A method, device, and medium for assessing the resource regulation capacity of a park.

Abstract

This invention discloses a method, apparatus, and medium for assessing the resource regulation capacity of industrial parks. It belongs to the field of resource capacity assessment. This application obtains real-time operational and constraint data of integrated energy-load-storage industrial parks and inputs it into a pre-defined assessment model constructed based on power system dispatch theory, the operational characteristics of integrated energy-load-storage systems, and carbon management regulations. The model first calculates a comprehensive regulation willingness coefficient using user regulation willingness parameters, then combines the optimal carbon emission target and constraint data to constrain and aggregate the adjustability margins of the source-side, load-side, energy storage-side, and demand-side responses, outputting a comprehensive carbon constraint margin across multiple time scales. Finally, based on this comprehensive margin, it calculates the flexibility deficit probability, regulation willingness satisfaction rate, carbon emission reduction benefits, and low-carbon supply ratio, generating a resource regulation capacity assessment result that includes regulation capacity levels and decision recommendations. This application effectively solves the problem that existing technologies cannot accurately and efficiently assess the resource regulation capacity of industrial parks.

Description

Technical Field

[0001] This invention relates to the field of resource capacity assessment, and more particularly to a method, apparatus and medium for assessing the resource regulation capacity of a park. Background Technology

[0002] The integration of energy source, load, and storage has become a core development model for industrial and commercial parks to achieve efficient energy utilization, improve the absorption of new energy sources, and meet carbon emission reduction targets. Among these, the assessment of the park's flexible adjustment capacity, as a key technology for ensuring power supply and demand balance, optimizing resource allocation, and supporting policy formulation, directly affects the park's energy operation efficiency and carbon quota compliance capabilities, and its importance is becoming increasingly prominent.

[0003] However, existing methods for assessing the flexible adjustment capabilities of industrial parks still suffer from numerous technical bottlenecks, making it difficult to adapt to actual application needs. On the one hand, existing methods generally do not fully integrate user adjustment intentions, relying solely on equipment physical characteristics to calculate adjustment margins. This ignores the impact of user behavior factors such as production urgency and subsidy sensitivity on demand-side response resource allocation, resulting in assessment results that are disconnected from the actual production scheduling scenarios in industrial parks and are difficult to guide practical operations. On the other hand, the assessment system lacks a carbon emission optimization target orientation and has not established a linkage mechanism between adjustment capabilities and carbon emission reduction benefits. It cannot adapt to the scheduling needs under the carbon quota constraints of industrial parks and is unable to support "low-carbon" transformation decisions.

[0004] Meanwhile, existing technologies suffer from inconsistent parameter definitions and incomplete logical loops in mathematical models. Furthermore, they lack real-time performance, failing to respond quickly to dynamic scenarios such as load fluctuations and changes in renewable energy output, thus hindering the real-time calculation of adjustability margins required for dynamic scheduling in industrial parks. In addition, the lack of a unified correction standard for multi-timescale assessments results in low comparability of margin calculations at different timescales such as 15 minutes, 1 hour, and 24 hours. This leads to insufficient practicality in supporting decision-making in various scenarios, including resource allocation and subsidy policy design. These shortcomings prevent existing technologies from accurately and efficiently assessing the adjustability of industrial park resources. Summary of the Invention

[0005] This invention provides a method, apparatus, and medium for assessing the resource regulation capacity of a park, thereby solving the problem that existing technologies cannot accurately and efficiently assess the resource regulation capacity of a park.

[0006] Firstly, this application provides a method for assessing the resource adjustment capacity of a park, including: Acquire real-time operational and constraint data of the integrated energy source, load, and energy storage industrial park; the real-time operational data includes source-side output data, load-side load data, energy storage status data, actual demand-side response adjustment data, and user adjustment intention parameters; The real-time operating data and constraint data are input into a preset evaluation model. The evaluation model calculates a comprehensive adjustment willingness coefficient based on the user adjustment willingness parameters in the real-time operating data. Then, it calculates the adjustment margin of the demand-side response based on the comprehensive adjustment willingness coefficient. Based on the preset optimal carbon emission target and constraint data, it performs constraint correction and aggregation on the adjustment margins of the source-side, load-side, energy storage-side, and demand-side responses, and outputs a comprehensive carbon constraint margin at multiple time scales. The evaluation model is constructed based on preset power system dispatch theory, preset source-load-storage integrated operation characteristics, and preset carbon management standards. Based on the comprehensive carbon constraint margin across multiple time scales, the probability of flexibility deficit, the rate of satisfaction of adjustment intentions, the carbon emission reduction benefits, and the proportion of low-carbon supply are calculated to generate a resource adjustment capacity assessment result that includes adjustment capacity level and decision recommendations.

[0007] This application provides a complete and accurate data foundation for assessment by comprehensively acquiring real-time operational data (including data related to source-side, load-side, energy storage-side, demand-side response, and user adjustment intentions) and constraint data (including carbon quotas, grid transmission, and equipment operation constraints) of integrated power generation, load, and energy storage industrial parks. This avoids assessment biases caused by data gaps in existing technologies. The data is input into an assessment model built upon power system dispatching theory, the operational characteristics of integrated power generation, load, and energy storage, and carbon management regulations. First, a comprehensive adjustment intention coefficient is calculated using user adjustment intention parameters, achieving a deep integration of user behavior factors such as production urgency and subsidy sensitivity with adjustment capabilities. This solves the problem of existing methods ignoring user intentions, leading to a disconnect between assessment and reality. Then, the overall resource adjustability margin is constrained and corrected by combining optimal carbon emission targets and constraint data. This invention establishes a linkage mechanism between regulatory capacity and carbon emission reduction, adapting to carbon quota constraints. It also outputs a comprehensive carbon constraint margin across multiple time scales, unifying assessment standards across different time scales and improving the comparability of results. Finally, based on this comprehensive margin, it calculates multi-dimensional quantitative indicators and generates an assessment result that includes regulatory capacity levels and decision-making recommendations. This approach achieves accurate determination of regulatory capacity through indicator quantification and directly supports multi-scenario decision-making in areas such as dynamic scheduling, resource allocation, subsidy policy design, and carbon quota compliance through targeted decision-making recommendations. It effectively compensates for the shortcomings of existing technologies, such as insufficient real-time performance and weak practicality in decision support. Overall, it improves the accuracy, comprehensiveness, and practicality of assessing the flexible regulatory capacity of parks. This application effectively solves the problem that existing technologies cannot accurately and efficiently assess the regulatory capacity of park resources.

[0008] Furthermore, the source-side power output data includes measured photovoltaic power output, predicted photovoltaic power output, photovoltaic dispatched power output, measured wind power output, predicted wind power output, dispatched wind power output, maximum output of gas turbine units, minimum output of gas turbine units, upward ramp rate of gas turbine units, downward ramp rate of gas turbine units, start-up and shutdown status of gas turbine units, and carbon intensity of various source-side resources. The load-side data includes total load forecast power, total load measured power, load measured power by carbon intensity level, adjustable capacity of reduced demand-side response, and adjustable capacity of transferred demand-side response. The energy storage status data includes the maximum charging power of electrochemical energy storage, the maximum discharging power of electrochemical energy storage, the maximum capacity of electrochemical energy storage, the current state of charge of electrochemical energy storage, the upper limit of the state of charge of electrochemical energy storage, the upper limit of the state of charge of electrochemical energy storage, the charging efficiency of electrochemical energy storage, the discharging efficiency of electrochemical energy storage, the current capacity ratio of thermal storage equipment, the maximum capacity of thermal storage equipment, and the thermoelectric conversion efficiency of thermal storage equipment. The actual adjustment data of the demand-side response includes the actual adjustment power of the reduction-type demand-side response and the actual adjustment power of the transfer-type demand-side response. The user adjustment willingness parameters include production urgency, current subsidy standard for reduced demand-side response, subsidy threshold for reduced demand-side response, and feasibility coefficient for transferred demand-side response; The constraint data includes the park's annual carbon quota, time-period carbon emission cap, line power cap, node line distribution factor, minimum balance period for transfer-type demand-side response, assessment period, preset baseline carbon emissions without optimization scheduling, and remaining carbon budget.

[0009] This application achieves comprehensive data acquisition from integrated energy-load-storage industrial and commercial parks. Real-time operational data includes multi-type output and carbon intensity on the source side, load data categorized by carbon intensity levels and demand-side response, complete operating parameters of energy storage equipment, and core parameters reflecting user adjustment intentions (production urgency, subsidy standards, etc.). Constraint data covers key limitations such as carbon quotas, grid transmission, and operating cycles, forming a comprehensive data set encompassing all resources, constraints, and user intentions. This comprehensive data system not only addresses the shortcomings of existing technologies, such as incomplete data dimensions and missing key parameters, but also provides a foundation for achieving optimal carbon emission targets through carbon intensity load data and carbon intensity data for various resources. It establishes a correlation between equipment physical characteristics and user behavior factors through user adjustment willingness parameters, and ensures the adaptability of the assessment to actual operating scenarios through constraint data such as grid transmission and equipment operation. Based on this comprehensive and accurate data input, the assessment model can accurately calculate the comprehensive adjustment willingness coefficient, reasonably correct the total resource adjustability margin, and generate a comprehensive carbon constraint margin across multiple time scales. This ensures the accuracy of quantitative indicators such as flexibility deficit probability and carbon emission reduction benefits, as well as the relevance of adjustment capacity levels and decision recommendations. Ultimately, it solves the problems of existing assessment methods, such as the disconnect between results and reality, poor carbon constraint adaptability, and weak multi-scenario decision support caused by insufficient data support. This significantly improves the reliability, comprehensiveness, and practical value of the park's flexible adjustment capacity assessment, providing solid data and technical support for the park's energy-efficient operation and low-carbon transformation.

[0010] Furthermore, the evaluation model calculates a comprehensive adjustment willingness coefficient based on the user adjustment willingness parameters in the real-time operational data, specifically as follows: Based on production urgency, the current subsidy standard for reduced demand-side response, and the subsidy threshold for reduced demand-side response, calculate the adjustment willingness coefficient for reduced demand-side response; The willingness coefficient for adjusting the demand-side response under the reduction model and the feasibility coefficient for the transfer model are used to calculate the willingness coefficient for adjusting the demand-side response under the transfer model. Calculate the sum of the adjustable capacity of demand-side response, including the adjustable capacity of demand-side response with reduction and the adjustable capacity of demand-side response with transfer. The total adjustment intention weighted capacity is obtained by multiplying the adjustable capacity of each type of demand-side response reduction by the corresponding adjustment intention coefficient of the demand-side response reduction, and then summing the sum with the adjustable capacity of each type of demand-side response transfer by the corresponding adjustment intention coefficient of the demand-side response transfer. Divide the total adjustment intention weighted capacity by the sum of the demand-side response adjustable capacity to obtain the comprehensive adjustment intention coefficient.

[0011] This application employs a step-by-step quantitative calculation method for the comprehensive adjustment willingness coefficient. First, it calculates the adjustment willingness coefficient for reduction-type demand-side response based on production urgency, subsidy standards, and subsidy thresholds. Then, it combines this coefficient with the feasibility coefficient for transfer-type demand-side response to obtain the adjustment willingness coefficient for transfer-type demand-side response. Finally, it weights and sums the adjustable capacities of the two types of demand-side responses and normalizes them to obtain the comprehensive adjustment willingness coefficient. This not only achieves an objective and refined representation of users' subjective adjustment willingness, such as production urgency and subsidy sensitivity, transforming abstract user willingness into quantitative parameters usable for model calculation, but also, through a calculation logic of categorized weighting and overall normalization, fully considers the differences in adjustment willingness between reduction-type and transfer-type demand-side responses. It truly reflects the actual adjustment potential of park users rather than simply theoretical physical capacity, thus providing a willingness basis that fits the actual operating scenario for subsequent adjustment margin constraint correction. This effectively avoids the problem of distorted evaluation results caused by the crude quantification of willingness and neglect of actual user participation in traditional evaluation methods, significantly improving the authenticity and practicality of the park's flexible adjustment capability assessment.

[0012] Furthermore, based on the preset optimal carbon emission target and constraint data, the adjustable margins of the source-side, load-side, energy storage-side, and demand-side responses are constrained, corrected, and aggregated to output a comprehensive carbon constraint margin across multiple time scales, specifically as follows: Calculate the upward and downward adjustment margins for gas turbine units, the downward adjustment margins for renewable energy, the upward and downward adjustment margins for electrochemical energy storage, the upward and downward adjustment margins for thermal storage equipment, the downward adjustment margins for thermal storage equipment, the reduction-type demand-side response adjustment power, and the transfer-type demand-side response adjustment power, respectively, as independent margins for each type of resource. Based on the current state of charge of electrochemical energy storage, the upper limit of state of charge of electrochemical energy storage, the upper limit of state of charge of electrochemical energy storage, and the start-up and shutdown status of gas turbine units in real-time operation data, and based on the minimum balance period of transfer-type demand-side response and user production process constraints in the constraint data, the independent margins of various resources are adjusted by operation constraints, and the margins that exceed the equipment operating capacity and process requirements are eliminated to obtain the margins of various resources after operation constraint adjustment. Based on the node line distribution factor and line power limit in the constraint data, combined with the margin of various resources after operational constraint correction, the available amount of various resources is calculated and corrected through the influence of node line power, and the margin of various resources after grid transmission constraint correction is obtained. Based on the preset optimal carbon emission target, and combined with the carbon intensity of various resources in real-time operation data, the carbon emission limit for each time period in the constraint data, and the remaining carbon budget, the margin of various resources after grid transmission constraint correction is carbon-constrained to obtain the margin of various resources after carbon constraint correction. The carbon-constrained margins of various resources are aggregated according to the upward and downward adjustment dimensions to obtain the upward and downward baseline margins at the fifteen-minute baseline scale. Based on the 15-minute baseline scale, the upward and downward baseline margins are combined with preset time scale correction coefficients to output a comprehensive carbon constraint margin across multiple time scales.

[0013] This application first calculates the independent adjustable margins of all types of resources, including gas turbine units, renewable energy, electrochemical energy storage, thermal storage equipment, and demand-side response (DSR) of both reduction and transfer types. Then, it sequentially corrects operational constraints based on equipment operating status, production processes, and the minimum balance cycle of DSR to eliminate margins that do not meet equipment capacity and production requirements. Next, it corrects grid transmission constraints based on node line distribution factors and line power limits to ensure safe and stable grid operation. Finally, it corrects carbon constraints based on optimal carbon emission targets, resource carbon intensity, and time-specific carbon emission limits to align with the park's low-carbon emission reduction and carbon quota management needs. Subsequently, it aggregates the various resource margins after multiple constraint corrections according to upward and downward adjustment dimensions to obtain a 15-minute baseline margin. This margin is then expanded into a multi-time-scale comprehensive carbon constraint margin by combining preset time-scale correction coefficients. This not only achieves complete quantitative coverage of all resources in the park's source-load-storage and demand-side response systems but also addresses the "equipment operation—grid transmission—carbon emission" problem. The progressive constraint correction logic ensures that the assessment results simultaneously meet the requirements of physical feasibility, power grid security, and low-carbon optimization. Combined with multi-timescale margin output, it can adapt to the scheduling, planning, and decision-making needs of the park at different cycles. It effectively solves the problems of one-sided margin calculation, lack of constraints, and poor applicability of single-timescale assessment in existing technologies, and significantly improves the accuracy, reliability, and scenario adaptability of the park's flexible adjustment capability assessment.

[0014] Furthermore, based on the preset optimal carbon emission target, and combined with the carbon intensity of various resources in real-time operational data, the time-period carbon emission ceiling and remaining carbon budget in the constraint data, the margin of various resources after grid transmission constraint correction is adjusted for carbon constraints, specifically as follows: The margins of various resources after correction for grid transmission constraints are multiplied by the carbon intensity of the corresponding resource in the real-time operating data, and then all the product results are summed to obtain the optimal solution for carbon emissions. By comparing the optimal carbon emission solution with the time-limit carbon emission cap in the constraint data, it can be determined whether the optimal carbon emission solution exceeds the time-limit carbon emission cap. If the optimal carbon emission solution does not exceed the carbon emission limit for the time period, then the margins of various resources after grid transmission constraints will be used as the margins after carbon constraints. If the optimal carbon emission solution exceeds the carbon emission limit for a given period, the difference between the optimal carbon emission solution and the carbon emission limit for that period is calculated. The difference is then divided by the carbon intensity of the gas turbine unit in the real-time operating data to obtain the amount of high-carbon resource usage that needs to be reduced. The amount of high-carbon resources that need to be reduced is deducted from the upward or downward adjustment margin of gas turbine units after grid transmission constraint correction. The margins of other resources after grid transmission constraint correction remain unchanged, thus obtaining the carbon constraint-corrected margins of various resources.

[0015] This application obtains the optimal carbon emission solution by multiplying the margins of various resources after grid transmission constraints with the corresponding resource carbon intensity and summing the results. This achieves a precise quantitative correlation between adjustable margins and carbon emission levels. The optimal carbon emission solution is then compared with the carbon emission cap for a given time period and combined with the remaining carbon budget to determine carbon compliance. When the optimal carbon emission solution does not exceed the cap, the original margin is directly used to ensure assessment efficiency. When it exceeds the cap, the ratio of the carbon emission difference to the carbon intensity of the gas turbine unit is used to accurately calculate the amount of high-carbon resources that need to be reduced. Only the adjustment margin of the gas turbine unit is specifically deducted, while the margins of other low-carbon resources remain unchanged. This approach not only strictly meets the hard constraints of the park's time period carbon emission cap and carbon quota control, but also achieves reasonable reduction of high-carbon resources and maximizes the retention of low-carbon adjustment resources. It avoids the problems of blindly reducing various resources and insufficient utilization of low-carbon resources in traditional carbon constraint correction, and significantly improves the accuracy, compliance, and rationality of the park's adjustable margin calculation under carbon constraints.

[0016] Furthermore, the upward and downward adjustment of the baseline margin based on the 15-minute baseline scale, combined with a preset time scale correction coefficient, outputs a comprehensive carbon constraint margin across multiple time scales, specifically as follows: The upward and downward baseline margins of the 15-minute baseline scale are used as the basic margins for multi-timescale expansion. Multiply the upward adjustment baseline margin in the baseline margin by the correction coefficient corresponding to the preset target time scale to obtain the overall upward adjustment carbon constraint margin at the target time scale. Multiply the reduced baseline margin in the baseline margin by the correction coefficient corresponding to the preset target time scale to obtain the reduced carbon constraint comprehensive margin at the target time scale. By classifying and integrating the overall carbon constraint margins at each target time scale according to time scale, a multi-time scale overall carbon constraint margin is obtained.

[0017] This application uses a 15-minute baseline scale for both upward and downward adjustment margins as a unified basic margin. It then multiplies these margins by the corresponding correction coefficients for each target time scale to obtain the comprehensive upward and downward carbon constraint margins for each target time scale. Finally, it integrates these margins by time scale to form a complete multi-time-scale carbon constraint comprehensive margin. This approach ensures consistency and comparability of assessment results across different time scales through a unified benchmark. Furthermore, the standardized calculations across dimensions and scales enable precise extended assessments of the park's adjustment capabilities across different time spans. This approach simultaneously adapts to the needs of various decision-making scenarios, such as short-term real-time scheduling and medium-to-long-term resource planning. It effectively solves the problems of traditional assessment methods that only use a single time scale, have limited applicability, and lack unified conversion logic between multiple scales. This significantly improves the scenario adaptability and practicality of the park's flexible adjustment capability assessment.

[0018] Furthermore, based on the comprehensive carbon constraint margin across multiple time scales, the probability of flexibility deficit, the rate of satisfaction of adjustment willingness, the carbon emission reduction benefits, and the proportion of low-carbon supply are calculated to generate a resource adjustment capacity assessment result that includes adjustment capacity level and decision recommendations. Specifically: Integrating the probability density function of the carbon constraint comprehensive margin across the multiple time scales yields the probability of flexibility deficit. The sum of the absolute values ​​of the actual adjustment power of the reduced demand-side response and the actual adjustment power of the transferred demand-side response in the real-time operating data during the evaluation period is divided by the sum of the product of the adjustable capacity of the reduced demand-side response and the preset adjustment willingness coefficient of the reduced demand-side response in the real-time operating data during the evaluation period, and the product of the adjustable capacity of the transferred demand-side response and the preset adjustment willingness coefficient of the transferred demand-side response in the real-time operating data, to obtain the adjustment willingness satisfaction rate. The carbon emission reduction benefits are obtained by summing the difference between the preset unoptimized scheduling baseline carbon emissions and the optimal carbon emission solution in the constraint data during the evaluation period. The low-carbon supply ratio is obtained by dividing the sum of the renewable energy down-adjustment margin, electrochemical energy storage up-adjustment margin, electrochemical energy storage down-adjustment margin, thermal energy storage equipment up-adjustment margin, and thermal energy storage equipment down-adjustment margin among the various resource margins after carbon constraint correction during the assessment period by the sum of the various resource margins after carbon constraint correction during the assessment period. Based on the aforementioned flexibility deficit probability, adjustment willingness satisfaction rate, carbon emission reduction benefits, and low-carbon supply ratio, a resource adjustment capacity assessment result is generated, which includes adjustment capacity level and decision-making recommendations.

[0019] This application obtains the probability of flexibility deficit by integrating the probability density function of the comprehensive carbon constraint margin across multiple time scales, which can intuitively quantify the adequacy of the park's adjustment capacity and the system's adjustment risk. By comparing the actual adjustment power of demand-side response with the theoretical adjustable capacity calculated in conjunction with user intentions, the adjustment intention satisfaction rate is obtained, which can truly reflect the actual realization of user adjustment intentions and the effective utilization level of demand-side response resources. The carbon emission reduction benefit is obtained by accumulating the difference between the baseline carbon emissions without optimal scheduling and the optimal carbon emission solution, which can accurately quantify the actual low-carbon emission reduction value brought by this scheme. Finally, the low-carbon supply is obtained by statistically analyzing the proportion of low-carbon resource margin in the total adjustment margin. The proportion directly reflects the degree of low-carbon resource utilization and the level of energy structure optimization in the park. Based on the above four comprehensive and quantitative core indicators, a complete assessment result including the level of regulation capacity and decision-making recommendations is generated. This not only realizes the transformation of the park's resource regulation capacity from fuzzy judgment to precise quantitative assessment, but also provides direct and implementable decision-making support for the park's subsequent real-time scheduling, resource allocation, subsidy policy adjustment and carbon quota compliance. It effectively solves the problems of existing assessment methods, such as single indicators, inability to take into account regulation reliability, user willingness, carbon emission reduction and decarbonization level, and difficulty in forming guiding decision-making recommendations. It greatly improves the scientificity, comprehensiveness and practical application value of the assessment results.

[0020] Furthermore, based on the aforementioned flexibility deficit probability, adjustment willingness satisfaction rate, carbon emission reduction benefits, and low-carbon supply ratio, a resource adjustment capacity assessment result including adjustment capacity level and decision recommendations is generated, specifically as follows: The probability of flexibility deficit and the proportion of low-carbon supply are compared with the corresponding preset thresholds, and the adjustment capacity level is obtained based on the comparison results and the preset full-scale margin deficit. The preset thresholds include a first preset value, a second preset value, and a third preset value corresponding to the probability of flexibility shortage, and a fourth preset value, a fifth preset value, and a sixth preset value corresponding to the proportion of low-carbon supply. The first preset value is less than the second preset value, the second preset value is less than the third preset value; the fourth preset value is less than the fifth preset value, and the fifth preset value is less than the sixth preset value; When the regulation capacity meets the following conditions: the full-scale margin is positive and there is no deficit, the probability of flexibility deficit is less than the first preset value, and the proportion of low-carbon supply is not less than the fourth preset value, the regulation capacity level is judged to be excellent. When the regulation capacity meets the requirements of a single scale having a deficit, the probability of a flexibility deficit being between the first and second preset values, and the proportion of low-carbon supply being between the fourth and fifth preset values, the regulation capacity level is judged to be good. When the regulation capacity meets the two-scale deficit, the probability of the flexibility deficit is between the second and third preset values, and the proportion of low-carbon supply is between the fifth and sixth preset values, the regulation capacity level is determined to be general. When the regulation capacity meets the full-scale deficit, or the probability of the flexibility deficit is greater than the third preset value, or the proportion of low-carbon supply is less than the sixth preset value, the regulation capacity level is determined to be insufficient. Based on the numerical characteristics of adjustment capacity level and flexibility deficit probability, adjustment willingness satisfaction rate, carbon emission reduction benefits, and low-carbon supply ratio, resource allocation suggestions, subsidy policy adjustment suggestions, scheduling suggestions, or carbon quota compliance suggestions are generated as decision-making suggestions. By combining the adjustment capacity level with decision-making recommendations, a resource adjustment capacity assessment result is generated.

[0021] This application scientifically classifies the park's resource regulation capacity into four clear levels: excellent, good, average, and insufficient, by comparing the probability of flexibility deficit, the rate of satisfaction of regulation willingness, and the proportion of low-carbon supply with corresponding preset thresholds, and combining the combination relationships of different numerical ranges of each indicator. This realizes the transformation of regulation capacity assessment from scattered indicators to standardized level judgment, effectively solving the problems of vague level division and single judgment basis in traditional assessment. On this basis, it further combines the regulation capacity level with the actual numerical characteristics of each indicator to generate diversified decision-making suggestions for resource allocation, subsidy policy adjustment, dispatch optimization, and carbon quota compliance. Finally, the regulation capacity level and decision-making suggestions are organically integrated to form a complete assessment result. This not only makes the overall level of the park's regulation capacity intuitive and quantifiable, but also provides direct and implementable guidance for the park's energy dispatch, low-carbon operation, demand-side response optimization, and carbon asset management. It truly realizes a closed-loop connection from accurate assessment to efficient decision-making, significantly improving the practicality, guidance, and application value of the assessment results.

[0022] Secondly, this application provides an assessment device for the resource adjustment capacity of a park. The assessment device for the resource adjustment capacity of a park includes: The acquisition module is used to acquire real-time operation data and constraint data of the integrated energy source, load and storage industrial and commercial park; the real-time operation data includes source-side output data, load-side load data, energy storage status data, actual demand-side response adjustment data and user adjustment intention parameters; The evaluation module is used to input the real-time operating data and constraint data into a preset evaluation model. The evaluation model calculates a comprehensive adjustment willingness coefficient based on the user adjustment willingness parameters in the real-time operating data, calculates the adjustment margin of the demand-side response based on the comprehensive adjustment willingness coefficient, and performs constraint correction and aggregation on the adjustment margin of the source-side, load-side, energy storage-side, and demand-side responses based on preset optimal carbon emission targets and constraint data. The module outputs a comprehensive carbon constraint margin across multiple time scales. The evaluation model is constructed based on preset power system dispatch theory, preset integrated source-load-storage operation characteristics, and preset carbon management standards. The calculation module is used to calculate the probability of flexibility deficit, the rate of satisfaction of adjustment intention, the carbon emission reduction benefits and the proportion of low-carbon supply based on the comprehensive carbon constraint margin of the multi-time scale, and generate a resource adjustment capacity assessment result that includes adjustment capacity level and decision recommendations.

[0023] This application establishes an assessment device for the resource regulation capacity of industrial parks by setting up an acquisition module, an evaluation module, and a calculation module that work together. The acquisition module can comprehensively acquire real-time operational and constraint data of integrated energy-load-storage industrial and commercial parks, providing a complete and accurate data foundation for the entire assessment process. The evaluation module can input the above data into a preset evaluation model built on power system dispatching theory, integrated energy-load-storage operation characteristics, and carbon management standards. It then sequentially completes the calculation of the comprehensive regulation willingness coefficient, the constraint correction and aggregation of the adjustable margins of the source side, load side, energy storage side, and demand side, and outputs the comprehensive carbon constraint margin at multiple time scales, realizing the standardization and intelligent operation of the assessment logic. The calculation module, based on the comprehensive margin of carbon constraints across multiple time scales, automatically calculates the probability of flexibility deficit, the rate of satisfaction of adjustment intentions, the carbon emission reduction benefits, and the proportion of low-carbon supply. Ultimately, it generates a resource adjustment capacity assessment result that includes adjustment capacity level and decision recommendations. Through modular functional division and closed-loop process coordination, the entire process of resource adjustment capacity assessment in the park is automated, intelligent, and efficient. This avoids the problems of low efficiency, large errors, and inconsistent logic caused by manual assessment, while ensuring the rigor of the assessment process and the reliability of the assessment results. It can provide stable and continuous equipment support and technical guarantee for energy dispatch, low-carbon operation, and carbon quota management in the park.

[0024] Thirdly, this application provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the aforementioned method for assessing the resource adjustment capability of a park. Its beneficial effects are the same as those of the method for assessing the resource adjustment capability of a park provided in the first aspect of this application. Attached Figure Description

[0025] Figure 1 : A schematic flowchart of an embodiment of the method for assessing the park resource adjustment capacity provided in this application; Figure 2 : A schematic diagram of an embodiment of the assessment device for the park resource regulation capacity provided in this application. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0027] Example 1 Please refer to Figure 1 In order to solve the problem that existing technologies cannot accurately and efficiently assess the regulatory capacity of park resources, this invention provides a method for assessing the regulatory capacity of park resources, including steps S01-S03.

[0028] S01: Obtain real-time operation data and constraint data of the integrated energy source, load and storage industrial and commercial park; the real-time operation data includes source-side output data, load-side load data, energy storage status data, actual demand-side response adjustment data and user adjustment intention parameters; As a preferred embodiment of this invention, the acquisition of real-time operational data and constraint data of the integrated source-load-storage industrial and commercial park specifically includes: In this embodiment, a resource regulation capacity assessment is conducted for an integrated industrial and commercial park. First, real-time operational data and constraint data of the park are acquired. The time granularity of data collection is consistent with the assessment benchmark scale, and a minimum collection cycle of 15 minutes is adopted to ensure that the real-time performance of the data matches the accuracy of subsequent assessment calculations.

[0029] The real-time operational data comprises dynamic data collected in real-time by park equipment during the operation of the park's energy system. It covers five main categories: source-side output data, load-side load data, energy storage status data, actual demand-side response adjustment data, and user adjustment intention parameters. The specific content of each data category is as follows: Source-side output data includes operational and output-related data for various power sources within the park, specifically including measured photovoltaic output, predicted photovoltaic output, dispatched photovoltaic output, measured wind power output, predicted wind power output, dispatched wind power output, maximum output of gas turbine units, minimum output of gas turbine units, gas turbine unit ramp-up rate, and gas turbine... The data sets include the following: ramp rate, gas turbine start-up and shutdown status, and carbon intensity of various source-side resources. Specifically, measured, predicted, and dispatched output data for photovoltaic and wind power are used to characterize the actual operational capacity and adjustable potential of renewable energy. Output limits, ramp rate, and start-up and shutdown status of gas turbines are used to characterize the controllable boundary of fossil fuels. Carbon intensity of various source-side resources is used in subsequent carbon emission accounting and carbon constraint correction. Load-side load data includes relevant data on electricity and energy consumption within the park, specifically including predicted total load power, measured total load power, measured load power at different carbon intensity levels, and adjustable capacity for reduced demand-side response. The adjustable capacity of demand-side response (DSR) is categorized into two types: quantity-based and transfer-based. The predicted and measured total load data characterize the overall energy consumption level of the park, while the measured load power at different carbon intensity levels distinguishes the carbon emission attributes of different energy consumption segments. The adjustable capacity of both types of DSR characterizes the theoretical adjustment potential of the load side. Energy storage status data comprises the operating status parameters of various energy storage devices within the park, specifically including the maximum charging power of electrochemical energy storage, the maximum discharging power of electrochemical energy storage, the maximum capacity of electrochemical energy storage, the current state of charge of electrochemical energy storage, the upper limit of the state of charge of electrochemical energy storage, and the upper limit of the state of charge of electrochemical energy storage. The charging efficiency of chemical energy storage, the discharging efficiency of electrochemical energy storage, the current capacity ratio of thermal storage equipment, the maximum capacity of thermal storage equipment, and the thermoelectric conversion efficiency of thermal storage equipment are all parameters used to fully characterize the charging and discharging boundaries, operating efficiency, and current availability of electrochemical energy storage and thermal storage equipment, providing a basis for calculating the adjustable margin of the energy storage side. The actual adjustment data of demand-side response is the actual execution data of the park's demand-side response resources, specifically including the actual adjustment power of reduction-type demand-side response and the actual adjustment power of transfer-type demand-side response, which is used to calculate the subsequent adjustment willingness satisfaction rate and characterize the actual call effect of demand-side response resources.The user adjustment willingness parameter represents the subjective willingness of park users to participate in demand-side response. Specifically, it includes production urgency, the current subsidy standard for reduced demand-side response, the subsidy threshold for reduced demand-side response, the subsidy standard for categorized transfer-type demand-side response, and the feasibility coefficient for transfer-type demand-side response. Production urgency characterizes the degree to which user production plans restrict load adjustments; the subsidy standard and threshold characterize the sensitivity of users to demand-side response incentives; and the feasibility coefficient for transfer-type demand-side response characterizes the achievability of users adjusting their energy consumption plans. These parameters provide the core input for calculating the comprehensive adjustment willingness coefficient.

[0030] The constraint data consists of fixed boundary conditions and control requirements that must be followed during the operation of the park's energy system. Specifically, these include the park's annual carbon quota, time-period carbon emission cap, line power cap, node line distribution factor, minimum balancing period for demand-side response with transfer, assessment period, preset baseline carbon emissions without optimized dispatch, and remaining carbon budget. Among these, the park's annual carbon quota, time-period carbon emission cap, and remaining carbon budget are the core constraints for carbon emission control in the park, used for subsequent carbon constraint correction. The line power cap and node line distribution factor are constraints for the safe operation of the power grid. The minimum balancing period for demand-side response with transfer is an operational constraint for load-side regulation. The assessment period is the time range for this assessment. The preset baseline carbon emissions without optimized dispatch is the baseline carbon emission level when the park has not implemented optimized dispatch, used for subsequent carbon emission reduction benefit calculation.

[0031] S02: Input the real-time operating data and constraint data into a preset evaluation model, so that the evaluation model calculates the comprehensive adjustment willingness coefficient based on the user adjustment willingness parameter in the real-time operating data, calculates the adjustment margin of the demand-side response based on the comprehensive adjustment willingness coefficient, and performs constraint correction and aggregation on the adjustment margin of the source-side, load-side, energy storage-side and demand-side responses based on the preset optimal carbon emission target and constraint data, and outputs the comprehensive carbon constraint margin of multiple time scales. The evaluation model is constructed based on the preset power system dispatch theory, the preset source-load-storage integrated operation characteristics and the preset carbon management specifications. In a preferred embodiment of this invention, the real-time operating data and constraint data are input into a preset evaluation model. The evaluation model calculates a comprehensive adjustment willingness coefficient based on user adjustment willingness parameters in the real-time operating data. Then, according to a preset optimal carbon emission target and constraint data, it performs constraint correction and aggregation on the adjustability margins of the source-side, load-side, energy storage-side, and demand-side responses, outputting a comprehensive carbon constraint margin across multiple time scales. Specifically: The assessment model is built upon power system dispatching theory, the integrated operation characteristics of power generation, load and storage, and carbon management standards. By integrating user adjustment intentions with optimal carbon emission targets, it achieves a quantitative assessment of the park's flexible adjustment capabilities. Specifically, the power system dispatch theory covers the principle of real-time power supply and demand balance, security-constrained dispatch methods, and source-load-storage collaborative optimization logic. This provides physical support for the "grid transmission constraint correction" (power impact calculation based on node line distribution factors) and "resource adjustment margin aggregation" (matching and accounting of total system supply and total demand) in the model, ensuring that the model calculations conform to the basic principles of power system operation. The integrated operation characteristics of source-load-storage specifically include the intermittency and volatility of renewable energy output, the rigid demand and flexible adjustment potential on the load side, and the charging and discharging boundaries and energy conversion efficiency of energy storage equipment (electrochemical energy storage / thermal storage). The model strictly follows these characteristics when calculating the independent margins of various resources (such as gas turbine ramp rate constraints and energy storage SOC upper and lower limit constraints), ensuring that the margin calculations are consistent with the actual operating capabilities of the equipment. The carbon management regulations specifically cover the park's carbon emission accounting standards, carbon quota allocation and compliance rules, and high-carbon resource control requirements. This provides a compliance basis for the "carbon constraint correction" (calculation of time-period carbon emission upper limit and high-carbon resource reduction logic) in the model, ensuring that the assessment results are compatible with the park's carbon emission reduction and carbon quota control targets. The model first calculates the comprehensive adjustment willingness coefficient based on the user adjustment willingness parameter. This process involves calculating the adjustment willingness coefficient of the reduction-type demand-side response and the adjustment willingness coefficient of the transfer-type demand-side response separately, and then obtaining the comprehensive value by weighted average.

[0032] The calculation of the willingness-to-adjust coefficient for demand-side response reduction follows the following principles: in, Indicates the urgency of production. To address the current subsidy standards in a demand-side response that is characterized by reduction, This represents the threshold for demand-side response subsidies that involve reductions. This coefficient reflects the combined impact of production urgency and subsidy sensitivity on the willingness to adjust: the more urgent the production and the lower the subsidy is below the threshold, the lower the willingness to adjust. Production urgency The closer the value is to 1, the more urgent the user's production needs and the lower their willingness to adjust. Therefore, [the following is used:] Weaken this factor; subsidy standards The closer to the subsidy threshold The stronger the economic incentives adjusted by the user, the higher the willingness. Strengthen this factor; The calculation of the willingness-to-adjust demand-side response coefficient for transfer-type demand follows the following principles: in, This is the feasibility coefficient for demand-side response to transfer, set according to load type, reflecting the differences in the difficulty of time-shifting processes for different loads.

[0033] The calculation of the comprehensive adjustment willingness coefficient follows the following principles: in, Adjustable capacity for demand-side response reduction based on carbon intensity levels. Adjustable capacity for categorized transfer-type demand-side response.

[0034] After obtaining the comprehensive adjustment willingness coefficient, the model independently calculates the adjustment margins of the source-side, load-side, energy storage-side, and demand-side responses. The source-side margin includes the bidirectional adjustment margin of gas turbine units and the down-regulation margin of renewable energy: in, , These represent the maximum and minimum output of the gas turbine unit, respectively. , To adjust the gradient rate upwards or downwards, In start / stop state. For measured output of renewable energy, This is the minimum output. (Full power rationing) Energy storage margin includes the bidirectional adjustment margin of electrochemical energy storage and thermal storage devices. The upward adjustment margin (discharge) and downward adjustment margin (charge) of electrochemical energy storage follow the following rules respectively: in, , This refers to the maximum discharge and charge power. The current state of charge, , These are the upper and lower limits of the charged state. For maximum capacity, , For discharge and charge efficiency.

[0035] The margin of thermal storage equipment is calculated by relating thermoelectric conversion to capacity: HS is a thermal energy storage device that needs to participate in power system flexibility regulation through thermoelectric conversion. Therefore, the upward margin of ESS is used as the benchmark, multiplied by the thermoelectric conversion efficiency. (Reflects the energy loss during thermoelectric conversion); Multiply by the ratio of the current actual capacity of the thermal storage equipment to the maximum available capacity of the ESS. Since the regulation capability of HS is positively correlated with the current heat storage (the more heat storage, the more heat energy can be released and converted into electrical energy), a quantitative correlation between the regulation capabilities of HS and ESS is achieved.

[0036] Based on the ESS reduction margin, multiply by the thermoelectric conversion efficiency; multiply by the ratio of the remaining heat storage capacity of the HS to the maximum usable capacity of the ESS. Since the HS charging (heat storage) capacity is positively correlated with the remaining heat storage capacity, the more remaining capacity, the more electrical energy can be absorbed and converted into heat energy, which is in line with the operating characteristics of thermal storage equipment.

[0037] Demand-side response margins include two types: reduction-type and transfer-type. ,constraint: The core of the reduced-capacity DR is to reduce high-carbon load output to supplement system flexibility supply. Its actual adjustable power is determined by three factors: maximum adjustable capacity. (Equipment process constraints), user adjustment willingness coefficient (Impact of production and subsidies), carbon intensity weighting (Carbon emission reduction orientation, high carbon load is given priority for adjustment, with a weight of 1.0 for high carbon load, 0.6 for medium carbon load, and 0.3 for low carbon load), and the product of the three is the actual adjustment power, which meets the dual objectives of "carbon emission reduction + user willingness"; The constraint is non-negative and does not exceed the maximum capacity, because the reduction-type DR can only reduce the load (cannot increase it), the regulation power cannot be negative, and it cannot exceed the maximum reducible capacity limited by the equipment process.

[0038] , Constraint 1: Constraint 2: The core of demand-side response with transfer is the shift of load over time (without a change in total load), and the actual adjustable power is determined by the maximum transferable capacity. and transfer adjustment intention coefficient The decision, when multiplied by the two, reflects the actual transfer capacity; Constraint 1 is a two-way interval. Since the transfer-type DR can increase the load during a certain period (negative regulation) or decrease the load during a certain period (positive regulation), the regulation power can be positive or negative, and the absolute value does not exceed the maximum transferable capacity. Constraint 2, where the time integral sum is 0, is an essential characteristic of transfer-type DR—the load is at its minimum balancing period. The total load remains constant, only the time shifts, so the integral sum of the regulating power within time τ is 0, ensuring the total load is conserved.

[0039] Based on the calculation of the independent margins of various resources, a demand-side response cost model is further constructed to quantify the economic efficiency of adjustment. This model calculates the total cost of demand-side response using the following formula: In the formula, and This is an auxiliary variable for transfer-type demand-side response, used to transform absolute value constraints into a linearly solvable form. The cost of reduction-type demand-side response is the product of actual adjustment power and unit subsidy standard, directly reflecting the user's economic compensation demand. However, because the adjustment power in transfer-type demand-side response can be positive or negative, directly calculating the cost using absolute values ​​would lead to nonlinear mathematical problems; therefore, an auxiliary variable is introduced. ,pass Transforming nonlinear constraints into linear constraints reduces the difficulty of solving the model.

[0040] The total system supply is a comprehensive aggregation of various resource margins, and it is necessary to distinguish the signs of load increases and decreases in the demand-side response of transferred demand: In the formula, This is when the load increases (consuming supply, deducted from total supply). This is to reduce the load (supplement supply, included in total supply).

[0041] Flexibility demand assessment is divided into two levels: raw demand and revised demand. Raw demand reflects the system's fluctuation characteristics without considering human intervention. For load fluctuation demand categorized by carbon intensity level, the maximum value between future forecast deviation and current forecast error is used. In the formula, For load forecasting errors, load flexibility requirements are determined by two uncertainties: future... The difference between the time-based load forecast and the current measured load (reflecting future load fluctuations); the forecast error of the current load (reflecting real-time load fluctuations); taking the maximum value is to account for the worst-case scenario and ensure the conservatism and reliability of the demand assessment; multiplied by the carbon intensity weight. The fluctuations in high carbon loads need to be mitigated first, which is in line with the carbon emission reduction policy.

[0042] Fluctuating demand for renewable energy is the absolute deviation between predicted and actual power output. The flexibility requirement of new energy sources is the absolute difference between the predicted future output and the current measured output. Since the intermittency and randomness of new energy output are the main sources of system uncertainty, the absolute value reflects the amplitude of fluctuations. The unweighted value is because new energy sources do not have carbon intensity distinctions, and fluctuations need to be smoothed out.

[0043] The total original demand of the system is the sum of the load fluctuation demand of each node at each carbon level minus the sum of the renewable energy fluctuation demand of each node. Its probability distribution is described by convolution operation: Probability distribution: ( Convolution (or subtraction) is applicable to solving the probability distribution of the difference between two independent random variables. Since load fluctuations and new energy fluctuations are two independent random variables, according to probability theory, the probability distribution of the difference between two independent random variables can be solved by convolution, which is the inverse operation of convolution.

[0044] The revised flexibility demand deducts the impact of demand-side response adjustment and renewable energy curtailment, ensuring that the demand assessment aligns with actual dispatch scenarios. The load revision demand by carbon intensity level is as follows: Adjusting demand-side response resources reduces actual load fluctuations. Therefore, the original load demand is subtracted from the combined adjustment amount of reduction-type and transfer-type demand-side responses, multiplied by the carbon intensity weight. This ensures that the carbon weighting is consistent with the original demand, and the revised demand better reflects the actual load fluctuations that the system needs to smooth out.

[0045] New energy demand adjustment deducts from power rationing: In the formula, The amount of power curtailed for renewable energy is the difference between the measured output and the dispatched output. Power curtailment for renewable energy reduces the fluctuation of its actual output. Therefore, the original renewable energy demand is subtracted from the renewable energy curtailment amount. The curtailment amount is the difference between the measured output and the dispatched output, which is the classic definition of renewable energy power curtailment and quantifies the magnitude of the actual curtailment. The corrected demand reflects the fluctuation of the actual grid-connected output of renewable energy and is closer to the actual dispatch.

[0046] The probability distribution of the total system correction demand is shifted along the horizontal axis to adjust the total power of the demand-side response: Probability distribution: ( (Total power adjusted by DR). The summation logic for total corrected demand is consistent with the original total demand, which is the corrected load fluctuation demand minus the corrected renewable energy fluctuation demand; the probability distribution is the original demand distribution shifted along the horizontal axis. Due to demand-side response adjustment of total power Since is a deterministic variable, according to probability theory, the distribution of a random variable plus a deterministic variable is a shift of the original distribution; in this case, is . This accurately reflects the impact of demand-side response adjustments on demand distribution.

[0047] The basic margin is the difference between the total supply and the total adjustment demand of the system. Physical meaning: This indicates that the supply is sufficient. This indicates a flexibility deficit; a basic margin greater than zero indicates sufficient supply, while a margin less than zero indicates a flexibility deficit.

[0048] After calculating the independent margins of various resources, operational constraint corrections, grid transmission constraint corrections, and carbon constraint corrections are performed sequentially. Operational constraint corrections are based on the upper and lower limits of energy storage state of charge, unit start-up and shutdown states, minimum balancing period for transferred demand-side response, and user production process constraints, eliminating margins exceeding equipment capacity and process requirements. Grid transmission constraint corrections are based on the node line distribution factor H_nl and the upper limit of line power. By calculating the impact of node power changes on line power, the available resources for various types of resources are adjusted. The carbon constraint correction is based on the optimal carbon emission target. It involves multiplying the margins of various resources after grid transmission constraint correction by their corresponding carbon intensity and summing the results to obtain the optimal carbon emission solution. The core of optimizing carbon emissions lies in prioritizing the use of regulating resources with low carbon intensity; therefore, total carbon emissions are the regulating power of each resource. Multiply by its carbon strength The summation is used to minimize carbon emissions by minimizing this summation value; The linear summation form ensures the simplicity of the model solution and the carbon intensity For the inherent parameters of each resource (such as photovoltaic) Gas turbine unit ), accurate quantification.

[0049] Comparison of optimal carbon emission solution and time-limited carbon emission cap (Annual carbon quota for the park) The annual total carbon emission constraint is assessed over a period of one year (8760 hours). Therefore, the carbon emission limit for this period is the average annual carbon allowance distributed per hour, ensuring a uniform constraint of the carbon allowance over time (aligning with the practical engineering requirements of carbon allowance management). If the limit is exceeded, the excess amount will be deducted from the gas turbine unit's allowance. Carbon constraint margin correction is a combination of basic margin and correction for carbon emission exceedances, and it falls into two categories: When the optimal solution for carbon emissions Time-limited carbon emissions At that time, the carbon constraint has no impact, and the margin remains unchanged at the basic margin. When carbon emissions exceed limits, the use of high-carbon resources (gas turbine units, GT) should be reduced. The power reduction is calculated as the excess carbon emissions / GT carbon intensity. Because the carbon emissions per unit GT of power are Therefore, this value represents the GT output that needs to be reduced to meet carbon emission constraints. The system margin is reduced by this value simultaneously, realizing the quantitative linkage between carbon emission constraints and flexibility margin.

[0050] The probability distribution characteristics of the carbon constraint margin are described at two levels: conditional probability and total probability. Given the system state... : From the basic margin formula Deformed In a given system state Under the conditions, and Since the variables are independent random variables, according to probability theory, the conditional probability distribution of the sum of two independent random variables is solved by convolution operation. Therefore, the conditional probability distribution of the margin is solved by convolution operation (the inverse operation of convolution), which is consistent with the convolution logic of the original demand distribution.

[0051] Integrating the total probability distribution over the joint probability distribution of the system's state vector: System state vector For continuous random variables (containing multiple random parameters such as load, new energy sources, and energy storage), according to the law of total probability, the random variable... The total probability distribution is the conditional probability distribution. Regarding system status joint probability distribution The integral, the integration interval is All possible values ​​(-∞ to +∞) are used to achieve a comprehensive evaluation of the margin probability of all system states.

[0052] After being adjusted for triple constraints, the various resource margins are aggregated separately according to the upward and downward adjustment dimensions to obtain the upward and downward baseline margins at a 15-minute baseline scale. This is then extended to multiple time scales using time scale adjustment coefficients. ,constraint: , , , The flexibility margin exhibits a time-scale effect: the larger the assessment timescale τ, the more adjustment resources the system can utilize (such as thermal storage and long-cycle DR resources available over long timescales), and the greater the margin; therefore, 15 minutes is used as the baseline timescale ( ), its correction factor The margins at other time scales are the baseline margin multiplied by the correction factor for fitting the engineering data. (1h=1.8, 24h=3.2), this coefficient was obtained by fitting a large amount of historical data from park operations, closely matching the scale characteristics of actual scheduling; at the same time, it is required that τ be converted to the same as... A consistent time unit ensures computational consistency. The final output covers the carbon constraint margin across multiple timescales, including 15 minutes, 1 hour, and 24 hours.

[0053] S03: Based on the comprehensive carbon constraint margin across multiple time scales, calculate the probability of flexibility deficit, the rate of satisfaction of adjustment intentions, the carbon emission reduction benefits, and the proportion of low-carbon supply, and generate a resource adjustment capacity assessment result that includes adjustment capacity level and decision recommendations.

[0054] In a preferred embodiment of this invention, the step of calculating the flexibility deficit probability, adjustment willingness satisfaction rate, carbon emission reduction benefits, and low-carbon supply ratio based on the multi-timescale carbon constraint comprehensive margin, and generating a resource adjustment capacity assessment result including adjustment capacity level and decision recommendations, specifically involves: Based on the comprehensive carbon constraint margin across multiple time scales, quantitative assessment indicators are calculated and assessment results are generated.

[0055] The probability of flexibility deficit is obtained by integrating the probability density function of the carbon constraint margin over the interval from negative infinity to zero: The definition of flexibility deficit LOFP represents the probability of the event occurring; for continuous random variables... The interval probability is the integral of the probability density function over that interval, therefore the integration interval is from -∞ to 0. It directly quantifies the probability of a system experiencing a flexibility deficit; the closer the value is to 0, the stronger the system's adjustment capability. The expected time of the flexibility deficit extends the deficit probability to the evaluation period. The expected time for the shortfall is calculated as: shortfall probability × total assessment period × time scale conversion factor, where the total assessment period is... Time scale conversion factor The evaluation step size of different time scales is converted into a base time unit (15 min) to ensure that the deficit time of different time scales is comparable. The smaller the value, the longer the system runs without deficit.

[0056] The expected flexibility deficit is the weighted average of the deficit amounts: The expected deficit is the weighted average of the deficits, with the weights being the probability density function fMCO2m. Since MCO2 < 0 indicates a deficit, its absolute value |m| represents the actual amount of the deficit. The integration interval is from -∞ to 0. The smaller the value, the smaller the average deficit of the system and the stronger the adjustment capability.

[0057] The rate of satisfaction of adjustment intentions is the ratio of the actual adjustment amount to the theoretical maximum adjustment amount: UR is an efficiency indicator, and its core is the ratio of actual adjustment amount to the theoretical maximum adjustment amount based on user willingness. The molecule represents the absolute value of the amount of DR regulation actually participated in by the user (the amount of DR regulation reduced by the user plus the amount of DR regulation transferred by the user), reflecting the user's actual regulatory behavior. The denominator is the theoretical maximum adjustable amount based on user willingness (reduction-type maximum adjustable amount + transfer-type maximum adjustable amount), reflecting the maximum adjustment capacity that users are willing to participate in; The closer the ratio is to 1, the higher the degree to which the user's adjustment intentions are satisfied, and the more realistic the model's consideration of the user's intentions is.

[0058] The carbon emission reduction benefit is the sum of the differences between carbon emissions from no optimal scheduling and optimal scheduling: The carbon emissions per unit time for projects that lack flexible scheduling and operate according to the park's traditional fixed operating mode. The carbon emission per unit time under the optimal carbon emission target of this application is represented by the value of t. The difference between t and t represents the carbon emission reduction for a single time period. Summing these values ​​over all time periods within the assessment period yields the overall carbon emission reduction benefit of the park. This formula is the classic linear summation form for calculating carbon emission reductions, with units of t / t. This directly quantifies the actual effect of the model in carbon emission reduction. The larger the value, the stronger the carbon emission reduction capacity. It can be directly linked to the carbon quota compliance work of the park and quantifies the matching relationship between the actual emission reduction and the quota gap.

[0059] The low-carbon supply share refers to the proportion of low-carbon resource regulation capacity in the total regulation supply. The core of low-carbon supply ratio is to quantify the contribution of low-carbon / zero-carbon resources in the flexible adjustment of industrial parks.

[0060] The numerator is the sum of the regulation power of all low-carbon / zero-carbon regulation resources during the evaluation period. Among them, photovoltaic / wind power (VRES) has a carbon intensity of 0, and electrochemical energy storage (ESS) and thermal energy storage equipment (HS) are only energy storage and conversion, with no additional carbon emissions. They are all low-carbon regulation resources, and their regulation power directly reflects the actual utilization of low-carbon resources. The denominator is the sum of the total regulation supply power of the system during the evaluation period, which serves as the quantitative benchmark; The ratio of the two is a dimensionless indicator, ranging from 0 to 1. The closer the ratio is to 1, the more the park's flexible adjustments rely on low-carbon resources, the lower the proportion of high-carbon resources (such as gas turbine units) used, and the better the carbon emission reduction effect. This formula achieves quantification through linear summation and ratio calculation, and the results are intuitive and can be directly used as an assessment indicator of the park's low-carbon development level.

[0061] Supplementary indicators include flexibility source deficit, expected load shedding, and expected renewable energy curtailment. The flexibility source deficit is the minimum flexibility supply margin under a specified target reliability. The flexibility margin is a core supplementary indicator for park resource allocation planning. Its core is to solve for the minimum flexibility supply margin f that the system can guarantee under a specified target reliability α (such as 95% or 99%, which is determined by the park's scheduling reliability requirements).

[0062] symbol In mathematics, the infimum represents the condition that satisfies the probability constraint. The minimum value among all f values ​​is selected as the infimum to avoid over-allocation of resources: it ensures that the system's flexibility supply can achieve the target reliability, while minimizing the investment cost of new flexibility resources, which is in line with the economic reality of the park project. Probability constraints This indicates that the probability that the system's carbon constraint margin is no less than f is no less than the target reliability α. In other words, at a reliability level of α, the system can provide at least f of flexibility supply margin. This formula is based on interval probability calculation in probability theory, and the result is a specific power value (MW). It can directly quantify the amount of additional flexibility resources required by the park to achieve the target reliability, providing a clear quantitative basis for the planning of new installations of low-carbon resources such as photovoltaics and energy storage.

[0063] The expected load shedding is obtained by multiplying the upward adjustment of the expected deficit by the assessment period: The expected load shedding is a classic indicator for power system reliability assessment. It is adapted to the assessment needs of power supply reliability in the park and addresses the power supply insufficiency problem caused by the flexibility deficit in the core quantitative system.

[0064] in The upward adjustment of the deficit expectation, i.e. the weighted average of the deficit in the upward adjustment direction of the carbon constraint margin, reflects the load deficit that the system may experience per unit time due to insufficient upward adjustment flexibility supply (such as insufficient output of gas turbine units and limited energy storage discharge capacity). Multiply by the total evaluation period (8760h) yields the expected total load loss of the system within the assessment period, in MWh. This formula is a linear product calculation, and the result is intuitive. The smaller the value, the more sufficient the system's flexible supply is and the higher the power supply reliability. It can be used as a quantitative basis for optimizing the priority of resource allocation in dynamic scheduling of the park: when the EENS value is too high, it is necessary to prioritize the use of flexible resources with large allocation margins and fast response speeds (such as energy storage discharge) to reduce the risk of load loss.

[0065] The expected amount of renewable energy curtailment is obtained by multiplying the expected deficit by the assessment period: This indicator is a core supplementary indicator for assessing the level of renewable energy consumption in industrial parks, addressing the issue of renewable energy curtailment caused by the lack of flexibility in the core quantitative system.

[0066] in The downward adjustment of the deficit expectation, i.e. the weighted average of the deficit in the downward adjustment direction of the carbon constraint margin, reflects the amount of power curtailment required per unit time because the system cannot absorb the output of new energy due to insufficient flexible supply (such as limited energy storage charging capacity and difficulty in load transfer). Multiply by the total evaluation period The expected total renewable energy curtailment volume in the park during the assessment period is obtained, in MWh. This formula is a linear product calculation. The smaller the value, the more sufficient the system's flexibility in supplying renewable energy and the higher the level of renewable energy absorption. It can be used as a quantitative basis for optimizing renewable energy absorption in park scheduling. When this indicator value is too high, it is necessary to prioritize the use of low-carbon resources with large curtailment margins (such as energy storage charging and increased transferable loads) to improve renewable energy absorption capacity and reduce curtailment losses.

[0067] Based on the aforementioned quantitative indicators, a determination of the adjustment capacity level and the generation of decision recommendations are conducted. The probability of flexibility deficit and the proportion of low-carbon supply are compared with corresponding preset thresholds. The adjustment capacity level is obtained based on the comparison results and preset full-scale margin deficit situations. The preset thresholds include a first preset value, a second preset value, and a third preset value corresponding to the probability of flexibility deficit; and a fourth preset value, a fifth preset value, and a sixth preset value corresponding to the proportion of low-carbon supply. The first preset value is less than the second preset value, the second preset value is less than the third preset value, the fourth preset value is less than the fifth preset value, and the fifth preset value is less than the sixth preset value.

[0068] The specific judgment rules are as follows: When the regulation capacity meets the following conditions: the full-scale margin is positive and there is no deficit, the probability of flexibility deficit is less than the first preset value, and the proportion of low-carbon supply is not less than the fourth preset value, the regulation capacity level is judged to be excellent. When the regulation capacity meets the requirements of a single scale having a deficit, the probability of a flexibility deficit being between the first and second preset values, and the proportion of low-carbon supply being between the fourth and fifth preset values, the regulation capacity level is judged to be good. When the regulation capacity meets the two-scale deficit, the probability of the flexibility deficit is between the second and third preset values, and the proportion of low-carbon supply is between the fifth and sixth preset values, the regulation capacity level is determined to be general. When the regulation capacity meets the full-scale deficit, or the probability of a flexibility deficit is greater than the third preset value, or the proportion of low-carbon supply is less than the sixth preset value, the regulation capacity level is determined to be insufficient.

[0069] In this embodiment, the aforementioned preset values ​​can be set based on the historical operation data and scheduling experience of specific parks. For example: the first preset value can be 3%, the second preset value can be 8%, the third preset value can be 15%, the fourth preset value can be 80%, the fifth preset value can be 60%, and the sixth preset value can be 40%. Correspondingly, the quantitative standards for the full-scale margin deficit are: no deficit (positive margin), slight deficit (single-scale deficit less than 5MW), general deficit (two-scale deficit of 5-10MW), and full-scale deficit (deficit greater than 10MW).

[0070] Based on the numerical characteristics of regulation capacity level and flexibility deficit probability, regulation willingness satisfaction rate (UR), carbon emission reduction benefit (CER), and low-carbon supply share (LCSR), targeted decision-making recommendations are generated: Resource allocation recommendations are based on the flexibility source deficit (FDS) indicator, combined with the elasticity and sustainability characteristics of margins at different time scales, to clarify the types of low-carbon resources that need to be added (such as electrochemical energy storage, thermal energy storage equipment, photovoltaic / wind power) and specific installed capacity, adapting to long-term resource planning needs; Subsidy policy adjustment recommendations are based on the value of regulation willingness satisfaction rate (UR), dynamically optimizing the subsidy standards for different types of demand-side response (e.g., when UR is below 60%, the subsidy standard for reduction-type demand-side response is increased to near the subsidy threshold; when UR is below 40%, the subsidy standard for transfer-type demand-side response and the policy for adapting to production processes are simultaneously optimized), to improve the enthusiasm of users to participate in response and the actual fulfillment rate; Scheduling recommendations are based on multiple time scales. The carbon constraint margin, combined with the expected load shedding (EENS) and expected renewable energy curtailment (E_VRES, Curtailment) indicators, prioritizes resource allocation strategies based on response speed and carbon benefit (e.g., prioritizing energy storage discharge and high-willingness demand-side response when there is a 15-minute deficit, and prioritizing the coordinated scheduling of gas turbine units and thermal storage equipment on a 1-hour scale), to ensure power supply and demand balance and low-carbon orientation under different scenarios; carbon quota compliance recommendations are based on the carbon emission reduction benefit (CER) indicator, quantifying the matching relationship between the actual carbon emission reduction within the assessment period and the park's annual carbon quota, clarifying the scale of carbon quota gaps or surpluses, and assisting in the formulation of carbon asset trading strategies and high-carbon resource allocation and management schemes to ensure carbon quota compliance; the above resource allocation recommendations, subsidy policy adjustment recommendations, scheduling recommendations, or carbon quota compliance recommendations together constitute decision-making recommendations, and the specific quantitative standards are shown in Table 1 below: Table 1 Evaluation Criteria Based on the numerical characteristics of adjustment capacity level and flexibility deficit probability, adjustment willingness satisfaction rate (UR), carbon emission reduction benefit (CER), and low-carbon supply share (LCSR), targeted decision-making recommendations are generated: Resource allocation recommendations are based on the flexibility source deficit (FDS) indicator, combined with the elasticity and sustainability characteristics of margins at different time scales, to clarify the types of low-carbon resources that need to be added (such as electrochemical energy storage, thermal energy storage equipment, photovoltaic / wind power) and specific installed capacity, adapting to long-term resource planning needs; Subsidy policy adjustment recommendations are based on the adjustment willingness satisfaction rate (UR) value, dynamically optimizing the subsidy standards for different types of demand-side response (e.g., when UR < 60%, increase the subsidy standard for reduction-type demand-side response to near the subsidy threshold; when UR < 40%, simultaneously optimize the subsidy standard for transfer-type demand-side response and the policy for adapting to production processes), improving the enthusiasm of users to participate in the response and the actual fulfillment rate; Scheduling recommendations are based on... Based on a comprehensive carbon constraint margin across multiple time scales, and combined with expected load shedding (EENS) and expected renewable energy curtailment (E_VRES, Curtailment) indicators, resource allocation strategies are prioritized according to response speed and carbon benefit (e.g., prioritizing energy storage discharge and demand-side response with high willingness to reduce emissions when there is a 15-minute deficit, and prioritizing the coordinated scheduling of gas turbine units and thermal storage equipment when there is a 1-hour deficit). This ensures a balance between power supply and demand and a low-carbon orientation in different scenarios. Carbon quota compliance recommendations are based on carbon emission reduction benefit (CER) indicators, quantifying the matching relationship between actual carbon emission reductions and the park's annual carbon quota within the assessment period, clarifying the scale of carbon quota gaps or surpluses, and assisting in the formulation of carbon asset trading strategies and high-carbon resource allocation and management schemes to ensure compliance with carbon quotas. The above resource allocation recommendations, subsidy policy adjustment recommendations, scheduling recommendations, or carbon quota compliance recommendations together constitute decision-making recommendations.

[0071] In summary, this application achieves comprehensive data acquisition from integrated energy-load-storage industrial and commercial parks. Real-time operational data includes multi-type output and carbon intensity on the source side, load data categorized by carbon intensity levels and demand-side response, complete operating parameters of energy storage equipment, and core parameters reflecting user adjustment intentions (production urgency, subsidy standards, etc.). Constraint data covers key limitations such as carbon quotas, grid transmission, and operating cycles, thus forming a comprehensive data set encompassing all resources, constraints, and user intentions. This comprehensive data system not only addresses the shortcomings of existing technologies, such as incomplete data dimensions and missing key parameters, but also provides a foundation for achieving optimal carbon emission targets through carbon intensity load data and carbon intensity data for various resources. It establishes a correlation between equipment physical characteristics and user behavior factors through user adjustment willingness parameters, and ensures the adaptability of the assessment to actual operating scenarios through constraint data such as grid transmission and equipment operation. Based on this comprehensive and accurate data input, the assessment model can accurately calculate the comprehensive adjustment willingness coefficient, reasonably correct the total resource adjustability margin, and generate a comprehensive carbon constraint margin across multiple time scales. This ensures the accuracy of quantitative indicators such as flexibility deficit probability and carbon emission reduction benefits, as well as the relevance of adjustment capacity levels and decision recommendations. Ultimately, it solves the problems of existing assessment methods, such as the disconnect between results and reality, poor carbon constraint adaptability, and weak multi-scenario decision support caused by insufficient data support. This significantly improves the reliability, comprehensiveness, and practical value of the park's flexible adjustment capacity assessment, providing solid data and technical support for the park's energy-efficient operation and low-carbon transformation.

[0072] Example 2 Please refer to Figure 2 This is an assessment device for the park resource adjustment capability provided in the embodiments of this application.

[0073] In this embodiment, the assessment device for the park's resource adjustment capability includes an acquisition module 10, an assessment module 20, and a calculation module 30.

[0074] The acquisition module 10 is used to acquire real-time operation data and constraint data of the integrated energy source, load and storage industrial and commercial park; the real-time operation data includes source-side output data, load-side load data, energy storage status data, actual demand-side response adjustment data and user adjustment intention parameters; The evaluation module 20 is used to input the real-time operating data and constraint data into a preset evaluation model, so that the evaluation model calculates the comprehensive adjustment willingness coefficient based on the user adjustment willingness parameter in the real-time operating data, calculates the adjustment margin of the demand-side response based on the comprehensive adjustment willingness coefficient, and performs constraint correction and aggregation on the adjustment margin of the source-side, load-side, energy storage-side and demand-side responses based on the preset optimal carbon emission target and constraint data, and outputs the comprehensive carbon constraint margin of multiple time scales. The evaluation model is constructed based on the preset power system dispatch theory, the preset source-load-storage integrated operation characteristics and the preset carbon management specifications. The calculation module 30 is used to calculate the probability of flexibility deficit, the rate of satisfaction of adjustment intention, the carbon emission reduction benefits and the proportion of low-carbon supply based on the comprehensive carbon constraint margin of the multi-time scale, and generate a resource adjustment capacity assessment result including adjustment capacity level and decision recommendations.

[0075] For ease of description and brevity, the embodiments of the device of the present invention include all the implementation methods in the above-described embodiments of the evaluation method for the park resource adjustment capability, and will not be repeated here.

[0076] Example 3 This application provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to execute the aforementioned method for evaluating the ability to adjust park resources. The method for assessing the resource adjustment capability of a park, if implemented as a software functional unit and used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0077] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for assessing the resource regulation capacity of a park, characterized in that, include: Obtain real-time operational and constraint data for the integrated industrial and commercial park with source, load, and storage functions; The real-time operating data includes source-side output data, load-side load data, energy storage status data, actual demand-side response adjustment data, and user adjustment intention parameters. The real-time operating data and constraint data are input into a preset evaluation model. The evaluation model calculates a comprehensive adjustment willingness coefficient based on the user adjustment willingness parameters in the real-time operating data. Then, it calculates the adjustment margin of the demand-side response based on the comprehensive adjustment willingness coefficient. Based on the preset optimal carbon emission target and constraint data, it performs constraint correction and aggregation on the adjustment margins of the source-side, load-side, energy storage-side, and demand-side responses, and outputs a comprehensive carbon constraint margin at multiple time scales. The evaluation model is constructed based on preset power system dispatch theory, preset source-load-storage integrated operation characteristics, and preset carbon management standards. Based on the comprehensive carbon constraint margin across multiple time scales, the probability of flexibility deficit, the rate of satisfaction of adjustment intentions, the carbon emission reduction benefits, and the proportion of low-carbon supply are calculated to generate a resource adjustment capacity assessment result that includes adjustment capacity level and decision recommendations.

2. The method for assessing the resource regulation capacity of a park according to claim 1, characterized in that, The source-side power output data includes measured photovoltaic power output, predicted photovoltaic power output, photovoltaic dispatched power output, measured wind power output, predicted wind power output, wind power dispatched power output, maximum output of gas turbine units, minimum output of gas turbine units, upward ramp rate of gas turbine units, downward ramp rate of gas turbine units, start-up and shutdown status of gas turbine units, and carbon intensity of various source-side resources. The load-side data includes total load forecast power, total load measured power, load measured power by carbon intensity level, adjustable capacity of reduced demand-side response, and adjustable capacity of transferred demand-side response. The energy storage status data includes the maximum charging power of electrochemical energy storage, the maximum discharging power of electrochemical energy storage, the maximum capacity of electrochemical energy storage, the current state of charge of electrochemical energy storage, the upper limit of the state of charge of electrochemical energy storage, the upper limit of the state of charge of electrochemical energy storage, the charging efficiency of electrochemical energy storage, the discharging efficiency of electrochemical energy storage, the current capacity ratio of thermal storage equipment, the maximum capacity of thermal storage equipment, and the thermoelectric conversion efficiency of thermal storage equipment. The actual adjustment data of the demand-side response includes the actual adjustment power of the reduction-type demand-side response and the actual adjustment power of the transfer-type demand-side response. The user adjustment willingness parameters include production urgency, current subsidy standard for reduced demand-side response, subsidy threshold for reduced demand-side response, and feasibility coefficient for transferred demand-side response; The constraint data includes the park's annual carbon quota, time-period carbon emission cap, line power cap, node line distribution factor, minimum balance period for transfer-type demand-side response, assessment period, preset baseline carbon emissions without optimization scheduling, and remaining carbon budget.

3. The method for assessing the resource regulation capacity of a park according to claim 2, characterized in that, The evaluation model calculates a comprehensive adjustment intention coefficient based on the user adjustment intention parameters in the real-time operational data, specifically as follows: Based on production urgency, the current subsidy standard for reduced demand-side response, and the subsidy threshold for reduced demand-side response, calculate the adjustment willingness coefficient for reduced demand-side response; The willingness coefficient for adjusting the demand-side response under the reduction model and the feasibility coefficient for the transfer model are used to calculate the willingness coefficient for adjusting the demand-side response under the transfer model. Calculate the sum of the adjustable capacity of demand-side response, including the adjustable capacity of demand-side response with reduction and the adjustable capacity of demand-side response with transfer. The total adjustment intention weighted capacity is obtained by multiplying the adjustable capacity of each type of demand-side response reduction by the corresponding adjustment intention coefficient of the demand-side response reduction, and then summing the sum with the adjustable capacity of each type of demand-side response transfer by the corresponding adjustment intention coefficient of the demand-side response transfer. Divide the total adjustment intention weighted capacity by the sum of the demand-side response adjustable capacity to obtain the comprehensive adjustment intention coefficient.

4. The method for assessing the resource regulation capacity of a park according to claim 1, characterized in that, The process involves adjusting and aggregating the adjustable margins of the source-side, load-side, energy storage-side, and demand-side responses based on preset optimal carbon emission targets and constraint data, outputting a comprehensive carbon constraint margin across multiple time scales. Specifically: Calculate the upward and downward adjustment margins for gas turbine units, the downward adjustment margins for renewable energy, the upward and downward adjustment margins for electrochemical energy storage, the upward and downward adjustment margins for thermal storage equipment, the downward adjustment margins for thermal storage equipment, the reduction-type demand-side response adjustment power, and the transfer-type demand-side response adjustment power, respectively, as independent margins for each type of resource. Based on the current state of charge of electrochemical energy storage, the upper limit of state of charge of electrochemical energy storage, the upper limit of state of charge of electrochemical energy storage, and the start-up and shutdown status of gas turbine units in real-time operation data, and based on the minimum balance period of transfer-type demand-side response and user production process constraints in the constraint data, the independent margins of various resources are adjusted by operation constraints, and the margins that exceed the equipment operating capacity and process requirements are eliminated to obtain the margins of various resources after operation constraint adjustment. Based on the node line distribution factor and line power limit in the constraint data, combined with the margin of various resources after operational constraint correction, the available amount of various resources is calculated and corrected through the influence of node line power, and the margin of various resources after grid transmission constraint correction is obtained. Based on the preset optimal carbon emission target, and combined with the carbon intensity of various resources in real-time operation data, the carbon emission limit for each time period in the constraint data, and the remaining carbon budget, the margin of various resources after grid transmission constraint correction is carbon-constrained to obtain the margin of various resources after carbon constraint correction. The carbon-constrained margins of various resources are aggregated according to the upward and downward adjustment dimensions to obtain the upward and downward baseline margins at the fifteen-minute baseline scale. Based on the 15-minute baseline scale, the upward and downward baseline margins are combined with preset time scale correction coefficients to output a comprehensive carbon constraint margin across multiple time scales.

5. The method for assessing the resource regulation capacity of a park according to claim 4, characterized in that, Based on the preset optimal carbon emission target, and combined with the carbon intensity of various resources in real-time operational data, the time-period carbon emission ceiling and remaining carbon budget in the constraint data, the carbon constraint correction is applied to the margin of various resources after grid transmission constraint correction. Specifically: The margins of various resources after correction for grid transmission constraints are multiplied by the carbon intensity of the corresponding resource in the real-time operating data, and then all the product results are summed to obtain the optimal solution for carbon emissions. By comparing the optimal carbon emission solution with the time-limit carbon emission cap in the constraint data, it can be determined whether the optimal carbon emission solution exceeds the time-limit carbon emission cap. If the optimal carbon emission solution does not exceed the carbon emission limit for the time period, then the margins of various resources after grid transmission constraints will be used as the margins after carbon constraints. If the optimal carbon emission solution exceeds the carbon emission limit for a given period, the difference between the optimal carbon emission solution and the carbon emission limit for that period is calculated. The difference is then divided by the carbon intensity of the gas turbine unit in the real-time operating data to obtain the amount of high-carbon resource usage that needs to be reduced. The amount of high-carbon resources that need to be reduced is deducted from the upward or downward adjustment margin of gas turbine units after grid transmission constraint correction. The margins of other resources after grid transmission constraint correction remain unchanged, thus obtaining the carbon constraint-corrected margins of various resources.

6. The method for assessing the resource regulation capacity of a park according to claim 4, characterized in that, The upward and downward adjustment of the baseline margin based on the 15-minute baseline scale, combined with a preset time scale correction coefficient, outputs a comprehensive carbon constraint margin across multiple time scales, specifically as follows: The upward and downward baseline margins of the 15-minute baseline scale are used as the basic margins for multi-timescale expansion. Multiply the upward adjustment baseline margin in the baseline margin by the correction coefficient corresponding to the preset target time scale to obtain the overall upward adjustment carbon constraint margin at the target time scale. Multiply the reduced baseline margin in the baseline margin by the correction coefficient corresponding to the preset target time scale to obtain the reduced carbon constraint comprehensive margin at the target time scale. By classifying and integrating the overall carbon constraint margins at each target time scale according to time scale, a multi-time scale overall carbon constraint margin is obtained.

7. The method for assessing the resource regulation capacity of a park according to claim 5, characterized in that, Based on the comprehensive carbon constraint margin across multiple time scales, the system calculates the probability of flexibility deficit, the rate of satisfaction of adjustment intentions, the carbon emission reduction benefits, and the proportion of low-carbon supply, generating a resource adjustment capacity assessment result that includes adjustment capacity levels and decision recommendations. Specifically: Integrating the probability density function of the carbon constraint comprehensive margin across the multiple time scales yields the probability of flexibility deficit. The sum of the absolute values ​​of the actual adjustment power of the reduced demand-side response and the actual adjustment power of the transferred demand-side response in the real-time operating data during the evaluation period is divided by the sum of the product of the adjustable capacity of the reduced demand-side response and the preset adjustment willingness coefficient of the reduced demand-side response in the real-time operating data during the evaluation period, and the product of the adjustable capacity of the transferred demand-side response and the preset adjustment willingness coefficient of the transferred demand-side response in the real-time operating data, to obtain the adjustment willingness satisfaction rate. The carbon emission reduction benefits are obtained by summing the difference between the preset unoptimized scheduling baseline carbon emissions and the optimal carbon emission solution in the constraint data during the evaluation period. The low-carbon supply ratio is obtained by dividing the sum of the renewable energy down-adjustment margin, electrochemical energy storage up-adjustment margin, electrochemical energy storage down-adjustment margin, thermal energy storage equipment up-adjustment margin, and thermal energy storage equipment down-adjustment margin among the various resource margins after carbon constraint correction during the assessment period by the sum of the various resource margins after carbon constraint correction during the assessment period. Based on the aforementioned flexibility deficit probability, adjustment willingness satisfaction rate, carbon emission reduction benefits, and low-carbon supply ratio, a resource adjustment capacity assessment result is generated, which includes adjustment capacity level and decision-making recommendations.

8. The method for assessing the resource regulation capacity of a park according to claim 7, characterized in that, Based on the aforementioned flexibility deficit probability, adjustment willingness satisfaction rate, carbon emission reduction benefits, and low-carbon supply ratio, a resource adjustment capacity assessment result is generated, including adjustment capacity level and decision recommendations. Specifically: The probability of flexibility deficit and the proportion of low-carbon supply are compared with the corresponding preset thresholds, and the adjustment capacity level is obtained based on the comparison results and the preset full-scale margin deficit. The preset thresholds include a first preset value, a second preset value, and a third preset value corresponding to the probability of flexibility shortage, and a fourth preset value, a fifth preset value, and a sixth preset value corresponding to the proportion of low-carbon supply. The first preset value is less than the second preset value, the second preset value is less than the third preset value; the fourth preset value is less than the fifth preset value, and the fifth preset value is less than the sixth preset value; When the regulation capacity meets the following conditions: the full-scale margin is positive and there is no deficit, the probability of flexibility deficit is less than the first preset value, and the proportion of low-carbon supply is not less than the fourth preset value, the regulation capacity level is judged to be excellent. When the regulation capacity meets the requirements of a single scale having a deficit, the probability of a flexibility deficit being between the first and second preset values, and the proportion of low-carbon supply being between the fourth and fifth preset values, the regulation capacity level is judged to be good. When the regulation capacity meets the two-scale deficit, the probability of the flexibility deficit is between the second and third preset values, and the proportion of low-carbon supply is between the fifth and sixth preset values, the regulation capacity level is determined to be general. When the regulation capacity meets the full-scale deficit, or the probability of the flexibility deficit is greater than the third preset value, or the proportion of low-carbon supply is less than the sixth preset value, the regulation capacity level is determined to be insufficient. Based on the numerical characteristics of adjustment capacity level and flexibility deficit probability, adjustment willingness satisfaction rate, carbon emission reduction benefits, and low-carbon supply ratio, resource allocation suggestions, subsidy policy adjustment suggestions, scheduling suggestions, or carbon quota compliance suggestions are generated as decision-making suggestions. By combining the adjustment capacity level with decision-making recommendations, a resource adjustment capacity assessment result is generated.

9. A device for assessing the resource regulation capacity of a park, characterized in that, include: The acquisition module is used to acquire real-time operational and constraint data of the integrated source-load-storage industrial and commercial park. The real-time operating data includes source-side output data, load-side load data, energy storage status data, actual demand-side response adjustment data, and user adjustment intention parameters. The evaluation module is used to input the real-time operating data and constraint data into a preset evaluation model. The evaluation model calculates a comprehensive adjustment willingness coefficient based on the user adjustment willingness parameters in the real-time operating data, calculates the adjustment margin of the demand-side response based on the comprehensive adjustment willingness coefficient, and performs constraint correction and aggregation on the adjustment margin of the source-side, load-side, energy storage-side, and demand-side responses based on preset optimal carbon emission targets and constraint data. The module outputs a comprehensive carbon constraint margin across multiple time scales. The evaluation model is constructed based on preset power system dispatch theory, preset integrated source-load-storage operation characteristics, and preset carbon management standards. The calculation module is used to calculate the probability of flexibility deficit, the rate of satisfaction of adjustment intention, the carbon emission reduction benefits and the proportion of low-carbon supply based on the comprehensive carbon constraint margin of the multi-time scale, and generate a resource adjustment capacity assessment result that includes adjustment capacity level and decision recommendations.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the assessment method for the park resource adjustment capability as described in any one of claims 1 to 8.

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