A park power quality treatment user side incentive method based on stochastic game

Abstract

The application relates to a park power quality treatment user side incentive method based on a random game, and belongs to the technical field of power system operation control and energy management. The method comprises the following steps: determining a user side resource set participating in collaborative treatment in a current power quality collaborative treatment period; obtaining long-term expected regular treatment cost and long-term expected collaborative treatment cost in the period based on park power grid data; obtaining user side resource allocable shares in the period based on the long-term expected regular treatment cost and the long-term expected collaborative treatment cost; calculating treatment contribution degrees of each user side resource in the period based on the user side resource allocable shares; obtaining incentive values of each user side resource based on the treatment contribution degrees and the user side resource allocable shares, and determining scheduling priorities of each resource in the next period according to the incentive values, and sequentially calling each resource to participate in collaborative treatment. The method is used for solving the technical problem of unreasonable treatment task allocation.

Description

Technical Field

[0001] This invention relates to the field of power system operation control and energy management technology, and in particular to a user-side incentive method for power quality governance in industrial parks based on random game theory. Background Technology

[0002] The large-scale integration of distributed energy sources such as photovoltaic systems, energy storage systems, and electric vehicle charging facilities with new loads in industrial parks has resulted in a high proportion of power electronics in the park's power distribution network. While these resources improve energy efficiency, they can also cause power quality issues such as voltage deviations. Therefore, how to fully utilize adjustable resources on the user side to participate in collaborative power quality management has become an important research direction in the current power management of industrial parks.

[0003] Currently, power quality management in industrial parks mainly adopts a centralized approach, which involves configuring specialized equipment such as static var compensators (SVCs), static var generators (SVAs), and active power filters at key nodes of the distribution network. This regulates bus voltage by adjusting reactive power, thus suppressing voltage deviations. However, centralized management suffers from high investment costs and low resource utilization. To address this issue, existing technologies are gradually incorporating user-side resources to collaboratively participate in power quality management. However, during collaborative management, the management capabilities of different user-side resources vary—if resources with strong management capabilities are assigned fewer management tasks, it leads to a waste of high-quality management resources; conversely, if resources with weak management capabilities are assigned more management tasks, it increases management costs.

[0004] Existing technologies lack effective assessment and quantification of the power quality governance capabilities of various user-side resources, making it impossible to rationally allocate governance tasks based on the actual governance capabilities of resources, resulting in unreasonable allocation of governance resources and low overall governance efficiency. Summary of the Invention

[0005] Based on the above analysis, the embodiments of the present invention aim to provide a user-side incentive method for power quality governance in industrial parks based on random game theory, in order to solve the technical problem that the existing technology cannot reasonably allocate governance tasks according to the actual governance capabilities of each user-side resource, resulting in waste of high-quality resources or increased governance costs.

[0006] On one hand, embodiments of the present invention provide a user-side incentive method for power quality management in industrial parks based on random game theory, including: Determine the set of user-side resources participating in collaborative governance within the current power quality collaborative governance cycle, and divide the cycle into multiple discrete operating segments; Obtain the park power grid data within the specified period, and based on the park power grid data, obtain the long-term expected conventional governance cost of the centralized governance equipment within the specified period, and the long-term expected collaborative governance cost of the user-side resource subset and the centralized governance equipment jointly participating in governance, wherein the user-side resource subset is any subset of the user-side resource set; Based on the long-term expected conventional governance cost and the long-term expected collaborative governance cost, the share of user-side resources that can be allocated within the period is obtained. Based on the allocatable share of user-side resources, the governance contribution of each user-side resource in the user-side resource set during the period is calculated using the Shapley formula. Based on the governance contribution and the allocable share of user-side resources, the incentive value of each user-side resource is obtained. Based on the incentive value of each user-side resource, the scheduling priority of each resource in the next power quality collaborative governance cycle is determined. Then, each resource is called to participate in collaborative governance in order of scheduling priority from high to low.

[0007] Based on a further improvement of the above method, the step of obtaining the long-term expected conventional governance cost of the centralized governance equipment within the period, and the long-term expected collaborative governance cost of the user-side resource subset and the centralized governance equipment jointly participating in governance, based on the park power grid data, includes: Based on the power grid data of the park, calculate the collaborative governance cost in each of the operating segments when the user-side resource subset and the centralized governance equipment jointly participate in governance, and the regular governance cost of the centralized governance equipment in each of the operating segments; The long-term expected collaborative governance cost is obtained by summing up the costs of each collaborative governance cost after discounting them according to a preset first discount factor. The long-term expected conventional governance cost is obtained by summing up the conventional governance costs after discounting each of them according to the preset second discount factor.

[0008] Based on a further improvement of the above method, the park power grid data includes the reactive power regulation of the centralized governance equipment and each resource in the user-side resource set during each operating period, the sensitivity coefficient of the centralized governance equipment to the bus voltage, and the bus voltage before collaborative governance during each operating period; the calculation of the collaborative governance cost and the conventional governance cost of the centralized governance equipment during each operating period when the user-side resource subset and the centralized governance equipment jointly participate in governance based on the park power grid data includes: For each of the aforementioned operating periods, the collaborative governance cost of the operating period is calculated based on the reactive power adjustment amount of each resource in the user-side resource subset during the operating period and the reactive power adjustment amount of the centralized governance equipment during the time period. Based on the bus voltage before the collaborative governance during the aforementioned operating period and the sensitivity coefficient of the centralized governance equipment to the bus voltage, the reactive power deficit of the park power grid during the aforementioned operating period is obtained. Based on the aforementioned reactive power deficit, the routine governance cost of the centralized governance equipment during the operating period is determined.

[0009] Based on a further improvement to the above method, the step of obtaining the allocatable share of user-side resources within the stated period based on the long-term expected conventional governance cost and the long-term expected collaborative governance cost includes: The difference between the long-term expected conventional governance cost and the long-term expected collaborative governance cost is used as the long-term cost savings, which represents the cost saved in the period after the user-side resource subset participates in the collaborative power quality governance. Based on the preset governance cost compensation ratio coefficient and the long-term cost savings, the share of user-side resources that can be allocated within the period is obtained.

[0010] A further improvement to the above method, the step of obtaining the incentive value for each user-side resource based on each governance contribution and the allocatable share of user-side resources, includes: The governance contributions described above are non-negatively processed; Based on the nonnegation of each governance contribution, the contribution ratio of each user-side resource is determined. The incentive value for each user-side resource is determined based on its contribution percentage and the allocatable share of each user-side resource.

[0011] A further improvement to the above method, the step of determining the scheduling priority of each resource in the next power quality collaborative governance cycle based on the incentive value of each user-side resource, includes: Sort the incentive values ​​of each user-side resource from largest to smallest; Based on the ranking results, the scheduling priority of each user-side resource participating in collaborative governance in the next power quality collaborative governance cycle is determined, with resources with higher incentive values ​​having higher priority than resources with lower incentive values.

[0012] Compared with the prior art, the present invention can achieve at least the following beneficial effects: By calculating the contribution of each user-side resource throughout the entire governance cycle using Shapley values, an effective quantitative assessment of the governance capabilities of each resource is achieved. Based on these contributions, incentive values ​​are determined for each resource, and the scheduling priority for the next cycle is then determined according to these incentive values. This ensures that resources with strong governance capabilities are given priority in undertaking governance tasks, avoiding waste of high-quality resources, while resources with weak governance capabilities are excluded from or given fewer governance tasks, preventing increased governance costs. Simultaneously, this invention utilizes the collaborative participation of user-side resources in power quality governance, reducing reliance on centralized governance equipment and effectively lowering centralized governance costs. In summary, this application, through quantitative assessment of governance capabilities and incentive-based scheduling, achieves optimized allocation of governance resources, improves resource utilization and overall power quality governance effectiveness, and significantly reduces governance costs.

[0013] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0014] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. Figure 1 is a flowchart of a user-side incentive method for power quality management in a park based on random game theory in an embodiment of the present invention. Detailed Implementation

[0015] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0016] A specific embodiment of the present invention discloses a user-side incentive method for power quality management in industrial parks based on random game theory, such as... Figure 1 As shown, it includes the following steps: S1. Determine the set of user-side resources participating in collaborative governance within the current power quality collaborative governance cycle, and divide the cycle into multiple discrete operating segments.

[0017] Bus voltage is a crucial indicator of power quality. Performance is optimal when the bus voltage operates near its rated voltage. However, due to load variations within the power distribution system, bus voltage deviations are inevitable. The essence of voltage deviation is reactive power imbalance; therefore, regulating bus voltage requires regulating the reactive power of the power grid. Within the power distribution system, this is primarily achieved through centralized reactive power management equipment, which absorbs or generates reactive power to regulate the grid's reactive power. This centralized equipment, including static var compensators (SVCs), static var generators (SVAs), and active power filters, is installed at key distribution nodes within the system. Simultaneously, user-side resources collaboratively participate in power management, absorbing or generating reactive power to coordinate the regulation of the grid's reactive power. User-side resources refer to equipment installed in the user-side distribution system.

[0018] In this application, the power quality collaborative governance cycle refers to the time period for quantitatively assessing the power quality governance capabilities of various user-side resources within the park. The duration of the collaborative governance cycle can be set according to actual operational needs, such as one month, one quarter, or one year; this application does not impose specific limitations on this. Simultaneously, the collaborative governance cycle is divided into multiple discrete operating segments, the duration of which can be set according to actual needs. The interval between two adjacent operating segments can be the same or different.

[0019] To quantitatively assess the power quality governance capabilities of each user-side resource within a power quality collaborative governance cycle, it is first necessary to determine the set of user-side resources participating in collaborative governance within the current cycle. :

[0020] in, The total number of user-side resources participating in collaborative governance within the park; set Each element in the list corresponds to an independent user-side resource, which includes energy storage devices, photovoltaic inverters, industrial static var generators, and electric vehicle charging piles. When there are multiple user-side resources of the same type, they are numbered separately.

[0021] For example, if the user-side resources include 2 energy storage devices, 3 photovoltaic inverters, 1 industrial static var generator, and 2 charging piles, then the energy storage devices A and B are numbered 1 and 2 respectively, the photovoltaic inverters A, B, and C are numbered 3, 4, and 5 respectively, the industrial static var generator is numbered 6, and the charging piles A and B are numbered 7 and 8 respectively. =8, each number uniquely corresponds to one resource.

[0022] S2. Based on the park power grid data, obtain the long-term expected conventional governance cost of the centralized governance equipment within the period, and the long-term expected collaborative governance cost of the user-side resource subset and the centralized governance equipment jointly participating in governance, wherein the user-side resource subset is any subset of the user-side resource set.

[0023] The data on the power grid in the park includes at least the reactive power regulation of centralized governance equipment and each user-side resource during each operating period in the current power quality collaborative governance cycle, the state of charge value of each energy storage device, the active power output value of each user-side resource, the sensitivity coefficient of centralized governance equipment and each user-side resource to the bus voltage, the bus voltage before collaborative governance and the measured voltage of the park bus in each time period, the total active power value of the park, the occupancy rate of charging piles, the rated voltage of the park bus, the electricity price in each time period and the operating efficiency of centralized governance equipment, as well as the rated capacity and opportunity cost coefficient of each user-side resource.

[0024] In this step, the operating status of each operating segment is determined in advance based on the acquired power grid data of the park. , means as follows:

[0025] in, runtime segment The deviation value of the busbar voltage in the park, that is, the difference between the measured voltage of the busbar and the rated voltage; It is the equivalent state of charge (SOC) value of the energy storage system in the park, which is the capacity-weighted average of the SOC values ​​of each energy storage device. This represents the total active power output of all photovoltaic inverters within the park. The occupancy rate of charging stations; Industrial load level, i.e., the operating hours of the industrial park. The total active power value.

[0026] At the same time, determine the collaborative governance actions for each user-side resource in each runtime segment. for:

[0027] in, Each resource in the user-side resource subset In the runtime segment The reactive power adjustment amount.

[0028] Step S2 specifically includes: S2-1. Based on the power grid data of the park, calculate the collaborative governance cost in each of the operating segments when the user-side resource subset and the centralized governance equipment jointly participate in governance, and the regular governance cost of the centralized governance equipment in each of the operating segments.

[0029] Specifically, for each runtime segment Based on each resource in the user-side resource subset In the runtime segment reactive power regulation and centralized treatment equipment during operation reactive power regulation Calculate the runtime segment corresponding to the user-side resource subset. Operating status Collaborative governance costs .

[0030] The cost of collaborative governance This is obtained by solving the following optimization problem:

[0031] in, A subset of user-side resources N， Represents any subset of the user-side resource set; Represents any resource in the user-side resource set. For resources In the runtime segment reactive power regulation amount, For centralized treatment equipment during operation t reactive power regulation; runtime segment The cost required for centralized power treatment equipment to regulate a unit of reactive power is determined by the electricity price during the current operating period and the operating efficiency of the centralized power treatment equipment. For user-side resource subset Resources In the runtime segment The corresponding opportunity cost.

[0032] Specifically, if resources When used as an energy storage device, its opportunity cost is expressed as follows:

[0033] If resources When it is a photovoltaic inverter, its opportunity cost is expressed as follows:

[0034] If resources When used as an industrial static var generator, its opportunity cost is expressed as follows:

[0035] If resources When it is used as a charging station, its opportunity cost is expressed as follows:

[0036] in, This is the energy storage lifespan degradation cost coefficient. This is the opportunity cost coefficient for energy storage capacity. The photovoltaic power generation revenue loss coefficient. This is the photovoltaic operating cost coefficient. Adjusting the unit reactive power cost coefficient for industrial static var generators. The charging service revenue loss coefficient. This represents the charging station occupancy rate for the current period. All the coefficients mentioned above are preset constant parameters based on the physical characteristics of the equipment.

[0037] The above optimization problem also satisfies the following constraints: Bus voltage constraint:

[0038] Voltage safety constraints

[0039] User-side resource capacity constraints:

[0040] Compensation constraints for centralized treatment equipment:

[0041] in, To control the voltage at the front bus, To address the voltage issue at the rear busbar, For user-side resources Sensitivity coefficient to bus voltage The sensitivity coefficient of the centralized control equipment to the bus voltage; and These are the lower and upper limits of the allowable bus voltage. Indicates user-side resources i In running state The maximum reactive power that can be generated under these conditions. Representing resources In running state The maximum reactive power that can be absorbed is The maximum reactive power that centralized treatment equipment can generate. This is the maximum reactive power that centralized treatment equipment can absorb.

[0042] Specifically, and Based on the line parameters and topology of the park's power distribution network, and according to the current operating status Bus voltage under In resources A small reactive power increment is applied to each of the grid connection points or centralized equipment access points. or Perform power flow calculations to obtain the bus voltage change. or And calculate the sensitivity coefficient using the following formula:

[0043] and It is usually determined by the national standard GB / T 12325-2008. If the internal standards of the park are more stringent than the national standards, then the park standards can also be used to determine the standards. , , and It is determined by the rated capacity of the resource or centralized treatment equipment and the current active power output value.

[0044] Specifically, the calculation steps for the conventional treatment cost of centralized treatment equipment during each of the aforementioned operating periods are as follows: (1) Based on the aforementioned runtime segment Before the coordinated management of bus voltage And the sensitivity coefficient of the centralized treatment equipment to the bus voltage. The runtime segment is obtained. Operating status Reactive power deficit in the lower industrial park power grid Unproductive capacity deficit The calculation formula is as follows:

[0045] in, This is the rated voltage of the park's busbar. When... When >0, it indicates that reactive power needs to be sent to the park's power distribution network; when When <0, it indicates that reactive power needs to be absorbed from the distribution network.

[0046] (2) Based on reactive power deficit The cost required for centralized treatment equipment to regulate unit reactive power Determine the operating time of centralized treatment equipment Operating status The conventional governance costs Governance costs The calculation formula is as follows:

[0047] S2-2. The collaborative governance costs described are discounted according to the preset first discount factor and then summed to obtain the long-term expected collaborative governance cost of the user-side resource subset and the centralized governance equipment participating in the governance.

[0048] Specifically, the long-term expected collaborative governance cost within the current power quality collaborative governance cycle. It is expressed as follows:

[0049] in, This represents the first discount factor, a preset constant with a value range of 0 < 0. ≤1; This indicates the last period of the current collaborative governance cycle for power quality.

[0050] It should be noted that the expansion of this formula is related to the state transition probability. The state transition probability is determined based on historical data from the park. The specific method is as follows: Historical operational data of the park is collected, for example, one year's worth of data, with each hour representing a time period, totaling 8760 time periods. For each time period... t Get the running status for this period. and reactive power regulation of each resource For each adjacent time period forming a state transition sample （ ）→ Statistics in status Next action Then, the status of the next period. Frequency of occurrence and the same （ ） Total number of samples under the condition Calculate the transition probability using the following formula:

[0051] For example, in the 8760 periods of a year, the state and actions There are a total of 100 time periods, and the next time period is analyzed from these 10 samples. If 30 of the samples are in a state where the state in the next time period is... The corresponding state transition probability is: =0.3 S2-3. The conventional governance costs described are discounted according to the preset second discount factor and then summed to obtain the long-term expected conventional governance cost of the centralized governance equipment.

[0052] The long-term expected conventional governance cost of centralized governance equipment refers to the total cost incurred throughout the entire power quality collaborative governance cycle, assuming only centralized governance equipment within the park is used for voltage deviation management without utilizing any user-side resources. Specifically, the long-term expected conventional governance cost... The calculation formula is:

[0053] in, This represents the second discount factor, a preset constant with a value range of 0 < 0. ≤1.

[0054] S3. Based on the long-term expected conventional governance cost and the long-term expected collaborative governance cost, obtain the share of user-side resources that can be allocated within the period.

[0055] Specifically, it includes: S3-1. Calculate the difference between the long-term expected conventional governance cost and the long-term expected collaborative governance cost, and use the difference as the long-term cost savings. The long-term cost savings represent the cost saved in the period after the user-side resource subset participates in the collaborative governance of power quality.

[0056] Specifically, user-side resource subset Long-term cost savings within the current power quality collaborative governance cycle for:

[0057] S3-2. Based on the preset governance cost compensation ratio coefficient and the long-term cost savings, obtain the share of user-side resources that can be allocated within the period.

[0058] Determining the subset of user-side resources After determining the long-term cost savings, it is necessary to allocate a share that can be distributed to the park management. And the share of user-side resources that can be allocated :

[0059]

[0060] in, The preset governance cost compensation ratio coefficient is determined based on the governance difficulty and cost-sharing needs within the current power quality collaborative governance cycle, where 0 ≤ ≤1.

[0061] S4. Based on the allocatable share of the user-side resources, use the Shapley formula to calculate the governance contribution of each user-side resource in the user-side resource set during the period.

[0062] Specifically, the Shapley formula is:

[0063] in, This refers to the set of user-side resources participating in collaborative governance within the current cycle. This indicates that no resources are included. Belongs to set any subset , Representing a subset The amount of resources in it equals , The total number of user-side resources participating in collaborative governance within the park; and The values ​​are all based on the user-side resource allocable share obtained in step S3-2. Determined, i.e., a subset of user-side resources S Take respectively and ; The Shapley value represents the user-side resources. Governance contribution during the cycle.

[0064] Based on the above formula, each user-side resource Governance contribution It is quantified into a numerical value that reflects the resource The actual contribution made to collaborative governance of power quality.

[0065] S5. Based on the governance contribution and the allocable share of user-side resources, obtain the incentive value of each user-side resource, and determine the scheduling priority of each resource in the next power quality collaborative governance cycle according to the incentive value of each user-side resource. Then, in order of the scheduling priority from high to low, call each resource to participate in collaborative governance.

[0066] Specifically, the incentive values ​​for each user-side resource The calculation formula is as follows:

[0067] in, Indicates the use of user-side resources Perform nonnegation processing; This is an index variable used to iterate through all user-side resources. Incentive value. Characterizing user-side resources The allocation share that should be obtained during the current governance cycle. The larger the value, the more resources are available. The greater the governance contribution during this cycle, the higher the share deserved.

[0068] Incentive values ​​obtained from various user-side resources Based on this, the incentive values ​​are sorted in descending order. According to the sorting results, the scheduling priority of each user-side resource participating in collaborative governance in the next power quality collaborative governance cycle is determined. The resource with the higher incentive value has the higher scheduling priority and will be called up first to participate in collaborative governance in the next governance cycle.

[0069] Specifically, during each operating period in the next governance cycle, the reactive power deficit will be determined based on the current voltage deviation. Then, it calls the resources sequentially, starting with the highest priority, until the sum of the maximum adjustable capacities of the resources already called is satisfied. Resources that fail to be invoked will not participate in governance during that period.

[0070] This application calculates the contribution of each user-side resource throughout the entire governance cycle using Shapley values, achieving an effective quantitative assessment of the governance capabilities of each resource. Based on these contributions, an incentive value is determined for each resource, and the scheduling priority for the next cycle is then determined according to the incentive value. This ensures that resources with strong governance capabilities are given priority in undertaking governance tasks, avoiding waste of high-quality resources, while resources with weak governance capabilities are exempt from or assigned fewer governance tasks, preventing increased governance costs. Simultaneously, this invention utilizes the collaborative participation of user-side resources in power quality governance, reducing reliance on centralized governance equipment and effectively lowering centralized governance costs. In summary, this application, through quantitative assessment of governance capabilities and incentive-based scheduling, achieves optimized allocation of governance resources, improves resource utilization and overall power quality governance effectiveness, and significantly reduces governance costs.

[0071] In an optional embodiment, when the number of user-side resources is large, the computational complexity of the Shapley value in step S4 increases exponentially with the number of resources. To reduce computational complexity, this application provides a hierarchical approximate Shapley calculation method to improve computational efficiency.

[0072] The formula for calculating the hierarchical approximation of Shapley is as follows:

[0073]

[0074] in, For all satisfied And does not include resources A set consisting of subsets; To from that A set consisting of a subset selected from a set. express for Any subset of, Represents a set The number of elements in the middle. This is an approximation of the Shapley value, representing user-side resources. Approximate governance contribution within the cycle.

[0075] Compared to the hierarchical approximation Shapley formula, the Shapley formula offers higher computational accuracy, but its computational complexity increases exponentially with the number of resources. Therefore, when the number of user-side resources exceeds a preset threshold (e.g., 15), the hierarchical approximation Shapley formula is preferred for calculation to reduce computational complexity and improve computational efficiency.

[0076] Accordingly, if the approximate governance contribution of each user-side resource is obtained using the hierarchical approximation Shapley formula, then the approximate incentive value of each user-side resource can be obtained. Approximate incentive values ​​for each user-side resource. for:

[0077] Obtain approximate incentive values ​​for each user-side resource. Then, based on each approximate excitation value The scheduling priority of each resource in the next power quality collaborative governance cycle is determined, and each resource is called in order of priority from high to low to participate in collaborative governance.

[0078] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A user-side incentive method for power quality management in industrial parks based on random game theory, characterized in that, The method includes: Determine the set of user-side resources participating in collaborative governance within the current power quality collaborative governance cycle, and divide the cycle into multiple discrete operating segments; Obtain the park power grid data within the specified period, and based on the park power grid data, obtain the long-term expected conventional governance cost of the centralized governance equipment within the specified period, and the long-term expected collaborative governance cost of the user-side resource subset and the centralized governance equipment jointly participating in governance, wherein the user-side resource subset is any subset of the user-side resource set; Based on the long-term expected conventional governance cost and the long-term expected collaborative governance cost, the share of user-side resources that can be allocated within the period is obtained. Based on the allocatable share of user-side resources, the governance contribution of each user-side resource in the user-side resource set during the period is calculated using the Shapley formula. Based on the governance contribution and the allocable share of user-side resources, the incentive value of each user-side resource is obtained. Based on the incentive value of each user-side resource, the scheduling priority of each resource in the next power quality collaborative governance cycle is determined. Then, each resource is called to participate in collaborative governance in order of scheduling priority from high to low.

2. The method according to claim 1, characterized in that, The method for obtaining the long-term expected conventional governance cost of the centralized governance equipment within the specified period based on the park power grid data, and the long-term expected collaborative governance cost of the user-side resource subset and the centralized governance equipment jointly participating in governance, includes: Based on the power grid data of the park, calculate the collaborative governance cost in each of the operating segments when the user-side resource subset and the centralized governance equipment jointly participate in governance, and the regular governance cost of the centralized governance equipment in each of the operating segments; The long-term expected collaborative governance cost is obtained by summing up the costs of each collaborative governance cost after discounting them according to a preset first discount factor. The long-term expected conventional governance cost is obtained by summing up the conventional governance costs after discounting each of them according to the preset second discount factor.

3. The method according to claim 2, characterized in that, The park power grid data includes the reactive power regulation of the centralized governance equipment and each resource in the user-side resource set during each operating period, the sensitivity coefficient of the centralized governance equipment to the bus voltage, and the bus voltage before collaborative governance during each operating period; the calculation of the collaborative governance cost and the conventional governance cost of the centralized governance equipment during each operating period when the user-side resource subset and the centralized governance equipment jointly participate in governance based on the park power grid data includes: For each of the aforementioned operating periods, the collaborative governance cost of the operating period is calculated based on the reactive power adjustment amount of each resource in the user-side resource subset during the operating period and the reactive power adjustment amount of the centralized governance equipment during the time period. Based on the bus voltage before the collaborative governance during the aforementioned operating period and the sensitivity coefficient of the centralized governance equipment to the bus voltage, the reactive power deficit of the park power grid during the aforementioned operating period is obtained. Based on the aforementioned reactive power deficit, the routine governance cost of the centralized governance equipment during the operating period is determined.

4. The method according to claim 1, characterized in that, The method of obtaining the allocatable share of user-side resources within the stated period based on the long-term expected conventional governance cost and the long-term expected collaborative governance cost includes: The difference between the long-term expected conventional governance cost and the long-term expected collaborative governance cost is used as the long-term cost savings, which represents the cost saved in the period after the user-side resource subset participates in the collaborative power quality governance. Based on the preset governance cost compensation ratio coefficient and the long-term cost savings, the share of user-side resources that can be allocated within the period is obtained.

5. The method according to claim 4, characterized in that, The allocatable share of user-side resources is determined by the following formula: in, Assign a share of user-side resources. For long-term cost savings, This is the preset governance cost compensation ratio coefficient.

6. The method according to claim 1, characterized in that, The governance contribution is calculated using the following formula: in, For user-side resource collection, Not containing resources any subset, Representing a subset The amount of resources in it equals , The total number of user-side resources. For subset The share of user-side resources that can be allocated For subset Add resources The subsequent user-side resource allocation share For user-side resources The contribution to governance.

7. The method according to claim 1, characterized in that, The incentive value for each user-side resource is obtained based on the governance contribution and the allocable share of user-side resources, including: The governance contributions described above are non-negatively processed; Based on the nonnegation of each governance contribution, the contribution ratio of each user-side resource is determined. The incentive value for each user-side resource is determined based on its contribution percentage and the allocatable share of each user-side resource.

8. The method according to claim 7, characterized in that, The excitation value is determined by the following formula: in, For user-side resources The degree of contribution to governance The total number of user-side resources. Nonnegation processing; This is an index variable used to iterate through all user-side resources; Incentive value represents user-side resources. The allocation share to be obtained within the period; Allocable share of resources on the user side.

9. The method according to claim 1, characterized in that, The step of determining the scheduling priority of each resource in the next power quality collaborative governance cycle based on the incentive values ​​of each user-side resource includes: Sort the incentive values ​​of each user-side resource from largest to smallest; Based on the ranking results, the scheduling priority of each user-side resource participating in collaborative governance in the next power quality collaborative governance cycle is determined, with resources with higher incentive values ​​having higher priority than resources with lower incentive values.

10. The method according to claim 1, characterized in that, The user-side resources include any one or more of the following: energy storage devices, photovoltaic inverters, industrial static var generators, and charging piles.

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