Virtual power plant optimization scheduling method based on blockchain system and related device

Abstract

A virtual power plant optimization scheduling method based on a blockchain system and related devices, including the following steps: step 1, a distributed particle swarm optimization algorithm based on a blockchain is proposed; step 2, the consensus algorithm used in the process of verifying the block by the distributed particle swarm optimization algorithm based on the blockchain in step 1 is improved, and an optimized computing proof of work consensus algorithm is proposed; step 3, the algorithm proposed in steps 1 and 2 is used to improve the blockchain system, and an optimized computing blockchain system is proposed to match the structure and operation requirements of the virtual power plant; step 4, an OCB-VPP model is established based on the optimized computing blockchain system OCB proposed in step 3; step 5, a scheduling strategy is established according to the characteristics of the OCB-VPP model proposed in step 4. The present application can effectively improve the VPP scheduling efficiency and information security level, reduce the VPP operation cost, improve the renewable energy consumption level, and reduce carbon dioxide emissions.

Description

Technical Field

[0001] This invention belongs to the field of virtual power plant scheduling technology, and specifically relates to a method and related apparatus for optimizing virtual power plant scheduling based on a blockchain system. Background Technology

[0002] In recent years, the number of distributed energy sources, represented by wind power, photovoltaics, electric vehicles, energy storage devices, and controllable loads, has grown rapidly. However, due to the small capacity of individual units, large number of devices, and wide distribution, large-scale integration of these distributed energy sources can impact the reliability and stability of the power grid. Virtual Power Provider (VPP) technology, through advanced metering, communication, and control technologies, integrates multiple stakeholders into a controllable virtual entity. This entity participates in grid dispatch and electricity market transactions, providing capacity, peak shaving, and valley filling services to the system. This reduces the operational risks and management complexity of distributed energy sources, promotes the consumption of renewable energy, and improves grid stability.

[0003] However, the distributed nature of VPP also leads to some problems. First, existing centralized, centralized-distributed, and fully distributed VPP control methods cannot achieve optimal consistency in terms of flexibility, compatibility, and scalability, affecting the overall performance of VPP.

[0004] Secondly, since the various units within a VPP rely on a network for bidirectional communication, the output and demand information data between the various power generation and load units are easily subject to malicious external attacks and tampering, causing errors in VPP scheduling and operation, resulting in increased costs or equipment failures.

[0005] Furthermore, the output of renewable energy sources such as wind and solar power is uncertain and random. If VPPs cannot make timely and rapid adjustments when there are sharp changes, it will lead to increased penalty costs and affect the overall efficiency of the system.

[0006] Finally, the distributed nature of VPP makes integration between its internal units difficult and scheduling and coordination challenging. Existing research typically uses intelligent optimization algorithms for computation, but these algorithms are not adapted to distributed characteristics and still have many problems in terms of computation time and accuracy, leading to increased transaction costs and decreased efficiency. Summary of the Invention

[0007] The purpose of this invention is to provide a method and related apparatus for optimizing the scheduling of virtual power plants based on a blockchain system, in order to solve the problems of poor overall operation of VPP, increased costs or equipment failures, rising penalty costs, affecting the overall efficiency of the system, and difficulties in scheduling and coordination.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] The virtual power plant optimization scheduling method based on the blockchain system comprises:

[0010] The scheduling optimization process of the virtual power plant VPP and the particle swarm algorithm used are improved by applying the blockchain system to obtain a distributed particle swarm optimization algorithm BD-PSO;

[0011] The consensus algorithm used by the distributed particle swarm optimization algorithm BD-PSO in the process of verifying the block at runtime is improved to obtain an optimized computing work proof POCW consensus algorithm;

[0012] Based on the distributed particle swarm optimization algorithm BD-PSO and the optimized computing work proof POCW consensus algorithm, an optimized computing blockchain OCB system is obtained;

[0013] Based on the optimized computing blockchain OCB system, an OCB-VPP model of the optimized computing blockchain OCB system matched with the virtual power plant VPP is established;

[0014] According to the OCB-VPP model, the scheduling strategy is divided into three stages of day-ahead scheduling stage, intra-day scheduling stage and intra-day evaluation stage for scheduling.

[0015] Further, the scheduling optimization of the VPP and the running process of the BD-PSO optimization algorithm are as follows:

[0016] (1) When the system has an optimization computing task, the control center divides all participating nodes into a plurality of node groups according to the number of participating nodes at the time and the operation solving difficulty, the number of groups is proportional to the solving speed and inversely proportional to the accuracy; the number of nodes in the group is proportional to the solving accuracy and inversely proportional to the speed, and each node group generates a corresponding blockchain; then the control center brings the solving problem into the particle swarm algorithm, randomly generates a large number of initial populations and divides them equally, and packs them with the remaining data required for calculation to form a block, which is linked to the blockchain corresponding to each node group as the initial block of the group blockchain;

[0017] (2) All nodes in each node group independently optimize the calculation according to the content of the initial block by using the linear decreasing weight particle swarm optimization algorithm Lin-WPSO;

[0018] (3) The BD-PSO optimization algorithm is set to stop verification once an effective block is generated, and then the block is formally linked to the main chain.

[0019] (4) The control center reads each group blockchain, takes the optimal solution in each node group result as the iteration calculation result, and then packages the block to link to all group blockchains. Each node in the group reads the block content to update the calculation result and perform the next iteration.

[0020] Further, the POCW consensus algorithm is improved based on the most widely used proof of work consensus algorithm PoW in the current blockchain system. The repeated hash operation of the SHA256 algorithm in the block verification process of the PoW consensus algorithm is replaced by the optimization calculation process of the BD-PSO optimization algorithm.

[0021] Further, the specific steps of the POCW consensus algorithm include:

[0022] (1) When a node needs to generate a new block, it is packaged with the received broadcast information first, and the Merkel root is generated, which is the same as PoW.

[0023] (2) In the verification link, the time-consuming SHA256 operation is cancelled first. When each node needs to verify whether a block is a valid block, if the block is the result of optimization calculation, each node verifies the calculation result contained in the block, brings the calculation result into the BD-PSO optimization algorithm to recalculate, judges whether the result meets the system constraints and boundary conditions, and whether the running cost is optimal. If the block is an instruction, operation or information simple data content, the BD-PSO optimization algorithm calculation result is defaulted to 1, indicating that the block is a simple data block, and the next step is directly performed.

[0024] (3) For the block containing optimization calculation result, if more than 50% of the nodes verify that the result is valid, the block passes the consensus algorithm verification and is a valid block.

[0025] (4) For the block containing simple data content, whether the block is valid is judged and voted. If more than 50% of the nodes vote for the data, the block passes the consensus algorithm verification and is a valid block.

[0026] Further, the optimization calculation type blockchain system is improved based on the blockchain system based on the PoW consensus algorithm.

[0027] The improvement of the optimization calculation type blockchain system includes:

[0028] First, the BD-PSO optimization algorithm is combined with the blockchain, added to the blockchain data layer, and the SHA256 algorithm used in the PoW consensus algorithm is changed to the improved BD-PSO optimization algorithm.

[0029] Secondly, the network networking mechanism will then be changed from the fully distributed PoW blockchain system to the adaptive distributed mechanism compatible with the VPP and BD-PSO algorithm;

[0030] The adaptive distributed networking mechanism is a new networking mechanism improved according to the actual operation characteristics of VPP combined with the improved optimization algorithm and consensus algorithm characteristics; that is, each node in the VPP system changes the networking structure according to the different current tasks.

[0031] Further, the OCB-VPP model includes multiple networked power generation, power consumption, and energy storage users, and communication and control between users and between users and the control center are carried out through the network. The power generation and power consumption demands between users are coordinated and scheduled by the control center. OCB is the data and information transmission carrier in the system, and the interaction between the control center and all nodes is carried out through the OCB network.

[0032] The interaction between nodes in the system is mainly divided into two categories. First, the optimization calculation process of scheduling needs to use OCB to cooperate with the BD-PSO optimization algorithm and combine the POCW consensus algorithm to complete. Second, the system also contains a large amount of one-way and two-way information data transmission that does not need to be optimized and calculated. Only the fast, reliable, and secure interaction information needs to be ensured, and the OCB applied to this process needs to set the BD-PSO calculation process to 1, that is, each node can generate a block without using BD-PSO for calculation.

[0033] Further, the scheduling and operation process of OCB-VPP is divided into three parts, and the specific steps include:

[0034] (1) In the day-ahead stage, each power generation, power consumption, and energy storage unit predicts its output and load curve for the next day according to the weather forecast data issued by the control center, generates a block after verification by the consensus algorithm, and links it to OCB. After reading and organizing the OCB data, the control center forms a problem to be optimized and calculates, distributes it to each node group for calculation, and generates a new block as the day-ahead scheduling scheme after verification by the consensus algorithm.

[0035] (2) The intra-day scheduling stage mainly includes real-time optimization scheduling and sudden optimization scheduling. For real-time optimization scheduling, short interval and multiple number of optimization calculations are adopted, and the process, objective function, and constraint condition are the same as in the day-ahead stage. For optimization scheduling under sudden conditions, the BS-PSO optimization algorithm is used to adjust the number of node groups and nodes in the group to complete the scheduling calculation while ensuring the calculation accuracy.

[0036] (3) The intraday evaluation phase mainly evaluates the performance of each unit node in the scheduling process, which serves as the basis for subsequent allocation of computing tasks and incentive settlement. For the evaluation of computing tasks, each unit node will broadcast its own computing results to the system after each calculation. The VPP control center compares the computing results of each unit with the actual value finally written into the OCB, and broadcasts the comparison results as the computing accuracy of the unit node. The selection of participating nodes is based on the computing accuracy from high to low.

[0037] Furthermore, a virtual power plant optimization scheduling system based on a blockchain system includes:

[0038] The distributed particle swarm optimization module is used to improve the scheduling optimization process of virtual power plants (VPPs) and the particle swarm algorithm used in the blockchain system, resulting in the distributed particle swarm optimization algorithm BD-PSO.

[0039] The optimized Proof-of-Work consensus algorithm module is used to improve the consensus algorithm used by the Distributed Particle Swarm Optimization Algorithm (BD-PSO) in the process of verifying blocks at runtime, resulting in the optimized Proof-of-Work (POCW) consensus algorithm.

[0040] An optimized computational blockchain system acquisition module is used to obtain an optimized computational blockchain OCB system based on the distributed particle swarm optimization algorithm BD-PSO and the optimized computational proof-of-work consensus algorithm POCW.

[0041] The OCB-VPP model building module is used to build an OCB-VPP model that matches the virtual power plant (VPP) based on the optimized computational blockchain OCB system.

[0042] The scheduling module is used to establish a scheduling strategy based on the OCB-VPP model, which is divided into three stages: day-ahead scheduling, intraday scheduling, and intraday evaluation.

[0043] Furthermore, a computer device includes 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 a virtual power plant optimization scheduling method based on a blockchain system.

[0044] Furthermore, a computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of a virtual power plant optimization scheduling method based on a blockchain system.

[0045] Compared with the prior art, the present invention has the following technical effects:

[0046] The BD-PSO algorithm provided by the application generates more calculation results than the original algorithm through multi-node parallel calculation, and compares the optimal results in a larger range on the basis of using the blockchain technology for data exchange to ensure safety, prevents falling into local optimization, can improve the algorithm accuracy while reducing the iteration number and calculation time, and effectively improves the VPP scheduling operation efficiency.

[0047] The POCW consensus algorithm provided by the application inherits the safety characteristics of the PoW algorithm, adapts to the structure of the blockchain VPP, can cooperate with the BD-PSO algorithm, improves the calculation speed and operation efficiency of the system, and lays a foundation for the operation of the blockchain VPP.

[0048] The optimized computing type blockchain system provided by the application combines the advantages of the blockchain technology and the VPP perfectly through a series of improvements, can adapt to various requirements of the VPP in scheduling operation, and simultaneously solves the problem that the existing VPP cannot achieve consistent optimization in flexibility, compatibility and expansibility by simply using a certain control mode, and further affects the overall operation effect of the VPP. The VPP model established based on the system can be applied to various scenes such as a community, a comprehensive energy system and a new energy power plant, and helps to improve the operation efficiency and information security level.

[0049] The OCB-VPP scheduling strategy provided by the application can reduce the calculation consumption time on the basis of ensuring the scheduling effect, realize reasonable collaborative operation of power generation, power consumption and energy storage units in the VPP, improve renewable energy consumption, ensure smooth execution of the scheduling task and reduce the penalty cost. Meanwhile, the application of the blockchain technology also makes the scheduling and transaction process more transparent and fair, can improve the trust degree of users to the VPP and the enthusiasm of users in participating in the scheduling and calculation task, and plays a positive role in the operation and maintenance of the VPP. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 It is the BD-PSO optimization algorithm structure diagram of the application.

[0051] Figure 2 It is the OCB system structure diagram of the application.

[0052] Figure 3 It is the OCB-VPP model structure diagram of the application.

[0053] Figure 4 It is the OCB-VPP scheduling flowchart of the application.

[0054] Figure 5 It is the VPP information security level comparison chart of each scheme in the embodiment.

[0055] Figure 6is a performance comparison chart of each scheme in the embodiment.

[0056] Figure 7 is a power scheduling chart of scheme one in the embodiment.

[0057] Figure 8 is a heat energy scheduling chart of scheme one in the embodiment.

[0058] Figure 9 is a power scheduling chart of scheme two in the embodiment.

[0059] Figure 10 is a heat energy scheduling chart of scheme two in the embodiment.

[0060] Figure 11 is a wind and solar power consumption comparison chart of each scheme in the embodiment.

[0061] Figure 12 is a carbon emission comparison chart of each scheme in the embodiment. DETAILED DESCRIPTION

[0062] The present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings and preferred embodiments, which should be understood to be merely illustrative of the present application and not restrictive thereof.

[0063] As shown in Figure 1 , the present application is based on a virtual power plant model and scheduling method of an optimization computing type block chain system, specifically including the following steps:

[0064] Step 1: In view of the distributed characteristics of a virtual power plant (VPP), the scheduling optimization process of the VPP and the particle swarm algorithm used are improved by applying block chain technology, and a distributed particle swarm optimization algorithm (BD-PSO) is proposed, and the algorithm structure is as shown in Figure 1 ;

[0065] The role of the step 1 block chain in the VPP scheduling optimization process and the particle swarm algorithm operation process is to store, distribute and collect the interaction data, information and instructions between each power generation, power consumption and energy storage unit in the VPP, improve the information security level of the VPP, and use its organization ability for distributed nodes to regard each scattered power generation, power consumption and energy storage unit in the VPP as a computing node to form a block chain operation network, and then apply the operation network combined with the particle swarm algorithm to calculate the scheduling optimization problem of the VPP, maximize the use of the computing power resources of the VPP, and improve the speed and accuracy of the VPP scheduling optimization;

[0066] The VPP scheduling optimization and BD-PSO optimization algorithm operation process are as follows:

[0067] (1) When the system has an optimization calculation task, the control center divides all participating nodes into several node groups according to the number of nodes participating at that time and the difficulty of operation solution (the number of groups is proportional to the solution speed and inversely proportional to the accuracy; the number of nodes in the group is proportional to the solution accuracy and inversely proportional to the speed), and each node group generates a corresponding blockchain. Then the control center brings the solution problem into the particle swarm algorithm, randomly generates a large number of initial populations and divides them, and packs them into blocks with the remaining data required for calculation, links to the blockchains corresponding to each node group, and serves as the initial block of the group blockchain;

[0068] (2) All nodes in each node group use the linearly decreasing weight particle swarm optimization algorithm (Lin-WPSO) to perform independent optimization calculation according to the initial block content. However, due to the uncertain nature of intelligent algorithms such as particle swarm optimization, the results generated by the nodes within each node group are unlikely to be the same, and the different initial data allocated to each node group will also result in different results between node groups. This feature will generate a large number of different calculation results in the system;

[0069] (3) In view of the above characteristics, the BD-PSO optimization algorithm sets that after all nodes in a node group complete a single calculation, each node broadcasts the calculation result within the group, and each node selects the best one from the received results, packs it into a block, and connects it in parallel to the side chain of the group blockchain. Then, in the side chain, the multiple parallel blocks generated by this calculation are tried one by one to link to the main chain, and each node in the group uses a consensus algorithm to verify the block. The block that passes the verification is the valid block (the block contains the optimal solution). Once a valid block is generated, the verification is stopped, and then the block is officially linked to the main chain;

[0070] (4) The control center reads each group blockchain and takes the optimal solution from each node group result as the iteration calculation result, then packs it into a block and links it to all group blockchains. Each node in the group reads the block content to update the calculation result and perform the next iteration.

[0071] Through the above process, it can be found that the BD-PSO optimization algorithm improved by distribution and combined with blockchain generates more calculation results than the original algorithm through multi-node parallel calculation. On the basis of using blockchain for information verification and transmission to ensure safety, a larger range of optimal result comparison is performed to prevent falling into local optimum, which can improve the accuracy of the algorithm while reducing the number of iterations and calculation time;

[0072] Step 2: Improve the consensus algorithm used by the BD-PSO optimization algorithm in step 1 when verifying blocks during runtime, and propose an optimized proof of work (POCW) consensus algorithm;

[0073] The POCW consensus algorithm proposed in step 2 is improved based on the most widely used proof of work consensus algorithm (PoW) in the current blockchain system, and the repeated hash operation of SHA256 algorithm in the block verification process of PoW consensus algorithm is replaced by the optimization calculation process using BD-PSO optimization algorithm.

[0074] The specific steps of the POCW consensus algorithm include:

[0075] (1) When a node needs to generate a new block, it is packaged with the received broadcast information first, and the Merkel root is generated, which is the same as PoW.

[0076] (2) In the verification link, the SHA256 operation consuming a lot of time is cancelled. When each node needs to verify whether a block is a valid block, if the block is the result of optimization calculation, each node verifies the calculation result contained in the block (brings the calculation result into the BD-PSO optimization algorithm to recalculate, judges whether the result meets the system constraints and boundary conditions, and whether the running cost is optimal). If the block is a simple data content (such as power generation, load prediction declaration and other information), the BD-PSO optimization algorithm calculation result is defaulted to 1, indicating that the block is a simple data block, and the next step is directly performed.

[0077] (3) For the block containing optimization calculation result, if more than 50% of the nodes verify that the result is valid, the block passes the consensus algorithm verification and is a valid block.

[0078] (4) For the block containing simple data content, the block is judged whether it is valid, and if more than 50% of the nodes vote for the data, the block passes the consensus algorithm verification and is a valid block.

[0079] Step 3: Based on steps 1 and 2, the blockchain system is improved, and an optimization calculation type blockchain (OCB) system is proposed to match the structure and operation requirements of VPP, and the structure is as shown in Figure 2 .

[0080] The optimization calculation type blockchain system proposed in step 3 is improved based on the blockchain system based on PoW consensus algorithm.

[0081] The improvement of the optimization calculation type blockchain system includes:

[0082] Firstly, the BD-PSO optimization algorithm is combined with the blockchain, the blockchain data layer is added, and the SHA256 algorithm used in the PoW consensus algorithm is changed to the improved BD-PSO optimization algorithm in this paper. On the basis of ensuring the security and transaction efficiency of the system, the node computing power in the system is fully utilized to improve the verification speed of block generation;

[0083] Secondly, the network networking mechanism is changed from the fully distributed PoW blockchain system to the adaptive distributed mechanism compatible with VPP and BD-PSO algorithm;

[0084] The adaptive distributed networking mechanism is a new networking mechanism improved according to the actual operation characteristics of VPP combined with the improved optimization algorithm and consensus algorithm in this paper, which combines the advantages of centralized and centralized-distributed VPP control methods. That is, each node in the VPP system can change the networking structure according to the different current tasks, so as to improve the control efficiency of VPP on the basis of adapting to the scheduling operation characteristics of VPP and the requirements of blockchain and algorithm. For example, when only data interaction is required, the calculation is small and will not have a great impact on the VPP control center, so the centralized structure can be used, and the control center directly controls each node through the OCB network; when optimization calculation is required, in addition to the main OCB network, the group OCB network in each group is also included, and the group OCB network can be regarded as the subordinate control center of VPP, at this time the VPP control center realizes centralized-distributed control through each group OCB network.

[0085] Step 4: Establish the OCB-VPP model based on the OCB system proposed in step 3, as shown in Figure 3

[0086] The OCB-VPP includes multiple connected power generation, power consumption and energy storage users. The users communicate and control with each other and with the control center through the network, and the power generation and power consumption demands between users are coordinated and scheduled by the control center. OCB is the data and information transmission carrier in the system, and the interaction between the control center and all nodes is carried out through the OCB network to ensure the security of communication and data;

[0087] ​The interaction between nodes in the system is mainly divided into two categories. First, the optimization calculation process of scheduling needs to use OCB to cooperate with BD-PSO optimization algorithm and combine POCW consensus algorithm to complete. Second, the system also contains a large amount of one-way and two-way information data transmission that does not need to be optimized and calculated, such as the control center sending weather forecast data to each node, nodes sending power generation and load prediction declaration information to each other, and the control center feeding back the evaluation of each node execution process. The above processes do not need complex calculation, only need to ensure the rapid, reliable and safe interaction of information, so the OCB applied to the process needs to set the BD-PSO calculation process to 1, that is, each node does not need to use BD-PSO for calculation to generate a block;

[0088] Step 5: Establish the scheduling strategy according to the characteristics of the OCB-VPP model proposed in step 4, as shown in Figure 4

[0089] The scheduling operation process of OCB-VPP is divided into three parts, and the specific steps include:

[0090] (1) In the day-ahead stage, each power generation, power consumption and energy storage unit predicts the output and load curve of the next day according to the weather forecast data issued by the control center, generates a block linked to the OCB through the consensus algorithm verification, and the control center reads the OCB data and organizes it to form a problem to be optimized and calculated, which is distributed to each node group for calculation. The control center generates a new block linked to the OCB as a day-ahead scheduling scheme after the calculation result is verified by the consensus algorithm.

[0091] (2) The day-ahead scheduling stage mainly includes real-time optimization scheduling and emergency optimization scheduling. For real-time optimization scheduling, this process benefits from the improved BS-PSO optimization algorithm, which can greatly reduce the time consumption, so short interval and multiple times of optimization calculation can be adopted in the optimization process to improve the accuracy of system scheduling. The interval time needs to be determined according to the specific situation of VPP, power grid scheduling and power market. The process, objective function and constraint condition are the same as those in the day-ahead stage and will not be repeated. For emergency optimization scheduling, such as scheduling adjustment caused by power grid failure, change of renewable energy output and other problems, the number of node groups and nodes in the group can be adjusted by using the characteristics of BS-PSO optimization algorithm to quickly complete the scheduling calculation and ensure the smooth execution of scheduling task and reduce the penalty cost under the condition of ensuring the calculation accuracy.

[0092] ​(3) Daily evaluation stage, mainly to evaluate the performance of each unit node in the scheduling process, as the basis for subsequent allocation of computing tasks and incentive settlement. For the evaluation of computing tasks, each unit node will broadcast its own computing results to the system after each computing is completed, and the VPP control center compares the unit computing results with the actual values written into the OCB, and the comparison results are broadcast as the computing accuracy of the unit node. Since the allocation of each computing task, the selection of participating nodes needs to be selected from high to low according to the computing accuracy, and its computing accuracy will also affect the selection of nodes in the optimization calculation, which is conducive to promoting the upgrading and maintenance of computers by each user.

[0093] The application will be further described below through the best embodiment of the application:

[0094] The best embodiment takes the data of a smart community as an example, compares and analyzes the VPP model and scheduling method constructed by the application with the traditional VPP in terms of system operation cost, new energy consumption, carbon emission and scheduling speed.

[0095] (1) Example data

[0096] The VPP is composed of wind, light generators, resident electric / heat load, energy storage batteries, gas turbines, electric heating devices and other auxiliary equipment in the community, and the parameters of each device are shown in Table 1.

[0097] Table 1 Device parameters

[0098]

[0099] (2) Scheme design

[0100] In order to verify the effectiveness of the VPP model and scheduling method proposed in the application, two schemes are set to compare and analyze:

[0101] Scheme 1: The VPP adopts the conventional centralized control model, and the VPP control center controls all power generation and load units in one direction. The scheduling optimization calculation process is independently carried out by the control center using the commonly used Lin-WPSO, WOA and LRO optimization algorithms.

[0102] Scheme 2: The OCB-VPP model constructed in this paper is adopted, and the units can communicate with each other in both directions through the block chain. The scheduling optimization calculation process also adopts the distributed optimization algorithm based on the block chain proposed in this paper.

[0103] The simulation environment is Matlab R2019a, and the computer configurations of each scheme are the same, which are CPU i5 8400, RAM 16 GB, and scheme 2 uses the Matlab parallel computing toolbox to write and simulate the blockchain network (simulate the optimization calculation of each node in the OCB network). Other parameters required in the calculation are shown in Table 2.

[0104] Table 2 Other parameters required for calculation

[0105]

[0106]

[0107] (3) Mathematical model of scheduling process

[0108] The OCB-VPP compared and analyzed is the same as the traditional VPP model, which includes wind power, photovoltaic generator set, energy storage battery, gas turbine (power generation and heat production), electric heating equipment (supplementary heat production) and residential load. The system minimizes the operation cost as the target, optimizes the scheduling of each unit in the system. The system benefits from peak clipping and valley filling, and the excess electricity on the grid. The cost includes the purchase of electricity, equipment investment and discount, carbon emission cost, and the penalty cost of load loss due to power shortage. The objective function is:

[0109]

[0110] In the formula: X is the daily operation cost of the system; T is the system scheduling period, which includes 24 scheduling periods; t is a scheduling period; G t , R t are the system operation cost and benefit at t period, respectively, which are represented as:

[0111]

[0112]

[0113] In the formula: V t 1 , V t 2 are the system purchase of electricity and load loss penalty cost, are the fuel cost of gas turbine and the system carbon emission cost; F t 1 , F t 2 , F t 3 are the wind, light generation, gas turbine excess electricity on the grid and energy storage battery peak clipping and valley filling benefits, respectively, as follows:

[0114]

[0115] V t 2 = W t 2 pr 2 (5)

[0116]

[0117]

[0118] F t 1 + F t 2 = (W t 4 + W t 5 )pr 5 (8)

[0119]

[0120] wherein: W t 1 , W t 2 , W t 3 , W t 4 , W t 5 are the system electricity purchase, load loss, gas turbine output and wind, light and gas turbine surplus electricity on-grid power at t period, respectively; are the wind, light generation and grid charging energy of the energy storage battery, respectively; are the charging and discharging efficiency of the energy storage battery, respectively; is the unit carbon emission of grid electricity purchase and gas turbine power generation; are the grid electricity price, load loss penalty, natural gas price, carbon emission cost and surplus electricity on-grid unit price at t period, respectively; pr H , pr L are the peak and valley electricity prices of the grid, respectively.

[0121] The power balance constraint is:

[0122]

[0123] wherein: P t pv , P t pw , P t g , P t b,dP t 1 P t b,c P t lo P t sw P

[0124] The thermal energy balance constraint is:

[0125]

[0126] In the formula: P h η P

[0127] The energy storage battery charging and discharging constraint is:

[0128]

[0129]

[0130]

[0131] In the formula: t b,c P P P P

[0132] The gas turbine constraint is:

[0133]

[0134] In the formula: P gas V t g In the formula: P P P

[0135] (4) Results of each scheme and analysis

[0136] VPP optimization scheduling models were constructed and solved for the two schemes mentioned above. The information security levels of the two schemes were compared as follows: Figure 5 As shown in Table 3, the computational performance of the optimization algorithms used in the two schemes is compared. Figure 6 As shown. A comparison of the operational status of each device in the two schemes is presented, as follows: Figures 7-10 As shown in Table 4, the operating costs and benefits of the two schemes are compared. The energy consumption of wind and solar power generated by the two schemes is also compared. Figure 11 As shown. A comparison of the carbon emissions of the two options is presented, as follows. Figure 12 As shown.

[0137] Table 3 Computational performance of the optimization algorithm scheduling process

[0138]

[0139] Table 4. Operating Costs and Benefits of Each Scheme

[0140]

[0141]

[0142] Through Tables 3-4 and Figures 5-12 Based on comprehensive comparative analysis, the following conclusions can be drawn.

[0143] 1) From Figure 5 It can be observed that as the number of nodes in the system increases, the probability of a successful compromise of a VPP using the POCW consensus algorithm decreases exponentially. With 30 nodes, the probability of a successful compromise is approximately 5.1%, which is statistically practically impossible. If the number of nodes increases further, the probability approaches zero. This is because the POCW consensus algorithm requires 50% node approval to generate a block; the more nodes there are, the more nodes need to be controlled for a compromise, making it more difficult. In contrast, traditional VPPs rely solely on a single computer or server at the control center for computation and control, making system security independent of the number of nodes. Therefore, once this server is compromised, the entire system is controlled.

[0144] 2) According to Table 3, the improved BD-PSO optimization algorithm of the application has obvious advantages in calculation speed and calculation accuracy compared with the other three algorithms. Due to the use of distributed calculation, the calculation time of the BD-PSO optimization algorithm is greatly reduced, which is reduced by 53.06%, 46.20% and 14.60% compared with Lin-WPSO, WOA and LRO algorithms respectively, the single-day operation cost is reduced by 351.58 yuan, 224.20 yuan and 504.65 yuan respectively, and the calculation accuracy is increased by 15.16%, 10.19% and 20.36% respectively. It can be found that although the optimal value iteration times of Lin-WPSO, WOA and LRO algorithms are less, but the optimal value is far less than that of BD-PSO algorithm, and the reason is that it is easy to fall into local optimum.

[0145] As shown in Figure 6 , Lin-WPSO, WOA and LRO algorithms have faster iteration speed in the early stage, among which the LRO algorithm is always in the leading position before 70 times, but then it basically stops updating the optimal value, which also shows that these three types of algorithms have poor global search ability and are easy to fall into local optimum, especially the LRO algorithm, although it has faster calculation speed, but it has the greatest possibility of falling into local optimum. Although the BD-PSO algorithm has slower iteration speed in the early stage, it still maintains strong optimization ability by comparing a large number of different calculation results generated by many nodes in the middle and late stages, and finally exceeds the other three algorithms.

[0146] 3) Due to the superiority of the OCB-VPP model and the scheduling method proposed in the application, the operation state of each device in scheme two is more reasonable. It can be found by comparing Figures 7-10 that the discharge capacity of the battery in the three scenarios of scheme two is significantly increased compared with scheme one, and in the typical daily environment of summer, winter and spring and autumn, the total discharge of the battery is 607.47kWh, 507.74kWh and 729.17kWh respectively, which is increased by 1.36%, 1.12% and 1.36% respectively compared with the proportion of the respective electrical load. The system electrical load purchase quantity is reduced by 635.44kWh, 659.04kWh and 709.72kWh respectively compared with scheme one.

[0147] At the same time, the power generation capacity of the gas turbine in each season accounts for the proportion of the electrical load, which is reduced by 0.31%, 2.05% and 0.81% respectively compared with scheme one; the heat production capacity accounts for the proportion of the heat load, which is reduced by-19.5%, 2.95% and 3.52% respectively compared with scheme one, and the excess part of the heat production capacity of the gas turbine is 4.35kWh, 0kWh and 12.36kWh respectively, which is reduced by 86.92%, 100% and 71.97% respectively compared with scheme one, and the operation state of the gas turbine is reduced, and the operation is concentrated in the period when the system heat and electrical load gap is large, which is more conducive to maximizing the economic benefit of the gas turbine.

[0148] 4) As shown in Table 4, compared with Scheme 1, the operating cost of the system in typical days of summer, winter and spring and autumn seasons decreased by 15.11%, 9.20% and 19.26% respectively, which fully demonstrates that the OCB-VPP model and scheduling method proposed in this invention have significant advantages over traditional VPP in improving system economy.

[0149] Table 4 shows that the advantage of Scheme 2 in terms of system operating costs is mainly due to the OCB-VPP's strong optimization capabilities, which allow for precise matching of power output and load demand. This significantly reduces electricity purchase costs and off-load penalty costs, decreasing them by 19.55% and 73.91% respectively compared to Scheme 1. Furthermore, precise matching improves the absorption of wind and solar power in Scheme 2, enabling the system to utilize more low-cost electricity and reducing unnecessary waste from additional power purchases, further lowering operating costs. Additionally, Scheme 2 also slightly reduces natural gas costs and carbon emission costs, making it more environmentally friendly while reducing system costs.

[0150] 5) By Figure 11 It can be seen that, compared with Scheme 1, Scheme 2 shows an increase in the wind and solar energy absorbed by the system in summer, winter, and spring and autumn. The reason for this is that Scheme 2's OCB-VPP benefits from the superior system architecture and optimization algorithm, which enables it to fully utilize the scheduling capabilities of each device, accurately match supply and demand, and increase the utilization rate of high-economic-efficiency devices while reducing the operation of low-economic-efficiency devices, while meeting load and safety requirements.

[0151] Option 2's proactive and precise use of batteries and gas turbines significantly reduces the system's electricity purchases, decreasing them by a total of 19.55% across all seasons compared to Option 1. This is beneficial for improving the utilization of wind and solar energy. Overall, Option 1 achieved seasonal wind and solar energy consumption percentages of 76.81%, 70.44%, and 62.90%, respectively. Option 2 increased these percentages by 7.07%, 13.56%, and 10.37%, respectively, fully demonstrating the advantages of the OCB-VPP in promoting renewable energy consumption.

[0152] 6) By Figure 12 It can be seen that, compared with Scheme 1, the carbon emissions of Scheme 2 on typical days in each season are lower. The cumulative carbon emissions of Scheme 1 on typical days in summer, winter and spring and autumn are 3707.11 kg, 4052.14 kg and 2683.15 kg respectively. Scheme 2 reduces carbon emissions by 14.53%, 15.58% and 12.94% respectively compared with Scheme 1.

[0153] Among them, the carbon emissions from natural gas and electricity on typical days in summer, winter, and spring / autumn seasons are 525.73 kg and 3181.38 kg for Scheme 1; 974.41 kg and 3077.73 kg for Scheme 2; and 637.34 kg and 2045.81 kg for Scheme 2. In Scheme 2, the carbon emissions from natural gas and electricity decrease by 4.53% and 16.18% for Scheme 2; 10.02% and 17.34% for Scheme 2; and 3.16% and 15.99% for Scheme 2. It can be seen that the reduction in carbon emissions is mostly due to the reduction in carbon emissions from electricity, while the reduction in carbon emissions from natural gas is smaller than that from electricity. This is consistent with the above analysis of electricity purchases and the operation of batteries and gas turbines.

[0154] In summary, the virtual power plant model and scheduling method based on the optimized computational blockchain system can effectively improve the scheduling efficiency and information security level of VPP, reduce the operating cost of VPP, improve the level of renewable energy consumption, and reduce carbon dioxide emissions.

[0155] In another embodiment of the present invention, a virtual power plant optimization scheduling system based on a blockchain system is provided, which can be used to implement the above-mentioned virtual power plant optimization scheduling method based on a blockchain system. Specifically, the system includes:

[0156] The distributed particle swarm optimization module is used to improve the scheduling optimization process of virtual power plants (VPPs) and the particle swarm algorithm used in the blockchain system, resulting in the distributed particle swarm optimization algorithm BD-PSO.

[0157] The optimized Proof-of-Work consensus algorithm module is used to improve the consensus algorithm used by the Distributed Particle Swarm Optimization Algorithm (BD-PSO) in the process of verifying blocks at runtime, resulting in the optimized Proof-of-Work (POCW) consensus algorithm.

[0158] An optimized computational blockchain system acquisition module is used to obtain an optimized computational blockchain OCB system based on the distributed particle swarm optimization algorithm BD-PSO and the optimized computational proof-of-work consensus algorithm POCW.

[0159] The OCB-VPP model building module is used to build an OCB-VPP model that matches the virtual power plant (VPP) based on the optimized computational blockchain OCB system.

[0160] The scheduling module is used to establish a scheduling strategy based on the OCB-VPP model, which is divided into three stages: day-ahead scheduling, intraday scheduling, and intraday evaluation.

[0161] The division of the modules in the embodiments of the present application is illustrative, and is merely logical function division. In actual implementation, another division manner can be used. In addition, each function module in each embodiment of the present application can be integrated in one processor, or can be physically separated, or two or more modules can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software function module.

[0162] In still another embodiment of the present application, a computer device is provided, which includes a processor and a memory. The memory is configured to store a computer program, and the computer program includes program instructions. The processor is configured to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be another general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or another programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, and the like. The processor is a computing core and a control core of the terminal, and is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement a corresponding method flow or a corresponding function. The processor in the embodiments of the present application can be used for the operation of the virtual power plant optimization scheduling method based on the blockchain system.

[0163] In still another embodiment of the present application, the present application also provides a storage medium, specifically a computer readable storage medium (Memory), which is a memory device in a computer device, used for storing programs and data. It can be understood that the computer readable storage medium herein can include a built-in storage medium in the computer device, and of course can also include an extended storage medium supported by the computer device. The computer readable storage medium provides a storage space, which stores an operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory. The one or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to realize the corresponding steps of the virtual power plant optimization scheduling method based on the blockchain system in the above embodiments.

[0164] Those skilled in the art should understand that embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0165] The present application is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more flows or blocks.

[0166] These computer program instructions can also be stored in a computer readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable memory produce a product including instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocksFigure 1 the function specified in the one or more blocks.

[0167] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operation steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, thus the instructions executed on the computer or other programmable data processing devices provide a process for implementing the flow Figure 1 the flow or flows and / or blocks Figure 1 the function specified in the one or more blocks.

[0168] Those ordinarily skilled in the art can understand that the above description is only the preferred examples of the application, and is not used to limit the application, although the application is described in detail with reference to the foregoing examples, those skilled in the art can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features. Any modification, equivalent replacement, etc. within the spirit and principle of the application shall be included in the protection scope of the application.

Claims

1. A virtual power plant optimization scheduling method based on a blockchain system, characterized in that, include: The scheduling optimization process of virtual power plants (VPPs) and the particle swarm optimization algorithm used are improved by applying a blockchain system, resulting in the distributed particle swarm optimization algorithm BD-PSO. An improved consensus algorithm is obtained by modifying the distributed particle swarm optimization algorithm BD-PSO during the process of verifying blocks at runtime, resulting in the optimized computational proof-of-work (POCW) consensus algorithm. Based on the distributed particle swarm optimization algorithm BD-PSO and the optimized computational proof-of-work consensus algorithm POCW, an optimized computational blockchain OCB system is obtained. An OCB-VPP model for matching the optimized computational blockchain OCB system with the virtual power plant (VPP) is established based on the optimized computational blockchain OCB system. The scheduling strategy based on the OCB-VPP model is divided into three stages: day-ahead scheduling, intraday scheduling, and intraday evaluation. The execution flow of VPP's scheduling optimization and BD-PSO optimization algorithms is as follows: (1) When the system has an optimization calculation task, the control center divides all participating nodes into several node groups according to the number of participating nodes and the difficulty of the calculation. The number of groups is directly proportional to the calculation speed and inversely proportional to the accuracy. The number of nodes in a group is directly proportional to the calculation accuracy and inversely proportional to the speed. Each node group generates a corresponding blockchain. Then the control center brings the problem to the particle swarm algorithm, randomly generates a large number of initial populations and divides them equally. The populations are packaged with the remaining data required for calculation to form blocks and linked to the blockchains corresponding to each node group as the initial blocks of the group blockchain. (2) All nodes in each node group perform independent optimization calculations based on the initial block content using the Lin-WPSO linear decreasing weight particle swarm optimization algorithm; (3) The BD-PSO optimization algorithm sets that after all nodes in the node group complete a single calculation, each node broadcasts the calculation result in the group. Each node selects the best result from the received results and packages it to generate a block and connects it in parallel to the sub-chain of the group blockchain. Subsequently, multiple parallel blocks generated in this calculation on the secondary chain are attempted to be linked to the main chain one by one. Each node in the group uses a consensus algorithm to verify the block. The block that passes the verification is a valid block. Once a valid block is generated, the verification stops, and then the block is officially linked to the main chain. (4) The control center reads the blockchain of each group, and then takes the optimal solution in the result of each node group as the calculation result of this iteration. Then it packages and generates a blockchain and connects it to the blockchain of all groups. Each node in the group reads the block content to update the calculation result and performs the next iteration. The Proof-of-Work (PoW) consensus algorithm is an improvement on the Proof-of-Work (PoW) consensus algorithm, which is currently the most widely used consensus algorithm in blockchain systems. It replaces the repeated hash calculations performed by the SHA256 algorithm in the block verification process of the PoW consensus algorithm with an optimized calculation process using the BD-PSO optimization algorithm.

2. The virtual power plant optimization scheduling method based on a blockchain system according to claim 1, characterized in that, The specific steps of the POCW consensus algorithm include: (1) When a node needs to generate a new block, it first packages the received broadcast information and generates the Merkel root, just like in PoW. (2) In the verification stage, the time-consuming SHA256 operation is taken first; when each node needs to verify whether a certain block is a valid block, if the block is the result of the optimization calculation, then each node is asked to verify the calculation result contained in the block, and the calculation result is brought into the BD-PSO optimization algorithm for recalculation to determine whether the result meets the system constraints and boundary conditions, and whether the running cost is optimal; if the block is a simple data content of instructions, operations or information, then the calculation result of the BD-PSO optimization algorithm is defaulted to 1 to indicate that the block is a simple data block, and proceed directly to the next step; (3) For a block containing the result of the optimization calculation, if more than 50% of the nodes verify that the result is valid, then the block is verified by the consensus algorithm and is a valid block; (4) For blocks containing only data content, a vote is taken to determine whether the block is valid. If more than 50% of the nodes vote in favor of the data, the block is verified by the consensus algorithm and is a valid block.

3. The virtual power plant optimization scheduling method based on a blockchain system according to claim 1, characterized in that, Optimized computational blockchain systems are improvements based on blockchain systems using the PoW consensus algorithm. The improvements to this optimized computational blockchain system include: First, the BD-PSO optimization algorithm is combined with the blockchain and added to the blockchain data layer, and the SHA256 algorithm used in the PoW consensus algorithm is changed to the improved BD-PSO optimization algorithm. Secondly, the network networking mechanism will be changed from the fully distributed PoW blockchain system to an adaptive distributed system that is compatible with VPP and BD-PSO algorithms. The adaptive distributed networking mechanism is a new networking mechanism formed by combining the characteristics of VPP's actual operation with improved optimization and consensus algorithms; that is, each node in the VPP system changes the networking structure according to the different tasks at hand.

4. The virtual power plant optimization scheduling method based on a blockchain system according to claim 1, characterized in that, The OCB-VPP model includes multiple networked power generation, power consumption, and energy storage users. Communication and control between users and between users and the control center are achieved through the network. The power generation and power consumption demands of each user are uniformly coordinated and scheduled by the control center. The OCB is the data and information transmission carrier within the system, and all interactions between the control center and all nodes are conducted through the OCB network. The interactions between nodes within the system fall into two categories. First, there is the optimization computation process for scheduling, which requires the use of OCB in conjunction with the BD-PSO optimization algorithm and the POCW consensus algorithm. Second, the system also contains a large amount of one-way and two-way information data transmission that does not require optimization computation. It is only necessary to ensure the speed, reliability, and security of the interactive information. The OCB used in this process needs to set the BD-PSO computation process to 1 by default, meaning that each node does not need to use BD-PSO to generate blocks.

5. The virtual power plant optimization scheduling method based on a blockchain system according to claim 1, characterized in that, The scheduling and operation process of OCB-VPP is divided into three parts, and the specific steps include: (1) During the day-ahead phase, each power generation, power consumption, and energy storage unit predicts its own output and load curves for the next day based on the meteorological forecast data issued by the control center. After verification by the consensus algorithm, a blockchain is generated and connected to the OCB. The control center reads and organizes the OCB data, forms a problem to be optimized, and distributes it to each node group for calculation. After the calculation results are verified by the consensus algorithm, the control center generates a new blockchain and connects it to the OCB as the day-ahead scheduling scheme. (2) The intraday scheduling phase includes two parts: real-time optimization scheduling and emergency optimization scheduling. For real-time optimization scheduling, short-interval and multiple optimization calculations are adopted. The process, objective function, and constraints are the same as those in the daytime phase. For emergency optimization scheduling, the characteristics of the BS-PSO optimization algorithm are utilized. By adjusting the number of nodes in the node group and within the group, the scheduling calculation is completed while ensuring the calculation accuracy. (3) The intraday evaluation phase is to evaluate the performance of each unit node in the scheduling process, which serves as the basis for subsequent allocation of computing tasks and incentive settlement. For the evaluation of computing tasks, each unit node will broadcast its own computing results to the system after each calculation. The VPP control center compares the computing results of each unit with the actual value finally written into the OCB, and broadcasts the comparison results as the computing accuracy of the unit node. The selection of participating nodes is based on the computing accuracy from high to low.

6. A virtual power plant optimization scheduling system based on a blockchain system, characterized in that, include: The distributed particle swarm optimization module is used to improve the scheduling optimization process of virtual power plants (VPPs) and the particle swarm algorithm used in the blockchain system, resulting in the distributed particle swarm optimization algorithm BD-PSO. The optimized Proof-of-Work consensus algorithm module is used to improve the consensus algorithm used by the Distributed Particle Swarm Optimization Algorithm (BD-PSO) in the process of verifying blocks at runtime, resulting in the optimized Proof-of-Work (POCW) consensus algorithm. An optimized computational blockchain system acquisition module is used to obtain an optimized computational blockchain OCB system based on the distributed particle swarm optimization algorithm BD-PSO and the optimized computational proof-of-work consensus algorithm POCW. The OCB-VPP model building module is used to build an OCB-VPP model that matches the virtual power plant (VPP) based on the optimized computational blockchain OCB system. The scheduling module is used to establish a scheduling strategy based on the OCB-VPP model, which is divided into three stages: day-ahead scheduling, intraday scheduling, and intraday evaluation. The execution flow of VPP's scheduling optimization and BD-PSO optimization algorithms is as follows: (1) When the system has an optimization calculation task, the control center divides all participating nodes into several node groups according to the number of participating nodes and the difficulty of the calculation. The number of groups is directly proportional to the calculation speed and inversely proportional to the accuracy. The number of nodes in a group is directly proportional to the calculation accuracy and inversely proportional to the speed. Each node group generates a corresponding blockchain. Then the control center brings the problem to the particle swarm algorithm, randomly generates a large number of initial populations and divides them equally. The populations are packaged with the remaining data required for calculation to form blocks and linked to the blockchains corresponding to each node group as the initial blocks of the group blockchain. (2) All nodes in each node group perform independent optimization calculations based on the initial block content using the Lin-WPSO linear decreasing weight particle swarm optimization algorithm; (3) The BD-PSO optimization algorithm sets that after all nodes in the node group complete a single calculation, each node broadcasts the calculation result in the group. Each node selects the best result from the received results and packages it to generate a block and connects it in parallel to the sub-chain of the group blockchain. Subsequently, multiple parallel blocks generated in this calculation on the secondary chain are attempted to be linked to the main chain one by one. Each node in the group uses a consensus algorithm to verify the block. The block that passes the verification is a valid block. Once a valid block is generated, the verification stops, and then the block is officially linked to the main chain. (4) The control center reads the blockchain of each group, and then takes the optimal solution in the result of each node group as the calculation result of this iteration. Then it packages and generates a blockchain and connects it to the blockchain of all groups. Each node in the group reads the block content to update the calculation result and performs the next iteration. The Proof-of-Work (PoW) consensus algorithm is an improvement on the Proof-of-Work (PoW) consensus algorithm, which is currently the most widely used consensus algorithm in blockchain systems. It replaces the repeated hash calculations performed by the SHA256 algorithm in the block verification process of the PoW consensus algorithm with an optimized calculation process using the BD-PSO optimization algorithm.

7. A computer 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 the steps of the virtual power plant optimization scheduling method based on a blockchain system as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the virtual power plant optimization scheduling method based on a blockchain system as described in any one of claims 1 to 5.

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