Virtual power plant resource matching method, system and computer program product

Abstract

The application discloses a virtual power plant resource matching method, system and computer program product, and belongs to the field of electric energy storage. The method comprises the following steps: constructing a power distribution network optimal power flow model containing a virtual power plant output variable through power distribution network power supply side operation information, determining a power supply gap variable by solving the virtual power plant output variable, evaluating the dynamic response upper limit of each virtual power plant resource based on real-time operation sensing data corresponding to different types of virtual power plant resources, and constructing a dynamic response upper limit constraint of each virtual power plant resource based on the dynamic response upper limit. A resource matching cost optimization model, a resource available margin optimization model and a supply-demand matching deviation optimization model are constructed, and the virtual power plant resource matching result is determined by solving under the dynamic response upper limit constraint of each virtual power plant resource, the power supply gap variable matching constraint and the resource output change rate constraint. The problems of power supply gap positioning distortion and lack of anti-risk margin to deal with secondary fluctuations in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, and in particular to a virtual power plant resource matching method, system, and computer program product. Background Technology

[0002] With the large-scale integration of distributed power sources, energy storage devices, and flexible loads, distribution networks are gradually evolving from the traditional unidirectional power supply mode to an active distribution network mode that integrates power generation, grid, load, and storage. Against this backdrop, virtual power plants, as a new dispatching mode capable of aggregating distributed adjustable resources and participating in grid regulation, integrate various resources such as energy storage resources, temperature-controlled loads, electric vehicles, and industrial interruptible loads. This provides flexible supply and demand regulation capabilities for distribution networks under peak load, localized power shortages, and sudden operational scenarios, making it an important technical means to improve the reliability and operational flexibility of distribution networks.

[0003] Existing virtual power plant resource scheduling systems typically estimate the power supply gap of the distribution network in advance during the target operating cycle through load forecasting or supply-demand balance analysis. Based on the pre-set adjustable capacity and response cost of each virtual power plant resource, a scheduling model is constructed. Target resources are selected for use through cost optimization to compensate for the power supply gap, thereby alleviating the operating pressure on the distribution network.

[0004] However, in existing technologies, power supply gap variables are usually determined by prediction results or static supply and demand analysis, which makes it difficult to reflect the actual power supply gap under real-time operating conditions of the distribution network. This can easily lead to distorted gap location and affect the accuracy of subsequent resource scheduling. At the same time, existing scheduling schemes usually take meeting the current gap variable or minimizing the call cost as the main optimization objective. The selected resources often only meet the current gap compensation variable and lack the margin of remaining callable capacity. When the power supply gap of the distribution network fluctuates twice, it is difficult to provide additional adjustment capabilities in a timely manner, resulting in insufficient system risk resistance. Summary of the Invention

[0005] To address the technical problems of distorted power supply gap positioning and lack of resilience against secondary fluctuations in existing technologies, this invention provides a virtual power plant resource matching method, system, and computer program product. The technical solution is as follows: The virtual power plant resource matching method includes the following steps: An optimal power flow model of the distribution network, including the output variables of virtual power plants, is constructed using the operation information of the power supply side of the distribution network. The power supply gap variable is determined by solving the output variables of the virtual power plants. The dynamic response limit of each virtual power plant resource is evaluated based on real-time operation perception data corresponding to different types of virtual power plant resources, and the dynamic response limit constraint of each virtual power plant resource is constructed based on the dynamic response limit. A resource matching cost optimization model, a resource availability margin optimization model, and a supply-demand matching deviation optimization model are constructed and solved under the constraints of the upper limit of the dynamic response of resources in each virtual power plant, the matching constraint of power supply gap variables, and the constraint of the rate of change of resource output, so as to determine the resource matching result of the virtual power plant and generate a scheduling control scheme.

[0006] Optionally, the resource matching cost optimization model specifically involves: based on the unit response cost parameters corresponding to each virtual power plant resource and the resource call status and response output within the target scheduling period, constructing a resource matching cost objective function with the goal of minimizing the total call cost of virtual power plant resources. The resource matching cost objective function is shown below: ; In the formula, The objective function for matching resources to costs. For resources exist The unit response cost coefficient at time t. Indicates the first The actual output of a virtual power plant resource at time t. , indicating the first The output status of a virtual power plant resource at time t, where 1 indicates it is in use and 0 indicates it is idle. Let I be the set of scheduling times, and let I be the set of virtual power plant resources participating in resource matching. By linking resource call costs with actual resource response behavior, the resource matching process can prioritize resources with lower response costs and that meet power supply demands for scheduling, thereby reducing the overall resource call cost of virtual power plants and improving the economic efficiency of resource scheduling.

[0007] Optionally, the resource availability margin optimization model includes: based on the dynamic response ceiling corresponding to each virtual power plant resource and the resource call status within the target scheduling period, constructing a resource availability margin objective function with the goal of maximizing the remaining callable capacity of the selected virtual power plant resources, as shown below: ; In the formula, The objective function is the resource availability margin. This indicates that for a certain virtual power plant resource To determine whether they exerted effort within a day, For control factors that are available when needed, This represents a dynamic upper limit for resources. By introducing a resource availability margin optimization objective during the resource matching process, resources can meet the current power supply gap compensation needs while retaining subsequent call-up capabilities, thereby improving the system's continuous response capability and risk resistance to secondary power supply fluctuations.

[0008] Optionally, the supply-demand matching deviation optimization model includes: based on the supply-demand deviation between the total response output of virtual power plant resources and the power supply gap variable within the target scheduling period, constructing a supply-demand matching deviation objective function with the goal of minimizing the supply-demand deviation, specifically: ; In the formula, The objective function is the supply-demand matching deviation. For nodes exist The virtual power plant output variable at time t, where B is the set of distribution network nodes. Let I be the set of scheduling times, and let I be the set of virtual power plant resources participating in resource matching. By optimizing the deviation between the total resource response capacity and the power supply gap demand, the resource response results can be made closer to the actual power supply demand, avoiding the problems of insufficient or excessive resource allocation, and improving the accuracy of supply and demand matching and resource utilization efficiency.

[0009] Optionally, an optimal power flow model of the distribution network, incorporating virtual power plant output variables, is constructed using the power supply side operation information of the distribution network. This includes: constructing an optimal power flow model of the distribution network based on distribution network topology data, node load data, branch parameter data, and conventional generator output constraints, and introducing virtual power plant output variables as power supply gap compensation variables into the optimal power flow model. By directly embedding the virtual power plant output variables into the optimal power flow model of the distribution network, the power supply gap identification process can be jointly solved with the actual supply and demand balance relationship, thereby improving the authenticity and accuracy of the power supply gap location results.

[0010] Optionally, a virtual power plant output variable is introduced as a power supply gap compensation variable, including: under the constraint that conventional generator units reach their maximum output, the solution result of the virtual power plant output variable is used as the power supply gap variable within the target time period. The specific process is as follows: ; In the formula, Let $ be the overall power supply cost objective function corresponding to the optimal power flow model of the distribution network. For nodes exist The regular generating units at any given time have active power output. Its power generation cost coefficient is a constant; For nodes exist The output variable of the virtual power plant at any given time is also the variable of the power supply gap. Cost of power output for virtual power plants For the set of scheduling times, For the set of distribution network nodes; where the upper limit constraint of the virtual power plant output variable is: ,in, for The sum of the total load demand of all nodes in the distribution network at any given time. for The sum of the maximum available generating capacity of all conventional generating units in the distribution network at any given time. By using the output variable of a virtual power plant to compensate for the supply-demand gap under the condition that the conventional generating units reach their maximum power supply capacity, the power supply gap variable can directly reflect the actual degree of power shortage in the current distribution network, thereby avoiding the problem of power supply gap positioning distortion caused by traditional static estimation methods.

[0011] Optionally, the dynamic response upper limit of each virtual power plant resource can be evaluated, including: obtaining the dynamic response upper limit of energy storage resources by acquiring real-time state of charge information and combining it with preset safe charge thresholds and rated operating parameters; obtaining the dynamic response upper limit of temperature-controlled load resources by acquiring real-time environmental state information and combining it with preset comfort boundary parameters; obtaining the dynamic response upper limit of electric vehicle resources by acquiring real-time state of charge information and combining it with preset target charge demand and preset dwell time information; obtaining the dynamic response upper limit of data center resources by acquiring real-time operating load information and combining it with preset core minimum operating load parameters; and obtaining the dynamic response upper limit of industrial interruptible load resources by acquiring real-time operating status information and combining it with preset interruption protocol constraint information. By dynamically evaluating the resource availability based on the real-time operating status of different types of resources, the resource scheduling process can be made more closely aligned with the current real operating status of the resources, thereby improving the execution feasibility and scheduling reliability of resource matching results.

[0012] Optionally, determining the virtual power plant resource matching results and generating a scheduling control scheme includes: weighting and fusing the resource matching cost objective function, resource availability margin objective function, and supply-demand matching deviation objective function based on preset weight coefficients to construct a comprehensive objective function; and performing collaborative optimization to solve the comprehensive objective function under constraints such as the upper limit of dynamic response of each virtual power plant resource, power supply gap variable matching constraints, and resource output change rate constraints to obtain the virtual power plant resource matching results. By collaboratively optimizing the economic efficiency of resource allocation, the subsequent adjustability of resources, and the accuracy of supply-demand matching, and combining the dynamic response capability constraints and resource operation constraints for joint solution, the resource matching results can simultaneously take into account economic efficiency, supply-demand matching accuracy, and subsequent risk resistance, thereby improving the overall stability and continuous response capability of virtual power plant resource scheduling.

[0013] A virtual power plant resource matching system is provided for executing the virtual power plant resource matching method described above. The virtual power plant resource matching system includes: one or more processors; and a storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the virtual power plant resource matching method described above.

[0014] A computer program product, including a computer program that, when executed by a processor, implements the steps of the virtual power plant resource matching method as described in any of the preceding claims.

[0015] The beneficial effects of the technical solutions provided by the embodiments of the present invention include at least the following: The virtual power plant resource matching method, system, and computer program product provided by this invention achieve accurate identification of power supply gaps by constructing an optimal power flow model of the distribution network that includes the output variables of virtual power plants. Based on real-time operational sensing data corresponding to different types of virtual power plant resources, the actual availability of each resource is dynamically evaluated. Furthermore, the resource matching cost optimization model, resource availability margin optimization model, and supply-demand matching deviation optimization model are combined for collaborative optimization solutions. This ensures that the resource matching results meet the current power supply gap compensation needs while retaining subsequent adjustability. Thus, it effectively solves the problems of power supply gap location distortion and lack of risk resistance margin for secondary power supply fluctuations in the prior art, and improves the matching accuracy, continuous response capability, and operational stability of virtual power plant resource scheduling. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating the virtual power plant resource matching method provided in this application embodiment. Detailed Implementation

[0018] Embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of the present disclosure are shown in the drawings, it should be understood that embodiments of the present disclosure may be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure.

[0019] It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure. In the description of the embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "this embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects.

[0020] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0021] In this specific embodiment, to support model calculations during power supply gap analysis, dynamic resource capacity assessment, and resource optimization matching, distribution network operating parameters and virtual power plant operating parameters are pre-acquired and organized to form the parameter set required for each model calculation, providing data support for subsequent parameter calculations and model solving in various formulas. The distribution network operating parameters can be collected through dispatch automation systems, distribution management systems, energy management systems, and field measurement terminals, and mainly include distribution network topology information, node load information, branch operating parameters, node operating status parameters, and conventional generator set operating parameters.

[0022] In this embodiment of the invention, virtual power plant resources refer to distributed adjustable units that are connected to the virtual power plant aggregation control platform and can respond to dispatch commands to adjust power. These include energy storage units, flexible load units, and distributed energy consumption units with power output adjustment capabilities, load reduction capabilities, or load transfer capabilities. Virtual power plant resources may include energy storage resources, temperature-controlled load resources, electric vehicle resources, data center resources, and industrial interruptible load resources, etc. Different types of virtual power plant resources are aggregated and connected to the virtual power plant management platform. After receiving dispatch control commands, they adjust their own power output status or load operation status according to the corresponding operating status and dispatch requirements to participate in power supply gap compensation, load adjustment, and distribution network operation balancing.

[0023] Distribution network topology information is used to characterize the connection relationships between nodes, such as node number information, branch connection relationship information, and node hierarchy relationship information; node load information is used to characterize the load demand of each node during different scheduling periods, such as node active load power, node reactive load power, and total regional load demand power; branch operating parameters are used to characterize line transmission characteristics, such as branch resistance parameters, branch reactance parameters, line impedance parameters, and line transmission capacity parameters; node operating status parameters are used to characterize the current distribution network operating status, such as node voltage amplitude, node voltage phase angle, and branch current value; conventional generator set operating parameters are used to characterize the power supply capacity of the power supply side, such as the current active power output of the unit, the upper limit of the maximum active power output of the unit, the generation cost coefficient, and the adjustable capacity parameters.

[0024] Virtual power plant resource operation parameters can be collected in real time through the virtual power plant management platform, edge sensing devices, user-side intelligent terminals, and equipment operation monitoring systems. These parameters mainly include energy storage resource operation parameters, temperature-controlled load operation parameters, electric vehicle operation parameters, data center operation parameters, and industrial interruptible load operation parameters. Specifically, energy storage resource operation parameters characterize the current operating status of energy storage equipment, such as current state of charge, rated capacity, rated charge / discharge power, and discharge efficiency parameters; temperature-controlled load operation parameters characterize the current operating status of temperature control equipment, such as current operating power, current ambient temperature, preset comfort temperature boundary, and thermal conversion coefficient parameters; electric vehicle operation parameters characterize the current charging status of electric vehicles, such as current state of charge, current charging power, target state of charge, estimated departure time, and charging efficiency parameters; data center operation parameters characterize the current business load status of the data center, such as real-time server operating power, core business minimum power, and energy utilization efficiency parameters; and industrial interruptible load operation parameters characterize the current operating status of industrial equipment, such as real-time production line operating power, allowed interruption period information, and cumulative interruption duration information.

[0025] In addition, the cost parameters, safe operation threshold parameters, and user behavior constraint parameters involved in the model calculation process can be determined based on historical operation records, equipment operation parameters, and user preset information.

[0026] The cost parameters include resource unit response cost parameters, power generation cost coefficient parameters, and resource allocation cost parameters; the safe operation threshold parameters include minimum safe state of charge threshold, maximum allowable voltage threshold, minimum allowable voltage threshold, and maximum output change rate threshold; and the user behavior constraint parameters include user comfort boundary parameters, electric vehicle target charging demand parameters, and industrial load interruption protocol constraint parameters.

[0027] Based on the aforementioned distribution network operation parameters, virtual power plant resource operation parameters, and cost parameters, safe operation threshold parameters, and user behavior constraint parameters involved in the model calculation process, the power supply side input parameters in the power supply gap analysis model, the resource side input parameters in the dynamic response upper limit assessment model, and the optimization input parameters in the resource optimization matching model are formed to support the model calculation and result solving in the power supply gap identification, resource capacity assessment, and resource optimization matching processes.

[0028] like Figure 1 The diagram shown is a flowchart of a virtual power plant resource matching method provided in an embodiment of this application. The method includes the following steps: Step S1: Construct an optimal power flow model of the distribution network that includes the output variables of virtual power plants by using the power supply side operation information of the distribution network, and determine the power supply gap variables by solving the output variables of virtual power plants.

[0029] Specifically, an optimal power flow model for the distribution network, incorporating virtual power plant output variables, is constructed using the power supply side operation information of the distribution network. This includes: constructing an optimal power flow model for the distribution network based on distribution network topology data, node load data, branch parameter data, and conventional generator output constraints, and introducing virtual power plant output variables as power supply gap compensation variables into the optimal power flow model.

[0030] The power supply side operation information of the target distribution network is obtained. The power supply side operation information includes distribution network topology data, node load data, branch parameter data, and conventional generator set operation constraint data. Among them, the distribution network topology data is used to characterize the connection relationship between each distribution node, the node load data is used to characterize the load demand of each node during the target scheduling cycle, the branch parameter data is used to characterize the branch impedance characteristics, and the conventional generator set operation constraint data is used to characterize the maximum output boundary of each conventional generator set.

[0031] Based on the distribution network topology data, node load data, and conventional generator unit operation constraint data, an optimal power flow model for the distribution network is constructed. The objective function of the optimal power flow model is expressed as: ; In the formula, For nodes exist Active power output of conventional generating units (non-virtual power plants) at any given time; Its power generation cost coefficient is a constant; For nodes exist The output variable of the virtual power plant at any given time is also the variable of the power supply gap; Cost of power output for virtual power plants ( ), is a constant; For the set of scheduling times; For distribution network nodes; For nodes exist The active power output cost of conventional generating units (non-virtual power plants) at any given time; For nodes exist The output cost of a virtual power plant at any given moment; For nodes The sum of the output cost of conventional generating units connected to the power plant and the output cost of the virtual power plant; For nodes exist The sum of the output cost of conventional generating units and the output cost of virtual power plants at any given time; The objective function of the optimal power flow model is denoted as .

[0032] The optimal power flow model includes the following constraints: The output constraints of conventional generator sets are as follows: ; In the formula, For nodes The output variable of conventional generating units; For nodes The maximum active power output limit of conventional generating units. The output constraint for conventional generating units is used to ensure that the actual active power output of these units remains within the permissible operating range. The lower limit is used to prevent non-physical situations such as negative output, while the upper limit reflects the rated power supply capacity boundary of the unit. This constraint accurately describes the maximum power supply capacity of the distribution network's conventional power resources under the current operating scenario, providing a realistic power supply benchmark for subsequent gap identification.

[0033] The branch has a power flow balance constraint, namely: ; Where a and b are the distribution network node numbers; Let be the active power at the beginning of branch bk at time t; For nodes Active load connected at the location; Let be the current flowing through branch ab during time period t; Let be the resistance of branch ab; b→k represents the set of all child nodes with node b as the parent node, and k represents the downstream child node number of node b; the branch active power flow balance constraint is used to describe the active power conservation relationship of the node. The active power entering the node, after deducting line losses, should be equal to the power difference between the node load consumption and the power injection. Through this constraint, it can be ensured that the power supply gap variable participates in the overall active power balance calculation, so that the obtained gap amount has physical consistency.

[0034] Node voltage recursion constraint, namely: ; in, , Let be the voltage values ​​of nodes a and b during time period t; , Let t represent the active and reactive power flowing through the first end of branch ab during time period t. Here, represents the reactance of branch ab; the node voltage recursion constraint, i.e., the voltage relationship constraint between nodes, is used to describe the voltage transmission relationship between parent and child nodes. This constraint determines the voltage variation between nodes through branch impedance, branch power flow, and line current, ensuring that the power flow transmission process meets the physical transmission laws of the distribution network.

[0035] The constraint on the relationship between branch current and power is as follows: ; This constraint is used to establish a mapping relationship between power and current, making the current variable in the power flow calculation process physically computable, and at the same time providing support for line loss calculation.

[0036] Branch reactive power flow balance constraint, namely: ; in, The reactive power injected into node b at time t; Let be the reactive power at the beginning of branch bk at time t; this constraint is used to maintain the conservation of reactive power at nodes. Since voltage stability in the distribution network is closely related to reactive power, the branch reactive power flow balance constraint can ensure that the power flow calculation process meets the reactive power support requirements of the real power grid, thereby improving the accuracy of power supply gap identification results.

[0037] Node voltage magnitude boundary constraints, namely: ; in, , These are the upper and lower limits for the voltage at low-voltage distribution network nodes. This constraint is used to limit the voltage operation of each node within a safe range, preventing voltage exceedances due to supply-demand imbalances or line overloads, and ensuring that the power supply gap identification process meets actual operational safety requirements.

[0038] Furthermore, it also includes upper limit constraints on the output variables of virtual power plants, specifically: ; in, for The sum of the total load demand of all nodes in the distribution network at any given time. for The sum of the maximum available generating capacity of all conventional generating units in the distribution network at any given time. This is because when the distribution network is in a power shortage scenario, its... > The total output of the virtual power plant is actually... - Therefore, we should consider taking... - The upper limit of the virtual power plant output is set at twice the value. The purpose of setting it to 2 is to make the upper limit as large as possible so that the final output result of the virtual power plant does not touch the boundary.

[0039] By introducing virtual power plant output variables into the optimal power flow model As a power supply gap compensation variable, it ensures that the optimal power flow model still has a feasible solution even when conventional generator sets cannot meet all load demands and constraints, and this is achieved through the virtual power plant output variable. The solution results directly characterize the actual power supply gap variables of the target distribution network, thereby improving the accuracy of power supply gap location and providing an accurate demand basis for subsequent virtual power plant resource matching.

[0040] Step S2: Evaluate the dynamic response upper limit of each virtual power plant resource based on the real-time operation perception data corresponding to different types of virtual power plant resources, and construct the dynamic response upper limit constraint of each virtual power plant resource based on the dynamic response upper limit.

[0041] Furthermore, assessing the dynamic response upper limit of each virtual power plant resource can accurately reflect the true adjustability of various resources under the current operating state, providing real capability boundary support for subsequent resource matching optimization. The dynamic response upper limit constraints of each virtual power plant resource include: dynamic response upper limit constraints of energy storage resources, dynamic response upper limit constraints of temperature-controlled load resources, dynamic response upper limit constraints of electric vehicle resources, dynamic response upper limit constraints of data center resources, and dynamic response upper limit constraints of industrial interruptible load resources.

[0042] Specifically, by acquiring real-time state of charge information of energy storage resources and combining it with preset safe charge thresholds and rated operating parameters, the upper limit of the dynamic response of energy storage resources is obtained. The specific formula is as follows: ; in, For the first One energy storage device in The dynamic maximum discharge power limit at any given time is the maximum active power output that can be called upon. For the first The rated maximum discharge power of each energy storage device; For smart devices to collect marketing data in real time One energy storage device in The current state of charge at any given moment; The minimum state of charge safe discharge threshold set to protect battery life; For the first The rated capacity of each energy storage device; For the first Discharge efficiency of individual energy storage devices; To optimize the time step of scheduling.

[0043] By acquiring real-time environmental status information of temperature-controlled load resources and combining it with preset comfort boundary parameters, the dynamic response upper limit of the temperature-controlled load resources is obtained. The specific formula is as follows: ; in, For the first A temperature-controlled load in The maximum dynamic power reduction limit at any given time; For the first A temperature-controlled load in The baseline operating power at any given time; The equivalent coefficient for electrothermal conversion of the building or room; The maximum tolerable indoor temperature limit set when the user signs the contract or determined by the system assessment; The first real-time data collection for smart devices Individual users The actual indoor temperature at any given time; To optimize the time step of scheduling.

[0044] By acquiring real-time state-of-charge information of electric vehicle resources and combining it with preset target charge demand and preset dwell time information, the dynamic response upper limit of electric vehicle resources is obtained. The specific formula is as follows: ; in, For the first electric vehicles The maximum reduction in charging power can be achieved dynamically at any given time. For the first electric vehicles The actual charging power at any given time; The target state of charge that must be achieved by the vehicle owner when removing the gun and leaving the site; The first real-time data collection for smart devices electric vehicles The current state of charge at any given moment; For the first The rated capacity of the onboard battery of an electric vehicle; The estimated time for the car owner to disconnect the charging gun and leave the site is preset through the charging station's interactive interface; At the current scheduling time, therefore Indicates the remaining parking / charging time of the vehicle; The charging efficiency of electric vehicle charging stations.

[0045] By obtaining real-time operational load information of data center resources and combining it with preset core minimum operational load parameters, the dynamic response limit of data center resources is obtained. The specific formula is as follows: ; in, For the first Data centers in The maximum dynamic power reduction limit at any given time; This represents the actual total operating power of IT servers in the data center at the current moment. To maintain a minimum core IT power level for uninterrupted services such as financial transactions and real-time communications; To calculate the power efficiency of the data center, reducing the heat generated by 1kW of servers will also save power for the supporting cooling system. Multiplying this by the power usage efficiency (PUE) will give you the actual power reduction for the entire power distribution network.

[0046] By acquiring real-time operational status information of industrial interruptible load resources and combining it with preset interruption protocol constraints, the dynamic response upper limit of industrial interruptible load resources is obtained. The specific formula is as follows: ; in, This represents the current real-time operating power of the high-energy-consuming production line. This is a 0-1 matrix parameter, representing the agreement between the factory and the power grid. Is it permissible to interrupt this specific shift during this time period? This represents the maximum cumulative number of hours that the production line is allowed to be interrupted throughout the day; the formula is used to express that only during the period permitted by the agreement, and provided that the number of hours that the factory h is allowed to be interrupted within a day does not reach the limit, can its current operating power be provided to the grid as a dynamic upper limit.

[0047] By dynamically assessing the response limit at the current moment based on real-time operational perception data corresponding to various resources, the static contracting capability of resources can be transformed into real-time executable capability, making the adjustable boundaries of each resource more in line with the actual operating state, and improving the physical feasibility and execution reliability of resource matching results. At the same time, the dynamic response limit can also serve as a capability boundary constraint in the subsequent resource optimization and matching process, so that the resource calling process avoids over-scheduling while satisfying power supply variables, thereby improving the overall resource scheduling accuracy and system operation stability.

[0048] Step S3: Construct resource matching cost optimization model, resource availability margin optimization model, and supply-demand matching deviation optimization model, and solve them under the constraints of the upper limit of dynamic response of resources in each virtual power plant, the matching constraint of power supply gap variables, and the constraint of resource output change rate, so as to determine the resource matching result of virtual power plants and generate a scheduling control scheme.

[0049] Specifically, a resource matching cost optimization model is constructed, including: based on the unit response cost parameters corresponding to each virtual power plant resource and the resource call status and response output within the target scheduling period, a resource matching cost objective function is constructed with the goal of minimizing the total call cost of virtual power plant resources. The resource matching cost objective function is shown below: ; In the formula, The objective function for matching resources to costs. For resources exist The unit response cost coefficient at any given time; For the first The actual response output of a virtual power plant resource at time t; For the first The output status of a virtual power plant resource at time t, where 1 represents being called up and 0 represents being idle; T is the time set corresponding to the target scheduling period; I is the set of virtual power plant resources participating in resource matching. This formula can quantify the actual call cost of a single resource at the corresponding scheduling time. By summing all resources and all scheduling periods, the total call cost of virtual power plant resources in the entire scheduling period can be obtained.

[0050] Specifically, a resource availability margin optimization model is constructed, including: based on the dynamic response limit corresponding to the resources of each virtual power plant and the resource call status within the target scheduling period, a resource availability margin objective function is constructed with the goal of maximizing the remaining callable capacity of the selected virtual power plant resources. The resource availability margin objective function is as follows: ; In the formula, The objective function is the resource availability margin. To determine the calling status of the i-th virtual power plant resource throughout the entire target scheduling cycle, the value is set to 1 if a calling behavior exists at any time, and 0 otherwise. For control factors that are available when needed; This represents the upper limit of the dynamic response of the i-th virtual power plant resource in the current scheduling cycle.

[0051] Furthermore, the state variable is used to identify whether a resource participates in the current scheduling. When a resource participates in power supply gap compensation at least once during the target scheduling period, it is activated through... Mark the resource as enabled and further extract its corresponding dynamic response limit. As an evaluation value of the overall callable capacity of the resource, the total overall callable capacity corresponding to the current resource matching scheme can be obtained by summing the dynamic response limits of all called resources. A negative sign is introduced before the objective function to transform the optimization objective of maximizing resource callable capacity into a unified minimization solution form. This allows for unified weighted fusion with the resource matching cost objective function and the supply-demand matching deviation objective function to form a comprehensive objective function for collaborative optimization. (Time-dependent availability control coefficient) This is used to adjust the weight of resource availability margin targets in comprehensive optimization, enabling the resource matching process to flexibly balance economic efficiency and risk resistance according to different operating scenarios. When secondary power supply fluctuations occur during distribution network operation, such as sudden load increases, distributed generation fluctuations, or further reductions in local power supply capacity, if the currently matched resources can continue to provide additional adjustment capabilities, there is no need to restart the large-scale resource screening and scheduling optimization process, thereby improving the system's secondary response speed.

[0052] Specifically, a supply-demand matching deviation optimization model is constructed, including: based on the supply-demand deviation between the total response output of virtual power plant resources and the power supply gap variable within the target scheduling period, a supply-demand matching deviation objective function is constructed with the goal of minimizing the supply-demand deviation, specifically: ; In the formula, Let B be the objective function for the supply-demand mismatch, and let B represent the set of nodes corresponding to the power supply gap. This represents the total response output of all virtual power plant resources invoked within the target time period; This represents the total power supply gap variable for all gap nodes within the target time.

[0053] At each scheduling moment, the actual output of the virtual power plant resources participating in the response is summarized to form the total supply capacity; at the same time, all power supply gap variables within the target time are summarized to form the total variable; the difference between the output and the variable is calculated by the least squares method, which can reflect the supply and demand matching degree of the current resource matching scheme. The smaller the difference or the closer it is to 0, the higher the matching degree.

[0054] The squared form is used to process the supply and demand deviation. On the one hand, it can eliminate the mutual cancellation problem of positive and negative deviations during the accumulation process, making the deviation evaluation more realistic. On the other hand, it can amplify the larger supply and demand deviations, improve the sensitivity of the optimization process to large deviations, and thus prioritize the correction of scheduling schemes with larger matching errors.

[0055] For example, if the output is 1kW less, the penalty is 1.2 =1. If the output is 10kW less, the penalty is 10. 2 =100. This mechanism will cause the solver to choose to have a difference of 1kW in 10 different time periods (total penalty of 10 points), rather than choosing to have a difference of 10kW in one time period (total penalty of 100 points).

[0056] In a specific example, the power supply gap variable is 100kW within a certain target scheduling period. Under the first resource matching scheme, the total response output of the virtual power plant is 90kW, so the supply-demand deviation is: 90−100=−10, which squares to a deviation value of 100. Under the second resource matching scheme, the total response output of the virtual power plant is 110kW, so the supply-demand deviation is: 110−100=10. It can be seen that after squaring, both insufficient and excessive supply can be uniformly quantified as positive values ​​for evaluation, avoiding the mutual cancellation of positive and negative deviations during the accumulation of multiple time periods. When the squared values ​​of the supply-demand deviations corresponding to different resource matching schemes are the same, a comprehensive evaluation can be conducted by combining the resource matching cost target and the resource availability margin target to determine the final resource matching scheme. Furthermore, if the total output of the virtual power plant is 70kW under the third resource matching scheme, then the supply-demand deviation is: 70−100=−30, which squares to a deviation of 900. Compared to the deviation of 100 in the first scheme, the deviation in the third scheme is significantly amplified. This indicates that the square form has a higher penalty for larger supply-demand deviations, enabling the optimization process to prioritize reducing scheduling schemes with larger deviations, thereby improving resource matching accuracy.

[0057] Furthermore, determining the virtual power plant resource matching results and generating a scheduling control scheme includes: weighting and fusing the resource matching cost objective function, the resource availability margin objective function, and the supply-demand matching deviation objective function based on preset weight coefficients to construct a comprehensive objective function; under the constraints of the upper limit of the dynamic response of each virtual power plant resource, the matching constraints of the power supply gap variable, and the constraints of the rate of change of resource output, collaboratively optimizing and solving the comprehensive objective function to obtain the virtual power plant resource optimization matching results; and generating a scheduling control scheme based on the virtual power plant resource optimization matching results.

[0058] Specifically, the overall objective function is as follows: ; In the formula, The overall objective function; Weighting coefficients for matching cost objectives to resources; The weighting coefficients for the resource availability margin target; The weighting coefficients for the supply-demand mismatch target, and satisfying Among them, the resource matching cost objective function is used to measure the economic cost of the resource allocation process; the resource availability margin objective function is used to measure the subsequent sustainable adjustment capability of the resource matching result; and the supply-demand matching deviation objective function is used to measure the matching accuracy between the total resource response capability and the power supply gap variable.

[0059] By introducing weighting coefficients, the importance of different optimization objectives can be dynamically adjusted according to different operating scenarios.

[0060] Furthermore, under the constraints of the upper limit of the dynamic response of each virtual power plant resource, the matching constraint of the power supply gap variable, and the constraint of the rate of change of resource output, the comprehensive objective function is solved by collaborative optimization to obtain the virtual power plant resource optimization matching result.

[0061] The upper limit constraints for the dynamic response of the above virtual power plant resources are as follows: ; In the formula, For virtual power plant resources The maximum available capacity is dynamically calculated based on the type of equipment and real-time marketing data received locally. This includes one or more of the following: the dynamic response limits for energy storage resources, temperature-controlled load resources, electric vehicle resources, data center resources, and industrial interruptible load resources, as mentioned earlier. This constraint ensures that the resource matching results are practically feasible.

[0062] The above power supply gap variable matching constraints are: ; This formula is used to ensure that the virtual power plant resource optimization matching results output by the resource matching cost optimization model, the resource availability margin optimization model, and the supply-demand matching deviation optimization model must satisfy the node gap variables.

[0063] The above resource output change rate constraint is: ; This formula constrains the output variable. Adjacent time steps The intense fluctuations within. and These represent the maximum upward ramp rate and the maximum downward ramp rate at full load for the i-th virtual power plant resource, respectively. This formula is used to limit the variation in resource output between adjacent scheduling times, avoiding sudden changes in output.

[0064] Furthermore, under the combined effect of the above-mentioned comprehensive objective function and constraints, the resource matching results are optimized and solved to obtain the virtual power plant resource optimization matching results, and a scheduling control scheme is generated based on the virtual power plant resource optimization matching results.

[0065] For nodes with power supply gaps, an I×T matrix structure is set up for the optimization decision variables, including the state matrix. = : , indicating the first The output state of each virtual power plant resource at time t, and the output matrix. = , indicating the first The actual output of a virtual power plant resource at time t. The scheduling and control scheme includes a state matrix. and output matrix .

[0066] By comprehensively considering resource allocation costs, subsequent resource availability margins, and supply-demand matching accuracy, resource scheduling results can simultaneously achieve economic efficiency, risk resistance, and supply-demand matching accuracy. Furthermore, dynamic response upper limit constraints ensure resource allocation feasibility, power supply gap variable matching constraints ensure power supply reliability, and resource output change rate constraints ensure resource operational stability. This results in a final scheduling and control scheme with higher execution reliability, scheduling stability, and risk response capabilities.

[0067] A virtual power plant resource matching system is provided for executing the virtual power plant resource matching method described above. The virtual power plant resource matching system includes: one or more processors; and a storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the virtual power plant resource matching method described above.

[0068] This embodiment provides a virtual power plant resource matching computer program product, including computer program instructions stored on a computer-readable storage medium. When a processor in a computing device executes the computer program instructions, it performs the following operations: Obtain the power supply side operation information of the distribution network, and construct an optimal power flow model of the distribution network that includes the output variables of virtual power plants based on the power supply side operation information, so as to determine the power supply gap variables.

[0069] Acquire real-time operational awareness data for different types of virtual power plant resources, and evaluate the dynamic response limit of each virtual power plant resource based on the real-time operational awareness data.

[0070] A resource matching cost optimization model, a resource availability margin optimization model, and a supply-demand matching deviation optimization model are constructed and solved under the constraints of the upper limit of the dynamic response of resources in each virtual power plant, the matching constraint of power supply gap variables, and the constraint of the rate of change of resource output, so as to determine the resource matching result of the virtual power plant and generate a scheduling control scheme.

[0071] A scheduling and control scheme is generated based on the virtual power plant resource matching results, and scheduling and control instructions are sent to the corresponding virtual power plant resources to achieve power supply gap compensation.

[0072] In this embodiment, the computer program can be deployed in a virtual power plant management platform, an energy dispatch terminal, or a cloud dispatch server. The processor executes the computer program instructions to achieve dynamic optimization matching and dispatch control of virtual power plant resources.

[0073] Through the above description of the implementation methods, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the above functions can be divided into different functional modules to complete all or part of the functions described above.

[0074] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0075] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units, located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0076] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0077] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the solution, or all or part of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0078] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for matching virtual power plant resources, characterized in that, Includes the following steps: An optimal power flow model of the distribution network, including the output variables of virtual power plants, is constructed using the operation information of the power supply side of the distribution network. The power supply gap variable is determined by solving the output variables of the virtual power plants. The dynamic response limit of each virtual power plant resource is evaluated based on real-time operation perception data corresponding to different types of virtual power plant resources, and the dynamic response limit constraint of each virtual power plant resource is constructed based on the dynamic response limit. A resource matching cost optimization model, a resource availability margin optimization model, and a supply-demand matching deviation optimization model are constructed and solved under the constraints of the upper limit of the dynamic response of resources in each virtual power plant, the matching constraints of power supply gap variables, and the constraints of the rate of change of resource output, so as to determine the resource matching results of the virtual power plant and generate a scheduling control scheme. The resource matching cost optimization model is as follows: Based on the unit response cost parameters corresponding to each virtual power plant resource and the resource call status and response output within the target scheduling period, a resource matching cost objective function is constructed with the goal of minimizing the total call cost of virtual power plant resources. The resource matching cost objective function is as follows: ； In the formula, The objective function for matching resources to costs. For the first Virtual power plant resources in The unit response cost coefficient at time t. Indicates the first The actual output of a virtual power plant resource at time t. , indicating the first The output status of a virtual power plant resource at time t, where 1 indicates it is in use and 0 indicates it is idle. Let I be the set of scheduling times, and let I be the set of virtual power plant resources participating in resource matching; The resource availability margin optimization model includes: Based on the dynamic response cap of each virtual power plant resource and the resource call status within the target scheduling period, a resource availability margin objective function is constructed with the goal of maximizing the remaining callable capacity of the selected virtual power plant resources. The resource availability margin objective function is as follows: ； In the formula, The objective function is the resource availability margin. Indicates the judgment of the first Does the virtual power plant resource generate power within a day? For control factors that are available when needed, This represents the dynamic upper limit of resources; The supply-demand matching deviation optimization model includes: Based on the supply-demand deviation between the total response output of virtual power plant resources and the power supply gap variable within the target scheduling period, a supply-demand matching deviation objective function is constructed with the goal of minimizing the supply-demand deviation, specifically: ； In the formula, The objective function is the supply-demand matching deviation. For nodes exist The virtual power plant output variable at time t, and B is the set of distribution network nodes.

2. The virtual power plant resource matching method as described in claim 1, characterized in that: The construction of an optimal power flow model for the distribution network, incorporating virtual power plant output variables, based on the distribution network's power supply side operation information includes: An optimal power flow model for the distribution network is constructed based on the distribution network topology data, node load data, branch parameter data, and conventional generator output constraints. A virtual power plant output variable is introduced into the optimal power flow model as a power supply gap compensation variable.

3. The virtual power plant resource matching method as described in claim 2, characterized in that: The introduction of virtual power plant output variables as power supply gap compensation variables includes: Under the constraint that conventional generator units reach their maximum output, the solution results of the virtual power plant output variable are used as the power supply gap variable within the target time period. The specific process is as follows: ； In the formula, Let $ be the overall power supply cost objective function corresponding to the optimal power flow model of the distribution network. For nodes exist The regular generating units at any given time have active power output. Its power generation cost coefficient is a constant. The output cost of the virtual power plant is a constant. For nodes exist The virtual power plant output variable at any given time is also the power supply gap variable, and B is the set of distribution network nodes; The upper limit constraint for the output variable of the virtual power plant is as follows: ,in, for The sum of the total load demand of all nodes in the distribution network at any given time. for The sum of the maximum available generating capacity of all conventional generator sets in the distribution network at any given time.

4. The virtual power plant resource matching method as described in claim 1, characterized in that: The assessment of the dynamic response limit of each virtual power plant resource includes: By acquiring real-time state of charge information of energy storage resources and combining it with preset safe charge thresholds and rated operating parameters, the dynamic response upper limit of energy storage resources can be obtained. By acquiring real-time environmental status information of temperature-controlled load resources and combining it with preset comfort boundary parameters, the dynamic response upper limit of temperature-controlled load resources can be obtained. By acquiring real-time charge status information of electric vehicle resources and combining it with preset target charge demand and preset dwell time information, the dynamic response limit of electric vehicle resources is obtained. By obtaining real-time operational load information of data center resources and combining it with preset core minimum operational load parameters, the dynamic response limit of data center resources can be obtained. By acquiring real-time operating status information of industrial interruptible load resources and combining it with preset interruption protocol constraint information, the dynamic response limit of industrial interruptible load resources can be obtained.

5. The virtual power plant resource matching method as described in claim 1, characterized in that: The process of determining the virtual power plant resource matching results and generating a scheduling control scheme includes: Based on preset weighting coefficients, the objective functions of resource matching cost, resource availability margin, and supply-demand matching deviation are weighted and integrated to construct a comprehensive objective function. Under the constraints of the upper limit of the dynamic response of each virtual power plant resource, the matching constraint of the power supply gap variable, and the constraint of the rate of change of resource output, the comprehensive objective function is solved by collaborative optimization to obtain the virtual power plant resource matching result.

6. A virtual power plant resource matching system, characterized in that, include: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a virtual power plant resource matching method as described in any one of claims 1 to 5.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the virtual power plant resource matching method as described in any one of claims 1 to 5.

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