Hierarchical planning scheme comprehensive decision-making method and system

Abstract

The invention discloses a hierarchical planning scheme comprehensive decision-making method and system, relates to the technical field of power system evaluation, guarantees subjective and objective balance of scheme evaluation, and further improves the evaluation accuracy. The method comprises the following steps: acquiring basic data of each candidate planning scheme; according to a preset multi-dimensional index system, index calculation of a multi-time scale scene is carried out on the basic data of each candidate planning scheme, and a multi-dimensional scheme index corresponding to each candidate planning scheme is obtained; determining an ideal decision scheme, and calculating a multi-dimensional index score of each candidate planning scheme in each time scale scene; utilizing an analytic hierarchy process to set a corresponding score weight and a scene weight for each dimension index score under each time scale scene; calculating a scheme total score corresponding to each candidate planning scheme based on each determined score weight and each scene weight; and generating and outputting a comprehensive decision result by referring to the scheme total score corresponding to each candidate planning scheme.

Description

Technical Field

[0001] This application relates to the field of power system assessment technology, and in particular to a hierarchical planning scheme integrated decision-making method and system. Background Technology

[0002] The goal of comprehensive evaluation of power system planning is to quantitatively examine how different schemes meet the requirements of safety, reliability, economy, and low carbon emissions at different time scales, as well as their adaptability to national economic and social development, thereby providing a basis for selecting a reasonable planning scheme. Safety and reliability are the primary considerations in power system planning, ensuring reliable power supply and possessing fault mitigation capabilities. Furthermore, planning must also consider economy and low carbon emissions, maximizing environmental benefits with minimal economic cost. A rational power system planning structure should adapt to multiple operating modes and meet the needs of future load growth and grid expansion; therefore, flexibility indicators are also essential.

[0003] In related technologies, to achieve a comprehensive evaluation of power system planning schemes, some methods start from key aspects such as the power supply reliability, economy, power supply quality, and security of the distribution network, and construct a comprehensive evaluation index system to conduct a comprehensive evaluation of the planning scheme. Other methods, targeting urban transmission network planning, based on specific technologies to explore limited scenarios that need to be considered in the evaluation process, and construct a comprehensive evaluation index system covering multiple aspects such as economy, safety and reliability, flexibility and adaptability, environmental protection, renewable energy absorption capacity, and AC / DC intensity. In addition, some methods comprehensively utilize multiple theories to establish a secondary evaluation index system that includes attributes such as economy, technology, environmental impact, and social impact, in order to achieve comprehensive decision-making.

[0004] However, the applicant recognizes that the relevant technology has at least the following technical problems: Although the subjective and objective weight vectors are integrated and the ambiguity of historical data and the uncertainty of operating scenarios are taken into account, the performance of the planning scheme varies significantly under different scenarios and time scales. This makes it difficult to effectively weigh multiple dimensions and indicators, which limits its widespread application and practical effect in the complex and ever-changing power system planning environment. It cannot ensure the balance between subjective and objective factors in the evaluation, and the evaluation accuracy is not high. Summary of the Invention

[0005] In view of this, this application provides a hierarchical planning scheme comprehensive decision-making method and system, the main purpose of which is to solve the problem that the current multi-dimensional indicators are difficult to effectively weigh, which limits the wide application and practical effect in the complex and ever-changing power system planning environment, and cannot ensure the balance between subjective and objective evaluation, resulting in low evaluation accuracy.

[0006] According to the first aspect of this application, a hierarchical integrated decision-making method for planning schemes is provided, the method comprising: Obtain basic data for each of the multiple candidate planning schemes to be evaluated; According to the preset multi-dimensional index system, the basic data of each candidate planning scheme are used to calculate the index for multiple time scale scenarios, so as to obtain the multi-dimensional scheme index corresponding to each candidate planning scheme. Based on the multi-dimensional scheme indicators of each candidate planning scheme in each time scale scenario, an ideal decision scheme is determined, and based on the ideal decision scheme, a multi-dimensional indicator score of each candidate planning scheme in each time scale scenario is calculated. Using the Analytic Hierarchy Process (AHP), corresponding scoring weights are set for the scores of each dimension index under each time scale scenario, and the scenario weights corresponding to each time scale scenario are determined. Based on the determined scoring weights and scenario weights, the multi-dimensional index scores of each candidate planning scheme under each time scale scenario are calculated to obtain the total score of each candidate planning scheme. Based on the total score of each candidate planning scheme, a comprehensive decision result is generated and output.

[0007] According to a second aspect of this application, a hierarchical integrated decision-making system for planning schemes is provided, comprising: The data input module is used to acquire basic data for each of the multiple candidate planning schemes to be evaluated; The multi-scenario, multi-time-scale indicator calculation module is used to perform multi-time-scale scenario indicator calculations on the basic data of each candidate planning scheme according to a preset multi-dimensional indicator system, so as to obtain the multi-dimensional scheme indicator corresponding to each candidate planning scheme. The multi-dimensional scoring module is used to determine the ideal decision scheme based on the multi-dimensional scheme indicators of each candidate planning scheme in each time scale scenario, and to calculate the multi-dimensional indicator scores of each candidate planning scheme in each time scale scenario based on the ideal decision scheme. The weighting module for each dimension is used to set corresponding scoring weights for the scores of each dimension indicator under each time scale scenario using the analytic hierarchy process, and to determine the scenario weights corresponding to each time scale scenario. The comprehensive evaluation module that coordinates long and short time scales is used to calculate the multi-dimensional index scores of each candidate planning scheme under each time scale scenario based on the determined score weights and scenario weights, so as to obtain the total score of each candidate planning scheme. The output module is used to generate and output a comprehensive decision result by referring to the total score of each candidate planning scheme.

[0008] According to a third aspect of this application, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any of the first aspects above.

[0009] According to a fourth aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of the first aspects above.

[0010] By employing the above technical solutions, this application provides a hierarchical planning scheme comprehensive decision-making method and system. This application acquires basic data for each candidate planning scheme among multiple candidate planning schemes to be evaluated. According to a preset multi-dimensional indicator system, it calculates indicators for each candidate planning scheme's basic data across multiple time scale scenarios, obtaining multi-dimensional scheme indicators corresponding to each candidate planning scheme. Based on the multi-dimensional scheme indicators of each candidate planning scheme in each time scale scenario, it determines the ideal decision scheme. Furthermore, based on the ideal decision scheme, it calculates the multi-dimensional indicator scores for each candidate planning scheme in each time scale scenario. Using the analytic hierarchy process (AHP), it sets corresponding scoring weights for the scores of each dimension indicator in each time scale scenario and determines the time scale scenario. Based on the determined scoring weights and scenario weights, the multi-dimensional indicator scores of each candidate planning scheme are calculated for each time scale scenario, resulting in a total score for each candidate planning scheme. Referring to the total score of each candidate planning scheme, a comprehensive decision result is generated and output. Based on a multi-dimensional indicator system, candidate planning schemes are evaluated across multiple time scale scenarios. This allows for a comprehensive measurement of planning schemes with different goal orientations, achieving synergy between long-term strategic and immediate needs. It addresses the problem of difficulty in weighing multiple dimensions and indicators due to varying performance of planning schemes across different scenarios and time scales, avoiding single-dominant decision-making, ensuring a balance between subjective and objective evaluation of schemes, and further improving evaluation accuracy.

[0011] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0012] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1This illustration shows a flowchart of a hierarchical planning scheme comprehensive decision-making method provided in an embodiment of this application; Figure 2 This illustration shows a schematic diagram of a security index system provided in an embodiment of this application; Figure 3 This illustration shows a schematic diagram of a reliability index system provided in an embodiment of this application; Figure 4 This illustration shows a schematic diagram of an economic indicator system provided in an embodiment of this application; Figure 5 This illustration shows a schematic diagram of a low-carbon performance index system provided in an embodiment of this application; Figure 6 This illustration shows a schematic diagram of a flexibility index system provided in an embodiment of this application; Figure 7 This paper illustrates a schematic diagram of the system architecture of a decision-making system provided in an embodiment of this application. Figure 8 A schematic diagram illustrating the overall similarity of the various solutions provided in the embodiments of this application is shown; Figure 9 A schematic diagram of the device structure of a computer device provided in an embodiment of this application is shown. Detailed Implementation

[0013] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0014] This application provides a hierarchical planning scheme comprehensive decision-making method, such as... Figure 1 As shown, the method includes: S10: Obtain basic data for each of the multiple candidate planning schemes to be evaluated.

[0015] The technical solution of this application embodiment can be applied to a decision-making system. In power system planning, the decision-making system first determines multiple candidate planning schemes to be evaluated, and then obtains the basic data of each candidate planning scheme among the multiple candidate planning schemes to be evaluated.

[0016] In acquiring basic data, the decision-making system collects historical data on various indicators within the planning scheme evaluation index system. These indicators cover multiple aspects of the power system, such as historical load data and equipment failure rate data, reflecting the system's past operation and providing a basic reference for evaluation. On the other hand, in addition to traditional planning scheme topology data (such as grid connection methods and substation layouts) and source-grid-load-storage operation parameter data (power output parameters, grid transmission parameters, load demand parameters, energy storage device parameters, etc.), the decision-making system also acquires typical and extreme multi-scenario operation modes at different time scales. Wind, solar, and hydropower output and load size, influenced by natural resources, are key data, such as wind, solar, and hydropower output in different seasons (quarterly and annual time scales), and load changes at different times of the day.

[0017] Using the data obtained in the above process, the decision-making system calculates future indicator data for each candidate planning scheme through power system simulation software, thereby obtaining the basic data for each candidate planning scheme. In this way, by comprehensively collecting various types of data related to the planning scheme, the basic data determined for each candidate planning scheme not only considers historical information but also incorporates operational data under different future scenarios, providing a rich and accurate foundation for subsequent evaluation and ensuring that the evaluation results reflect the performance of the planning scheme under various conditions.

[0018] For example, in a regional power grid planning, there are three candidate planning schemes. The decision system collects the load data of each month in the region over the past five years as historical indicator data. At the same time, it obtains the typical wind speed data of local wind farms in different seasons, the sunshine duration data of photovoltaic power stations in different seasons, and the hydrological data of hydropower stations. Combined with the peak and valley changes of daily load, the system uses power system simulation software to calculate the various indicator data of each planning scheme under different future scenarios.

[0019] S20: According to the preset multi-dimensional indicator system, perform multi-time-scale scenario indicator calculations on the basic data of each candidate planning scheme to obtain the multi-dimensional scheme indicators corresponding to each candidate planning scheme.

[0020] The multi-dimensional indicator system includes five dimensions: safety, reliability, economy, low carbon emissions, and flexibility. Safety primarily refers to the power system's ability to avoid faults and accidents during operation, such as the probability of line overload and voltage exceeding limits. Reliability emphasizes the system's ability to provide continuous and stable power supply, commonly measured by indicators such as the probability of power shortage and the expected value of power shortage. Economy involves economic factors such as the annual investment cost and operating costs of the planning scheme. Low carbon emissions focus on the scheme's performance in reducing carbon emissions. Flexibility reflects the system's ability to cope with load changes and renewable energy fluctuations. Multiple time-scale scenarios include long-term scenarios (quarterly, annual) and short-term scenarios (daily).

[0021] The decision-making system performs routine power system analysis operations on each candidate planning scheme under different time scale scenarios, including power flow calculations (determining the voltage and power distribution of each node in the power system) and safety and stability verification (assessing the system's stability under various disturbances). For example, at the annual time scale, power flow calculations are performed considering seasonal load changes and variations in wind, solar, and hydropower output to obtain corresponding safety and reliability indicators; at the intraday time scale, calculations are performed based on real-time load fluctuations and renewable energy output fluctuations to obtain various indicators for short-term scenarios. In this way, through a multi-dimensional indicator system and indicator calculations across multiple time scale scenarios, the performance of the planning scheme can be comprehensively and meticulously reflected in different aspects, providing rich indicator information for subsequent scoring and decision-making.

[0022] In step S20, according to the preset multi-dimensional indicator system, the basic data of each candidate planning scheme are used to calculate the indicators for multiple time scale scenarios, resulting in the multi-dimensional scheme indicators corresponding to each candidate planning scheme, including: S21: Obtain the preset multi-dimensional indicator system.

[0023] The multidimensional indicator system includes a safety indicator system, a reliability indicator system, an economic indicator system, a low-carbon indicator system, and a flexibility indicator system.

[0024] like Figure 2As shown, the safety indicator system includes reserve rate, short-circuit current adequacy, single component failure pass rate (i.e., N-1 pass rate), minimum frequency drop, frequency qualification rate, voltage qualification rate, and power flow distribution rationality index. Among these, the reserve rate reflects the power system's reserve capacity; the short-circuit current adequacy reflects the system's ability to withstand current during short-circuit faults; the single component failure pass rate (N-1 pass rate) measures the system's ability to maintain normal operation under single component failure conditions; the minimum frequency drop and frequency qualification rate relate to the system's frequency stability; the voltage qualification rate reflects voltage quality; and the power flow distribution rationality index is used to assess whether the power flow distribution in the system is reasonable. The safety indicator system covers both long-term and short-term scenarios, and because it involves the real-time stability of power grid operation, short-term recovery capability, long-term power flow rationality, and planning verification, it is suitable for safety assessments across the entire time scale.

[0025] like Figure 3 As shown, the reliability index system includes the probability of power shortage, the expected value of power shortage, the frequency of power shortage, the duration of power shortage, the expected value of power shortage, and the contribution coefficient of renewable energy to the probability of load shedding. The reliability index system is mainly applicable to long-term scenarios because it assesses the system's continuous power supply capability through statistical models, typically requiring a long period of data accumulation, and is not suitable for short-term analysis.

[0026] like Figure 4 As shown, the economic performance indicator system includes annualized investment costs, operating costs, internal rate of return (IRR), dynamic payback period, power generation utilization hours, and transmission loss rate. Among these, annualized investment costs and operating costs reflect the investment and operating costs of the planning scheme; the IRR and dynamic payback period are used to assess the project's economic benefits and investment recovery; power generation utilization hours reflect the utilization efficiency of power generation equipment; and the transmission loss rate reflects energy losses during power transmission. This economic performance indicator system is applicable to long-term scenarios, primarily reflecting the economic feasibility and operational efficiency of the system plan, while in the short term, economic fluctuations are relatively small.

[0027] like Figure 5 As shown, the low-carbon performance indicator system includes curtailment rate, renewable energy penetration rate, carbon emissions, and energy storage mitigation rate. The curtailment rate reflects the proportion of renewable energy not being utilized; the renewable energy penetration rate reflects the proportion of renewable energy in the energy structure; carbon emissions directly reflect the system's carbon emissions; and the energy storage mitigation rate reflects the role of energy storage systems in mitigating energy fluctuations. The low-carbon performance indicator system covers both short-term and long-term time scales, enabling adjustments to the power structure based on short-term data results and assessment of carbon reduction effectiveness through long-term planning.

[0028] like Figure 6As shown, the flexibility index system includes the capacity-to-load ratio, the probability of insufficient upward / downward adjustment flexibility, the proportion of regulating resources, load resource regulation capability, average net load peak-to-valley ratio, and grid expansion margin. Among these, the capacity-to-load ratio reflects the grid's capacity configuration; the probability of insufficient upward / downward adjustment flexibility reflects the system's insufficient ability to regulate power increases or decreases; the proportion of regulating resources reflects the proportion of resources available for regulation in the system; the load resource regulation capability reflects the ability to regulate load resources; the average net load peak-to-valley ratio reflects the fluctuation of net load; and the grid expansion margin reflects the grid's potential for future expansion. The flexibility index system also covers both short-term and long-term time scales, comprehensively reflecting the grid's real-time regulation capability, short-term dispatch optimization, and long-term flexible expansion capability, demonstrating broad applicability across time scales.

[0029] It should be noted that most of the above indicators are common evaluation indicators in power systems and can be found in national standards, industry standards, enterprise standards, and publicly available literature. Only a few indicators require further explanation. Therefore, the calculation process for these few indicators is described below: I. Contribution coefficient of renewable energy to load shedding probability.

[0030] The contribution coefficient of renewable energy to the load shedding probability is defined as the change in the system load shedding probability caused by whether or not renewable energy is connected. Considering the volatility of renewable energy, Formula 1 is used to calculate the contribution coefficient of renewable energy to the load shedding probability for each candidate planning scheme on a long-term time scale. Formula 1:

[0031] in, This represents the calculated contribution coefficient. This represents the system load shedding probability of the candidate planning scheme when renewable energy is integrated into the current candidate planning scheme. This represents the probability of system load shedding in the candidate planning schemes after the withdrawal of renewable energy in the currently calculated candidate planning schemes. It should be noted that the larger the contribution coefficient value, the greater the contribution of renewable energy to the system load reduction, so it belongs to the benefit-type indicator.

[0032] II. Energy storage damping rate.

[0033] The energy storage mitigation rate refers to the proportion of renewable energy fluctuations that an energy storage system can mitigate during grid operation, thus reducing the impact of these fluctuations on the system load. Given the randomness and volatility of renewable energy, the energy storage mitigation rate is assessed on a short-term timescale. Its core purpose is to smooth out fluctuations in renewable energy generation through the charging and discharging regulation of the energy storage system, thereby reducing dependence on fossil fuel power generation and lowering carbon emissions. Specifically, on a short-term timescale, Formula 2 is used to calculate the energy storage mitigation rate for each candidate planning scheme. Formula 2:

[0034] in, This represents the calculated energy storage mitigation rate. This represents the energy storage output of the currently calculated candidate planning scheme. This represents the imbalance capacity of the currently calculated candidate planning schemes. Imbalance capacity is the capacity caused by the imbalance between load demand in the power grid and the output of renewable energy sources such as wind and solar power, reflecting the portion of the power grid that requires energy storage for regulation. Specifically, it is the grid load minus the output of all available power sources; a high energy storage smoothing rate indicates that energy storage has a more significant effect on smoothing system load fluctuations.

[0035] 3. Probability of insufficient flexibility in adjusting up / down.

[0036] The probability of insufficient upward / downward adjustment flexibility refers to the probability that, within a specific timeframe, power generation equipment or the power system will be unable to meet load increases / decreases due to insufficient upward / downward adjustment flexibility. A lower probability value is better. This indicator directly reflects the system's real-time adjustment capability and is typically considered in the short term. Therefore, on a short-term timescale, the following formula (Formula 3) is used to calculate the probability of insufficient upward / downward adjustment flexibility for each candidate planning scheme. Formula 3:

[0037]

[0038] in, This represents the calculated probability of insufficient upward adjustment flexibility. This indicates that among the candidate planning schemes currently being calculated, the system is... Available capacity increase at any time This indicates that among the candidate planning schemes currently being calculated, the system... Net load at any given time This indicates that among the candidate planning schemes currently being calculated, the system... Net load at any given time This represents the calculated probability of insufficient flexibility in downsizing. This indicates that among the candidate planning schemes currently being calculated, the system is... Downsizing capacity available at any time.

[0039] IV. Adjusting the proportion of resources.

[0040] The proportion of regulating resources measures the percentage of resources in a power grid that can participate in regulation. This includes long-cycle adjustable hydropower, large-scale energy storage, and inter-regional power grids. It is suitable for evaluating whether the system's resource regulation capacity meets demand on a long-term scale and is a benefit-oriented indicator. Therefore, on a long-term time scale, the following formula (4) is used to calculate the proportion of regulating resources for each candidate planning scheme. Formula 4:

[0041] in, This represents the calculated proportion of adjustment resources. This represents the adjustable installed capacity of resources within the corresponding time period in the currently calculated candidate planning schemes. This represents the total installed capacity of all power generation resources in the power grid among the candidate planning schemes currently being calculated.

[0042] V. Load resource regulation capability.

[0043] Load resource regulation capability characterizes the flexible and adjustable level of load within the balance zone, expressed as the proportion of controllable load power to total load power, and is a benefit-oriented indicator. These resources can participate in regulation during peak demand periods by peak shaving or during off-peak periods by increasing load. These load resources mainly come from the user end, especially flexible electricity loads in industrial and commercial sectors. Load resource regulation capability is suitable for short-term daily dispatching and peak shaving / valley filling analysis, and is also suitable for long-term assessment of changes in overall regulation capability over a longer period. Therefore, the following formula 5 is used to calculate the load resource regulation capability for each candidate planning scheme on both long-term and short-term time scales. Formula 5:

[0044] in, This represents the calculated load resource regulation capability over either a long-term or short-term time scale. This represents the adjustable load capacity among the candidate planning schemes currently being calculated. This represents the total load capacity of the power grid in the currently calculated candidate planning scheme.

[0045] S22: In accordance with the instructions of the multi-timescale scenario, on the long-term timescale, based on the safety index system, reliability index system, economic index system, low-carbon index system and flexibility index system, calculate the multi-dimensional scheme index on the long-term timescale for each candidate planning scheme.

[0046] As described in step S21, safety, reliability, economy, low carbon emissions, and flexibility need to be scored separately on a long-term time scale. Therefore, the decision-making system will calculate multi-dimensional scheme indicators for each candidate planning scheme on a long-term time scale according to the calculation method indicated in step S21, based on the safety index system, reliability index system, economy index system, low carbon emissions index system, and flexibility index system.

[0047] S23: In accordance with the instructions of the multi-timescale scenario, in the short-term timescale, based on the safety index system, low-carbon index system and flexibility index system, calculate the multi-dimensional scheme indexes for each candidate planning scheme in the short-term timescale.

[0048] As described in step S21, safety, low carbon emissions, and flexibility need to be scored separately on a short-term time scale. Therefore, the decision-making system will calculate multi-dimensional scheme indicators for each candidate planning scheme on a short-term time scale according to the calculation method indicated in step S21, based on the safety index system, low carbon emission index system, and flexibility index system.

[0049] S30: Based on the multi-dimensional scheme indicators of each candidate planning scheme in various time scale scenarios, determine the ideal decision scheme, and based on the ideal decision scheme, calculate the multi-dimensional indicator scores of each candidate planning scheme in various time scale scenarios.

[0050] The ideal decision-making solution is the one that achieves optimal performance across all dimensions across different time scales. Since the number of indicators covered by different dimensions varies, and the actual number of obtainable indicators is unpredictable, the decision-making system scores safety, reliability, economy, low-carbon performance, and flexibility separately for long-term scenarios, and also scores them separately for short-term scenarios. This allows for a direct comparison of the merits of different planning schemes across different dimensions and time scales, laying the foundation for subsequent overall score calculations.

[0051] In step S30, the ideal decision scheme is determined based on the multi-dimensional scheme indicators of each candidate planning scheme in various time-scale scenarios, and the multi-dimensional indicator scores of each candidate planning scheme in various time-scale scenarios are calculated based on the ideal decision scheme, including: S31: Integrate the multi-dimensional scheme indicators of multiple candidate planning schemes under various time scale scenarios to obtain the decision matrix, and normalize the decision matrix. Based on the normalized decision matrix, determine the ideal decision scheme.

[0052] This involves integrating multi-dimensional scheme indicators from multiple candidate planning schemes across various time scales to obtain a decision matrix, and then normalizing the decision matrix, including the following processes: First, the multi-dimensional scheme indicators of multiple candidate planning schemes under various time scales are integrated to obtain the following decision matrix.

[0053] in, Represents the decision matrix. Indicates the first The candidate programming scheme is the th candidate program in the decision matrix before normalization. Individual indicator values, This indicates the number of candidate planning schemes. This represents the number of indicator values ​​for the multi-dimensional solution indicators. In other words, the decision matrix is ​​composed of... Candidate planning schemes and It is constructed from individual indicator values.

[0054] Next, it is necessary to clarify whether each indicator is a benefit-type indicator or a cost-type indicator. Benefit-type indicators are those with higher values, while cost-type indicators are those with lower values. Therefore, Formula 6 is used to normalize the benefit-type indicators in the decision matrix. Formula 6:

[0055] in, Indicates the normalized result , Indicates the first The minimum value of a benefit-type indicator. Indicates the first The maximum value of each benefit-type indicator.

[0056] Simultaneously, Formula 7 is used to normalize the cost-related indicators in the decision matrix, thereby completing the normalization process and eliminating the influence of different dimensions. Formula 7:

[0057] in, Indicates the normalized result , Indicates the first The minimum value of a cost-related indicator. Indicates the first The maximum value of each cost-related indicator.

[0058] After normalizing the decision matrix through the above process, the ideal decision scheme is determined based on the normalized decision matrix. The ideal decision scheme includes positive ideal schemes. and negative ideal solution , , , Indicates the first The maximum value of each indicator among multiple candidate planning schemes Indicates the first The minimum value of each indicator among multiple candidate planning schemes.

[0059] S32: Calculate the Eulerian distance between each candidate programming scheme and the positive ideal scheme and the negative ideal scheme using the following formula 8.

[0060] Formula 8:

[0061] in, Indicates the first The Euler distance between each candidate program and the ideal program. Indicates the first The Euler distance between each candidate programming solution and the negative ideal solution Indicates the number of indicators for the multidimensional scheme. Indicates the first The candidate programming scheme is the th one in the normalized decision matrix. A normalized index value, Indicating the first ideal solution Individual indicator values, Indicates the first The Euler distance between each candidate programming solution and the negative ideal solution In the negative ideal solution, the first... Individual indicator values.

[0062] S33: Using the following formula 9, calculate the positive grey correlation coefficient between each candidate planning scheme and the positive ideal scheme, and calculate the negative grey correlation coefficient between each candidate planning scheme and the negative ideal scheme.

[0063] Among them, the power system, due to its massive system structure and intricate direct and indirect couplings, exhibits uncertainty and incompleteness, essentially constituting a grey system. Grey relational coefficients and grey relational degrees are used to determine the magnitude of the difference between two states from the perspective of curve shape coupling. The larger the grey relational degree, the smaller the difference between the two states, and vice versa. Therefore, in this embodiment, calculating the grey relational degree between each candidate planning scheme and the positive and negative ideal schemes allows for comparison of the closeness between each candidate planning scheme and the positive and negative ideal schemes. Specifically, the positive and negative grey relational coefficients need to be calculated first, and the specific formula used for calculation is as follows (Formula 9). Formula 9:

[0064] in, Indicates the first The positive grey relational coefficient of each candidate planning scheme. Indicates the first The positive grey relational coefficient of each candidate planning scheme. This represents the preset resolution coefficient. In practical applications, The value can be set to 0.5.

[0065] S34: Using the following formula 10, calculate the positive grey correlation degree between each candidate planning scheme and the positive ideal scheme, and calculate the negative grey correlation degree between each candidate planning scheme and the negative ideal scheme.

[0066] After obtaining the positive and negative grey correlation coefficients through step S34 above, the following formula 10 is used to calculate the positive grey correlation degree between each candidate planning scheme and the positive ideal scheme, and the negative grey correlation degree between each candidate planning scheme and the negative ideal scheme. Formula 10:

[0067] in, Indicates the first The positive grey relational degree of each candidate planning scheme, Indicates the first The negative grey relational degree of each candidate planning scheme.

[0068] S35: Using the following formula 11, calculate the proximity distance between each candidate planning scheme and the positive ideal scheme and the negative ideal scheme.

[0069] Formula 11:

[0070] in, Indicates the first The closest approximation between each candidate planning scheme and the ideal scheme Indicates the first The approximation distance between each candidate planning scheme and the negative ideal scheme This represents the preset position coefficient. This represents the preset shape factor. In practical applications, and The value can be set to 0.5.

[0071] S36: Using the following formula 12, calculate the comprehensive approximation degree of each candidate planning scheme corresponding to its various dimension index system.

[0072] Formula 12:

[0073] in, Indicates the first The comprehensive approximation of each candidate planning scheme under a certain dimension index system.

[0074] S37: The calculated comprehensive approximation is used as the index score of each candidate planning scheme under the indicator system of each dimension, so as to obtain the multi-dimensional index score of each candidate planning scheme under each time scale scenario.

[0075] In the embodiments of this application, the comprehensive approximation obtained through the above process is... The evaluation of each candidate planning scheme is integrated based on its strengths and weaknesses in terms of "location distance" and "trend consistency," directly serving as the comprehensive score for each scheme. A higher comprehensive approximation value indicates that the candidate planning scheme is closer to the ideal state (positive ideal solutions have high correlation, negative ideal solutions have low correlation), and thus has a higher ranking priority. Therefore, in this embodiment, [the following is used]. The score of each candidate planning scheme in each dimension.

[0076] S40: Using the Analytic Hierarchy Process (AHP), set corresponding scoring weights for each dimension of indicators in each time scale scenario, and determine the scenario weights for each time scale scenario.

[0077] Based on the safety, reliability, economy, low-carbon, and flexibility scores obtained in long-term scenarios, as well as in short-term scenarios, the decision-making system uses the Analytic Hierarchy Process (AHP) to assign weights to each dimension. This allows for the reasonable setting of weights for each dimension's indicators and each time-scale scenario, reflecting the importance of different indicators and scenarios in planning decisions and making the evaluation results more in line with actual needs.

[0078] In step S40, the analytic hierarchy process (AHP) is used to assign corresponding scoring weights to the scores of each dimension of indicators in each time scale scenario, including: S41: Utilize the safety, reliability, economy, low-carbon, and flexibility indicators in the multi-dimensional indicator system to construct the corresponding first factor set for long-term time scales.

[0079] The first set of factors constructed is designed for long-term time scales and includes safety, reliability, economy, low carbon emissions, and flexibility.

[0080] S42: Using the safety indicator system, low-carbon indicator system and flexibility indicator system in the multidimensional indicator system, construct the corresponding second factor set for the short-term time scale.

[0081] The second set of factors constructed is for short-term time scales and includes safety, low carbon emissions, and flexibility.

[0082] S43: Extract any two factors from the first factor set and the second factor set respectively, and assign values ​​to the relative influence between any two factors using a hierarchical comparison scale table indicated by the analytic hierarchy process to obtain the judgment matrix.

[0083] In practical applications, any two matrix factors are selected. , ,use express , The relative degree of influence on the target, The values ​​assigned are defined by the AHP hierarchical comparison scale table, and the constructed judgment matrix is ​​as follows:

[0084] Furthermore, after assigning values ​​to the relative influence between any two factors using a hierarchical comparison scale table indicated by the analytic hierarchy process (AHP), the assigned values ​​are obtained and sent to the verification recipient. The verification recipient then adjusts the assigned values, and a judgment matrix is ​​generated using the adjusted values. In practical application, this is combined with the Delphi method, where the indicator data (i.e., the assigned values) obtained from the planning scheme simulation are submitted to an expert group for cyclical surveys. In the first round of surveys, experts independently complete questionnaires and provide their opinions and scores. After collecting and organizing the experts' responses, the results of the first round of surveys are fed back to the expert group. The purpose of this is to allow experts to understand the opinions of other experts and to re-evaluate and improve their own suggestions and scores. The survey is then repeated until the experts' opinions reach a consensus and pass the convergence test.

[0085] Furthermore, after constructing the judgment matrix, a consistency check can be performed on the judgment matrix. After the consistency check passes, the judgment matrix can be used to calculate the corresponding score weights for each dimension indicator in each time scale scenario, thereby avoiding logical errors.

[0086] S44: Define the largest eigenvalue of the judgment matrix as... The eigenvector corresponding to the largest eigenvalue is .

[0087] Among them, for the judgment matrix The weights can be found using the eigenvalue method, where the largest eigenvalue of the matrix is ​​defined as... Its corresponding feature vector is In this way, it can be determined .

[0088] S45: The feature vector is normalized using the following formula 13 to obtain the score weights corresponding to the score calculation of each dimension index under each time scale scenario.

[0089] Formula 13:

[0090] in, Indicates the first The scoring weights corresponding to the scores of each dimension indicator Indicates the first The subvectors corresponding to the scores of each dimension index in the feature vector.

[0091] S50: Based on the determined weights of each score and each scenario, calculate the multi-dimensional index scores of each candidate planning scheme under each time scale scenario to obtain the total score of each candidate planning scheme.

[0092] The decision-making system will calculate the multi-dimensional index scores of each candidate planning scheme under different time scales and scenarios based on the determined scoring weights and scenario weights, and obtain the total score of each candidate planning scheme. In order to quantify the planning orientation under different time dimensions through differentiated index selection and weight allocation, achieve the synergistic optimization of strategic and immediate needs, and solve the dynamic trade-off between long-term and short-term goals of planning schemes.

[0093] In step S50, based on the determined scoring weights and scenario weights, the multi-dimensional index scores of each candidate planning scheme under each time scale scenario are calculated to obtain the total score of each candidate planning scheme, including: For each candidate planning scheme, the following formula 14 is used to calculate the corresponding total score for each candidate planning scheme. Formula 14:

[0094] in, This represents the total score of the candidate planning scheme currently being calculated. This represents the scene weight corresponding to the long-term timescale scene within each timescale scene. This represents the scene weight corresponding to the short-term timescale scene within each timescale scene. Among them, That is, the weights of each dimension's weighting module are obtained using the analytic hierarchy process (AHP), and That is, the comprehensive closeness of the five (long-term time scale scenarios) / three (short-term time scale scenarios) primary indicators calculated by the scoring modules of each dimension using the improved TOPSIS method.

[0095] S60: Based on the total score of each candidate planning scheme, generate and output the comprehensive decision result.

[0096] The decision-making system ranks multiple candidate planning schemes based on their comprehensive evaluation values. The scheme with the highest evaluation value is the optimal planning scheme, and the optimal planning scheme is output as the comprehensive decision result. This provides a basis for power system planning, clear guidance for actual power system planning, and improves the efficiency and accuracy of planning decisions.

[0097] In step S60, that is, referring to the total score of each candidate planning scheme, a comprehensive decision result is generated and output, including: S61: Based on the total score of each candidate planning scheme, determine the target candidate planning scheme with the highest total score among multiple candidate planning schemes.

[0098] As described above, the overall score is calculated based on a multi-dimensional indicator system across multiple time scales. It comprehensively considers the planning scheme's performance in multiple dimensions, including safety, reliability, economy, low carbon emissions, and flexibility, as well as its overall performance at different time scales (such as long-term and short-term). Therefore, by comparing the overall scores of each candidate planning scheme, it is possible to intuitively see which scheme performs best in the overall evaluation, and the candidate planning scheme with the highest overall score is determined as the target candidate planning scheme.

[0099] In this way, by using the quantitative overall score of the plan as a basis, the best-performing plan can be objectively and accurately selected from multiple candidate plans, avoiding the bias of subjective judgment, and fully considering the comprehensive performance of the plan under different goal orientations, thus providing a foundation for achieving the synergy between long-term strategic and immediate needs.

[0100] S62: Generate a comprehensive decision result to indicate that the target candidate planning scheme is the optimal planning scheme, and output the comprehensive decision result.

[0101] After determining the target candidate planning scheme, the decision-making system will generate a comprehensive decision result, which will clearly indicate that the target candidate planning scheme is the optimal planning scheme. The comprehensive decision result fully considers the multi-dimensional performance of the planning scheme under different scenarios and time scales.

[0102] The decision-making system outputs comprehensive decision results, providing clear guidance for power system planners to conduct subsequent planning and construction work based on these results. In addition, the output can take various forms, such as generating reports or displaying prompts in the system, enabling decision-makers to quickly and clearly understand the optimal planning scheme, improving decision-making efficiency, ensuring that the scheme evaluation results can be effectively applied to actual planning, and further enhancing the practicality and accuracy of the evaluation.

[0103] In another alternative implementation, such as Figure 7 As shown, the decision-making system may include a data input module, a multi-scenario, multi-timescale indicator calculation module, a multi-dimensional scoring module, a multi-dimensional weighting module, a comprehensive evaluation module that coordinates long-term and short-term timescales, and an output module. The following is a description of each module: The data input module is used to acquire basic data for each candidate planning scheme among multiple candidate planning schemes to be evaluated. In practical applications, the data input module will also input historical data of various indicators in the planning scheme evaluation index system, as well as data such as the planning scheme topology, source-grid-load-storage operation parameters, and multi-scenario operation modes used for index calculation.

[0104] The multi-scenario, multi-timescale indicator calculation module is used to calculate indicators for the basic data of each candidate planning scheme in multiple timescale scenarios according to a preset multi-dimensional indicator system, thereby obtaining the multi-dimensional scheme indicators corresponding to each candidate planning scheme. In actual application, 29 indicators will be calculated under the five-dimensional system of security, reliability, economy, low carbon, and flexibility.

[0105] The multi-dimensional scoring modules are used to determine the ideal decision scheme based on the multi-dimensional scheme indicators of each candidate planning scheme in various time-scale scenarios, and to calculate the multi-dimensional indicator scores of each candidate planning scheme in various time-scale scenarios based on the ideal decision scheme. In practical applications, the multi-dimensional scoring modules will use an improved approximation ideal value ranking method incorporating grey relational analysis to score each dimension.

[0106] The weighting modules for each dimension utilize the analytic hierarchy process (AHP) to assign corresponding scoring weights to the indicators for each dimension across different time scales, and to determine the scene weights for each time scale. In practical applications, these modules calculate the contribution of each dimension using AHP weights.

[0107] The integrated evaluation module, which coordinates long-term and short-term time scales, calculates the multi-dimensional indicator scores of each candidate planning scheme under different time scale scenarios based on determined scoring weights and scenario weights, thus obtaining the total score for each candidate planning scheme. In practical applications, the integrated evaluation module coordinates long-term and short-term time scales and flexibly adjusts the indicator weights at different time scales using a weighted average method, based on the planning scheme's objective orientation, to achieve a comprehensive evaluation.

[0108] The output module is used to generate and output a comprehensive decision result based on the total score of each candidate planning scheme. In practical applications, the output module will provide the comprehensive decision result, i.e., the optimal planning scheme.

[0109] The following is a detailed example illustrating the detailed logic of the technical solution in this application: For a certain regional power grid, the basic data of the four planning schemes in Example 1 are shown in Table 1. The proportion of new energy installed capacity gradually increases from Scheme 1 to Scheme 4.

[0110] Table 1 Basic Data of the Four Planning Schemes

[0111] First, calculate the overall similarity of each scheme, such as... Figure 8 As shown. By Figure 8The data shows that the comprehensive closeness data of each scheme calculated based on the improved TOPSIS method clearly reveals the differences in the performance of power grid planning schemes across multiple dimensions under different renewable energy ratios. In terms of economic indicators, the overall trend is a steady increase with the increase in the renewable energy ratio. This indicates that increasing the renewable energy ratio helps improve the economics of power grid planning to a certain extent, mainly due to the gradual optimization of conventional unit power generation costs caused by the increase in renewable energy. Regarding safety indicators, Schemes 1 to 3 all remain at low levels, and their equal values ​​are due to the equal number of lines constructed. The low-carbon performance indicators demonstrate the positive effect of increasing the renewable energy ratio on enhancing the low-carbon performance of the power grid, which aligns with the current green development concept of energy transition. In terms of reliability indicators, since the capacity of conventional units remains unchanged, the reliability indicators do not change significantly in Schemes 1 to 3; however, after the renewable energy ratio increases to 130%, the reliability indicators drop sharply, mainly because the capacity of conventional units decreases while the probability of system load failure increases under the fluctuating output of renewable energy. Regarding flexibility indicators, Scheme 4 shows the largest deviation from the ideal scheme, because the volatility of renewable energy power generation places higher demands on the power grid's regulation capabilities. Therefore, after calculating the overall closeness, the weights of the economy, safety, low carbon emissions, reliability, and flexibility indicators were calculated using the analytic hierarchy process (AHP) to be 0.15, 0.3, 0.2, 0.25, and 0.1, respectively. The specific indicator values ​​and overall indicators for each scheme are shown in the table below: Table 2 Calculation Results of Multi-Dimensional Evaluation Indicators for the Planning Scheme

[0112] In terms of comprehensive evaluation indicators, cross-analysis of multi-dimensional indicators shows that: Schemes 2 and 3 maintain stable investment costs in terms of economic efficiency; the short-circuit current adequacy in the safety indicator is on par with Scheme 1, showing no downward trend; in terms of reliability indicators, the failure probability of Scheme 4 shows an upward trend, rising to 1.49%; the low-carbon performance indicator is good, with no rebound in carbon emissions due to insufficient new energy consumption, and Scheme 4's carbon emissions are 48.835 million tons, further reducing from Scheme 3's 49.488 million tons. Scheme 4's investment cost is lower than the previous three schemes, ranking first in comprehensive score; its insufficient flexibility probability is 2.34%, slightly higher than Scheme 1's 2.13%, but not significantly worsened.

[0113] A comparison of the performance of each scheme across five dimensions—economic efficiency, safety, low carbon emissions, reliability, and flexibility—reveals that Scheme 4, with the highest proportion of renewable energy, not only demonstrates significant advantages in core indicators such as investment costs and carbon emissions but also achieves the highest overall evaluation due to its stable safety performance. After comprehensively balancing the weights of each indicator, Scheme 4, with a perfect overall evaluation score, fully demonstrates the optimal suitability of the power grid planning scheme for economic efficiency, safety, low carbon emissions, reliability, and flexibility under a 130% renewable energy installed capacity ratio. This indicates that, under the current research boundary conditions, this scheme is the optimal choice for achieving comprehensive power grid performance optimization.

[0114] The method provided in this application embodiment obtains basic data for each candidate planning scheme among multiple candidate planning schemes to be evaluated. According to a preset multi-dimensional index system, it calculates the indexes for each candidate planning scheme's basic data across multiple time scales, obtaining multi-dimensional scheme indicators for each candidate planning scheme. Based on the multi-dimensional scheme indicators of each candidate planning scheme in each time scale scenario, it determines the ideal decision scheme. Based on the ideal decision scheme, it calculates the multi-dimensional index scores for each candidate planning scheme in each time scale scenario. Using the analytic hierarchy process (AHP), it sets corresponding score weights for each dimension index score in each time scale scenario and determines the scenario weights corresponding to each time scale scenario. Each scoring weight and scenario weight is used to calculate the multi-dimensional indicator scores of each candidate planning scheme under different time scales and scenarios, resulting in the total score for each candidate planning scheme. Based on the total score of each candidate planning scheme, a comprehensive decision result is generated and output. The candidate planning schemes are evaluated under multiple time scales based on a multi-dimensional indicator system. This can comprehensively measure planning schemes with different goal orientations, achieve synergy between long-term strategic and immediate needs, solve the problem of difficulty in balancing multiple dimensions and indicators due to the different performance of planning schemes under different scenarios and time scales, avoid single-dominant decision-making, ensure the subjective and objective balance of scheme evaluation, and further improve the accuracy of evaluation.

[0115] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0116] The above embodiments and the technical features in the embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0117] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

[0118] In an exemplary embodiment, see Figure 9 The invention also provides a computer device including a bus, a processor, a memory, and a communication interface. It may also include an input / output interface and a display device, wherein the various functional units can communicate with each other via the bus. The memory stores a computer program, and the processor executes the program stored in the memory to perform the hierarchical planning scheme comprehensive decision-making method described in the above embodiments.

[0119] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the hierarchical planning scheme comprehensive decision-making method.

[0120] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented in hardware or by using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0121] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application.

[0122] Those skilled in the art will understand that the modules in the apparatus of the implementation scenario can be distributed within the apparatus of the implementation scenario as described, or they can be located in one or more apparatuses different from this implementation scenario, with corresponding changes. The modules of the above-described implementation scenario can be combined into one module, or they can be further divided into multiple sub-modules.

[0123] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of the implementation scenario.

[0124] The above disclosures are only a few specific implementation scenarios of this application. However, this application is not limited to these. Any variations that can be conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A hierarchical comprehensive decision-making method for planning schemes, characterized in that, include: Obtain basic data for each of the multiple candidate planning schemes to be evaluated; According to the preset multidimensional index system, the basic data of each candidate planning scheme are used to calculate the index for multiple time scale scenarios, so as to obtain the multidimensional scheme index corresponding to each candidate planning scheme. Based on the multi-dimensional scheme indicators of each candidate planning scheme in each time scale scenario, an ideal decision scheme is determined, and based on the ideal decision scheme, a multi-dimensional indicator score of each candidate planning scheme in each time scale scenario is calculated. Using the Analytic Hierarchy Process (AHP), corresponding scoring weights are set for the scores of each dimension index under each time scale scenario, and the scenario weights corresponding to each time scale scenario are determined. Based on the determined scoring weights and scenario weights, the multi-dimensional index scores of each candidate planning scheme under each time scale scenario are calculated to obtain the total score of each candidate planning scheme. Based on the total score of each candidate planning scheme, a comprehensive decision result is generated and output.

2. The method according to claim 1, characterized in that, The step involves calculating the indicators for each candidate planning scheme across multiple time scales based on a preset multi-dimensional indicator system, resulting in multi-dimensional scheme indicators corresponding to each candidate planning scheme, including: The system acquires a pre-defined multi-dimensional indicator system, which includes a safety indicator system, a reliability indicator system, an economic indicator system, a low-carbon indicator system, and a flexibility indicator system. The safety indicator system includes reserve rate, short-circuit current adequacy, single component failure pass rate, minimum frequency drop, frequency qualification rate, voltage qualification rate, and power flow distribution rationality index. The reliability indicator system includes power shortage probability, expected power shortage value, power shortage frequency, power shortage duration, expected power shortage value, and the contribution coefficient of renewable energy to load shedding probability. The economic indicator system includes equivalent annual investment cost, operating cost, internal rate of return, dynamic investment payback period, power generation utilization hours, and transmission loss rate. The low-carbon indicator system includes energy curtailment rate, renewable energy penetration rate, carbon emissions, and energy storage mitigation rate. The flexibility indicator system includes capacity-to-load ratio, probability of insufficient upward / downward adjustment flexibility, proportion of regulating resources, load resource regulation capability, average net load peak-to-valley ratio, and grid expansion margin. In accordance with the instructions of the multi-timescale scenario, on the long-term timescale, based on the security index system, the reliability index system, the economic index system, the low-carbon index system, and the flexibility index system, multi-dimensional scheme indicators are calculated for each candidate planning scheme on the long-term timescale. In accordance with the instructions of the multi-timescale scenario, in the short-term timescale, based on the security index system, the low-carbon index system, and the flexibility index system, multi-dimensional scheme indicators are calculated for each candidate planning scheme in the short-term timescale.

3. The method according to claim 2, characterized in that, On the long-term timescale, the following formula is used to calculate the contribution coefficient of renewable energy to the load shedding probability for each candidate planning scheme. in, This represents the calculated contribution coefficient. This represents the system load shedding probability of the candidate planning scheme when renewable energy is added in the currently calculated candidate planning scheme. This represents the system load shedding probability of the candidate planning scheme after the withdrawal of renewable energy in the currently calculated candidate planning scheme; On the short-term timescale, the energy storage mitigation rate is calculated for each candidate planning scheme using the following formula. in, This represents the calculated energy storage mitigation rate. This represents the energy storage output of the currently calculated candidate planning scheme. This represents the unbalanced capacity of the currently calculated candidate planning schemes; On the short-term timescale, the following formula is used to calculate the probability of insufficient flexibility for each candidate planning scheme. in, This represents the calculated probability of insufficient upward adjustment flexibility. This indicates that among the candidate planning schemes currently being calculated, the system is... Available capacity increase at any time This indicates that among the candidate planning schemes currently being calculated, the system... Net load at any given time This indicates that among the candidate planning schemes currently being calculated, the system... Net load at any given time This represents the calculated probability of insufficient flexibility in downsizing. This indicates that among the candidate planning schemes currently being calculated, the system is... Available capacity at all times; On the long-term timescale, the following formula is used to calculate the proportion of adjustment resources for each candidate planning scheme. in, This represents the calculated proportion of adjustment resources. This represents the adjustable installed capacity of resources within the corresponding time period in the currently calculated candidate planning schemes. This represents the total installed capacity of all power generation resources in the power grid among the candidate planning schemes currently being calculated; The following formula is used to calculate the load resource regulation capacity for each candidate planning scheme on both the long-term and short-term time scales. in, This represents the calculated load resource regulation capability on the long-term or short-term time scale. This represents the adjustable load capacity among the candidate planning schemes currently being calculated. This represents the total load capacity of the power grid in the currently calculated candidate planning scheme.

4. The method according to claim 1, characterized in that, The process of determining the ideal decision scheme based on the multi-dimensional scheme indicators of each candidate planning scheme in each time scale scenario, and calculating the multi-dimensional indicator scores of each candidate planning scheme in each time scale scenario based on the ideal decision scheme, includes: The multi-dimensional scheme indicators of the multiple candidate planning schemes under various time scales are integrated to obtain a decision matrix. The decision matrix is ​​then normalized. Based on the normalized decision matrix, the ideal decision scheme is determined, wherein the ideal decision scheme includes positive ideal schemes. and negative ideal solution , , , Indicates the first The maximum value of each indicator among the multiple candidate planning schemes Indicates the first The minimum value of each indicator among the multiple candidate planning schemes; The Eulerian distance between each candidate planning scheme and the positive ideal scheme and the negative ideal scheme is calculated using the following formula. in, Indicates the first The Euler distance between each candidate programming solution and the ideal solution. Indicates the first The Euler distance between each candidate programming solution and the negative ideal solution This indicates the number of indicators in the multidimensional scheme. Indicates the first The candidate programming scheme is in the normalized decision matrix. A normalized index value, In the positive ideal solution, the first... Individual indicator values, Indicates the first The Euler distance between each candidate programming solution and the negative ideal solution In the negative ideal solution, the first... Individual indicator values; The following formulas are used to calculate the positive grey correlation coefficient between each candidate planning scheme and the positive ideal scheme, and the negative grey correlation coefficient between each candidate planning scheme and the negative ideal scheme. in, Indicates the first The positive grey relational coefficient of each candidate planning scheme. Indicates the first The positive grey relational coefficient of each candidate planning scheme. Indicates the preset resolution coefficient; The following formulas are used to calculate the positive grey correlation degree between each candidate planning scheme and the positive ideal scheme, and the negative grey correlation degree between each candidate planning scheme and the negative ideal scheme. in, Indicates the first The positive grey relational degree of each candidate planning scheme, Indicates the first The negative grey correlation of each candidate planning scheme; The following formula is used to calculate the proximity distance between each candidate planning scheme and the positive ideal scheme and the negative ideal scheme. in, Indicates the first The closest approximation distance between each candidate planning scheme and the ideal scheme. Indicates the first The proximity between each candidate planning scheme and the negative ideal scheme This represents the preset position coefficient. Indicates the preset shape factor; The following formula is used to calculate the comprehensive approximation degree of each candidate planning scheme corresponding to its various dimension index systems. in, Indicates the first The comprehensive approximation of each candidate planning scheme under a certain dimension index system; The calculated comprehensive approximation is used as the index score of each candidate planning scheme under each dimension index system, thus obtaining the multi-dimensional index score of each candidate planning scheme under each time scale scenario.

5. The method according to claim 4, characterized in that, The process of integrating the multi-dimensional scheme indicators of the multiple candidate planning schemes under various time scale scenarios to obtain a decision matrix, and normalizing the decision matrix, includes: By integrating the multi-dimensional scheme indicators of the multiple candidate planning schemes under various time scales, the following decision matrix is ​​obtained. in, Represents the decision matrix. Indicates the first The candidate programming scheme is in the decision matrix before normalization. Individual indicator values, This indicates the number of the multiple candidate planning schemes. This indicates the number of index values ​​for the multidimensional scheme index; The benefit-type indicators in the decision matrix are normalized using the following formula. in, Indicates the normalized result , Indicates the first The minimum value of a benefit-type indicator. Indicates the first The maximum value of each benefit-type indicator; The cost-related indicators in the decision matrix are normalized using the following formula to complete the normalization process for the decision matrix. in, Indicates the normalized result , Indicates the first The minimum value of a cost-related indicator. Indicates the first The maximum value of each cost-related indicator.

6. The method according to claim 1, characterized in that, The use of the analytic hierarchy process (AHP) to assign corresponding scoring weights to the scores of each dimension of indicators in each time scale scenario includes: Using the safety, reliability, economy, low-carbon, and flexibility indicators in the multidimensional indicator system, a first factor set is constructed for the long-term time scale. Using the safety index system, low-carbon index system, and flexibility index system in the multidimensional index system, a corresponding second factor set is constructed for the short-term time scale. Extract any two factors from the first factor set and the second factor set respectively, and assign values ​​to the relative influence between the two factors using a hierarchical comparison scale table indicated by the analytic hierarchy process (AHP) to obtain the following judgment matrix. The largest eigenvalue of the judgment matrix is ​​defined as... The eigenvector corresponding to the largest eigenvalue is ,in, ; The feature vector is normalized using the following formula to obtain the score weights corresponding to the score calculation of each dimension index under each time scale scenario. in, Indicates the first The scoring weights corresponding to the scores of each dimension indicator Indicates the first Each dimension's score corresponds to a subvector within the feature vector.

7. The method according to claim 6, characterized in that, The method further includes: The judgment matrix is ​​subjected to consistency verification, and after the consistency verification passes, the judgment matrix is ​​used to calculate the corresponding score weights for each dimension indicator in each time scale scenario; and / or, After assigning values ​​to the relative influence between any two factors using a hierarchical comparison scale table indicated by the analytic hierarchy process, the assignment results are obtained, the assignment results are pushed to the verification recipient, and the verification recipient's adjustment of the assignment results is received. The judgment matrix is ​​then generated using the adjusted assignment results.

8. The method according to claim 1, characterized in that, Based on the determined scoring weights and scenario weights, the multi-dimensional index scores of each candidate planning scheme under each time scale scenario are calculated to obtain the total score of each candidate planning scheme, including: For each candidate planning scheme, the following formula is used to calculate the corresponding total score for each candidate planning scheme. in, This represents the total score of the candidate planning scheme currently being calculated. This represents the scene weight corresponding to the long-term timescale scene among the various timescale scenes. This represents the scene weight corresponding to the short-term timescale scene among the various timescale scenes.

9. The method according to claim 1, characterized in that, The process of generating and outputting a comprehensive decision result by referring to the total score of each candidate planning scheme includes: Based on the total score of each candidate planning scheme, the target candidate planning scheme with the highest total score is determined from the plurality of candidate planning schemes; Generate the comprehensive decision result indicating that the target candidate planning scheme is the optimal planning scheme, and output the comprehensive decision result.

10. A hierarchical planning scheme integrated decision-making system, characterized in that, include: The data input module is used to acquire basic data for each of the multiple candidate planning schemes to be evaluated; The multi-scenario, multi-time-scale indicator calculation module is used to perform multi-time-scale scenario indicator calculations on the basic data of each candidate planning scheme according to a preset multi-dimensional indicator system, so as to obtain the multi-dimensional scheme indicator corresponding to each candidate planning scheme. The multi-dimensional scoring module is used to determine the ideal decision scheme based on the multi-dimensional scheme indicators of each candidate planning scheme in each time scale scenario, and to calculate the multi-dimensional indicator scores of each candidate planning scheme in each time scale scenario based on the ideal decision scheme. The weighting module for each dimension is used to set corresponding scoring weights for the scores of each dimension indicator under each time scale scenario using the analytic hierarchy process, and to determine the scenario weights corresponding to each time scale scenario. The comprehensive evaluation module that coordinates long and short time scales is used to calculate the multi-dimensional index scores of each candidate planning scheme under each time scale scenario based on the determined score weights and scenario weights, so as to obtain the total score of each candidate planning scheme. The output module is used to generate and output a comprehensive decision result by referring to the total score of each candidate planning scheme.

Images