balance adjustment on the boundary of the initial time window. The 36-hour power parameters provide basic data for subsequent analysis. The steady-state power flow parameters generated by network power flow analysis provide necessary information for power grid status evaluation. The data adjudication of the first 24 hours improves the pertinence of data processing. The generated 24-hour steady-state data provides a reliable basis for power grid operation. The state feature summary provides a comprehensive perspective for the extraction of steady-state feature data. The dynamic topology reconstruction enhances the adaptability of the power grid to a changing environment. The dynamic topology model provides a flexible structural basis for subsequent analysis. The 24-hour section segmentation realizes a detailed analysis of the power grid status. The initial value of the segmented section status provides a starting condition for transient response calculation. The acquisition of transient calculation values ​​lays the foundation for the analysis of the dynamic characteristics of the power grid. The feature parameter extraction provides a deep insight into the understanding of the behavior of the power grid under dynamic changes, which overall improves the response capability and scheduling flexibility of the power system under the condition of a high proportion of new energy access, and promotes the stability and security of the power grid.

[0060] Preferably, step S5 comprises the following steps:

[0061] Step S51: screening qualified combinations of combined load capacities based on preset expected load demands to obtain threshold screening combinations;

[0062] Step S52: Perform reliability evaluation on the threshold screening combination to obtain the combination reliability; repeatedly screen the threshold screening combination according to the combination reliability to generate a screening qualified combination;

[0063] Step S53: Calculate the start-stop consumption of the screened qualified combination to obtain the combined start-stop cost; perform full life cycle cost accounting on the screened qualified combination based on the combined start-stop cost to generate the combined accounting cost;

[0064] Step S54: Perform cost optimization deduction on the screened qualified combinations according to the combined accounting costs to generate the optimal low-cost units.

[0065] The present invention screens qualified combinations of combined load capacities based on preset expected load demands, thereby ensuring the basis for the power system to meet load demands. The threshold screening combination provides a clear selection range for subsequent reliability evaluation, and the evaluation of combined reliability provides a quantitative basis for the operating safety of the unit. Repeated screening ensures the reliability and stability of the final combination. The start-stop consumption calculation of the screened qualified combinations provides important data for economic analysis, and the accounting of combined start-stop costs provides a basis for a comprehensive evaluation of the operating efficiency of the unit. The full life cycle cost accounting provides a perspective for the long-term economic analysis of the unit. The cost optimization derivation realizes the accurate identification of low-cost units, and the generation of optimized low-cost units provides a guarantee for the economic operation of the power system, which overall improves the economy and reliability of the power system under the condition of a high proportion of new energy access, and promotes the efficient use and flexible scheduling of renewable energy.

[0066] The present invention also provides a power grid access simulation system for a high proportion of renewable energy, which is used to execute the power grid access simulation method for a high proportion of renewable energy as described above. The power grid access simulation system for a high proportion of renewable energy includes:

[0067] The operating parameter acquisition module is used to collect the operating parameters of the thermal power unit and the operating parameters of the new energy unit; the operating parameters of the thermal power unit and the new energy unit are combined to construct tensors and generate the combined unit feature tensors;

[0068] The constraint analysis module is used to derive the unit's own constraints on the combined unit's characteristic tensor to obtain its own safety constraints; perform network topology constraint analysis on the combined unit's characteristic tensor to generate network security constraints;

[0069] The unit combination modeling module is used to perform collaborative fusion processing on its own safety constraints and network security constraints to obtain a collaborative constraint spectrum; according to the collaborative constraint spectrum, the combined unit characteristic tensors are combined and enumerated to model and generate a unit combination model;

[0070] The grid-connected simulation module is used to perform long-term grid-connected simulation on the unit combination model based on the collaborative constraint spectrum to obtain grid-connected simulation data; the load capacity of the unit combination model is calculated based on the grid-connected simulation data to obtain the combined load capacity;

[0071] The combination screening module is used to screen qualified combinations based on the combined load capacity to obtain qualified combinations; perform cost optimization deduction on the qualified combinations to generate optimized low-cost units.

[0072] The present invention realizes comprehensive monitoring and data integration of the operating status of thermal power units and new energy units through the operation parameter acquisition module. The generated combined unit characteristic tensor provides a rich information basis for subsequent analysis. The implementation of the constraint analysis module ensures the safety of the unit itself and the stability of the network. Through collaborative fusion processing, the obtained collaborative constraint spectrum enhances the understanding of the combined behavior of the units. The unit combination modeling module improves the accuracy and applicability of the model through combined enumeration modeling. The long-cycle simulation of the grid-connected simulation module can truly simulate the grid operation environment, so as to obtain reliable grid-connected simulation data. The load capacity calculation provides a scientific basis for the flexible scheduling of the unit. The combined screening module ensures the effectiveness and economy of the unit based on the screening of load capacity. Through cost optimization derivation, the operating cost is significantly reduced, and the efficiency and reliability of the access of a high proportion of new energy to the power grid are optimized as a whole, and the adaptability and response speed of the power system to the fluctuation of new energy are enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 A schematic diagram of a step flow of a method for simulating access to a power grid with a high proportion of renewable energy;

[0074] Figure 2 Detailed implementation flow chart of step S2;

[0075] Figure 3 is a schematic diagram of a detailed implementation process of step S3;

[0076] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0077] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0078] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0079] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0080] To achieve this, please refer to Figures 1 to 3 , a grid access simulation method for high-proportion renewable energy, comprising the following steps:

[0081] Step S1: collecting the operating parameters of the thermal power unit and the operating parameters of the new energy unit; constructing a combined tensor of the operating parameters of the thermal power unit and the operating parameters of the new energy unit to generate a combined unit feature tensor;

[0082] Step S2: deriving the unit's own constraints on the combined unit's characteristic tensor to obtain its own safety constraints; performing network topology constraint analysis on the combined unit's characteristic tensor to generate network security constraints;

[0083] Step S3: Perform collaborative fusion processing on the self-security constraints and network security constraints to obtain a collaborative constraint spectrum; perform combined enumeration modeling on the combined unit characteristic tensors according to the collaborative constraint spectrum to generate a unit combination model;

[0084] Step S4: performing a long-period grid-connected simulation on the unit combination model based on the collaborative constraint spectrum to obtain grid-connected simulation data; performing load capacity calculation on the unit combination model according to the grid-connected simulation data to obtain a combined load capacity;

[0085] Step S5: Screen qualified combinations based on combined load capacity to obtain qualified combinations; perform cost optimization deduction on the qualified combinations to generate optimized low-cost units.

[0086] The present invention collects the operating parameters of thermal power units and new energy units, constructs the characteristic tensor of the combined units, and provides a comprehensive data basis for subsequent analysis. The derivation of the unit's own constraints is combined with the network topology constraint analysis to ensure the safety of the unit operation and the stability of the network. The constraint spectrum after collaborative fusion processing provides a scientific basis for the establishment of the unit combination model. The long-term grid-connected simulation can simulate the actual operating conditions and obtain more reliable grid-connected simulation data. The load capacity calculation provides a basis for the reasonable configuration of the units. The qualified combination screening based on load capacity improves the effectiveness of the unit combination. The cost optimization derivation significantly reduces the operating cost, optimizes the economy and reliability of the grid access as a whole, and improves the adaptability and flexibility of the power system to a high proportion of new energy.

[0087] In the embodiment of the present invention, refer to Figure 1 , is a schematic diagram of the steps of a method for simulating access to a power grid with a high proportion of renewable energy according to the present invention. In this example, the method for simulating access to a power grid with a high proportion of renewable energy comprises the following steps:

[0088] Step S1: collecting the operating parameters of the thermal power unit and the operating parameters of the new energy unit; constructing a combined tensor of the operating parameters of the thermal power unit and the operating parameters of the new energy unit to generate a combined unit feature tensor;

[0089] In this embodiment, various key parameters of the operation of thermal power units and new energy units are collected. The operating parameters of thermal power units include unit power generation, coal consumption, boiler temperature and pressure, etc. The operating parameters of new energy units include wind speed, wind direction, power generation of wind power units, solar radiation intensity, temperature, inverter efficiency, etc. of photovoltaic power generation units. These parameters are stored in a database in the form of time series. The above operating parameters are divided into dimensions according to unit category, time dimension, operating status, etc. by a multidimensional data tensor method. The tensor construction function tf.constant in the TensorFlow framework is used to construct initial tensors for the parameters of the thermal power unit and the new energy unit respectively, and then the two initial tensors are combined according to the time dimension to generate a combined unit feature tensor. The tensor concatenation function tf.concat is used in the combination process, and finally a feature tensor with a unified time series and dimension definition is obtained as the input data basis. All data are normalized using the data structure in the IEEE standardized model. The size of the feature tensor of the thermal power unit and the new energy unit is N×T×F, where N is the number of units, T is the time series length, and F is the number of features.

[0090] Step S2: deriving the unit's own constraints on the combined unit's characteristic tensor to obtain its own safety constraints; performing network topology constraint analysis on the combined unit's characteristic tensor to generate network security constraints;

[0091] In this embodiment, when the characteristic tensor of the combined unit is used to derive the unit's own constraints, the data matrix decomposition method is used to analyze the operating constraints of each unit. The upper and lower limits of the output power, the safe operating current range, and the tolerance temperature of the computer unit are calculated by the model, and the constraint parameters are written into the constraint matrix. The network topology constraints are analyzed by the power system flow analysis tool (such as PSS / E software, power system simulation software) on the topological structure of the power grid. The input data is the power grid node and branch connection information. During the analysis process, the flow tool calculates the maximum load capacity and safety margin of each line, and finally generates a three-dimensional network security constraint matrix. Each value of the matrix represents the safe operation constraint condition of each node pair.

[0092] Step S3: Perform collaborative fusion processing on the self-security constraints and network security constraints to obtain a collaborative constraint spectrum; perform combined enumeration modeling on the combined unit characteristic tensors according to the collaborative constraint spectrum to generate a unit combination model;

[0093] In this embodiment, the self-safety constraints and network security constraints are collaboratively fused through a multi-level weighted method. The weighted fusion algorithm written in Matlab is used to weight the two sets of matrices with different weight values. The weight setting is adjusted according to the system stability requirements. After fusion, a collaborative constraint spectrum is generated. The collaborative constraint spectrum is a multi-dimensional matrix structure, which is input into the combinatorial enumeration modeling algorithm for processing. The combinatorial enumeration model is constructed using Python's TensorFlow deep learning framework. The input layer in the model corresponds to the unit feature tensor, and the output layer is the combined result of the unit's operating characteristics, and finally a unit combination model is generated.

[0094] Step S4: performing a long-period grid-connected simulation on the unit combination model based on the collaborative constraint spectrum to obtain grid-connected simulation data; performing load capacity calculation on the unit combination model according to the grid-connected simulation data to obtain a combined load capacity;

[0095] In this embodiment, when the long-period grid-connected simulation of the unit combination model is performed based on the collaborative constraint spectrum, a single-machine multi-node power flow simulation analysis method is adopted, and the input data is the unit combination model and the grid topology information. During the simulation process, the tool dynamically updates the input of the power flow calculation according to the time step, and the output is the power flow distribution results and load data of each time period. The simulation results are saved in the database for subsequent load capacity calculation. When the load capacity is calculated, the simulation data is called, and the maximum load capacity value of the computer group combination is calculated according to the dynamic balance relationship between the generated power and the grid load demand, and the output is the load capacity data file of each combination under different operating scenarios.

[0096] Step S5: Screen qualified combinations based on combined load capacity to obtain qualified combinations; perform cost optimization deduction on the qualified combinations to generate optimized low-cost units.

[0097] In this embodiment, the combined load capacity data is programmed to screen out qualified unit combinations that meet the load demand. The screening process compares the maximum load capacity value of the combination with the minimum demand of the power grid through a conditional screening algorithm. The qualified combinations are saved in a list, and then the cost optimization is derived for the screened qualified combinations. The operating cost of each combination is calculated using an optimization algorithm based on linear programming. The operating cost includes fuel consumption, startup expenses and maintenance costs. The optimization goal is to minimize the operating cost. The weight parameters are set according to the requirements of different operating scenarios. Finally, the unit combination with the lowest operating cost is selected as the preferred low-cost unit, and the output result is stored in a standard table in the format of "combination number-unit composition-operating cost".

[0098] In this embodiment, step S1 includes the following steps:

[0099] Step S11: Collecting the operating parameters of thermal power units and new energy units;

[0100] Step S12: performing time sequence alignment on the operating parameters of the thermal power unit and the operating parameters of the new energy unit to generate synchronous operating parameters;

[0101] Step S13: performing principal component analysis on the synchronous operation parameters to obtain dimension-reduced operation features;

[0102] Step S14: Perform three-boundary tensor transformation on the dimension-reduced operation characteristics to generate a combined unit characteristic tensor.

[0103] In this embodiment, the operating parameters of the thermal power unit are obtained through a real-time data acquisition system (SCADA, data acquisition and monitoring system) installed in the unit control room. The operating parameters involved include main steam pressure, main steam temperature, generator active power, unit speed and flue gas emission indicators. The operating parameters of the new energy unit are collected through the Modbus communication protocol connected to the unit controller. The parameters involved include fan speed, wind speed and direction, solar radiation intensity, power output and ambient temperature. All data are in standard timestamp (ISO 8601 format) and stored as CSV files according to the equipment number. The collected thermal power unit operating parameters and new energy unit operating parameters are aligned in time series. First, the stored operating parameter CSV file is read through Python's Pandas library. The time resolution of the unified data is 5 minutes. The missing data is filled with a linear interpolation algorithm to ensure the continuity of the parameter sequence in the time dimension. The interpolation calculation formula is as follows: y = y1 + (y2-y1) × (t-t1) / (t2-t1); where t1 and t2 are adjacent time points, and y1 and y2 are parameter values ​​at corresponding time points. After the interpolation is completed, the thermal power and new energy parameters are merged into a single table file according to the time points. The principal component analysis is performed on the synchronous operation parameter file, and the PCA module in Python's Scikit-learn library is called. The synchronous operation parameter matrix is ​​standardized to zero mean and unit variance. The formula of the standardization method is: z = (xv) / σ; where v is the mean and σ is the standard deviation. After the standardization is completed, the principal components of the operation parameter matrix are extracted, and the dimension with the cumulative variance contribution rate of the principal component reaching 90% is selected. The extracted principal component data is stored in the form of a matrix. Each column of the matrix represents a principal component and each row represents a time point. The reduced dimension operation features are converted into three-bounded tensors. The NumPy library is called to read the reduced dimension feature file, and it is constructed into a three-dimensional tensor structure using a multidimensional array method. The first dimension of the tensor represents the unit number, the second dimension represents the time point, and the third dimension represents the principal component eigenvalue after dimensionality reduction. During the conversion process, the time points are screened and aligned to ensure the consistency of all unit data in the time dimension. The generated three-bounded tensor is saved in the NumPy array format.

[0104] In this embodiment, step S2 includes the following steps:

[0105] Step S21: Deconstruct the dynamic characteristics of the combined unit characteristic tensor to obtain the thermal power unit constraints; analyze the new energy output characteristics of the combined unit characteristic tensor to generate the new energy unit constraints;

[0106] Step S22: performing group constraint connection on the thermal power unit constraints and the new energy unit constraints to obtain their own safety constraints;

[0107] Step S23: quantifying stability based on the constraints of thermal power units and new energy units, and generating stability constraints;

[0108] Step S24: performing a balanced power tensor operation on the characteristic tensor of the combined unit according to the stability constraint to obtain a node constraint; performing a phase angle sensitivity matrix decomposition on the node constraint to obtain a phase angle constraint;

[0109] Step S25: Calculate the power flow safety region for the phase angle constraint to generate a power flow constraint; perform topological integration processing on the node constraint, phase angle constraint and power flow constraint to generate a network topology constraint;

[0110] Step S26: Expand and map the network topology constraints based on the stability constraints to generate network security constraints.

[0111] In this embodiment, the characteristic tensor of the combined unit is extracted, and the tensor contains historical operation data of the thermal power unit and the new energy unit, including information such as power output, load fluctuation, and regulation capability. The principal component analysis (PCA) is applied to reduce the dimension of the data to extract key features related to the dynamic response characteristics. The frequency domain characteristics of the tensor are analyzed by Fourier transform to obtain the response frequency and regulation capability of the thermal power unit. The load regulation time, response time and power fluctuation amplitude of the thermal power unit are analyzed to obtain the constraints of the thermal power unit. The constraints include parameters such as minimum load, maximum load, response time, etc., including unit power constraints. The unit power constraints refer to that the output level of the generator unit should be between its minimum technical output and maximum technical output:

[0112]

[0113] u i,t Indicates the online status of unit i in time period t, 1 means in operation, and 0 means off-grid operation; P i Indicates the maximum / minimum output power of unit i; P i,t Represents the output power of unit i in time period t.

[0114] In addition, because thermal power units need to participate in the electricity energy market and ancillary service market, the electricity energy and ancillary services they provide must also meet the following constraints:

[0115]

[0116] Finally, the thermal power unit adopts segmented bidding, so the following power constraints are also met:

[0117]

[0118] Then, the output characteristics of the new energy units are analyzed. The output data of new energy units such as wind power and photovoltaic power are extracted through time series analysis methods (such as time domain convolution network, Temporal Convolution Network, TCN). The characteristics are modeled in combination with the historical data of the units to obtain the output volatility of the new energy units and generate the corresponding new energy unit constraints. The constraints include the maximum output, minimum output, output fluctuation range, etc. of wind power. The constraints of thermal power units and new energy units are integrated according to load scheduling, power output fluctuation and operation timing. The optimization constraint programming algorithm (such as linear programming) is used to integrate the two constraints into a comprehensive constraint model. The coordination between the two is considered during optimization. By modeling the load demand curve of the power grid, it is ensured that the output of thermal power units and new energy units does not conflict, avoid overload or power gap, and obtain a comprehensive safety constraint. In the specific steps, by optimizing the objective function in the model, the constraints are combined with the load changes to ensure the synergy of the units and avoid the problem of power supply imbalance caused by the independence of each constraint. This objective function is established to minimize the system cost under a high absorption rate of new energy. By F 1 and F 2 It consists of two parts. Among them, F 1 represents the combined power generation cost of the units, F 2 represents the cost of auxiliary services provided by conventional thermal power units, from which the system minimum operating cost F under different new energy penetration rates can be obtained:

[0119] F=F 1 +F 2

[0120] The first part of the objective function F 1 The expression is:

[0121] min F 1 =f 1 +f 2 +f 3

[0122]

[0123] Where: f 1 represents the operating cost of thermal power units, f 2 represents the operating cost of wind turbines, f 3 Represents the operating cost of a photovoltaic system. Represents the electricity price of thermal power units, P i,m,t Represents the output of thermal power unit i at time t in period m. and They represent the startup, shutdown and no-load operation costs of thermal power units, vi,t Indicates the start-up action of unit i at time t, which is a 0-1 variable; w i,t Indicates the shutdown action of unit i at time t, which is a 0-1 variable; u i,t Indicates whether it is in no-load running state, which is a 0-1 variable. represents the bid of wind power plant station j, represents the price offered by photovoltaic plant k, P j,t represents the output power of the wind turbine in period j, P k,t Represents the output power of the photovoltaic unit in time period t.

[0124] Part II Objective Function F 2 The expression is as follows:

[0125] min F 2 =f 1 +f 2 +f 3

[0126]

[0127] Where: f 1 The cost of providing up and down backup for conventional thermal power units, f 2 represents the cost of deep peak load regulation auxiliary services provided by thermal power units, f 3 Represents the cost of providing primary and secondary frequency regulation by thermal power units. represents the upper reserve quotation of thermal power unit i at time t; represents the next reserve price of thermal power unit i at time t, Indicates the upper reserve power provided by unit i; Indicates the lower reserve power provided by unit i. Indicates the deep peak load regulation quotation of thermal power units. Indicates the deep peak regulation capacity of the thermal power unit that won the bid. and Indicates the price of primary / secondary frequency regulation mileage of thermal power units, and Indicates the price of primary / secondary frequency regulation capacity of thermal power units, and It represents the primary / secondary frequency regulation performance index of the unit, and finally outputs a safety constraint including the coordinated operation of thermal power units and new energy units. The small disturbance stability analysis method of the power system is used to dynamically simulate the unit constraints. The eigenvalue decomposition method is used to quantify the stability of the power system. By calculating the eigenvalues ​​of the system, the stability limit of the system is analyzed to ensure that the power system can remain stable under load fluctuations. By solving the state equation of the power system, the power regulation capability, inertial response and load changes of each unit are considered to generate stability constraints. In the specific steps, the constraints are optimized through linear matrix inequality (LMI) to ensure that the system can quickly recover to a stable state under any operating conditions. After generating the stability constraints, the stability control strategy is further optimized according to the sensitivity analysis of the system to cope with a large range of load changes. According to the power output characteristics of thermal power units and new energy units, the power flow calculation method (such as Newton-Raphson method, Newton-Raphson method, etc.) is used to calculate the stability limit of the system. Method) is used to calculate the balanced power, obtain the power distribution of each grid node, ensure the power balance of the grid at each node, and generate node constraints. The node constraints include the power output and load balance restrictions of each node. Subsequently, the phase angle sensitivity matrix is ​​decomposed based on the node constraints, and the sensitivity analysis method is used to calculate the rate of change of the phase angle. By performing eigenvalue decomposition on the grid phase angle sensitivity matrix (Angle Sensitivity Matrix), the sensitivity of the phase angle of each grid node to the power change is obtained, and then the phase angle constraint is generated. The phase angle constraint refers to the value range of the bus phase angle. The phase angle of the balanced bus is taken as 0 as a reference. During normal operation, the phase angles of other buses should be near 0. The specific equation constraints are as follows:

[0128] -π≤θ(s,t)≤π

[0129] , to ensure that the phase angle change will not cause system instability, use the power flow calculation method (such as DC Power Flow) to calculate the safety domain, determine the power flow safety area of ​​the power grid under different operating conditions, ensure that the system is not overloaded, and then generate power flow constraints. Among them, the power flow constraint of the transmission line means that the line flow should be less than the allowable limit of the line flow. At present, the most widely used method is to use the DC power flow model to solve the line flow. The specific model is as follows:

[0130]

[0131] Where θ(s,t) represents the phase angle of bus s at time t; x l It represents the reactance parameter of the transmission line; P l is the maximum / minimum power constraint of line l.

[0132] After calculating the power flow transfer distribution factor matrix G, the power flow safety constraint is rewritten as

[0133]

[0134] In the specific operation, the grid topology is analyzed, the flow distribution is calculated, and the boundary of the safe flow domain is obtained. Then, the node constraints, phase angle constraints and flow constraints are topologically integrated, and the grid topology is optimized by network optimization algorithms (such as mixed integer linear programming, MILP) to ensure the safety and stability of the grid and generate the final network topology constraints. The whole process adjusts the network connection and current flow direction to ensure that the network can recover quickly and maintain the stability of the system when a fault or load fluctuation occurs. The topology of the grid is deeply analyzed to identify the weak links and potential fault points in the grid. The network flow model is used for expansion mapping to extend the results of the stability constraint to the grid topology to ensure that the grid can maintain efficient power transmission and distribution under any circumstances. The topology of the grid is analyzed by graph theory to identify key nodes and branches to avoid safety hazards caused by unreasonable topology. Network security constraints are generated to ensure that the grid can still maintain safe operation under various emergencies, and finally complete the safety constraint mapping and optimization of the entire grid system.

[0135] In this embodiment, step S21 includes the following steps:

[0136] Extract power balance features from the combined unit feature tensor to obtain a power balance constraint set; calculate the power boundary of the thermal power unit on the power balance constraint set to obtain a power boundary constraint set;

[0137] The thermal power unit ramp rate analysis is performed on the power boundary constraint set to obtain the ramp constraint set; the start and stop time modeling is performed on the ramp constraint set to obtain the time constraint set;

[0138] The power balance constraint set, power boundary constraint set, ramp constraint set and time constraint set are dynamically integrated to obtain the constraints of the thermal power unit;

[0139] Analyze the new energy output characteristics of the combined unit characteristic tensor to obtain the output characteristic set;

[0140] The wind power prediction model is performed on the output characteristic set to obtain the wind power constraint set; the photovoltaic power generation characteristics of the wind power constraint set are analyzed to obtain the photovoltaic constraint set;

[0141] Based on the wind power constraint set and the photovoltaic constraint set, the absorption capacity is predicted to obtain the predicted absorption capacity; the absorption constraint mapping is performed on the predicted absorption capacity to obtain the absorption constraint set;

[0142] The output characteristics of the output feature set, wind power constraint set, photovoltaic constraint set and consumption constraint set are integrated to generate constraints for new energy units.

[0143] In this embodiment, the historical power output data of the combined units is analyzed in time series, and the power output characteristics of each unit are extracted, including real-time power, load changes and regulation responses. The power flow balance of the combined units at different load levels is calculated, and the power balance calculation is performed according to the power grid topology and the unit output data using a power flow algorithm (such as a power flow calculation method). The power balance characteristics of each unit are obtained, and then a power balance constraint set is generated, in which the output of the new energy and conventional units is equal to the load size, and the power of the system needs to be balanced at every moment. The equality constraint of the model is described as:

[0144]

[0145] N G Indicates the number of thermal power units; N L Indicates the number of load nodes; N W Indicates the number of wind power plants; N S represents the number of photovoltaic power plants. The constraint set includes parameters such as the balance of power output and input, the load regulation capability of the unit, etc. The minimum and maximum output values ​​are extracted from the output power characteristics of the thermal power unit. By analyzing historical data, the power boundary of the thermal power unit is calculated using the maximum power curve and the minimum power curve. The power boundary constraint set is generated by combining the response time and regulation capability of the unit with the method based on the dynamic simulation of the power system. The constraint set includes the adjustable power range of the unit and the dynamic change of the constraint boundary under different load conditions. The historical operation data is used to model the ramp rate of the unit. The ramp rate refers to the regulation rate of the unit from the minimum power to the maximum power in unit time. The control strategy data of the thermal power unit is used to analyze the ramp rate of the unit using mathematical modeling methods (such as optimization algorithms) to obtain the ramp rate under different load conditions. Further, according to the load demand of the power system and the response capability of the unit, a ramp constraint set is generated. Among them, the unit ramp constraint means that the output change value of the unit in two adjacent time periods should be within the upper and lower ramp allowable range:

[0146]

[0147] R Indicates the up and down climbing rate of the unit; P i,trepresents the output power of unit i in period t; P i,t represents the output power of unit i in period t; P i,t-1 Represents the output of unit i in t-1.

[0148] In addition, when the minimum power for unit startup is greater than the ramp rate, the unit ramp constraint will cause all shut-down units to fail to start, so the ramp constraint is rewritten as:

[0149]

[0150] In addition, in order to simplify the calculation, the maximum startup rate and the maximum shutdown rate can be taken as

[0151]

[0152] The constraint set includes the maximum ramp rate that the unit can reach within a specific time and the rate adjustment range under different operation modes. According to the historical start-stop records and scheduling plans of the unit, the start-stop process of the thermal power unit is modeled using the time series analysis method. By analyzing the characteristics of the unit's start-up time, shutdown time and power adjustment during the start-stop process, combined with the unit's technical parameters, the start-stop time model is obtained. When the unit is turned on, it must be guaranteed to run for a period of time, and it can only be turned on once during this period of time (minimum running time); similarly, when the unit is turned off, it must also be guaranteed to stop running for a period of time (minimum shutdown time):

[0153]

[0154] The three Boolean variables have the following relationship, which means that the units that have been turned on can only be turned off, and the units that have been turned off can only be turned on:

[0155] u i,t -u i,t-1 =v i,t -w i,t

[0156] TU i ,TD i Indicates the minimum start / stop time of unit i; v i,t Indicates the start-up action of unit i at time t, 1 means start-up, 0 means others; u i,t represents the online status of unit i in time period t, 1 means in operation, 0 means off-grid operation; w i,tIndicates the shutdown action of unit i at time t, 1 is shutdown, 0 is other, the linear programming method is used to optimize the start and stop time, ensure that the power regulation during the start and stop process meets the stability requirements, and generate a time constraint set. The constraint set includes the time limit during the start and stop process, the correlation between the start and stop time and the power change, and the various constraint sets are multi-dimensionally integrated according to power output, ramp rate, start and stop time and load regulation capability. The weighted sum method is used to synthesize the constraint set, considering the load demand of the power grid and the response capability of the thermal power unit, and combining the various constraints to form a comprehensive thermal power unit constraint, and then through the optimization algorithm (such as particle swarm optimization, Particle Swarm Optimization, The constraint set is optimized by using the time series analysis (such as LSTM neural network) method to ensure that the unit can meet the load demand during operation and maintain stability in the dynamically changing power environment. Finally, the comprehensive constraints of thermal power units suitable for different load demand conditions are generated. The historical output data of new energy units such as wind power and photovoltaic are collected and sorted. The power output of new energy units is modeled by time series analysis (such as LSTM neural network) method, and the power volatility, maximum power generation, minimum output and other characteristics of new energy units are extracted. The output characteristics of new energy units under different weather conditions, seasons and time periods are calculated to generate the output feature set, which includes the fluctuation range, time series fluctuation law and maximum and minimum output capacity of wind power and photovoltaic power generation. The relationship model between wind speed and wind turbine output power is adopted, combined with historical meteorological data, and regression analysis methods (such as support vector machine regression, SVR) are used to predict wind power. Based on real-time meteorological data and wind turbine characteristics, the power output of wind turbines in the future is predicted. The wind power constraint set is generated by analyzing the changes in prediction errors. Among them, wind power output is mainly affected by wind speed. At present, the two-parameter Weibull distribution model is widely used to describe wind speed distribution. The probability density function expression of this type of model is as follows:

[0157]

[0158] In the formula, v represents wind speed; k represents shape parameter, and c represents size parameter. Their specific expressions are:

[0159]

[0160] Among them, μ w , σ w are the mean and standard deviation of wind speed respectively.

[0161] The probability cumulative density function of the Weibull distribution is as follows:

[0162]

[0163] This gives the wind speed:

[0164]

[0165] Among them, r is a random variable uniformly distributed between [0,1], which can be randomly generated by Monte Carlo simulation.

[0166] The characteristic curve between wind speed and wind power can be expressed by the following piecewise function:

[0167]

[0168] In the formula, Indicates the active power output by the fan; P G,w,N Indicates the rated output of the fan; v in 、v N 、v out They are cut-in wind speed, rated wind speed, and cut-out wind speed respectively. The constraint set includes the maximum output value, minimum output value, and power fluctuation range of wind power. The historical data of photovoltaic generator sets are collected to analyze the output characteristics of photovoltaic power generation, including the influence of factors such as light intensity and temperature on photovoltaic power output. The photovoltaic power generation output is modeled using a photovoltaic power generation model (such as photovoltaic power calculation based on a radiation model) to generate a photovoltaic constraint set. The constraint set includes the maximum output, minimum output, and seasonal changes of photovoltaic power. The photovoltaic output level is mainly related to the light radiation of the location. Similar to wind speed, light radiation itself has a certain degree of randomness and generally obeys the beta distribution. Its probability density expression is as follows:

[0169]

[0170] In the formula, q and q max are the actual light radiation intensity and the maximum light radiation intensity respectively; Γ() is the gamma function; α and β are the shape parameters of the beta distribution, which can be obtained by the following formulas respectively.

[0171]

[0172] In the formula, μ pv , σ pv They are the mean and standard deviation of sunlight radiation. Generally speaking, the sampling period for sunlight intensity measurement is 1 minute or an integer multiple of 1 minute. The system can calculate its mean and variance based on a large number of sampling results in one day.

[0173] In addition, the gamma function is an extension of the factorial function, which is defined over the real number field as:

[0174]

[0175] Finally, the following formula can be used to obtain the output power of photovoltaic cells under any light intensity.

[0176]

[0177] Among them, P G,pv (q) represents the output value of photovoltaic power generation; P G,pv,N is the rated output power of the photovoltaic cell; s is the temperature coefficient, which is generally -0.47; q N 、T pv,N are the light radiation intensity and temperature under standard conditions, which are 100W / m2 and 25℃ respectively; T pv is the actual temperature of the photovoltaic cell, which is calculated by the following formula.

[0178]

[0179] Where, T en represents the ambient temperature. By simulating the sunshine conditions and climate factors, it ensures that the photovoltaic constraints are compatible with the wind power constraints to form a complete new energy output constraint. Among them, the wind power output constraint

[0180] 0<P W,t <P W,t,Max

[0181] P W,t represents the wind farm output at time t, P W,t,Max represents the predicted maximum output of the wind power plant at time t

[0182] Photovoltaic output constraints

[0183] 0<P PV,t <P PV,t,Max

[0184] P PV,t represents the photovoltaic field output at time t, P PV,t,MaxIt represents the predicted maximum output of the photovoltaic power plant at time t. The grid load demand and renewable energy generation capacity are used for collaborative prediction. The multivariate regression analysis method is used to combine wind power, photovoltaic power and grid load factors to predict the grid's absorption capacity and obtain the maximum renewable energy power that the grid can accept in the future time period. By modeling and predicting the grid absorption capacity, the predicted absorption capacity data is generated. The predicted absorption capacity is compared with the load dispatching demand of the grid. Combining the grid dispatching strategy with the characteristics of renewable energy generation, the nonlinear programming method is used to constrain the absorption capacity to ensure that the absorption constraint can reflect the actual absorption capacity of the grid. Taking into account the grid load fluctuation and renewable energy power fluctuation, the final absorption constraint set is obtained. The constraint set includes the upper and lower limits of the absorption capacity and the maximum support degree of the grid for renewable energy absorption. The absorption rate calculation formula is:

[0185]

[0186] In the formula, They represent the actual maximum possible output of the i-th wind turbine and the j-th photovoltaic unit in time period t; h 1 and h 2 They are the minimum renewable energy absorption rates for wind power and photovoltaic systems, respectively. Each constraint set is integrated in multiple dimensions according to power fluctuation, load demand and power generation capacity. A multi-objective optimization algorithm (such as genetic algorithm) is used to optimize each constraint set to ensure the compatibility of all constraints. The output of renewable energy units is reasonably allocated in power grid dispatching to generate constraints for renewable energy units. The constraint set includes characteristics such as output fluctuation, absorption capacity and maximum output of wind power and photovoltaic power generation.

[0187] In this embodiment, step S24 includes the following steps:

[0188] Perform unbalanced power analysis on the characteristic tensor of the combined unit to obtain the power deviation;

[0189] According to the power deviation, the characteristic tensor of the combined unit is distributed and mapped to generate the unit power distribution;

[0190] According to the stability constraint, the power distribution of the unit is subjected to balance correction processing to obtain the balanced power tensor;

[0191] Based on the balanced power tensor, the node power of the combined unit characteristic tensor is readjusted to generate node constraints;

[0192] The phase angle sensitivity of the node constraint is calculated to obtain the phase angle sensitivity parameter; the matrix eigenvalue decomposition of the phase angle sensitivity parameter is performed to obtain the phase angle constraint.

[0193] In this embodiment, the real-time power data of each unit in the system is obtained, and the power deviation of each unit is calculated using the weighted mean method. The power deviation refers to the difference between the actual output power of each unit and the ideal power. For new energy units such as wind power and photovoltaic power, real-time meteorological data (such as wind speed and light intensity) are used to estimate their theoretical maximum power output. In the process of unbalanced power analysis, data cleaning technology is used to remove abnormal values ​​to ensure the accuracy of power data. After that, the deviation is statistically analyzed, and a distribution map of the power deviation is generated, and then the maximum and minimum values ​​of the deviation are extracted. According to the power deviation, the characteristic tensor of the combined unit is distributed and mapped to generate the unit power distribution. The power deviation is classified according to the type of power output (such as wind power, photovoltaic power, thermal power, etc.), and the Gaussian distribution model (Gaussian The power value is fitted by using the power distribution function to generate the probability distribution of the combined unit power. The power distribution in different time periods is corrected using the estimation method based on Bayesian reasoning to ensure the rationality and accuracy of the power distribution in the scenario with a high proportion of renewable energy access. Then, the probability distribution diagram of the unit power output at each moment is obtained, and the unit power distribution is balanced and corrected according to the stability constraint. The stability constraint is: to ensure the frequency safety of the system, after disturbances occur on the renewable energy and load sides, the frequency rise or fall must be within the safe range that the system can bear. Therefore, the minimum frequency constraint can be constructed as follows.

[0194] f a ≤f N +Δf≤f b

[0195] f a Indicates the frequency lower limit, which is usually set to the system frequency lower limit;

[0196] f b Indicates the upper frequency limit value, which is usually set to the upper frequency limit of the system;

[0197] f N Indicates the frequency reference value;

[0198]

[0199] Among them, ΔP NE Indicates the active power fluctuation caused by the new energy unit, ΔP L Indicates the active power fluctuation caused by the load, K GThe power-frequency static characteristic coefficient of the conventional generator set is expressed to obtain the balanced power tensor. Combined with the stability constraints of the power grid (such as voltage and frequency stability), the Lagrange Multiplier Method is used to correct the power distribution of the unit to ensure that the corrected power distribution can meet the balance conditions of the power grid. The power flow algorithm is used to optimize the power of each unit in the process of calculating the corrected power, so that the output power of each unit can minimize the power deviation while meeting the stability requirements of the power grid. The balanced power tensor is obtained, which contains the balanced power values ​​of each unit at each moment. According to the topological structure of the power grid, the key nodes in the system are identified, and the power of each node is readjusted using the node current balance principle (Kirchhoff's Current Law). The power output of each node is adjusted by the weighted average method to ensure that the power of all nodes meets the overall power balance of the system, and the change amplitude of the node power does not exceed the set maximum value, thereby generating node constraints, calculating the phase sensitivity of the node constraints, and obtaining the phase sensitivity parameters. In this process, the admittance matrix based on the power grid structure is applied. Matrix) is used for calculation, the relationship between phase angle and power is analyzed through linearized power flow equations, the sensitivity analysis method is used to calculate the sensitivity of each node power change to the phase angle change, and the phase angle sensitivity parameters are obtained. The matrix eigenvalue decomposition of the phase angle sensitivity parameters is performed to obtain the phase angle constraint, and the eigenvalue decomposition technology is used to decompose the matrix composed of the phase angle sensitivity parameters, and the eigenvalues ​​and eigenvectors of the matrix are extracted. The stability boundary of the power grid is judged from the eigenvalues, and specific constraints are set, such as the upper limit of the phase angle change rate, to ensure that the system will not exceed the stability range during operation, and finally the phase angle constraint is obtained.

[0200] In this embodiment, step S3 includes the following steps:

[0201] Step S31: Based on the stability constraint, correlation mining is performed on the self-security constraint and the network security constraint to obtain constraint correlation; conflict resolution is performed on the constraint correlation to generate constraint association data;

[0202] Step S32: construct a constraint spectrum for the self-security constraint and the network security constraint according to the constraint association data to obtain a collaborative constraint spectrum;

[0203] Step S33: enumerating combination schemes for the characteristic tensors of the combined units according to the collaborative constraint spectrum to obtain an enumerated combination scheme;

[0204] Step S34: Perform combined mathematical modeling based on the enumerated combination scheme to generate a unit combination model.

[0205] In this embodiment, the grid operation data under the scenario of high proportion of new energy access is collected, including voltage fluctuation range, current distribution law, frequency change curve, etc., and a mathematical model of self-safety constraint and network security constraint is established. The self-safety constraint model includes the safety boundary of single unit operation, and the network security constraint model includes the stability boundary of the grid. The interactive relationship between the two constraints is analyzed by using the association rule mining technology in the data mining algorithm to generate a constraint correlation matrix. Then, the conflict resolution algorithm based on conflict detection is applied to optimize and adjust the contradictory constraints in the constraint correlation. For example, in the case where different constraint priorities exist in the same power node, the constraint priority is sorted based on the analytic hierarchy process (AHP). When the constraint spectrum of the self-safety constraint and the network security constraint is constructed according to the constraint association data, the constraint association data is decomposed into the self-constraint spectrum and the network constraint spectrum by using the matrix decomposition method, and the singular value decomposition (Singular Value Decomposition) is used. The SVD (Subjective Decomposition) technique is used to extract the principal component information of the constraint matrix, thereby generating a synergistic constraint spectrum containing global synergistic constraint information. The synergistic constraint spectrum is represented in the form of a multidimensional tensor, where the first dimension of the tensor represents the constraint type, the second dimension represents the time series of the constraint, and the third dimension represents the importance weight of the constraint. The synergistic constraint spectrum also contains the mutual coupling relationship between the constraints. By reducing the dimension of the spectrum data, the key constraint factors are extracted. Based on the constraint coupling relationship in the synergistic constraint spectrum, the combined unit feature tensor is divided into different feature groups. For example, the new energy unit features, traditional unit features and energy storage system features are respectively taken as independent feature groups. These feature groups are enumerated using a permutation and combination algorithm. During the enumeration process, an optimization search algorithm is introduced, such as a particle swarm optimization algorithm (PSO), to screen out all effective solutions that meet the synergistic constraint spectrum. The performance indicators of each solution, such as output power deviation, operating cost, stability factor, etc., are recorded in the solution table. Finally, a complete set of enumerated combination solutions is obtained. When performing combinatorial mathematical modeling based on the enumerated combination solution, the key feature indicators in the enumerated combination solution are selected as input variables. The mixed integer linear programming (Mixed Integer Linear Programming) is used to perform the combined mathematical modeling. The unit combination model is constructed by the method of Multi-Integer Linear Programming (MILP). The objective function of the unit combination model is the comprehensive optimization target of power output and stability constraints. The constraints include the coupling constraints provided by the collaborative constraint spectrum and the constraint boundaries in the enumeration scheme table. The solution of the model is solved by high-performance computing tools such as Gurobi optimizer. The results include the specific output power distribution, operating status and quantitative value of the impact on grid stability of each combination scheme, thus generating a unit combination model.

[0206] In this embodiment, step S4 includes the following steps:

[0207] Step S41: construct a 36-hour rolling time window for the unit combination model to obtain a unit rolling time window; set boundary conditions for the unit rolling time window to generate an initial time window boundary;

[0208] Step S42: performing steady-state grid-connected simulation on the initial time window boundary based on the cooperative constraint spectrum to obtain steady-state characteristic data; performing transient grid-connected simulation on the steady-state characteristic data based on the cooperative constraint spectrum to generate transient response parameters;

[0209] Step S43: Perform rolling time domain advancement on the transient response parameters to obtain time domain advancement data; perform unit coordination simulation on the time domain advancement data to generate simulated output coordination information;

[0210] Step S44: performing new energy fluctuation response processing according to the simulation processing coordination information to generate a fluctuation response signal; performing long-period dynamic characteristic evaluation based on the fluctuation response signal to generate grid-connected simulation data;

[0211] Step S45: Based on the grid-connected simulation data, load tracking rating is performed on the unit combination model to obtain tracking capability information; load capacity evaluation is performed on the tracking capability information to obtain the combined load capacity.

[0212] In this embodiment, the time range and step parameters of the rolling window are defined. The time range is 36 hours, and the step parameter is 1 hour. Each time period in the time range is used as an independent time window node, and each time window node is subjected to rolling update processing. During the rolling process, all units in the unit combination model are traversed to extract the output power time series of each unit, and a rolling window data covering 36 hours is generated. Then, the boundary conditions of the unit rolling window are set. When setting the boundary conditions, the initial state variables need to be clarified, including the initial output power of the unit, the network voltage boundary value, and the frequency stability range. The boundary conditions are used to generate the initial time window boundary, and the generated initial time window boundary is used as the input condition for subsequent simulation operations. When the initial time window boundary is subjected to steady-state grid-connected simulation based on the collaborative constraint spectrum, a steady-state simulation tool such as PSSE (Power System Simulator for Engineering, a power system simulation tool) is used for operation. During the simulation process, the rolling time window and cooperative constraint spectrum of the unit are imported into the simulation model. The initial simulation conditions are set, including the rated power of the unit, the network topology and the constraint boundary value. The steady-state simulation is run to calculate the steady-state characteristic parameters of each unit, such as the node voltage amplitude, reactive power distribution, etc. The calculation results of the steady-state characteristic data are output in the form of a matrix. Then, the steady-state characteristic data is subjected to transient grid-connected simulation based on the cooperative constraint spectrum. The transient simulation is implemented by electromagnetic transient simulation software such as MATLAB Simulink (a mathematical modeling simulation tool). The input variables are the steady-state characteristic data. The simulation outputs transient response parameters, including the frequency response curve, the voltage recovery time and the power fluctuation amplitude. The data output by the simulation is imported into the analysis module to generate complete transient response parameters. The time axis of the transient response parameters is divided according to the time step parameter. The transient response data in each time step is integrated into a rolling advancement data sequence. Then, the rolling advancement data is numerically calculated by the finite difference method. The time coupling characteristics in the data are extracted to generate time domain advancement data. When the time domain advancement data is used for unit coordination simulation, the optimization scheduling algorithm such as the genetic algorithm is used. The power output of each unit is optimized and allocated by the GA (Generator Algorithm). During the simulation, the dynamic characteristics in the time domain advancement data are used as input variables, the minimization of power deviation is used as the objective function, and the constraint condition is the physical boundary value of the unit. Finally, the simulated output coordination information is output. The simulation results include the power allocation plan and dynamic characteristic adjustment amount of each unit in each time step. When processing the fluctuation response of new energy according to the simulated output coordination information, the fluctuation analysis algorithm such as Fast Fourier Transform (Fast Fourier Transform,The power change data in the coordination information is analyzed in the frequency domain by using FFT, and the amplitude, frequency and phase characteristics of the fluctuation signal are extracted. The fluctuation response signal is generated with the frequency characteristics as the main parameter, and then the long-period dynamic characteristics are evaluated based on the fluctuation response signal. During the evaluation process, a dynamic response model is constructed by using dynamic response modeling tools such as DIgSILENT (power system dynamic simulation tool). The input variables are the fluctuation response signal and the coordinated constraint spectrum, and the model output is the long-period dynamic characteristics data. The key indicators such as the response amplitude attenuation rate, phase stability and system damping ratio in the output are analyzed to generate complete grid-connected simulation data. The output power curve in the simulation data is fitted and analyzed by data fitting algorithms such as the least squares method to extract the dynamic load response capability of the unit. Based on this, a load tracking rating index system is constructed. The rating indicators include dynamic power tracking error, frequency recovery time and steady-state deviation. A multi-objective optimization algorithm is used to rate and calculate each unit, and a rating report containing tracking capability values ​​is generated. When evaluating the load capacity of the tracking capability information, a large-scale simulation platform such as RTDS (Real-Time Digital Simulator, a real-time digital simulator, re-simulates and verifies the load response performance of each unit, and outputs the combined load capacity based on the verification results and the collaborative constraint spectrum data. The output data of the load capacity includes the maximum traceable power value of each unit under different load conditions and the operating stability parameters.

[0213] In this embodiment, step S42 includes the following steps:

[0214] Based on the collaborative constraint spectrum, the power balance adjustment is performed on the initial time window boundary to obtain the 36-hour power parameters; the network power flow analysis is performed on the 36-hour power parameters to generate the steady-state power flow parameters;

[0215] The steady-state power flow parameters are adjudicated by the data of the first 24 hours to obtain the 24-hour steady-state data; the state characteristics of the 24-hour steady-state data are summarized to generate steady-state characteristic data;

[0216] Based on the collaborative constraint spectrum, the steady-state characteristic data is dynamically reconstructed to obtain a dynamic topological model; the dynamic topological model is sectioned at 24 moments to generate the initial state value of the sectioned section;

[0217] The transient response calculation is performed on the initial value of the cut section state to obtain the transient calculation value; the characteristic parameters are extracted from the transient calculation value to obtain the transient response parameters.

[0218] In this embodiment, when the power balance is regulated for the initial time window boundary based on the collaborative constraint spectrum, the collaborative optimization algorithm and the linear programming method are used to construct a power balance model of the power grid, set the objective function to minimize the total power deviation, and set the power input of the power grid node to be equal to the output. The rated power of the unit, the load demand, and the line transmission capacity in the initial time window boundary are used as input parameters, and the iterative optimization solution method is used to generate the power parameters covering 36 hours. When the network flow analysis is performed on the 36-hour power parameters, a power flow calculation tool such as PSSE (Power System Simulator for Engineering, a power system simulation tool, is used to calculate the power flow. The power parameters are used as input variables. The node voltage reference value, branch impedance and phase angle difference range are set. The network power flow is analyzed and calculated by the Newton-Raphson method. The steady-state power flow parameters are output, including the voltage amplitude of each node, reactive power distribution and line power transmission. When the steady-state power flow parameters are judged for the first 24 hours of data, the power flow parameters are extracted according to the time series segmentation. The judgment process is implemented through programming tools such as Python. The judgment rule is to retain the data of the first 24 hours of the time series and exclude the data of other time periods. The invalid data and outliers are cleared by using data cleaning libraries such as Pandas. Finally, 24-hour steady-state data is generated. When the state characteristics of the 24-hour steady-state data are summarized, feature extraction algorithms such as principal component analysis (Principal Component The data is processed by PCA to reduce the dimension, extract key characteristic parameters such as steady-state voltage amplitude change, reactive power fluctuation rate and line load factor, generate characteristic distribution diagram through data visualization tools such as Matplotlib, and store the summary results in the form of steady-state characteristic data, where each characteristic parameter includes its mean, variance and fluctuation range. When the steady-state characteristic data is dynamically reconstructed based on the collaborative constraint spectrum, optimization algorithms such as genetic algorithm (Genetic Algorithm,The dynamic coupling relationship in the characteristic data is modeled and analyzed by GA, and the topological relationship between nodes is described by the dynamic connection matrix. The input constraints are the line transmission capacity and the load demand distribution, and the output is the dynamic topological model, including the dynamic connection strength of the node and the network coupling parameters. When the dynamic topological model is divided into 24 sections at 24 moments, the dynamic parameters in the model are divided into 24 sections according to the time points. The section data is sorted by the time series grouping method, and each section is stored as an independent structured data file. The index operation is used to ensure the parameter integrity at each moment during the segmentation process, and the initial state value of the segmented section is generated. The initial value data includes the node power distribution, the line voltage amplitude and the load rate. When the transient response calculation is performed on the initial state value of the segmented section, the electromagnetic transient simulation tool such as EMTP (Electromagnetic Transients Program, electromagnetic transient program) performs transient analysis on each section. The input variables include the initial value of the section and the parameters of the dynamic topology model. The simulation outputs the transient calculation value, which includes the transient current, voltage recovery time and frequency fluctuation rate. When extracting characteristic parameters from the transient calculation value, the transient data is analyzed in the frequency domain based on signal processing algorithms such as Fast Fourier Transform (FFT), and the key frequency characteristics, damping ratio and fluctuation amplitude in the transient response are extracted. The extracted results are stored as transient response parameters.

[0219] In this embodiment, step S5 includes the following steps:

[0220] Step S51: screening qualified combinations of combined load capacities based on preset expected load demands to obtain threshold screening combinations;

[0221] Step S52: Perform reliability evaluation on the threshold screening combination to obtain the combination reliability; repeatedly screen the threshold screening combination according to the combination reliability to generate a screening qualified combination;

[0222] Step S53: Calculate the start-stop consumption of the screened qualified combination to obtain the combined start-stop cost; perform full life cycle cost accounting on the screened qualified combination based on the combined start-stop cost to generate the combined accounting cost;

[0223] Step S54: Perform cost optimization deduction on the screened qualified combinations according to the combined accounting costs to generate the optimal low-cost units.

[0224] In this embodiment, when the qualified combination of combined load capacity is screened based on the preset expected load demand, the expected load demand data is read, and the combination capacity screening operation is performed using a database query tool such as MySQL, a matching model between the load demand and the unit load capacity is constructed, and the constraint condition is set so that the load capacity of each combination meets the expected demand. A linear optimization algorithm is used to iteratively screen all unit combinations, and combinations with insufficient load capacity or exceeding the expected range are eliminated to generate a threshold screening combination. When the threshold screening combination is subjected to reliability evaluation, the operating parameters of each combination are calculated based on the reliability evaluation model, and the input data are the historical operation failure rate and load capacity fluctuation rate of the unit. Monte Carlo simulation is used to simulate the historical operation failure rate and load capacity fluctuation rate of the unit. The simulation method is used to generate large-scale simulation samples, and the reliability indicators of each combination, such as the mean time between failures (MTBF) and the fault recovery time (MTTR), are calculated. The combinations that do not meet the threshold requirements are eliminated according to the reliability indicators. The combinations that meet the reliability standards are retained through repeated screening, and finally the qualified combinations are generated. The start-stop cost model is established based on the start-stop power consumption curve of each unit in the combination, and the start-stop consumption data, including the start-stop power demand, start-stop time and unit efficiency, are read. The total start-stop cost of each combination is calculated by the numerical integration method. The start-stop cost calculation result is based on the consumption value per unit time and stored as a CSV file. When the combination is further calculated for the entire life cycle cost according to the start-stop cost, the start-stop cost is superimposed with the operation and maintenance cost, fuel cost and depreciation cost of the unit. The life cycle analysis method (Life Cycle Assessment, LCA) is used to calculate the total cost value of each combination to generate the combination accounting cost. Based on optimization algorithms such as dynamic programming ( Programming) analyzes the cost data of all qualified combinations, sets the objective function to minimize the full life cycle cost, takes the combination accounting cost as the input variable, calculates the cost optimization score of each combination, selects the optimal combination with the lowest cost according to the score ranking, and stores the optimal combination number and the corresponding unit configuration as an independent XML format file, which contains the load distribution, operation cycle and corresponding optimization score of the unit, and generates a visual analysis chart to show the cost distribution of each combination.

[0225] The present invention also provides a power grid access simulation system for a high proportion of renewable energy, which is used to execute the power grid access simulation method for a high proportion of renewable energy as described above. The power grid access simulation system for a high proportion of renewable energy includes:

[0226] The operating parameter acquisition module is used to collect the operating parameters of the thermal power unit and the operating parameters of the new energy unit; the operating parameters of the thermal power unit and the new energy unit are combined to construct tensors and generate the combined unit feature tensors;

[0227] The constraint analysis module is used to derive the unit's own constraints on the combined unit's characteristic tensor to obtain its own safety constraints; perform network topology constraint analysis on the combined unit's characteristic tensor to generate network security constraints;

[0228] The unit combination modeling module is used to perform collaborative fusion processing on its own safety constraints and network security constraints to obtain a collaborative constraint spectrum; according to the collaborative constraint spectrum, the combined unit characteristic tensors are combined and enumerated to model and generate a unit combination model;

[0229] The grid-connected simulation module is used to perform long-term grid-connected simulation on the unit combination model based on the collaborative constraint spectrum to obtain grid-connected simulation data; the load capacity of the unit combination model is calculated based on the grid-connected simulation data to obtain the combined load capacity;

[0230] The combination screening module is used to screen qualified combinations based on the combined load capacity to obtain qualified combinations; perform cost optimization deduction on the qualified combinations to generate optimized low-cost units.

[0231] The present invention realizes comprehensive monitoring and data integration of the operating status of thermal power units and new energy units through the operation parameter acquisition module. The generated combined unit characteristic tensor provides a rich information basis for subsequent analysis. The implementation of the constraint analysis module ensures the safety of the unit itself and the stability of the network. Through collaborative fusion processing, the obtained collaborative constraint spectrum enhances the understanding of the combined behavior of the units. The unit combination modeling module improves the accuracy and applicability of the model through combined enumeration modeling. The long-cycle simulation of the grid-connected simulation module can truly simulate the grid operation environment, so as to obtain reliable grid-connected simulation data. The load capacity calculation provides a scientific basis for the flexible scheduling of the unit. The combined screening module ensures the effectiveness and economy of the unit based on the screening of load capacity. Through cost optimization derivation, the operating cost is significantly reduced, and the efficiency and reliability of the access of a high proportion of new energy to the power grid are optimized as a whole, and the adaptability and response speed of the power system to the fluctuation of new energy are enhanced.

[0232] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0233] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for simulating access to a power grid with a high proportion of new energy, characterized in that: The following steps are involved: Step S1: collecting the operating parameters of the thermal power unit and the operating parameters of the new energy unit; constructing a combined tensor of the operating parameters of the thermal power unit and the operating parameters of the new energy unit to generate a combined unit feature tensor; Step S2: deriving the unit's own constraints on the combined unit's characteristic tensor to obtain its own safety constraints; performing network topology constraint analysis on the combined unit's characteristic tensor to generate network security constraints; Step S3: Perform collaborative fusion processing on the self-security constraints and network security constraints to obtain a collaborative constraint spectrum; perform combined enumeration modeling on the combined unit characteristic tensors according to the collaborative constraint spectrum to generate a unit combination model; Step S4: performing a long-period grid-connected simulation on the unit combination model based on the collaborative constraint spectrum to obtain grid-connected simulation data; The load capacity of the unit combination model is calculated based on the grid-connected simulation data to obtain the combined load capacity; Step S5: Screen qualified combinations based on combined load capacity to obtain qualified combinations; perform cost optimization deduction on the qualified combinations to generate optimized low-cost units.

2. The grid access simulation method for high-proportion new energy according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Collecting the operating parameters of thermal power units and new energy units; Step S12: performing time sequence alignment on the operating parameters of the thermal power unit and the operating parameters of the new energy unit to generate synchronous operating parameters; Step S13: performing principal component analysis on the synchronous operation parameters to obtain dimension-reduced operation features; Step S14: Perform three-boundary tensor transformation on the dimension-reduced operation characteristics to generate a combined unit characteristic tensor.

3. The grid access simulation method for high-proportion renewable energy according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Deconstruct the dynamic characteristics of the combined unit characteristic tensor to obtain the thermal power unit constraints; analyze the new energy output characteristics of the combined unit characteristic tensor to generate the new energy unit constraints; Step S22: performing group constraint connection on the thermal power unit constraints and the new energy unit constraints to obtain their own safety constraints; Step S23: quantifying stability based on the constraints of thermal power units and new energy units, and generating stability constraints; Step S24: performing a balanced power tensor operation on the characteristic tensor of the combined unit according to the stability constraint to obtain a node constraint; performing a phase angle sensitivity matrix decomposition on the node constraint to obtain a phase angle constraint; Step S25: Calculate the power flow safety region for the phase angle constraint to generate a power flow constraint; perform topological integration processing on the node constraint, phase angle constraint and power flow constraint to generate a network topology constraint; Step S26: Expand and map the network topology constraints based on the stability constraints to generate network security constraints.

4. The grid access simulation method for high-proportion renewable energy according to claim 3, characterized in that: Step S21 includes the following steps: Extract power balance features from the combined unit feature tensor to obtain a power balance constraint set; calculate the power boundary of the thermal power unit on the power balance constraint set to obtain a power boundary constraint set; The thermal power unit ramp rate analysis is performed on the power boundary constraint set to obtain the ramp constraint set; the start and stop time modeling is performed on the ramp constraint set to obtain the time constraint set; The power balance constraint set, power boundary constraint set, ramp constraint set and time constraint set are dynamically integrated to obtain the constraints of the thermal power unit; Analyze the new energy output characteristics of the combined unit characteristic tensor to obtain the output characteristic set; The wind power prediction model is performed on the output characteristic set to obtain the wind power constraint set; the photovoltaic power generation characteristics of the wind power constraint set are analyzed to obtain the photovoltaic constraint set; Based on the wind power constraint set and the photovoltaic constraint set, the absorption capacity is predicted to obtain the predicted absorption capacity; the absorption constraint mapping is performed on the predicted absorption capacity to obtain the absorption constraint set; The output characteristics of the output feature set, wind power constraint set, photovoltaic constraint set and consumption constraint set are integrated to generate constraints for new energy units.

5. The grid access simulation method for high-proportion renewable energy according to claim 3, characterized in that: Step S24 includes the following steps: Perform unbalanced power analysis on the characteristic tensor of the combined unit to obtain the power deviation; Perform distribution mapping on the combined unit characteristic tensor according to the power deviation to generate the unit power distribution; According to the stability constraint, the power distribution of the unit is subjected to balance correction processing to obtain the balanced power tensor; Based on the balanced power tensor, the node power of the combined unit characteristic tensor is readjusted to generate node constraints; The phase angle sensitivity of the node constraint is calculated to obtain the phase angle sensitivity parameter; the matrix eigenvalue decomposition of the phase angle sensitivity parameter is performed to obtain the phase angle constraint.

6. The grid access simulation method for high-proportion renewable energy according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Based on the stability constraint, correlation mining is performed on the self-security constraint and the network security constraint to obtain constraint correlation; conflict resolution is performed on the constraint correlation to generate constraint association data; Step S32: construct a constraint spectrum for the self-security constraint and the network security constraint according to the constraint association data to obtain a collaborative constraint spectrum; Step S33: enumerating combination schemes for the characteristic tensors of the combined units according to the collaborative constraint spectrum to obtain an enumerated combination scheme; Step S34: Perform combined mathematical modeling based on the enumerated combination scheme to generate a unit combination model.

7. The grid access simulation method for high-proportion renewable energy according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: construct a 36-hour rolling time window for the unit combination model to obtain a unit rolling time window; set boundary conditions for the unit rolling time window to generate an initial time window boundary; Step S42: performing steady-state grid-connected simulation on the initial time window boundary based on the cooperative constraint spectrum to obtain steady-state characteristic data; performing transient grid-connected simulation on the steady-state characteristic data based on the cooperative constraint spectrum to generate transient response parameters; Step S43: Perform rolling time domain advancement on the transient response parameters to obtain time domain advancement data; perform unit coordination simulation on the time domain advancement data to generate simulated output coordination information; Step S44: performing new energy fluctuation response processing according to the simulation processing coordination information to generate a fluctuation response signal; performing long-period dynamic characteristic evaluation based on the fluctuation response signal to generate grid-connected simulation data; Step S45: Based on the grid-connected simulation data, load tracking rating is performed on the unit combination model to obtain tracking capability information; load capacity evaluation is performed on the tracking capability information to obtain the combined load capacity.

8. The grid access simulation method for high-proportion renewable energy according to claim 7, characterized in that: Step S42 includes the following steps: Based on the collaborative constraint spectrum, the power balance of the initial time window boundary is adjusted to obtain the 36-hour power parameters; the network power flow analysis is performed on the 36-hour power parameters to generate steady-state power flow parameters; The steady-state power flow parameters are adjudicated by the data of the first 24 hours to obtain the 24-hour steady-state data; the state characteristics of the 24-hour steady-state data are summarized to generate steady-state characteristic data; Based on the collaborative constraint spectrum, the steady-state characteristic data is dynamically reconstructed to obtain a dynamic topological model; the dynamic topological model is sectioned at 24 moments to generate the initial state value of the sectioned section; The transient response calculation is performed on the initial value of the cut section state to obtain the transient calculation value; the characteristic parameters are extracted from the transient calculation value to obtain the transient response parameters.

9. The grid access simulation method for high-proportion renewable energy according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: screening qualified combinations of combined load capacities based on preset expected load demands to obtain threshold screening combinations; Step S52: Perform reliability evaluation on the threshold screening combination to obtain the combination reliability; repeatedly screen the threshold screening combination according to the combination reliability to generate a screening qualified combination; Step S53: Calculate the start-stop consumption of the screened qualified combination to obtain the combined start-stop cost; perform full life cycle cost accounting on the screened qualified combination based on the combined start-stop cost to generate the combined accounting cost; Step S54: Perform cost optimization deduction on the screened qualified combinations according to the combined accounting costs to generate the optimal low-cost units.

10. A grid access simulation system for high-proportion renewable energy, characterized in that: For executing the power grid access simulation method for high-proportion renewable energy as claimed in claim 1, the power grid access simulation system for high-proportion renewable energy comprises: The operating parameter acquisition module is used to collect the operating parameters of the thermal power unit and the operating parameters of the new energy unit; the operating parameters of the thermal power unit and the new energy unit are combined to construct tensors and generate the combined unit feature tensors; The constraint analysis module is used to derive the unit's own constraints on the combined unit's characteristic tensor to obtain its own safety constraints; perform network topology constraint analysis on the combined unit's characteristic tensor to generate network security constraints; The unit combination modeling module is used to perform collaborative fusion processing on its own safety constraints and network security constraints to obtain a collaborative constraint spectrum; according to the collaborative constraint spectrum, the combined unit characteristic tensors are combined and enumerated to model and generate a unit combination model; The grid-connected simulation module is used to perform long-term grid-connected simulation on the unit combination model based on the collaborative constraint spectrum to obtain grid-connected simulation data; the load capacity of the unit combination model is calculated based on the grid-connected simulation data to obtain the combined load capacity; The combination screening module is used to screen qualified combinations based on the combined load capacity to obtain qualified combinations; perform cost optimization deduction on the qualified combinations to generate optimized low-cost units.

Images