Method and device for evaluating new energy base dispatching mode

Abstract

The application belongs to the field of power system dispatching automation, and discloses a new energy base dispatching mode evaluation method and related device, which comprises the following steps: based on a preset optimization dispatching model, optimization dispatching of a power system containing a new energy base is carried out by using each dispatching mode to obtain optimization dispatching results of each dispatching mode; according to the optimization dispatching results of each dispatching mode, each dispatching mode evaluation index value of each dispatching mode is obtained based on preset dispatching mode evaluation indexes; and according to each dispatching mode evaluation index value of each dispatching mode, a grey correlation degree of each dispatching mode is obtained as an evaluation result of each dispatching mode based on a grey correlation analysis method. The closeness of each dispatching mode to an ideal scheme can be objectively measured, so that the operation effect of the power system containing the new energy base under different dispatching modes can be scientifically and reasonably quantitatively evaluated, the advantages and disadvantages of each dispatching mode can be accurately determined, and the safe, economic and efficient operation of the new energy base can be effectively promoted.

Description

Technical Field

[0001] This invention belongs to the field of power system dispatch automation, and relates to a method and related apparatus for evaluating dispatch modes of new energy bases. Background Technology

[0002] In recent years, the construction of large-scale wind and solar power bases, focusing on desert, Gobi, and arid regions, has become an important strategic deployment for energy transformation. Compared with traditional power sources, the power sources supporting large-scale new energy bases are characterized by strong policy influence, a high proportion of new energy, concentrated power generation entities, relatively weak grid connection points, and complex grid connection modes. With the continuous increase in the penetration rate of new energy, the scheduling and operation of large-scale new energy bases face the urgent need for multi-objective synergistic optimization. The operating characteristics of the bases differ significantly under different scheduling modes. Therefore, how to scientifically evaluate the advantages and disadvantages of scheduling modes has become a key issue that urgently needs to be addressed.

[0003] However, current research in the field of power system operation evaluation mainly focuses on the macro-level assessment of the overall operating status of the power system. Research on evaluation systems specifically targeting dispatch modes is severely lacking. In practical work, there is a lack of scientific basis and practical tools for systematically evaluating dispatch modes in new energy bases, which restricts the optimization process and the level of scientific decision-making in new energy base dispatch operation schemes. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and related apparatus for evaluating the scheduling mode of new energy bases.

[0005] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, the present invention provides a method for evaluating the dispatching modes of new energy bases, comprising: performing optimized dispatching of a power system including new energy bases using various dispatching modes based on a preset optimized dispatching model, and obtaining optimized dispatching results for each dispatching mode; obtaining evaluation index values ​​for each dispatching mode based on preset evaluation indexes for each dispatching mode according to the optimized dispatching results of each dispatching mode; and obtaining the grey relational degree of each dispatching mode as the evaluation result of each dispatching mode based on grey relational analysis according to the evaluation index values ​​of each dispatching mode.

[0006] Optionally, the objective function of the preset optimized scheduling model is:

[0007] in, Where T is the number of power system generating units, and T is the number of optimization time periods. For the nth generating unit, the electricity market price during time period t. Let represent the amount of electricity won in the electricity market by the nth generating unit during time period t.

[0008] The constraints of the preset optimized scheduling model include: power balance constraints, reserve constraints, network constraints, unit ramping constraints, and unit output constraints.

[0009] Optionally, the power balance constraint is: ,

[0010] in, Let be the total load of the power system during time period t. The number of power generating units to support the new energy base The output of the power generation unit supporting the m-th new energy base during time period t. It supplies electricity to the new energy base.

[0011] The backup constraint is: ,

[0012] ,

[0013] in, This represents the upper limit of the active power of the nth unit. This is the lower limit of the active power of the nth generating unit. For the reserve demand of the power system during time period t, For the power system's reserve demand during time period t, To meet the backup demand of the new energy base during period t. This is for the backup demand of the new energy base during period t.

[0014] The network constraints are:

[0015] in, This represents the maximum transmission capacity of line l. Let n be the power transfer factor of the nth generator unit to line l. This represents the number of load nodes in the power system. Let n be the power transfer factor of the nth load node to line l. Let be the power of the nth load node during time period t.

[0016] The unit's ramp-up constraint is:

[0017] in, Let n be the output of the nth unit during time period t. Let n be the output of the nth unit during time period t-1. This represents the maximum ramp rate for the nth unit.

[0018] The unit output constraint is:

[0019] in, This represents the upper limit of the active power of the nth unit. This is the lower limit of the active power of the nth generating unit. It provides power output for the nth unit during time period t.

[0020] Optionally, the preset evaluation indicators for each scheduling mode include evaluation indicators for safety-related scheduling modes, evaluation indicators for economic-related scheduling modes, and evaluation indicators for green-related scheduling modes.

[0021] Optionally, the preset evaluation indicators for each scheduling mode include one or more of the following: power flow knowability indicators, critical channel load rate, ramp-up executability rate, reserve margin of new energy bases, power system supply cost, power system generator revenue, power system load electricity cost, marginal supply cost of new energy bases, new energy absorption rate, and proportion of green electricity transmitted from new energy bases to other regions.

[0022] Optionally, the step of obtaining the grey relational degree of each scheduling mode as the evaluation result of each scheduling mode based on the evaluation index values ​​of each scheduling mode using the grey relational analysis method includes: obtaining the optimal evaluation index values ​​of each scheduling mode among all scheduling modes based on the evaluation index values ​​of each scheduling mode, thus obtaining the optimal evaluation index sequence; after performing dimensionless processing on the evaluation index values ​​of each scheduling mode and the evaluation index values ​​of each scheduling mode in the optimal evaluation index sequence, obtaining the grey relational coefficient between the evaluation index values ​​of each scheduling mode and the evaluation index values ​​of each scheduling mode in the optimal evaluation index sequence based on the grey relational analysis method, and taking the average value to obtain the grey relational degree of each scheduling mode as the evaluation result of each scheduling mode.

[0023] Optionally, the grey relational coefficient is obtained by the following formula:

[0024] in, Let be the grey relational coefficient between the evaluation index value of the z-th scheduling mode of the i-th scheduling mode and the evaluation index value of the z-th scheduling mode in the optimal evaluation index sequence; The resolution coefficient; It is the absolute value of the difference between the evaluation index value of the z-th scheduling mode of the i-th scheduling mode and the evaluation index value of the z-th scheduling mode in the optimal evaluation index sequence; It is the minimum absolute value of the difference between the evaluation index value of each scheduling mode and the corresponding evaluation index value of each scheduling mode in the optimal evaluation index sequence. It represents the maximum absolute value of the difference between the evaluation index value of each scheduling mode and the corresponding evaluation index value of each scheduling mode in the optimal evaluation index sequence.

[0025] Optionally, before taking the average value, the method further includes: using a preset adjustment rule based on business logic to adjust the grey correlation coefficient between the evaluation index values ​​of each scheduling mode and the evaluation index values ​​of each scheduling mode in the optimal evaluation index sequence.

[0026] Optionally, the scheduling mode evaluation indicators include power flow awareness indicators and critical channel load rate; the preset adjustment rules based on business logic include: when the power flow awareness indicator value of the current scheduling mode is 0, adjusting the gray correlation coefficient between the critical channel load rate value of the current scheduling mode and the critical channel load rate value in the optimal evaluation indicator sequence to a first preset value or reducing the second preset value; wherein, a power flow awareness indicator value of 0 indicates that the power flow awareness is such that complete power flow cannot be obtained.

[0027] In a second aspect, the present invention provides a new energy base dispatching mode evaluation system, comprising: a dispatching module, configured to perform optimized dispatching of a power system including a new energy base using various dispatching modes based on a preset optimized dispatching model, and obtain optimized dispatching results for each dispatching mode; an index module, configured to obtain evaluation index values ​​for each dispatching mode based on preset evaluation indexes for each dispatching mode according to the optimized dispatching results of each dispatching mode; and an evaluation module, configured to obtain the grey relational degree of each dispatching mode as the evaluation result of each dispatching mode based on the evaluation index values ​​of each dispatching mode using a grey relational analysis method.

[0028] Optionally, the objective function of the preset optimized scheduling model is:

[0029] in, Where T is the number of power system generating units, and T is the number of optimization time periods. For the nth generating unit, the electricity market price during time period t. Let represent the amount of electricity won in the electricity market by the nth generating unit during time period t.

[0030] The constraints of the preset optimized scheduling model include: power balance constraints, reserve constraints, network constraints, unit ramping constraints, and unit output constraints.

[0031] Optionally, the power balance constraint is: ,

[0032] in, Let be the total load of the power system during time period t. The number of power generating units to support the new energy base The output of the power generation unit supporting the m-th new energy base during time period t. It supplies electricity to the new energy base.

[0033] The backup constraint is: ,

[0034] ,

[0035] in, This represents the upper limit of the active power of the nth unit. This is the lower limit of the active power of the nth generating unit. For the reserve demand of the power system during time period t, For the power system's reserve demand during time period t, To meet the backup demand of the new energy base during period t. This is for the backup demand of the new energy base during period t.

[0036] The network constraints are:

[0037] in, This represents the maximum transmission capacity of line l. Let n be the power transfer factor of the nth generator unit to line l. This represents the number of load nodes in the power system. Let n be the power transfer factor of the nth load node to line l. Let be the power of the nth load node during time period t.

[0038] The unit's ramp-up constraint is:

[0039] in, Let n be the output of the nth unit during time period t. Let n be the output of the nth unit during time period t-1. This represents the maximum ramp rate for the nth unit.

[0040] The unit output constraint is:

[0041] in, This represents the upper limit of the active power of the nth unit. This is the lower limit of the active power of the nth generating unit. It provides power output for the nth unit during time period t.

[0042] Optionally, the preset evaluation indicators for each scheduling mode include evaluation indicators for safety-related scheduling modes, evaluation indicators for economic-related scheduling modes, and evaluation indicators for green-related scheduling modes.

[0043] Optionally, the preset evaluation indicators for each scheduling mode include one or more of the following: power flow knowability indicators, critical channel load rate, ramp-up executability rate, reserve margin of new energy bases, power system supply cost, power system generator revenue, power system load electricity cost, marginal supply cost of new energy bases, new energy absorption rate, and proportion of green electricity transmitted from new energy bases to other regions.

[0044] Optionally, the evaluation module is specifically used to: obtain the optimal evaluation index value of each scheduling mode among all scheduling modes based on the evaluation index values ​​of each scheduling mode of each scheduling mode, and obtain the optimal evaluation index sequence; after performing dimensionless processing on the evaluation index values ​​of each scheduling mode of each scheduling mode and the evaluation index values ​​of each scheduling mode in the optimal evaluation index sequence, obtain the grey relational coefficient between the evaluation index values ​​of each scheduling mode of each scheduling mode and the evaluation index values ​​of each scheduling mode in the optimal evaluation index sequence based on the grey relational analysis method, and take the average value to obtain the grey relational degree of each scheduling mode as the evaluation result of each scheduling mode.

[0045] Optionally, the grey relational coefficient is obtained by the following formula:

[0046] in, Let be the grey relational coefficient between the evaluation index value of the z-th scheduling mode of the i-th scheduling mode and the evaluation index value of the z-th scheduling mode in the optimal evaluation index sequence; The resolution coefficient; It is the absolute value of the difference between the evaluation index value of the z-th scheduling mode of the i-th scheduling mode and the evaluation index value of the z-th scheduling mode in the optimal evaluation index sequence; It is the minimum absolute value of the difference between the evaluation index value of each scheduling mode and the corresponding evaluation index value of each scheduling mode in the optimal evaluation index sequence. It represents the maximum absolute value of the difference between the evaluation index value of each scheduling mode and the corresponding evaluation index value of each scheduling mode in the optimal evaluation index sequence.

[0047] Optionally, the evaluation module is further configured to: before taking the average value, use a preset adjustment rule based on business logic to adjust the grey correlation coefficient between the evaluation index values ​​of each scheduling mode and the evaluation index values ​​of each scheduling mode in the optimal evaluation index sequence.

[0048] Optionally, the scheduling mode evaluation indicators include power flow awareness indicators and critical channel load rate; the preset adjustment rules based on business logic include: when the power flow awareness indicator value of the current scheduling mode is 0, adjusting the gray correlation coefficient between the critical channel load rate value of the current scheduling mode and the critical channel load rate value in the optimal evaluation indicator sequence to a first preset value or reducing the second preset value; wherein, a power flow awareness indicator value of 0 indicates that the power flow awareness is such that complete power flow cannot be obtained.

[0049] In a third aspect, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the new energy base scheduling mode evaluation method.

[0050] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the evaluation method for the scheduling mode of the new energy base.

[0051] Compared with the prior art, the present invention has the following beneficial effects: This invention provides an evaluation method for dispatching modes in new energy bases. Based on a pre-set optimized dispatching model, it optimizes the dispatching of power systems including new energy bases using various dispatching modes. It acquires panoramic simulation data of system operation under different dispatching modes, then calculates pre-set evaluation index values ​​for each dispatching mode based on the optimized dispatching results to quantitatively characterize the dispatching effect. Finally, it calculates the grey relational degree based on the evaluation index values ​​of each dispatching mode using grey relational analysis as the evaluation result. This method objectively measures the degree of closeness of each dispatching mode to the ideal solution, thereby scientifically and rationally quantifying and evaluating the operational effect of power systems including new energy bases under different dispatching modes. It accurately determines the merits of each dispatching mode, providing quantitative decision support for dispatching agencies to optimize dispatching modes and ensure the safe, economical, and efficient operation of power systems, effectively promoting the safe, economical, and efficient operation of new energy bases. Attached Figure Description

[0052] Figure 1 This is a flowchart of the evaluation method for the scheduling mode of new energy bases according to an embodiment of the present invention.

[0053] Figure 2 This is a structural block diagram of the new energy base scheduling mode evaluation system according to an embodiment of the present invention. Detailed Implementation

[0054] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0055] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0056] The present invention will now be described in further detail with reference to the accompanying drawings: See Figure 1 In one embodiment of the present invention, a method for evaluating the scheduling mode of a new energy base is provided to solve the problem that it is currently impossible to effectively evaluate the various scheduling modes of new energy bases (especially new energy bases in desert, Gobi and arid regions).

[0057] Specifically, the evaluation method for the dispatching mode of the new energy base of this invention includes the following steps: S1: Based on the preset optimized scheduling model, optimize the scheduling of the power system including the new energy base using various scheduling modes, and obtain the optimized scheduling results of each scheduling mode.

[0058] Specifically, step S1 is used to obtain the operating status data of the power system including the renewable energy base under different dispatch modes. Here, dispatch mode refers to the different dispatch organization methods and market mechanisms adopted in the power system dispatch operation. For renewable energy bases, common dispatch modes include independent control area dispatch mode, decentralized dispatch mode, and overall dispatch mode, among others. Under different dispatch modes, the power generation planning of the renewable energy base's supporting power sources, the coordination method with external transmission channels, and the cooperation method with other power sources within the system differ. Therefore, it is necessary to perform optimization dispatch calculations for each dispatch mode to be evaluated to obtain the system operating status under that mode.

[0059] Specifically, a power system model is constructed based on the actual power system topology. Input data includes load demand curves, power source operating parameters, and renewable energy output curves. Optimization periods are measured in hours, and the power system operation over a certain future time span is uniformly optimized based on a pre-defined optimization scheduling model. For example, the total number of optimization periods T is typically 96 points (i.e., 15 minutes per period) or 24 points (i.e., 1 hour per period), but can also be flexibly set according to actual scheduling needs.

[0060] S2: Based on the optimized scheduling results of each scheduling mode, and based on the preset evaluation indicators of each scheduling mode, obtain the evaluation index values ​​of each scheduling mode.

[0061] Specifically, step S2 transforms the optimized scheduling results obtained in step S1 into quantifiable scheduling mode evaluation index values. For example, optimized scheduling results typically include information such as the output of each unit in each time period, system power flow distribution, and nodal electricity prices. This raw data is massive in volume and complex in dimensions, making it difficult to directly compare the merits of different solutions. This step, through a pre-defined evaluation index system, extracts these raw data into quantifiable indicators with clear physical and operational meanings, thereby providing standardized input for subsequent comprehensive evaluation.

[0062] The explanatory, pre-defined evaluation indicators for each dispatch mode are set based on the operational characteristics of new energy bases and the actual needs of dispatching agencies. These evaluation indicators typically cover multiple dimensions of power system operation to comprehensively reflect the impact of different dispatch modes on operational performance.

[0063] S3: Based on the evaluation index values ​​of each scheduling mode, the grey relational degree of each scheduling mode is obtained using the grey relational analysis method as the evaluation result of each scheduling mode.

[0064] Specifically, step S3 involves comprehensively processing the evaluation index values ​​of each scheduling mode obtained in step S2 to form a single quantitative evaluation result for each scheduling mode, thereby determining the relative merits of each scheduling mode. Grey relational analysis is a multi-factor comprehensive evaluation method suitable for small-sample, incomplete-information systems. Its basic idea is to determine the degree of correlation between factors based on the similarity of the geometric shapes of sequence curves. The grey relational degree of each scheduling mode is used as the evaluation result. The higher the grey relational degree of a scheduling mode, the closer it is to the optimal solution, i.e., the better the comprehensive evaluation effect. Through the grey relational analysis process, even when the importance of the evaluation indicators of each scheduling mode is not fully clear and there may be correlations between the indicators, an objective and comprehensive ranking of the merits of each scheduling mode can be given.

[0065] In summary, the new energy base dispatching mode evaluation method of this invention forms a complete quantitative evaluation process for dispatching modes by organically combining three stages: optimized dispatching simulation, calculation of dispatching mode evaluation index values, and grey relational analysis. Specifically, the optimized dispatching simulation stage provides an objective data foundation for the evaluation; the calculation of dispatching mode evaluation index values ​​quantifies the operational effectiveness of the dispatching modes; and the grey relational analysis stage enables a comprehensive quantitative comparison of various dispatching modes under limited data conditions. These three steps are interconnected and progressively enhance each other, jointly achieving a scientific and reasonable evaluation of the dispatching modes of new energy bases and providing strong quantitative support for the operational decisions of dispatching agencies. This method can be widely applied to the selection and optimization of dispatching modes for large-scale wind and solar power bases in desert, Gobi, and other arid regions, and has significant engineering application value.

[0066] In one possible implementation, the objective function of the preset optimization scheduling model is:

[0067] in, Where T is the number of power system generating units, and T is the number of optimization time periods. For the nth generating unit, the electricity market price during time period t. Let represent the amount of electricity won in the electricity market by the nth generating unit during time period t.

[0068] Specifically, the objective function of the optimized dispatch model aims to minimize the total power supply cost of the power system. In optimized power system dispatch, the total power supply cost equals the sum of the products of each generating unit's electricity market bid and its awarded electricity volume in each time period. This objective function reflects the economic requirements of power system operation, namely, meeting load demand at the lowest possible total cost while satisfying various safety and operational constraints. The design of this objective function is based on the fundamental mechanism of unit bidding and clearing in the electricity market environment. Each generating unit submits its bid curve based on its own generation cost. The optimized dispatch model, under the premise of satisfying various constraints, performs clearing calculations with the goal of minimizing the total electricity purchase cost, obtaining the awarded electricity volume for each unit in each time period. By solving the above optimized dispatch model, it is possible to simulate the market clearing results and generating unit plans of the power system under different dispatch modes.

[0069] Optionally, the constraints of the preset optimized scheduling model include: power balance constraints, reserve constraints, network constraints, unit ramping constraints, and unit output constraints.

[0070] Specifically, power balance constraints are used to ensure that the power generation output and load demand of the system remain in real time balance at any time; reserve constraints are used to ensure that the system has sufficient spinning reserve capacity to cope with sudden disturbances; network constraints are used to ensure that the power flow of each line does not exceed its transmission capacity limit; unit ramping constraints are used to ensure that the adjustment range of unit output between adjacent time periods does not exceed the physical ramping capacity of the unit; and unit output constraints are used to ensure that the output of each unit is within its technical output upper and lower limits.

[0071] In one possible implementation, the power balance constraint is: ,

[0072] in, Let be the total load of the power system during time period t. The number of power generating units to support the new energy base The output of the power generation unit supporting the m-th new energy base during time period t. It supplies electricity to the new energy base.

[0073] It should be noted that, under different dispatch modes, the total external power transmitted from the new energy base varies. The determination method may differ. For example, in a unified scheduling mode, this value is determined by the system scheduling agency through unified optimization; in a hierarchical scheduling mode, this value may be determined by the internal scheduling agency of the new energy base in coordination with the system scheduling agency.

[0074] The backup constraint is: ,

[0075] ,

[0076] in, This represents the upper limit of the active power of the nth unit. This is the lower limit of the active power of the nth generating unit. For the reserve demand of the power system during time period t, For the power system's reserve demand during time period t, To meet the backup demand of the new energy base during period t. This is for the backup demand of the new energy base during period t.

[0077] Explaining this, reserve capacity is a crucial guarantee for the safe operation of a power system, used to quickly adjust output to maintain system power balance in the event of sudden events such as unit tripping or load surges. System reserve constraints ensure that the entire power system has sufficient reserve capacity to cope with uncertainties; while reserve constraints for renewable energy bases are specific to these bases, requiring them to maintain a certain reserve margin within themselves. The reserve requirements of renewable energy bases vary under different dispatch modes. and The determination methods may differ. For example, in a unified scheduling mode, the base's backup requirements are set uniformly by the system, while in a hierarchical scheduling mode, the base's backup requirements may be set independently by the base based on its own operating conditions.

[0078] The network constraints are:

[0079] in, This represents the maximum transmission capacity of line l. Let n be the power transfer factor of the nth generator unit to line l. This represents the number of load nodes in the power system. Let n be the power transfer factor of the nth load node to line l. Let be the power of the nth load node during time period t.

[0080] Interpretively, network constraints are one of the core constraints for the safe operation of power systems, used to prevent transmission line overload. Large-scale renewable energy bases are typically located in remote areas such as deserts, Gobi, and arid regions, with relatively weak grid connection points. The transmission capacity of external transmission channels often constitutes a key bottleneck restricting the output of renewable energy bases. Therefore, accurately simulating the power flow constraints of key transmission channels in network constraints is of great significance for evaluating the power transmission capacity of renewable energy bases and the grid security level under different dispatching modes. Specifically, the power transfer factor... and It is a constant calculated based on the power system network topology and line parameters, reflecting the basic physical laws of power flow distribution in the power system network.

[0081] The unit's ramp-up constraint is:

[0082] in, Let n be the output of the nth unit during time period t. Let n be the output of the nth unit during time period t-1. This represents the maximum ramp rate for the nth unit.

[0083] Explanatoryly, the output adjustment of generator units is subject to physical limitations imposed by equipment such as boilers and turbines, preventing rapid increases or decreases in output within a certain timeframe. This constraint is particularly important in power systems with a high proportion of renewable energy, as rapid fluctuations in renewable energy output require a quick response from conventional power sources to maintain system balance, and the ramp-up capability of conventional power sources may constitute a bottleneck to the system's flexible adjustment capabilities. By setting ramp-up constraints, the optimized scheduling model can more realistically reflect the actual operating characteristics of the units.

[0084] The unit output constraint is:

[0085] in, This represents the upper limit of the active power of the nth unit. This is the lower limit of the active power of the nth generating unit. It provides power output for the nth unit during time period t.

[0086] In one possible implementation, the preset evaluation indicators for each scheduling mode include evaluation indicators for safety-related scheduling modes, evaluation indicators for economic-related scheduling modes, and evaluation indicators for green-related scheduling modes.

[0087] Specifically, to comprehensively reflect the operational effectiveness of new energy bases under different dispatch modes, this implementation method divides the dispatch mode evaluation indicators into three categories: safety indicators, economic indicators, and green indicators. Safety indicators evaluate the impact of dispatch modes on the safe and stable operation of the power system, reflecting the dispatch mode's ability to ensure grid security. Economic indicators evaluate the economic efficiency of dispatch modes, reflecting their effectiveness in reducing system operating costs and optimizing resource allocation. Green indicators evaluate the dispatch mode's promotion of new energy consumption and environmental friendliness, reflecting its contribution to promoting the clean and low-carbon transformation of energy. This three-dimensional division fully considers the diverse objectives of new energy base dispatch operation. New energy bases typically include large-scale new energy sources, and their dispatch operation not only needs to meet the basic requirements of safe and reliable power system operation but also needs to consider economic efficiency and new energy consumption objectives. By constructing a three-dimensional evaluation indicator system, the comprehensive performance of different dispatch modes can be reflected more comprehensively and scientifically.

[0088] In one possible implementation, the preset evaluation indicators for each scheduling mode include one or more of the following: power flow knowability indicators, critical channel load rate, ramp-up executability rate, reserve margin of new energy bases, power system supply cost, power system generator revenue, power system load electricity cost, marginal supply cost of new energy bases, new energy absorption rate, and proportion of green electricity transmitted from new energy bases to other regions.

[0089] Preferably, the above 10 scheduling mode evaluation indicators are used together to form a specific evaluation indicator system for the scheduling mode of new energy bases.

[0090] Specifically, the power flow knowability index reflects whether the dispatching agency can obtain complete power flow distribution information. This index is a binary index, with a value of 1 indicating that complete power flow is available and a value of 0 indicating that complete power flow is not available. Under different dispatching modes, the power flow distribution information that the dispatching agency can obtain may be different, and incomplete power flow distribution information may affect the accuracy of dispatching decisions.

[0091] Specifically, the critical channel load factor reflects the power flow load level of critical transmission channels and is used to evaluate the power flow carrying capacity pressure of critical channels. It is defined as follows:

[0092] Where T is the number of optimization time periods. The key channel is the power flow during time period t.

[0093] Specifically, the ramp-up feasibility rate reflects the feasibility of the unit's ramp-up plan and is defined as:

[0094] in, To optimize the number of executable time periods for hill climbing in the scheduling results.

[0095] Specifically, the reserve margin of a new energy base reflects the adequacy of its reserve capacity, and is defined as:

[0096] in, To reduce the backup capacity required for large-scale base operations, To reduce the backup capacity required for large-scale base operations, The total external power transmitted to the new energy base during period t.

[0097] Specifically, the power supply cost of a power system reflects the total cost of purchasing electricity in the system, and is defined as:

[0098] Specifically, the revenue of power generation entities reflects their total income in the electricity market, and is defined as:

[0099] in, Set the clearing price for the electricity market for the nth generating unit during time period t.

[0100] Specifically, the electricity cost of the power system load reflects the total electricity expenditure on the load side, and is defined as:

[0101] in, For load numbers, The electricity market clearing price for the nth load in time period t. This represents the electricity market bid amount for the nth load during time period t.

[0102] Specifically, the marginal power supply cost of a new energy base reflects the cost increase when the new energy base increases its unit output.

[0103] Specifically, the renewable energy absorption rate reflects the degree of utilization of renewable energy power generation, and is defined as:

[0104] in, To optimize the cumulative grid-connected power generation of new energy units throughout the entire period, To predict the power generation of new energy units for the entire optimized time period.

[0105] Specifically, the proportion of green electricity transmitted from new energy bases is defined as the percentage of green electricity in the total electricity transmitted from new energy bases:

[0106] In one possible implementation, the step of obtaining the grey relational degree of each scheduling mode as the evaluation result of each scheduling mode based on the evaluation index values ​​of each scheduling mode using the grey relational analysis method includes: obtaining the optimal evaluation index values ​​of each scheduling mode among all scheduling modes based on the evaluation index values ​​of each scheduling mode, thus obtaining the optimal evaluation index sequence; after performing dimensionless processing on the evaluation index values ​​of each scheduling mode and the evaluation index values ​​of each scheduling mode in the optimal evaluation index sequence, obtaining the grey relational coefficient between the evaluation index values ​​of each scheduling mode and the evaluation index values ​​of each scheduling mode in the optimal evaluation index sequence based on the grey relational analysis method, and taking the average value to obtain the grey relational degree of each scheduling mode as the evaluation result of each scheduling mode.

[0107] Specifically, for each scheduling mode evaluation index, the optimal index value is first selected from all scheduling modes to be evaluated. The optimal index sequence is then formed from the optimal values ​​of all scheduling mode evaluation indices. This optimal index sequence represents the ideal solution, meaning that each scheduling mode evaluation index has reached the best level among all scheduling modes, and serves as the benchmark for subsequent comparisons.

[0108] For explanatory purposes, for positive indicators (indicators where a larger value is better, such as the renewable energy absorption rate), the optimal value is the maximum value among all dispatch modes; for negative indicators (indicators where a smaller value is better, such as the electricity cost of the power system load), the optimal value is the minimum value among all dispatch modes.

[0109] Then, the evaluation index values ​​of each scheduling mode and the evaluation index values ​​of each scheduling mode in the optimal evaluation index sequence are dimensionless to eliminate the influence of different dimensions of the indicators on the analysis results. For example, the mean normalization method, range normalization method, or initial value method can be used for dimensionless processing. Based on this, the grey relational coefficient between the evaluation index values ​​of each scheduling mode and the corresponding evaluation index values ​​of the optimal sequence is calculated. This coefficient reflects the degree of closeness between the evaluation index values ​​of the current scheduling mode and the evaluation index values ​​of the optimal scheduling mode. Finally, the arithmetic mean of the grey relational coefficients of all scheduling mode evaluation index values ​​for each scheduling mode is taken to obtain the grey relational degree of that scheduling mode. A higher grey relational degree indicates that the overall evaluation index values ​​of each scheduling mode are closer to the optimal level.

[0110] In one possible implementation, the grey relational coefficient is obtained by the following formula:

[0111] in, Let be the grey relational coefficient between the evaluation index value of the z-th scheduling mode of the i-th scheduling mode and the evaluation index value of the z-th scheduling mode in the optimal evaluation index sequence; The resolution coefficient; It is the absolute value of the difference between the evaluation index value of the z-th scheduling mode of the i-th scheduling mode and the evaluation index value of the z-th scheduling mode in the optimal evaluation index sequence; It is the minimum absolute value of the difference between the evaluation index value of each scheduling mode and the corresponding evaluation index value of each scheduling mode in the optimal evaluation index sequence. It represents the maximum absolute value of the difference between the evaluation index value of each scheduling mode and the corresponding evaluation index value of each scheduling mode in the optimal evaluation index sequence.

[0112] Explanatory, by introducing a resolution coefficient This allows adjustment of the sensitivity of the grey relational coefficient to different difference levels. For example, when... When the grey relational coefficient is smaller, the discrimination is higher, and the differences between the indicators are more obvious; when When a larger value is taken, the grey relational coefficient tends to be averaged, and the differences between the indicators are smoothed out.

[0113] For example, the grey relational degree of each scheduling mode is obtained by the following formula:

[0114] in, Let gray relation be the degree of the i-th scheduling mode. This represents the number of evaluation indicators for scheduling modes.

[0115] In one possible implementation, before taking the average value, the method further includes: using a preset adjustment rule based on business logic to adjust the grey correlation coefficient between the evaluation index values ​​of each scheduling mode and the evaluation index values ​​of each scheduling mode in the optimal evaluation index sequence.

[0116] Specifically, before calculating the grey relational degree, the grey relational coefficients of some dispatch mode evaluation indicators are adaptively adjusted based on the actual business logic of power system dispatch operation. Interpretatively, the assumption of independence between dispatch mode evaluation indicators in the standard grey relational analysis method may deviate from reality. In the actual scenario of new energy base dispatch evaluation, there are logical correlations or priority relationships between certain dispatch mode evaluation indicators. For example, when a key dispatch mode evaluation indicator fails to meet basic requirements, the evaluation significance of other related dispatch mode evaluation indicators will decrease accordingly.

[0117] Therefore, this implementation proposes adjustment rules based on business logic. These rules correct the grey relational coefficients according to the inherent correlations between scheduling mode evaluation indicators, making the evaluation results more aligned with actual business needs. Specifically, the content of the adjustment rules can be flexibly configured according to actual business requirements.

[0118] For example, the adjustment rules may include lowering the overall grey relational coefficient between the relevant dispatch mode evaluation index value and the optimal value when the evaluation index value of a certain prerequisite dispatch mode does not meet the requirements; or reducing the grey relational coefficient of the dispatch mode in terms of the economic dispatch mode evaluation index when the evaluation index of a certain safety-type dispatch mode does not reach the basic threshold. Through the above adjustments, the grey relational analysis results can be made more consistent with the actual business logic of power system dispatch operation.

[0119] In one possible implementation, the scheduling mode evaluation index includes a power flow awareness index and a critical channel load rate; the preset adjustment rules based on business logic include: when the power flow awareness index value of the current scheduling mode is 0, adjusting the grey correlation coefficient between the critical channel load rate value of the current scheduling mode and the critical channel load rate value in the optimal evaluation index sequence to a first preset value or reducing it to a second preset value; wherein, a power flow awareness index value of 0 indicates that the power flow awareness is such that a complete power flow cannot be obtained.

[0120] Specifically, the power flow awareness index reflects whether the scheduling agency can obtain complete power flow distribution information, which is the basis for accurate scheduling decisions. If the scheduling agency cannot obtain complete power flow information (i.e., the power flow awareness index value is 0), the calculated value of the critical channel load rate may be based on incomplete network information, and its confidence level will decrease accordingly. Therefore, when the power flow awareness index value of a certain scheduling mode is 0, the grey relational coefficient between the critical channel load rate and the optimal value of that scheduling mode is adjusted. Specifically, it can be adjusted to a first preset value (e.g., uniformly adjusted to a certain fixed value) or reduced to a second preset value (e.g., multiplied by 0.5 or subtracted by 0.2 from the original value). Explanatoryly, the above adjustment rules reflect the business logic that information is the basis of decision-making: when power flow information is incomplete, the reliability of the critical channel load rate index calculated based on this information decreases, thus reducing the contribution of this index to the final evaluation result in the comprehensive evaluation.

[0121] Through the above adjustments, this invention enables evaluation results to better align with actual business needs, avoiding distorted evaluation conclusions due to incomplete data. It should be noted that the first and second preset values ​​can be flexibly set according to actual business requirements, and no specific limitations are imposed.

[0122] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the apparatus embodiments, please refer to the embodiments of the method of the present invention.

[0123] See Figure 2 In another embodiment of the present invention, a new energy base scheduling mode evaluation system is provided, which can be used to implement the above-mentioned new energy base scheduling mode evaluation method. Specifically, the new energy base scheduling mode evaluation system includes a scheduling module, an indicator module and an evaluation module.

[0124] The scheduling module is used to optimize the scheduling of the power system including the new energy base based on a preset optimized scheduling model and various scheduling modes, and obtain the optimized scheduling results of each scheduling mode; the index module is used to obtain the evaluation index value of each scheduling mode based on the optimized scheduling results of each scheduling mode and preset evaluation indexes of each scheduling mode; the evaluation module is used to obtain the grey relational degree of each scheduling mode as the evaluation result of each scheduling mode based on the evaluation index value of each scheduling mode and the grey relational analysis method.

[0125] In one possible implementation, the objective function of the preset optimization scheduling model is:

[0126] in, Where T is the number of power system generating units, and T is the number of optimization time periods. For the nth generating unit, the electricity market price during time period t. Let represent the amount of electricity won in the electricity market by the nth generating unit during time period t.

[0127] The constraints of the preset optimized scheduling model include: power balance constraints, reserve constraints, network constraints, unit ramping constraints, and unit output constraints.

[0128] In one possible implementation, the power balance constraint is: ,

[0129] in, Let be the total load of the power system during time period t. The number of power generating units to support the new energy base The output of the power generation unit supporting the m-th new energy base during time period t. It supplies electricity to the new energy base.

[0130] The backup constraint is: ,

[0131] ,

[0132] in, This represents the upper limit of the active power of the nth unit. This is the lower limit of the active power of the nth generating unit. For the reserve demand of the power system during time period t, For the power system's reserve demand during time period t, To meet the backup demand of the new energy base during period t. This is for the backup demand of the new energy base during period t.

[0133] The network constraints are:

[0134] in, This represents the maximum transmission capacity of line l. Let n be the power transfer factor of the nth generator unit to line l. This represents the number of load nodes in the power system. Let n be the power transfer factor of the nth load node to line l. Let be the power of the nth load node during time period t.

[0135] The unit's ramp-up constraint is:

[0136] in, Let n be the output of the nth unit during time period t. Let n be the output of the nth unit during time period t-1. This represents the maximum ramp rate for the nth unit.

[0137] The unit output constraint is:

[0138] in, This represents the upper limit of the active power of the nth unit. This is the lower limit of the active power of the nth generating unit. It provides power output for the nth unit during time period t.

[0139] In one possible implementation, the preset evaluation indicators for each scheduling mode include evaluation indicators for safety-related scheduling modes, evaluation indicators for economic-related scheduling modes, and evaluation indicators for green-related scheduling modes.

[0140] In one possible implementation, the preset evaluation indicators for each scheduling mode include one or more of the following: power flow knowability indicators, critical channel load rate, ramp-up executability rate, reserve margin of new energy bases, power system supply cost, power system generator revenue, power system load electricity cost, marginal supply cost of new energy bases, new energy absorption rate, and proportion of green electricity transmitted from new energy bases to other regions.

[0141] In one possible implementation, the evaluation module is specifically used to: obtain the optimal evaluation index value of each scheduling mode among all scheduling modes based on the evaluation index values ​​of each scheduling mode, and obtain the optimal evaluation index sequence; after performing dimensionless processing on the evaluation index values ​​of each scheduling mode and the evaluation index values ​​of each scheduling mode in the optimal evaluation index sequence, obtain the grey relational coefficient between the evaluation index values ​​of each scheduling mode and the evaluation index values ​​of each scheduling mode in the optimal evaluation index sequence based on the grey relational analysis method, and take the average value to obtain the grey relational degree of each scheduling mode as the evaluation result of each scheduling mode.

[0142] In one possible implementation, the grey relational coefficient is obtained by the following formula:

[0143] in, Let be the grey relational coefficient between the evaluation index value of the z-th scheduling mode of the i-th scheduling mode and the evaluation index value of the z-th scheduling mode in the optimal evaluation index sequence; The resolution coefficient; It is the absolute value of the difference between the evaluation index value of the z-th scheduling mode of the i-th scheduling mode and the evaluation index value of the z-th scheduling mode in the optimal evaluation index sequence; It is the minimum absolute value of the difference between the evaluation index value of each scheduling mode and the corresponding evaluation index value of each scheduling mode in the optimal evaluation index sequence. It represents the maximum absolute value of the difference between the evaluation index value of each scheduling mode and the corresponding evaluation index value of each scheduling mode in the optimal evaluation index sequence.

[0144] In one possible implementation, the evaluation module is further configured to: before averaging, use a preset adjustment rule based on business logic to adjust the grey correlation coefficient between the evaluation index values ​​of each scheduling mode and the evaluation index values ​​of each scheduling mode in the optimal evaluation index sequence.

[0145] In one possible implementation, the scheduling mode evaluation index includes a power flow awareness index and a critical channel load rate; the preset adjustment rules based on business logic include: when the power flow awareness index value of the current scheduling mode is 0, adjusting the grey correlation coefficient between the critical channel load rate value of the current scheduling mode and the critical channel load rate value in the optimal evaluation index sequence to a first preset value or reducing it to a second preset value; wherein, a power flow awareness index value of 0 indicates that the power flow awareness is such that a complete power flow cannot be obtained.

[0146] All relevant content of each step involved in the aforementioned embodiments of the evaluation method for new energy base scheduling mode can be referenced to the functional description of the corresponding functional module of the evaluation system for new energy base scheduling mode in the embodiments of the present invention, and will not be repeated here.

[0147] The module division in this embodiment of the invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0148] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of a new energy base scheduling mode evaluation method.

[0149] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory). This computer-readable storage medium is a memory device within a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium of the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space containing the terminal's operating system. Furthermore, this storage space also contains one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the new energy base scheduling mode evaluation method in the above embodiments.

[0150] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0151] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0152] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0153] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating the scheduling mode of a new energy base, characterized in that, include: Based on the preset optimized scheduling model, the optimized scheduling of the power system including the new energy base is carried out using various scheduling modes, and the optimized scheduling results of each scheduling mode are obtained. Based on the optimized scheduling results of each scheduling mode, and based on the preset evaluation indicators of each scheduling mode, the evaluation index values ​​of each scheduling mode are obtained. Based on the evaluation index values ​​of each scheduling mode, the grey relational degree of each scheduling mode is obtained using the grey relational analysis method as the evaluation result of each scheduling mode.

2. The evaluation method for the dispatching mode of new energy bases according to claim 1, characterized in that, The objective function of the preset optimized scheduling model is: in, Where T is the number of power system generating units, and T is the number of optimization time periods. For the nth generating unit, the electricity market price during time period t. Let n be the amount of electricity the nth generating unit won in the electricity market during time period t. The constraints of the preset optimized scheduling model include: power balance constraints, reserve constraints, network constraints, unit ramping constraints, and unit output constraints.

3. The evaluation method for the dispatching mode of new energy bases according to claim 2, characterized in that, The power balance constraint is: ， in, Let be the total load of the power system during time period t. The number of power generating units to support the new energy base The output of the power generation unit supporting the m-th new energy base during time period t. This is the total external power transmission for the new energy base; The backup constraint is: ， ， in, This represents the upper limit of the active power of the nth unit. This represents the lower limit of the active power of the nth generating unit. For the reserve demand of the power system during time period t, For the power system's reserve demand during time period t, To meet the backup demand of the new energy base during period t. This is for the backup demand of the new energy base during period t; The network constraints are: in, This represents the maximum transmission capacity of line l. Let n be the power transfer factor of the nth generator unit to line l. The number of load nodes in the power system. Let n be the power transfer factor of the nth load node to line l. Let be the power of the nth load node during time period t; The unit's ramp-up constraint is: in, Let n be the output of the nth unit during time period t. Let n be the output of the nth unit during time period t-1. This represents the maximum ramp rate for the nth generator unit. The unit output constraint is: in, This represents the upper limit of the active power of the nth unit. This represents the lower limit of the active power of the nth generating unit. It provides power output for the nth unit during time period t.

4. The evaluation method for the dispatching mode of new energy bases according to claim 1, characterized in that, The preset evaluation indicators for each scheduling mode include evaluation indicators for safety-related scheduling modes, economic-related scheduling modes, and green-related scheduling modes.

5. The evaluation method for the dispatching mode of new energy bases according to claim 1, characterized in that, The preset evaluation indicators for each scheduling mode include one or more of the following: The indicators include: power flow visibility, critical channel load rate, ramp-up achievable rate, reserve margin of new energy bases, power system supply cost, power system generation entity revenue, power system load electricity cost, marginal supply cost of new energy bases, new energy absorption rate, and the proportion of green electricity transmitted from new energy bases to other regions.

6. The evaluation method for the dispatching mode of new energy bases according to claim 1, characterized in that, The step of obtaining the grey relational degree of each scheduling mode as the evaluation result of each scheduling mode based on the evaluation index values ​​of each scheduling mode and using the grey relational analysis method includes: Based on the evaluation index values ​​of each scheduling mode, the optimal evaluation index value among all scheduling modes is obtained, thus yielding the optimal evaluation index sequence. After dimensionless processing of the evaluation index values ​​of each scheduling mode and the evaluation index values ​​of each scheduling mode in the optimal evaluation index sequence, the grey relational analysis method is used to obtain the grey relational coefficient between the evaluation index values ​​of each scheduling mode and the evaluation index values ​​of each scheduling mode in the optimal evaluation index sequence. The average value is then taken to obtain the grey relational degree of each scheduling mode as the evaluation result of each scheduling mode.

7. The evaluation method for the dispatching mode of new energy bases according to claim 6, characterized in that, The grey relational coefficient is obtained by the following formula: in, Let be the grey relational coefficient between the evaluation index value of the z-th scheduling mode of the i-th scheduling mode and the evaluation index value of the z-th scheduling mode in the optimal evaluation index sequence; The resolution coefficient; It is the absolute value of the difference between the evaluation index value of the z-th scheduling mode of the i-th scheduling mode and the evaluation index value of the z-th scheduling mode in the optimal evaluation index sequence; It is the minimum absolute value of the difference between the evaluation index value of each scheduling mode and the corresponding evaluation index value of each scheduling mode in the optimal evaluation index sequence. It represents the maximum absolute value of the difference between the evaluation index value of each scheduling mode and the corresponding evaluation index value of each scheduling mode in the optimal evaluation index sequence.

8. The evaluation method for the dispatching mode of new energy bases according to claim 6, characterized in that, Before taking the average value, the following is also included: Using preset adjustment rules based on business logic, the grey correlation coefficient between the evaluation index values ​​of each scheduling mode and the evaluation index values ​​of each scheduling mode in the optimal evaluation index sequence is adjusted.

9. The evaluation method for the dispatching mode of new energy bases according to claim 8, characterized in that, The evaluation indicators for the scheduling mode include power flow predictability indicators and critical channel load rate; The preset adjustment rules based on business logic include: when the power flow awareness index value of the current scheduling mode is 0, the gray correlation coefficient between the key channel load rate value of the current scheduling mode and the key channel load rate value in the optimal evaluation index sequence is adjusted to a first preset value or reduced to a second preset value. Among them, a trend knowability index value of 0 indicates that the trend knowability is not complete and the trend cannot be obtained.

10. A new energy base scheduling mode evaluation system, characterized in that, include: The scheduling module is used to optimize the scheduling of the power system, which includes new energy bases, based on a preset optimized scheduling model and using various scheduling modes, and to obtain the optimized scheduling results of each scheduling mode. The indicator module is used to obtain the evaluation index value of each scheduling mode based on the optimized scheduling results of each scheduling mode and the preset evaluation index of each scheduling mode. The evaluation module is used to obtain the grey relational degree of each scheduling mode based on the evaluation index values ​​of each scheduling mode and the grey relational analysis method, which serves as the evaluation result of each scheduling mode.

11. The new energy base scheduling mode evaluation system according to claim 10, characterized in that, The objective function of the preset optimized scheduling model is: in, Where T is the number of power system generating units, and T is the number of optimization time periods. For the nth generating unit, the electricity market price during time period t. Let n be the amount of electricity the nth generating unit won in the electricity market during time period t. The constraints of the preset optimized scheduling model include: power balance constraints, reserve constraints, network constraints, unit ramping constraints, and unit output constraints.

12. The evaluation system for the scheduling mode of new energy bases according to claim 11, characterized in that, The power balance constraint is: ， in, Let be the total load of the power system during time period t. The number of power generating units to support the new energy base The output of the power generation unit supporting the m-th new energy base during time period t. This is the total external power transmission for the new energy base; The backup constraint is: ， ， in, This represents the upper limit of the active power of the nth unit. This represents the lower limit of the active power of the nth generating unit. For the reserve demand of the power system during time period t, For the power system's reserve demand during time period t, To meet the backup demand of the new energy base during period t. This is for the backup demand of the new energy base during period t; The network constraints are: in, This represents the maximum transmission capacity of line l. Let n be the power transfer factor of the nth generator unit to line l. The number of load nodes in the power system. Let n be the power transfer factor of the nth load node to line l. Let be the power of the nth load node during time period t; The unit's ramp-up constraint is: in, Let n be the output of the nth unit during time period t. Let n be the output of the nth unit during time period t-1. This represents the maximum ramp rate for the nth generator unit. The unit output constraint is: in, This represents the upper limit of the active power of the nth unit. This represents the lower limit of the active power of the nth generating unit. It provides power output for the nth unit during time period t.

13. The evaluation system for the scheduling mode of new energy bases according to claim 10, characterized in that, The preset evaluation indicators for each scheduling mode include evaluation indicators for safety-related scheduling modes, economic-related scheduling modes, and green-related scheduling modes.

14. The evaluation system for the scheduling mode of new energy bases according to claim 10, characterized in that, The preset evaluation indicators for each scheduling mode include one or more of the following: The indicators include: power flow visibility, critical channel load rate, ramp-up achievable rate, reserve margin of new energy bases, power system supply cost, power system generation entity revenue, power system load electricity cost, marginal supply cost of new energy bases, new energy absorption rate, and the proportion of green electricity transmitted from new energy bases to other regions.

15. The evaluation system for the scheduling mode of new energy bases according to claim 10, characterized in that, The evaluation module is specifically used for: Based on the evaluation index values ​​of each scheduling mode, the optimal evaluation index value among all scheduling modes is obtained, thus yielding the optimal evaluation index sequence. After dimensionless processing of the evaluation index values ​​of each scheduling mode and the evaluation index values ​​of each scheduling mode in the optimal evaluation index sequence, the grey relational analysis method is used to obtain the grey relational coefficient between the evaluation index values ​​of each scheduling mode and the evaluation index values ​​of each scheduling mode in the optimal evaluation index sequence. The average value is then taken to obtain the grey relational degree of each scheduling mode as the evaluation result of each scheduling mode.

16. The evaluation system for the scheduling mode of new energy bases according to claim 15, characterized in that, The grey relational coefficient is obtained by the following formula: in, Let be the grey relational coefficient between the evaluation index value of the z-th scheduling mode of the i-th scheduling mode and the evaluation index value of the z-th scheduling mode in the optimal evaluation index sequence; The resolution coefficient; It is the absolute value of the difference between the evaluation index value of the z-th scheduling mode of the i-th scheduling mode and the evaluation index value of the z-th scheduling mode in the optimal evaluation index sequence; It is the minimum absolute value of the difference between the evaluation index value of each scheduling mode and the corresponding evaluation index value of each scheduling mode in the optimal evaluation index sequence. It represents the maximum absolute value of the difference between the evaluation index value of each scheduling mode and the corresponding evaluation index value of each scheduling mode in the optimal evaluation index sequence.

17. The evaluation system for the scheduling mode of new energy bases according to claim 15, characterized in that, The evaluation module is also used to: before taking the average value, use a preset adjustment rule based on business logic to adjust the grey correlation coefficient between the evaluation index values ​​of each scheduling mode and the evaluation index values ​​of each scheduling mode in the optimal evaluation index sequence.

18. The evaluation system for the scheduling mode of new energy bases according to claim 17, characterized in that, The evaluation indicators for the scheduling mode include power flow predictability indicators and critical channel load rate; The preset adjustment rules based on business logic include: when the power flow awareness index value of the current scheduling mode is 0, the gray correlation coefficient between the key channel load rate value of the current scheduling mode and the key channel load rate value in the optimal evaluation index sequence is adjusted to a first preset value or reduced to a second preset value. Among them, a trend knowability index value of 0 indicates that the trend knowability is not complete and the trend cannot be obtained.

19. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the evaluation method for the scheduling mode of new energy bases as described in any one of claims 1 to 9.

20. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the evaluation method for the scheduling mode of new energy bases as described in any one of claims 1 to 9.

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