Resource regulation capability assessment method, device, equipment, medium and product

Abstract

The invention relates to the technical field of power dispatching, and discloses a resource regulation capability assessment method, device and equipment, a medium and a product, and the method comprises the steps: obtaining a resource assessment index and a feature tag of a to-be-assessed industrial and commercial load; calculating the similarity between the industrial and commercial load in the weight knowledge base and the industrial and commercial load to be evaluated; if the quantity of the industrial and commercial loads with the similarity meeting the condition is smaller than a preset quantity, determining a weight correction strategy in a fuzzy rule base according to the expert experience confidence and the data feature adaptation degree of the resource evaluation index; correcting the initial weight of the resource evaluation index according to the strategy to obtain an intermediate weight set; and calculating a resource adjustment capability evaluation value in combination with the resource evaluation index and the intermediate weight set. According to the method, the resource evaluation indexes are firstly obtained to determine the initial weight, then the weight is corrected through the fuzzy rule base, the evaluation value used for evaluating the industrial and commercial load regulation capability is calculated, and the problem that new users entering the market cannot predict self-regulation capability and market value positioning is solved.

Description

Technical Field

[0001] This invention relates to the field of power dispatching technology, specifically to a method, apparatus, equipment, medium, and product for assessing resource regulation capabilities. Background Technology

[0002] With the increasing penetration of new energy sources, Virtual Power Plants (VPPs), as key carriers for aggregating distributed energy resources, have become an important technological path to improve power system flexibility and promote renewable energy consumption. The demand for grid regulation of industrial and commercial flexibility resources is increasingly urgent, and a comprehensive and scientific assessment of regulation capabilities is a core prerequisite for achieving efficient regulation. Currently, newly entering industrial and commercial load users (such as industrial parks and commercial complexes) are important resource entities for VPPs, and their regulation capabilities directly affect the market competitiveness of aggregators and user participation. Existing assessment methods mostly focus on single-dimensional indicators, failing to form a complete indicator system, especially neglecting scenario-based indicators such as "second-level / minute-level adjustable capacity differentiation" and "sustainable duration of power limit regulation," leading to a disconnect between assessment and actual regulation needs. Methods focusing on multi-dimensional indicators suffer from difficulties in determining the weight of each indicator. This results in new market entrants being unable to predict the value positioning of their regulation capabilities in the market, leading to a lack of basis for their decisions regarding participation in electricity market services (such as the spot market, peak-shaving ancillary service market, and frequency regulation ancillary service market). Aggregators are also unable to formulate differentiated contract strategies based on objective data, resulting in low efficiency in resource aggregation. Summary of the Invention

[0003] This invention provides a method, apparatus, equipment, medium, and product for assessing resource adjustment capabilities, in order to solve the problem that new market entrants cannot predict the value of their adjustment capabilities in the market.

[0004] In a first aspect, the present invention provides a method for assessing resource regulation capacity, comprising:

[0005] Obtain various resource assessment indicators and feature tags for the industrial and commercial loads to be assessed. The resource assessment indicators include unit regulation cost, second-level adjustable capacity, minute-level adjustable capacity, power limit regulation duration, response time, regulation accuracy, and total regulation contribution. The similarity between the industrial and commercial load in the weighted knowledge base and the industrial and commercial load to be evaluated is calculated based on the feature labels. If the number of industrial and commercial loads in the weight knowledge base that meet the similarity conditions with the industrial and commercial loads to be evaluated is less than the preset number, a weight correction strategy is determined in the fuzzy rule base based on the expert experience confidence and data feature fit of each resource assessment indicator. The fuzzy rule base contains the correspondence between different expert experience confidence and data feature fit and correction strategies. The data feature fit is used to characterize the correlation between resource assessment indicators and core assessment indicators. The initial weights of each resource assessment indicator are adjusted according to the weight adjustment strategy to obtain an intermediate weight set containing the intermediate weights of each resource assessment indicator. The resource adjustment capacity assessment value is calculated based on various resource assessment indicators and intermediate weight sets.

[0006] Various resource assessment indicators affecting the load regulation capacity of industrial and commercial enterprises were defined. The resource assessment indicators of the industrial and commercial loads to be assessed were obtained, and the initial weights corresponding to each indicator were determined. A weight correction strategy was then used to correct the initial weights, resulting in more accurate weight information. Therefore, calculating the resource regulation capacity assessment value based on the corrected weight information allows for a more accurate assessment of the response and regulation capacity of the industrial and commercial loads to be assessed. This enables newly entering industrial and commercial load users to determine their own market value positioning in terms of regulation capacity.

[0007] In one optional implementation, various resource assessment indicators for the industrial and commercial load to be assessed are obtained, including: Determine the initial resource assessment indicators for the industrial and commercial load to be assessed; Each initial resource assessment indicator is preprocessed to obtain the various resource assessment indicators. The preprocessing includes dimensionless calculation and positive and inverse index quantification calculation of each initial resource assessment indicator.

[0008] By acquiring various initial resource assessment indicators and performing dimensionless and positive / inverse indicator quantification calculations, the evaluation scale and direction of various resource assessment indicators were unified, allowing multi-dimensional data to participate in comprehensive analysis fairly, comparablely, and logically consistently, thereby improving the objectivity and interpretability of the evaluation results.

[0009] In one optional implementation, the initial weights of each resource evaluation indicator are adjusted according to a weight adjustment strategy to obtain an intermediate weight set containing the intermediate weights of each resource evaluation indicator, including: Determine the initial weights for each resource assessment indicator; Based on the expert experience confidence and data feature fit of each resource assessment indicator, a weight correction strategy is determined in the fuzzy rule base. The initial weights of each resource assessment indicator are then corrected according to the weight correction strategy to obtain the intermediate weights of each resource assessment indicator. The intermediate weight set contains the intermediate weights of each resource assessment indicator for the same industrial and commercial load.

[0010] By first obtaining the initial weights of various resource evaluation indicators and then constructing a fuzzy rule base to correct the initial weights, the corrected intermediate weights are obtained. This method combines the objectivity of the initial weights with the flexibility of fuzzy reasoning, allowing the intermediate weights to not only fit the data foundation but also adapt to complex actual situations through expert experience, thereby improving the rationality of the weights and the accuracy of decision-making.

[0011] In an optional implementation, if the number of industrial and commercial loads in the weighted knowledge base that meet the similarity criteria to the industrial and commercial loads to be evaluated is greater than or equal to a preset number, the method further includes: Based on the similarity between each industrial and commercial load in the weighted knowledge base and the industrial and commercial load to be evaluated, a preset number of industrial and commercial loads are selected as source industrial and commercial loads. Based on the weights of various resource assessment indicators of the source industrial and commercial load recorded in the weight knowledge base, calculate the intermediate weight set of various resource assessment indicators of the industrial and commercial load to be assessed. The resource adjustment capacity assessment value is calculated based on various resource assessment indicators and intermediate weight sets.

[0012] Another method is proposed to obtain the intermediate weights of various resource assessment indicators for the industrial and commercial load to be evaluated. Since the weight knowledge base records the final weights of various resource assessment indicators of various industrial and commercial loads after calibration, the data is more accurate than the intermediate weights obtained after calibrating the initial weights. Furthermore, since the various industrial and commercial loads selected from the weight knowledge base have a high degree of similarity with the industrial and commercial load to be evaluated, selecting multiple sets of highly similar industrial and commercial loads for evaluation calculation can obtain a more accurate intermediate weight set for the industrial and commercial load to be evaluated.

[0013] In one alternative implementation, the method further includes: Obtain actual data on the industrial and commercial loads to be evaluated when they participate in power regulation; The deviation is determined based on actual data and resource adjustment capacity assessment values; If the deviation is greater than the preset deviation threshold, the intermediate weight set of each indicator is optimized to obtain the final weight set. The feature labels and final weight sets of the industrial and commercial load to be evaluated are stored in the weight knowledge base.

[0014] By using measured data obtained after participating in actual power regulation to correct the deviation of the estimated resource regulation capacity assessment value, the final weight set calculated is more accurate, and at the same time, it provides a data basis for judging the intermediate weights of new industrial and commercial loads to be evaluated.

[0015] In one optional implementation, the feature tags include industry type, main equipment type, and load characteristics. The similarity between the industrial and commercial loads in the weighted knowledge base and the industrial and commercial loads to be evaluated is calculated based on the feature tags, including: Determine the feature labels of the industrial and commercial load in the weighted knowledge base and the industrial and commercial load to be evaluated respectively; Calculate the similarity of each feature in the feature labels of the industrial and commercial load and the industrial and commercial load to be assessed; The similarity between the industrial and commercial load and the industrial and commercial load to be evaluated is obtained by weighting and summing the similarity of each feature according to empirical weights.

[0016] A method is presented to determine the similarity between industrial and commercial loads and the industrial and commercial loads to be evaluated by using feature labels of industrial and commercial loads. The similarity judgment results are more accurate by using weighted summation calculations across multiple dimensions.

[0017] In a second aspect, the present invention provides a resource adjustment capability assessment device, comprising: The indicator acquisition module is used to acquire various resource assessment indicators and feature tags of the industrial and commercial load to be assessed. The various resource assessment indicators include unit regulation cost, second-level adjustable capacity, minute-level adjustable capacity, power limit regulation duration, response time, regulation accuracy, and total regulation contribution power. The similarity judgment module is used to calculate the similarity between the industrial and commercial load in the weighted knowledge base and the industrial and commercial load to be evaluated based on the feature labels. The strategy confirmation module is used to determine the weight correction strategy in the fuzzy rule base based on the expert experience confidence and data feature fit of each resource assessment indicator if the number of industrial and commercial loads in the weight knowledge base that meet the similarity conditions to the industrial and commercial loads to be evaluated is less than the preset number. The weight correction module is used to correct the initial weights of each resource assessment indicator according to the weight correction strategy, and obtain an intermediate weight set containing the intermediate weights of each resource assessment indicator. The fuzzy rule base contains the correspondence between different expert experience confidence and data feature fit and the correction strategy. The data feature fit is used to characterize the correlation between resource assessment indicators and core assessment indicators. The assessment and calculation module is used to calculate the resource adjustment capacity assessment value based on various resource assessment indicators and intermediate weight sets.

[0018] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform a resource adjustment capability assessment method of the first aspect or any corresponding embodiment described above.

[0019] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform a resource adjustment capability assessment method as described in the first aspect or any corresponding embodiment thereof.

[0020] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute a resource adjustment capability assessment method according to the first aspect above or any corresponding embodiment thereof. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process of a resource adjustment capability assessment method according to an embodiment of the present invention; Figure 3 This is a structural block diagram of a resource adjustment capability assessment device according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0024] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0025] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0026] As an optional application scenario of this invention, such as Figure 1 As shown, this resource adjustment capability assessment system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0027] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0028] This invention provides a method for assessing resource regulation capacity. By obtaining resource assessment indicators to determine preliminary weights, and then correcting the weights through a fuzzy rule base, an assessment value for evaluating the regulation capacity of industrial and commercial loads is calculated. This allows newly entering industrial and commercial load users to obtain a market value positioning of their own regulation capacity.

[0029] According to an embodiment of the present invention, a method for assessing resource adjustment capabilities is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0030] This embodiment provides a method for evaluating resource adjustment capabilities, which can be used in mobile terminals such as mobile phones and tablets. Figure 2 This is a flowchart of a resource adjustment capability assessment method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Obtain the various resource assessment indicators and feature labels of the industrial and commercial load to be assessed.

[0031] The various resource assessment indicators refer to multiple indicators defined based on the core scenario of industrial and commercial loads participating in power grid regulation. These include unit regulation cost, second-level adjustable capacity, minute-level adjustable capacity, power limit regulation duration, response time, regulation accuracy, and total regulation contribution.

[0032] In the process of participating in power regulation, the control terminal is a dedicated intelligent device deployed on the user side, responsible for remotely receiving instructions, locally parsing them, and controlling electrical equipment to complete load regulation. Unit regulation cost refers to the comprehensive cost incurred by industrial and commercial loads to provide power regulation services, from the initial deployment of control terminals and construction of regulation capacity to each actual load reduction or increase, averaged per megawatt of adjustable capacity or per megawatt-hour of regulated electricity. The smaller this indicator, the more valuable the corresponding industrial and commercial user's investment. Second-level adjustable capacity refers to the adjustable capacity of the main production equipment of industrial and commercial users with installed control terminals that can respond to grid frequency regulation within seconds. Minute-level adjustable capacity refers to the adjustable capacity of the main production equipment with installed control terminals... Compared to other start-stop devices, the adjustable capacity can respond to grid peak shaving within minutes; the sustainable duration of power limit regulation refers to the duration during which industrial and commercial loads participate in grid regulation while maintaining their upper or lower power limits without significantly impacting production and daily life; response time refers to the time interval from receiving an industrial or commercial regulation command to the output power of the regulating resource reaching the target value; regulation accuracy refers to the degree of deviation between the actual output power of the industrial and commercial regulating resource and the target power required by the command; total regulation contribution refers to the total amount of electricity regulated by industrial and commercial regulating resources through power adjustment within a specific time period. The total regulation contribution is the sum of the contribution electricity of industrial and commercial loads participating in different types of ancillary services after being allocated according to a certain proportion. When judging the sustainable duration of power limit regulation, considering the coupling relationship between temperature and electricity of the main electrical equipment of thermal storage industrial loads, prolonged power regulation will cause the temperature to exceed the range required for production. Therefore, the sustainable duration of power limit regulation for this type of industrial load is relatively short.

[0033] Feature tags refer to the user feature tags of the industrial and commercial loads to be evaluated, which may include industry type, main equipment type and load type.

[0034] Step S202: Calculate the similarity between the industrial and commercial load in the weighted knowledge base and the industrial and commercial load to be evaluated based on the feature labels.

[0035] The weighted knowledge base contains the weights and feature labels of various resource assessment indicators for industrial and commercial loads that are currently or have previously participated in regulation. The similarity calculation in this process is used to determine whether there are industrial and commercial loads in the weighted knowledge base that are similar to the current industrial and commercial load to be assessed, thus informing the subsequent selection of an appropriate weight calculation method.

[0036] Step S203: If the number of industrial and commercial loads in the weight knowledge base that meet the similarity conditions with the industrial and commercial loads to be evaluated is less than the preset number, a weight correction strategy is determined in the fuzzy rule base based on the expert experience confidence and data feature fit of each resource evaluation index.

[0037] The fuzzy rule base contains the correspondence between different expert experience confidence levels and data feature fit degrees with correction strategies. Data feature fit degree is used to characterize the correlation between resource assessment indicators and core assessment indicators. If there are no industrial or commercial loads in the weighted knowledge base that meet the similarity criteria to the current industrial or commercial load to be assessed, or if the number of industrial or commercial loads that meet the similarity criteria is less than a preset number, then the current industrial or commercial load to be assessed cannot be judged based on the industrial or commercial loads in the weighted knowledge base. Instead, it is necessary to calculate the various resource assessment indicators corresponding to the industrial or commercial load to be assessed using the fuzzy rule base. As an example, the preset number can be 5.

[0038] Step S204: Adjust the initial weights of each resource evaluation indicator according to the weight adjustment strategy to obtain an intermediate weight set containing the intermediate weights of each resource evaluation indicator.

[0039] Initial weights refer to the weight information of each resource assessment indicator obtained through a preliminary judgment of the weights of each resource assessment indicator. The initial weights need to be corrected to obtain relatively accurate weight data. The resource assessment indicator weight information obtained after correcting the initial weights is called intermediate weights. The intermediate weight set contains the intermediate weights of each resource assessment indicator corresponding to a given industrial and commercial load.

[0040] Step S205: Calculate the resource adjustment capacity assessment value based on the various resource assessment indicators and the intermediate weight set.

[0041] The resource regulation capacity assessment value is used to preliminarily evaluate the current industrial and commercial load's ability to respond to grid regulation. Based on the various resource assessment indicators and their corresponding weights, the resource regulation capacity assessment value for the industrial and commercial load to be assessed can be calculated.

[0042] For example, the resource adjustment capacity assessment value can be calculated using the following formula:

[0043] in, This is a value used to assess resource adjustment capacity. S represents the quantitative value of the j-th indicator. The closer S is to 1, the stronger the adjustment capability. Let be the weight of the j-th term.

[0044] This embodiment provides a resource regulation capacity assessment method, which defines various resource assessment indicators affecting the regulation capacity of industrial and commercial loads. The method acquires these resource assessment indicators for the industrial and commercial loads to be assessed, thereby confirming the initial weights for each indicator. A weight correction strategy is then used to correct the initial weights, resulting in more accurate weight information. Therefore, by calculating the resource regulation capacity assessment value based on the corrected weight information, a more accurate response and regulation capacity of the industrial and commercial loads to be assessed can be obtained. This achieves the effect of enabling newly entering industrial and commercial load users to determine their own market value positioning in terms of regulation capacity.

[0045] In an optional embodiment, step S201 above involves obtaining various resource assessment indicators and feature tags for the industrial and commercial load to be assessed, wherein obtaining the various resource assessment indicators for the industrial and commercial load to be assessed includes: Step a1: Determine the initial resource assessment indicators for the industrial and commercial load to be assessed.

[0046] Initial resource assessment indicators refer to the original resource assessment indicators obtained through direct calculation, including unit adjustment cost, second-level adjustable capacity, minute-level adjustable capacity, power limit adjustment duration, response time, adjustment accuracy, and total adjustment contribution power.

[0047] For example, the unit adjustment cost can be determined by the quotient of the total investment cost of deploying control terminals by industrial and commercial users and the adjustable capacity that industrial and commercial enterprises gain after the terminal equipment is developed:

[0048] Where C represents the unit adjustment cost. The total investment cost for deploying control terminals for this industrial and commercial user, Adjustable capacity for industrial and commercial enterprises after the development of terminal equipment.

[0049] For example, the second-level adjustable capacity can be determined by the product of the adjustable coefficient set by the control terminal when industrial and commercial loads participate in grid frequency regulation and the power consumption of the main production equipment controlled by the control terminal:

[0050] in, Capacity adjustable in seconds. Adjustable coefficients set for control terminals when industrial and commercial loads participate in power grid frequency regulation. This refers to the power consumption of the main production equipment controlled by the control terminal.

[0051] For example, the minute-level adjustable capacity can be determined by the sum of the adjustable coefficient set by the control terminal when industrial and commercial loads participate in grid peak shaving and the capacity of the equipment that can be started and stopped when participating in peak shaving among industrial and commercial users:

[0052] in, Adjustable capacity in minutes. Adjustable coefficients set for control terminals when industrial and commercial loads participate in grid peak shaving. This refers to the total capacity of equipment that can be started and stopped during peak shaving for industrial and commercial users. It takes into account the negative impacts that could result from prolonged continuous regulation of heat storage loads, etc. Usually less than .

[0053] For example, the duration of power limit regulation is relatively short because the temperature and power consumption of the main electrical equipment in thermal storage industrial loads are coupled. Long-term power regulation can cause the temperature to exceed the range required for production.

[0054] For example, the response time can be determined by the sum of the inherent action delay of the terminal device, the average delay of the communication network, and the resolution delay of the control strategy:

[0055] in, For response time, This is an inherent delay in the terminal device's operation, which can be found in the device's performance parameters or factory testing. The average delay of the communication network, The control strategy analysis delay is calculated as follows: For new users not participating in the control, the response time is the theoretical sum of the inherent action delay of the terminal device, the communication delay, and the control logic delay.

[0056] For example, the adjustment accuracy can be determined by multiplying the quotient of the adjustment accuracy and the rated power of the equipment by the correction factor for the production disturbance:

[0057] in, To adjust the accuracy, This is the minimum adjustment step size for the equipment. Rated power of the equipment To account for correction factors related to production disturbances. As an example. It can be 1.2-1.5.

[0058] For example, the total regulation contribution can be determined by adding the products of the primary frequency regulation contribution of industrial and commercial loads and the contribution ratio coefficient, the secondary frequency regulation contribution of industrial and commercial loads and the contribution ratio coefficient, and the peak shaving contribution of industrial and commercial loads and the contribution ratio coefficient:

[0059] in, , and These represent the electricity contribution from primary frequency regulation of industrial and commercial loads, the electricity contribution from secondary frequency regulation of industrial and commercial loads, and the electricity contribution from peak shaving of industrial and commercial loads, respectively. , and Let represent the contribution ratio coefficients of the three frequency modulation methods, and let the three satisfy . .

[0060] For example, a dual-track indicator system is constructed for the response time indicator in the flexibility resource assessment of industrial and commercial users: for new users not participating in regulation, theoretical evaluation indicators based on equipment parameters and industry experience are used; for users with existing regulation records, measured evaluation indicators based on operational data are used. The two types of indicators are aligned in dimensions to ensure the comparability of the comprehensive evaluation results. As an example, regulation records can refer to records of participation in grid dispatching, or records of regulation generated by the user itself.

[0061] Step a2 involves preprocessing the initial resource assessment indicators to obtain the various resource assessment indicators.

[0062] Preprocessing refers to the removal of dimensions and the quantification of positive and negative indicators for each initial resource assessment indicator.

[0063] For example, the elimination of dimensions can be calculated using the standard deviation method, the linear proportion method, or the extreme value processing method. The standard deviation method is suitable for indicators where the data is normally distributed, such as response time; the linear proportion method is suitable for indicators with a well-defined data range, such as unit adjustment cost; and the extreme value processing method is suitable for indicators with clearly defined data extreme values, such as adjustment accuracy.

[0064] As an example, the standard deviation method transforms raw data into standardized data with a mean of 0 and a standard deviation of 1, based on the mean and standard deviation of the data, thus preserving the normal distribution characteristics of the data. This can be achieved through... Perform the calculation, where x is the original data. The mean of the data. The standard deviation is denoted as .

[0065] As an example, the linear scaling method linearly maps the original data to the [0,1] interval, adjusting the mapping direction according to the positive or negative value of the indicator. Relying on a defined range of values, positive indicators can be... Calculations can be performed using inverse indicators. Calculations can be performed using the extreme value processing method. Mapped to the interval [0,1], where, All are the minimum values ​​of the indicators. All are the maximum values ​​of the indicators.

[0066] As an example, the quantification of positive and negative indicators is completed while eliminating dimensions, ensuring that the larger the value of all indicators, the stronger the adjustment capability.

[0067] This embodiment provides a resource adjustment capability assessment method that, by acquiring various initial resource assessment indicators and performing dimensionless and positive / inverse indicator quantification calculations, unifies the evaluation scale and direction of various resource assessment indicators, allowing multi-dimensional data to participate in comprehensive analysis fairly, comparablely, and logically consistently, thereby improving the objectivity and interpretability of the evaluation results.

[0068] In an optional embodiment, step S204 above, which involves correcting the initial weights of each resource evaluation indicator according to a weight correction strategy to obtain an intermediate weight set containing the intermediate weights of each resource evaluation indicator, includes: Step b1: Determine the initial weights of each resource assessment indicator.

[0069] Initial weights refer to the weights of each resource assessment indicator obtained by making a preliminary weight judgment on each resource assessment indicator to be evaluated for industrial and commercial load, providing initial values ​​for subsequent weight adjustments.

[0070] For example, initial weights can be assigned based on expert experience. As an example, select 5-10 experts with ≥5 years of experience in the fields of "power grid dispatching," "industrial load management," and "power economics," and use a 1-9 scale to compare the relative importance of the seven indicators pairwise, forming a judgment matrix. Then, perform a consistency check on the judgment matrix, remove unqualified matrices, and calculate the initial weights using the arithmetic mean method.

[0071] For example, in the 1-9 scale, 1 represents equal importance, and 9 represents extreme importance. The judgment matrix is ​​as follows: ,in, This represents the relative importance of indicator i to indicator j. It can be expressed as follows: , The initial weights are calculated using the arithmetic mean method, where... k The number of qualified matrices, Let t be the initial weight of the j-th indicator given by the t-th expert.

[0072] Step b2: Determine the weight correction strategy in the fuzzy rule base based on the expert experience confidence and data feature fit of each resource assessment indicator, and correct the initial weights of each resource assessment indicator according to the weight correction strategy to obtain the intermediate weights of each resource assessment indicator. The intermediate weight set contains the intermediate weights of each resource assessment indicator for the same industrial and commercial load.

[0073] The intermediate weights are obtained by correcting the initial weights according to the correction strategy in the fuzzy rule base, and are used to directly calculate the resource regulation capacity assessment value of the industrial and commercial load to be evaluated. The fuzzy rule base is constructed based on two data points: expert experience confidence and data feature fit. Expert experience confidence reflects the consistency of experts' judgments on the same indicator, while data feature fit reflects the correlation between each indicator and the core assessment indicator. As an example, the core assessment indicator can be the total regulation contribution electricity.

[0074] For example, the confidence level of expert experience can be calculated using the following formula:

[0075] in, CV stands for expert confidence level, and it is the coefficient of variation, used to measure the dispersion of different experts' judgments on the weight of the same indicator. Let the initial weight of the j-th indicator be given by the t-th expert. This represents taking the maximum value of the coefficient of variation for all indicators, used for normalization, so that... It falls between 0 and 1.

[0076] For example, the data feature fit can be calculated using the following formula:

[0077] in, For data feature fit, The correlation coefficient measures the degree of linear correlation between the j-th indicator and core evaluation indicators (such as total regulation contribution). The coefficient of variation for all loads on the same index j is used to measure the dispersion of the index data. The CV indicates the data stability of the indicator. The smaller the CV, the closer the value is to 1, indicating higher data quality.

[0078] For example, the expert experience confidence level and the data feature fit can be divided into five fuzzy subsets, namely, unimportant (UN), relatively unimportant (LN), moderate (M), relatively important (LM), and important (N). A fuzzy rule base is constructed based on these fuzzy subsets of expert experience confidence level and data feature fit. An example of a fuzzy rule base is as follows:

[0079] The trigger strength of a rule can be calculated using the max-min method, which is a calculation method in a fuzzy inference system to determine the degree to which each fuzzy rule is activated under the current input conditions.

[0080] The centroid method is used to convert the fuzzy results into correction coefficients, and the initial weights can be corrected using a correction formula:

[0081] in, Let j be the initial weight of the resource evaluation index. Let j be the j-th resource evaluation index with intermediate weight. is the correction coefficient for the j-th term.

[0082] This embodiment provides a resource adjustment capability assessment method. By first obtaining the initial weights of various resource assessment indicators and then constructing a fuzzy rule base to correct the initial weights, the corrected intermediate weights are obtained. This method combines the objectivity of the initial weights with the flexibility of fuzzy reasoning, allowing the intermediate weights to not only conform to the data foundation but also adapt to complex actual situations through expert experience, thereby improving the rationality of the weights and the accuracy of decision-making.

[0083] In an optional embodiment, when performing step S203 above, if the number of industrial and commercial loads in the weighted knowledge base that meet the similarity criteria with the industrial and commercial loads to be evaluated is greater than or equal to a preset number, the method further includes: Step c1: Select a preset number of industrial and commercial loads as source industrial and commercial loads based on the similarity between each industrial and commercial load in the weighted knowledge base and the industrial and commercial load to be evaluated.

[0084] After comparing the industrial and commercial loads to be evaluated with the industrial and commercial loads in the weight knowledge base, if there are more than a preset number of highly matched industrial and commercial loads, then a preset number of industrial and commercial loads are selected as source industrial and commercial loads from all the highly matched industrial and commercial loads that meet the similarity criteria. The source industrial and commercial loads serve as the basis for subsequently calculating the intermediate weights of the industrial and commercial loads to be evaluated, and include the weights of various resource evaluation indicators for the preset number of industrial and commercial loads that meet the matching requirements. As an example, the preset number is the same as in step S203, which is 5.

[0085] Step c2: Calculate the intermediate weight set of each resource assessment index of the industrial and commercial load to be assessed based on the weights of each resource assessment index of the source industrial and commercial load recorded in the weight knowledge base.

[0086] Since the weight knowledge base records the weights of various resource assessment indicators for each source industrial and commercial load, and the source industrial and commercial loads also have a high degree of similarity with the industrial and commercial loads to be assessed, the weights of various resource assessment indicators for the industrial and commercial loads to be assessed can be directly calculated based on the weights of various resource assessment indicators for each source industrial and commercial load.

[0087] For example, the intermediate weight set of each resource assessment index of the industrial and commercial load to be assessed can be calculated by averaging the weights of each resource assessment index of the source industrial and commercial load.

[0088] Since the source industrial and commercial loads contain a preset number of resource assessment index weights for industrial and commercial loads that meet the matching requirements, the method for determining the intermediate weights of each resource assessment index for the industrial and commercial load to be assessed can be achieved by averaging each resource assessment index in the source industrial and commercial loads, obtaining the mean of each resource assessment index in the source industrial and commercial loads, and using this mean as the intermediate weight of each resource assessment index for the industrial and commercial load to be assessed. The intermediate weights of each resource assessment index for the same industrial and commercial load to be assessed are then used as the intermediate weight set.

[0089] Step c3 involves calculating the resource adjustment capacity assessment value based on the various resource assessment indicators and the intermediate weight set. For details, please refer to [link to relevant documentation]. Figure 2 Step S205 of the illustrated embodiment will not be described again here.

[0090] This embodiment provides a method for assessing resource regulation capacity, offering another approach to obtaining intermediate weights for various resource assessment indicators of the industrial and commercial load to be assessed. Since the weight knowledge base records the final weights of various resource assessment indicators of different types of industrial and commercial loads after calibration, the data is more accurate than the intermediate weights obtained after calibrating the initial weights. Furthermore, because the various types of industrial and commercial loads selected from the weight knowledge base have a high degree of similarity to the industrial and commercial load to be assessed, selecting multiple sets of highly similar industrial and commercial loads for evaluation and calculation can yield a more accurate set of intermediate weights for the industrial and commercial load to be assessed.

[0091] In an optional embodiment, after obtaining the intermediate weight set and calculating the resource adjustment capability assessment value, the method further includes: Step d1: Obtain actual data on the industrial and commercial loads to be evaluated when they participate in power regulation.

[0092] Actual data is used to characterize the actual regulation capacity of the industrial and commercial loads under evaluation after they actually participate in power regulation, and it serves as the basis for subsequent judgments on resource regulation capacity assessment values.

[0093] For example, the actual regulation capacity of the industrial and commercial loads to be assessed is standardized and calculated, transforming it into specific values ​​in the 0-1 range. Since different industrial and commercial loads to be assessed have different electricity consumption attributes and therefore different ways of responding to power regulation, the regulation capacity of each load can be standardized based on its strengths. As an example, different regulation capacities could be frequency regulation response speed or peak-shaving capacity compliance rate.

[0094] Step d2: Determine the deviation based on actual data and resource adjustment capacity assessment values.

[0095] The deviation is used to represent the difference between the assessed value and the actual value of the intermediate weight. It is obtained by the difference between the assessed value of resource adjustment capability and the actual data. Since the assessed value of resource adjustment capability is a value in the range of 0-1, and the actual data is also a value in the range of 0-1, it can be compared and calculated from the same dimension.

[0096] As an example, if the resource adjustment capability assessment value is 0.5 and the actual data is 0.6, the deviation between the two is 10%; if the resource adjustment capability assessment value is 0.9 and the actual data is 0.85, the deviation between the two is 5%.

[0097] Step d3: If the deviation is greater than the preset deviation threshold, the intermediate weight set of each indicator is optimized to obtain the final weight set.

[0098] If the deviation exceeds the preset deviation threshold, it indicates that the calculated value of the resource adjustment capability assessment is inaccurate, and therefore the intermediate weight set for each indicator is inaccurate. The intermediate weight set needs to be recalculated and optimized to obtain accurate weights for each indicator as the final weight set.

[0099] For example, the deviation threshold can be 5%, 10%, etc., and there is no limitation here. Optimization of the intermediate weight set of each indicator can be achieved by adjusting the fuzzy rules. As an example, methods such as optimizing the membership function parameters can be used until the deviation of the calculated resource adjustment capability assessment value is less than the preset deviation threshold.

[0100] Step d4: Store the feature labels and final weight sets of the industrial and commercial load to be evaluated into the weight knowledge base.

[0101] The weight knowledge base is used to store the final weight set and feature labels corresponding to each industrial and commercial load. Therefore, storing the accurate feature labels and final weight set of the industrial and commercial load to be evaluated into the weight knowledge base can be used for subsequent judgment and calculation of new industrial and commercial loads to be evaluated.

[0102] This embodiment provides a resource regulation capacity assessment method that uses measured data obtained after participating in actual power regulation to correct the deviation of the estimated resource regulation capacity assessment value, making the final weight set more accurate, and providing a data basis for judging the intermediate weights of new industrial and commercial loads to be assessed.

[0103] In an optional embodiment, step S202 above, calculating the similarity between the industrial and commercial load in the weighted knowledge base and the industrial and commercial load to be evaluated based on feature labels, includes: Step e1: Determine the feature labels of the industrial and commercial loads in the weighted knowledge base and the industrial and commercial loads to be evaluated.

[0104] Feature labels describe the characteristics of industrial and commercial loads in the weighted knowledge base and the industrial and commercial loads to be evaluated from multiple dimensions. Furthermore, subsequent similarity calculations are also based on the features provided by the feature labels. As an example, feature labels include industry type, main equipment type, and load characteristics.

[0105] Step e2: Calculate the similarity of each feature in the feature labels of the industrial and commercial load and the industrial and commercial load to be evaluated.

[0106] In the subsequent calculation of the overall similarity between the industrial and commercial load and the industrial and commercial load to be assessed, it is necessary to calculate the similarity based on the similarity of each feature. Therefore, it is necessary to judge each feature in the feature label separately to obtain the similarity of each feature. As an example, Indicates the industry type; if the industries are the same, then... =1, otherwise =0; The similarity between two sets of devices. The calculation formula can be J(A,B)=|A∩B| / |A∪B|, with a value range between 0 and 1.

[0107] Step e3: The similarity of each feature is weighted and summed according to empirical weights to obtain the similarity between the industrial and commercial load and the industrial and commercial load to be evaluated.

[0108] For example, the similarity between industrial and commercial loads and the industrial and commercial loads to be assessed can be determined using the following formula:

[0109] in, Indicates industry type. The similarity between two sets of devices. For load characteristic similarity. , and Empirical weights, and ω1+ω2+ω3=1, as an example, It can be 0.3, It can be 0.4, It can be 0.3.

[0110] This embodiment provides a resource regulation capacity assessment method, which proposes a method to judge the similarity between industrial and commercial loads and the industrial and commercial loads to be assessed by using the feature labels of industrial and commercial loads. The method uses weighted summation calculations across multiple dimensions to make the similarity judgment results more accurate.

[0111] This embodiment also provides a resource adjustment capability assessment device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0112] This embodiment provides a resource adjustment capability assessment device, such as... Figure 3 As shown, it includes: The indicator acquisition module 301 is used to acquire various resource assessment indicators and feature tags of the industrial and commercial load to be evaluated. The various resource assessment indicators include unit regulation cost, second-level adjustable capacity, minute-level adjustable capacity, power limit regulation duration, response time, regulation accuracy, and total regulation contribution power.

[0113] The similarity judgment module 302 is used to calculate the similarity between the industrial and commercial load in the weighted knowledge base and the industrial and commercial load to be evaluated based on the feature labels.

[0114] The strategy confirmation module 303 is used to determine the weight correction strategy in the fuzzy rule base based on the expert experience confidence and data feature fit of each resource assessment indicator if the number of industrial and commercial loads in the weight knowledge base that meet the similarity conditions to the industrial and commercial loads to be evaluated is less than the preset number.

[0115] The weight correction module 304 is used to correct the initial weights of each resource assessment indicator according to the weight correction strategy, so as to obtain an intermediate weight set containing the intermediate weights of each resource assessment indicator. The fuzzy rule base contains the correspondence between different expert experience confidence and data feature fit and correction strategy. The data feature fit is used to characterize the correlation between resource assessment indicators and core assessment indicators.

[0116] The assessment calculation module 305 is used to calculate the resource adjustment capacity assessment value based on various resource assessment indicators and intermediate weight sets.

[0117] The resource adjustment capability assessment device provided in this embodiment of the invention can execute a resource adjustment capability assessment method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0118] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0119] The following is a detailed reference. Figure 4 This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from memory 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device. The processor 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0120] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0121] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a memory 408, or installed from a ROM 402. When the computer program is executed by the processor 401, it performs the functions defined in the resource adjustment capability assessment method of an embodiment of the present invention.

[0122] Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0123] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, it implements the resource adjustment capability assessment method shown in the above embodiments.

[0124] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0125] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for assessing resource regulation capacity, characterized in that, The method includes: Obtain various resource assessment indicators and feature tags of the industrial and commercial load to be assessed. The various resource assessment indicators include unit regulation cost, second-level adjustable capacity, minute-level adjustable capacity, power limit regulation duration, response time, regulation accuracy, and total regulation contribution. The similarity between the industrial and commercial load in the weighted knowledge base and the industrial and commercial load to be evaluated is calculated based on the feature labels. If the number of industrial and commercial loads in the weighted knowledge base that meet the similarity condition with the industrial and commercial load to be evaluated is less than a preset number, a weight correction strategy is determined in the fuzzy rule base based on the expert experience confidence and data feature fit of each resource assessment indicator. The fuzzy rule base contains the correspondence between different expert experience confidence and data feature fit and correction strategies. The data feature fit is used to characterize the correlation between resource assessment indicators and core assessment indicators. The initial weights of each resource assessment indicator are adjusted according to the weight adjustment strategy to obtain an intermediate weight set containing the intermediate weights of each resource assessment indicator. The resource adjustment capacity assessment value is calculated based on the aforementioned resource assessment indicators and the intermediate weight set.

2. The method according to claim 1, characterized in that, The resource assessment indicators for obtaining the industrial and commercial load to be assessed include: Determine the initial resource assessment indicators for the industrial and commercial load to be assessed; The initial resource assessment indicators are preprocessed to obtain the various resource assessment indicators. The preprocessing includes dimensionless calculation and positive and inverse index quantification calculation of the initial resource assessment indicators.

3. The method according to claim 1, characterized in that, The step of adjusting the initial weights of each resource assessment indicator according to the weight adjustment strategy to obtain an intermediate weight set containing the intermediate weights of each resource assessment indicator includes: Determine the initial weights of each resource assessment indicator; Based on the expert experience confidence and data feature fit of each resource assessment indicator, a weight correction strategy is determined in the fuzzy rule base. The initial weights of each resource assessment indicator are then corrected according to the weight correction strategy to obtain the intermediate weights of each resource assessment indicator. The intermediate weight set includes the intermediate weights of each resource assessment indicator for the same industrial and commercial load.

4. The method according to claim 1, characterized in that, If the number of industrial and commercial loads in the weighted knowledge base that meet the similarity criteria with the industrial and commercial load to be evaluated is greater than or equal to the preset number, the method further includes: Based on the similarity between each industrial and commercial load in the weighted knowledge base and the industrial and commercial load to be evaluated, a preset number of industrial and commercial loads are selected as source industrial and commercial loads. Based on the weights of various resource assessment indicators of the source industrial and commercial load recorded in the weight knowledge base, calculate the intermediate weight set of various resource assessment indicators of the industrial and commercial load to be assessed. The resource adjustment capacity assessment value is calculated based on the aforementioned resource assessment indicators and the intermediate weight set.

5. The method according to any one of claims 1 to 4, characterized in that, The method further includes: Obtain actual data on the industrial and commercial loads to be evaluated when they participate in power regulation; The deviation is determined based on actual data and resource adjustment capacity assessment values; If the deviation is greater than the preset deviation threshold, the intermediate weight set of each indicator is optimized to obtain the final weight set. The feature labels and final weight sets of the industrial and commercial load to be evaluated are stored in the weight knowledge base.

6. The method according to claim 1, characterized in that, The feature tags include industry type, main equipment type, and load characteristics. The calculation of the similarity between the industrial and commercial load in the weighted knowledge base and the industrial and commercial load to be evaluated based on the feature tags includes: Determine the feature labels of the industrial and commercial load in the weighted knowledge base and the industrial and commercial load to be evaluated respectively; Calculate the similarity of each feature in the feature labels of the industrial and commercial load and the industrial and commercial load to be assessed; The similarity between the industrial and commercial load and the industrial and commercial load to be evaluated is obtained by weighting and summing the similarities of the features according to empirical weights.

7. A resource regulation capacity assessment device, characterized in that, The device includes: The indicator acquisition module is used to acquire various resource assessment indicators and feature tags of the industrial and commercial load to be assessed. The various resource assessment indicators include unit adjustment cost, second-level adjustable capacity, minute-level adjustable capacity, power limit adjustment duration, response time, adjustment accuracy, and total adjustment contribution power. The similarity judgment module is used to calculate the similarity between the industrial and commercial load in the weighted knowledge base and the industrial and commercial load to be evaluated based on the feature labels; The strategy confirmation module is used to determine a weight correction strategy in the fuzzy rule base based on the expert experience confidence and data feature fit of each resource assessment index if the number of industrial and commercial loads in the weight knowledge base that meet the similarity conditions with the industrial and commercial loads to be evaluated is less than a preset number. The weight correction module is used to correct the initial weights of each resource evaluation indicator according to the weight correction strategy to obtain an intermediate weight set containing the intermediate weights of each resource evaluation indicator. The fuzzy rule base contains the correspondence between different expert experience confidence and data feature fit and the correction strategy. The data feature fit is used to characterize the correlation between the resource evaluation indicators and the core evaluation indicators. The evaluation calculation module is used to calculate the resource adjustment capacity evaluation value based on the various resource evaluation indicators and the intermediate weight set.

8. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the computer instructions to perform a resource adjustment capability assessment method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute a resource adjustment capability assessment method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes computer instructions for causing a computer to perform a resource adjustment capability assessment method according to any one of claims 1 to 6.

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