A multi-energy principal benefit distribution method based on contribution degree

Abstract

This invention discloses a multi-energy entity revenue allocation method based on contribution. After determining the basic contribution of different types of power generation entities, real-time operating data and real-time output data of various types of power generation entities are collected. Based on the real-time operating data and real-time output data, the congestion type, congestion period, and output fluctuation period are obtained. Granger causality verification is used to verify the causal relationship between the congestion period and the output fluctuation period, and power generation entities with causal relationships are output as those to be corrected. The basic contribution is negatively corrected based on the impact of output fluctuations on the target power grid, and positively corrected based on the response processing of the power generation entities. Finally, revenue is allocated to different types of power generation entities based on the corrected basic contribution. By constructing a complete logical chain of basic contribution setting, congestion and fluctuation correlation analysis, bidirectional contribution correction, and revenue allocation, the method achieves precise and dynamic revenue allocation for multiple energy entities.

Description

Technical Field

[0001] This invention relates to the field of power grid revenue distribution technology, and in particular to a method for distributing revenue among multiple energy entities based on contribution. Background Technology

[0002] With the increasing penetration rate of new energy sources, the coordinated operation of multiple energy sources such as wind power and thermal power has become an important feature of the new power system. The rationality of its revenue distribution directly affects the efficient and stable operation of the energy system. Existing revenue distribution methods are mostly based on static indicators such as power generation and investment costs, and fail to fully consider the dynamic impact of grid congestion on the output fluctuations of multiple energy sources. For example, line congestion may force wind power to adjust its output through forced load reduction, and thermal power may also experience unplanned output fluctuations due to voltage constraints caused by congestion. These output changes caused by congestion are not included in the contribution assessment, resulting in the allocation results being out of touch with the actual grid value, and even failing to reflect the operating costs of the entities under congestion scenarios. There is a significant correlation between grid congestion and power output fluctuations of multiple energy entities. After congestion occurs, dispatch intervention or equipment self-protection mechanisms often trigger passive adjustments in the power output of energy entities. For example, exceeding the line load limit may cause a sudden drop in wind power output, and voltage anomalies may force thermal power to temporarily change its operating status. Existing technologies lack a quantitative mechanism for this "congestion → fluctuation" correlation. They neither distinguish the differentiated impact of different congestion types on power output fluctuations nor incorporate the adaptive performance of entities during fluctuations into contribution correction. As a result, the distribution of benefits cannot reflect the actual value of entities in the safe operation of the grid, which is not conducive to guiding multiple energy entities to optimize congestion response strategies. Summary of the Invention

[0003] In view of this, the present invention proposes a multi-energy subject revenue allocation method based on contribution, which comprehensively considers the impact of congestion on power output and the processing response of the energy subject to modify the contribution, thereby achieving accurate allocation of revenue from multiple energy subjects.

[0004] The technical solution of this invention is implemented as follows: A method for distributing revenue among multiple energy entities based on contribution includes the following steps: Step S1: Obtain basic information on various types of power generation entities and set the basic contribution level of different types of power generation entities in combination with the topology of the target power grid; Step S2: Collect real-time operation data and real-time output data of various types of power generation entities respectively; obtain the blockage type and blockage period based on the real-time operation data; and obtain the output fluctuation period based on the real-time output data. Step S3: Use Granger causality check to verify the causal relationship between the blocking period and the power output fluctuation period, and output the power generation entities with causal relationship as the power generation entities to be corrected. Step S4: Assess the impact of the power generation entity to be corrected on the target power grid in conjunction with the power output fluctuation period, and make a negative correlation correction to the basic contribution based on the impact level; Step S5: Obtain the response processing of the power generation entity to be corrected after the blockage period occurs, and make a positive correlation correction to the basic contribution based on the response processing and the blockage type; Step S6: Distribute revenue among different types of power generation entities based on the revised base contribution.

[0005] Preferably, step S1 includes the following steps: Step S11: Obtain the type, installed capacity, connection location, and connection voltage of the power generation entity connected to the target power grid; Step S12: Assess the technology maturity based on the type of power generation entity and the importance based on the access location; Step S13: Input the type of power generation entity, installed capacity, technological maturity, importance, and access voltage into the trained neural network, and then have the neural network process the data to obtain the basic contribution.

[0006] Preferably, the types of power generation entities include wind power, thermal power, photovoltaic power, and energy storage, and the neural network is obtained through training with a large amount of historical data.

[0007] Preferably, step S2 includes the following specific steps: Step S21: Collect real-time current, node voltage, conductor temperature and power flow monitoring data on the main power generation line, and record them as real-time operating data; Step S22: Compare the real-time running data with the standard data, and output the time periods with differences as the blocking periods; Step S23: Collect the real-time active power of the power generation unit, remove outliers, smooth the noise using the moving average method, and record it as real-time output data. Step S24: Set the fluctuation threshold, and use a sliding window to calculate the output change rate of real-time output data. When the fluctuation threshold is exceeded and the duration is greater than the time threshold, the fluctuation start time and fluctuation end time are recorded as the output fluctuation period.

[0008] Preferably, in step S22, the blocking type is determined based on the difference between real-time operating data and standard data. The blocking type includes line load blocking, voltage blocking, and power flow transfer blocking.

[0009] Preferably, step S3 includes the following specific steps: Step S31: Define blockage as the cause variable and power output fluctuation as the result variable, and extract the blockage sequence from the real-time operating data corresponding to the blockage period and the power output fluctuation sequence from the real-time power output data corresponding to the power output fluctuation period, respectively. Step S32: Perform time-series alignment on the blocking sequence and the power output fluctuation sequence, and perform data stationarity verification on the blocking sequence and the power output fluctuation sequence respectively; Step S33: Set the lag order and significance level parameters, and construct the regression equation of the power fluctuation sequence with respect to the blockage sequence based on the lag order and significance level parameters; Step S34: Calculate the P value based on the regression equation using F-verification. When the P value is <0.05, it is determined that the power output fluctuation is affected by the blockage, and the corresponding power generation entity is output as the power generation entity to be corrected.

[0010] Preferably, in step S32, before timing alignment, a time window is selected from 30 minutes before the blockage occurs to 60 minutes after the blockage occurs.

[0011] Preferably, step S4 includes the following specific steps: Step S41: Check whether the power output fluctuation period has triggered a safety event. Safety events include line overload, voltage over-limit, or protection device activation. Step S42: Statistical scheduling intervention measures taken to mitigate the impact of power output fluctuations; Step S43: Classify the degree of impact based on the severity of the security incident, the intensity of the intervention measures, and the cost; Step S44: Set a deduction value according to the level of influence, and deduct the basic contribution value accordingly.

[0012] Preferably, step S5 includes the following specific steps: Step S51: Collect the automatic adjustment and manual maintenance records of the generator to be corrected after the blockage as a response processing record; Step S52: Evaluate the type suitability of the response processing record in conjunction with the blocking type; Step S53: Classify the processing level based on the response speed, blockage mitigation effect, and type adaptability of the response processing records; Step S54: Set an additional value according to the processing level, and adjust the basic contribution value accordingly.

[0013] Preferably, step S6 includes the following specific steps: Step S61: Obtain the electricity sales revenue and operation and maintenance costs of the target power grid, and calculate the total revenue based on the electricity sales revenue and operation and maintenance costs; Step S62: Multiply the total revenue by the base contribution after negative correlation correction and positive correlation correction respectively, and then allocate it to different types of power generation entities.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention discloses a multi-energy entity revenue allocation method based on contribution. First, it sets the basic contribution level for different types of power generation entities according to the topology of the target power grid. Then, it obtains congestion periods and output fluctuation periods using real-time operating and processing data of the power generation entities. Next, it introduces the Granger causality check method to verify the causal relationship between the congestion periods and the output fluctuation periods, determining whether the output fluctuation was caused by congestion. Power generation entities with a causal relationship are then output as those to be corrected. Negative correlation correction and positive correlation correction are then applied to these entities. Negative correlation correction considers the impact of the output fluctuation of the entity to be corrected on the target power grid, while positive correlation correction considers the entity's response to congestion. By combining the objective impact of congestion with the subjective handling of the entity, the contribution level is corrected, taking into account not only the objective impact on the target power grid but also the entity's attitude, thus achieving accurate revenue allocation. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only preferred embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a multi-energy entity revenue distribution method based on contribution according to the present invention; Figure 2 This is a flowchart of step S1 of a multi-energy subject revenue allocation method based on contribution according to the present invention; Figure 3 This is a flowchart of step S2 of a multi-energy subject revenue allocation method based on contribution according to the present invention; Figure 4 This is a flowchart of step S3 of a multi-energy subject revenue distribution method based on contribution according to the present invention; Figure 5 This is a flowchart of step S4 of a multi-energy entity revenue allocation method based on contribution according to the present invention; Figure 6 This is a flowchart of step S5 of a multi-energy subject revenue distribution method based on contribution according to the present invention; Figure 7 This is a flowchart of step S6 of a multi-energy subject revenue distribution method based on contribution according to the present invention. Detailed Implementation

[0017] To better understand the technical content of this invention, a specific embodiment is provided below, and the invention will be further described in conjunction with the accompanying drawings.

[0018] See Figures 1 to 7 The present invention provides a method for distributing the benefits of multiple energy entities based on contribution, comprising the following steps: Step S1: Obtain basic information on various types of power generation entities and set the basic contribution level of different types of power generation entities in combination with the topology of the target power grid; Step S2: Collect real-time operation data and real-time output data of various types of power generation entities respectively; obtain the blockage type and blockage period based on the real-time operation data; and obtain the output fluctuation period based on the real-time output data. Step S3: Use Granger causality check to verify the causal relationship between the blocking period and the power output fluctuation period, and output the power generation entities with causal relationship as the power generation entities to be corrected. Step S4: Assess the impact of the power generation entity to be corrected on the target power grid in conjunction with the power output fluctuation period, and make a negative correlation correction to the basic contribution based on the impact level; Step S5: Obtain the response processing of the power generation entity to be corrected after the blockage period occurs, and make a positive correlation correction to the basic contribution based on the response processing and the blockage type; Step S6: Distribute revenue among different types of power generation entities based on the revised base contribution.

[0019] This invention discloses a multi-energy entity revenue allocation method based on contribution. First, it acquires basic information on various types of power generation entities connected to the target power grid. Then, it sets the basic contribution level for different types of power generation entities based on the topology of the target power grid, ensuring initial fairness in allocation and avoiding bias caused by relying solely on single indicators such as power generation. Next, it obtains congestion periods and power fluctuation periods through collected real-time operational and output data, and also identifies the congestion type. Finally, it introduces Granger causality verification to check the causal relationship between congestion periods and power fluctuation periods, determining whether power fluctuations are caused by congestion and accurately identifying the source of the congestion to be corrected. The power generation entity model addresses the issue of ambiguous responsibility allocation in traditional methods. After identifying the power generation entity to be corrected, a dynamic adjustment mechanism for the basic contribution is established, including negative and positive correlation corrections. Negative correlation corrections are applied based on the impact of power generation entity output fluctuations on the target power grid, while positive correlation corrections are applied based on the response taken by the power generation entity after a congestion occurs. The correction of the basic contribution incorporates both the objective impact of congestion and the subjective attitude of the power generation entity towards congestion, achieving a comprehensive consideration of objective factors and subjective initiative. The corrected basic contribution can be directly used for revenue distribution, ensuring accurate and dynamic allocation.

[0020] Preferably, step S1 includes the following steps: Step S11: Obtain the type, installed capacity, connection location, and connection voltage of the power generation entity connected to the target power grid; Step S12: Assess the technology maturity based on the type of power generation entity and the importance based on the access location. The types of power generation entities include wind power, thermal power, photovoltaic power, and energy storage. Step S13: The basic contribution of the power generation entity type, installed capacity, technological maturity, importance, and access voltage input into the trained neural network is obtained by the neural network. The neural network is trained with a large amount of historical data.

[0021] Based on the peripheral planning map of the target power grid, the power generation entities connected to the target power grid can be identified, and their types, installed capacities, connection locations, and access voltages can be determined. These power generation entities include wind power, thermal power, photovoltaic power, and energy storage, among others. Different energy entities have different power generation methods and varying levels of technological maturity. Therefore, the technological maturity can be directly assessed based on the type of power generation entity. Furthermore, different power generation entities are connected to the target power grid at different locations, and some nodes in the target power grid perform important functions. Therefore, the importance of the power generation entities can be assessed based on their connection locations. Then, a neural network is introduced to directly process and obtain the basic contribution. The neural network is trained on a large amount of historical data, and its basic contribution assessment accuracy can reach over 95%.

[0022] Preferably, step S2 includes the following specific steps: Step S21: Collect real-time current, node voltage, conductor temperature and power flow monitoring data on the main power generation line, and record them as real-time operating data; Step S22: Compare the real-time running data with the standard data, and determine the blocking type based on the differences between the real-time running data and the standard data. The blocking types include line load type blocking, voltage type blocking and power flow transfer type blocking. Output the time periods with differences as blocking periods. Step S23: Collect the real-time active power of the power generation unit, remove outliers, smooth the noise using the moving average method, and record it as real-time output data. Step S24: Set the fluctuation threshold, and use a sliding window to calculate the output change rate of real-time output data. When the fluctuation threshold is exceeded and the duration is greater than the time threshold, the fluctuation start time and fluctuation end time are recorded as the output fluctuation period.

[0023] After determining the basic contribution level, the revenue is not directly distributed based on the basic contribution level. This invention also provides a dynamic adjustment method. Before making the adjustment, it is necessary to determine the adjustment method. First, real-time operating data and real-time output data of each type of power generation entity are collected. The real-time operating data is compared with standard data. The standard data sets a threshold. If the real-time operating data is not within the threshold range of the standard data and continues for a period of time, it can be judged that a blockage has occurred. At the same time, the type of blockage can be determined based on the abnormal real-time operating data. For example, if the voltage deviation is ±5 and continues for 5 minutes, it can be judged as a voltage-type blockage.

[0024] The real-time active power of the power generation entity is collected and preprocessed to output real-time power output data. The preprocessing includes removing outliers and noise reduction. Then, a fluctuation threshold is set, and the power output fluctuation period is determined by calculating whether the power output change rate of the real-time power output data exceeds the fluctuation threshold and whether the duration is greater than the time threshold.

[0025] Preferably, step S3 includes the following specific steps: Step S31: Define blockage as the cause variable and power output fluctuation as the result variable, and extract the blockage sequence from the real-time operating data corresponding to the blockage period and the power output fluctuation sequence from the real-time power output data corresponding to the power output fluctuation period, respectively. Step S32: Extract a time window from 30 minutes before the blockage occurs to 60 minutes after the blockage occurs. Then, perform time-series alignment on the blockage sequence and the power output fluctuation sequence, and perform data stationarity verification on the blockage sequence and the power output fluctuation sequence respectively. Step S33: Set the lag order and significance level parameters, and construct the regression equation of the power fluctuation sequence with respect to the blockage sequence based on the lag order and significance level parameters; Step S34: Calculate the P value based on the regression equation using F-verification. When the P value is <0.05, it is determined that the power output fluctuation is affected by the blockage, and the corresponding power generation entity is output as the power generation entity to be corrected.

[0026] After identifying the periods of blockage and power output fluctuation, a Granger causality test is introduced, where blockage is treated as the causal variable and power output fluctuation as the outcome variable. Blockage and power output fluctuation sequences are then extracted from real-time operational and output data, respectively. Preprocessing of these sequences is then required. The blockage sequence first undergoes time window truncation, extracting the sequence from 30 minutes before the blockage occurs to 60 minutes after its end. This ensures coverage of the lagged impact of blockage on power output fluctuation. The extracted blockage and power output fluctuation sequences are then time-series aligned and their stationarity verified. After processing, the lag order and significance level parameters for the Granger causality test are set. The lag order is determined based on the AIC criterion, typically set to 5-10, to test whether the blockage in the first 5-10 minutes affects power output fluctuation. The significance level parameter is set to 0.05. A regression equation is then established, expressed as: Y = α + Σ(lag order × X) + Error term, where Y is output fluctuation, α is significance level parameter, and X is blockage. Based on the regression equation, it is assumed that blockage is not a Granger cause of output fluctuation. The P value is calculated by F test. If the P value < 0.05, the hypothesis is not valid. It is determined that blockage significantly affects output fluctuation, that is, there is a causal relationship between blockage and output fluctuation. The corresponding power generation entity is output as the power generation entity to be corrected.

[0027] Preferably, step S4 includes the following specific steps: Step S41: Check whether the power output fluctuation period has triggered a safety event. Safety events include line overload, voltage over-limit, or protection device activation. Step S42: Statistical scheduling intervention measures taken to mitigate the impact of power output fluctuations; Step S43: Classify the degree of impact based on the severity of the security incident, the intensity of the intervention measures, and the cost; Step S44: Set a deduction value according to the level of influence, and deduct the basic contribution value accordingly.

[0028] When a power generation system experiences a blockage, it can cause power output fluctuations. If these fluctuations are severe, they can lead to safety incidents in the target power grid, including line overloads, voltage exceeding limits, and the activation of protective devices. The target power grid will also implement corresponding intervention measures to address these fluctuations, selecting the intensity of these measures based on their severity. Different levels of intervention will have different costs. Therefore, the impact level of power output fluctuations can be classified based on the severity of the safety incident, the intensity of the intervention measures, and the cost. Different impact levels correspond to different deduction values: high, medium, and low, with deduction values ​​of 20%, 10%, and 5% respectively. The base contribution is deducted based on the deduction value. For example, when the impact level is medium, the corrected expression for the base contribution is: base contribution × (1-10%).

[0029] Preferably, step S5 includes the following specific steps: Step S51: Collect the automatic adjustment and manual maintenance records of the generator to be corrected after the blockage as a response processing record; Step S52: Evaluate the type suitability of the response processing record in conjunction with the blocking type; Step S53: Classify the processing level based on the response speed, blockage mitigation effect, and type adaptability of the response processing records; Step S54: Set an additional value according to the processing level, and adjust the basic contribution value accordingly.

[0030] When a power generation entity experiences a blockage, its response determines its handling attitude. A faster response results in a smaller impact on the target power grid, allowing for an appropriate increase in the base contribution value. The power generation entity's response includes automatic adjustment and manual maintenance. Different handling methods have varying adaptability to different blockage types. Therefore, combining the blockage type allows for an assessment of the type adaptability of the response handling records. Ultimately, the handling level is determined by combining the response speed, blockage mitigation effect, and type adaptability of the response handling records. The handling levels are excellent, medium, and poor, with corresponding additional values ​​of 20%, 10%, and 5%, respectively. The base contribution can be increased based on the additional value. For example, when the handling level is medium, the modified expression for the base contribution is: base contribution × (1 + 10%).

[0031] Preferably, step S6 includes the following specific steps: Step S61: Obtain the electricity sales revenue and operation and maintenance costs of the target power grid, and calculate the total revenue based on the electricity sales revenue and operation and maintenance costs; Step S62: Multiply the total revenue by the base contribution after negative correlation correction and positive correlation correction respectively, and then allocate it to different types of power generation entities.

[0032] After the basic contribution is adjusted for negative and positive correlation, it can be directly multiplied by the total revenue of the target power grid to obtain the revenue allocated to different types of power generation entities. The total revenue of the target power grid needs to take into account electricity sales revenue and operation and maintenance costs. The total revenue can be obtained by subtracting operation and maintenance costs from electricity sales revenue.

[0033] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A contribution-based multi-energy principal benefit distribution method, characterized in that, Includes the following steps: Step S1: Obtain basic information on various types of power generation entities and set the basic contribution level of different types of power generation entities in combination with the topology of the target power grid; Step S2: Collect real-time operation data and real-time output data of various types of power generation entities respectively; obtain the blockage type and blockage period based on the real-time operation data; and obtain the output fluctuation period based on the real-time output data. Step S3: Use Granger causality check to verify the causal relationship between the blocking period and the power output fluctuation period, and output the power generation entities with causal relationship as the power generation entities to be corrected. Step S4: Assess the impact of the power generation entity to be corrected on the target power grid in conjunction with the power output fluctuation period, and make a negative correlation correction to the basic contribution based on the impact level; Step S5: Obtain the response processing of the power generation entity to be corrected after the blockage period occurs, and make a positive correlation correction to the basic contribution based on the response processing and the blockage type; Step S6: Distribute revenue to different types of power generation entities based on the revised base contribution. The specific steps of step S3 include: Step S31: Define blockage as the cause variable and power output fluctuation as the result variable, and extract the blockage sequence from the real-time operating data corresponding to the blockage period and the power output fluctuation sequence from the real-time power output data corresponding to the power output fluctuation period, respectively. Step S32: Perform time-series alignment on the blocking sequence and the power output fluctuation sequence, and perform data stationarity verification on the blocking sequence and the power output fluctuation sequence respectively; Step S33: Set the lag order and significance level parameters, and construct the regression equation of the power fluctuation sequence with respect to the blockage sequence based on the lag order and significance level parameters; Step S34: Calculate the P value based on the regression equation using F-verification. When the P value is <0.05, it is determined that the power output fluctuation is affected by the blockage, and the corresponding power generation entity is output as the power generation entity to be corrected. 2.The method of claim 1, wherein, The specific steps of step S1 include: Step S11: Obtain the type, installed capacity, connection location, and connection voltage of the power generation entity connected to the target power grid; Step S12: Assess the technology maturity based on the type of power generation entity and the importance based on the access location; Step S13: Input the type of power generation entity, installed capacity, technological maturity, importance, and access voltage into the trained neural network, and then have the neural network process the data to obtain the basic contribution. 3.The method of claim 2, wherein, The types of power generation entities include wind power, thermal power, photovoltaic power, and energy storage, and the neural network is obtained through training with a large amount of historical data. 4.The method of claim 1, wherein, The specific steps of step S2 include: Step S21: Collect real-time current, node voltage, conductor temperature and power flow monitoring data on the main power generation line, and record them as real-time operating data; Step S22: Compare the real-time running data with the standard data, and output the time periods with differences as the blocking periods; Step S23: Collect the real-time active power of the power generation unit, remove outliers, smooth the noise using the moving average method, and record it as real-time output data. Step S24: Set the fluctuation threshold, and use a sliding window to calculate the output change rate of the real-time output data. When the fluctuation threshold is exceeded and the duration is greater than the time threshold, the fluctuation start time and fluctuation end time are recorded as the output fluctuation period.

5. The method of claim 4, wherein, In step S22, the blocking type is determined based on the difference between real-time operating data and standard data. The blocking type includes line load blocking, voltage blocking, and power flow transfer blocking.

6. The method for distributing revenue among multiple energy entities based on contribution as described in claim 1, characterized in that, Before performing timing alignment, step S32 extracts a time window from 30 minutes before the blockage occurs to 60 minutes after it occurs.

7. The method for distributing revenue among multiple energy entities based on contribution as described in claim 1, characterized in that, The specific steps of step S4 include: Step S41: Check whether the power output fluctuation period has triggered a safety event. Safety events include line overload, voltage over-limit, or protection device activation. Step S42: Statistical scheduling intervention measures taken to mitigate the impact of power output fluctuations; Step S43: Classify the degree of impact based on the severity of the security incident, the intensity of the intervention measures, and the cost; Step S44: Set a deduction value according to the level of influence, and deduct the basic contribution value accordingly.

8. The method for distributing revenue among multiple energy entities based on contribution as described in claim 1, characterized in that, The specific steps of step S5 include: Step S51: Collect the automatic adjustment and manual maintenance records of the generator to be corrected after the blockage as a response processing record; Step S52: Evaluate the type suitability of the response processing record in conjunction with the blocking type; Step S53: Classify the processing level based on the response speed, blockage mitigation effect, and type adaptability of the response processing records; Step S54: Set an additional value according to the processing level, and adjust the basic contribution value accordingly.

9. A method for distributing revenue among multiple energy entities based on contribution as described in claim 1, characterized in that, The specific steps of step S6 include: Step S61: Obtain the electricity sales revenue and operation and maintenance costs of the target power grid, and calculate the total revenue based on the electricity sales revenue and operation and maintenance costs; Step S62: Multiply the total revenue by the base contribution after negative correlation correction and positive correlation correction respectively, and then allocate it to different types of power generation entities.

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