Internal income comprehensive distribution method, system and device capable of adjusting load after large-scale aggregation and storage medium

Abstract

The invention discloses an internal income comprehensive distribution method, system and device capable of adjusting load after large-scale aggregation and a storage medium, and relates to the technical field of energy optimization scheduling and energy management, and the method comprises the steps: constructing a comprehensive contribution index system based on safety, economy and risk indexes; outputting the comprehensive contribution degree of each load resource according to the comprehensive contribution degree index system by adopting a comprehensive evaluation method fusing TOPSIS, grey correlation and an entropy weight method; creating an energy-auxiliary service market joint income model, and quantifying the value contribution of the adjustable load polymer; and according to the comprehensive contribution degree of each load resource and the energy-auxiliary service market joint income model, performing internal income distribution by using a double-layer settlement framework of fixed protocol electricity price and contribution degree assessment. According to the method, a comprehensive contribution degree model based on safety, economy and risk indexes is constructed, TOPSIS grey correlation and an entropy weight method are fused, and accurate quantification and fair distribution of load resource values are achieved.

Description

Technical Field

[0001] This invention relates to the field of energy optimization scheduling and energy management technology, specifically to a method, system, device, and storage medium for the comprehensive allocation of internal benefits of adjustable loads after large-scale aggregation. Background Technology

[0002] With the rapid increase in renewable energy penetration and the deepening of power market reforms, large-scale aggregated regulation of adjustable load (AL) has become a key technological path to improve the flexibility of the power system. According to statistics from the International Renewable Energy Agency (IRENA), global wind power and photovoltaic installed capacity reached 940 GW and 1190 GW respectively in 2023, but their output volatility led to a widening of the system's net load peak-to-valley difference to 30%–40%. Adjustable load, through aggregation and participation in market transactions, can provide peak-shaving capacity equivalent to that of thermal power units. However, the differences in the technical characteristics of heterogeneous load resources and the challenges in quantifying their market value present some core technological bottlenecks for large-scale aggregated regulation.

[0003] Adjustable loads encompass various resource types, including industrial motors, energy storage systems, and commercial air conditioning, exhibiting significant heterogeneity in their regulation characteristics. For instance, industrial motors have regulation delays ranging from 10 to 30 minutes, while energy storage systems can respond at the millisecond level, making it difficult for traditional unified scheduling models to achieve collaborative optimization across time scales. Load aggregation needs to accommodate the interests of multiple investors, and existing centralized control architectures pose risks of communication latency and privacy leaks. When the aggregation scale exceeds 1,000 nodes, the command transmission latency of traditional SCADA systems exceeds 500 ms, resulting in a 40% decrease in frequency regulation performance. Therefore, there is an urgent need to develop distributed collaborative control architectures and edge computing technologies to support the efficient aggregation of large-scale heterogeneous loads.

[0004] Current benefit-sharing mechanisms mainly rely on negotiated electricity prices or shared allocation models, making it difficult to achieve precise incentives. Theoretically, while the Shapley value method can quantify individual contributions in cooperative games, its computational complexity is high, making it difficult to apply in real time when the aggregated load exceeds 100. Although Data Envelopment Analysis (DEA) can assess relative performance through the efficiency frontier, it cannot analyze the coupling relationship between loads in the dimensions of security (such as frequency response) and economy (such as electricity price arbitrage). Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by this invention is that the existing benefit sharing mechanism has the following problems: it is difficult to achieve accurate incentives, it does not distinguish the value difference between frequency regulation and peak regulation services, resulting in the marginal contribution of high response speed loads not being fully compensated, it is difficult to apply in real time when the number of aggregated loads n>100, and it cannot analyze the coupling relationship between loads in the dimensions of security (such as frequency response) and economy (such as electricity price arbitrage).

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a method for the comprehensive allocation of internal revenue for adjustable loads after large-scale aggregation, comprising: constructing a comprehensive contribution index system based on safety, economy, and risk indicators; employing a comprehensive evaluation method integrating TOPSIS, grey relational analysis, and entropy weighting to output the comprehensive contribution of each load resource according to the comprehensive contribution index system; creating an energy-ancillary service market joint revenue model to quantify the value contribution of the adjustable load aggregate; and allocating internal revenue based on the comprehensive contribution of each load resource and the energy-ancillary service market joint revenue model, using a two-tier settlement framework of fixed contract electricity price and contribution assessment.

[0008] As a preferred embodiment of the method for comprehensive allocation of internal revenue of adjustable load after large-scale aggregation as described in this invention, the comprehensive contribution index system includes: safety indicators, covering any one of response capability, plan execution, adjustment quality or operational reliability; economic indicators, including at least one of market revenue, cost optimization and marginal contribution; and risk indicators, including at least one of equipment risk and information risk.

[0009] As a preferred embodiment of the internal benefit allocation method for adjustable loads after large-scale aggregation as described in this invention, the comprehensive evaluation method using TOPSIS, grey relational analysis, and entropy weighting includes: determining the weight of each indicator in the comprehensive contribution index system using entropy weighting; calculating the grey relational degree and Euclidean distance of each sample to the positive and negative ideal solutions; and calculating the relative proximity of each sample according to the grey relational degree and Euclidean distance, which is expressed as the comprehensive contribution.

[0010] As a preferred embodiment of the method for comprehensive allocation of internal revenue of adjustable load after large-scale aggregation as described in this invention, the joint revenue model of the energy-ancillary services market includes the use of economic indicators such as the revenue ratio of conventional generating units, the optimization rate of revenue of new energy generating units, and the optimization rate of electricity purchase cost per unit of load.

[0011] As a preferred embodiment of the internal revenue allocation method for adjustable load after large-scale aggregation as described in this invention, the two-layer settlement framework includes: calculating the initial allocation amount based on the agreed electricity price and the contributed electricity volume, and assessing and adjusting the initial allocation amount based on the comprehensive contribution index system, and outputting the final allocation amount.

[0012] As a preferred embodiment of the internal revenue allocation method for adjustable load after large-scale aggregation as described in this invention, the revenue allocation includes two stages: monthly allocation and annual allocation.

[0013] As a preferred embodiment of the internal revenue comprehensive allocation method for adjustable load after large-scale aggregation as described in this invention, the monthly allocation and annual allocation include a monthly allocation ratio of 70% to 90% and an annual allocation ratio of 10% to 30%.

[0014] Another objective of this invention is to provide an internal revenue allocation system for adjustable loads after large-scale aggregation. This system can allocate internal revenue based on the comprehensive contribution of each load resource and the joint revenue model of the energy-ancillary services market, using a two-tier settlement framework of fixed contract electricity price and contribution assessment. This solves the problem that the current benefit sharing mechanism cannot resolve the coupling relationship between loads in the dimensions of security (such as frequency response) and economy (such as electricity price arbitrage).

[0015] As a preferred embodiment of the internal revenue allocation system for adjustable loads after large-scale aggregation as described in this invention, the system includes: an indicator formation module, a contribution calculation module, a revenue model creation module, and a revenue allocation module. The indicator formation module forms a comprehensive contribution indicator system encompassing safety, economic, and risk-related indicators. The contribution calculation module uses a comprehensive evaluation method integrating TOPSIS grey relational analysis and entropy weighting to calculate the comprehensive contribution of each load resource according to the comprehensive contribution indicator system. The revenue model creation module forms a revenue model relating energy and ancillary services markets, quantifying the value contribution of the adjustable load aggregation to the overall system's economic efficiency. The revenue allocation module uses a two-tier settlement architecture, incorporating both contribution and joint revenue, to execute internal revenue allocation.

[0016] Another object of the present invention is to provide an internal revenue allocation device for adjustable loads after large-scale aggregation, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of an internal revenue allocation method for adjustable loads after large-scale aggregation.

[0017] Another object of the present invention is to provide a storage medium for the comprehensive allocation of internal revenues of adjustable loads after scaling up and amplification, wherein a computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method for comprehensive allocation of internal revenues of adjustable loads after scaling up and amplification are implemented.

[0018] The beneficial effects of this invention are: This invention provides a method for comprehensively allocating the internal revenue of adjustable loads after large-scale aggregation. It constructs a comprehensive contribution model based on safety, economic, and risk indicators, integrating TOPSIS grey relational analysis and entropy weighting to achieve accurate quantification and fair allocation of load resource value. A joint revenue model is established, introducing three-dimensional economic indicators to comprehensively quantify the value contribution of adjustable loads to the system's economic efficiency and uncover joint revenue. A two-tier settlement architecture is designed, selecting a load model that emphasizes economic value to balance operational stability and allocation fairness, thereby improving the system's economic benefits and operational efficiency. Attached Figure Description

[0019] 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.

[0020] Figure 1 This is a flowchart of a method for comprehensively allocating the internal revenue of an adjustable load after large-scale aggregation, as provided in Embodiment 1 of the present invention. Detailed Implementation

[0021] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0022] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for comprehensively allocating the internal revenue of adjustable loads after large-scale aggregation is provided, comprising: S1: Construct a comprehensive contribution indicator system based on safety, economy, and risk indicators.

[0023] Furthermore, the adjustable load aggregation model (Multiple Investment Entities - Aggregation for Load Participation) integrates dispersed adjustable load resources to participate in electricity spot and ancillary service market transactions, achieving economies of scale. Aggregators sign agreements with multiple investment entities to optimize load dispatching schemes and conduct internal settlements through negotiated fixed electricity prices and contribution assessments. The profit model includes the electricity spot market and the ancillary service market. In the spot market, adjustable loads adjust their electricity consumption based on price signals, participating in supply and demand interactions, or feeding surplus electricity back into the market. In the ancillary service market, aggregated loads provide services such as peak shaving and frequency regulation. The contribution of an individual load is comprehensively measured through factors such as regulation capacity and response speed to ensure a reasonable distribution of transaction revenue.

[0024] The revenue from adjustable loads participating in electricity trading is expressed as follows: , in, To generate revenue for adjustable loads participating in the energy market, To generate revenue from adjustable load participation in the ancillary services market.

[0025] The clusters formed by the aggregation of adjustable loads include various flexibly controllable electrical devices, responsive energy storage components, and aggregated user loads. These clusters participate in electricity spot and ancillary service transactions, providing key ancillary services such as frequency regulation and peak shaving to ensure grid stability. Therefore, the revenue generated by adjustable load clusters needs to be assessed through a scientific individual load contribution index system to accurately evaluate each component and achieve a reasonable distribution of benefits. For the members within the adjustable load cluster, their main contributions can be analyzed and evaluated based on three aspects: safety, economy, and risk, as shown in Table 1.

[0026] Table 1. Evaluation Index System for Internal Member Contributions of Adjustable Load After Large-Scale Aggregation

[0027] Plan completion rate refers to the completion status of generator unit power generation plan or controllable load response plan, expressed as... express: , in, This represents the actual controlled electricity consumption of a single load within a month. The planned controlled electricity volume to be achieved for a single load in the monthly plan.

[0028] The marginal contribution value is an indicator based on cooperative game theory that reflects the utility of each member to the adjustable load, expressed as: , in, This represents the marginal contribution value. For the benefits of adjustable load; The marginal contribution ranges between [0,1], representing the revenue obtained by removing member i from the adjustable load. The larger the value, the greater its marginal contribution, and the more important its role in adjustable loads.

[0029] S2: A comprehensive evaluation method integrating TOPSIS, grey relational analysis, and entropy weighting is adopted to output the comprehensive contribution of each load resource according to the comprehensive contribution index system.

[0030] Furthermore, a comprehensive evaluation of grey relational analysis based on TOPSIS is conducted.

[0031] The standardization process for the indicator matrix includes: Assuming shared Each evaluation unit, from... The indicators are calculated and evaluated, and the results are used to construct an initial evaluation matrix according to the formula. .

[0032] , Indicator matrix normalization. Since the evaluation indicators differ in nature, order of magnitude, and units, their numerical values ​​also vary significantly. Therefore, normalization is performed first, using vector normalization for numerical processing: , in, Indicates the initial evaluation. X represents the normalized index value, and the evaluation X is a dimensionless index matrix.

[0033] The entropy weight method is used to determine the weights of the indicators. The concept of entropy originates from thermodynamics and is a measure of the degree of microscopic disorder in a system; the smaller the entropy value, the weaker the uncertainty of the system. The entropy weight method is used to calculate the weights of each indicator. The normalized evaluation indicators are then calculated. (The proportion of the i-th item under the i-th indicator to the indicator value of the plan): , The entropy value of the j-th indicator express: , Then the entropy weight of each index express: , , Indicator weights are standardized.

[0034] The grey relational degree and Euclidean distance are determined by weighted standardization of the indicators, and expressed as follows: , , in, Indicates the degree of grey relational relationship. It indicates a desire to understand.

[0035] It should be noted that positive and negative ideal solutions are determined. The positive ideal solution is constructed using the maximum value of the positive index and the minimum value of the negative index. The negative ideal solution is constructed using the minimum value of the positive index and the maximum value of the negative index. : , , in, It is a positive indicator; It is a negative indicator.

[0036] Calculate the grey relational degree from each sample to the positive and negative ideal solutions.

[0037] Weighted index matrix mean transformation: , , Based on the initial value index matrix Y, calculate the grey relational coefficient between the i-th sample and the positive ideal sample with respect to the j-th index. .

[0038] , The grey relational degree between the i-th sample and the positive ideal solution can be calculated. : , Similarly, the grey relational degree of the negative ideal solution of the i-th sample can be calculated. : , Calculate the Euclidean distance from sample i to the positive and negative ideal solutions. and negative ideal solution The Euclidean distances between them are as follows: , , The overall contribution is determined by calculating the relative similarity of the samples.

[0039] We combine grey relational analysis and Euclidean distance. First, we perform dimensionless processing on both the Euclidean distance and grey relational analysis values: , in, This represents the value after dimensionless processing, used to eliminate the influence of dimensions and make different indicators comparable. , ... Let represent the dimensionless grey relational degree and Euclidean distance, and , The larger the value, the closer the sample is to the positive ideal solution and the further it deviates from the negative ideal solution; , A larger value indicates that the sample is closer to the negative ideal solution and deviates from the positive ideal solution. Considering the combined effects of Euclidean distance and grey relational degree, it can be expressed as: , , in, and Each represents the decision-maker's preference weight for distance and shape. , , Each represents the decision-maker's preference for distance and shape. These reflect the degree of similarity between the sample and the positive and negative ideal solutions, respectively.

[0040] The relative closeness calculation is expressed as: , Among them, relative closeness This reflects how close the sample is to the negative ideal solution under the decision-maker's preferences.

[0041] S3: Create a joint revenue model for the energy-ancillary services market to quantify the value contribution of adjustable load aggregates.

[0042] Furthermore, the economic value of the aggregated adjustable load is calculated.

[0043] The revenue and expenditure of conventional generating units participating in the spot market consists of three parts: the power generation revenue from the spot market, the reserve revenue obtained from participating in the standby ancillary services market, and the load shedding compensation costs that need to be compensated to the load.

[0044] , , in, This represents the total revenue of a conventional unit under the baseline scenario; It is the total benefit of conventional units after adjustable loads are involved; The total number of conventional generating units participating in the market; This is the power generation of the i-th conventional unit during time period t; This is the reserve capacity provided by the i-th conventional unit during time period t; and These are the marginal clearing electricity prices for the spot market period t before and after the participation of adjustable loads; The compensation price for conventional generating units during time period t in the standby ancillary services market; The compensation price paid for the standby capacity provided to conventional units in the standby ancillary services market during time period t; This is the cost allocation coefficient for load shedding compensation of conventional generating units. The price is the compensation price for loss of load. The total power loss of the system during time period t.

[0045] Therefore, the power generation revenue ratio of conventional units The expression is as follows: , Since renewable energy power generators have low marginal costs, they can make a profit as long as they can generate electricity. The revenue expression for power generation in scenario 2 is as follows: , , in, This represents the total revenue of new energy generating units under the baseline scenario; It is the total benefit of new energy units after adjustable loads participate; It represents the total number of new energy generating units participating in the market; and These are the marginal clearing electricity prices for the spot market period t before and after the participation of adjustable loads; This represents the actual power generation of the l-th renewable energy unit during time period t. This is the cost allocation coefficient for load shedding compensation of new energy generating units; The price is for loss of load compensation. The total load shedding power of the system during time period t. Therefore, the optimization rate of power generation revenue for new energy units. The expression is as follows: , in, The total revenue is calculated based on the on-grid purchase price of new energy generating units.

[0046] When adjustable loads participate in the spot market, they can increase the absorption of new energy sources and reduce marginal electricity prices by adjusting their electricity consumption at different times, thereby reducing the electricity purchase costs that the loads need to pay.

[0047] , , , in, This represents the unit electricity purchase cost on the load side under the baseline scenario. The unit electricity purchase cost for adjustable loads participating in the load side; This represents the total number of load nodes. Total number of adjustable loads; and These are the marginal clearing electricity prices for the spot market period t before and after the participation of adjustable loads; The price is for compensation for loss of load. This represents the initial power consumption of load node m during time period t. The total power loss of the system during time period t; The actual load power of the system during the period t after the adjustable load participates in the spot market; The unit compensation price for adjusting the i-th adjustable load; Let be the adjustable power of the i-th adjustable load during time period t. Therefore, the optimization rate of the unit electricity purchase cost per load is... The expression is as follows: , For ease of calculation, The cost of purchasing electricity per unit of load within the scheduling cycle is taken.

[0048] Calculate the internal benefit sharing mechanism for adjustable load.

[0049] S4: Based on the comprehensive contribution of each load resource and the joint revenue model of the energy-ancillary services market, internal revenue is distributed using a two-tier settlement framework of fixed contract electricity price and contribution assessment.

[0050] Furthermore, the internal benefit allocation for adjustable loads is settled and allocated through two levels: the agreed-upon electricity price and the contribution assessment. Specifically, the benefit allocation is based on the proximity calculation results and is conducted twice, monthly and annually. 80% of the allocation amount is allocated monthly, and the remaining 20% ​​is allocated annually. Monthly allocation amount Annual apportionment amount The calculation methods are as follows: , in, The total market revenue of adjustable load polymers; For wind and solar power contracted electricity prices; It contributes electricity to wind and solar power. The agreed-upon electricity price for gas turbines; It contributes electricity to the gas turbine; For flexible load agreement electricity pricing; Contribute electricity to flexible loads; The agreed electricity price for energy storage systems; It contributes electricity to the energy storage system.

[0051] , , in, This represents the monthly internal benefit allocation amount for the m-th month.

[0052] The monthly allocation formula for each internal member of the adjustable load is as follows: , , , , in, This represents the apportionment amount for wind power / solar power in month m. Let m be the amount allocated to the gas turbine in month m. This represents the amount of flexible load allocated in month m. Let m be the amount allocated to the energy storage system in month m. The combined contribution of wind power / solar power in month m; The overall contribution of the gas turbine in month m; The overall contribution of the flexible load in month m; The overall contribution of the energy storage system in month m.

[0053] The annual allocation formula for adjustable loads among its internal members is as follows: , , , , in, This represents the allocated amount for wind / solar power throughout the year. This represents the cost allocated to the gas turbine throughout the entire year. This is the amount allocated to flexible loads throughout the year; This represents the annual cost allocated to the energy storage system. This is the amount allocated annually. The overall contribution of wind power / solar power to the entire year; The overall contribution of gas turbines to the entire year; The overall contribution to the flexible load for the entire year; The overall contribution of the energy storage system to the entire year.

[0054] Each indicator is evaluated using a quantitative method, and the weights among the indicators are determined using an empirical method. The indicators are divided into two categories: single-item evaluation and dimensional evaluation.

[0055] , , in, This is the i-th dimension indicator in the multi-dimensional value assessment system for adjustable loads participating in the spot market. The weight of the i-th dimension. This represents the weight of the j-th indicator in the i-th dimension.

[0056] The monthly allocation formula for each internal member of the adjustable load is as follows: , , , , in, This represents the apportionment amount for wind power / solar power in month m. Let m be the amount allocated to the gas turbine in month m. This represents the amount of flexible load allocated in month m. Let m be the amount allocated to the energy storage system in month m. This represents the amount to be allocated in month m. The multi-dimensional value assessment score for photovoltaics in month m; The multi-dimensional value assessment score for the gas turbine in month m; The multi-dimensional value assessment score of the flexible load in month m; The multi-dimensional value assessment score of the energy storage system in month m.

[0057] The annual allocation formula for adjustable loads among its internal members is as follows: , , , , in, This represents the allocated amount for wind / solar power throughout the year. This represents the cost allocated to the gas turbine throughout the entire year. This is the amount allocated to flexible loads throughout the year; This represents the annual cost allocated to the energy storage system. This is the amount allocated annually. A multi-dimensional value assessment score for photovoltaics throughout the year; The multi-dimensional value assessment score for the gas turbine throughout the year; A multi-dimensional value assessment score for the entire year's flexible load; The score is used to assess the multi-dimensional value of the energy storage system for the entire year.

[0058] Example 2, an embodiment of the present invention, provides an internal revenue comprehensive allocation system for adjustable loads after large-scale aggregation, including an indicator formation module, a contribution calculation module, a revenue model creation module, and a revenue allocation module.

[0059] The indicator formation module is used to form a comprehensive contribution indicator system that includes safety, economic and risk-related indicators; the contribution calculation module is used to calculate the comprehensive contribution of each load resource according to the comprehensive contribution indicator system by using a comprehensive evaluation method that integrates TOPSIS grey relational analysis and entropy weight method; the revenue model creation module is used to form a revenue model related to the energy-ancillary service market and quantify the value contribution of the adjustable load aggregate to the overall system economy; the revenue distribution module is used to implement internal revenue distribution based on comprehensive contribution and joint revenue, with a two-tier settlement architecture that includes fixed contract electricity price and contribution assessment.

[0060] This embodiment also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the method for comprehensive allocation of internal revenue of adjustable load after large-scale aggregation as proposed in the above embodiment.

[0061] This embodiment also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the method for comprehensive allocation of internal revenue of adjustable load after large-scale aggregation as proposed in the above embodiment.

[0062] 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.

[0063] 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-included 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.

[0064] 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.

[0065] 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 using any one or a combination 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.

[0066] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to 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 comprehensively allocating the internal revenue of adjustable loads after large-scale aggregation, characterized in that, include: Construct a comprehensive contribution indicator system based on safety, economy, and risk indicators; A comprehensive evaluation method integrating TOPSIS, grey relational analysis, and entropy weighting is adopted to output the comprehensive contribution of each load resource according to the comprehensive contribution index system. Create a joint revenue model for the energy-ancillary services market to quantify the value contribution of adjustable load aggregates; Based on the comprehensive contribution of each load resource and the joint revenue model of the energy-ancillary services market, internal revenue is distributed using a two-tier settlement framework of fixed contract electricity price and contribution assessment.

2. The method for comprehensive allocation of internal benefits of adjustable loads after large-scale aggregation as described in claim 1, characterized in that: The comprehensive contribution index system includes, Safety metrics encompass any one of the following: responsiveness, plan execution, regulation quality, or operational reliability. Economic indicators, including at least one of market revenue, cost optimization, and marginal contribution; Risk indicators include at least one of equipment risk and information risk.

3. The method for comprehensive allocation of internal benefits of adjustable loads after large-scale aggregation as described in claim 1 or 2, characterized in that: The comprehensive evaluation method employing TOPSIS, grey relational analysis, and entropy weighting includes: The weights of each indicator in the comprehensive contribution index system are determined using the entropy weight method. Calculate the grey relational degree and Euclidean distance of each sample to the positive and negative ideal solutions; The relative closeness of each sample is calculated based on grey relational degree and Euclidean distance, and expressed as the comprehensive contribution.

4. The method for comprehensive allocation of internal benefits of adjustable loads after large-scale aggregation as described in claim 3, characterized in that: The energy-ancillary services market joint revenue model includes: The economic indicators used include the revenue ratio of conventional generating units, the optimization rate of revenue from renewable energy generating units, and the optimization rate of electricity purchase cost per unit of load.

5. The method for comprehensive allocation of internal revenue from adjustable loads after large-scale aggregation as described in any one of claims 1, 2, and 4, characterized in that: The two-tier settlement framework includes, The preliminary allocation amount is calculated based on the agreed electricity price and the contributed electricity volume, and the preliminary allocation amount is assessed and adjusted based on the comprehensive contribution index system to output the final allocation amount.

6. The method for comprehensive allocation of internal benefits of adjustable loads after large-scale aggregation as described in claim 5, characterized in that: The distribution of profits includes, The allocation process is divided into two phases: monthly allocation and annual allocation.

7. The method for comprehensive allocation of internal benefits of adjustable load after large-scale aggregation as described in claim 6, characterized in that: The monthly and annual allocations include, The monthly allocation ratio is 70% to 90%, and the annual allocation ratio is 10% to 30%.

8. A comprehensive internal revenue allocation system for adjustable loads after large-scale aggregation, employing the comprehensive internal revenue allocation method for adjustable loads after large-scale aggregation as described in any one of claims 1 to 7, characterized in that: It includes an indicator generation module, a contribution calculation module, a revenue model creation module, and a revenue distribution module; The indicator generation module is used to generate a comprehensive contribution indicator system that includes safety, economic and risk-related indicators; The contribution calculation module is used to calculate the comprehensive contribution of each load resource by applying a comprehensive evaluation method that integrates TOPSIS grey relational analysis and entropy weight method, according to the comprehensive contribution index system. The revenue model creation module is used to form a revenue model related to the energy-ancillary services market, quantifying the value contribution of the adjustable load aggregate to the overall system economy. The revenue distribution module is used to integrate contribution and joint revenue, and executes internal revenue distribution through a two-tier settlement architecture that includes fixed contract electricity price and contribution assessment.

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 method for comprehensive allocation of internal revenue of adjustable load after scaling up and aggregating, 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 method for comprehensive allocation of internal revenue of adjustable load after scaling up and aggregating as described in any one of claims 1 to 7.

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