Virtual power plant resource allocation 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 allocation 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; after the resource scheduling of the virtual power plant is completed, determining the respective resource allocation values corresponding to each supply end node and each demand end node according to 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 the smart contract; and finally, allocating the resources generated by the virtual power plant according to the respective resource allocation values corresponding to each supply end node and each demand end node. According to the present application, the resources generated by the virtual power plant are allocated according to the demands of each supply end node and each demand end node through the smart contract, thereby improving the resource allocation adaptability of the virtual power plant.

Description

Technical Field

[0001] This application belongs to the field of virtual power plant technology, and particularly relates to a resource allocation 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, the resource allocation rules of virtual power plants are usually forcibly specified by the virtual power plant itself. Therefore, they often fail to take into account the demands of each supply-side node and each demand-side node, thus reducing the adaptability of the virtual power plant's resource allocation. Summary of the Invention

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

[0005] In a first aspect, embodiments of this application provide a resource allocation 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. After the virtual power plant completes resource scheduling, the smart contract determines the resource allocation value for each supply-side node and each demand-side node based on the supply-side demand information, historical supply data, demand-side demand information, historical demand data, supply-side node weight, and demand-side node weight of each supply-side node. The resources generated by the virtual power plant are allocated according to the resource allocation values ​​corresponding to each of the supply-side nodes and each of the demand-side nodes.

[0006] Optionally, the supply-side demand information includes the maximum schedulable capacity, and the historical supply data includes execution rate, response speed, and adjustment accuracy; determining the supply-side node weight corresponding to each of the supply-side nodes based on the supply-side demand information and historical supply data corresponding to each of the supply-side nodes includes: For each of the supply-side nodes, the adjustment capability score corresponding to the supply-side node is determined based on the execution rate, response speed and adjustment accuracy of the supply-side node, and the first reputation coefficient corresponding to the supply-side node is determined based on the execution rate of the supply-side node. The weight of each supply-side node is determined based on its maximum schedulable capacity, its adjustment capability score, and its first reputation coefficient.

[0007] Optionally, the demand-side request information includes the rated electricity consumption scale, and the historical demand data includes power supply reliability requirements, load interruptibility duration, and fulfillment rate; determining the demand-side node weight corresponding to each demand-side node based on the demand-side request information and the historical demand data corresponding to each demand-side node includes: For each demand-side node, the load importance level score corresponding to the demand-side node is determined based on the power supply reliability requirement and the load interruptibility duration corresponding to the demand-side node, and the second reputation 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 rating, and second reputation coefficient.

[0008] Optionally, the supply-side demand information includes the maximum dispatchable capacity, the demand-side demand information includes the rated electricity consumption, and both the historical supply data and the historical demand data include the execution rate; determining the resource allocation value corresponding to each supply-side node and each demand-side node based on the supply-side demand information, the historical supply data, the demand-side demand information, the historical demand data, the weight of each supply-side node, and the weight of each demand-side node includes: The basic resources to be allocated for the virtual power plant are determined based on the maximum dispatchable capacity of each supply-side node and the rated power consumption of each demand-side node. The contribution of the virtual power plant to be allocated is determined based on the electricity market transaction revenue and the grid ancillary service revenue of the virtual power plant. Based on the execution rate of each supply-side node and each demand-side node, the execution reward and default penalty for each supply-side node and each demand-side node are determined respectively. For each of the supply-side nodes, the resource allocation value corresponding to the supply-side node is determined based on the supply-side node weight, the basic resources to be allocated, the contribution resources to be allocated, the execution reward and the default penalty corresponding to the supply-side node. For each demand-side node, the resource allocation value corresponding to that demand-side node is determined based on the demand-side node weight, the basic resources to be allocated, the contribution resources to be allocated, the execution reward, and the breach penalty corresponding to that demand-side node.

[0009] Optionally, determining the resource allocation value corresponding to the supply-side node based on the supply-side node weight, the basic resources to be allocated, the contribution resources to be allocated, and the execution reward and the default penalty corresponding to the supply-side node includes: Determine the first weight corresponding to the basic resources to be allocated, the second weight corresponding to the contribution resources to be allocated, the third weight corresponding to the execution reward, and the fourth weight corresponding to the breach penalty; The resource allocation value corresponding to the supply-side node is determined based on the product of the basic unallocated resources and the first weight, the product of the contribution unallocated resources, the supply-side node weight, and the second weight, the product of the execution reward and the third weight, and the product of the default penalty and the fourth weight.

[0010] Optionally, determining the resource allocation value corresponding to the demand-side node based on the demand-side node weight, the basic resources to be allocated, the contribution resources to be allocated, and the execution reward and the breach penalty corresponding to the demand-side node includes: Determine the first weight corresponding to the basic resources to be allocated, the second weight corresponding to the contribution resources to be allocated, the third weight corresponding to the execution reward, and the fourth weight corresponding to the breach penalty; The resource allocation value corresponding to the demand-side node is determined based on the product of the basic unallocated resources and the first weight, the product of the contribution unallocated resources, the demand-side node weight, and the second weight, the product of the execution reward and the third weight, and the product of the default penalty and the fourth weight.

[0011] Optionally, the supply-side demand information includes resource allocation weight demands; determining the first weight corresponding to the basic resources to be allocated, the second weight corresponding to the contribution resources to be allocated, the third weight corresponding to the execution reward, and the fourth weight corresponding to the breach penalty includes: Based on the resource allocation weight request corresponding to each of the supply-side nodes, the supply-side node weight corresponding to each of the supply-side nodes, the resource allocation weight request corresponding to each of the demand-side nodes, and the demand-side node weight corresponding to each of the demand-side nodes, the first weight, the second weight, the third weight, and the fourth weight are determined respectively.

[0012] Secondly, embodiments of this application provide a resource allocation 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 resource allocation determination unit is used to determine the resource allocation value for each supply-side node and each demand-side node respectively through the smart contract after the virtual power plant completes resource scheduling, based on the supply-side demand information, historical supply data, demand-side demand information, historical demand data, supply-side node weight, and demand-side node weight of each supply-side node. The resource allocation unit is used to allocate the resources generated by the virtual power plant according to the resource allocation values ​​corresponding to each of the supply-side nodes and each of the demand-side nodes.

[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, wherein the processor executes the computer program to implement the steps of the resource allocation 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 allocation 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 allocation method for a virtual power plant as described in any of the first aspects above.

[0016] The beneficial effects of the resource allocation method for virtual power plants provided in this application embodiment are as follows: The resource allocation 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, and determining the weight of each demand-side node based on its corresponding demand-side demand information and historical demand data, through the smart contract; determining the resource allocation value for each supply-side node and each demand-side node respectively, based on their respective supply-side demand information, historical supply data, demand-side demand information, historical demand data, weights, and weights; and allocating the resources generated by the virtual power plant according to their respective resource allocation values.

[0017] The resource allocation method for virtual power plants provided in this application integrates node demand information and historical data through smart contracts based on consortium blockchains to dynamically calculate weights and allocate resources according to the weights, which can effectively improve the resource allocation adaptability of 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 allocation 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 allocation 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 generated unilaterally by the platform, lacking a two-way consensus mechanism between supply and demand. This results in an inability to balance the resource lifespan degradation on the supply side with the reliability requirements of electricity demand, leading to a low dispatching command execution rate. Fixed dispatching frequencies cannot adapt to dynamic changes in supply and demand, resulting in delayed responses during high-load scenarios and resource idleness during low-load scenarios, leading to insufficient average resource utilization. The rule generation logic is opaque, resulting in high trust costs among multiple stakeholders. The lack of economic incentive mechanisms leads to low node participation. The absence of a standardized rotating pairing mechanism results in high pairing deviation rates during large-scale resource access. The lack of clear triggering and convergence logic for adaptive updates of dispatching rules leads to frequent rule fluctuations affecting system stability. The lack of privacy protection mechanisms makes sensitive node information easily leaked. Furthermore, the non-standardized interface with the upper-level power grid hinders participation in grid peak shaving, frequency regulation, and other ancillary services. These issues significantly restrict key system performance indicators such as dispatching execution efficiency, resource utilization, and operational stability.

[0023] For example, in a virtual power plant that includes distributed photovoltaic, energy storage, controllable loads, and electric vehicle V2G nodes, the lack of a two-way voting mechanism for supply and demand leads to an imbalance in supply and demand matching when electric vehicle mobility resources are accessed, resulting in a higher matching deviation rate. When scheduling rules are updated, the lack of consensus calculation causes frequent rule adjustments, leading to disputes among multiple stakeholders and reduced system stability.

[0024] If the aforementioned problems are not addressed, the rigidity of scheduling rules will lead to a coexistence of resource idleness and demand gaps, resulting in a continuous deterioration of the overall system operating efficiency. This will ultimately limit the application of virtual power plants in the ancillary services market, affecting the safe and stable operation of the power system and its marketization process. Therefore, there is an urgent need for a resource allocation mechanism that can achieve two-way coordination between supply and demand, dynamic adaptive updates, and privacy protection.

[0025] 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.

[0026] For ease of understanding, the following explains some key terms in this embodiment: A virtual power plant is a system that aggregates various distributed energy sources, such as distributed power sources, energy storage systems, and controllable loads, into a single controllable unit for participation in the electricity market and grid operation management, using advanced information and communication technologies and control technologies. This system aims to optimize resource allocation, improve energy utilization efficiency, and provide ancillary services to the power grid.

[0027] A consortium blockchain is a blockchain network jointly maintained by multiple pre-selected nodes. Compared to public blockchains, consortium blockchains offer higher transaction processing speeds, lower transaction costs, and stronger privacy protection. In this virtual power plant application, the consortium blockchain provides all participants with a decentralized, trustworthy, and transparent platform for transactions and information sharing.

[0028] Supply-side nodes refer to entities that provide electrical energy or regulation capabilities within a virtual power plant, such as distributed photovoltaic systems, wind turbines, and energy storage devices. These nodes generate revenue by providing resources to the virtual power plant.

[0029] Demand-side nodes refer to entities that consume electricity or have controllable loads in the virtual power plant, such as industrial users, commercial buildings, and electric vehicle charging stations. These nodes optimize electricity costs or receive incentives by participating in the scheduling of the virtual power plant.

[0030] A smart contract is a piece of code stored on the blockchain that executes automatically when preset conditions are met. In this method, smart contracts are used to handle the submission of node information, the calculation of weights, and the determination of resource allocation values, thereby ensuring the automation, transparency, and immutability of transactions.

[0031] Supply-side demand information refers to information submitted by supply-side nodes to smart contracts, expressing the amount of resources or adjustment capabilities they can provide within a specific time period.

[0032] Demand-side request information refers to information submitted by demand-side nodes to smart contracts, expressing their demand for electricity or the interruptibility of their load within a specific time period.

[0033] Historical supply data refers to the actual operating data of supply-side nodes in the past process of participating in virtual power plant scheduling. This data is used to evaluate the reliability and performance of the nodes.

[0034] Historical demand data refers to the actual electricity consumption data of demand-side nodes in the past when they participated in virtual power plant scheduling. This data is used to evaluate the demand characteristics and importance of the nodes.

[0035] The supply-side node weight is a quantitative indicator calculated based on the demand information and historical supply data of the supply-side node. It is used to measure the importance or priority of the supply-side node in resource allocation.

[0036] Demand-side node weight is a quantitative indicator calculated based on the demand information and historical demand data of the demand-side node, used to measure the importance or priority of the demand-side node in resource allocation.

[0037] Resource allocation value refers to the specific amount of resources or economic value allocated to each node after the virtual power plant completes resource scheduling, calculated through smart contracts based on factors such as the contribution, demand, historical performance, and weight of each node.

[0038] Please see Figure 1 , Figure 1 The following is a flowchart illustrating the implementation of a resource allocation method for a virtual power plant, as provided in an embodiment of this application. This resource allocation method may include steps S101 to S104, 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.

[0039] In this embodiment, the supply-side demand information may include maximum dispatchable capacity, which refers to the maximum power 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 plan, and other operational constraints.

[0040] Demand-side demand information includes rated power consumption, which 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.

[0041] Specifically, both supply-side nodes and demand-side nodes can 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 through a blockchain client.

[0042] 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.

[0043] In this embodiment of the application, historical supply data may include execution rate, response speed, and adjustment accuracy.

[0044] 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.

[0045] 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).

[0046] 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.

[0047] 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.

[0048] 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.

[0049] More specifically, smart contracts can determine the weight of each supply-side node using the following formula:

[0050] 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.

[0051] More specifically, smart contracts can determine the regulation capability score corresponding to a supply-side node using the following formula:

[0052] 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.

[0053] 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.

[0054] 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.

[0055] In this embodiment of the application, historical demand data includes power supply reliability requirements, load interruptibility duration, and fulfillment rate.

[0056] 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.

[0057] 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 calculated as the ratio of the actual number of responses to the total number of scheduling operations.

[0058] 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.

[0059] 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.

[0060] More specifically, smart contracts can determine the weight of each demand-side node using the following formula:

[0061] 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.

[0062] More specifically, the smart contract can determine the load importance level score of the demand-side node using the following formula:

[0063] 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.

[0064] 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.

[0065] 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.

[0066] In S103, after the virtual power plant completes resource scheduling, the resource allocation value for each supply-side node and each demand-side node is determined through smart contracts based on the supply-side demand information, historical supply data, demand-side demand information, historical demand data, supply-side node weight, and demand-side node weight of each supply-side node.

[0067] In this embodiment, the supply-side demand information includes the maximum dispatchable capacity, and the demand-side demand information includes the rated electricity consumption. Both historical supply data and historical demand data include the execution rate. Based on this, the resource allocation value corresponding to each supply-side node and each demand-side node can be determined separately in the following ways: Based on the maximum dispatchable capacity of each supply-side node and the rated electricity consumption of each demand-side node, the basic resources to be allocated for the virtual power plant are determined; based on the electricity market transaction revenue and grid ancillary service revenue of the virtual power plant, the contribution resources to be allocated for the virtual power plant are determined; based on the execution rate of each supply-side node and each demand-side node, the execution rewards and default penalties for each supply-side node are determined; for each supply-side node, the resource allocation value for that supply-side node is determined based on its corresponding supply-side node weight, basic resources to be allocated, contribution resources to be allocated, execution rewards, and default penalties; for each demand-side node, the resource allocation value for that demand-side node is determined based on its corresponding demand-side node weight, basic resources to be allocated, contribution resources to be allocated, execution rewards, and default penalties.

[0068] The proposed solution uses smart contracts to calculate the basic resources to be allocated in the virtual power plant after the virtual power plant completes resource scheduling. This is based on the maximum dispatchable capacity of each supply-side node and the rated power consumption of each demand-side node. This constitutes the core resource pool that meets the basic needs of the internal members.

[0069] At the same time, the smart contract will also calculate the resources to be allocated based on the revenue that the virtual power plant obtains in electricity market transactions and grid ancillary services. These resources reflect the value created by the virtual power plant as a whole in the external market, aiming to incentivize all participants to jointly improve the overall efficiency of the virtual power plant.

[0070] To further enhance the performance of each node, the smart contract will also determine the execution rewards or penalties for breach of contract that each supply-side and demand-side node should receive based on their respective execution rates, thereby forming an effective incentive and constraint mechanism.

[0071] Ultimately, for each supply-side and demand-side node, its resource allocation value will comprehensively consider its respective node weight, the virtual power plant's basic unallocated resources, its contributed unallocated resources, and its own performance rewards and penalties for breach of contract. This comprehensive approach ensures that resource allocation no longer relies solely on node weight and basic demands, but rather more fully integrates the overall benefits of the virtual power plant and the actual performance of each node. This makes the resource allocation results more fair and reasonable, and effectively incentivizes each node to improve its participation and performance quality. This mechanism can more accurately reflect each node's actual contribution to the virtual power plant and its importance within it, thereby promoting the healthy development and efficient operation of the virtual power plant.

[0072] More specifically, smart contracts can achieve the following: "Determine the resource allocation value corresponding to a supply-side node based on its weight, basic unallocated resources, contribution unallocated resources, execution rewards, and default penalties," detailed below: Determine the first weight corresponding to the basic unallocated resources, the second weight corresponding to the contribution unallocated resources, the third weight corresponding to the execution reward, and the fourth weight corresponding to the default penalty; determine the resource allocation value corresponding to the supply-side node based on the product of the basic unallocated resources and the first weight, the product of the contribution unallocated resources, the supply-side node weight and the second weight, the product of the execution reward and the third weight, and the product of the default penalty and the fourth weight.

[0073] The first, second, third, and fourth weights are used to quantify the relative importance of basic resources to be allocated, contribution resources to be allocated, execution rewards, and default penalties in the final resource allocation value calculation. These weights are designed to ensure that resource allocation accurately reflects the value contribution and impact of each factor on the virtual power plant.

[0074] The product calculation involves multiplying the four components—basic unallocated resources, contribution unallocated resources, execution rewards, and breach of contract penalties—by their respective first, second, third, and fourth weights to obtain their weighted values.

[0075] In one possible implementation, the smart contract can determine the resource allocation value corresponding to the supply-side node using the following formula:

[0076] in, This is the resource allocation value corresponding to the supply-side node. Basic resources to be allocated, This represents the weight of the supply-side node corresponding to that supply-side node. To contribute resources to be allocated, In order to implement the reward, This is a penalty for breach of contract.

[0077] This weighted calculation mechanism transforms resource allocation from a simple summation into a flexible adjustment of the influence of various factors based on the actual needs and strategies of the virtual power plant. For example, when it's necessary to incentivize nodes to respond actively, the weight of execution rewards can be increased; when emphasizing basic capacity guarantees, the weight of basic resources to be allocated can be increased. In this way, the virtual power plant can more accurately assess the comprehensive value of supply-side nodes and allocate resources accordingly, thereby effectively guiding the behavior of supply-side nodes and improving the overall operational efficiency and economic benefits of the virtual power plant.

[0078] More specifically, smart contracts can achieve the following: "Determine the resource allocation value corresponding to a demand-side node based on its weight, basic resources to be allocated, contribution resources to be allocated, execution rewards, and default penalties," as detailed below: Determine the first weight corresponding to the basic unallocated resources, the second weight corresponding to the contribution unallocated resources, the third weight corresponding to the execution reward, and the fourth weight corresponding to the breach penalty; determine the resource allocation value corresponding to the demand-side node based on the product of the basic unallocated resources and the first weight, the product of the contribution unallocated resources, the weight of the demand-side node, and the second weight, the product of the execution reward and the third weight, and the product of the breach penalty and the fourth weight.

[0079] The first, second, third, and fourth weights are used to quantify the relative importance of basic resources to be allocated, contribution resources to be allocated, execution rewards, and default penalties in the final resource allocation value calculation. These weights are designed to ensure that resource allocation accurately reflects the value contribution and impact of each factor on the virtual power plant.

[0080] The product calculation involves multiplying the four components—basic unallocated resources, contribution unallocated resources, execution rewards, and breach of contract penalties—by their respective first, second, third, and fourth weights to obtain their weighted values.

[0081] In one possible implementation, the smart contract can determine the resource allocation value corresponding to the demand-side node using the following formula:

[0082] in, Allocate resource values ​​to the node corresponding to this demand side. Basic resources to be allocated, This represents the weight of the demand-side node corresponding to this demand-side node. To contribute resources to be allocated, In order to implement the reward, This is a penalty for breach of contract.

[0083] This weighted calculation mechanism transforms resource allocation from a simple accumulation into a flexible adjustment of the influence of various factors based on the actual needs and strategies of the virtual power plant. For example, when it's necessary to incentivize nodes to respond actively, the weight of execution rewards can be increased; when emphasizing basic capacity guarantees, the weight of basic resources to be allocated can be increased. In this way, the virtual power plant can more accurately assess the comprehensive value of demand-side nodes and allocate resources accordingly, thereby effectively guiding the behavior of demand-side nodes and improving the overall operational efficiency and economic benefits of the virtual power plant.

[0084] In some embodiments of this application, a method is proposed to determine the resource allocation values ​​for supply-side and demand-side nodes based on basic unallocated resources, contributed unallocated resources, execution rewards, and default penalties, combined with corresponding weights. However, in practice, how to reasonably and dynamically determine these weights to better reflect the actual demands and importance of each participant in the virtual power plant, thereby achieving fairer and more efficient resource allocation, is a problem that needs to be solved. To address this, this application further proposes a specific method for determining each weight, detailed below: Supply-side demand information includes resource allocation weight demands, and demand-side demand information includes resource allocation weight demands. The determination of the first weight corresponding to the basic resources to be allocated, the second weight corresponding to the contribution resources to be allocated, the third weight corresponding to the execution reward, and the fourth weight corresponding to the breach penalty involves: determining the first weight, second weight, third weight, and fourth weight based on the resource allocation weight demands of each supply-side node, the supply-side node weights of each supply-side node, the resource allocation weight demands of each demand-side node, and the demand-side node weights of each demand-side node.

[0085] In this context, the resource allocation weight request refers to the expression by which supply-side or demand-side nodes, during the resource allocation process, indicate the proportion or importance of various resources (such as basic resources to be allocated, contribution resources to be allocated, execution rewards, and penalties for breach of contract) in the final allocation result. This allows each node to submit its preference for weight allocation to the smart contract based on its own business strategy, risk appetite, or emphasis on different types of returns.

[0086] The above-mentioned first, second, third, and fourth weights are determined based on the resource allocation weight demands of each supply-side node, the corresponding supply-side node weights, the resource allocation weight demands of each demand-side node, and the corresponding demand-side node weights. This aims to comprehensively consider the weight demands of all participants and their importance in the virtual power plant (reflected by node weights), thereby dynamically and fairly determining the weights used for resource allocation. This avoids the drawbacks of fixed weights or weights determined by a single centralized entity, enhancing the flexibility of the allocation mechanism and participant satisfaction. Specifically, the smart contract can use a weighted average method. For example, for the first weight, a weighted average can be calculated based on the resource allocation weight demands of all supply-side nodes (regarding the portion of basic resources to be allocated) and their respective supply-side node weights. Simultaneously, a weighted average can be calculated by considering the resource allocation weight demands of all demand-side nodes (regarding the portion of basic resources to be allocated) and their respective demand-side node weights, ultimately resulting in the first weight. The determination of other weights (second, third, and fourth weights) is similar. In addition, smart contracts can also adopt game theory models, where each node submits its weight requests as strategies, and the smart contract finds an optimal weight combination by iteratively calculating or solving Nash equilibrium, so as to maximize the overall utility or achieve a balance of interests among all parties.

[0087] This application's solution allows supply-side and demand-side nodes in a virtual power plant to submit their respective resource allocation weight requests. Combined with the supply-side and demand-side node weights already determined by the smart contract, it dynamically calculates a first weight corresponding to the basic resources to be allocated, a second weight corresponding to the contributed resources to be allocated, a third weight corresponding to the execution reward, and a fourth weight corresponding to the default penalty. This process first collects the preferences of each participant for different components of resource allocation; these preferences are reflected in the resource allocation weight requests. Simultaneously, the smart contract, using historical data and the current state, has already evaluated and determined the supply-side and demand-side node weights of each node. These node weights reflect the reliability, contribution, and importance of each participant in the virtual power plant. Subsequently, the smart contract comprehensively considers these individual requests with the predetermined weights of each node, for example, through weighted averaging or other aggregation algorithms, to derive the resource allocation weights applicable to the entire virtual power plant. This mechanism ensures that the final resource allocation weights are not fixed or determined by a single entity, but rather dynamically reflect the collective preferences of all participants and their actual status within the virtual power plant. In this way, the resource allocation mechanism can adapt more flexibly to changes in the operation of virtual power plants and incentivize all participants more fairly, thereby optimizing the overall resource scheduling and allocation efficiency.

[0088] In S104, the resources generated by the virtual power plant are allocated according to the resource allocation values ​​corresponding to each supply-side node and each demand-side node.

[0089] In this embodiment of the application, after the smart contract determines the resource allocation values ​​corresponding to each supply-side node and each demand-side node, it can allocate the resources generated by the virtual power plant in a preset manner.

[0090] In some of the embodiments described above in this application, the virtual power plant determines node weights based on supply and demand information and historical data through smart contracts, and then allocates resources accordingly. 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 results from continuous resource scheduling and pairing. 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] As can be seen from the above, this application integrates node demand information and historical data with smart contracts based on consortium blockchains to dynamically calculate weights and allocate resources according to the weights, which can effectively improve the resource allocation adaptability of virtual power plants.

[0106] Based on the resource allocation method for virtual power plants provided in the above embodiments, this application further provides a resource allocation apparatus for implementing the above method embodiments of virtual power plants. Please refer to [link to relevant documentation]. Figure 2 , Figure 2 This is a schematic diagram of the structure of a resource allocation device for a virtual power plant provided in an embodiment of this application, as shown below. Figure 2 As shown, the resource allocation device 20 of the virtual power plant may include: a request submission unit 21, a node weight determination unit 22, a resource allocation determination unit 23, and a resource allocation 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.

[0107] 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.

[0108] The resource allocation determination unit 23 is used to determine the resource allocation value for each supply-side node and each demand-side node respectively through smart contracts after the virtual power plant completes resource scheduling, based on the supply-side demand information, historical supply data, demand-side demand information, historical demand data, supply-side node weight, and demand-side node weight of each supply-side node.

[0109] The resource allocation unit 24 is used to allocate the resources generated by the virtual power plant according to the resource allocation values ​​corresponding to each supply-side node and each demand-side node.

[0110] Optionally, supply-side demand information includes maximum schedulable capacity, and historical supply data includes execution rate, response speed, and adjustment accuracy; the node weight determination unit 22 is specifically used for: 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, and the first reputation coefficient of the supply-side node is determined based on its execution rate. The weight of each supply-side node is determined based on its maximum schedulable capacity, adjustment capability score, and first reputation coefficient.

[0111] Optionally, demand-side information includes rated power consumption, and historical demand data includes power supply reliability requirements, load interruptibility duration, and fulfillment rate; the node weight determination unit 22 is specifically used for: 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.

[0112] Optionally, the supply-side demand information includes the maximum dispatchable capacity, and the demand-side demand information includes the rated electricity consumption scale. Both historical supply data and historical demand data include the execution rate. The resource allocation determination unit 23 is specifically used for: The basic resources to be allocated for the virtual power plant are determined based on the maximum dispatchable capacity of each supply-side node and the rated power consumption of each demand-side node. The contribution of virtual power plants to be allocated resources is determined based on their electricity market transaction revenue and grid ancillary service revenue. Based on the execution rate of each supply-side node and each demand-side node, the corresponding execution rewards and default penalties for each supply-side node and each demand-side node are determined respectively. For each supply-side node, the resource allocation value corresponding to that supply-side node is determined based on its weight, basic resources to be allocated, contribution resources to be allocated, execution rewards, and default penalties. For each demand-side node, the resource allocation value corresponding to that demand-side node is determined based on its corresponding demand-side node weight, basic resources to be allocated, contribution resources to be allocated, execution rewards, and default penalties.

[0113] Optionally, the resource allocation determination unit 23 is specifically used for: Determine the first weight corresponding to the basic resources to be allocated, the second weight corresponding to the contribution resources to be allocated, the third weight corresponding to the execution reward, and the fourth weight corresponding to the penalty for breach of contract; The resource allocation value corresponding to the supply-side node is determined by multiplying the basic unallocated resources by the first weight, the contribution unallocated resources, the supply-side node weight, and the second weight, the execution reward by the third weight, and the default penalty by the fourth weight.

[0114] Optionally, the resource allocation determination unit 23 is specifically used for: Determine the first weight corresponding to the basic resources to be allocated, the second weight corresponding to the contribution resources to be allocated, the third weight corresponding to the execution reward, and the fourth weight corresponding to the penalty for breach of contract; The resource allocation value corresponding to the demand-side node is determined by multiplying the basic unallocated resources by the first weight, the contribution unallocated resources, the demand-side node weight, and the second weight, the execution reward by the third weight, and the default penalty by the fourth weight.

[0115] Optionally, the supply-side demand information includes resource allocation weight demands; the resource allocation determination unit 23 is specifically used for: Based on the resource allocation weight requirements of each supply-side node, the supply-side node weight of each supply-side node, the resource allocation weight requirements of each demand-side node, and the demand-side node weight of each demand-side node, the first weight, the second weight, the third weight, and the fourth weight are determined respectively.

[0116] 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.

[0117] 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 allocation 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 allocation 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 allocation device embodiment, for example... Figure 2 The functions of units 21-24 shown.

[0118] 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 resource allocation determination unit 23, and a resource allocation unit 24. For the specific functions of each unit, please refer to [link to relevant documentation]. Figure 2 The relevant descriptions in the corresponding embodiments are not repeated here.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 allocation 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The above 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 allocation 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. After the virtual power plant completes resource scheduling, the smart contract determines the resource allocation value for each supply-side node and each demand-side node based on the supply-side demand information, historical supply data, demand-side demand information, historical demand data, supply-side node weight, and demand-side node weight of each supply-side node. The resources generated by the virtual power plant are allocated according to the resource allocation values ​​corresponding to each of the supply-side nodes and each of the demand-side nodes.

2. The method according to claim 1, characterized in that, The supply-side demand information includes the maximum schedulable capacity, and the historical supply data includes execution rate, response speed, and adjustment accuracy; determining the weight of each supply-side node based on its corresponding supply-side demand information and historical supply data includes: For each of the supply-side nodes, the adjustment capability score corresponding to the supply-side node is determined based on the execution rate, response speed and adjustment accuracy of the supply-side node, and the first reputation coefficient corresponding to the supply-side node is determined based on the execution rate of the supply-side node. The weight of each supply-side node is determined based on its maximum schedulable capacity, its adjustment capability score, and its first reputation coefficient.

3. The method according to claim 1, characterized in that, The demand-side request information includes the rated electricity consumption scale, and the historical demand data includes power supply reliability requirements, load interruptibility duration, and fulfillment rate; determining the demand-side node weight corresponding to each demand-side node based on the demand-side request information and the historical demand data corresponding to each demand-side node includes: For each demand-side node, the load importance level score corresponding to the demand-side node is determined based on the power supply reliability requirement and the load interruptibility duration corresponding to the demand-side node, and the second reputation 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 rating, and second reputation coefficient.

4. The method according to claim 1, characterized in that, The supply-side demand information includes the maximum dispatchable capacity, the demand-side demand information includes the rated electricity consumption, and both the historical supply data and the historical demand data include the execution rate; the process of determining the resource allocation value for each supply-side node and each demand-side node based on the supply-side demand information, the historical supply data, the demand-side demand information, the historical demand data, the supply-side node weight, and the demand-side node weight for each supply-side node includes: The basic resources to be allocated for the virtual power plant are determined based on the maximum dispatchable capacity of each supply-side node and the rated power consumption of each demand-side node. The contribution of the virtual power plant to be allocated is determined based on the electricity market transaction revenue and the grid ancillary service revenue of the virtual power plant. Based on the execution rate of each supply-side node and each demand-side node, the execution reward and default penalty for each supply-side node and each demand-side node are determined respectively. For each of the supply-side nodes, the resource allocation value corresponding to the supply-side node is determined based on the supply-side node weight, the basic resources to be allocated, the contribution resources to be allocated, the execution reward and the default penalty corresponding to the supply-side node. For each demand-side node, the resource allocation value corresponding to that demand-side node is determined based on the demand-side node weight, the basic resources to be allocated, the contribution resources to be allocated, the execution reward, and the breach penalty corresponding to that demand-side node.

5. The method according to claim 4, characterized in that, The step of determining the resource allocation value corresponding to the supply-side node based on the supply-side node weight, the basic resources to be allocated, the contribution resources to be allocated, and the execution reward and the default penalty corresponding to the supply-side node includes: Determine the first weight corresponding to the basic resources to be allocated, the second weight corresponding to the contribution resources to be allocated, the third weight corresponding to the execution reward, and the fourth weight corresponding to the breach penalty; The resource allocation value corresponding to the supply-side node is determined based on the product of the basic unallocated resources and the first weight, the product of the contribution unallocated resources, the supply-side node weight, and the second weight, the product of the execution reward and the third weight, and the product of the default penalty and the fourth weight.

6. The method according to claim 4, characterized in that, The step of determining the resource allocation value corresponding to the demand-side node based on the demand-side node weight, the basic resources to be allocated, the contribution resources to be allocated, and the execution reward and the breach penalty corresponding to the demand-side node includes: Determine the first weight corresponding to the basic resources to be allocated, the second weight corresponding to the contribution resources to be allocated, the third weight corresponding to the execution reward, and the fourth weight corresponding to the breach penalty; The resource allocation value corresponding to the demand-side node is determined based on the product of the basic unallocated resources and the first weight, the product of the contribution unallocated resources, the demand-side node weight, and the second weight, the product of the execution reward and the third weight, and the product of the default penalty and the fourth weight.

7. The method according to any one of claims 5 to 6, characterized in that, The supply-side demand information includes resource allocation weight demands; determining the first weight corresponding to the basic resources to be allocated, the second weight corresponding to the contribution resources to be allocated, the third weight corresponding to the execution reward, and the fourth weight corresponding to the breach penalty includes: Based on the resource allocation weight request corresponding to each of the supply-side nodes, the supply-side node weight corresponding to each of the supply-side nodes, the resource allocation weight request corresponding to each of the demand-side nodes, and the demand-side node weight corresponding to each of the demand-side nodes, the first weight, the second weight, the third weight, and the fourth weight are determined respectively.

8. A resource allocation 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 resource allocation determination unit is used to determine the resource allocation value for each supply-side node and each demand-side node respectively through the smart contract after the virtual power plant completes resource scheduling, based on the supply-side demand information, historical supply data, demand-side demand information, historical demand data, supply-side node weight, and demand-side node weight of each supply-side node. The resource allocation unit is used to allocate the resources generated by the virtual power plant according to the resource allocation values ​​corresponding to each of the supply-side nodes and each of the demand-side nodes.

9. 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 allocation method for the virtual power plant as described in any one of claims 1 to 7.

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

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