Individual contribution degree calculation method, system and device for load cluster regulation and control and medium

Abstract

The invention relates to the technical field of calculation of contribution degrees of adjustable loads, and discloses an individual contribution degree calculation method, system and device for load cluster regulation and control and a medium, and the method comprises the steps: collecting multi-source operation data of an adjustable load cluster participating in power grid regulation and control, and carrying out the preprocessing; constructing a contribution degree calculation model of load individuals in a cluster according to the preprocessed multi-source operation data to obtain comprehensive contribution degrees of the load individuals; constructing a cluster adjustable load benefit distribution strategy based on a static distribution proportion and a dynamic adjustment mechanism according to the comprehensive contribution degree of the load individuals; and based on the cluster adjustable load benefit distribution strategy, outputting an individual load contribution degree evaluation result and a benefit distribution scheme after the adjustable load cluster participates in power grid regulation and control. According to the method, high-contribution load individuals are stimulated, the overall response capability of the cluster is improved, regulation and control resource allocation is optimized, and the supporting effect of the adjustable load cluster on the power grid is improved.

Description

Technical Field

[0001] This invention relates to the field of adjustable load contribution calculation technology, and in particular to a method, system, device and medium for calculating individual contribution of load cluster regulation. Background Technology

[0002] In the context of new power system construction, adjustable loads, as important flexible resources, have been widely used in power grid operation and control. Especially during the construction of new power systems, the extensive integration of demand-side resources has enabled loads to participate in ancillary services such as peak shaving, frequency regulation, and voltage support, becoming a key means to improve system regulation capacity and operational resilience. Currently, the regulation of adjustable loads is mostly carried out in a clustered manner, achieving overall response through aggregated control. However, in the process of clustered control, different individual loads differ in response capability, response speed, and regulation accuracy, resulting in inconsistent actual contributions.

[0003] In existing research and engineering practice, there is a general lack of quantitative evaluation methods for assessing the role of individual adjustable loads in cluster regulation. Existing adjustable load regulation technologies primarily focus on the overall cluster regulation strategy and control algorithms, with less attention paid to evaluating the contribution of individuals within the cluster. Some studies have proposed regulation methods based on overall cluster performance indicators, such as optimizing the cluster's total regulation capacity and response speed. However, these methods cannot accurately distinguish the contribution of each individual within the cluster, failing to meet the needs of refined management. Other studies attempt to address the distribution of benefits within the cluster through simple proportional or average allocation, but these methods ignore individual differences, failing to reflect individual contributions and leading to unfair incentive distribution. Furthermore, some studies have proposed rule-based individual evaluation methods, but these methods typically rely on pre-set rules and assumptions, lacking flexibility and adaptability, and failing to accurately reflect actual conditions.

[0004] The lack of effective methods for evaluating individual contributions hinders refined management in areas such as load resource optimization, incentive allocation, and operational evaluation. In load resource optimization, it is impossible to accurately identify and screen loads that contribute significantly to control objectives, thus affecting the optimal allocation of adjustable load resources. Regarding incentive allocation, the inability to accurately measure the contribution of each individual makes it difficult to develop reasonable incentive mechanisms, leading to unfair incentive distribution and negatively impacting the enthusiasm of loads to participate in control. In operational evaluation, the lack of quantitative assessment of individual contributions makes it difficult to accurately evaluate the overall operational effectiveness of adjustable load clusters, affecting the optimization and improvement of control strategies.

[0005] In summary, existing technical solutions have significant shortcomings in the regulation and control of adjustable load clusters. The lack of a quantitative assessment method for individual load contributions hinders refined management in areas such as load resource optimization, incentive allocation, and operational evaluation, thus restricting the full utilization of adjustable load resources. Therefore, there is an urgent need to establish a method for calculating individual contributions in the regulation and control of adjustable load clusters. This method should accurately identify and quantify the actual contribution of each individual load to the regulation objective under a unified cluster control goal, thereby providing theoretical support and a technical foundation for the optimal control, response evaluation, and incentive allocation of load clusters. Summary of the Invention

[0006] In view of the aforementioned existing problems, the present invention is proposed.

[0007] Therefore, the present invention provides a method, system, device and medium for calculating the individual contribution of load cluster regulation. The problem solved is that the existing technology generally lacks a quantitative evaluation method for the role of individual adjustable loads in cluster regulation, which makes it difficult to achieve refined management in load resource selection, incentive allocation and operation evaluation, and restricts the full utilization of adjustable load resources.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0009] In a first aspect, the present invention provides a method for calculating the individual contribution of load cluster regulation, comprising:

[0010] Collect multi-source operation data of adjustable load clusters participating in power grid regulation and perform preprocessing;

[0011] Based on the preprocessed multi-source operational data, a contribution calculation model for individual loads within the cluster is constructed. This model is used to calculate the marginal contribution and collaborative contribution of each individual load.

[0012] The marginal contribution and collaborative contribution of the load individuals are weighted and calculated to obtain the comprehensive contribution of the load individuals;

[0013] Based on the overall contribution of the individual loads, a cluster-adjustable load benefit allocation strategy based on static allocation ratio and dynamic adjustment mechanism is constructed.

[0014] Based on the aforementioned cluster adjustable load benefit allocation strategy, and combining the comprehensive contribution of individual loads, static allocation ratio, dynamic adjustment parameters, and total cluster regulation benefits, the evaluation results of individual load contributions and benefit allocation schemes after the adjustable load cluster participates in grid regulation are output.

[0015] As a preferred embodiment of the method for calculating the individual contribution of load cluster regulation according to the present invention, the contribution calculation model of the individual loads within the cluster includes a marginal contribution calculation model and a collaborative contribution calculation model, wherein the marginal contribution calculation model includes:

[0016] The adjustable load cluster is defined as consisting of adjustable load individuals n and load individuals i whose contribution is to be calculated;

[0017] Based on the first and second adjustment capabilities of the adjustable load cluster, the increment of adjustment capability after adding the load individual whose contribution is to be calculated is expressed as:

[0018] Δv i (S)=v(S∪{i})-v(S)

[0019] Where v(S) represents the adjustment capability of the adjustable load cluster N when it does not include load individual i whose contribution is to be calculated, and v(S∪{i}) represents the adjustment capability of the adjustable load cluster N when it includes load individual i whose contribution is to be calculated.

[0020] Based on the incremental adjustment capacity, the marginal contribution of the load individual whose contribution is to be calculated is obtained, and expressed as:

[0021]

[0022] As a preferred embodiment of the individual contribution calculation method for load cluster regulation according to the present invention, the collaborative contribution calculation model includes:

[0023] Based on the first and second adjustment capabilities of the adjustable load cluster, the collaborative contribution of the individual loads whose contribution is to be calculated is obtained to measure the collaborative effect of the individual loads in the load cluster. The formula is expressed as follows:

[0024]

[0025] As a preferred embodiment of the method for calculating the individual contribution of load cluster regulation according to the present invention, the step of obtaining the comprehensive contribution of individual loads includes:

[0026] Based on the marginal and collaborative contributions of the load individuals whose contributions are to be calculated, the comprehensive contribution of the load individuals is calculated using weighted averages with weighting coefficients, as expressed in the formula:

[0027]

[0028] Wherein, ω1 and ω2 are the weighting coefficients of marginal contribution and collaborative contribution, respectively.

[0029] The beneficial effects of this preferred technical solution are: to achieve a quantitative assessment of the independent adjustment capability and synergistic effect of individual loads in cluster regulation, and to ensure the scientificity and fairness of contribution calculation.

[0030] As a preferred embodiment of the method for calculating the individual contribution of load cluster regulation according to the present invention, the construction of a cluster-adjustable load benefit allocation strategy based on static allocation ratio and dynamic adjustment mechanism includes:

[0031] Considering the overall contribution of the individual loads, market price fluctuations, and the historical revenue of the individual loads, the revenue distribution strategy is optimized through static allocation ratios and dynamic adjustment mechanisms, and a static allocation ratio model based on the individual load contribution and a dynamic allocation ratio model based on market price signals are constructed.

[0032] When the total benefit of the cluster in a single power grid dispatching operation is R N At that time, the benefit of load individual i in the cluster is:

[0033] R i =(θ1G i +θ2λ i )·R N

[0034]

[0035]

[0036] Among them, R i G represents the benefit of load individual i in the cluster, θ1 and θ2 are the weight coefficients for static allocation and dynamic allocation, respectively. i λ is the static return adjustment coefficient based on individual contribution. i C is a dynamic return adjustment factor based on market price fluctuations and the historical returns of individual loads. i This represents the overall contribution of individual loads, where k1 and k2 are the market price adjustment coefficient and the historical return equilibrium coefficient, respectively, and p m p represents the current market price. a U represents the average market price over a past period. i R is the historical return balance factor for individual load i. c,i R represents the cumulative return of individual load i over a past period. a This represents the average cumulative return for all individuals in the cluster.

[0037] The beneficial effects of this preferred technical solution are: to make the distribution of benefits more fair and reasonable, to incentivize high-contribution load individuals to actively participate, and to improve the overall regulation and control capabilities of the load cluster.

[0038] As a preferred embodiment of the method for calculating the individual contribution of load cluster regulation according to the present invention, the step of collecting multi-source operation data of adjustable load clusters participating in power grid regulation and performing preprocessing includes:

[0039] Collect load curves, load regulation capacity, equipment operating status, and response history of adjustable load clusters to provide load-side data for performance evaluation; collect grid load, frequency fluctuations, renewable energy generation, market electricity prices, and ancillary service demand to provide grid-side data for performance evaluation.

[0040] The preprocessing steps include:

[0041] Remove invalid data caused by missing text, communication failures, or equipment malfunctions;

[0042] Outliers were identified and unreasonable data were filtered out using box plot analysis and Z-score normalization.

[0043] The maximum-minimum normalization method is used to eliminate the differences in data dimensions, resulting in processed multi-source running data.

[0044] Secondly, the present invention provides an individual contribution calculation system for load cluster regulation, comprising:

[0045] The data acquisition module is used to collect multi-source operational data of adjustable load clusters participating in power grid regulation and to perform preprocessing.

[0046] The contribution calculation module is used to construct a contribution calculation model for individual loads within the cluster based on the preprocessed multi-source operating data. The contribution calculation model for individual loads within the cluster is used to calculate the marginal contribution and collaborative contribution of individual loads. The marginal contribution and collaborative contribution of individual loads are weighted and calculated to obtain the comprehensive contribution of individual loads.

[0047] The benefit decomposition module is used to construct a cluster adjustable load benefit allocation strategy based on static allocation ratio and dynamic adjustment mechanism according to the comprehensive contribution of the individual loads; based on the cluster adjustable load benefit allocation strategy, combined with the comprehensive contribution of the individual loads, static allocation ratio, dynamic adjustment parameters and total benefit of cluster regulation, it outputs the individual load contribution evaluation results and benefit allocation scheme after the adjustable load cluster participates in grid regulation.

[0048] As a preferred embodiment of the individual contribution calculation system for load cluster regulation according to the present invention, the contribution calculation module includes:

[0049] The marginal contribution calculation unit is used to set the adjustable load cluster to include adjustable load individuals n and load individuals i whose contribution is to be calculated; based on the first and second adjustment capabilities of the adjustable load cluster, calculate the adjustment capability increment after adding the load individuals whose contribution is to be calculated; and obtain the marginal contribution of the load individuals whose contribution is to be calculated based on the adjustment capability increment.

[0050] The collaborative contribution calculation unit is used to obtain the collaborative contribution of the load individual whose contribution is to be calculated based on the first and second adjustment capabilities of the adjustable load cluster, so as to measure the collaborative effect of the load individual in the load cluster.

[0051] The comprehensive contribution calculation unit is used to calculate the comprehensive contribution of the load individual based on the marginal contribution and collaborative contribution of the load individual to be calculated, by weighting it with a weighting coefficient.

[0052] Thirdly, the present invention provides an electronic device, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor executes the computer-executable instructions to implement the steps of a method for calculating the individual contribution of load cluster regulation.

[0053] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of a method for calculating the individual contribution of load cluster regulation.

[0054] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention constructs an individual load contribution calculation method based on marginal contribution and collaborative contribution, thereby achieving a quantitative assessment of the independent adjustment capability and collaborative effect of individual loads in cluster regulation, ensuring the scientific nature and fairness of contribution calculation; combined with market price fluctuations and historical revenue data, it proposes a revenue decomposition method based on individual contribution, and adopts a dynamic adjustment mechanism driven by static allocation ratios and market signals, making revenue distribution more fair and reasonable, incentivizing high-contribution loads to actively participate, and improving the overall regulation capability of the load cluster. Attached Figure Description

[0055] 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.

[0056] Figure 1 This is a schematic diagram of the overall process logic of the method for calculating the individual contribution of load cluster regulation according to an embodiment of the present invention. Detailed Implementation

[0057] 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.

[0058] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for calculating the individual contribution of load cluster regulation is provided, such as... Figure 1 The specific steps shown are as follows:

[0059] S100: Collects multi-source operation data of adjustable load clusters participating in power grid regulation and performs preprocessing;

[0060] S200: Construct a contribution calculation model for individual loads within the cluster based on preprocessed multi-source operational data. This model is used to calculate the marginal contribution and collaborative contribution of individual loads. The marginal contribution and collaborative contribution of individual loads are weighted to obtain the comprehensive contribution of individual loads.

[0061] S300: Based on the comprehensive contribution of individual loads, construct a cluster-adjustable load benefit allocation strategy based on static allocation ratio and dynamic adjustment mechanism;

[0062] S400: Based on the cluster adjustable load benefit allocation strategy, and combining the comprehensive contribution of individual loads, static allocation ratio, dynamic adjustment parameters and total benefits of cluster regulation, it outputs the evaluation results of individual load contribution and benefit allocation scheme after the adjustable load cluster participates in grid regulation.

[0063] It should be noted that existing technologies generally lack quantitative assessment methods for the role of individual adjustable loads in cluster regulation, making it difficult to achieve refined management in load resource selection, incentive allocation, and operational evaluation, thus restricting the full utilization of adjustable load resources. Steps S100–S400 above construct an individual load contribution calculation method based on marginal contribution and collaborative contribution, enabling a quantitative assessment of the independent adjustment capability and collaborative effect of individual loads in cluster regulation, ensuring the scientific and fair nature of contribution calculation. Combining market price fluctuations and historical revenue data, a revenue decomposition method based on individual contribution is proposed, employing a dynamic adjustment mechanism driven by static allocation ratios and market signals to make revenue distribution more equitable and reasonable, incentivizing high-contribution loads to actively participate, and improving the overall regulation capability of the load cluster.

[0064] In this embodiment of the application, step S100 above, which involves collecting multi-source operational data of adjustable load clusters participating in power grid regulation and performing preprocessing, includes:

[0065] Specifically, the system collects load curves, load regulation capabilities, equipment operating status, and response history of adjustable load clusters to provide load-side data for performance evaluation; it also collects grid load, frequency fluctuations, renewable energy generation, market electricity prices, and ancillary service demand to provide grid-side data for performance evaluation.

[0066] Specifically, the preprocessing steps include:

[0067] Remove invalid data caused by missing text, communication failures, or equipment malfunctions;

[0068] Outliers were identified and unreasonable data were filtered out using box plot analysis and Z-score normalization.

[0069] The maximum-minimum normalization method is used to eliminate the differences in data dimensions, resulting in processed multi-source running data.

[0070] In an optional embodiment, the preprocessing step can also be to use a moving average filter combined with wavelet transform. First, the original data is smoothed through a sliding window to suppress random noise. Then, based on the multi-scale decomposition characteristics of wavelet transform, the high-frequency disturbance component and the low-frequency effective component of the signal are separated, thereby effectively filtering out abnormal fluctuations while preserving the true characteristics of the data.

[0071] In another optional embodiment, the preprocessing step can also be to combine the K-nearest neighbor algorithm with local outlier detection, identify potential outliers by calculating the local density deviation of data points, and then perform interpolation repair based on the nearest neighbor relationship of normal data. This is suitable for cleaning and reconstructing high-dimensional non-uniformly distributed data and can adapt to the data distribution characteristics under different working conditions.

[0072] It should be noted that step S100 above provides a reliable data foundation for subsequent contribution calculation and benefit allocation, solves the evaluation bias problem caused by poor data quality or missing data in traditional methods, and supports real-time monitoring and analysis of the operating status of the load cluster, thereby improving the overall control response speed and decision-making accuracy.

[0073] In this embodiment of the application, step S200 above constructs a contribution calculation model for individual loads within the cluster based on preprocessed multi-source operational data to obtain the comprehensive contribution of individual loads, including the following sub-steps B1 to B3:

[0074] In B1: Based on the preprocessed multi-source operational data, a model for calculating the marginal contribution of individual loads within the cluster is constructed. The steps include:

[0075] The adjustable load cluster is defined to include adjustable load individuals and load individuals whose contribution is to be calculated;

[0076] Based on the first and second adjustment capabilities of the adjustable load cluster, calculate the increment of adjustment capability after adding load individuals whose contribution is to be calculated.

[0077] Based on the increment of adjustment capacity, obtain the marginal contribution of the load individual whose contribution is to be calculated.

[0078] Specifically, the marginal contribution rate measures the independent contribution of an individual load to power grid regulation without considering clustering effects. Let N be a controllable load cluster containing n controllable load individuals, where i is the load individual whose contribution is to be calculated, S is the subset of cluster N that does not contain individual i, and j is the other individual in cluster N besides individual i. The regulation capacity of the controllable load cluster N without load individual i is v(S), and the regulation capacity with load individual i is v(S∪{i}). The increment in regulation capacity after adding load individual i is expressed as:

[0079] Δv i (S)=v(S∪{i})-v(S)

[0080] Specifically, the marginal contribution of the load individual whose contribution is to be calculated is obtained based on the increment of the adjustment capacity, and is expressed as follows:

[0081]

[0082] In an optional embodiment, the first and second regulation capabilities of the adjustable load cluster can also be evaluated using a dynamic assessment method based on power response rate. The first regulation capability can be defined as the instantaneous power adjustment rate of an individual load when the grid frequency fluctuates, reflecting its rapid response capability, while the second regulation capability is measured by the power regulation accuracy and stability of the load during continuous frequency regulation, reflecting its reliable support role over a long time scale.

[0083] In another optional embodiment, the first and second regulation capabilities of the adjustable load cluster can also be: the first regulation capability is designed as the interruptible load capacity of an individual load in a peak shaving and valley filling scenario, directly characterizing its potential to participate in grid peak regulation, while the second regulation capability is evaluated by the duration and dispatchable frequency of the load participating in demand response, thereby forming a dual contribution measurement system that takes into account both capacity and time dimensions.

[0084] In B2: A collaborative contribution calculation model for individual loads within the cluster is constructed based on preprocessed multi-source operational data; the steps include:

[0085] Based on the first and second adjustment capabilities of the adjustable load cluster, the collaborative contribution of the load individuals whose contribution is to be calculated is obtained to measure the collaborative effect of the load individuals in the load cluster.

[0086] Specifically, the synergistic contribution measures the synergistic effect of individual loads within a load cluster, i.e., whether the addition of an individual load enhances the overall regulation capacity, rather than simply providing regulation capacity individually. For example, some individual loads possess peak-shaving capabilities, while others only possess valley-filling capabilities; when they form a cluster, they synergistically acquire peak-shaving and valley-filling capabilities. Using the same basic assumptions as step B1, the synergistic contribution of load individual i is expressed as:

[0087]

[0088] In B3: Based on the constructed contribution calculation model of individual loads within the cluster, the comprehensive contribution of each load is obtained; the steps include:

[0089] Based on the marginal and collaborative contributions of individual loads to be calculated, the comprehensive contribution of each individual load is calculated using weighted averages with weighting coefficients. The formula is as follows:

[0090]

[0091] Wherein, ω1 and ω2 are the weighting coefficients of marginal contribution and collaborative contribution, respectively.

[0092] It should be noted that step S200 above breaks through the limitations of traditional methods that only focus on total quantity control while ignoring individual differences, and achieves scientific quantification of the value of individual loads, enabling the accurate identification of high-contribution loads and improving the level of refined management of cluster control.

[0093] In this embodiment of the application, step S300, which constructs a cluster-adjustable load benefit allocation strategy based on static allocation ratios and dynamic adjustment mechanisms according to the comprehensive contribution of individual loads, includes:

[0094] Considering the overall contribution of individual loads, market price fluctuations, and the historical revenue of individual loads, the revenue distribution strategy is optimized through static allocation ratios and dynamic adjustment mechanisms, and a static allocation ratio model based on individual load contribution and a dynamic allocation ratio model based on market price signals are constructed.

[0095] When the total benefit of the cluster in a single power grid dispatching operation is R N At that time, the benefit of load individual i in the cluster is:

[0096] R i =(θ1G i +θ2λ i )·R N

[0097]

[0098] Among them, R i G represents the benefit of load individual i in the cluster, θ1 and θ2 are the weight coefficients for static allocation and dynamic allocation, respectively. i λ is the static return adjustment coefficient based on individual contribution. i C is a dynamic return adjustment factor based on market price fluctuations and the historical returns of individual loads. i This represents the overall contribution of individual loads, where k1 and k2 are the market price adjustment coefficient and the historical return equilibrium coefficient, respectively, and p m p represents the current market price. a U is the average market price over a past period (e.g., 30 days). i R is the historical return balance factor for individual load i. c,i R represents the cumulative return of individual load i over a period of time (e.g., 30 days). a This represents the average cumulative return for all individuals in the cluster.

[0099] It should be noted that the above step S300 not only ensures the stability of revenue distribution and avoids damage to low-contribution loads due to short-term fluctuations, but also dynamically adjusts the incentive intensity according to market electricity prices and regulatory needs, thereby enhancing the participation enthusiasm of high-contribution loads, thus optimizing the overall regulation capability of the cluster and promoting the long-term sustainable use of resources.

[0100] In this embodiment of the application, step S400 above is based on the cluster adjustable load benefit allocation strategy, and combines the comprehensive contribution of individual loads, static allocation ratio, dynamic adjustment parameters and total benefits of cluster regulation to output the individual load contribution evaluation results and benefit allocation scheme after the adjustable load cluster participates in grid regulation.

[0101] It should be noted that step S400 above provides grid operators with a transparent load regulation performance evaluation tool to help formulate differentiated incentive policies; at the same time, users can clearly understand the relationship between their own contributions and benefits, enhance their participation and trust, form a positive cycle of "high contribution - high return", and further improve the aggregation benefits and market competitiveness of adjustable load resources.

[0102] Example 2, based on the previous example, provides an application example of a method, system, device and medium for calculating the individual contribution of load cluster regulation, to verify and illustrate the technical effects used in this method.

[0103] This embodiment focuses on an adjustable load cluster comprising five individual adjustable loads, and decomposes the benefits of its regulation. The individual loads in this adjustable load cluster are air conditioning loads, electric water heaters, industrial loads, electric vehicle charging stations, and commercial lighting. Table 1 presents the basic parameters and regulation performance of each individual load, where regulation capacity represents the maximum power regulation range that each load can provide during the regulation task; historical average response rate reflects the proportion of actual response power to regulation capacity when the load previously participated in regulation; and current regulation output represents the actual power regulation value provided by each load in the current regulation task.

[0104] Table 1: Individual control status in adjustable load clusters.

[0105] Load type Regulated capacity (kW) Historical average response rate (%) Output power adjusted this time (kW) air conditioner 20 90 18 electric water heater 15 100 15 Industrial load 50 70 35 Electric vehicle charging stations 40 80 32 Commercial lighting 25 80 20

[0106] During the duration of this regulation task, the adjustable load cluster needs to provide a total load regulation of 120kW. The market side grants the adjustable load cluster a total revenue of 1000 yuan. In this embodiment, this total revenue will be divided among individual loads according to different allocation mechanisms to verify the fairness advantage of the method of the present invention. First, the marginal contribution, synergistic contribution, and comprehensive contribution of each load are calculated according to the parameters shown in Table 1, as shown in Table 2.

[0107] Table 2: Contribution of individual loads within an adjustable load cluster.

[0108]

[0109]

[0110] Based on the comprehensive contribution calculated above, the static-dynamic composite revenue allocation mechanism proposed in this invention allocates revenue according to the proportion of the comprehensive contribution of each load. The allocation results of this invention are compared with traditional methods, which include average allocation (equal distribution to all loads, with total revenue equally divided among each load at 200 yuan) and allocation according to regulation capacity (allocation based on the size of the regulation capacity). Table 3 summarizes the specific revenue values ​​obtained by each load under the three methods.

[0111] Table 3: Revenue distribution of individual loads within an adjustable load cluster under different allocation methods.

[0112] Load type Average distribution (yuan) Allocated according to capacity ratio (yuan) This invention allocates (yuan). air conditioner 200 133 182 electric water heater 200 100 182 Industrial load 200 333 229 Electric vehicle charging stations 200 267 228 Commercial lighting 200 167 179

[0113] The above analysis shows that under the average allocation scheme, all loads receive the same benefit, without considering differences. Under the capacity-proportion allocation scheme, industrial loads, due to their largest regulating capacity, receive the highest benefit, while electric water heaters, with the smallest capacity, receive the lowest benefit, resulting in significant differences in benefit among loads. In contrast, the static-dynamic composite allocation method of this invention allocates benefits based on the comprehensive contribution of individual loads. Loads with small capacity but high response reliability, such as electric water heaters, receive significantly higher benefits than those allocated by capacity, increasing from 100 yuan to 182 yuan. Industrial loads, although large in capacity, suffer from insufficient response, resulting in a corresponding decrease in benefit from 333 yuan to 229 yuan. Loads with medium capacity and good response, such as air conditioners, receive benefits matching their actual contribution, allocated 182 yuan, falling between the average allocation and capacity allocation results. Overall, the allocation result of this invention's method falls between the two extreme schemes of average allocation and capacity allocation, with more balanced values, while also reflecting the differences in their actual contributions to the control task.

[0114] Therefore, this invention constructs an individual load contribution calculation method based on marginal contribution and collaborative contribution, thereby achieving a quantitative assessment of the independent adjustment capability and collaborative effect of individual loads in cluster regulation, ensuring the scientific nature and fairness of contribution calculation. Combining market price fluctuations and historical returns, it proposes a return decomposition method based on individual contribution, adopting a dynamic adjustment mechanism driven by static allocation ratios and market signals to make return distribution more fair and reasonable, incentivize high-contribution loads to actively participate, and improve the overall regulation capability of the load cluster.

[0115] Example 3: This example provides an individual contribution calculation system for load cluster regulation, including:

[0116] The data acquisition module is used to collect multi-source operational data of adjustable load clusters participating in power grid regulation and to perform preprocessing.

[0117] The contribution calculation module is used to construct a contribution calculation model for individual loads within the cluster based on preprocessed multi-source operational data, in order to obtain the comprehensive contribution of each load.

[0118] The benefit decomposition module is used to construct a cluster adjustable load benefit allocation strategy based on static allocation ratio and dynamic adjustment mechanism according to the comprehensive contribution of individual loads. Based on the cluster adjustable load benefit allocation strategy, combined with the comprehensive contribution of individual loads, static allocation ratio, dynamic adjustment parameters and total benefit of cluster regulation, it outputs the evaluation results of individual load contribution after the adjustable load cluster participates in grid regulation and the benefit allocation scheme.

[0119] Specifically, the contribution calculation module includes:

[0120] The marginal contribution calculation unit is used to set the adjustable load cluster to include adjustable load individuals n and load individuals i whose contribution is to be calculated; based on the first and second adjustment capabilities of the adjustable load cluster, it calculates the adjustment capability increment after adding the load individuals whose contribution is to be calculated; and obtains the marginal contribution of the load individuals whose contribution is to be calculated based on the adjustment capability increment.

[0121] The collaborative contribution calculation unit is used to obtain the collaborative contribution of the load individual to be calculated based on the first and second adjustment capabilities of the adjustable load cluster, so as to measure the collaborative effect of the load individual in the load cluster.

[0122] The comprehensive contribution calculation unit is used to calculate the comprehensive contribution of a load individual based on its marginal contribution and collaborative contribution, using weighted coefficients.

[0123] It should be noted that the technical solution of the individual contribution calculation system for load cluster regulation is based on the same concept as the technical solution of the individual contribution calculation method for load cluster regulation described above. For details not described in detail in the technical solution of the individual contribution calculation system for load cluster regulation in this embodiment, please refer to the description of the technical solution of the individual contribution calculation method for load cluster regulation described above.

[0124] The above-mentioned unit modules can be embedded in the processor of the electronic device in hardware form or independent of it, or they can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0125] This embodiment also provides an electronic device, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for calculating the individual contribution of load cluster regulation. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0126] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method proposed in the above embodiments.

[0127] The storage medium proposed in this embodiment belongs to the same inventive concept as the method proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0128] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute the method of the embodiments of the present invention.

[0129] 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 calculating the individual contribution of load cluster regulation, characterized in that, include: Collect multi-source operation data of adjustable load clusters participating in power grid regulation and perform preprocessing; Based on the preprocessed multi-source operational data, a contribution calculation model for individual loads within the cluster is constructed. This model is used to calculate the marginal contribution and collaborative contribution of each individual load. The marginal contribution and collaborative contribution of the load individuals are weighted and calculated to obtain the comprehensive contribution of the load individuals; Based on the overall contribution of the individual loads, a cluster-adjustable load benefit allocation strategy based on static allocation ratio and dynamic adjustment mechanism is constructed. Based on the aforementioned cluster adjustable load benefit allocation strategy, and combining the comprehensive contribution of individual loads, static allocation ratio, dynamic adjustment parameters, and total cluster regulation benefits, the evaluation results of individual load contributions and benefit allocation schemes after the adjustable load cluster participates in grid regulation are output.

2. The method for calculating the individual contribution of load cluster regulation as described in claim 1, characterized in that, The contribution calculation model for individual loads within the cluster includes a marginal contribution calculation model and a collaborative contribution calculation model, wherein the marginal contribution calculation model includes: The adjustable load cluster is defined as consisting of adjustable load individuals n and load individuals i whose contribution is to be calculated; Based on the first and second adjustment capabilities of the adjustable load cluster, the increment of adjustment capability after adding the load individual whose contribution is to be calculated is expressed as: Δv i (S)=v(S∪{i})-v(S) Where v(S) represents the adjustment capability of the adjustable load cluster N when it does not include load individual i whose contribution is to be calculated, and v(S∪{i}) represents the adjustment capability of the adjustable load cluster N when it includes load individual i whose contribution is to be calculated. Based on the incremental adjustment capacity, the marginal contribution of the load individual whose contribution is to be calculated is obtained, and expressed as:

3. The method for calculating the individual contribution of load cluster regulation as described in claim 2, characterized in that, The collaborative contribution calculation model includes: Based on the first and second adjustment capabilities of the adjustable load cluster, the collaborative contribution of the individual loads whose contribution is to be calculated is obtained to measure the collaborative effect of the individual loads in the load cluster. The formula is expressed as follows:

4. The method for calculating the individual contribution of load cluster regulation as described in claim 3, characterized in that, The overall contribution of the individual load is obtained as follows: Based on the marginal and collaborative contributions of the load individuals whose contributions are to be calculated, the comprehensive contribution of the load individuals is calculated using weighted averages with weighting coefficients, as expressed in the formula: Wherein, ω1 and ω2 are the weighting coefficients of marginal contribution and collaborative contribution, respectively.

5. The method for calculating the individual contribution of load cluster regulation as described in claim 4, characterized in that, The proposed cluster-adjustable load benefit allocation strategy, based on static allocation ratios and dynamic adjustment mechanisms, includes: Considering the overall contribution of the individual loads, market price fluctuations, and the historical revenue of the individual loads, the revenue distribution strategy is optimized through static allocation ratios and dynamic adjustment mechanisms, and a static allocation ratio model based on the individual load contribution and a dynamic allocation ratio model based on market price signals are constructed. When the total benefit of the cluster in a single power grid dispatching operation is R N At that time, the benefit of load individual i in the cluster is: R i =(θ1G i +θ2λ i )·R N Among them, R i G represents the benefit of load individual i in the cluster, θ1 and θ2 are the weight coefficients for static allocation and dynamic allocation, respectively. i λ is the static return adjustment coefficient based on individual contribution. i C is a dynamic return adjustment factor based on market price fluctuations and the historical returns of individual loads. i This represents the overall contribution of individual loads, where k1 and k2 are the market price adjustment coefficient and the historical return equilibrium coefficient, respectively, and p m p represents the current market price. a U represents the average market price over a past period. i R is the historical return balance factor for individual load i. c,i R represents the cumulative return of individual load i over a past period. a This represents the average cumulative return for all individuals in the cluster.

6. The method for calculating the individual contribution of load cluster regulation as described in claim 1, characterized in that, The process of collecting and preprocessing multi-source operational data from adjustable load clusters participating in power grid regulation includes: Collect load curves, load regulation capacity, equipment operating status, and response history of adjustable load clusters to provide load-side data for performance evaluation; collect grid load, frequency fluctuations, renewable energy generation, market electricity prices, and ancillary service demand to provide grid-side data for performance evaluation. The preprocessing steps include: Remove invalid data caused by missing text, communication failures, or equipment malfunctions; Outliers were identified and unreasonable data were filtered out using box plot analysis and Z-score normalization. The maximum-minimum normalization method is used to eliminate the differences in data dimensions, resulting in processed multi-source running data.

7. A system for calculating the individual contribution of load cluster regulation, using the method for calculating the individual contribution of load cluster regulation as described in any one of claims 1 to 6, characterized in that, include: The data acquisition module is used to collect multi-source operational data of adjustable load clusters participating in power grid regulation and to perform preprocessing. The contribution calculation module is used to construct a contribution calculation model for individual loads within the cluster based on the preprocessed multi-source operating data. The contribution calculation model for individual loads within the cluster is used to calculate the marginal contribution and collaborative contribution of individual loads. The marginal contribution and collaborative contribution of individual loads are weighted and calculated to obtain the comprehensive contribution of individual loads. The benefit decomposition module is used to construct a cluster-adjustable load benefit allocation strategy based on static allocation ratios and dynamic adjustment mechanisms according to the comprehensive contribution of the individual loads. Based on the aforementioned cluster adjustable load benefit allocation strategy, and combining the comprehensive contribution of individual loads, static allocation ratio, dynamic adjustment parameters, and total cluster regulation benefits, the evaluation results of individual load contributions and benefit allocation schemes after the adjustable load cluster participates in grid regulation are output.

8. The individual contribution calculation system for load cluster regulation as described in claim 7, characterized in that, The contribution calculation module includes: The marginal contribution calculation unit is used to set the adjustable load cluster to include adjustable load individuals n and load individuals i whose contribution is to be calculated; based on the first and second adjustment capabilities of the adjustable load cluster, calculate the adjustment capability increment after adding the load individuals whose contribution is to be calculated; and obtain the marginal contribution of the load individuals whose contribution is to be calculated based on the adjustment capability increment. The collaborative contribution calculation unit is used to obtain the collaborative contribution of the load individual whose contribution is to be calculated based on the first and second adjustment capabilities of the adjustable load cluster, so as to measure the collaborative effect of the load individual in the load cluster. The comprehensive contribution calculation unit is used to calculate the comprehensive contribution of the load individual based on the marginal contribution and collaborative contribution of the load individual to be calculated, by weighting it with a weighting coefficient.

9. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store computer-executable instructions, and when the processor executes the computer-executable instructions, it implements the steps of the individual contribution calculation method for load cluster regulation as described in any one of claims 1 to 6.

10. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that: When the computer-executable instructions are executed by the processor, they implement the steps of the individual contribution calculation method for load cluster regulation as described in any one of claims 1 to 6.

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