Network source adaptability comprehensive improvement method for new energy bearing capacity

Abstract

The invention discloses a network source adaptability comprehensive improvement method for the new energy bearing capacity, relates to the technical field of network source adaptability, and comprises the following steps: the invention discloses the network source adaptability comprehensive improvement method for the new energy bearing capacity. The method comprises the following steps: analyzing a network source adaptability improvement measure oriented to the new energy bearing capacity, and establishing a multi-type resource adjustment model oriented to the new energy bearing capacity; carrying out correlation analysis on different network source adaptability transformation means and the new energy bearing capacity of the system; and establishing a network source adaptability comprehensive improvement strategy planning model for the new energy bearing capacity according to correlation analysis of different network source adaptability transformation means and the new energy bearing capacity of the system. The invention provides a network source adaptability comprehensive improvement strategy oriented to the new energy bearing capacity, and provides a technical means for filling up the increasingly expanded adjusting capacity gap of the power grid and improving the new energy bearing and absorption capacity.

Description

Technical Field

[0001] The present invention relates to the field of new power system planning with a high proportion of new energy access, and more specifically, to a method for comprehensively improving network source adaptability for new energy carrying capacity. Background Art

[0002] In recent years, with the introduction of strategies such as "energy structure transformation", high-proportion new energy has flourished, and various flexible resources have exploded. The access of high-proportion new energy has made it difficult for traditional power grid carrying capacity and regulation methods to meet the safe and economic operation of the power grid under the new situation. Among them, the adaptability of the grid source is the main factor that affects the regulation of power resources and limits the improvement of the carrying capacity of high-proportion new energy. Through the study of the adaptability of the power system grid source, the high-proportion new energy carrying capacity of the power grid can be analyzed, and the grid source transformation planning research can be carried out based on the analysis results, which plays a key role in filling the growing gap in the regulation capacity of the power grid and improving the carrying and absorption capacity of new energy.

[0003] The above disclosed technical solutions have at least the following technical problems: when dealing with a high proportion of new energy, the current power grid may not be able to fully adapt to the volatility and uncertainty of new energy generation, affecting the stability of the power grid, and the existing regulation means of the power grid may not be able to meet the regulation needs brought by a high proportion of new energy, resulting in insufficient regulation capacity and affecting the safe operation of the power grid. In view of the above problems, the present invention proposes a solution. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for comprehensively improving the adaptability of a network source for new energy carrying capacity, in order to construct a comprehensive improvement strategy for targeted network source adaptability, achieve the improvement of a high proportion of new energy carrying capacity coordinated by the network source, and solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A method for comprehensively improving the adaptability of a network source for new energy carrying capacity comprises the following steps: analyzing measures for improving the adaptability of a network source for new energy carrying capacity, and establishing a multi-type regulating resource model for new energy carrying capacity; based on the multi-type regulating resource model for new energy carrying capacity, performing a correlation analysis between different network source adaptability transformation methods and the new energy carrying capacity of the system to obtain a comprehensive weighted index of carrying capacity; according to the multi-type regulating resource model and the comprehensive weighted index of carrying capacity, establishing a planning model for a comprehensive improvement strategy for the adaptability of a network source for new energy carrying capacity, and proposing a comprehensive improvement strategy for the adaptability of a network source for new energy carrying capacity.

[0006] In a preferred embodiment, the analysis is conducted on measures to improve the adaptability of network sources for new energy carrying capacity, and a multi-type regulating resource model for new energy carrying capacity is established, specifically: an energy storage model is established for improving the carrying capacity of new energy, and the parameters of the energy storage model include energy storage capacity, charging and discharging efficiency, and charging and discharging power; a thermal power unit transformation model is established for improving the carrying capacity of new energy.

[0007] In a preferred embodiment, the correlation analysis between different grid source adaptive transformation means and the system's new energy carrying capacity is performed to obtain a comprehensive weighted index of carrying capacity, specifically: based on the grey correlation theory, a correlation analysis is performed, by calculating the correlation between different transformation means and the new energy carrying capacity, based on the influence relationship between different transformation means and the new energy carrying capacity, the transformation means that has the greatest effect on improving the carrying capacity is found; based on the entropy weight method, the influence relationship between the parameter variables of the characteristics of the grid source adaptive transformation means and the indicator sequence composed of the new energy carrying capacity indicators of each system is analyzed, an evaluation index for comprehensively evaluating the new energy carrying capacity of the system is obtained, and a comprehensive weighted index is calculated.

[0008] In a preferred embodiment, the specific steps of establishing the energy storage model for improving the carrying capacity of new energy are as follows: the energy storage model for improving the carrying capacity of new energy includes energy storage constraints and energy storage operation costs, and the energy storage operation costs include energy storage unit power and unit capacity investment costs; Energy storage constraints are established based on the parameters of the energy storage model, and the energy storage constraints include energy storage energy balance, charge and discharge state restrictions, and maximum charge and discharge constraints.

[0009] In a preferred embodiment, a thermal power unit transformation model for improving the carrying capacity of new energy is established, specifically: the thermal power unit transformation model for improving the carrying capacity of new energy includes constraints of three discrete variables: online startup capacity, startup capacity and shutdown capacity; clustering the thermal power units, selecting clustering indicators, and using a single integer variable to describe the number of online units of each cluster unit at each time step, thereby reducing the decision variables required for the unit. The method for obtaining the number of online units is as follows: selecting clustering indicators that can effectively describe the thermal power units, including unit load, fuel consumption, operating efficiency, failure rate and power generation cost; obtaining the operating data of each unit within G time and constructing a data table, wherein the rows of the data table represent different units and the columns represent the selected clustering indicators; preprocessing the data table, wherein the preprocessing includes standardization and missing value processing; based on the k-means clustering algorithm, calculating the silhouette coefficients of different k values, selecting the k with the largest silhouette coefficient, and assigning a cluster label to each unit; According to each time step, calculate the number of units online at time point G, and create a time series data table based on the cluster label, the rows of the time series data table represent the time steps, the columns represent the clusters, and the cells are filled with the number of online units; define a variable , which is used to represent the number of online units of cluster g at time step t.

[0010] In a preferred embodiment, the correlation analysis is performed based on the grey correlation theory, specifically: determining an analysis sequence and establishing a parameter variable data table; the analysis sequence includes transformation means and new energy carrying capacity indicators, the transformation means include energy storage systems, thermal power unit flexibility transformation and line capacity expansion, and the new energy carrying capacity indicators include power grid load and energy supply stability; Select parameter variables that characterize the characteristics of different grid source adaptive transformation means to form a system transformation characteristic parameter sequence; perform dimensionless processing on the characteristic parameter sequence based on extreme difference standardization; form an analysis matrix composed of an indicator sequence composed of several indicators for evaluating the new energy carrying capacity of the system, wherein the parameter variables that characterize the characteristics of different grid source adaptive transformation means include different types of regulating resource configuration capacity; perform dimensionless processing on the analysis matrix, modify the dimensions and orders of magnitude between the system transformation means characteristic sequence and the carrying capacity evaluation indicator sequence to the same value, and obtain an initial value matrix, wherein the dimensionless processing includes initialization and averaging; perform difference operation on the elements in the initial value matrix based on a preset formula to obtain a difference matrix ; Calculate the correlation coefficient of the carrying capacity assessment index and form a correlation coefficient matrix. Take the mean of each column in the correlation coefficient matrix to obtain the grey correlation between the system transformation means parameter variables and the new energy carrying capacity assessment indicators of each system.

[0011] In a preferred embodiment, the specific steps of calculating the comprehensive weighted index are: The index sequence composed of the new energy carrying capacity indicators of each system forms an initial matrix, and the range method is used to perform dimensionless processing according to the index characteristics, and the index characteristics include negative indicators and positive indicators.

[0012] In a preferred embodiment, the comprehensive improvement strategy of network source adaptability for new energy carrying capacity is as follows: Basic data construction: generating basic data for this month based on the power supply planning results of last month; Model input: input the constructed basic data into the comprehensive improvement strategy planning model for network source adaptability facing the new energy carrying capacity; investment decision-making layer processing: carry out energy storage system planning; plan the flexibility transformation of thermal power units; formulate line capacity expansion plan; operation simulation layer processing: carry out short-term operation simulation to evaluate the actual performance of the power grid; resource allocation adjustment: if insufficient resource allocation is found during the operation simulation, return to the investment decision-making layer for optimization and adjustment, including energy storage system planning, flexibility transformation of thermal power units and line capacity expansion; if insufficient resource allocation is found during the operation simulation, output the power supply planning plan for the year, and use the plan as the calculation data for the next year.

[0013] The technical effects and advantages of the method for comprehensively improving network source adaptability for new energy carrying capacity of the present invention are as follows: 1. The present invention analyzes the influence characteristics of different source adaptive transformation methods on the system's high-proportion new energy carrying capacity from the perspective of multi-type regulation resource allocation; 2. The present invention combines the influencing characteristics of the network source adaptability transformation means and adopts the grey correlation theory to analyze the correlation between different network source adaptability transformation means and the system's new energy carrying capacity, and identifies the key links for improving the high-proportion new energy carrying capacity; 3. The present invention aims to optimize the comprehensive cost and network source adaptability, considers the system's multi-time scale operation indicators, power balance indicators, electricity balance and other indicators, establishes multi-type regulation resources and grid planning models, and proposes a comprehensive strategy to improve the network source adaptability for new energy carrying capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The present invention is a flow chart of a method for comprehensively improving network source adaptability for new energy carrying capacity.

[0015] Figure 2 It is a schematic diagram of the solution process of the network source adaptability comprehensive improvement strategy planning scheme of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Embodiment 1, Figure 1 The present invention provides a method for comprehensively improving network source adaptability for new energy carrying capacity, and the steps are as follows: Step S1 analyzes the grid source adaptability improvement measures for new energy carrying capacity, and establishes a multi-type regulation resource model for new energy carrying capacity: Step S11 establishes an energy storage model for improving the carrying capacity of new energy, wherein the model parameters include energy storage capacity, charging and discharging efficiency, and charging and discharging power; Step S111 establishes energy storage constraints, including energy storage energy balance, charge and discharge state restrictions, and maximum charge and discharge constraints, as shown in the following formula: (1.1) (1.2) (1.3) (1.4) (1.5) (1.6) (1.7) (1.8) Formula (1.1) is the energy balance equation of energy storage in adjacent time periods. represents the capacity of energy storage at time t, and are the charging and discharging efficiency of energy storage, and are the charging and discharging power of energy storage respectively; Formula (1.2) limits the real-time capacity of energy storage to not exceed the rated capacity of energy storage; Formulas (1.3) and (1.4) limit the real-time charging and discharging power of energy storage to not exceed the rated power of energy storage. represents the rated power of the energy storage; equations (1.5) and (1.6) are the energy storage charge and discharge state restriction constraints, which stipulate that the energy storage can only be in the charging state or the discharging state at the same time. is a variable of 0 or 1, 1 means the energy storage is in the discharge state, M is a large positive real number; Formula (1.7) is the maximum discharge capacity constraint of the energy storage, represents the minimum state of charge of energy storage; formula (1.8) is the maximum charging capacity constraint of energy storage, Indicates the maximum state of charge of the energy storage.

[0018] Step S112, calculating the energy storage operation cost; The energy storage operation cost includes the energy storage unit power and unit capacity investment cost. Since the annual operation cost coefficient and annual maintenance cost coefficient of energy storage are difficult to determine, the energy storage operation and maintenance fee is approximately estimated based on a certain proportion of the initial investment of energy storage. is the estimated coefficient of operation and maintenance cost of energy storage, generally 0.5%. is the equal-annual value coefficient, which can be expressed by formula (1.10).

[0019] (1.9) In formula (1.9), represents the energy storage operating cost; and are the investment cost per unit power and per unit capacity of energy storage respectively.

[0020] (1.10) In formula (1.10): is the base discount rate, The duration of energy storage operation (years).

[0021] Step S12 establishes a thermal power unit transformation model for improving the new energy carrying capacity: Step S121 clusters the thermal power units, selects clustering indicators, and uses a single integer variable to describe the number of online units of each cluster unit at each time step, thereby reducing the decision variables required for the units; The specific steps are as follows: The clustering indicators that can effectively describe the thermal power units include unit load, fuel consumption, operating efficiency, failure rate and power generation cost; Collect data from the monitoring system or historical records of the units and integrate the collected operating data of each unit within the G time into a data table, wherein the rows of the data table represent different units and the columns represent the selected clustering indicators; Preprocessing the data table, wherein the preprocessing includes standardization and missing value processing; Based on the k-means clustering algorithm, the silhouette coefficients of different k values ​​are calculated, the k with the largest silhouette coefficient is selected, and a cluster label is assigned to each unit; At each time step, calculate the number of units online at time point G and create a time series data table with rows representing time steps, columns representing clusters, and cells filled with the number of online units; Defining a variable , which is used to represent the number of online units of cluster c at time step t.

[0022] The k-means clustering algorithm is specifically as follows: for each K value, K-means clustering is performed; a(i) and b(i) of the sample points in each data table are calculated; The silhouette coefficient of each sample point is calculated using a preset formula; the silhouette coefficients of all sample points are averaged to obtain the overall silhouette coefficient under the K value.

[0023]

[0024] Among them, a(i) is the average distance from sample point i to other sample points in the same cluster, and b(i) is the average distance from sample point i to the nearest sample point in a different cluster.

[0025] In cluster analysis of thermal power units, selecting a clustering index and using a single integer variable to describe the number of online units of each cluster unit at each time step has the following main benefits: Simplify model complexity: By representing the number of online units in each cluster as a single integer variable, the number of decision variables that need to be processed can be significantly reduced. This simplification helps reduce the complexity of the model, making the calculations more efficient and manageable. This is especially important when dealing with large-scale thermal power plant systems.

[0026] Improve computational efficiency: Reducing decision variables can significantly improve the computational efficiency of optimization algorithms. Fewer variables means a smaller optimization problem to solve, resulting in faster solutions. Reducing the computational burden is critical for data that requires real-time decision making or frequent updates.

[0027] Ease of data management and analysis: Simplifying the number of online units into a single integer variable can make data management and analysis more intuitive and convenient. Data simplification not only reduces storage requirements, but also makes data analysis and visualization clearer.

[0028] Reduce uncertainty in model parameters: When the number of online units is described as a single integer variable, the model parameters are more explicit, reducing parameter uncertainty caused by too many variables. This allows for better control of model stability and accuracy.

[0029] Improved decision support: The simplified model can make decision support easier, helping decision makers to more quickly understand and evaluate the status of different cluster units and their impact on the system. This is very helpful for formulating optimized scheduling and operation strategies.

[0030] Easy to integrate with other indicators: The simplified variable of the number of online units can be more easily integrated and analyzed with other important indicators such as load demand, equipment status, etc. This helps to achieve more comprehensive system optimization and decision support.

[0031] Reduced complexity of data collection and processing: A simplified model may reduce the complexity of data collection and processing. By reducing the amount of detailed data that needs to be tracked, data collection, organization, and processing can be more convenient.

[0032] Improve model interpretability: Simplified models are usually easier to interpret and understand. Decision makers can more clearly understand the state of each cluster unit within a time step and the impact of these states on the overall system.

[0033] Step S122 performs cluster unit operation constraint modeling: After clustering the units, online startup capacity is introduced , startup capacity Shutdown capacity Three discrete variables are used to model the overall behavior of the cluster for the online operation, startup and shutdown states of the cluster units. The three discrete variables need to meet the following requirements: (1.11) (1.12) (1.13) In equations (1.11) to (1.13): Ng is the total number of individual units contained in cluster unit g; is the rated capacity of unit i; , and They respectively represent the sum of the rated capacities of all individual units of cluster unit g that are in the grid-connected, startup and shutdown states at time t.

[0034] Step 2: Based on the multi-type regulation resource model for renewable energy carrying capacity, conduct a correlation analysis between different grid source adaptability transformation methods and the system's renewable energy carrying capacity: Step 2.1, collect the performance data of various mediation resources and conduct correlation analysis based on the grey correlation theory. First, determine the analysis sequence, then perform dimensionless processing on the original sequence, and then calculate the correlation coefficient, thereby calculating the degree of correlation.

[0035] Step 2.1.1, forming an analysis matrix; Select parameter variables that characterize the characteristics of different network source adaptive transformation methods to form a system transformation characteristic parameter sequence , where the parameter variables that characterize the characteristics of different network source adaptive transformation methods can configure capacity for different types of adjustable resources; An indicator sequence consisting of indicators for evaluating the new energy carrying capacity of the system , the final analysis matrix can be expressed as: (1.14) Where: is an integer representing the number of scenarios generated by changing the parameter variables of the characteristics of different network source adaptive transformation means.

[0036] Step 2.1.2, generate the initial value matrix; In the analysis matrix, the dimensions and magnitudes of the system transformation means characteristic sequence and the carrying capacity assessment index sequence are different, which makes it difficult to compare them. Therefore, before conducting quantitative analysis, the original data must be dimensionless. The commonly used dimensionless processing methods are initialization and averaging. The formula for averaging the carrying capacity assessment index sequence is shown in formula (1.15), and the formula for initialization is shown in formula (1.16). The initialization method is selected to complete the dimensionless processing of the analysis matrix, and the resulting initial value matrix is ​​shown in formula (1.17).

[0037] (1.15) (1.16) (1.17) Step 2.1.3, generate a difference matrix; According to formula (1.18), the difference operation is performed on the elements in the initial value matrix, and finally the difference matrix is ​​obtained .

[0038] (1.18) (1.19) The maximum value of the difference matrix is ​​calculated by formula (1.20) and formula (1.21): Minimum : (1.20) (1.21) Step 2.1.4, calculation of grey relational degree; Calculated by formula (1.22) Correlation coefficient between parameter variables of different system transformation methods and the carrying capacity evaluation index composition of each system's new energy carrying capacity evaluation index under different scenarios , and form their correlation coefficient matrix , as shown in formula (1.23): (1.22) (1.23) Where: It is the resolution coefficient, which is used to reduce the influence of the maximum difference being too large and causing the correlation coefficient to be distorted. It is a number between 0 and 1, and is generally 0.5.

[0039] The correlation coefficient matrix By taking the mean of each column in , we can get the grey correlation between the system transformation means parameter variables and the evaluation indicators of the new energy carrying capacity of each system. : (1.24) Furthermore, this example conducts correlation analysis based on the grey correlation theory, which has the following advantages: Applied to the context of multi-type regulatory resources, grey relational theory has the following advantages: Adapt to diverse data types: Multi-type regulating resources may involve different types of data, including energy storage, flexible load, renewable energy, etc. Grey correlation theory does not need to assume the statistical distribution of data, so it can handle various types of data, including digital and non-digital data, and is suitable for correlation analysis of multi-type regulating resources.

[0040] Processing partial information data: In practical applications, the data of regulatory resources may be missing or incomplete. Grey correlation theory can effectively process partial information data and extract correlation from it through the calculation of fuzzy sets and correlation coefficients, which is conducive to correlation analysis in a multi-type regulatory resource environment.

[0041] Flexibility and robustness: Grey correlation theory has relatively low requirements on data, is not restricted by data distribution, and has high flexibility. In multi-type regulatory resources, different types of data may present different distribution forms. Grey correlation theory can cope with this situation and maintain the robustness of the analysis.

[0042] Information fusion capability: Multi-type regulation resources usually include multi-source data, such as energy market prices, power demand, renewable energy output, etc. Grey correlation theory can integrate information from different data sources, help analysts comprehensively consider the correlation between multiple factors, and provide a comprehensive reference for decision-making.

[0043] Step 2.2: Based on the entropy weight method, analyze the influence relationship between the parameter variables of the characteristics of the grid source adaptive transformation means and the indicator sequence composed of the new energy carrying capacity indicators of each system, provide importance weights for the new energy carrying capacity indicators of the system, obtain the evaluation indicators for the comprehensive evaluation of the new energy carrying capacity of the system, and calculate the comprehensive weighted indicators; Step 2.2.1. Matrix preprocessing Will The indicator sequence composed of the new energy carrying capacity indicators of the system An initial matrix is ​​formed, and the range method is used to perform dimensionless processing based on the indicator characteristics, where negative indicators use formula (1.25) and positive indicators use formula (1.26).

[0044] (1.25) (1.26) Where: and for Sequence The samples after non-dimensionalization of equations (1.25) and (1.26) form a matrix and are recorded as , and calculate the first The proportion of the new energy carrying capacity index of a system in the index sequence.

[0045] (1.27) Step 2.2.2. Calculation of entropy value and entropy weight No. The entropy value calculation of each indicator is as follows: (1.28) No. The entropy weight calculation of each indicator is as follows: (1.29) Taking into account the different importance of each indicator, the grey correlation calculated by formula (1.24) is weighted. Therefore, the correlation between the final weighted comprehensive index of the system's new energy carrying capacity and different system source network transformation methods is the product of formula (1.24) and formula (1.29).

[0046] The comprehensive weighted index The calculation formula is:

[0047] It should be noted that the correlation between the weighted comprehensive index of the system's new energy carrying capacity and the different system source-grid transformation methods is used to quantify the contribution of various resources to the new energy system.

[0048] Step 3: Based on the multi-type regulation resource model and the weighted comprehensive index of new energy carrying capacity, a comprehensive improvement strategy planning model for network source adaptability for new energy carrying capacity is established, and a comprehensive improvement strategy for network source adaptability for new energy carrying capacity is proposed; The goal is to minimize the total system cost, which is divided into two parts: investment cost and operating cost. The investment cost is composed of energy storage investment cost, line capacity expansion cost, and thermal power unit flexibility transformation cost; the operating cost is composed of fixed unit operation brought about by power generation, fuel cost, and pollution discharge cost; Step 3.1, establish the objective function: (1.30) Where: F is the total cost, Planning stage costs, Cost of the operation phase.

[0049] Step 3.2: Establish the cost model for the planning stage: (1.31) (1.32) (1.33) (1.34) (1.35) (1.36) (1.37) (1.38) Where: Costs for the planning stage; They are energy storage planning cost, thermal power unit flexibility transformation cost, and line capacity increase cost respectively; M is the total number of months, which is equal to the number of planned years multiplied by 12; m is the subscript of the month; i is the subscript of the unit; is the unit capacity construction cost of energy storage i, is the flexibility transformation cost of thermal power unit j, is the static capacity increase cost of line k; They respectively indicate whether energy storage i is built, whether thermal power unit j is modified for flexibility, and whether line k is increased in capacity in month m; , are the annual growth rates of construction costs for thermal power and energy storage equipment, respectively; is the discount value coefficient of the mth month; ESS, G, and LINE are energy storage devices, thermal power units, and line collections, respectively.

[0050] Step 3.3: Establish the cost model for the operation phase: (1.39) Where: is the fixed operating rate of a unit of power source i in month m, is the fuel price of power source i in the mth month; Penalties for the discharge of pollutants from power source i; is the coal consumption coefficient per unit electricity of power source i in the mth month.

[0052] Step 3.4: Establish monthly power balance constraints: (1.40) Where: The available capacity of each type of power source unit when participating in power balancing; is the maximum load value in the mth month; is the static capacity reserve coefficient of the mth month; A is the collection of various types of units.

[0054] Step 3.5: Establish monthly power balance constraints: (1.41) Where: and are the power generation and storage capacity of energy storage station i in the mth month respectively; is the power generation of the i-th power plant in month m, is the maximum electricity demand in month m.

[0056] The process of the network source adaptability comprehensive improvement strategy planning scheme is as follows: Based on the power planning results of last month, we first build the basic data for this month. Input this data into a comprehensive improvement strategy planning model for network source adaptability for new energy carrying capacity. The process of the model is as follows: First, the data will be reviewed by the investment decision-making layer to make decisions on energy storage planning, flexibility transformation of thermal power units, and line capacity expansion. If these decisions are approved after review, the model will enter the operation simulation layer for short-term operation simulation. If there are problems with insufficient resource allocation during the simulation process, these problems will be fed back to the investment decision-making layer to optimize the strategies for energy storage planning, flexibility transformation of thermal power units, and line capacity expansion. If the simulation results show that the resource allocation is sufficient, the model will generate a power planning plan for the year and use the plan as the basic data for calculations in the next year.

[0057] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0058] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0059] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0060] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0061] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0062] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for comprehensively improving network source adaptability for new energy carrying capacity, characterized in that: The steps are as follows: S1, analyze the measures to improve the adaptability of the grid source for the carrying capacity of new energy, and establish a multi-type regulation resource model for the carrying capacity of new energy; S2, based on the multi-type regulation resource model for renewable energy carrying capacity, the correlation analysis between different grid source adaptive transformation methods and the system renewable energy carrying capacity is carried out to obtain a comprehensive weighted index of carrying capacity; S3, based on the multi-type regulation resource model and the comprehensive weighted index of carrying capacity, establish a planning model for the comprehensive improvement strategy of network source adaptability for new energy carrying capacity, and propose a comprehensive improvement strategy of network source adaptability for new energy carrying capacity.

2. The method for comprehensively improving network source adaptability for new energy carrying capacity according to claim 1 is characterized in that: The analysis of the grid source adaptability improvement measures for the new energy carrying capacity establishes a multi-type regulation resource model for the new energy carrying capacity, specifically: S11, establishing an energy storage model for improving the carrying capacity of new energy, wherein the parameters of the energy storage model include energy storage capacity, charging and discharging efficiency, and charging and discharging power; S12, establish a thermal power unit transformation model for improving the new energy carrying capacity.

3. The method for comprehensively improving network source adaptability for new energy carrying capacity according to claim 1 is characterized in that: The correlation analysis between different grid source adaptive transformation methods and the system's new energy carrying capacity is used to obtain a comprehensive weighted index of carrying capacity, which is specifically: S21, based on the grey correlation theory, conduct correlation analysis, calculate the correlation between different transformation methods and new energy carrying capacity, and find out the transformation method that can improve the carrying capacity the most based on the influence relationship between different transformation methods and new energy carrying capacity; S22, based on the entropy weight method, analyze the influence relationship between the parameter variables of the characteristics of the grid source adaptive transformation means and the indicator sequence composed of the new energy carrying capacity indicators of each system, obtain the evaluation index of the new energy carrying capacity of the comprehensive evaluation system, and calculate the comprehensive weighted index.

4. The method for comprehensively improving network source adaptability for new energy carrying capacity according to claim 2 is characterized in that: The specific steps of establishing an energy storage model for improving the carrying capacity of new energy are as follows: The energy storage model for improving the carrying capacity of new energy includes energy storage constraints and energy storage operation costs, and the energy storage operation costs include energy storage unit power and unit capacity investment costs; Establishing energy storage constraints based on parameters of the energy storage model, wherein the energy storage constraints include energy storage energy balance, charge and discharge state restrictions, and maximum charge and discharge constraints; The energy storage energy balance is specifically: in, represents the capacity of energy storage at time t, and are the charging and discharging efficiency of energy storage, and are the charging and discharging power of energy storage respectively; The formula for limiting the real-time capacity of energy storage to no more than the rated capacity of energy storage is as follows: The formula for the real-time charging and discharging power of energy storage not exceeding the rated power limit of energy storage is as follows: in Indicates the rated power of energy storage, and are the charging and discharging power of energy storage respectively; The charge and discharge state restrictions are specifically: It stipulates that energy storage can only be in charging or discharging state at the same time. is a 0, 1 variable, 1 means the energy storage is in the discharge state, and M is a positive real number; The maximum charge and discharge constraints are specifically: in, Indicates the minimum state of charge of energy storage, It represents the maximum state of charge of energy storage, and Δt is the time interval.

5. The method for comprehensively improving network source adaptability for new energy carrying capacity according to claim 2 is characterized in that: Establish a thermal power unit transformation model for improving the carrying capacity of new energy, specifically: The thermal power unit transformation model for improving the carrying capacity of new energy sources includes constraints on three discrete variables: online startup capacity, startup capacity, and shutdown capacity; Cluster the thermal power units, select clustering indicators, use a single integer variable to describe the number of online units of each cluster unit at each time step, and reduce the decision variables required for the unit. The method for obtaining the number of online units is as follows: The clustering indicators that can effectively describe the thermal power units include unit load, fuel consumption, operating efficiency, failure rate and power generation cost; Obtaining the operating data of each unit within the G time and constructing a data table, wherein the rows of the data table represent different units and the columns represent the selected clustering indicators; Preprocessing the data table, wherein the preprocessing includes standardization and missing value processing; Based on the k-means clustering algorithm, the silhouette coefficients of different k values ​​are calculated, the k with the largest silhouette coefficient is selected, and a cluster label is assigned to each unit; According to each time step, the number of units online at time point G is calculated, and a time series data table is created based on the cluster label, wherein the rows of the time series data table represent the time steps, the columns represent the clusters, and the cells are filled with the number of online units; Defining a variable , used to represent the number of online units of cluster g at time step t; After clustering the units, online startup capacity is introduced , startup capacity Shutdown capacity Three discrete variables are used to model the overall behavior of the cluster for the online operation, startup and shutdown states of the cluster units. The constraints of the three discrete variables are as follows: Where: is the total number of individual units contained in the cluster unit g; is the rated capacity of unit i; , and They respectively represent the sum of the rated capacities of all individual units of cluster unit g that are in the grid-connected, startup and shutdown states at time t.

6. The method for comprehensively improving network source adaptability for new energy carrying capacity according to claim 3 is characterized in that: The correlation analysis is carried out based on the grey correlation theory, specifically: Determine an analysis sequence and establish a parameter variable data table; the analysis sequence includes transformation means and new energy carrying capacity indicators, the transformation means include energy storage system, thermal power unit flexibility transformation and line capacity increase, and the new energy carrying capacity indicators include power grid load and energy supply stability; Select parameter variables that characterize the characteristics of different network source adaptive transformation methods to form a system transformation characteristic parameter sequence; The characteristic parameter sequence is dimensionless processed based on range standardization; An analysis matrix is ​​formed by an indicator sequence composed of several indicators for evaluating the new energy carrying capacity of the system, wherein the parameter variables characterizing the characteristics of different grid source adaptive transformation means include different types of regulating resource configuration capacities; The analysis matrix is ​​dimensionally processed, and the dimensions and magnitudes between the system transformation means characteristic sequence and the carrying capacity assessment index sequence are modified to the same value to obtain an initial value matrix, wherein the dimensionless processing includes initialization and averaging; Based on the preset formula, the elements in the initial value matrix are interpolated to obtain the difference matrix ; The correlation coefficient of the carrying capacity assessment index is calculated and a correlation coefficient matrix is ​​formed. The mean of each column in the correlation coefficient matrix is ​​obtained to obtain the grey correlation between the system transformation means parameter variables and the new energy carrying capacity assessment index of each system.

7. The method for comprehensively improving network source adaptability for new energy carrying capacity according to claim 6 is characterized in that: The specific calculation formula of the grey relational degree is: in is the correlation coefficient of the carrying capacity assessment indicators, and n is the number of different scenarios.

8. The method for comprehensively improving network source adaptability for new energy carrying capacity according to claim 3 is characterized in that: The specific steps of calculating the comprehensive weighted index are: Will The index sequence composed of the new energy carrying capacity index of each system forms an initial matrix, and the range method is used to perform dimensionless processing according to the index characteristics, and the index characteristics include negative indicators and positive indicators; The specific steps for dimensionless processing of negative indicators are as follows: The specific steps for dimensionless processing of positive indicators are as follows: Where: and for Sequence The maximum and minimum values ​​of ; The dimensionless indicators are formed into a matrix and the The proportion of the new energy carrying capacity index of each system in the index sequence, the specific calculation formula of the proportion is as follows; The entropy value calculation of the bth indicator is as follows: No. The entropy weight calculation of each indicator is as follows: The comprehensive weighted index The calculation formula is: 。 9. The method for comprehensively improving network source adaptability for new energy carrying capacity according to claim 1 is characterized in that: S3, based on the multi-type regulation resource model and the comprehensive weighted index of carrying capacity, establishes a comprehensive improvement strategy planning model for network source adaptability oriented to new energy carrying capacity, specifically: The network source adaptability comprehensive improvement strategy planning model includes an objective function, an operation phase cost model, a monthly power balance constraint, and a monthly electricity balance constraint; The specific calculation formula of the objective function is as follows: Where F is the total cost, Planning stage costs, Cost of operation Build a planning phase cost model: Where: Costs for the planning stage; They are energy storage planning cost, thermal power unit flexibility transformation cost, and line capacity increase cost respectively; M is the total number of months, which is equal to the number of planned years multiplied by 12; m is the subscript of the month; i is the subscript of the unit; is the construction cost per unit capacity of energy storage i, is the flexibility transformation cost of thermal power unit j, is the static capacity increase cost of line k; They respectively indicate whether energy storage i is built, whether thermal power unit j is modified for flexibility, and whether line k is increased in capacity in month m; , are the annual growth rates of construction costs for thermal power and energy storage equipment, respectively; is the discount value coefficient of the mth month; ESS, G, and LINE are energy storage devices, thermal power units, and line collections, respectively; The specific calculation formula of the cost model in the operation phase is as follows: Where: is the fixed operating rate of a unit of power source i in month m, is the fuel price of power source i in the mth month; Penalties for the discharge of pollutants from power source i; is the unit power consumption coefficient of power source i in the mth month; The monthly power balance constraints are specifically as follows: Where: The available capacity of each type of power source unit when participating in power balancing; is the maximum load value in the mth month; is the static capacity reserve coefficient of the mth month; A is the set of various units; The monthly power balance constraints are as follows: Where: and are the power generation and storage capacity of energy storage station i in the mth month respectively; is the power generation of the i-th power plant in month m, is the maximum electricity demand in month m.

10. The method for comprehensively improving network source adaptability for new energy carrying capacity according to claim 1, characterized in that: The comprehensive strategy for improving the adaptability of the network source for the new energy carrying capacity is as follows: Basic data construction: Generate this month's basic data based on last month's power planning results; Model input: Input the constructed basic data into the comprehensive improvement strategy planning model for network source adaptability oriented to new energy carrying capacity; Investment decision-making level processing: energy storage system planning; Plan the flexibility transformation of thermal power units; formulate line capacity expansion plans; Operational simulation layer processing: short-term operational simulation is performed to evaluate the actual performance of the power grid; Resource allocation adjustment: If insufficient resource allocation is found during the simulation, the system will return to the investment decision-making layer for optimization and adjustment, including energy storage system planning, flexibility transformation of thermal power units and line capacity expansion. If insufficient resource allocation is found during the simulation, the power supply planning plan for the year will be output and used as the calculation data for the next year.

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