An optimization method and system based on virtual power plant multi-source aggregated resource collaborative scheduling

Abstract

The application discloses a kind of based on virtual power plant multi-source aggregation resource collaborative scheduling optimization method and system, it is related to virtual power plant resource aggregation and power dispatching technical field, including: obtaining photovoltaic, energy storage, charging pile and adjustable load operating data, determine current physical adjustable power;According to historical dispatching instruction, actual response power and station grid-connected boundary power determine instruction realization coefficient and power transfer coefficient, and revise current physical adjustable power, obtain deliverable regulation capacity;According to current dispatching task, determine the resource target regulation power of aggregated resource and aggregated target boundary power;According to the actual response result during scheduling execution, update instruction realization coefficient and power transfer coefficient, and be used for next dispatching period.The application can improve the consistency of aggregated resource regulation capacity evaluation and actual deliverable capacity, improve the accuracy and adaptability of multi-source aggregated resource collaborative scheduling.

Description

Technical Field

[0001] This invention relates to the field of virtual power plant resource aggregation and power collaborative scheduling technology, and in particular to an optimization method and system based on virtual power plant multi-source aggregation resource collaborative scheduling. Background Technology

[0002] With the continuous expansion of new energy installed capacity and the large-scale integration of distributed energy, energy storage, charging facilities, and flexible loads, virtual power plants are gradually evolving from a single load aggregation model to a unified aggregation and collaborative scheduling model for multiple types of distributed resources. Existing virtual power plants typically determine the adjustable capacity of resources based on the real-time operating status, rated power, state of charge, load adjustment range, and equipment operating constraints of photovoltaic resources, energy storage resources, charging pile resources, and adjustable load resources. Scheduling tasks are then allocated through day-ahead optimization, intraday rolling optimization, or real-time control. However, after receiving scheduling instructions, different aggregated resources are affected by factors such as equipment control accuracy, user behavior, communication delays, local control strategies, and operating environments, resulting in a discrepancy between their actual response power and theoretical adjustable power. Furthermore, power changes generated on the resource side, after passing through load changes, equipment losses, and other power disturbances within the power plant, may not be proportionally reflected at the grid connection boundary. Therefore, determining resource adjustability solely based on physical operational constraints can easily lead to discrepancies between the adjustment capabilities declared or issued by virtual power plants and the actual deliverable adjustment capabilities. Furthermore, the existing scheduling process does not adequately utilize feedback from historical scheduling execution results, making it difficult to continuously revise subsequent scheduling capability evaluations based on the actual response characteristics of resources.

[0003] For example, CN121239699A discloses a resource coordination method and system for a multi-level architecture model of a virtual power plant. The method includes: systematically modeling distributed resources based on resource heterogeneity compatibility criteria; using a spatiotemporal decoupling mechanism for inter-level information interaction to perform multi-level architecture interaction; the distributed resource physical layer performing unified abstraction and standardized access for heterogeneous devices to execute the "sensing-conversion-execution" function; the regional aggregation agent layer executing dynamic aggregation and optimized scheduling of heterogeneous resources within the region through a distributed decision-making mechanism; the global coordination and optimization layer executing cross-regional and cross-timescale resource coordination optimization; and the dynamic communication topology model performing inter-level interaction through an event-triggered information update mechanism and a communication delay compensation algorithm. This invention significantly improves the collaborative operation efficiency of different types of distributed resources through systematic modeling of a multi-dimensional fusion framework, ensuring system stability when large-scale heterogeneous resources are accessed. This technology mainly focuses on resource collaboration from the perspectives of multi-level architecture, unified modeling of heterogeneous resources, and information interaction between levels. However, it does not establish a resource-level quantitative correction mechanism for the fulfillment relationship between historical scheduling instructions and actual response power, nor does it evaluate the actual deliverability of resources by combining the transmission relationship between resource power changes and power changes at the grid connection boundary of the power station. Therefore, it is difficult to reflect the differences in the process of different resources transforming from "physically adjustable" to "deliverable at the grid connection boundary".

[0004] CN120999640B This invention relates to the field of intelligent power management technology and discloses a collaborative scheduling method and system for virtual power plants. The method includes the following steps: collecting real-time data from distributed power sources, loads, and energy storage systems within the virtual power plant, and performing ultra-short-term forecasts to obtain forecast parameters; dynamically calculating the dynamic operating boundary based on the real-time status of the energy storage system; generating a pre-schedule plan through collaborative decision-making using a multi-objective fuzzy satisfaction function one day before the current operating day; updating the boundary and forecast parameters on a rolling basis within the current operating day using the pre-schedule plan as a reference, and generating real-time scheduling instructions through model prediction; and monitoring the deviation between the actual output of each resource and the real-time scheduling instructions in real time. When the deviation exceeds a threshold, a collaborative deviation compensation mechanism is activated to achieve power balancing. This invention enables refined collaborative scheduling of different types of distributed resources in complex environments with high uncertainty. This technology can compensate for actual output deviations in real time, but its deviations are mainly used for power balance in the current scheduling process. It does not further convert the historical command execution results into parameters that characterize the degree of fulfillment of resource commands, nor does it use the correspondence between resource-side power changes and grid-connected boundary power changes to make secondary corrections to the adjustable capacity. Therefore, it is still insufficient to characterize the differences in long-term resource response and the differences in power transfer within the station. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of the embodiments of the present invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this section, the abstract and title of the invention. Such simplifications or omissions shall not be used to limit the scope of the present invention.

[0006] In view of the problems in the existing virtual power plant multi-source aggregation resource collaborative scheduling process, such as the discrepancy between the physical adjustability of resources and the actual deliverable adjustment capability, the failure of historical scheduling execution results to be effectively used for subsequent adjustment capability correction, and the insufficient consideration of the transmission difference between resource-side power changes and power changes at the grid connection boundary of the power plant, this invention is proposed.

[0007] Therefore, the problem to be solved by this invention is how to combine the historical instruction execution status of aggregated resources and the actual transmission status of resource power to the grid connection boundary of the power station to correct the physical adjustability of aggregated resources, and continuously update the resource adjustment capability evaluation parameters based on the scheduling execution results, thereby improving the consistency between the scheduling capability of multi-source aggregated resources and the actual deliverable adjustment capability.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide an optimization method for collaborative scheduling of multi-source aggregated resources based on virtual power plants, comprising: Obtain the current operating data, equipment parameters, historical dispatch instructions, corresponding actual response power, and grid connection boundary power of the aggregated resources within the virtual power plant; determine the operating constraints of the aggregated resources based on the current operating data and equipment parameters; and obtain the current physical adjustable power based on the operating constraints. Based on historical dispatch instructions and actual response power, the instruction fulfillment coefficient of aggregated resources is determined, and the power transfer coefficient of aggregated resources is determined according to the change in actual response power of aggregated resources and the change in grid connection boundary power of the same period. The current physical adjustable power is corrected according to the instruction fulfillment coefficient and the power transfer coefficient to obtain the deliverable adjustment capability of aggregated resources. Obtain the task target adjustment power, adjustment direction and duration of the current scheduled task; determine the resource target adjustment power based on the deliverable adjustment capability and operational constraints; and obtain the aggregate target boundary power corresponding to the current scheduled task based on the resource target adjustment power. Dispatch instructions are issued according to the resource target adjustment power. The actual response power of aggregated resources and the actual grid connection boundary power of the actual power station are obtained during the scheduling execution period. The instruction fulfillment coefficient is updated according to the change in the resource target adjustment power and the actual response power. The power transfer coefficient is updated according to the change in the actual response power and the actual grid connection boundary power. The updated instruction fulfillment coefficient and power transfer coefficient are used to determine the deliverable adjustment capability of aggregated resources in the next scheduling cycle.

[0009] Secondly, embodiments of the present invention provide an optimization system based on multi-source aggregation resource collaborative scheduling of a virtual power plant, comprising: The power determination module is used to acquire the current operating data, equipment parameters, historical dispatch instructions, corresponding actual response power, and grid connection boundary power of the aggregated resources in the virtual power plant. Based on the current operating data and equipment parameters, it determines the operating constraints of the aggregated resources and obtains the current physical adjustable power based on the operating constraints. The power correction module determines the command fulfillment coefficient of aggregated resources based on historical dispatch commands and actual response power, and determines the power transfer coefficient of aggregated resources based on the actual response power change of aggregated resources and the power change of the grid connection boundary of the power station in the same period. Based on the command fulfillment coefficient and the power transfer coefficient, the current physical adjustable power is corrected to obtain the deliverable adjustment capability of aggregated resources. The scheduling target allocation module is used to obtain the task target adjustment power, adjustment direction and duration of the current scheduling task, determine the resource target adjustment power based on the deliverable adjustment capability and operating constraints, and obtain the aggregate target boundary power corresponding to the current scheduling task based on the resource target adjustment power. The scheduling feedback update module is used to issue scheduling instructions according to the resource target adjustment power, obtain the actual response power of aggregated resources and the actual grid-connected boundary power of the power station during the scheduling execution period; update the instruction fulfillment coefficient according to the change in the resource target adjustment power and the actual response power, and update the power transfer coefficient according to the change in the actual response power and the actual grid-connected boundary power of the power station; and use the updated instruction fulfillment coefficient and power transfer coefficient to determine the deliverable adjustment capability of aggregated resources in the next scheduling cycle.

[0010] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any step of the above-described optimization method for multi-source aggregation resource collaborative scheduling based on virtual power plants.

[0011] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the above-described optimization method for multi-source aggregation resource collaborative scheduling based on virtual power plants.

[0012] Compared with existing technologies, the beneficial effects of this invention are as follows: By constructing a layer-by-layer progressive scheduling optimization architecture, it achieves refined management of the entire process of collaborative scheduling of multi-source aggregated resources in virtual power plants; First, based on accurate modeling of real-time operating data and multi-type resource operating constraints, it obtains the current physically adjustable power, providing a reliable physical boundary for scheduling decisions; It introduces command fulfillment coefficients and power transfer coefficients to bidirectionally correct the physically adjustable power from two dimensions: historical resource response accuracy and grid connection transmission efficiency, effectively eliminating false reporting of adjustment capabilities caused by user behavior uncertainty and network transmission loss, significantly improving the authenticity and reliability of deliverable adjustment capabilities; Based on this, it combines... By defining the target power, direction, and duration of the scheduling task, and through determining the deliverable adjustment limit during the task period and scientifically allocating resources, the overall scheduling demand is optimized and decomposed among various aggregated resources to generate the station-level aggregated boundary power. This ensures that the task objectives are achieved while avoiding the imbalance problem of resource overload or idleness. Through the collection of actual response data after each scheduling cycle and the dynamic updating of the two coefficients, an adaptive mechanism is formed, which solves the core problems in virtual power plant scheduling, such as the disconnect between physical capacity and actual available capacity, the difficulty of adapting static evaluation to dynamic changes, and the inability of single optimization to guarantee continuous optimality. This provides full-link technical support for the safe, efficient, and reliable participation of virtual power plants in grid regulation. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. 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. Wherein: Figure 1 The flowchart shows an optimization method for multi-source aggregation and collaborative scheduling based on virtual power plants. Figure 2 This is a structural diagram of an optimization system based on multi-source aggregation and collaborative scheduling of virtual power plants. Detailed Implementation

[0014] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0015] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort should fall within the scope of protection of this invention.

[0016] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0017] As mentioned in the background section, existing virtual power plant scheduling methods typically determine adjustable capacity based on resource operating status, operating boundaries, and prediction results, and perform real-time compensation when actual output deviates. However, they lack joint quantification of the degree of fulfillment of instructions for different aggregated resources and the degree of transmission of actual resource response power changes to power changes at the power plant grid connection boundary. This makes it difficult for the adjustable capacity determined based on physical operating constraints to accurately characterize the effective adjustment capacity that can be formed at the grid connection boundary during actual scheduling. To address these issues, this invention provides an optimization method based on the collaborative scheduling of multi-source aggregated resources in a virtual power plant.

[0018] Reference Figures 1-2 , Figure 1 This is a flowchart illustrating an optimization method for multi-source aggregation and collaborative scheduling of virtual power plants according to an embodiment of the present invention. Figure 1 As shown, an optimization method based on multi-source aggregation and collaborative scheduling of virtual power plants includes: S1: Obtain the current operating data, equipment parameters, historical dispatch instructions, corresponding actual response power, and grid connection boundary power of the aggregated resources within the virtual power plant. Determine the operating constraints of the aggregated resources based on the current operating data and equipment parameters, and obtain the current physical adjustable power based on the operating constraints. Among them, the aggregated resources include photovoltaic resources, energy storage resources, charging pile resources, and adjustable load resources.

[0019] S1.1: Establish the association between aggregated resources, their respective power stations, and the grid connection boundaries of the power stations, and obtain the current operating data and equipment parameters of the aggregated resources.

[0020] Based on the resource identifiers and site identifiers pre-registered by the virtual power plant, photovoltaic resources, energy storage resources, charging pile resources, and adjustable load resources are distinguished. The corresponding site and grid connection boundary of the aggregated resources are determined according to their electrical access location, and the association between resource identifiers, site identifiers, and grid connection boundaries is established. The resource identifier includes at least the resource type identifier, the site identifier, and the resource serial number. Each aggregated resource has a unique resource identifier. When a site includes multiple aggregated resources, the multiple aggregated resources are associated with the same site identifier and the same site grid connection boundary through their respective resource identifiers, so that the actual response power obtained subsequently corresponds to the power at the site grid connection boundary according to the site identifier.

[0021] The current operating data of aggregated resources are obtained according to a unified sampling time. The current operating data of photovoltaic resources includes current active power, current irradiance, current module temperature, inverter operating status, and current active power limit of the inverter. The current operating data of energy storage resources includes current charging and discharging power, state of charge, operating status of energy storage converter, allowable discharge power, and allowable charging power. The current operating data of charging pile resources includes charging pile access status, current charging power, rated charging power, vehicle remaining charging demand, and vehicle planned departure time. The current operating data of adjustable load resources includes current load power, allowable adjustable power range, current operating status, and continuous operating time limit.

[0022] The equipment parameters corresponding to the aggregated resources are obtained according to the resource identifier; among them, the equipment parameters of photovoltaic resources include the active power change rate of the inverter; the equipment parameters of energy storage resources include the upper limit of state of charge, the lower limit of state of charge, and the active power change rate of the energy storage converter; the equipment parameters are determined by the equipment nameplate values ​​or pre-calibrated values ​​of the corresponding aggregated resources, remain unchanged within the preset calibration period, and are recorded separately from the values ​​that change in real time with the operating conditions in the current operating data.

[0023] It should be noted that the current charging and discharging power of energy storage resources adopts a unified positive and negative direction, where the discharging power is recorded as a positive value and the charging power is recorded as a negative value; the grid connection boundary power of the power station adopts a unified power exchange direction, where the active power sent by the power station to the distribution network is recorded as a positive value and the active power taken by the power station from the distribution network is recorded as a negative value.

[0024] S1.2: Obtain the historical scheduling instructions for the aggregated resources and the actual response power corresponding to the historical scheduling instructions.

[0025] According to the resource identifier, the historical dispatch instructions received by the aggregated resource within the preset historical time period are read from the virtual power plant dispatch record, and the dispatch task identifier, instruction issuance time, adjustment direction, historical target adjustment power and duration corresponding to each historical dispatch instruction are recorded.

[0026] Based on the scheduling task identifier and resource identifier, the preset baseline time window (the preset baseline time window is set according to the resource type, usually 1 to 5 minutes before the instruction) and the actual response power within the corresponding scheduling execution period are extracted from the historical operation records of the aggregated resources, so that each historical scheduling instruction is associated with the actual response power of the same aggregated resource within the corresponding time range.

[0027] It should be noted that the preset historical time period is determined according to the scheduling cycle and covers at least several completed historical scheduling tasks. When selecting historical scheduling instructions, priority is given to retaining historical scheduling records under the resource type, adjustment direction and operating conditions corresponding to the current operating status, so that the parameters determined subsequently based on historical scheduling instructions and actual response power correspond to the same resource adjustment direction and similar operating status.

[0028] S1.3: Obtain the grid-connected boundary power of the power station corresponding to the historical dispatch instructions, and perform time alignment of the historical dispatch instructions, actual response power and grid-connected boundary power of the power station to form historical dispatch data.

[0029] Based on the association between resource identifiers, station identifiers, and station grid connection boundaries established in S1.1, the station grid connection boundary power recorded by the corresponding grid connection metering device of each station is obtained, and the actual response power corresponding to the aggregated resource is associated with the station grid connection boundary power of the station according to the station identifier.

[0030] Using the sampling time of the power grid connection boundary of the power station as a unified time reference, the actual response power is time-matched. For each historical scheduling instruction, the preset reference time window before the instruction is issued and the actual response power and power grid connection boundary power of the power station during the scheduling execution period are retained respectively, so that the actual response power and power grid connection boundary power corresponding to the same historical scheduling task are in the same time range.

[0031] For power sequences with isolated missing sampling points and valid sampling values ​​on both sides of the missing sampling point, interpolation is performed on the isolated missing sampling point based on adjacent valid sampling values. For historical scheduling records with consecutive missing sampling points reaching a preset number, lacking valid power data in a preset reference time window, or with a power data integrity rate lower than a preset integrity rate threshold during the scheduling execution period, the corresponding historical scheduling records are removed, and power data from different dates or other scheduling tasks are not used to replace the missing power data in the historical scheduling records. For sampling values ​​that exceed the allowable power range of the corresponding equipment or exceed the metering range of the grid-connected metering device at the site, the corresponding sampling values ​​are marked as abnormal sampling values. When abnormal sampling values ​​do not affect the calculation of power changes within the preset reference time window and scheduling execution period, abnormal sampling values ​​are removed. When abnormal sampling values ​​cause the inability to obtain valid power changes within the corresponding time range, the corresponding historical scheduling records are removed.

[0032] The historical dispatch instructions, actual response power, and grid connection boundary power of the power station, after time alignment and the above-mentioned cleaning process, are used together as historical dispatch data for S2.

[0033] S1.4: Determine the operating constraints based on the current operating data and equipment parameters of the aggregated resources to obtain the current physical adjustable power of the aggregated resources.

[0034] S1.4.1: Determine operating constraints; where operating constraints are calculated based on current operating data and equipment parameters, and are intermediate parameters in the process of determining the current physical adjustable power based on current operating data.

[0035] For photovoltaic resources, the upper limit of the current power generation is determined based on the current irradiance and the current module temperature, and the smaller value between the current upper limit of the current power generation and the current active power limit of the inverter is taken as the upper limit of the active power that the photovoltaic resources are currently allowed to reach. The lower limit of the current active power that the photovoltaic resources are currently allowed to reach is determined based on the range of allowable active power corresponding to the inverter's operating status, and the rate of change of the inverter's active power is used as the limiting condition for the allowable change in power amplitude within the current scheduling cycle.

[0036] It should be noted that the current scheduling cycle is the continuous execution time interval during which the virtual power plant recalculates the current physical adjustable power, deliverable adjustment capacity, and target adjustment power of aggregated resources based on the current operating status. It is determined by the master station scheduling interval, and the typical value is 1 minute, 5 minutes, or 15 minutes. Adjacent scheduling cycles are set consecutively in chronological order. After the current scheduling cycle ends, the next scheduling cycle begins based on the updated current operating data.

[0037] For energy storage resources, the positive power boundary is determined based on the allowable discharge power, and the negative power boundary is determined based on the allowable charging power. The dischargeable energy constraint power is determined based on the remaining dischargeable energy corresponding to the state of charge and the lower limit of the state of charge, as well as the current scheduling cycle duration. The rechargeable energy constraint power is determined based on the remaining rechargeable energy corresponding to the upper limit of the state of charge and the current scheduling cycle duration. The positive and negative power boundaries are limited by the dischargeable energy constraint power and the rechargeable energy constraint power, respectively. The active power change rate of the energy storage converter is used as a constraint condition for the allowable change in power amplitude within the current scheduling cycle.

[0038] For charging pile resources, the minimum maintenance charging power is determined based on the vehicle's remaining charging demand and the vehicle's planned departure time. The minimum maintenance charging power is used as the lower limit of the charging power after the charging pile resource is allowed to be reduced, and the rated charging power is used as the upper limit of the charging power after the charging pile resource is allowed to be increased. When the charging pile access status indicates that no vehicle is connected or no charging task is being performed, the allowable adjustable power range of the corresponding charging pile resource is recorded as zero.

[0039] For adjustable load resources, the lower limit of the allowable adjustable power range is used as the lower limit of the allowable reduced load power, and the upper limit of the allowable adjustable power range is used as the upper limit of the allowable increased load power. The lower limit and upper limit of load power are limited according to the current operating status and continuous operating time limit.

[0040] Operating constraints are recorded separately for upward and downward adjustment directions. The upward adjustment direction corresponds to increasing the power sent from the power station's grid connection boundary to the distribution network or decreasing the power taken from the distribution network by the power station. The downward adjustment direction corresponds to decreasing the power sent from the power station's grid connection boundary to the distribution network or increasing the power taken from the distribution network by the power station.

[0041] S1.4.2: Determine the current physically adjustable power of the photovoltaic resources based on the current operating data and operating constraints of the photovoltaic resources.

[0042] For any photovoltaic resource, read the current active power and the determined upper and lower limits of active power; calculate the power difference between the upper and current active power, and use the power difference as a candidate power for upward adjustment; calculate the power difference between the current active power and the lower limit of active power, and use the power difference as a candidate power for downward adjustment.

[0043] The allowable power change in the current scheduling cycle is determined based on the inverter's active power change rate and the current scheduling cycle duration. The minimum value between the allowable power change in the current scheduling cycle and the candidate power corresponding to the two adjustment directions is taken as the current physically adjustable power of the photovoltaic resource in the corresponding adjustment direction.

[0044] When photovoltaic resources are under dispatch and limited power generation, the upper limit of active power is less than or equal to the upper limit of current power generation determined based on the current irradiance and current module temperature. When the current active power of photovoltaic resources has reached the upper limit of active power, the current physical adjustable power in the upward adjustment direction is recorded as zero. When the inverter is in a shutdown state, fault state, or grid disconnection state, the current physical adjustable power in both adjustment directions is recorded as zero.

[0045] S1.4.3: Determine the current physical adjustable power of the energy storage resource based on the current operating data and operating constraints of the energy storage resource.

[0046] For any energy storage resource, read the current charging / discharging power, positive power boundary, and negative power boundary according to the power direction where the discharge power is positive and the charging power is negative.

[0047] Calculate the power difference between the positive power boundary and the current charge / discharge power, and use the power difference as a candidate power for the upward adjustment direction of energy storage resources; calculate the power difference between the current charge / discharge power and the negative power boundary, and use the power difference as a candidate power for the downward adjustment direction of energy storage resources.

[0048] The allowable power change of energy storage resources in the current scheduling cycle is determined based on the active power change rate of the energy storage converter and the current scheduling cycle duration. The minimum value between the power change and the candidate power in the two adjustment directions is taken as the current physically adjustable power of the energy storage resources in the corresponding adjustment direction.

[0049] When the state of charge reaches the upper limit of the state of charge, the current physically adjustable power corresponding to the continued increase in discharge power will be recorded as zero; when the state of charge reaches the upper limit of the state of charge, the current physically adjustable power corresponding to the continued increase in charging power will be recorded as zero; when the energy storage converter is not allowed to perform charging and discharging operations, the current physically adjustable power in the corresponding adjustment direction will be recorded as zero.

[0050] S1.4.4: Determine the current physically adjustable power of the charging pile resources based on the current operating data and operating constraints of the charging pile resources.

[0051] Filter charging pile resources that are connected and currently performing charging tasks, and read the current charging power, minimum sustaining charging power, and rated charging power.

[0052] Calculate the power difference between the current charging power and the minimum sustaining charging power, and use this power difference as a candidate power for upward adjustment of charging pile resources; calculate the power difference between the rated charging power and the current charging power, and use this power difference as a candidate power for downward adjustment of charging pile resources. Verify the candidate power for upward adjustment based on the vehicle's remaining charging demand and planned departure time. If, after reducing the current charging power according to the candidate power for upward adjustment, the remaining rechargeable energy calculated based on the remaining charging time and rated charging power is less than the vehicle's remaining charging demand, further reduce the candidate power for upward adjustment until the reduced charging power exceeds the minimum sustaining charging power.

[0053] The candidate power for the upward adjustment direction and the candidate power for the downward adjustment direction after verification are recorded as the current physically adjustable power of the charging pile resource in the corresponding adjustment direction. For charging pile resources whose access status is not connected, whose charging task is not currently being performed, or whose current operating status does not allow power adjustment, the corresponding current physically adjustable power is recorded as zero.

[0054] S1.4.5: Determine the current physical adjustable power of the adjustable load resources based on the current operating data and operating constraints of the adjustable load resources, and aggregate the current physical adjustable power of the aggregated resources.

[0055] For any adjustable load resource, read the current load power, the lower limit of the load power, and the upper limit of the load power; calculate the power difference between the current load power and the lower limit of the load power, and use the power difference as a candidate power for the upward adjustment direction of the adjustable load resource; calculate the power difference between the upper limit of the load power and the current load power, and use the power difference as a candidate power for the downward adjustment direction of the adjustable load resource.

[0056] The candidate power in the two adjustment directions is checked based on the current operating status and continuous operating time limit. When adjusting according to the corresponding candidate power will cause the adjustable load resource to exceed the continuous operating time limit, the candidate power in the corresponding adjustment direction is reduced. The candidate power in the two adjustment directions after the check is recorded as the current physical adjustable power corresponding to the adjustable load resource.

[0057] Record the current physically adjustable power of photovoltaic resources, energy storage resources, charging pile resources, and adjustable load resources in the upward and downward adjustment directions according to the resource identifiers. Maintain the correlation between the current physically adjustable power of aggregated resources and the corresponding operating constraints, historical dispatch instructions, actual response power, grid connection boundary power of the power station, resource identifiers, and power station identifiers. This allows S2 to correct the current physically adjustable power of aggregated resources based on historical dispatch instructions, actual response power, and grid connection boundary power of the power station.

[0058] S2: Based on historical dispatch instructions and actual response power, determine the instruction fulfillment coefficient of aggregated resources, and determine the power transfer coefficient of aggregated resources according to the change in actual response power of aggregated resources and the change in grid connection boundary power of the same period. Correct the current physical adjustable power according to the instruction fulfillment coefficient and the power transfer coefficient to obtain the deliverable adjustment capability of aggregated resources.

[0059] S2.1: Construct historical scheduling response samples corresponding to aggregated resources based on historical scheduling data.

[0060] According to the resource identifier, read the historical scheduling instructions corresponding to the aggregated resources in S1.2, and according to the scheduling task identifier, associate each historical scheduling instruction with the actual response power and the grid connection boundary power of the station that have been time aligned in S1.3 to form a historical scheduling response sample with a single historical scheduling task as the unit.

[0061] For any historical dispatch response sample, retain the corresponding resource identifier, station identifier, dispatch task identifier, adjustment direction, historical target adjustment power, preset reference time window, dispatch execution period, actual response power, and station grid connection boundary power; wherein, the actual response power and station grid connection boundary power in the same historical dispatch response sample adopt the same time reference determined by S1.3.

[0062] Historical scheduling response samples corresponding to aggregated resources are collected separately according to the adjustment direction, so that historical scheduling response samples corresponding to the upward adjustment direction and the downward adjustment direction participate in subsequent calculations respectively; historical scheduling records that have been removed in S1.3 are not re-included in the historical scheduling response samples. When multiple aggregated resources in the same site participate in the same historical scheduling task, the actual response power corresponding to the multiple aggregated resources is associated with the same scheduling task identifier and the same site identifier, and the power change records of multiple aggregated resources during the scheduling execution period are retained, so as to jointly determine the power transfer coefficient based on the actual response power changes of multiple aggregated resources later.

[0063] S2.2: Determine the actual response power change of the aggregated resources based on the preset baseline time window and the actual response power within the scheduling execution period.

[0064] S2.2.1: For any historical scheduling response sample corresponding to any aggregated resource, calculate the average value of multiple actual response powers within a preset benchmark time window, and determine the calculated average value as the response benchmark power of the corresponding historical scheduling response sample.

[0065] S2.2.2: Calculate the average value of multiple actual response powers within the scheduling execution period, and determine the difference between the average value of the actual response power within the scheduling execution period and the response baseline power as the actual response power change of the corresponding historical scheduling response sample.

[0066] It should be noted that the actual change in response power is recorded according to the unified adjustment direction determined by S1. For photovoltaic resources and energy storage resources, the power change generated along the direction of increasing the power output to the distribution network from the grid connection boundary corresponds to the upward adjustment direction, and the power change generated in the opposite direction corresponds to the downward adjustment direction; for charging pile resources and adjustable load resources, reducing the power consumption corresponds to the upward adjustment direction, and increasing the power consumption corresponds to the downward adjustment direction.

[0067] S2.2.3: For any historical dispatch response sample, determine whether the direction of change of the actual response power is consistent with the adjustment direction in the corresponding historical dispatch instruction, and determine the effective actual response power change.

[0068] When the direction of change of the actual response power is the same as the adjustment direction in the corresponding historical dispatch instruction, the absolute value of the actual response power change is determined as the effective actual response power change; when the direction of change of the actual response power is opposite to the adjustment direction in the corresponding historical dispatch instruction, the effective actual response power change of the corresponding historical dispatch response sample is recorded as zero.

[0069] If the absolute value of the actual response power change is higher than the historical target adjustment power in the corresponding historical scheduling instruction, then when calculating the instruction fulfillment coefficient, the effective actual response power change involved in the calculation will be limited to the corresponding historical target adjustment power to avoid a single overshoot causing the subsequently determined deliverable adjustment capacity to exceed the current physical adjustable power determined by S1 based on the physical operating status of the equipment.

[0070] S2.3: Determine the command fulfillment coefficient based on the changes in historical target adjustment power and effective actual response power corresponding to the aggregated resources.

[0071] S2.3.1: For any aggregated resource, read the historical target adjustment power and the change in effective actual response power corresponding to multiple historical scheduling response samples in the upward adjustment direction and the downward adjustment direction, respectively.

[0072] S2.3.2: For multiple historical scheduling response samples of the same aggregated resource under the same adjustment direction, accumulate the effective actual response power change corresponding to each historical scheduling response sample, and accumulate the historical target adjustment power corresponding to each historical scheduling response sample; calculate the ratio between the accumulated effective actual response power change and the accumulated historical target adjustment power, and determine the ratio as the command fulfillment coefficient of the corresponding aggregated resource in the corresponding adjustment direction.

[0073] The value range of the order fulfillment coefficient is limited to [0,1]. When the calculated ratio is greater than 1, the corresponding order fulfillment coefficient is recorded as 1. When the calculated ratio is between 0 and 1, the ratio is directly determined as the order fulfillment coefficient.

[0074] It should be noted that the command fulfillment coefficient is recorded separately according to the resource identifier and the adjustment direction, so that the same aggregated resource corresponds to different command fulfillment coefficients in the upward and downward adjustment directions. Therefore, for aggregated resources that frequently experience incomplete responses, incorrect response directions, or actual response power changes lower than the historical target adjustment power during historical scheduling, the command fulfillment coefficient in the corresponding adjustment direction decreases as the proportion of the effective actual response power change to the historical target adjustment power decreases.

[0075] If a certain aggregated resource does not have a valid historical scheduling response sample in the corresponding adjustment direction, the average value of the command fulfillment coefficients of the same type of aggregated resources under the same station identifier in the same adjustment direction will be read first as the initial command fulfillment coefficient of the aggregated resource. If there is no valid command fulfillment coefficient for the same type of aggregated resource in the same station, the initial command fulfillment coefficient of the aggregated resource will be recorded as 1, and the command fulfillment coefficient will be re-determined after a new valid historical scheduling response sample is obtained in the subsequent scheduling cycle.

[0076] S2.4: Determine the change in grid-connected boundary power of the station during the same period based on the preset reference time window and the station grid-connected boundary power within the scheduling execution period.

[0077] For any historical dispatch response sample, the corresponding grid-connected boundary power of the station is read according to the station identifier. The average value of the grid-connected boundary power of multiple stations within the preset reference time window is calculated, and the calculated average value is determined as the boundary reference power of the corresponding historical dispatch response sample. The average value of the grid-connected boundary power of multiple stations within the dispatch execution period is calculated, and the difference between the average value of the grid-connected boundary power of the stations within the dispatch execution period and the boundary reference power is determined as the change in the grid-connected boundary power of the stations during the same period.

[0078] The change in power at the grid connection boundary of the power station is recorded according to the power exchange direction determined in S1.1. Specifically, an increase in the active power sent from the grid connection boundary of the power station to the distribution network corresponds to a positive change, while an increase in the active power taken from the distribution network by the power station corresponds to a negative change.

[0079] The power change at the grid connection boundary of the same power station and the actual response power change determined in S2.2 use the same preset reference time window and the same scheduling execution period, so that they correspond to the resource-side power change and the power change at the grid connection boundary of the same historical scheduling task. In the case where multiple aggregated resources within the same power station simultaneously generate actual response power changes within the same scheduling execution period, the actual response power changes corresponding to the aggregated resources under the same power station identifier are jointly associated with the power change at the grid connection boundary of the same power station corresponding to that historical scheduling response sample, and the power change at the grid connection boundary of the same power station is not separately attributed to any one of the aggregated resources.

[0080] S2.5: Determine the power transfer coefficient of aggregated resources by comparing the actual response power change of aggregated resources within the same power station with the power change at the grid connection boundary of the power station during the same period.

[0081] For any given station identifier, multiple historical dispatch response samples belonging to that station are arranged according to the dispatch task identifier and occurrence time, and the actual response power change corresponding to the aggregated resources within the same station is arranged sequentially according to the resource identifier, forming a resource response change matrix. Each row of the resource response change matrix corresponds to a historical dispatch response sample, and each column corresponds to an aggregated resource within the same station. When an aggregated resource does not experience a power change in its corresponding historical dispatch response sample, the actual response power change at the corresponding position in the resource response change matrix is ​​recorded as zero.

[0082] Following the same historical dispatch response sample arrangement order as the resource response change matrix, the corresponding synchronous grid-connected boundary power change quantities of each historical dispatch response sample are sequentially arranged to form a boundary power change vector.

[0083] Using the power transfer coefficient of aggregated resources within the same power station as the variable to be determined, the actual response power change corresponding to the aggregated resource in any historical dispatch response sample is multiplied by the corresponding power transfer coefficient, and the results are summed to obtain the calculated boundary power change value corresponding to that historical dispatch response sample. The calculated boundary power change values ​​corresponding to multiple historical dispatch response samples are calculated sequentially, and the residual between each calculated boundary power change value and the corresponding concurrent grid-connected boundary power change value is calculated. Under the condition that the power transfer coefficient corresponding to the aggregated resource is greater than 0 and less than 1, the minimum sum of squared residuals corresponding to multiple historical dispatch response samples is used as the solution condition to determine the power transfer coefficient corresponding to the aggregated resource within the same power station, specifically: 1. Solve for feasibility conditions; For any given power station identifier, let N be the number of aggregated resources involved in determining the power transfer coefficient within that station, and M be the number of corresponding valid historical scheduling response samples. Let A be the resource response change matrix (dimension M×N), b be the boundary power change vector (dimension M×1), and x be the vector of the power transfer coefficient to be determined (dimension N×1). Then the solution relationship can be expressed as: A·x = b + ε, where ε is the residual vector. To ensure that the power transfer coefficient x can be uniquely and stably determined, the following condition must be met: Condition 1 (Sample Quantity Condition): M≥N+1; that is, the number of valid historical dispatch response samples is at least one more than the number of aggregated resources with undetermined coefficients in the station, so as to ensure that the equation system is an overdetermined equation system and that the least squares solution has a unique solution.

[0084] Condition 2 (Full Rank Matrix Column Condition): The columns of the resource response change matrix A are linearly independent. That is, the actual response power changes of different aggregated resources within the same site should not exhibit completely identical change patterns in historical samples. When there is an approximately linear correlation between the response changes of a certain aggregated resource and other resources (correlation coefficient greater than 0.95), aggregated resources with fewer response samples or later equipment commissioning times are marked as strongly correlated resources. Their power transfer coefficients are not solved separately for the time being. Instead, the average power transfer coefficient of the same type of resources within the same site is used as its initial value, and it is solved separately after a sufficient number of independent response samples are accumulated.

[0085] 2. Rules for handling insufficient samples; When the number of valid historical scheduling response samples M does not satisfy M≥N+1, the following priority order applies: Priority 1: The average power transfer coefficient of the same type of aggregated resources in the same adjustment direction within the same site is used as the initial power transfer coefficient of the aggregated resources with insufficient samples; Priority 2: If there is no valid power transfer coefficient corresponding to the same type of aggregated resources within the same site, but there are power transfer coefficients of other types of aggregated resources in the same adjustment direction within the same site, the average power transfer coefficient of the other types of aggregated resources is taken as the initial value; Priority 3: If there is no valid power transfer coefficient within the same site, the initial power transfer coefficient of the corresponding aggregated resource is recorded as 1, and after accumulating M≥N+1 valid historical scheduling response samples in subsequent scheduling cycles, the power transfer coefficient is re-determined according to the solution relationship in S2.5.

[0086] 3. Coefficient updates after adding new historical samples; When a certain aggregated resource originally used an initial power transfer coefficient (priority 1, 2 or 3), and the number of valid historical scheduling response samples added in subsequent scheduling cycles makes the total number of valid samples M of the station reach or exceed N+1, the power transfer coefficients of all aggregated resources in the station should be recalculated according to the solution relationship in S2.5, and the initial value should no longer be retained; after recalculation, boundary abnormal samples should be removed according to the residual screening method in S2.6, and the solution should be performed again, and the final result should be used as the updated power transfer coefficient; Power transfer coefficients are recorded separately according to resource identifier, station identifier, and adjustment direction. For aggregated resources with valid historical scheduling response samples in both the upward and downward adjustment directions, the corresponding power transfer coefficients are determined using the historical scheduling response samples in the corresponding adjustment direction. The power transfer coefficient corresponding to one adjustment direction is not directly replaced by the power transfer coefficient corresponding to another adjustment direction.

[0087] With the above joint determination method, when multiple aggregated resources in the same power station experience power changes simultaneously, the power change at the grid connection boundary of the same power station is calculated by the actual response power change of the aggregated resources, rather than by directly comparing the actual response power change of a single aggregated resource with the power change at the grid connection boundary of the entire power station.

[0088] S2.6: Select valid historical dispatch response samples corresponding to the power transfer coefficient based on the residual between the calculated boundary power change value and the concurrent grid-connected boundary power change of the power station.

[0089] Based on the power transfer coefficient of the aggregated resources initially determined in S2.5, the calculated value of the boundary power change corresponding to each historical scheduling response sample is recalculated, and the absolute value of the residual between the calculated value of the boundary power change and the corresponding change in the boundary power of the grid-connected station during the same period is calculated.

[0090] The absolute value of the residual is compared with a preset boundary residual threshold. The preset boundary residual threshold is determined based on the metering error range of the grid-connected metering device of the corresponding power station and the natural fluctuation range of the load that is not involved in the current historical dispatch task, and is usually taken as 1% to 3% of the rated installed capacity of the power station. When the absolute value of the residual corresponding to any historical dispatch response sample is less than or equal to the preset boundary residual threshold, the corresponding historical dispatch response sample is retained; when the absolute value of the residual corresponding to any historical dispatch response sample is greater than the preset boundary residual threshold, the corresponding historical dispatch response sample is marked as a boundary abnormal sample, and the data corresponding to the boundary abnormal sample is removed from the resource response change matrix and the boundary power change vector.

[0091] Based on the resource response change matrix and boundary power change vector after removing boundary anomaly samples, the power transfer coefficient of the aggregated resources is re-determined according to the solution relationship in S2.5.

[0092] When there are insufficient valid historical dispatch response samples for the same site to determine the power transfer coefficient of aggregated resources, the average power transfer coefficient of aggregated resources of the same type under the same site identification in the same adjustment direction shall be used as the initial power transfer coefficient of the corresponding aggregated resource. When there is no valid power transfer coefficient for aggregated resources of the same type in the same site, the initial power transfer coefficient of the corresponding aggregated resource shall be recorded as 1, and the power transfer coefficient shall be re-determined after obtaining new valid historical dispatch response samples in subsequent dispatch cycles.

[0093] S2.7: Adjust the current physical adjustable power of the aggregated resources according to the instruction fulfillment coefficient and power transfer coefficient to obtain the deliverable adjustable capability.

[0094] For any aggregated resource, read the current physical adjustable power corresponding to the upward adjustment direction and the downward adjustment direction determined in S1.4, and read the command fulfillment coefficient determined in S2.3 and the power transfer coefficient determined in S2.6 according to the resource identifier and the corresponding adjustment direction.

[0095] For any adjustment direction, the current physical adjustable power of the corresponding aggregated resource in that adjustment direction is multiplied by the corresponding command fulfillment coefficient, and then multiplied by the corresponding power transfer coefficient. The resulting power is determined as the deliverable adjustment capability of the corresponding aggregated resource in that adjustment direction.

[0096] The instruction fulfillment coefficient corresponds to the historical fulfillment ratio of the actual power change on the resource side after the issuance of historical dispatch instructions relative to the target adjustment power; the power transfer coefficient corresponds to the historical transfer ratio of the actual response power change on the resource side to the concurrent grid-connected boundary power change of the corresponding power station. Both the instruction fulfillment coefficient and the power transfer coefficient are less than 1, therefore the deliverable adjustment capability corresponding to the aggregated resources is less than the current physically adjustable power in the corresponding adjustment direction.

[0097] The deliverable adjustment capabilities of photovoltaic resources, energy storage resources, charging pile resources, and adjustable load resources in the upward and downward adjustment directions are obtained respectively.

[0098] S2.8: Record the deliverable adjustability of aggregated resources according to resource identifiers, and establish the association between deliverable adjustability and corresponding parameters.

[0099] Record the deliverable adjustment capabilities of aggregated resources in the upward and downward adjustment directions according to the resource identifier, and associate and store the deliverable adjustment capabilities with the corresponding current physical adjustable power, command fulfillment coefficient, power transfer coefficient, site identifier, and adjustment direction.

[0100] For aggregated resources that use initial instruction fulfillment coefficients or initial power transfer coefficients, the corresponding initialization state is retained; when new valid historical scheduling response samples are generated in subsequent scheduling cycles, the corresponding historical scheduling response samples are added to the historical scheduling response samples in S2.1, and the corresponding instruction fulfillment coefficients and power transfer coefficients are re-determined according to S2.2 to S2.6.

[0101] The deliverable adjustment capabilities of aggregated resources and their corresponding operational constraints are retained in the current scheduling cycle, so that S3 can determine the target adjustment power of aggregated resources based on the target adjustment power, adjustment direction, and duration of the current scheduling task.

[0102] S3: Obtain the task target adjustment power, adjustment direction and duration of the current scheduled task, determine the resource target adjustment power based on the deliverable adjustment capability and operational constraints, and obtain the aggregate target boundary power corresponding to the current scheduled task based on the resource target adjustment power.

[0103] S3.1: Obtain the target adjustment power, adjustment direction and duration of the current scheduled task, and determine the adjustment period corresponding to the current scheduling cycle.

[0104] Read the current scheduling task received by the virtual power plant, and extract the task target adjustment power, adjustment direction and duration from the current scheduling task; where the task target adjustment power represents the amount of power adjustment required by the current scheduling task for the virtual power plant as a whole, and the adjustment direction is recorded according to the unified adjustment direction determined by S1.

[0105] If the adjustment direction is upward, the power sent from the power station's grid connection boundary to the distribution network will increase or the power taken from the distribution network by the power station will decrease. If the adjustment direction is downward, the power sent from the power station's grid connection boundary to the distribution network will decrease or the power taken from the distribution network by the power station will increase.

[0106] Read the current scheduling cycle duration determined by S1; if the duration is within the current scheduling cycle duration, then the time range corresponding to the duration is determined as the adjustment period; if the duration exceeds the current scheduling cycle duration, then the time range corresponding to the current scheduling cycle is determined as the adjustment period. For current scheduling tasks spanning multiple scheduling cycles, the current scheduling cycle only determines the resource target adjustment power corresponding to each aggregated resource in this cycle, and subsequent scheduling cycles reallocate power based on the updated current operating data, deliverable adjustment capabilities, and operating constraints.

[0107] S3.2: Read the deliverable adjustment capabilities corresponding to each aggregate resource according to the adjustment direction, and determine the aggregate resources participating in the current scheduling task.

[0108] According to the resource identifier, read the deliverable adjustment capacity of photovoltaic resources, energy storage resources, charging pile resources and adjustable load resources recorded in S2 under the adjustment direction, and simultaneously read the operation constraints associated with the corresponding resource identifier in S1, the current physical adjustable power, and the instruction fulfillment coefficient and power transfer coefficient associated with the corresponding resource identifier and adjustment direction in S2.

[0109] When the deliverable adjustment capability corresponding to any aggregated resource in the adjustment direction is zero, the corresponding aggregated resource is excluded from the power allocation range of the current scheduling cycle; when the deliverable adjustment capability corresponding to any aggregated resource in the adjustment direction is higher than zero, the corresponding aggregated resource is determined to participate in the current scheduling task.

[0110] It should be noted that the deliverable adjustability has already been corrected for the current physical adjustable power based on the command fulfillment factor and power transfer factor. Therefore, this step will not use the command fulfillment factor and power transfer factor again to reduce the deliverable adjustability. The command fulfillment factor and power transfer factor will be used in the calculation when converting the boundary-side allocated power into the resource-side issued power.

[0111] S3.3: By adjusting the time period and operating constraints, determine the physical adjustment power corresponding to each aggregated resource during the task period, and obtain the upper limit of the deliverable adjustment during the task period.

[0112] For any aggregated resource participating in the current scheduling task, the current physical adjustable power in the corresponding adjustment direction determined by S1 is used as the initial physical adjustment power for the task period. The corresponding aggregated resource is then continuously adjusted according to the initial physical adjustment power for the task period until the end of the adjustment period, based on the adjustment period.

[0113] When the calculated operating state is within the corresponding operating constraint limit, the initial task period physical adjustment power is retained and determined as the task period physical adjustment power of the corresponding aggregated resource; when the calculated operating state touches or exceeds the corresponding operating constraint boundary, the initial task period physical adjustment power is reduced according to the remaining power space or remaining energy space between the current operating state and the corresponding operating constraint boundary, until the reduced power continues until the end of the adjustment period, the corresponding operating state is within the operating constraint limit, and the reduced power is determined as the task period physical adjustment power.

[0114] For photovoltaic resources, the physical regulation power during the task period is checked based on the current active power, the upper limit of active power, the lower limit of active power, and the rate of change of active power of the inverter. When the active power corresponding to the adjustment according to the physical regulation power during the task period exceeds the upper limit or the lower limit of active power, the physical regulation power during the task period is reduced.

[0115] For energy storage resources, the state of charge at the end of the adjustment is calculated based on the current charging and discharging power, state of charge, upper limit of state of charge, lower limit of state of charge, and adjustment period. In the upward adjustment direction, the physical adjustment power during the mission period is reduced based on the remaining dischargeable energy corresponding to the state of charge and the lower limit of state of charge. In the downward adjustment direction, the physical adjustment power during the mission period is reduced based on the remaining rechargeable energy corresponding to the upper limit of state of charge and the state of charge.

[0116] For charging pile resources, the remaining charging demand and remaining charging time after the adjustment are calculated based on the current charging power, minimum maintenance charging power, vehicle remaining charging demand, vehicle planned departure time, and adjustment period. When the current charging power is reduced by the physical adjustment power of the task period corresponding to the upward adjustment direction, and the rechargeable amount corresponding to the remaining charging time is lower than the remaining charging demand, the physical adjustment power of the task period is reduced until the remaining charging demand can be completed before the vehicle's planned departure time.

[0117] For adjustable load resources, the adjusted continuous operating time is calculated based on the current load power, upper limit of load power, lower limit of load power, current operating status, continuous operating time limit, and adjustment period. When the continuous operating time exceeds the continuous operating time limit, the physical adjustment power during the task period is reduced based on the current remaining allowable operating time.

[0118] Furthermore, when the physical adjustment power during the mission period is consistent with the current physical adjustable power determined in S1, the deliverable adjustment capacity in the corresponding adjustment direction in S2 is determined as the upper limit of the deliverable adjustment capacity for the corresponding aggregate resource during the mission period; when the physical adjustment power during the mission period is reduced relative to the current physical adjustable power, the proportion of the physical adjustment power during the mission period to the current physical adjustable power is calculated, and the deliverable adjustment capacity is reduced synchronously according to the proportion, and the reduced deliverable adjustment capacity is determined as the upper limit of the deliverable adjustment capacity for the corresponding aggregate resource during the mission period.

[0119] S3.4: Aggregate the task-period deliverable adjustment limit of each aggregated resource and determine the allocatable adjustment power corresponding to the current scheduling cycle.

[0120] The task-period deliverable adjustment limits for each aggregated resource participating in the current scheduling task are aggregated according to the resource identifier, and the sum of the task-period deliverable adjustment limits for each aggregated resource is calculated.

[0121] The sum of the deliverable adjustment limits for the task period is compared with the target adjustment power. When the sum of the deliverable adjustment limits for the task period reaches or exceeds the target adjustment power, the target adjustment power is determined as the allocatable adjustment power corresponding to the current scheduling period; when the sum of the deliverable adjustment limits for the task period is lower than the target adjustment power, the sum of the deliverable adjustment limits for the task period is determined as the allocatable adjustment power corresponding to the current scheduling period.

[0122] Therefore, the allocatable adjustable power represents the amount of power that can be allocated to the grid connection boundary of the station within the current scheduling cycle based on the historical fulfillment of each aggregated resource, the transmission of the station grid connection boundary, and the operating constraints during the adjustment period.

[0123] S3.5: Allocate allocatable adjustable power according to the upper limit of deliverable adjustment during the mission period to obtain the resource boundary target adjustment power of each aggregated resource.

[0124] For any aggregated resource participating in the current scheduling task, calculate the proportion of the corresponding task-period deliverable adjustment limit to the sum of the task-period deliverable adjustment limits of all aggregated resources, and allocate the allocable adjustment power according to the proportion. The power allocated to the corresponding aggregated resource is determined as the initial resource boundary target adjustment power of that aggregated resource.

[0125] The resource boundary target adjustment power represents the effective adjustment power that the corresponding aggregated resource is expected to contribute to the grid connection boundary of its respective station within the current scheduling cycle, and its power direction is consistent with the adjustment direction of the current scheduling task.

[0126] Compare the initial resource boundary target adjustment power of each aggregated resource with the corresponding deliverable adjustment limit for the mission period. When the initial resource boundary target adjustment power is within the power range of the corresponding deliverable adjustment limit for the mission period, the initial resource boundary target adjustment power is determined as the resource boundary target adjustment power of the corresponding aggregated resource.

[0127] When any aggregated resource is affected by the device power adjustment step size and needs to have its initial resource boundary target adjustment power discretely adjusted, the initial resource boundary target adjustment power is adjusted according to the device power adjustment step size of the corresponding aggregated resource, and the power difference before and after the adjustment is calculated. The power difference is redistributed according to the remaining task period deliverable adjustment limit of other aggregated resources until the allocable adjustment power is fully allocated or all aggregated resources participating in the current scheduling task reach the corresponding task period deliverable adjustment limit.

[0128] Record the resource boundary target adjustment power of each aggregated resource according to the resource identifier, and maintain the correlation between the resource boundary target adjustment power and the corresponding task period deliverable adjustment limit, adjustment direction and site identifier.

[0129] S3.6: Based on the resource boundary target adjustment power, command fulfillment coefficient, and power transfer coefficient, the resource target adjustment power of each aggregated resource is obtained.

[0130] For any aggregated resource, read the instruction fulfillment coefficient and power transfer coefficient in the corresponding adjustment direction. Since the deliverable adjustment capability of the aggregated resources participating in the current scheduling task is higher than zero, the corresponding instruction fulfillment coefficient and power transfer coefficient both have valid values.

[0131] The resource boundary target adjustment power of the corresponding aggregated resource is divided by the instruction fulfillment coefficient and the power transfer coefficient in sequence, and the resulting power is determined as the initial resource target adjustment power of the corresponding aggregated resource. The initial resource target adjustment power represents the amount of power adjustment that needs to be issued from the resource side to the corresponding aggregated resource to obtain the corresponding resource boundary target adjustment power.

[0132] The initial resource target adjustment power is compared with the task-period physical adjustment power determined in S3.3. When the initial resource target adjustment power is within the power range limited by the task-period physical adjustment power, the initial resource target adjustment power is determined as the resource target adjustment power of the corresponding aggregated resource.

[0133] When the corresponding aggregated resource has a preset power adjustment step size, the initial resource target adjustment power is rounded according to the preset power adjustment step size, and the corresponding resource boundary adjustment power is recalculated based on the rounded power, instruction fulfillment coefficient and power transfer coefficient; when there is a power difference between the recalculated resource boundary adjustment power and the resource boundary target adjustment power, the power difference is returned to S3.5, and reallocated according to the remaining task period deliverable adjustment limit of other aggregated resources.

[0134] After the above conversion, the target adjustment power of photovoltaic resources, energy storage resources, charging pile resources and adjustable load resources are recorded according to the resource identifier, so that S4 can generate corresponding scheduling instructions.

[0135] S3.7: Determine the target boundary power corresponding to each station by adjusting the resource boundary target power to obtain the aggregated target boundary power.

[0136] Based on the association between resource identifiers, station identifiers, and station grid connection boundaries established in S1.1, before the scheduling instruction corresponding to the current scheduling task is issued, the station grid connection boundary power of each station within the preset boundary reference time window is obtained according to the station identifier.

[0137] For any given power station, calculate the average value of the grid-connected boundary power of multiple power stations within a preset boundary reference time window, and determine the calculated average value as the boundary reference power of the corresponding power station; the preset boundary reference time window adopts the same sampling time reference and power exchange direction as when S2 determines the change in the grid-connected boundary power of power stations in the same period.

[0138] The resource boundary target adjustment power of each aggregated resource is collected according to the station identification, and the resource boundary target adjustment power corresponding to multiple aggregated resources belonging to the same station is summed to obtain the station boundary target adjustment power of the corresponding station.

[0139] When the adjustment direction is upward, the target adjustment power of the station boundary is added to the corresponding boundary reference power to obtain the target boundary power of the corresponding station; when the adjustment direction is downward, the target adjustment power of the station boundary is subtracted from the corresponding boundary reference power to obtain the target boundary power of the corresponding station.

[0140] According to the unified power exchange direction of the grid-connected boundary power of the power station determined by S1, the target boundary power corresponding to each power station participating in the current scheduling task is summed, and the resulting power is determined as the aggregated target boundary power corresponding to the current scheduling task.

[0141] It should be noted that the aggregated target boundary power is determined based on the resource boundary target adjustment power, rather than directly based on the resource target adjustment power issued by the resource side. The resource target adjustment power has been converted by the resource side based on the instruction fulfillment coefficient and the power transfer coefficient, thereby maintaining the correspondence between the resource side scheduling instruction and the target power of the grid connection boundary side of the power station.

[0142] S3.8: Establish the correlation between the current scheduling task, resource target adjustment power, resource boundary target adjustment power, and aggregate target boundary power.

[0143] Record the target adjustment power, adjustment direction, duration, adjustment period, and allocable adjustment power corresponding to the current scheduling task according to the scheduling task identifier.

[0144] Record the physical adjustment power, deliverable adjustment limit, resource boundary target adjustment power, and resource target adjustment power corresponding to each aggregated resource according to the resource identifier, and maintain the correlation between each parameter and the corresponding instruction fulfillment coefficient, power transfer coefficient, and site identifier.

[0145] Record the boundary reference power, the target adjustment power of the site boundary, and the target boundary power of each site according to the site identification, and establish the correlation between the target boundary power of each site and the aggregated target boundary power.

[0146] The target adjustment power of each aggregated resource, the target adjustment power of the resource boundary, and the target boundary power of the aggregated resources are retained in the current scheduling cycle. S4 issues scheduling instructions according to the target adjustment power of each aggregated resource, and evaluates the fulfillment of resource-side instructions and the power transfer at the grid connection boundary based on the actual response power and the actual power at the grid connection boundary obtained during the scheduling execution.

[0147] S4: Issue scheduling instructions according to the resource target adjustment power, obtain the actual response power of aggregated resources and the actual grid-connected boundary power of the actual power station during the scheduling execution period; update the instruction fulfillment coefficient according to the change in the resource target adjustment power and the actual response power, and update the power transfer coefficient according to the change in the actual response power and the actual grid-connected boundary power; use the updated instruction fulfillment coefficient and power transfer coefficient to determine the deliverable adjustment capability of aggregated resources in the next scheduling cycle.

[0148] S4.1: Generate scheduling instructions corresponding to aggregated resources by adjusting power according to resource targets, and issue scheduling instructions according to resource identifiers.

[0149] For any aggregated resource participating in the current scheduling task, read the resource target adjustment power determined in S3.6, and generate a scheduling instruction for the corresponding aggregated resource by combining the adjustment direction, adjustment period and scheduling task identifier recorded in 8. The scheduling instruction includes the instruction issuance time, scheduling task identifier, resource identifier, adjustment direction, resource target adjustment power and duration.

[0150] The generated scheduling instructions are sent to the control terminals of the corresponding aggregated resources according to the resource identifiers, so that photovoltaic resources, energy storage resources, charging pile resources and adjustable load resources can adjust their power according to the corresponding resource target adjustment power. The time when the scheduling instructions are issued is used as the starting point for determining the reference time window and the execution period of this scheduling.

[0151] S4.2: Obtain the actual response power of aggregated resources and the grid connection boundary power of the power station during the scheduling execution, and perform time alignment to form the scheduling response data for this time.

[0152] The actual response power of aggregated resources within the preset time range before the issuance of the resource identification and within the continuous duration range after the issuance of the scheduling instruction is recorded as the actual response power within the current reference time window and the actual response power within the current scheduling execution period, respectively. According to the association relationship between resource identification, station identification and station grid connection boundary established in S1.1, the power of the corresponding station grid connection boundary within the current reference time window and the current scheduling execution period is obtained synchronously.

[0153] Following the time alignment method in S1.3, the sampling time of the power grid connection boundary of the power station is used as a unified time reference. The actual response power within the current reference time window and the current scheduling execution period is time-matched. The power sequences with isolated missing sampling points, consecutive missing sampling points, or abnormal sampling values ​​are processed according to the data cleaning method in S1.3. The actual response power after time alignment and cleaning, together with the power grid connection boundary power of the power station, is determined as the current scheduling response data, which is used in this step to determine the change in the actual response power and the change in the actual power grid connection boundary power of the power station.

[0154] S4.3: Determine the change in actual response power of aggregated resources based on the current scheduling response data, and determine the change in effective actual response power based on the resource target adjustment power.

[0155] For any aggregated resource, calculate the average value of multiple actual response powers within the current baseline time window, and determine the calculated average value as the current response baseline power; calculate the average value of multiple actual response powers within the current scheduling execution period, and determine the difference between the average value of actual response powers within the current scheduling execution period and the current response baseline power as the current actual response power change of the corresponding aggregated resource; record the current actual response power change according to the unified adjustment direction determined by S1.

[0156] Determine whether the direction of the actual response power change is the same as the adjustment direction in the current scheduling instruction: if they are the same, the absolute value of the actual response power change is determined as the effective actual response power change; if they are opposite, the effective actual response power change for the corresponding aggregated resource is recorded as zero. When the absolute value of the actual response power change is higher than the resource target adjustment power of the corresponding aggregated resource, the effective actual response power change is limited to the resource target adjustment power.

[0157] S4.4: Determine the actual grid connection boundary power change of the current station corresponding to the current scheduling task based on the current scheduling response data.

[0158] For any given power station, read the corresponding grid-connected boundary power of the power station within the current reference time window and the current scheduling execution period. Calculate the average value of the grid-connected boundary power of multiple power stations within the current reference time window, and determine the calculated average value as the current boundary reference power. Calculate the average value of the grid-connected boundary power of multiple power stations within the current scheduling execution period, and determine the difference between the average value of the grid-connected boundary power of the power stations within the current scheduling execution period and the current boundary reference power as the actual change in the grid-connected boundary power of the corresponding power station. The actual change in the grid-connected boundary power of the power station is recorded according to the power exchange direction determined in S1.1.

[0159] S4.5: Determine the instantaneous command fulfillment coefficient based on the change in resource target adjustment power and the effective actual response power, and update it in conjunction with the command fulfillment coefficient determined in S2.3 to obtain the updated command fulfillment coefficient.

[0160] For any aggregated resource, calculate the ratio between the change in the effective actual response power and the target adjustment power of the resource, and determine the calculated ratio as the instantaneous command fulfillment coefficient of the corresponding aggregated resource in the corresponding adjustment direction. The value range of the instantaneous command fulfillment coefficient is limited to [0,1]. When the calculated ratio is greater than 1, the instantaneous command fulfillment coefficient is recorded as 1.

[0161] According to the preset forgetting coefficient, the command fulfillment coefficient determined in S2.3 is weighted and summed with the current instantaneous command fulfillment coefficient, and the resulting weighted sum is determined as the updated command fulfillment coefficient. The preset forgetting coefficient is determined based on the cumulative number of times the corresponding aggregated resource has participated in scheduling tasks. The more times it has participated in scheduling tasks, the lower the weight of the corresponding instantaneous command fulfillment coefficient in the preset forgetting coefficient, and the higher the weight of the corresponding command fulfillment coefficient determined in S2.3. The initial value of this coefficient is set to 0.9, gradually approaching 1.0 as the number of scheduling tasks increases. The updated command fulfillment coefficient is recorded separately according to the resource identifier and adjustment direction, ensuring that the command fulfillment coefficients for the same aggregated resource in the upward and downward adjustment directions are updated separately and do not replace each other.

[0162] In this embodiment, the preset thresholds and cycle duration parameters mentioned above are engineering parameters that can be routinely adjusted by those skilled in the art based on actual engineering conditions and measurement accuracy, and are not exhaustively limited here.

[0163] S4.6: Based on the actual response power change corresponding to the aggregated resources within the same power station and the actual power change at the grid connection boundary of the power station, the power transfer coefficient is re-determined to obtain the updated power transfer coefficient.

[0164] For any station identifier, the current scheduling response data corresponding to the current scheduling task is added to the historical scheduling response sample in S2.1. Following the same method as in S2.5, the actual response power change corresponding to the aggregated resources within the same station is arranged together with the actual response power change corresponding to the historical scheduling response sample retained in S2.1 to form an updated resource response change matrix. The corresponding actual station grid connection boundary power change is arranged together with the concurrent station grid connection boundary power change retained in S2.1 to form an updated boundary power change vector.

[0165] Using the power transfer coefficient of aggregated resources within the same site as the variable to be determined, and under the condition that the power transfer coefficient is greater than 0 and less than 1, the power transfer coefficient corresponding to the aggregated resources within the same site is re-determined according to the solution condition S2.5, with the minimum sum of squared residuals between the updated resource response change matrix and the updated boundary power change vector as the solution condition. This yields the updated power transfer coefficient. Following the screening method in S2.6, historical scheduling response samples with residual absolute values ​​exceeding the preset boundary residual threshold are removed before resolving the problem. The updated power transfer coefficient is recorded separately according to resource identifier, site identifier, and adjustment direction, without replacing the power transfer coefficient corresponding to one adjustment direction with the power transfer coefficient corresponding to another adjustment direction.

[0166] S4.7: Store the updated instruction fulfillment coefficient and the updated power transfer coefficient to the corresponding aggregated resource and associate them with the deliverable adjustment capability determination process for the next scheduling cycle.

[0167] According to the resource identifier and adjustment direction, the updated command fulfillment coefficient and updated power transfer coefficient replace the original command fulfillment coefficient and power transfer coefficient of the corresponding aggregated resources in S2.3 and S2.6 respectively, and maintain the correlation between the replaced command fulfillment coefficient and power transfer coefficient and the corresponding resource identifier, station identifier and adjustment direction.

[0168] Upon entering the next scheduling cycle, based on the current operating data and equipment parameters obtained in the next scheduling cycle, the current physical adjustable power of the aggregated resources is re-determined according to S1; and according to the correction method in S2.7, the current physical adjustable power of the next scheduling cycle is corrected using the replaced instruction fulfillment coefficient and power transfer coefficient to obtain the deliverable adjustment capability of the aggregated resources in the next scheduling cycle. This allows the next scheduling cycle to determine the corresponding resource target adjustment power according to the new scheduling task in S3. This forms a closed-loop scheduling process of resource-side scheduling instruction issuance, actual response power and power feedback at the grid connection boundary of the power station, and dynamic updating of instruction fulfillment coefficient and power transfer coefficient.

[0169] In summary, this invention achieves refined management of the entire process of collaborative scheduling of multi-source aggregated resources in virtual power plants by constructing a layer-by-layer progressive scheduling optimization architecture. First, based on accurate modeling of real-time operational data and multi-type resource operational constraints, the current physically adjustable power is obtained, providing a reliable physical boundary for scheduling decisions. Then, by introducing command fulfillment coefficients and power transfer coefficients, the physically adjustable power is bidirectionally corrected from two dimensions: the historical response accuracy of resources and the grid connection efficiency of the power plant. This effectively eliminates false reporting of adjustment capabilities caused by user behavior uncertainty and network transmission losses, significantly improving the authenticity and reliability of deliverable adjustment capabilities. Based on this, combined with scheduling tasks… By determining the target power, direction, and duration, and through the determination of the deliverable adjustment limit during the task period and the scientific allocation of resources, the overall scheduling demand is optimized and decomposed among various aggregated resources to generate the station-level aggregated boundary power. This ensures that the task objectives are achieved while avoiding the imbalance problem of resource overload or idleness. Through the collection of actual response data after each scheduling cycle and the dynamic updating of the two coefficients, an adaptive mechanism is formed, which solves the core problems in virtual power plant scheduling, such as the disconnect between physical capacity and actual available capacity, the difficulty of adapting static evaluation to dynamic changes, and the inability of single optimization to guarantee continuous optimality. This provides full-link technical support for the safe, efficient, and reliable participation of virtual power plants in grid regulation.

[0170] Based on the teachings of the above embodiments, other aspects of the present invention also propose an optimization system based on multi-source aggregation resource collaborative scheduling of virtual power plants, such as... Figure 2 As shown, it includes: The power determination module is used to acquire the current operating data, equipment parameters, historical dispatch instructions, corresponding actual response power, and grid connection boundary power of the aggregated resources in the virtual power plant. Based on the current operating data and equipment parameters, it determines the operating constraints of the aggregated resources and obtains the current physical adjustable power based on the operating constraints. The power correction module determines the command fulfillment coefficient of aggregated resources based on historical dispatch commands and actual response power, and determines the power transfer coefficient of aggregated resources based on the actual response power change of aggregated resources and the power change of the grid connection boundary of the power station in the same period. Based on the command fulfillment coefficient and the power transfer coefficient, the current physical adjustable power is corrected to obtain the deliverable adjustment capability of aggregated resources. The scheduling target allocation module is used to obtain the task target adjustment power, adjustment direction and duration of the current scheduling task, determine the resource target adjustment power based on the deliverable adjustment capability and operating constraints, and obtain the aggregate target boundary power corresponding to the current scheduling task based on the resource target adjustment power. The scheduling feedback update module is used to issue scheduling instructions according to the resource target adjustment power, obtain the actual response power of aggregated resources and the actual grid-connected boundary power of the power station during the scheduling execution period; update the instruction fulfillment coefficient according to the change in the resource target adjustment power and the actual response power, and update the power transfer coefficient according to the change in the actual response power and the actual grid-connected boundary power of the power station; and use the updated instruction fulfillment coefficient and power transfer coefficient to determine the deliverable adjustment capability of aggregated resources in the next scheduling cycle.

[0171] This embodiment also provides a computer device applicable to the optimization method of multi-source aggregated resource collaborative scheduling based on virtual power plants, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the optimization method of multi-source aggregated resource collaborative scheduling based on virtual power plants as proposed in the above embodiment.

[0172] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. 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. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0173] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the optimization method for collaborative scheduling of multi-source aggregated resources based on a virtual power plant, as proposed in the above embodiments.

[0174] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiments belong to the same inventive concept. 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.

[0175] 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. An optimization method for multi-source aggregation and collaborative scheduling based on virtual power plants, characterized in that, include: The system acquires the current operating data, equipment parameters, historical dispatch instructions, corresponding actual response power, and grid connection boundary power of the aggregated resources within the virtual power plant. Based on the current operating data and equipment parameters, it determines the operating constraints of the aggregated resources and obtains the current physical adjustable power based on the operating constraints. Based on the historical scheduling instructions and the actual response power, the instruction fulfillment coefficient of the aggregated resource is determined, and the power transfer coefficient of the aggregated resource is determined according to the change in the actual response power of the aggregated resource and the change in the grid connection boundary power of the power station during the same period. The current physical adjustable power is corrected according to the instruction fulfillment coefficient and the power transfer coefficient to obtain the deliverable adjustment capability of the aggregated resource. Obtain the task target adjustment power, adjustment direction, and duration of the current scheduled task; determine the resource target adjustment power based on the deliverable adjustment capability and the operational constraints; and obtain the aggregate target boundary power corresponding to the current scheduled task based on the resource target adjustment power. Dispatch instructions are issued according to the resource target adjustment power; the actual response power and actual grid connection boundary power of the aggregated resources during the scheduling execution period are obtained; the instruction fulfillment coefficient is updated according to the change between the resource target adjustment power and the actual response power; the power transfer coefficient is updated according to the change between the actual response power and the actual grid connection boundary power; the updated instruction fulfillment coefficient and the power transfer coefficient are used to determine the deliverable adjustment capability of the aggregated resources in the next scheduling cycle.

2. The optimization method based on multi-source aggregation and collaborative scheduling of virtual power plants as described in claim 1, characterized in that, The acquisition of the aggregated target boundary power includes: Aggregate the task-period deliverable adjustment limits of each aggregated resource and determine the allocatable adjustment power corresponding to the current scheduling cycle; The allocable adjustable power is allocated according to the deliverable adjustment limit for the task period, and the resource boundary target adjustment power of each aggregated resource is obtained. Based on the resource boundary target adjustment power, the instruction fulfillment coefficient, and the power transfer coefficient, the resource target adjustment power of each aggregated resource is obtained; The target boundary power corresponding to each station is determined by adjusting the resource boundary target power, and the aggregated target boundary power is obtained.

3. The optimization method based on multi-source aggregation and collaborative scheduling of virtual power plants as described in claim 2, characterized in that, The method for determining the upper limit of deliverable adjustment for the mission period includes: Obtain the target adjustment power, adjustment direction, and duration of the current scheduled task, and determine the adjustment period corresponding to the current scheduling cycle; Based on the adjustment direction, read the deliverable adjustment capabilities corresponding to each aggregated resource and determine the aggregated resources participating in the current scheduling task; By determining the physical adjustment power of each aggregated resource during the task period based on the adjustment period and operational constraints, the upper limit of the deliverable adjustment for the task period is obtained.

4. The optimization method based on multi-source aggregation and collaborative scheduling of virtual power plants as described in claim 3, characterized in that, The acquisition of the deliverable adjustability includes: The command fulfillment coefficient is determined based on the changes in historical target adjustment power and effective actual response power corresponding to the aggregated resources. The power transfer coefficient of aggregated resources is determined by comparing the actual response power change of aggregated resources within the same power station with the power change at the grid connection boundary of the power station during the same period. The current physical adjustable power of the aggregated resources is corrected based on the instruction fulfillment coefficient and the power transfer coefficient to obtain the deliverable adjustable capability.

5. The optimization method based on multi-source aggregation and collaborative scheduling of virtual power plants as described in claim 4, characterized in that, The determination of the execution factor of the instruction includes: For any aggregated resource, read the historical target adjustment power and the change in effective actual response power corresponding to multiple historical scheduling response samples according to the upward adjustment direction and the downward adjustment direction, respectively; For multiple historical scheduling response samples of the same aggregated resource under the same adjustment direction, the effective actual response power change corresponding to each historical scheduling response sample is accumulated, and the historical target adjustment power corresponding to each historical scheduling response sample is also accumulated; the ratio between the accumulated effective actual response power change and the accumulated historical target adjustment power is calculated, and the ratio is determined as the command fulfillment coefficient of the corresponding aggregated resource in the corresponding adjustment direction.

6. The optimization method based on multi-source aggregation and collaborative scheduling of virtual power plants as described in claim 5, characterized in that, The effective actual response power change includes: Construct historical scheduling response samples corresponding to aggregated resources based on historical scheduling data; The actual response power change of aggregated resources is determined based on the preset baseline time window and the actual response power during the scheduling execution period. When the direction of change of the actual response power is the same as the adjustment direction in the corresponding historical scheduling instruction, the absolute value of the actual response power change is determined as the effective actual response power change; when the direction of change of the actual response power is opposite to the adjustment direction in the corresponding historical scheduling instruction, the effective actual response power change of the corresponding historical scheduling response sample is recorded as zero.

7. The optimization method based on multi-source aggregation and collaborative scheduling of virtual power plants as described in claim 4, characterized in that, The determination of the current physically adjustable power includes: Operating constraints are determined based on the current operating data of the aggregated resources and the equipment parameters to obtain the current physically adjustable power of the aggregated resources. The aggregated resources include photovoltaic resources, energy storage resources, charging pile resources, and adjustable load resources. The current operating data of the photovoltaic resources includes current active power, current irradiance, current module temperature, inverter operating status, and the inverter's current active power limit. The current operating data of the energy storage resources includes current charge / discharge power, state of charge, energy storage converter operating status, allowable discharge power, and allowable charging power. The current operating data of the charging pile resources includes charging pile access status, current charging power, rated charging power, vehicle remaining charging demand, and vehicle planned departure time. The current operating data of the adjustable load resources includes current load power, allowable adjustable power range, current operating status, and continuous operating time limit.

8. An optimization system based on multi-source aggregation resource collaborative scheduling of virtual power plants, based on the optimization method based on multi-source aggregation resource collaborative scheduling of virtual power plants as described in any one of claims 1 to 7, characterized in that, include: The power determination module is used to acquire the current operating data, equipment parameters, historical dispatch instructions, corresponding actual response power, and grid connection boundary power of the aggregated resources in the virtual power plant, determine the operating constraints of the aggregated resources based on the current operating data and the equipment parameters, and obtain the current physical adjustable power based on the operating constraints. The power correction module determines the command fulfillment coefficient of the aggregated resource based on the historical scheduling command and the actual response power, and determines the power transfer coefficient of the aggregated resource based on the change in the actual response power of the aggregated resource and the change in the grid connection boundary power of the power station during the same period; and corrects the current physical adjustable power based on the command fulfillment coefficient and the power transfer coefficient to obtain the deliverable adjustment capability of the aggregated resource. The scheduling target allocation module is used to obtain the task target adjustment power, adjustment direction and duration of the current scheduling task, determine the resource target adjustment power according to the deliverable adjustment capability and the operation constraints, and obtain the aggregate target boundary power corresponding to the current scheduling task according to the resource target adjustment power. The scheduling feedback update module is used to issue scheduling instructions according to the resource target adjustment power, obtain the actual response power of aggregated resources and the actual grid-connected boundary power of the power station during the scheduling execution period; update the instruction fulfillment coefficient according to the change between the resource target adjustment power and the actual response power, and update the power transfer coefficient according to the change between the actual response power and the actual grid-connected boundary power; and use the updated instruction fulfillment coefficient and the power transfer coefficient to determine the deliverable adjustment capability of aggregated resources in the next scheduling cycle.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the optimization method based on multi-source aggregation resource collaborative scheduling of virtual power plants as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the optimization method based on multi-source aggregation resource collaborative scheduling of virtual power plants as described in any one of claims 1 to 7.

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