Method, system and equipment for sharing user participating in auxiliary service based on dynamic analysis and medium

Abstract

The invention discloses a method, a system, equipment and a medium for sharing a user participating in an auxiliary service based on dynamic analysis, and relates to the technical field of electric power auxiliary services, and the method comprises the steps: building a user participating in auxiliary service benefit degree quantification model, and quantifying the benefit degree of each user obtained by participating in the auxiliary service; determining a corresponding cost sharing proportion based on the total benefit degree of the user, and calculating the sharing amount of each user in combination with the supply cost, the operation cost and the risk cost of the auxiliary service; setting a stepped excitation coefficient and an influence factor according to the sharing amount, the participation frequency and the response quality of each user, and calculating the final excitation amount of the user; and establishing a dynamic adjustment and constraint model for mechanism operation, and periodically adjusting each parameter and factor. According to the method, the user benefit degree is taken as the calculation basis of cost sharing, and the participation behavior quality is brought into incentive adjustment, so that the fairness of auxiliary service cost distribution is improved, and the participation enthusiasm of the user and the operation flexibility are enhanced.

Description

Technical Field

[0001] This invention relates to the field of power ancillary services technology, and in particular to a method, system, device and medium for user participation in ancillary service sharing based on dynamic analysis. Background Technology

[0002] In new power systems, ancillary services play a crucial role in maintaining grid frequency stability. However, at the user participation level, the ancillary service mechanism still has the following shortcomings: The existing mechanism for the mismatch between beneficiaries and bearers is that ancillary service costs are mostly concentrated on power generation companies or grid companies, while the cost-bearing responsibility of users, as the direct beneficiaries of ancillary services, is not clearly defined, resulting in the unfair phenomenon of beneficiaries not bearing the costs.

[0003] The traditional mechanism for incentivizing user participation is lacking: no incentive model has been established for user participation in the traditional way. Users are only guided to participate through fixed subsidies or simple electricity price adjustments, which results in insufficient depth of user participation.

[0004] Traditional mechanisms present a state of ambiguity in cost and benefit accounting: the degree of benefit from ancillary services and the proportion of cost sharing are not quantified, resulting in a lack of data support for the cost-sharing mechanism.

[0005] Existing methods exhibit insufficient dynamic adjustment capabilities: they mostly employ static rules and fail to consider dynamic factors such as fluctuations in user electricity load and changes in ancillary service market prices. As a result, when the peak-to-valley difference in grid load increases or ancillary service prices fluctuate drastically, the mechanism cannot adapt in real time.

[0006] Therefore, the ancillary service cost-sharing mechanism based on the principle of "whoever benefits bears the cost" is the concept of this invention, and combined with dynamic analysis methods, it fairly distributes the costs and benefits of ancillary services. Summary of the Invention

[0007] In view of the above-mentioned problems, the present invention provides a user-participatory auxiliary service sharing method based on dynamic analysis.

[0008] Therefore, the problem that this invention aims to solve is that the cost of ancillary services is concentrated in the hands of power generation companies or power grid companies, while the cost-bearing responsibility of users, as the direct beneficiaries of ancillary services, is not clearly defined, resulting in an unfair situation where the beneficiaries do not bear the costs.

[0009] To address the aforementioned technical problems, this invention provides the following technical solution: a user-participatory ancillary service cost-sharing method based on dynamic analysis, comprising: establishing a quantification model for user participation in ancillary services benefits, quantifying the benefit gained by each user from participating in ancillary services; determining the corresponding cost-sharing ratio based on the total benefit of users, and calculating the cost-sharing amount for each user by combining the supply cost, operating cost, and risk cost of the ancillary services; setting tiered incentive coefficients and influencing factors according to the cost-sharing amount, participation frequency, and response quality of each user, and calculating the final incentive amount for the user; and establishing a dynamic adjustment and constraint model for the mechanism operation, periodically adjusting each parameter and factor.

[0010] As a preferred embodiment of the user-participatory ancillary service sharing method based on dynamic analysis described in this invention, the establishment of a user-participatory ancillary service benefit quantification model includes: classifying the benefits obtained by users from ancillary services into direct benefits and indirect benefits; extracting feature information reflecting the benefits obtained by users from ancillary services based on the classification results of user benefit dimensions, and organizing it into a structured quantitative expression for benefit calculation; performing weighted quantitative calculation of the total benefit of each user in the current ancillary service cycle, and combining actual power grid operation data and user electricity bill information, using a normalization method to unify the benefit scale among different users.

[0011] The beneficial effects of this preferred technical solution are: by establishing a dual-dimensional quantitative model that includes both direct and indirect benefits, it covers the immediate and long-term value that users obtain from ancillary services, breaking through the limitations of traditional methods that cannot quantify benefits.

[0012] As a preferred embodiment of the user-participatory ancillary service cost-sharing method based on dynamic analysis described in this invention, the calculation of each user's share includes: calculating the total cost of the ancillary services involved in the target period; calculating the proportion of each user in the set of all benefiting users based on the total benefit obtained by each user in the benefit measurement model, and determining the corresponding cost-sharing coefficient; and, based on the known total cost and cost-sharing coefficient, introducing an adjustment coefficient that reflects the current fluctuation of ancillary service market prices to correct the original share amount, thereby obtaining the final share amount that the user should bear.

[0013] As a preferred embodiment of the user participation ancillary service cost-sharing method based on dynamic analysis described in this invention, the calculation of the user's final incentive amount includes: constructing a mapping relationship based on the user's cost-sharing amount in the ancillary service to generate a corresponding tiered incentive coefficient; extracting behavioral characteristics to construct an influencing factor based on the user's participation frequency and response quality in the ancillary service; and combining the tiered incentive coefficient and the influencing factor to calculate the user's final incentive amount.

[0014] As a preferred embodiment of the user-participatory ancillary service cost-sharing method based on dynamic analysis described in this invention, the periodic adjustment of various parameters and factors includes: setting an upper limit on the amount of user cost-sharing to ensure that the user's share does not exceed a set proportion of the monthly electricity bill; setting an upper limit on the total incentive expenditure of the system to prevent the incentive amount from exceeding the market revenue of the ancillary service; constructing a dynamic adjustment mechanism for updating various parameters and factors in response to changes in the external environment during the operation of the ancillary service; and recalculating and deploying various parameters and factors using a fixed period as a trigger condition.

[0015] The beneficial effects of this preferred technical solution are: setting an upper limit for the amount shared by users and a constraint on the total amount of system incentives to prevent incentive imbalance or excessive burden on individuals in extreme scenarios, thereby improving the sustainability of the mechanism; and dynamically adjusting key parameters in conjunction with the external environment of the power grid to ensure that the mechanism can continuously adapt to the actual operating conditions.

[0016] As a preferred embodiment of the user-participatory auxiliary service sharing method based on dynamic analysis described in this invention, the formula for calculating the degree of direct benefit is expressed as follows: in, This represents the direct benefit user u receives from auxiliary services. Expressed as the cost of loss per unit of power outage time for user u. This is expressed as the average power outage power for user u. This represents the reduction in power outage time for user u as an ancillary service. It is represented as the power reliability requirement coefficient for user u.

[0017] As a preferred embodiment of the user-participatory auxiliary service sharing method based on dynamic analysis described in this invention, the formula for calculating the degree of benefit of the indirect beneficiaries is expressed as follows: in, This represents the indirect benefit of user u from ancillary services, reflecting the long-term benefits the user gains from grid stability. This represents the annual basic electricity cost for user u. This represents the power grid stability improvement factor resulting from ancillary services. Let be the grid dependence coefficient of user u; the formula for the weighted quantitative calculation of the total benefit of each user in the current ancillary service period is expressed as follows: in, This represents the total benefit of user u from auxiliary services. This is represented as the weight of direct benefit. This is represented as the indirect benefit weight.

[0018] To address the aforementioned technical problems, this invention provides the following technical solution: a user-participatory ancillary service cost-sharing system based on dynamic analysis, comprising: a benefit quantification module, a cost-sharing amount calculation module, an incentive amount calculation module, and an adjustment module; the benefit quantification module is used to establish a user-participatory ancillary service benefit quantification model, quantifying the benefit obtained by each user from participating in the ancillary service; the cost-sharing amount calculation module determines the corresponding cost-sharing ratio based on the user's total benefit, and calculates the cost-sharing amount for each user by combining the supply cost, operating cost, and risk cost of the ancillary service; the incentive amount calculation module sets a tiered incentive coefficient and influencing factor based on each user's cost-sharing amount, participation frequency, and response quality, and calculates the user's final incentive amount; the adjustment module is used to establish a dynamic adjustment and constraint model for the mechanism's operation, periodically adjusting each parameter and factor.

[0019] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of a user-participatory auxiliary service sharing method based on dynamic analysis as described above.

[0020] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a user-participatory auxiliary service sharing method based on dynamic analysis as described above.

[0021] The beneficial effects of this invention are as follows: By constructing a user-participatory ancillary service cost-sharing mechanism that integrates benefit assessment, cost sharing, incentive feedback, and dynamic adjustment, this invention effectively solves the core problems of misalignment between benefits and responsibilities, insufficient incentives, and lack of adaptability in traditional mechanisms. By using user benefit as the basis for cost sharing calculation, incorporating the quality of participation behavior into incentive adjustment factors, and incorporating market changes into the dynamic adjustment logic of the mechanism, this invention not only improves the fairness of ancillary service cost allocation but also enhances user participation enthusiasm and the flexibility of mechanism operation, thereby improving the economic operating efficiency and service coordination capabilities of the power system. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1This is a flowchart of a user participation auxiliary service sharing method based on dynamic analysis in Example 1. Detailed Implementation

[0024] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0025] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0026] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a method for sharing the burden of user-participated auxiliary services based on dynamic analysis, including: S1: Establish a quantification model for the benefits of user participation in ancillary services to quantify the benefits each user gains from participating in ancillary services.

[0027] S2: Determine the corresponding cost-sharing ratio based on the total user benefit, and calculate the amount each user shares by combining the supply cost, operating cost and risk cost of ancillary services.

[0028] S3: Based on each user's contribution amount, participation frequency, and response quality, set a tiered incentive coefficient and influence factor to calculate the user's final incentive amount.

[0029] S4: Establish a dynamic adjustment and constraint model for the mechanism's operation, and periodically adjust each parameter and factor.

[0030] It should be noted that most traditional mechanisms concentrate ancillary service costs on power generation companies or grid companies, failing to accurately identify the cost responsibilities that users, as direct beneficiaries, should bear. This leads to a structural unfairness where beneficiaries do not contribute and investors do not benefit, thus inhibiting the enthusiasm of both the supply and demand sides of services. Current mechanisms generally rely on fixed subsidies or simple electricity price adjustments to guide user participation, lacking dynamic incentives linked to users' actual contributions and the quality of their participation, and thus failing to fully mobilize users' initiative and depth in participating in ancillary services.

[0031] Therefore, in response to the above problems, such as Figure 1As shown, through steps S1-S4, the multidimensional benefits that users receive from ancillary services are first quantified, and then the service costs are dynamically allocated among users based on the degree of benefit. On this basis, an incentive mechanism linked to user participation behavior is introduced to provide differentiated rewards to users with high cost contribution and excellent response performance. Finally, by constructing a parameter update and boundary control mechanism, the above process can be periodically and adaptively adjusted according to the operating status and market changes, thereby realizing closed-loop management of benefit-sharing-incentive-adjustment in ancillary services.

[0032] Example 2, the second embodiment of the present invention, differs from the first embodiment in that: a user-participatory ancillary service sharing method based on dynamic analysis further includes, in step S1, establishing a quantification model of user-participatory ancillary service benefit, comprising the following steps A1-A3: A1: Divide the benefits that users gain from ancillary services into direct benefits and indirect benefits.

[0033] A2: Based on the classification results of user benefit dimension, extract feature information that reflects the benefits that users obtain from auxiliary services, and organize it into a structured quantitative expression for benefit calculation.

[0034] A3: The total benefit of each user in the current ancillary service period is calculated using a weighted quantitative method. Combining actual power grid operation data and user electricity bill information, a normalization method is used to unify the benefit scale among different users.

[0035] Specifically, direct benefits refer to the value that ancillary services bring directly to users, such as backup services to avoid production losses caused by power outages and frequency regulation services to reduce equipment losses by improving the stability of users' electricity voltage; indirect benefits refer to the long-term value that ancillary services bring to users indirectly by ensuring the overall stability of the power grid, such as grid frequency stability to extend the service life of users' electrical equipment and sufficient regional reserves to reduce the risk of future electricity price increases.

[0036] Traditional methods only consider direct benefits or ignore the distinction between benefit dimensions. By differentiating between direct and indirect benefits, this method covers all scenarios of user benefit and makes the calculation of benefit more comprehensive.

[0037] The formula for calculating the degree of benefit of direct beneficiaries is expressed as follows: in, This represents the direct benefit of user u from ancillary services, reflecting the direct losses avoided by the user due to ancillary services; It is expressed as the loss cost per unit of power outage time for user u, and is determined according to the characteristics of the user's industry (e.g., higher for industrial users than for commercial users). This is expressed as the average power outage power of user u, which is the average power consumption of the user during a power outage. This represents the reduction in power outage time for user u due to ancillary services, calculated from grid dispatch data (e.g., if backup services reduce user outage time from 4 hours to 1 hour, then...). =3); The power reliability requirement coefficient for user u is expressed as [0.8, 1.2], with a larger coefficient for higher requirements (such as data center users), reflecting the additional benefits that users with high reliability requirements gain from ancillary services.

[0038] The formula for calculating the degree of benefit from indirect benefits is expressed as follows: in, This represents the indirect benefit of user u from ancillary services, reflecting the long-term benefits the user gains from grid stability. This represents the annual basic electricity cost for user u, which is the total amount of electricity bills paid by the user each year. This represents the grid stability improvement factor (range [0, 1]) brought about by ancillary services, calculated from indicators such as grid frequency compliance rate and voltage deviation rate (e.g., if ancillary services improve the grid frequency compliance rate from 99.5% to 99.9%, then...). =0.004), The grid dependence coefficient of user u is expressed as [0.5, 1.5], with high-energy-consuming industrial users (such as electrolytic aluminum enterprises) having a high dependence and a large coefficient, while ordinary residential users have a low dependence and a small coefficient.

[0039] The formula for weighted quantitative calculation of the total benefit of each user during the current ancillary service period is expressed as follows: in, This represents the total benefit of user u from auxiliary services. This is represented as the direct benefit weight (range [0.6, 0.8]). It is represented as the indirect benefit weight (range [0.2, 0.4]).

[0040] In this embodiment of the application, step A2 involves extracting feature information reflecting the benefits users gain from ancillary services. This is achieved using a feature extraction method based on power grid operation data, and includes the following steps A211-A213: A211: Collect data such as the user's industry, power capacity, annual electricity bill, and historical power outage records, and extract indicators such as frequency qualification rate, voltage deviation rate, and ancillary service response time from the power grid operation data.

[0041] A212: Determine personalized weighting factors such as power reliability demand coefficient and grid dependence coefficient based on user type (e.g., high-energy-consuming industrial users, commercial users, residential users).

[0042] A213: Construct a set of benefit factors including unit outage time loss cost, reduced outage time, and grid stability coefficient, and standardize and weight them to synthesize a user benefit vector as the basis for subsequent calculations.

[0043] In an optional implementation, feature information reflecting the benefits a user gains from assistive services is extracted. A feature extraction method based on user behavior modeling using perceptual feedback can also be employed, including the following steps A221-A223: A221: Design a standardized questionnaire to collect user feedback data on their satisfaction with backup and frequency regulation ancillary services, their expected value, and their tolerance for power outages.

[0044] A222: Extract the main influencing factors and remove redundant dimensions through principal component analysis or factor analysis.

[0045] A223: Construct a quantitative feature representation based on user feedback scores and use it as an input item for user benefit.

[0046] In another alternative implementation, feature information reflecting the benefits users gain from ancillary services can be extracted using a feature extraction method based on an expert rule base and a multi-level weighting system, including the following steps A231-A233: A231: Establish an expert group composed of power dispatch, market operation and user-side management personnel to formulate a matrix of typical benefit scenarios corresponding to ancillary service types.

[0047] A232: The Analytic Hierarchy Process (AHP) is used to construct the weight hierarchy of various types of users in each benefit factor. For example, the sensitivity of industrial load to standby service is higher than that of residential load.

[0048] A233: A multi-level benefit factor system is formed, which includes expert empowerment rules, user attribute mapping, and indicator weight library. The matching results are used for subsequent calculations, and the system is traceable and interpretable.

[0049] It should be noted that this step establishes a dual-dimensional quantitative model of direct and indirect benefits, covering both the immediate and long-term value that users gain from ancillary services, thus overcoming the limitations of traditional methods that cannot quantify benefits. The benefit level serves as the basis for calculating the sharing ratio, directly reflecting how much each user benefits, avoiding the unfair phenomenon of using more but receiving less. By introducing parameters such as user electricity consumption characteristics, reliability requirements, and grid dependence, the model can differentiate between industrial users, commercial users, and residential users, thereby improving its applicability.

[0050] Furthermore, in step S2, calculating the share of each user includes the following steps B1-B3: B1: Calculate the total cost of ancillary services involved in the target period. Other costs are divided into three categories: supply costs, operating costs, and risk costs.

[0051] B2: Based on the total benefit obtained by each user in the benefit measurement model, calculate the proportion in the set of all benefiting users and determine the corresponding cost sharing coefficient.

[0052] B3: Based on the known total cost and cost-sharing coefficient, an adjustment coefficient reflecting the current fluctuations in the ancillary service market price is introduced to correct the original cost-sharing amount, thus obtaining the final cost-sharing amount that the user should bear.

[0053] Specifically, the total cost of ancillary services within a certain period is expressed by the formula: in, This represents the total cost of ancillary service s, encompassing the entire cost chain of ancillary services. This is expressed as the supply cost of ancillary services s, which includes the costs incurred by power generation companies in providing ancillary services such as unit modification and fuel consumption. This refers to the operating cost of ancillary service s, which includes the personnel, equipment maintenance, and other costs incurred by the power grid company in dispatching ancillary services. This represents the risk cost of ancillary services s, which is the grid loss reserve caused by ancillary services failing to achieve the expected results (such as frequency regulation failure).

[0054] Traditional cost accounting only considers Ignoring operational and risk costs leads to an underestimation of total costs. This model integrates three types of costs to ensure complete cost accounting and avoids insufficient user share due to cost omissions.

[0055] The proportion of ancillary service costs that user u should bear, i.e., the cost-sharing coefficient, is determined based on the proportion of its total benefit to the sum of the total benefits of all users, expressed by the formula: in, This represents the cost-sharing ratio of ancillary services for user u, reflecting the principle that the more one benefits, the more one bears; U represents the set of users who benefit from participating in ancillary services. It represents the sum of the total benefits for all beneficiary users.

[0056] Traditional models use a fixed ratio (such as based on the proportion of electricity consumption per user) to share costs, which is not linked to the degree of benefit, leading to an unfair situation where those who benefit less bear more of the costs. This model addresses this by... By directly linking the sharing ratio with the degree of benefit, a fair distribution can be achieved.

[0057] Considering the price fluctuations in the ancillary services market, a price adjustment factor is introduced to calculate the final share of costs to be borne by user u. The formula is as follows: in, This represents the dynamic cost-sharing amount for auxiliary services for user u, i.e., the actual cost that the user needs to pay. This is expressed as the price adjustment factor for ancillary service s (range [0.8, 1.2]), calculated based on the deviation between the market price and the benchmark price of the ancillary service (e.g., if the market price is 1.1 times the benchmark price, then...). =1.1); the benchmark price is set by the government or power grid company based on historical data.

[0058] In this embodiment of the application, step B3, where the adjustment coefficient is calculated based on a dynamic adjustment mechanism of the market settlement price, includes the following steps B311-B313: B313: Obtain the market settlement price of ancillary services for the current period and compare it with the historical benchmark price set by the dispatching agency.

[0059] B313: Calculate the price deviation ratio to form a price adjustment coefficient (e.g., if the market price is 10% higher than the benchmark, the adjustment coefficient is set to 1.1).

[0060] B313: Apply this coefficient to the original amount that the user should bear to obtain the final corrected dynamic sharing amount, so as to realize the real-time linkage between the user's sharing amount and the market cost.

[0061] In an optional implementation, the adjustment factor can also be calculated based on a smoothing adjustment mechanism of the rolling period average price, including the following steps B321-B323: B321: The average settlement price of ancillary services over several recent periods is used as a rolling benchmark average price.

[0062] B322: Compare the current market price with the rolling average price to calculate the volatility trend factor (the coefficient is adjusted upwards if there is a continuous upward trend).

[0063] B323: By smoothing the adjustment of user contribution amounts through trend factors, the mechanism avoids drastic changes in contribution results due to short-term abnormal fluctuations, thereby improving the stability of the mechanism.

[0064] In another alternative implementation, the adjustment coefficient can also be calculated based on a target-oriented adjustment mechanism of the policy intervention factor, including the following steps B331-B333: B331: Policy guidance factors formulated by power grid companies or regulatory agencies to reflect the state's support orientation for specific ancillary services or user groups (e.g., encouraging voltage regulation services, supporting small and medium-sized enterprises).

[0065] B332: Set policy adjustment coefficients for different user categories or service scenarios (e.g., 0.95 for industrial users and 1.0 for residential users).

[0066] B333: Applying policy coefficients to the calculation results of user-shared amounts makes the mechanism reflect policy-oriented goals and enhances the acceptability and strategic alignment of the system.

[0067] It should be noted that this step incorporates the three components of supply cost, operating cost, and risk cost into the calculation in a unified manner, avoiding the problem of a low sharing base caused by neglecting operational or systemic risks in traditional models; it adopts the proportion of total benefit as the sharing coefficient, so that cost bearing is strictly aligned with the degree of benefit, and strengthens the incentive and constraint mechanism of "whoever benefits bears the cost"; it introduces a price adjustment coefficient, so that the amount shared by the end user can fluctuate with the price of ancillary services, effectively avoiding the imbalance of static mechanism in dynamic market adaptation.

[0068] Furthermore, in step S3, calculating the user's final incentive amount includes the following steps C1-C3: C1: Construct a mapping relationship based on the cost-sharing amount of users in ancillary services, and generate corresponding tiered incentive coefficients.

[0069] C2: Based on the frequency of user participation in auxiliary services and the quality of response, behavioral features are extracted to construct influencing factors.

[0070] C3: Combine the tiered incentive coefficients with the influencing factors to calculate the final incentive amount received by the user.

[0071] Specifically, the incentive base is linked to the amount of cost shared by users, ensuring that the more users contribute, the higher the incentive base. The formula is as follows: in, This represents the base amount for the auxiliary service incentive for user u, which serves as the basis for calculating the incentive amount. This is expressed as an incentive coefficient (range [0.1, 0.3]), which is adjusted by the power grid company based on the supply and demand of ancillary services (e.g., when backup services are in short supply). =0.3, to enhance incentives).

[0072] Traditional incentive models are decoupled from cost-sharing (e.g., a fixed incentive of 50 yuan per household), failing to reflect the link between cost-sharing and incentives. This model addresses this by... Linking incentives to the amount of contribution avoids the unreasonable phenomenon of receiving incentives without taking responsibility.

[0073] Based on the depth of user participation in auxiliary services (such as participation frequency and response speed), a tiered incentive coefficient is set, expressed by the formula: in, It is represented as a stepped incentive coefficient for user u, with the coefficient increasing as the depth of participation increases; This represents the frequency of user u's participation in auxiliary services, i.e., the number of times the user responds to auxiliary service scheduling per month; , Represented as the participation frequency threshold (e.g.) = 2 times / month =5 times / month), set by the power grid company according to the needs of ancillary services.

[0074] Traditional incentives use fixed coefficients, which cannot differentiate the depth of user engagement. This model uses a tiered coefficient to provide higher incentives to users with high engagement. For example, users who participate more than 5 times per month have a coefficient of 1.5, which is higher than the 1.0 coefficient for users who participate less than or equal to 2 times per month, effectively improving user engagement.

[0075] The final incentive amount for user u is the product of the incentive base and the tiered incentive coefficient, expressed by the formula: in, This represents the final incentive amount for user u's auxiliary services, i.e., the incentive the user actually receives. This is expressed as the response quality factor for user u (range [0.9, 1.1]), calculated based on the on-time and completion rates of the user's response assistance services (e.g., if the on-time response rate is 100% and the completion rate is 100%). =1.1; On-time rate 80%, completion rate 80%, then =0.9).

[0076] In this embodiment of the application, step C2, where the influence factor is calculated based on a rule-based adjustment mechanism for participation in the classification of levels, includes the following steps C211-C213: C213: Based on the frequency with which users respond to auxiliary services each month, participation levels are divided, for example: high-frequency users (≥5 times / month), medium-frequency users (3-4 times / month), and low-frequency users (≤2 times / month).

[0077] C213: Configure corresponding step excitation coefficients for different levels, such as 1.5 for high-frequency users, 1.2 for medium-frequency users, and 1.0 for low-frequency users.

[0078] C213: The user's actual incentive adjustment factor is directly determined by the coefficient corresponding to their current level and participates in the calculation of the final incentive amount.

[0079] In an optional implementation, the impact factor can also be calculated based on a scoring-based moderating mechanism of statistical performance analysis, including the following steps C221-C223: C223: Extract key indicators such as response time, instruction completion rate, and error deviation for each user's last N auxiliary services from power grid dispatch data.

[0080] C223: Set the reference range and scoring criteria for each indicator (e.g., response time < 10 seconds gets 5 points, instruction completion rate ≥ 95% gets 5 points, error < ± 2% gets 5 points).

[0081] C223: The weighted total score is normalized to the interval [0.9, 1.1] to form a continuous response quality factor, which is used to adjust the incentive amount.

[0082] In another alternative implementation, the influencing factor can also be calculated based on the intelligent adjustment mechanism of the behavioral prediction model, including the following steps C231-C233: C233: Construct a user engagement behavior prediction model (such as random forest, logistic regression, neural network, etc.), using historical response data as training samples, and the model output is a user prediction service completion quality score.

[0083] C233: Standardize the predicted score to form an incentive adjustment factor.

[0084] C233: This factor is used as a dynamic adjustment value in the calculation of user incentive amounts, supporting continuous learning and refined evaluation.

[0085] It should be noted that this step uses the amount contributed by users as the incentive base, so that the more users contribute, the greater the potential incentive, thus preventing the reverse incentive problem of "taking incentives without participating". By constructing adjustment coefficients through behavioral factors such as participation frequency, response speed, and completion rate, users are encouraged not only to participate, but also to "participate well", thereby improving the quality of auxiliary service execution. The tiered incentive mechanism linked to response quality can achieve a positive incentive difference of "the better get more and the worse get less", promoting healthy competition among users.

[0086] Furthermore, in step S4, the periodic adjustment of each parameter and factor includes the following steps D1-D3: D1: Set an upper limit on the amount of cost sharing for users to ensure that users do not exceed the set proportion of their monthly electricity bill, and set an upper limit on the incentive budget for the total incentive expenditure of the system to prevent the incentive amount from exceeding the market revenue of ancillary services.

[0087] D2: To address changes in the external environment during the operation of auxiliary services, a dynamic adjustment mechanism is constructed to update various parameters and factors.

[0088] D3: Using a fixed period as the trigger condition, recalculate and deploy each parameter and factor.

[0089] Specifically, to prevent users from consuming excessive amounts that would place an undue burden, a cap on the amount to be shared is set, expressed by the formula: in, This is represented as the upper limit coefficient for the shared amount (range [0.05, 0.15]), meaning that the user's shared amount shall not exceed 15% of their monthly electricity bill; This represents the monthly electricity cost for user u.

[0090] To prevent the total incentive amount from exceeding the total revenue from ancillary services, an upper limit constraint is set on the total incentive amount, expressed by the formula: in, This represents the total incentive amount for all users. This is represented as the upper limit coefficient for the total incentive amount (range [0.2, 0.4]), meaning the total incentive amount shall not exceed 40% of the total revenue from ancillary services; The total revenue of ancillary services s is represented by ancillary service market transaction revenue, government subsidies, etc.

[0091] Traditional mechanisms lack constraints on the total incentive amount, which may lead to excessively high incentive costs and affect the sustainability of the mechanism. This constraint addresses this issue through… Control the total amount of incentives to ensure that the mechanism operates within an economically feasible range.

[0092] In this embodiment of the application, in step D2, the dynamic adjustment mechanism is based on a rule-based threshold triggering mechanism for load fluctuations and price deviations, including the following steps D211-D213: D211: Set monitoring rules for key operational indicators, such as: Peak-valley difference in power grid load ,in Represented as peak load of the power grid. Represented as grid valley load, It is expressed as the grid average.

[0093] Ancillary service price offset rate ,in This is represented as the current market settlement price. Represented as historical benchmark price, This is represented as the benchmark price.

[0094] D212: Set threshold judgment conditions for the above indicators, for example: like If so, the base amount for the backup service incentive will be adjusted to 1.2 times the original value.

[0095] like If so, the price adjustment coefficient will be increased to 1.1.

[0096] D213: Periodically check whether the indicators have exceeded the limits. If the conditions are met, update the relevant parameter values ​​and make them effective in the next settlement cycle.

[0097] In an optional implementation, the dynamic adjustment mechanism may also be based on a trend-driven adjustment mechanism of a time series forecasting model, including the following steps D221-D223: D221: Construct a time-series database of key parameters (such as market settlement prices, grid load, service call frequency, etc.) and use a sliding window to process historical data.

[0098] D222: Using a predictive model, the operating conditions of the next cycle are predicted, generating estimates of future price levels, grid load change trends, etc.

[0099] D223: Adjust mechanism parameters based on forecast results. For example, if the forecast model outputs a future price growth rate of 15%, the price adjustment coefficient will be set to 1.15 in advance; if the forecast shows that the load peak-valley difference is widening, the upper limit of user sharing will be dynamically increased.

[0100] In another alternative implementation, the dynamic adjustment mechanism can also be based on a structured adjustment mechanism using expert systems and user profiles, including the following steps D231-D233: D231: Establish a rule base based on expert experience, covering the mapping relationship of "user type ↔ recommended parameter range", for example: Users with high grid dependence → Increase the upper limit of incentive weight.

[0101] Commercial load → Increase the adjustment weight of response quality coefficient.

[0102] D232: Establish a user profile system, extract features such as user's industry, average monthly load, electricity price sensitivity, and ancillary service participation history, and match them to the tag categories in the expert rule model.

[0103] D233: Dynamically set personalized mechanism parameters based on matching results, for example: increase the indirect benefit weight of industrial users to 0.5; widen the range of response quality coefficient for data center users to improve incentive sensitivity.

[0104] It should be noted that this step sets an upper limit for the amount shared by users and a constraint on the total amount of system incentives to prevent incentive imbalances or excessive burdens on individuals in extreme scenarios, thereby improving the sustainability of the mechanism. In combination with external environments such as changes in grid load, market price fluctuations, and changes in user structure, key parameters are dynamically adjusted to ensure that the mechanism can continuously adapt to the actual operating situation. The parameter update triggering mechanism enables the system to perceive and respond to operational fluctuations, breaking through the lag problem of traditional "static allocation rules" and enhancing the adaptability of the system.

[0105] Example 3, the third embodiment of the present invention, differs from the previous two embodiments in that it provides a user-participatory ancillary service cost-sharing system based on dynamic analysis. This system includes a benefit quantification module, a cost-sharing amount calculation module, an incentive amount calculation module, and an adjustment module. The benefit quantification module establishes a benefit quantification model for user participation in ancillary services, quantifying the benefit gained by each user from participating in the ancillary services. The cost-sharing amount calculation module determines the corresponding cost-sharing ratio based on the user's total benefit, and calculates the cost-sharing amount for each user by combining the supply cost, operating cost, and risk cost of the ancillary services. The incentive amount calculation module sets a tiered incentive coefficient and influencing factor based on each user's cost-sharing amount, participation frequency, and response quality, calculating the user's final incentive amount. The adjustment module establishes a dynamic adjustment and constraint model for the mechanism's operation, periodically adjusting the parameters and factors.

[0106] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0107] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0108] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0109] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented in combination with any of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0110] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for sharing and distributing user-participated auxiliary services based on dynamic analysis, characterized in that: include, Establish a quantification model for the benefits of user participation in ancillary services to quantify the degree of benefit each user gains from participating in ancillary services; The corresponding cost-sharing ratio is determined based on the total benefit of users, and the amount borne by each user is calculated by combining the supply cost, operating cost and risk cost of ancillary services. Based on each user's contribution amount, participation frequency, and response quality, a tiered incentive coefficient and influence factor are set to calculate the user's final incentive amount. Establish a dynamic adjustment and constraint model for the mechanism's operation, and periodically adjust each parameter and factor.

2. The method for sharing and distributing user-participated auxiliary services based on dynamic analysis as described in claim 1, characterized in that: The establishment of a user-participatory metric model for ancillary service benefits includes, The benefits that users gain from ancillary services are divided into direct benefits and indirect benefits; Based on the classification results of user benefit dimension, feature information reflecting the benefits that users obtain from ancillary services is extracted and organized into a structured quantitative expression for benefit calculation; The total benefit of each user in the current ancillary service period is calculated using a weighted quantitative method. Combining actual power grid operation data and user electricity bill information, a normalization method is used to unify the benefit scale among different users.

3. The method for sharing and distributing user-participated auxiliary services based on dynamic analysis as described in claim 2, characterized in that: The calculation of each user's share of the cost includes, Perform total cost accounting for the ancillary services involved within the target period; Based on the total benefit obtained by each user in the benefit measurement model, calculate the proportion in the set of all benefiting users and determine the corresponding cost sharing coefficient; Based on the known total cost and cost-sharing coefficient, an adjustment coefficient reflecting the current fluctuations in ancillary service market prices is introduced to correct the original cost-sharing amount, thus obtaining the final cost-sharing amount that the user should bear.

4. The user participation auxiliary service sharing method based on dynamic analysis as described in claim 3, characterized in that: The calculation of the user's final incentive amount includes, A mapping relationship is constructed based on the cost-sharing amount of users in ancillary services, and corresponding tiered incentive coefficients are generated; Based on the frequency and quality of user participation in auxiliary services, behavioral features are extracted to construct influencing factors; The tiered incentive coefficients and influencing factors are combined to calculate the final incentive amount received by the user.

5. The method for sharing and distributing user-participated auxiliary services based on dynamic analysis as described in claim 4, characterized in that: The periodic adjustment of each parameter and factor includes, Set an upper limit on the amount of user cost sharing to ensure that users do not exceed the set proportion of their monthly electricity bill, and set an upper limit on the total incentive expenditure of the system to prevent the incentive amount from exceeding the market revenue of ancillary services; To address changes in the external environment during the operation of auxiliary services, a dynamic adjustment mechanism is constructed to update various parameters and factors. Using a fixed period as the trigger condition, the parameters and factors are recalculated and deployed.

6. The method for sharing and distributing user-participated auxiliary services based on dynamic analysis as described in claim 5, characterized in that: The formula for calculating the degree of benefit of the direct beneficiary is expressed as follows: in, This represents the direct benefit user u receives from auxiliary services. Expressed as the cost of power outage per unit of user u. This is expressed as the average power outage power for user u. This represents the reduction in power outage time for user u as an ancillary service. It is represented as the power reliability requirement coefficient for user u.

7. The method for sharing and distributing user-participated auxiliary services based on dynamic analysis as described in claim 6, characterized in that: The formula for calculating the degree of benefit of the indirect benefit is expressed as follows: in, This represents the indirect benefit of user u from ancillary services, reflecting the long-term benefits the user gains from grid stability. This represents the annual basic electricity cost for user u. This represents the power grid stability improvement factor resulting from ancillary services. The grid dependence coefficient of user u is expressed as follows; The formula for weighted quantitative calculation of the total benefit of each user in the current auxiliary service period is expressed as follows: in, This represents the total benefit of user u from auxiliary services. This is represented as the weight of direct benefit. This is represented as the indirect benefit weight.

8. A user-participatory auxiliary service sharing system based on dynamic analysis, employing the user-participatory auxiliary service sharing method based on dynamic analysis as described in any one of claims 1 to 7, characterized in that: It includes a benefit quantification module, a cost-sharing calculation module, an incentive amount calculation module, and an adjustment module; The benefit quantification module is used to establish a benefit quantification model for user participation in ancillary services, and to quantify the degree of benefit obtained by each user from participating in ancillary services. The cost-sharing calculation module determines the corresponding cost-sharing ratio based on the total benefit of the users, and calculates the cost-sharing amount for each user by combining the supply cost, operating cost and risk cost of the auxiliary services. The incentive amount calculation module sets a tiered incentive coefficient and an influence factor based on each user's share of the incentive amount, participation frequency, and response quality, and calculates the user's final incentive amount. The adjustment module is used to establish a dynamic adjustment and constraint model for the operation of the mechanism and to periodically adjust various parameters and factors.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the user participation auxiliary service sharing method based on dynamic analysis as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the user participation auxiliary service sharing method based on dynamic analysis as described in any one of claims 1 to 7.

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