Virtual power plant resource scheduling method and device, electronic equipment and program product

Abstract

The application is suitable for the technical field of virtual power plants, and provides a resource scheduling method and device for a virtual power plant, an electronic device and a program product. The method comprises the following steps: determining the respective supply end node weights corresponding to each supply end node and the respective demand end node weights corresponding to each demand end node through a smart contract, and determining a resource scheduling period according to the supply end node weights, the demand end node weights, supply end appeal information and demand end appeal information. In each resource scheduling period, the supply end nodes and the demand end nodes are paired for resource scheduling according to a pairing constraint condition, the supply end appeal information, the demand end appeal information, historical supply data, the supply end node weights, historical demand data and the demand end node weights. According to the appeal of each supply end node and each demand end node, the smart contract is used to schedule the resources of the virtual power plant, and the adaptability of the resource scheduling of the virtual power plant is improved.

Description

Technical Field

[0001] This application belongs to the field of virtual power plant technology, and particularly relates to a resource scheduling method, apparatus, electronic equipment and program product for a virtual power plant. Background Technology

[0002] With the large-scale integration of diverse distributed resources such as distributed photovoltaics, energy storage, controllable loads, and electric vehicles, virtual power plants serve as a core platform for achieving coordinated interaction between power generation, grid, load, and storage, ensuring a balance between power supply and demand, and participating in power market transactions.

[0003] Currently, resource scheduling methods in virtual power plants typically follow pre-set resource scheduling rules. However, this approach struggles to adapt to dynamic changes in supply and demand, often resulting in response delays under high-load conditions and resource idleness and insufficient average resource utilization under low-load conditions. It is evident that the resource scheduling capabilities of existing virtual power plant technologies are relatively low. Summary of the Invention

[0004] In view of this, embodiments of this application provide a resource scheduling method, apparatus, electronic device, and program product for virtual power plants to solve the technical problem of low adaptability of resource scheduling in existing virtual power plants.

[0005] In a first aspect, embodiments of this application provide a resource scheduling method for a virtual power plant, applied to a virtual power plant built on a consortium blockchain, wherein the virtual power plant includes a plurality of supply-side nodes and a plurality of demand-side nodes; the method includes: Each of the supply-side nodes submits its corresponding supply-side demand information to the smart contract, and each of the demand-side nodes submits its corresponding demand-side demand information to the smart contract. Through the smart contract, the weight of each supply-side node is determined based on the supply-side demand information and historical supply data of each supply-side node. Similarly, the weight of each demand-side node is determined based on the demand-side demand information and historical demand data of each demand-side node. The resource scheduling cycle is determined by the smart contract based on the weights of each supply-side node, the weights of each demand-side node, the demand information of each supply-side node, and the demand information of each demand-side node. In each resource scheduling cycle, the smart contract performs resource scheduling and pairing of each supply-side node and each demand-side node based on preset pairing constraints, each supply-side demand information, each demand-side demand information, each historical supply data, each supply-side node weight, each historical demand data, and each demand-side node weight.

[0006] Optionally, the supply-side demand information includes supply-side periodic demand values, and the demand-side demand information includes demand-side periodic demand values; determining the resource scheduling cycle through the smart contract based on the weights of each supply-side node, the weights of each demand-side node, the supply-side demand information, and the demand-side demand information includes: Based on the preset periodic threshold, the periodic demand values ​​of each supply side and each periodic demand value of each demand side are filtered to obtain the filtered periodic demand values ​​of each supply side and each periodic demand value of each demand side. The first cycle demand value is determined based on the supply-side node weight corresponding to each of the selected supply-side cycle demand values. The second periodic demand value is determined based on the demand node weight corresponding to each of the selected demand periodic demand values. Based on the preset fitting coefficients, the first periodic appeal value and the second periodic appeal value are fitted to obtain the range of periodic appeal values. If the error of the periodic demand value range meets the preset convergence condition, then the median value of the periodic demand value range is determined as the resource scheduling period.

[0007] Optionally, the supply-side demand information includes maximum schedulable capacity and resource response time, and the demand-side demand information includes load demand capacity; the step of performing resource scheduling pairing for each supply-side node and each demand-side node based on preset pairing constraints, each of the supply-side demand information, each of the demand-side demand information, each of the historical supply data, each of the supply-side node weights, each of the historical demand data, and each of the demand-side node weights includes: The pairing order of each demand-side node is determined based on its weight. According to the pairing order, for each demand-side node, determine a number of candidate supply-side nodes that meet the pairing constraints. Then, based on the historical supply data, the maximum schedulable capacity, the resource response time, the historical demand data, and the load demand capacity of each candidate supply-side node, determine the target supply-side node from among the candidate supply-side nodes.

[0008] Optionally, the supply-side demand information may further include resource type, and the demand-side demand information may further include maximum allowable response time and load demand type. The pairing constraints include: the maximum schedulable capacity of the candidate supply-side node is greater than the load demand capacity corresponding to the demand-side node; the resource response time of the candidate supply-side node is not greater than the maximum allowable response time corresponding to the demand-side node; and the resource type of the candidate supply-side node matches the load demand type corresponding to the demand-side node.

[0009] Optional, also includes: If multiple demand-side nodes match the same target supply-side node, the pairing order of the multiple demand-side nodes matching the same target supply-side node is determined according to the weight of each demand-side node. Then, the schedulable capacity of the target supply-side node is sequentially allocated to the multiple demand-side nodes matching the same target supply-side node according to the pairing order.

[0010] Optional, also includes: If the ratio between the change in the total schedulable resource capacity of each supply-side node in the virtual power plant and the current total schedulable resource capacity of each supply-side node in the virtual power plant is greater than a first preset threshold, or the ratio between the real-time total load of each demand-side node in the virtual power plant and a preset baseline load is greater than a second preset threshold, or the number of supply-side nodes whose weight ranking is greater than a third preset threshold in the virtual power plant has changed is greater than a fourth preset threshold, or the number of demand-side nodes whose weight ranking is greater than a fifth preset threshold in the virtual power plant has changed is greater than a sixth preset threshold, or the supply-demand matching success rate corresponding to two consecutive resource scheduling cycles is less than a seventh preset threshold, then the supply-side node weights corresponding to each supply-side node and the demand-side node weights corresponding to each demand-side node are re-determined.

[0011] Optional, also includes: Determine the threshold update frequency based on the resource scheduling frequency of the virtual power plant in the current time period; According to the threshold update frequency, the first preset threshold, the second preset threshold, the third preset threshold, the fourth preset threshold, the fifth preset threshold, the sixth preset threshold, and the seventh preset threshold are updated based on the virtual power plant's pairing success rate, load fluctuation curve, node fulfillment rate, and system response delay.

[0012] Secondly, embodiments of this application provide a resource scheduling device for a virtual power plant, applied to a virtual power plant built on a consortium blockchain, wherein the virtual power plant includes a plurality of supply-side nodes and a plurality of demand-side nodes; the device includes: The request submission unit is used to submit the supply-side request information corresponding to each supply-side node to the smart contract through each of the supply-side nodes, and to submit the demand-side request information corresponding to each demand-side node to the smart contract through each of the demand-side nodes. The node weight determination unit is used to determine the weight of each supply-side node based on the supply-side demand information and historical supply data of each supply-side node through the smart contract, and to determine the weight of each demand-side node based on the demand-side demand information and historical demand data of each demand-side node. The cycle determination unit is used to determine the resource scheduling cycle through the smart contract based on the weights of each supply-side node, the weights of each demand-side node, the demand information of each supply-side node, and the demand information of each demand-side node. The resource scheduling and pairing unit is used to perform resource scheduling and pairing on each supply-side node and each demand-side node in each resource scheduling cycle through the smart contract, based on preset pairing constraints, each supply-side demand information, each demand-side demand information, each historical supply data, each supply-side node weight, each historical demand data, and each demand-side node weight.

[0013] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the resource scheduling method for a virtual power plant as described in any of the first aspects above.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the resource scheduling method for a virtual power plant as described in any of the first aspects above.

[0015] Fifthly, embodiments of this application provide a computer program product that, when run on a display device, causes the display device to perform the steps of the resource scheduling method for a virtual power plant as described in any of the first aspects above.

[0016] The beneficial effects of the resource scheduling method for virtual power plants provided in this application embodiment are as follows: The resource scheduling method for a virtual power plant provided in this application includes: submitting supply-side demand information corresponding to each supply-side node to a smart contract through each supply-side node, and submitting demand-side demand information corresponding to each demand-side node to a smart contract through each demand-side node; determining the weight of each supply-side node based on its corresponding supply-side demand information and historical supply data through the smart contract, and determining the weight of each demand-side node based on its corresponding demand-side demand information and historical demand data; determining a resource scheduling cycle based on the weights of each supply-side node, the weights of each demand-side node, the supply-side demand information, and the demand-side demand information through the smart contract; and performing resource scheduling pairing between each supply-side node and each demand-side node through the smart contract based on preset pairing constraints, the supply-side demand information, the demand-side demand information, historical supply data, the weights of each supply-side node, historical demand data, and the weights of each demand-side node.

[0017] The resource scheduling method for virtual power plants provided in this application, through a virtual power plant architecture based on a consortium blockchain, enables the submission of demand information from supply and demand nodes and the weighted consensus calculation of smart contracts, dynamically determining the resource scheduling scheme, thereby improving the adaptability of resource scheduling in virtual power plants. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating the implementation of a resource scheduling method for a virtual power plant, as provided in an embodiment of this application. Figure 2 A schematic diagram of the structure of a resource scheduling device for a virtual power plant provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0020] It should be noted that the terminology used in the embodiments of this application is only for explaining specific embodiments of this application and is not intended to limit this application. In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more, "at least one" or "one or more" means one, two or more. The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0021] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0022] In traditional virtual power plant dispatching technologies, the dispatching strategy is unilaterally determined by the platform, lacking a two-way consensus mechanism between supply and demand. This prevents the effective integration of demands from supply-side and demand-side nodes, resulting in a low dispatching command execution rate. Furthermore, the fixed or dynamically adjusted dispatching frequency logic lacks adaptability, failing to adapt to dynamic changes in supply and demand. This leads to response delays under high load conditions and resource idleness under low load conditions, resulting in insufficient resource utilization. All these issues contribute to the low adaptability of resource dispatching in existing virtual power plant technologies.

[0023] If the above problems are not resolved, virtual power plants will be unable to guarantee the balance of power supply and demand when participating in electricity market transactions due to low dispatch command execution rates, resulting in decreased system operational stability; insufficient resource utilization will continue to worsen the supply and demand imbalance, affecting the coordinated interaction of power generation, grid, load, and storage; high trust costs and low participation enthusiasm may lead to node loss, weakening the economies of scale of virtual power plants; in cross-regional collaboration and mobile resource dispatch scenarios, insufficient adaptability will hinder virtual power plants from effectively participating in grid peak shaving, frequency regulation, and other ancillary services, thus limiting their application value.

[0024] In response, this application proposes a resource allocation method for a virtual power plant, which is applied to a virtual power plant built on a consortium blockchain. The virtual power plant includes several supply-side nodes and several demand-side nodes.

[0025] For ease of understanding, the following explains some key terms in this embodiment: Virtual power plant: A virtual power plant is a system that aggregates various distributed energy resources, such as distributed power sources, energy storage systems, and controllable loads, through advanced information and communication technologies and software systems. It coordinates and optimizes these resources for control, allowing the virtual power plant to participate as a special controllable unit in the electricity market and grid operation management. This virtual power plant enables the collaborative operation of distributed resources, improving the flexibility and reliability of the power grid.

[0026] Consortium blockchain: This is a blockchain technology that falls between public and private blockchains. Its key feature is that the nodes participating in the consortium blockchain are pre-selected and are typically maintained jointly by multiple institutions or organizations. Consortium blockchains offer high transaction speeds, controllable access permissions, and good privacy protection capabilities, making them suitable for scenarios requiring multi-party collaboration but also demanding a certain degree of centralized management, such as resource scheduling in virtual power plants.

[0027] Supply-side node: This refers to the entity that provides electrical energy or ancillary services in the virtual power plant, such as distributed photovoltaic systems, wind turbines, and energy storage devices. This supply-side node can submit information about the resources it can provide to the virtual power plant.

[0028] Demand-side node: This refers to the entity that consumes electricity or submits electricity demand in the virtual power plant, such as industrial load, commercial load, residential load, electric vehicle charging station, etc. This demand-side node can submit its electricity demand information to the virtual power plant.

[0029] Smart Contracts: A smart contract is a computer program stored on the blockchain that can be executed automatically when preset conditions are met. In the resource scheduling of a virtual power plant, this smart contract is used to receive node information, calculate node weights, determine scheduling cycles, and perform resource pairing operations, ensuring the automation, transparency, and immutability of the scheduling process.

[0030] Supply-side demand information: This refers to the detailed data on the resource supply capacity submitted by the supply-side nodes to the virtual power plant. This information is an important basis for smart contracts to schedule resources.

[0031] Demand-side request information: This refers to the detailed data on the electricity demand submitted by the demand-side nodes to the virtual power plant. This information is an important basis for the smart contract to schedule resources.

[0032] Historical supply data: This refers to the resource supply records of supply-side nodes over a past period, including actual supply volume, supply stability, and fulfillment status. This data is used to assess the reliability and performance of supply-side nodes, thereby affecting their weight calculation.

[0033] Historical demand data: This refers to the electricity demand records of demand-side nodes over a past period, including actual demand, demand volatility, and response status. This data is used to assess the stability of demand-side nodes, thus affecting their weight calculation.

[0034] Supply-side node weight: This weight is an indicator that measures the importance or reliability of a supply-side node in a virtual power plant. This weight can be dynamically calculated based on factors such as the node's historical performance and current demand information, influencing its priority in resource scheduling.

[0035] Demand-side node weight: This weight measures the importance or priority of a demand-side node in the virtual power plant. The weight is dynamically calculated based on factors such as the node's historical performance and current demand information, influencing its priority in resource scheduling.

[0036] Resource scheduling cycle: This refers to the time interval between a virtual power plant's resource scheduling pairing. This cycle can be fixed or dynamically adjusted according to supply and demand to adapt to real-time changes in the power system.

[0037] Pairing constraints: These constraints refer to a series of rules and restrictions that must be met during resource scheduling and pairing, such as supply capacity must be greater than demand capacity, response time must meet requirements, and resource types must match. These conditions ensure the effectiveness and rationality of the pairing.

[0038] Resource scheduling and matching: This refers to the smart contract matching the supply-side nodes and demand-side nodes in the virtual power plant according to preset rules and conditions, so as to achieve effective allocation and utilization of resources.

[0039] Please see Figure 1 , Figure 1 The flowchart illustrates the implementation of a resource scheduling method for a virtual power plant according to an embodiment of this application. This resource scheduling method for the virtual power plant may include steps S101 to S104, as detailed below: In S101, each supply-side node submits its corresponding supply-side demand information to the smart contract, and each demand-side node submits its corresponding demand-side demand information to the smart contract.

[0040] In this embodiment of the application, the supply-side demand information may include the maximum schedulable capacity, the supply-side periodic demand value, and the resource response time; The maximum dispatchable capacity refers to the maximum amount of electricity or energy that a supply-side node can provide to a virtual power plant within a specific time period. This is typically determined based on the node's installed capacity, available fuel, energy storage status, maintenance schedule, and other operational constraints.

[0041] Among them, the supply-side cycle demand value is the expected value of the supply-side nodes for the resource scheduling cycle.

[0042] Resource response time refers to the time required for a supply-side node to actually provide resources from receiving a scheduling instruction.

[0043] Demand-side demand information may include rated electricity consumption, demand-side periodic demand value, and load demand capacity.

[0044] Rated power consumption refers to the standard or maximum power consumption required by the equipment or system of a demand-side node under normal operating conditions, reflecting the basic power demand level of that demand-side node.

[0045] Among them, the demand-side cycle demand value is the expected value of the resource scheduling cycle by the demand-side nodes.

[0046] Among them, load demand capacity refers to the amount of resources required by demand-side nodes within a specific time period.

[0047] In practical applications, both supply-side nodes and demand-side nodes can use blockchain clients to send the supply-side demand information corresponding to the supply-side node and the demand-side demand information corresponding to the demand-side node to the smart contract.

[0048] In S102, through smart contracts, the weight of each supply-side node is determined based on the supply-side demand information and historical supply data of each supply-side node. Similarly, the weight of each demand-side node is determined based on the demand-side demand information and historical demand data of each demand-side node.

[0049] In this embodiment of the application, historical supply data may include execution rate, response speed, and adjustment accuracy.

[0050] In one possible implementation, the smart contract can determine the weight of each supply-side node in the following way: For each supply-side node, the adjustment capability score of the supply-side node is determined based on its execution rate, response speed, and adjustment accuracy. The first reputation coefficient of the supply-side node is determined based on its execution rate. Finally, the weight of each supply-side node is determined based on its maximum schedulable capacity, adjustment capability score, and first reputation coefficient.

[0051] In this implementation, execution rate is a metric measuring the ability of a supply-side node to fulfill its dispatch instructions or commitments, typically expressed as the ratio between actual execution and planned execution. For example, if a supply-side node is dispatched to provide 100 MWh of electricity but only provides 95 MWh, its execution rate is 95%. Response speed refers to the time required for a supply-side node to reach its target output level from receiving the dispatch instruction. For example, a fast-response energy storage system may reach its target power within seconds, while a traditional generator may take several minutes. Regulation accuracy refers to the degree of deviation between the actual output and the target output of a supply-side node when executing dispatch instructions. For example, if a supply-side node is required to output 50 MW but its output fluctuates between 49 MW and 51 MW, its regulation accuracy can be quantified using statistical methods (such as standard deviation).

[0052] The regulation capability score is a comprehensive performance evaluation of the supply-side nodes in power regulation, combining multiple dimensions such as execution rate, response speed, and regulation accuracy. This score can be calculated using various methods, including weighted average, fuzzy comprehensive evaluation, or analytic hierarchy process (AHP), to reflect the node's technical capabilities under different dispatching scenarios.

[0053] The first reputation score is a metric that measures the reliability and credibility of supply-side nodes in fulfilling contracts and scheduling instructions over a long period of time, and is usually directly related to the execution rate. For example, nodes with consistently high execution rates will have higher reputation scores, while nodes with frequent defaults will have lower reputation scores. This can be determined by the average historical execution rate, volatility, or penalty mechanisms.

[0054] The supply-side node weight is a numerical value that comprehensively reflects the importance and contribution of the supply-side node in the virtual power plant. It integrates the maximum dispatchable capacity (representing potential contribution), the regulation capability score (representing technical strength), and the first reputation coefficient (representing reliability) to form a comprehensive evaluation index.

[0055] More specifically, smart contracts can determine the weight of each supply-side node using the following formula:

[0056] in, Let be the weight of the i-th supply-side node. This represents the maximum schedulable capacity of the supply-side node. The adjustment capability of this supply-side node is scored. Let n be the first reputation coefficient of the supply-side node, and n be the number of supply-side nodes.

[0057] More specifically, smart contracts can determine the regulation capability score corresponding to a supply-side node using the following formula:

[0058] in, The adjustment capability of this supply-side node is scored. The response speed is scored. To adjust the accuracy score, Score the historical execution rate.

[0059] It is understandable that the response speed score can be determined based on the response speed of the supply-side node, the adjustment accuracy score can be determined based on the adjustment accuracy of the supply-side node, and the historical execution rate score can be determined based on the execution rate of the supply-side node.

[0060] Through the aforementioned technical solutions, virtual power plants can more accurately and comprehensively assess the value and capabilities of each supply-side node. By introducing maximum dispatchable capacity, regulation capacity scoring, and a first reputation coefficient, this method not only considers the potential contribution of supply-side nodes but also fully evaluates their technical performance and contract fulfillment reliability. This enables smart contracts to distinguish high-quality resources that not only have large capacity but also fast response, accurate regulation, and high reputation when determining the weight of supply-side nodes, thereby giving them higher priority or more reasonable incentives during resource scheduling and allocation. This helps optimize the overall operating efficiency of virtual power plants, improve the stability and reliability of the power system, and promote the fair and effective use of resources.

[0061] In this embodiment of the application, historical demand data includes power supply reliability requirements, load interruptibility duration, and fulfillment rate.

[0062] In one possible implementation, the smart contract can determine the weight of each demand-side node in the following way: For each demand-side node, the load importance level score corresponding to the demand-side node is determined based on the power supply reliability requirements and load interruptibility duration corresponding to the demand-side node, and the second credit coefficient corresponding to the demand-side node is determined based on the fulfillment rate corresponding to the demand-side node. The weight of each demand-side node is determined based on its rated electricity consumption, load importance level score, and second credit coefficient.

[0063] In this implementation, power supply reliability requirements refer to the degree of continuity and stability of power supply required by demand-side nodes. For example, some critical loads have extremely low tolerance for power outages, while others can accept short-term interruptions. Power supply reliability requirements can be declared by demand-side nodes when registering or submitting requests, for example, divided into high, medium, and low levels; or automatically assessed by analyzing the types of equipment they are connected to. Load interruptibility duration refers to the maximum time a demand-side node can withstand when accepting load reduction or interruption, reflecting the flexibility and dispatchability of the load. Load interruptibility duration can be declared by demand-side nodes when submitting requests, for example, declaring that their load can be interrupted for 1 hour, 2 hours, etc.; or inferred by smart contracts based on their historical load response data, analyzing the actual interruption duration when responding to dispatch instructions. Fulfillment rate refers to the degree to which the actual execution of virtual power plant dispatch instructions by demand-side nodes in the past conforms to their commitments, reflecting their reliability and integrity in participating in the operation of the virtual power plant. The fulfillment rate can be calculated by the smart contract by comparing the historical scheduling instruction issuance records with the actual response data of the demand-side nodes. For example, it can be the ratio of the actual number of responses to the total number of scheduling operations.

[0064] For each demand-side node, a load importance level score is determined based on its corresponding power supply reliability requirement and load interruptibility duration. This aims to quantify the importance and flexibility of demand-side loads within the virtual power plant. This score can be achieved using a scoring model. For example, power supply reliability requirements can be divided into different levels, and load interruptibility durations can be divided into different intervals. Different weights or scores can then be assigned to these levels and intervals, and the load importance level score is obtained through weighted summation or table lookup. For instance, loads with higher power supply reliability requirements and shorter interruptibility durations receive higher importance level scores.

[0065] Based on the fulfillment rate of the demand-side node, a second reputation coefficient is determined to assess the node's integrity and reliability in the virtual power plant operation. This reputation coefficient can be directly derived from the fulfillment rate, or it can be mapped to a reputation coefficient between 0 and 1 using a linear function. For example, a higher fulfillment rate results in a larger second reputation coefficient.

[0066] More specifically, smart contracts can determine the weight of each demand-side node using the following formula:

[0067] in, Let J be the demand-side node weight of the j-th demand-side node. This represents the rated power consumption of this demand-side node. Assess the load importance level of this demand-side node. is the second reputation coefficient of the demand-side node, and m is the number of nodes of the demand-side node.

[0068] More specifically, the smart contract can determine the load importance level score of the demand-side node using the following formula:

[0069] in, Assess the load importance level of this demand-side node. Score the power supply reliability requirements of this demand-side node. Score the load interruptibility duration for this demand-side node.

[0070] Understandably, the power supply reliability requirement score for this demand-side node can be determined based on its power supply reliability requirements. For example, a Level 1 load (uninterruptible) receives 1 point, a Level 2 load (short-term interruptible) receives 0.6 points, and a Level 3 load (interruptible) receives 0.3 points. The load interruptibility duration score for this demand-side node can be determined based on its interruptibility duration. For example, an interruptibility duration ≤ 15 minutes receives 1 point, 15-60 minutes receives 0.7 points, and > 60 minutes receives 0.4 points.

[0071] Through the aforementioned technical solution, when determining the weights of demand-side nodes, the virtual power plant no longer relies solely on rough demand information and historical data. Instead, it conducts in-depth analysis of the rated power consumption of each demand-side node, its specific requirements for power supply reliability, load flexibility (reflected by load interruptibility duration), and its historical performance. This multi-dimensional and refined evaluation mechanism allows for a more comprehensive and accurate quantification of the characteristics of each demand-side node. Consequently, the calculated weights of these nodes more accurately reflect their actual value and contribution within the virtual power plant. This not only improves the fairness and efficiency of resource allocation and avoids resource misallocation due to inaccurate weighting, but also helps incentivize demand-side nodes to actively participate in the virtual power plant's dispatch response, thereby enhancing the overall operational stability and economic benefits of the virtual power plant.

[0072] In S103, resource scheduling cycles are determined through smart contracts based on the weights of each supply-side node, the weights of each demand-side node, the demand information of each supply-side node, and the demand information of each demand-side node.

[0073] In this embodiment of the application, this step aims to dynamically adjust the scheduling frequency to adapt to changes in supply and demand. For example, if the determined resource scheduling period is 15 minutes, then the virtual power plant's resource scheduling is performed every 15 minutes.

[0074] One possible implementation is to determine the resource scheduling cycle based on the weights of each supply-side node, the weights of each demand-side node, the demand information of each supply-side node, and the demand information of each demand-side node, as detailed below: Based on preset periodic thresholds, the periodic demand values ​​of each supply side and each demand side are filtered to obtain the filtered periodic demand values ​​of each supply side and each demand side. The first periodic demand value is determined based on the supply-side node weights corresponding to each filtered supply-side periodic demand value. The second periodic demand value is determined based on the demand-side node weights corresponding to each filtered demand-side periodic demand value. The first and second periodic demand values ​​are fitted using preset fitting coefficients to obtain a range of periodic demand values. If the error within the range of periodic demand values ​​meets preset convergence conditions, the median value of the range is determined as the resource scheduling period.

[0075] A preset cycle threshold is a pre-defined numerical range or condition used to filter out abnormal, unreasonable, or non-compliant supply-side and demand-side cycle request values. For example, a minimum or maximum capacity threshold can be set; requests below or above this threshold will be ignored or adjusted. Alternatively, it can be a reasonable fluctuation range derived from historical data statistics. The filtering process involves cleaning and optimizing the original supply-side and demand-side cycle request values ​​by applying the preset cycle threshold. For example, extreme values ​​exceeding a reasonable range can be removed, or requests below a certain minimum value can be merged or discarded to reduce noise and improve data quality. Statistical methods can also be used to identify and remove outliers. The filtered supply-side and demand-side cycle request values, after cycle threshold processing, represent more representative and reliable supply-side and demand-side cycle request data, which are then used for subsequent cycle determination calculations.

[0076] The first-cycle demand value is a representative value obtained by comprehensively considering the filtered supply-side cycle demand values ​​and their corresponding supply-side node weights. For example, it can be the result of a weighted average or weighted summation of the filtered supply-side cycle demand values, where the weights reflect the reliability or importance of the supply-side nodes. Alternatively, it can be the optimal supply value calculated based on a certain optimization objective function.

[0077] The second-cycle demand value is a representative value obtained by comprehensively considering the filtered demand-side cycle demand values ​​and their corresponding demand-side node weights. For example, it can be the result of a weighted average or weighted summation of the filtered demand-side cycle demand values, where the weights reflect the priority or stability of the demand-side nodes, or it can be the optimal demand value calculated based on a certain priority strategy.

[0078] The preset fitting coefficients are parameters used when fitting the demand values ​​of the first and second cycles. They are used to adjust the degree of matching between the demands of supply and demand, and can also be parameters trained based on historical supply and demand relationship models.

[0079] Fitting processing refers to the mathematical modeling and analysis of the first-cycle and second-cycle demand values ​​by applying preset fitting coefficients in order to find the best matching relationship or trend between the two.

[0080] The cyclical demand value range is an interval obtained after fitting, which reflects the balance point of supply and demand. For example, it can be a time period, indicating that the demands of both supply and demand can be well met within that period, or it can be a capacity range.

[0081] The convergence condition is a preset criterion used to evaluate the stability and accuracy of the periodic target value range. For example, it can be set as the width of the periodic target value range being less than a certain threshold, or the fluctuation of several consecutive fitting results being less than a certain percentage, or the sum of squared residuals of the fitted model being less than a certain preset value.

[0082] The median value of the periodic demand range refers to the center point of the range when the error of the periodic demand range meets the convergence condition, which is taken as the final resource scheduling cycle. For example, if the range is [T1, T2], the median value can be (T1+T2) / 2, or the mode or median of the range.

[0083] By employing the aforementioned technical solutions to filter and process the cyclical demand values ​​from both the supply and demand sides, abnormal or unreasonable demand data can be effectively removed, improving data quality. Combining the weights of supply-side and demand-side nodes allows for a more accurate reflection of the actual capabilities and importance of each node, making the demand values ​​from the first and second cycles more representative. Through fitting processing and convergence condition judgment, a stable and reasonable resource scheduling cycle can be dynamically and intelligently determined, avoiding the inflexibility and inaccuracy of fixed cycles. This enables the virtual power plant's resource scheduling to better adapt to actual supply and demand changes, improving resource utilization efficiency and system operational stability.

[0084] In S104, during each resource scheduling cycle, the smart contract performs resource scheduling and pairing for each supply-side node and each demand-side node based on preset pairing constraints, demand information from each supply-side node, demand information from each demand-side node, historical supply data, weight of each supply-side node, historical demand data, and weight of each demand-side node.

[0085] In this embodiment of the application, the smart contract can achieve "resource scheduling and pairing of each supply-side node and each demand-side node based on preset pairing constraints, demand information of each supply-side node, demand information of each demand-side node, historical supply data, weight of each supply-side node, historical demand data, and weight of each demand-side node", as detailed below: The pairing order of each demand-side node is determined based on its weight. For each demand-side node in the pairing order, identify several candidate supply-side nodes that meet the pairing constraints. Then, based on the historical supply data, the maximum schedulable capacity, the resource response time, the historical demand data, and the load demand capacity of each candidate supply-side node, determine the target supply-side node from among the candidate supply-side nodes.

[0086] The section on "determining the pairing order of each demand-side node based on its weight" aims to provide a priority basis for subsequent resource pairing. For example, demand-side nodes can be sorted in descending order based on their weight values, with higher-weight nodes being paired first; alternatively, the pairing order can be determined by comprehensively evaluating factors such as the urgency of the demand-side nodes and their historical performance.

[0087] The step of "identifying several candidate supply nodes that meet the pairing constraints for each demand-side node in the order of pairing" aims to perform preliminary screening of potential supply-side nodes to narrow down the scope of subsequent refined matching. For example, a smart contract can iterate through all available supply-side nodes and judge them according to preset pairing constraints, identifying all supply-side nodes that meet the constraints as candidate supply-side nodes.

[0088] More specifically, the supply-side demand information also includes resource type, and the demand-side demand information includes maximum allowable response time and load demand type. Based on this, the pairing constraints include: The maximum schedulable capacity of the candidate supply node is greater than the load demand capacity corresponding to the demand node, the resource response time of the candidate supply node is not greater than the maximum allowable response time corresponding to the demand node, and the resource type of the candidate supply node matches the load demand type corresponding to the demand node.

[0089] These constraints not only require that the available capacity of the candidate supply-side nodes must exceed the load demand capacity of the demand-side nodes to ensure basic supply-demand balance, but also require that the resource response time of the candidate supply-side nodes must not exceed the maximum acceptable response time of the demand-side nodes, thus guaranteeing the timeliness of the matching. Furthermore, the resource type of the candidate supply-side nodes must match the load demand type required by the demand-side nodes, ensuring a high degree of compatibility between the nature of the supplied resources and the functional requirements of the demand load. Through these multi-dimensional and refined matching constraints, smart contracts can comprehensively consider key factors such as capacity, timeliness, and type matching when determining target supply-side nodes from the candidate supply-side nodes, thereby significantly improving the accuracy and effectiveness of resource scheduling.

[0090] The step of "determining the target supply node corresponding to the demand node from among the candidate supply nodes based on the historical supply data, the maximum schedulable capacity, the resource response time, the historical demand data, and the load demand capacity of each candidate supply node" aims to select the most suitable target supply node from the initially selected candidate supply nodes through more comprehensive data analysis and evaluation.

[0091] Specifically, after identifying potential supply-side nodes, the smart contract further utilizes more detailed data, including historical supply data, maximum schedulable capacity, and resource response time of the potential supply-side nodes, as well as historical demand data and load capacity of the current demand-side nodes, to conduct an in-depth evaluation of these potential supply-side nodes. By comprehensively analyzing this multi-dimensional data, the smart contract can accurately measure the degree of matching between each potential supply-side node and the current demand-side node. For example, it can assess whether the supply side can fully cover the demand in terms of capacity, whether the response time meets the timeliness requirements of the demand side, and whether the historical performance is reliable. Ultimately, the smart contract will select the node that best meets the requirements of the demand-side node and has the highest matching degree from these potential supply-side nodes as the target supply-side node.

[0092] In some of the embodiments described above in this application, a technical solution is proposed to determine the pairing order based on the weight of the demand-side nodes and to determine the target supply-side node for each demand-side node. However, during its implementation, multiple demand-side nodes may be matched with the same target supply-side node simultaneously. In this case, how to fairly and effectively allocate the limited schedulable capacity of the target supply-side node to avoid resource allocation conflicts or uneven distribution is a problem that needs to be further addressed.

[0093] In response, this application further proposes that if multiple demand-side nodes match the same target supply-side node, the pairing order of the multiple demand-side nodes matched with the same target supply-side node is determined according to the respective weights of the multiple demand-side nodes matched with the same target supply-side node, and the schedulable capacity of the target supply-side node is sequentially allocated to the multiple demand-side nodes matched with the same target supply-side node according to the pairing order of the multiple demand-side nodes matched with the same target supply-side node.

[0094] To address this issue, this application determines the pairing order of multiple demand-side nodes that are matched with the same target supply-side node based on their respective demand-side node weights.

[0095] This feature aims to establish a prioritization mechanism for multiple demand-side nodes competing for the same supply-side resources. Demand-side node weights are indicators that measure the importance, reliability, or priority of a demand-side node, and can be calculated based on a combination of factors such as its historical performance, reputation score, load stability, and contribution to the virtual power plant. By comparing these weights, these demand-side nodes can be ranked, thus determining their order of resource acquisition. For example, demand-side nodes with higher weight values ​​are paired earlier; alternatively, demand-side nodes can be divided into different priority levels based on their weight values, and nodes within the same level can be further ranked according to other rules (such as the order in which requests were submitted).

[0096] After determining the pairing order of the demand-side nodes, this application allocates the schedulable capacity of the target supply-side node to the multiple demand-side nodes that are matched with the same target supply-side node in sequence according to their respective pairing order.

[0097] Following a pre-determined pairing order, the smart contract processes resource requests from demand-side nodes one by one. For demand-side nodes at the front of the queue, their required capacity is deducted from the total schedulable capacity of the target supply-side nodes. If the remaining capacity of the target supply-side nodes is insufficient to meet all the demands of the current demand-side nodes, the remaining capacity will be allocated as much as possible. This sequential allocation method ensures that high-priority demand-side nodes receive the necessary resources.

[0098] This application's solution improves the resource scheduling process of a virtual power plant by introducing a mechanism for allocating resources from multiple demand-side nodes competing for the same supply-side node. When the smart contract is performing resource scheduling and matching, if it finds that a target supply-side node is matched by multiple demand-side nodes simultaneously, indicating resource competition, a refined allocation strategy will be initiated. First, the smart contract uses the pre-determined weights of each demand-side node to prioritize these competing nodes, thus establishing a clear matching order. This matching order reflects the priority level of each demand-side node in resource allocation. Subsequently, the smart contract strictly follows this matching order, starting with the highest-priority demand-side node, and allocates the schedulable capacity of the target supply-side nodes sequentially. Each demand-side node will obtain resources from the remaining capacity of the target supply-side node according to its load demand capacity, until the demand of that demand-side node is met or the capacity of the target supply-side node is exhausted. This mechanism ensures fair and efficient resource allocation based on preset priority rules when resources are limited, avoiding resource conflicts and uneven allocation, thereby improving the overall resource utilization and scheduling efficiency of the virtual power plant.

[0099] In some of the embodiments described above in this application, the virtual power plant determines node weights based on supply-side and demand-side demand information and historical data through smart contracts, and then performs resource scheduling and pairing. However, in actual operation, the operating environment of the virtual power plant may change dynamically, such as fluctuations in supply-side resource capacity, drastic changes in demand-side load, or significant changes in the behavior patterns of key participating nodes, or even poor continuous resource scheduling and pairing results. In these cases, if the old node weights are still used for scheduling, it may lead to low resource allocation efficiency or even scheduling failure, affecting the overall operational stability and economic benefits of the virtual power plant.

[0100] In response, this application further proposes that if the ratio between the change in the total schedulable resource capacity of each supply-side node in the virtual power plant and the current total schedulable resource capacity of each supply-side node in the virtual power plant is greater than a first preset threshold, or the ratio between the real-time total load of each demand-side node in the virtual power plant and the preset baseline load is greater than a second preset threshold, or the number of changed supply-side nodes among the supply-side nodes whose weight ranking is greater than a third preset threshold in the virtual power plant is greater than a fourth preset threshold, or the number of changed demand-side nodes among the demand-side nodes whose weight ranking is greater than a fifth preset threshold in the virtual power plant is greater than a sixth preset threshold, or the supply-demand matching success rate corresponding to two consecutive resource scheduling cycles is less than a seventh preset threshold, then the supply-side node weights corresponding to each supply-side node and the demand-side node weights corresponding to each demand-side node shall be re-determined.

[0101] Specifically, the ratio between the change in the total schedulable resource capacity of each supply-side node in the virtual power plant and the current total schedulable resource capacity of each supply-side node in the virtual power plant is greater than a first preset threshold. This is intended to monitor whether the overall supply capacity of the virtual power plant has changed significantly. When the total schedulable resource capacity provided by all supply-side nodes in the virtual power plant fluctuates significantly, it indicates that the overall supply-side situation is no longer stable. The smart contract can periodically summarize the latest schedulable capacity information submitted by all supply-side nodes, calculate its sum, and compare it with the total capacity of the previous period or the baseline period to obtain the change value and ratio. Alternatively, when the schedulable capacity of any supply-side node changes significantly, the smart contract can be proactively triggered to calculate the total capacity ratio. The first preset threshold is a pre-set value used to define the significance of changes in the total supply-side capacity. It can be empirically set based on the scale of the virtual power plant, stability requirements, and historical operating data, such as 5% or 10%, or dynamically adjusted based on historical data through a machine learning model.

[0102] The ratio of the real-time total load of each demand-side node in the virtual power plant to a preset baseline load is greater than a second preset threshold. This is intended to monitor whether there have been significant changes in the overall demand load of the virtual power plant. When the real-time total load of all demand-side nodes in the virtual power plant deviates significantly from the preset baseline load, it indicates that the overall demand-side situation is no longer stable. Smart contracts can periodically obtain real-time load data from each demand-side node, calculate the total load, and compare it with the preset baseline load to obtain the ratio. The baseline load can be the average load obtained through historical data statistics or the expected load obtained based on a prediction model. The second preset threshold is a pre-set value used to define the significance of changes in the total demand-side load. It can be empirically set based on the load characteristics of the virtual power plant, dispatch sensitivity requirements, and historical operating data, for example, set to 5%, 10%, etc., or adjusted according to the system operating status through an adaptive algorithm.

[0103] In a virtual power plant, the number of supply-side nodes whose weight ranking is greater than a third preset threshold and whose changes exceed a fourth preset threshold is used to monitor whether the behavior or status of key supply-side nodes in the virtual power plant has changed significantly. When a large number of top-ranked (i.e., high-weight) supply-side nodes experience changes in their status or behavior, it indicates that the stability of the core supply side has been affected. Smart contracts can periodically monitor the behavioral data (such as fulfillment rate, response time, etc.) of supply-side nodes or their submitted requests and compare them with historical data to identify nodes that have changed. Simultaneously, based on the current weight ranking, nodes with weight rankings higher than the third preset threshold are selected, and the number of nodes that have changed among them is counted. The third preset threshold is used to define the weight ranking boundary of key supply-side nodes, for example, the top 20% of nodes. The fourth preset threshold is used to define the significance of the number of changes in key supply-side nodes, for example, when more than 3 or 5 key supply-side nodes have changed.

[0104] In a virtual power plant, if the number of demand-side nodes whose weight ranking is greater than the fifth preset threshold and the number of changed demand-side nodes exceeds the sixth preset threshold, the system aims to monitor whether the behavior or status of key demand-side nodes in the virtual power plant has undergone significant changes. When a large number of nodes among the top-ranked (i.e., higher-weight) demand-side nodes experience changes in status or behavior, it indicates that the stability of the core demand side has been affected. The smart contract can periodically monitor the behavioral data (such as load fluctuations, response status, etc.) of demand-side nodes or their submitted requests, and compare this data with historical data to identify nodes that have changed. Simultaneously, based on the current weight ranking, nodes with weight rankings higher than the fifth preset threshold are selected, and the number of nodes among them that have changed is counted. The fifth preset threshold is used to define the weight ranking boundary of key demand-side nodes, for example, the top 20% of nodes. The sixth preset threshold is used to define the significance of the number of changes in key demand-side nodes, for example, when more than 3 or 5 key demand-side nodes have changed.

[0105] The goal of monitoring whether the supply-demand matching success rate is consistently below a seventh preset threshold for two consecutive resource scheduling cycles is to determine if the resource scheduling matching effect of the virtual power plant remains poor. When the supply-demand matching success rate is below a preset level for consecutive scheduling cycles, it indicates that the current scheduling strategy or node weights may no longer be applicable. The smart contract can calculate the matching success rate by calculating the ratio of successfully matched resources to total demand or total supply at the end of each resource scheduling cycle. Then, the matching success rate for two consecutive cycles is compared with the seventh preset threshold. The seventh preset threshold is a preset minimum matching success rate, such as 70% or 80%; a value below this is considered an unsatisfactory matching effect.

[0106] When any of the above conditions is triggered, it indicates that the current node weights may no longer be accurate or applicable. Continuing to use these weights for resource scheduling may lead to inefficiency or scheduling failure. Therefore, the smart contract will immediately initiate a weight re-determination process. Based on the latest supply-side demand information, historical supply data, demand-side demand information, and historical demand data, the weights of all supply-side and demand-side nodes will be recalculated and updated. In other words, steps S101 to S102 will be re-executed.

[0107] The proposed solution enables the virtual power plant's resource scheduling system to have adaptive capabilities. When faced with a dynamically changing operating environment, it can adjust its evaluation of each participating node in a timely manner, thereby ensuring the accuracy and effectiveness of resource scheduling pairing and avoiding performance degradation due to outdated weights.

[0108] This application proposes a resource scheduling method for a virtual power plant. This method uses multiple preset thresholds to determine whether it is necessary to re-determine the weights of supply-side and demand-side nodes. However, in actual operation, factors such as the operating environment, market demand, and node behavior of the virtual power plant are dynamically changing. If these thresholds remain fixed, they may not accurately reflect the actual operating status of the virtual power plant, resulting in insufficient sensitivity or accuracy of the weight re-determination mechanism, thereby affecting the efficiency of resource scheduling and the success rate of matching.

[0109] In response, this application further proposes to determine the threshold update frequency based on the resource scheduling frequency of the virtual power plant in the current time period; and to update the first preset threshold, the second preset threshold, the third preset threshold, the fourth preset threshold, the fifth preset threshold, the sixth preset threshold, and the seventh preset threshold according to the threshold update frequency, based on the pairing success rate of the virtual power plant, the load fluctuation curve, the node fulfillment rate, and the system response delay.

[0110] The resource scheduling frequency of the virtual power plant in the current time period refers to the number or rate at which the virtual power plant performs resource scheduling pairing operations within a specific time window, reflecting the intensity of the virtual power plant's scheduling activities. This frequency can be obtained by statistically analyzing the number of resource scheduling cycles completed per unit time, or it can be preset by the virtual power plant's operation strategy. Determining the threshold update frequency refers to setting a cycle for checking and adjusting the aforementioned preset thresholds based on the virtual power plant's resource scheduling frequency. Its purpose is to ensure the timeliness and rationality of threshold updates.

[0111] The pairing success rate of a virtual power plant (VFP) is a key indicator for measuring the effectiveness of VFP resource scheduling. It represents the proportion of supply-side and demand-side node pairs that successfully complete resource scheduling pairing within a certain time period, out of the total number of attempted pairings. This success rate can be obtained by statistically analyzing the ratio of the number of completed pairings to the total number of scheduling requests. The load fluctuation curve is a trend graph or data sequence describing the change of the total load of the VFP over time, reflecting the stability and predictability of the overall load. This curve can be obtained by real-time collection and aggregation of load data from each demand-side node, or by prediction and modeling using historical load data. The node fulfillment rate is a reliability indicator measuring the ability of each node in the VFP to execute scheduling instructions according to agreement, reflecting the reliability of node behavior and the overall stability of the system. This fulfillment rate can be calculated by recording and statistically analyzing the degree to which the actual behavior of nodes matches their promised behavior within the scheduling cycle. System response latency refers to the time interval required from the VFP issuing a scheduling instruction to the node actually responding and executing the instruction. It reflects the real-time response capability and scheduling efficiency of the VFP. This latency can be obtained by monitoring the time difference between the time the scheduling instruction is issued and the time the node responds and completes the execution.

[0112] Updating the first, second, third, fourth, fifth, sixth, and seventh preset thresholds refers to adjusting each threshold used to trigger the weight re-determination mechanism based on the dynamically acquired virtual power plant operating status parameters. Update methods may include rule-based adjustments, adaptive adjustments based on historical data analysis, or dynamic optimization using optimization algorithms.

[0113] This application's solution introduces a dynamic threshold update mechanism, enabling the virtual power plant's weight determination strategy to better adapt to constantly changing operating environments. Through this technical solution, the virtual power plant's resource scheduling method can dynamically and adaptively adjust the weight re-determination threshold. This significantly improves the adaptability and robustness of the virtual power plant in complex and volatile operating environments. Since the threshold is no longer fixed but intelligently updated based on the virtual power plant's real-time operating status (such as pairing success rate, load fluctuations, node fulfillment rate, and system response latency), the weight re-determination mechanism can be triggered more promptly and accurately when the virtual power plant's operating conditions change—for example, when supply and demand fluctuate significantly, node behavior reliability decreases, or system response slows down. This avoids weight re-determination delays or misjudgments caused by threshold mismatches with the current situation, thus ensuring that the evaluation of supply-side and demand-side node weights always reflects the latest system status and node performance. Ultimately, this dynamic threshold update mechanism helps maintain high efficiency and high pairing success rate in virtual power plant resource scheduling, effectively improving the operational stability and economic benefits of the entire virtual power plant system.

[0114] As can be seen from the above, the resource scheduling method for virtual power plants provided in this application, through a virtual power plant architecture based on a consortium blockchain, enables the submission of demand information from supply and demand nodes and the weighted consensus calculation of smart contracts, dynamically determining the resource scheduling scheme, thereby improving the adaptability of resource scheduling in virtual power plants.

[0115] Based on the resource scheduling method for virtual power plants provided in the above embodiments, this application further provides a resource scheduling apparatus for virtual power plants that implements the above method embodiments. Please refer to [link to relevant documentation]. Figure 2 , Figure 2 This is a schematic diagram of the structure of a resource scheduling device for a virtual power plant provided in an embodiment of this application, as shown below. Figure 2 As shown, the resource scheduling device 20 of the virtual power plant may include: a request submission unit 21, a node weight determination unit 22, a period determination unit 23, and a resource scheduling pairing unit 24. Wherein: The request submission unit 21 is used to submit the supply-side request information corresponding to the supply-side node to the smart contract through each supply-side node, and to submit the demand-side request information corresponding to the demand-side node to the smart contract through each demand-side node.

[0116] The node weight determination unit 22 is used to determine the weight of each supply-side node by means of a smart contract, based on the supply-side demand information and historical supply data of each supply-side node, and to determine the weight of each demand-side node by means of the demand-side demand information and historical demand data of each demand-side node.

[0117] The cycle determination unit 23 is used to determine the resource scheduling cycle through smart contracts based on the weights of each supply-side node, the weights of each demand-side node, the demand information of each supply-side node, and the demand information of each demand-side node.

[0118] The resource scheduling and matching unit 24 is used to perform resource scheduling and matching on each supply-side node and each demand-side node in each resource scheduling cycle through smart contracts, based on preset matching constraints, demand information of each supply-side node, demand information of each demand-side node, historical supply data, weight of each supply-side node, historical demand data, and weight of each demand-side node.

[0119] Optionally, the supply-side demand information includes supply-side cyclical demand values, and the demand-side demand information includes demand-side cyclical demand values; the cycle determination unit 23 is specifically used for: Based on the preset cycle threshold, the cycle demand values ​​of each supply side and each demand side are filtered to obtain the filtered cycle demand values ​​of each supply side and each demand side. The first cycle demand value is determined based on the supply-side node weights corresponding to each of the selected supply-side cycle demand values. The second cycle demand value is determined based on the demand node weight corresponding to each of the selected demand cycle demand values. Based on the preset fitting coefficients, the first and second cycle appeal values ​​are fitted to obtain the range of cycle appeal values. If the error within the range of periodic demand values ​​meets the preset convergence condition, then the median value of the range of periodic demand values ​​will be determined as the resource scheduling cycle.

[0120] Optionally, the supply-side demand information includes the maximum schedulable capacity and resource response time, while the demand-side demand information includes the load demand capacity; the resource scheduling and matching unit 24 is specifically used for: The pairing order of each demand-side node is determined based on its weight. For each demand-side node in the pairing order, identify several candidate supply-side nodes that meet the pairing constraints. Then, based on the historical supply data, the maximum schedulable capacity, the resource response time, the historical demand data, and the load demand capacity of each candidate supply-side node, determine the target supply-side node from among the candidate supply-side nodes.

[0121] Optionally, the supply-side demand information may also include resource type, and the demand-side demand information may also include maximum allowable response time and load demand type. The pairing constraints include: the maximum schedulable capacity of the candidate supply-side node is greater than the load demand capacity corresponding to the demand-side node; the resource response time of the candidate supply-side node is not greater than the maximum allowable response time corresponding to the demand-side node; and the resource type of the candidate supply-side node matches the load demand type corresponding to the demand-side node.

[0122] Optionally, the resource scheduling pairing unit 24 is further used for: If multiple demand-side nodes match the same target supply-side node, the pairing order of the multiple demand-side nodes matching the same target supply-side node is determined according to their respective weights. Then, the schedulable capacity of the target supply-side node is allocated to the multiple demand-side nodes matching the same target supply-side node in sequence according to their pairing order.

[0123] Optionally, the node weight determination unit 22 is specifically used for: If the ratio between the change in the total schedulable resource capacity of each supply-side node in the virtual power plant and the current total schedulable resource capacity of each supply-side node in the virtual power plant is greater than a first preset threshold, or the ratio between the real-time total load of each demand-side node in the virtual power plant and the preset baseline load is greater than a second preset threshold, or the number of changed supply-side nodes among the supply-side nodes whose weight ranking is greater than a third preset threshold in the virtual power plant is greater than a fourth preset threshold, or the number of changed demand-side nodes among the demand-side nodes whose weight ranking is greater than a fifth preset threshold in the virtual power plant is greater than a sixth preset threshold, or the supply-demand matching success rate corresponding to two consecutive resource scheduling cycles is less than a seventh preset threshold, then the supply-side node weights corresponding to each supply-side node and the demand-side node weights corresponding to each demand-side node will be re-determined.

[0124] Optionally, the node weight determination unit 22 is specifically used for: The threshold update frequency is determined based on the resource scheduling frequency of the virtual power plant in the current time period; According to the threshold update frequency, the first preset threshold, the second preset threshold, the third preset threshold, the fourth preset threshold, the fifth preset threshold, the sixth preset threshold, and the seventh preset threshold are updated based on the virtual power plant's pairing success rate, load fluctuation curve, node fulfillment rate, and system response delay.

[0125] It should be noted that the information interaction and execution process between the above-mentioned units are based on the same concept as the method embodiments of this application. Their specific functions and technical effects can be referred to the method embodiments section, and will not be repeated here.

[0126] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 As shown, the electronic device 3 provided in this embodiment may include: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as a program corresponding to a resource scheduling method for a virtual power plant. When the processor 30 executes the computer program 32, it implements the steps described above in the embodiment of the resource scheduling method applied to a virtual power plant, for example... Figure 1 S101~S104 are shown. Alternatively, when processor 30 executes computer program 32, it implements the functions of each module / unit in the above-described virtual power plant resource scheduling device embodiment, for example... Figure 2 The functions of units 21-24 shown.

[0127] For example, computer program 32 can be divided into one or more modules / units, one or more of which are stored in memory 31 and executed by processor 30 to complete this application. One or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of computer program 32 in electronic device 3. For example, computer program 32 can be divided into a request submission unit 21, a node weight determination unit 22, a period determination unit 23, and a resource scheduling and matching unit 24. For the specific functions of each unit, please refer to... Figure 2 The relevant descriptions in the corresponding embodiments are not repeated here.

[0128] Those skilled in the art will understand that Figure 3 This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or combine certain components, or use different components.

[0129] The processor 30 can be a central processing unit (CPU), a graphics processing unit (GPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0130] The memory 31 can be an internal storage unit of the electronic device 3, such as a hard disk or RAM. The memory 31 can also be an external storage device of the electronic device 3, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, or flash card. Furthermore, the memory 31 can include both internal and external storage units of the electronic device 3. The memory 31 is used to store computer programs and other programs and data required by the electronic device. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0131] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units is merely an example. In practical applications, the above functions can be assigned to different functional units as needed, that is, the internal structure of the resource scheduling device of the virtual power plant can be divided into different functional units to complete all or part of the functions described above. The functional units in the embodiments 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. Furthermore, the specific names of the functional units are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0132] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps in the various method embodiments described above.

[0133] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0134] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, refer to the relevant descriptions of other embodiments.

[0135] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0136] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A resource scheduling method for a virtual power plant, characterized in that, The method is applied to a virtual power plant built on a consortium blockchain, wherein the virtual power plant includes several supply-side nodes and several demand-side nodes; the method includes: Each of the supply-side nodes submits its corresponding supply-side demand information to the smart contract, and each of the demand-side nodes submits its corresponding demand-side demand information to the smart contract. Through the smart contract, the weight of each supply-side node is determined based on the supply-side demand information and historical supply data of each supply-side node. Similarly, the weight of each demand-side node is determined based on the demand-side demand information and historical demand data of each demand-side node. The resource scheduling cycle is determined by the smart contract based on the weights of each supply-side node, the weights of each demand-side node, the demand information of each supply-side node, and the demand information of each demand-side node. In each resource scheduling cycle, the smart contract performs resource scheduling pairing for each supply-side node and each demand-side node based on preset pairing constraints, each supply-side demand information, each demand-side demand information, each historical supply data, each supply-side node weight, each historical demand data, and each demand-side node weight. The supply-side demand information includes maximum schedulable capacity and resource response time, and the demand-side demand information includes load demand capacity; the process of resource scheduling and pairing of each supply-side node and each demand-side node according to preset pairing constraints, each supply-side demand information, each demand-side demand information, each historical supply data, each supply-side node weight, each historical demand data, and each demand-side node weight includes: The pairing order of each demand-side node is determined based on its weight. According to the pairing order, for each demand-side node, determine a number of candidate supply-side nodes that meet the pairing constraints. Then, based on the historical supply data, the maximum schedulable capacity, the resource response time, the historical demand data, and the load demand capacity of each candidate supply-side node, determine the target supply-side node from among the candidate supply-side nodes.

2. The method according to claim 1, characterized in that, The supply-side demand information includes supply-side periodic demand values, and the demand-side demand information includes demand-side periodic demand values; determining the resource scheduling cycle through the smart contract based on the weights of each supply-side node, the weights of each demand-side node, the supply-side demand information, and the demand-side demand information includes: Based on the preset periodic threshold, the periodic demand values ​​of each supply side and each periodic demand value of each demand side are filtered to obtain the filtered periodic demand values ​​of each supply side and each periodic demand value of each demand side. The first cycle demand value is determined based on the supply-side node weight corresponding to each of the selected supply-side cycle demand values. The second periodic demand value is determined based on the demand node weight corresponding to each of the selected demand periodic demand values. Based on the preset fitting coefficients, the first periodic appeal value and the second periodic appeal value are fitted to obtain the range of periodic appeal values. If the error of the periodic demand value range meets the preset convergence condition, then the median value of the periodic demand value range is determined as the resource scheduling period.

3. The method according to claim 1, characterized in that, The supply-side demand information also includes resource type, and the demand-side demand information also includes maximum allowable response time and load demand type. The pairing constraints include: the maximum schedulable capacity of the candidate supply-side node is greater than the load demand capacity corresponding to the demand-side node, the resource response time of the candidate supply-side node is not greater than the maximum allowable response time corresponding to the demand-side node, and the resource type of the candidate supply-side node matches the load demand type corresponding to the demand-side node.

4. The method according to claim 1, characterized in that, Also includes: If multiple demand-side nodes match the same target supply-side node, the pairing order of the multiple demand-side nodes matching the same target supply-side node is determined according to the weight of each demand-side node. Then, the schedulable capacity of the target supply-side node is sequentially allocated to the multiple demand-side nodes matching the same target supply-side node according to the pairing order.

5. The method according to any one of claims 1 to 4, characterized in that, Also includes: If the ratio between the change in the total schedulable resource capacity of each supply-side node in the virtual power plant and the current total schedulable resource capacity of each supply-side node in the virtual power plant is greater than a first preset threshold, or the ratio between the real-time total load of each demand-side node in the virtual power plant and a preset baseline load is greater than a second preset threshold, or the number of supply-side nodes whose weight ranking is greater than a third preset threshold in the virtual power plant has changed is greater than a fourth preset threshold, or the number of demand-side nodes whose weight ranking is greater than a fifth preset threshold in the virtual power plant has changed is greater than a sixth preset threshold, or the supply-demand matching success rate corresponding to two consecutive resource scheduling cycles is less than a seventh preset threshold, then the supply-side node weights corresponding to each supply-side node and the demand-side node weights corresponding to each demand-side node are re-determined.

6. The method according to claim 5, characterized in that, Also includes: Determine the threshold update frequency based on the resource scheduling frequency of the virtual power plant in the current time period; According to the threshold update frequency, the first preset threshold, the second preset threshold, the third preset threshold, the fourth preset threshold, the fifth preset threshold, the sixth preset threshold, and the seventh preset threshold are updated based on the virtual power plant's pairing success rate, load fluctuation curve, node fulfillment rate, and system response delay.

7. A resource scheduling device for a virtual power plant, characterized in that, Applied to a virtual power plant built on a consortium blockchain, the virtual power plant includes several supply-side nodes and several demand-side nodes; the device includes: The request submission unit is used to submit the supply-side request information corresponding to each supply-side node to the smart contract through each of the supply-side nodes, and to submit the demand-side request information corresponding to each demand-side node to the smart contract through each of the demand-side nodes. The node weight determination unit is used to determine the weight of each supply-side node based on the supply-side demand information and historical supply data of each supply-side node through the smart contract, and to determine the weight of each demand-side node based on the demand-side demand information and historical demand data of each demand-side node. The cycle determination unit is used to determine the resource scheduling cycle through the smart contract based on the weights of each supply-side node, the weights of each demand-side node, the demand information of each supply-side node, and the demand information of each demand-side node. The resource scheduling and pairing unit is used to perform resource scheduling and pairing on each supply-side node and each demand-side node in each resource scheduling cycle through the smart contract, based on preset pairing constraints, each supply-side demand information, each demand-side demand information, each historical supply data, each supply-side node weight, each historical demand data, and each demand-side node weight. The supply-side demand information includes maximum schedulable capacity and resource response time; the demand-side demand information includes load demand capacity; the resource scheduling and matching unit is specifically used for: The pairing order of each demand-side node is determined based on its weight. According to the pairing order, for each demand-side node, determine a number of candidate supply-side nodes that meet the pairing constraints. Then, based on the historical supply data, the maximum schedulable capacity, the resource response time, the historical demand data, and the load demand capacity of each candidate supply-side node, determine the target supply-side node from among the candidate supply-side nodes.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements each step of the resource scheduling method for the virtual power plant as described in any one of claims 1 to 6.

9. A computer program product, characterized in that, When the computer program product is executed by a processor, it implements the steps of the resource scheduling method for the virtual power plant as described in any one of claims 1 to 6.

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