Microgrid Distributed Power Supply Co-control Method Based on Blockchain Technology

Abstract

This invention relates to a microgrid distributed power source collaborative control method based on blockchain technology, comprising: constructing a microgrid blockchain network; calculating the minimum total operating cost of the microgrid and the supply and demand relationship of system power; constructing a Lagrangian function to obtain the optimal solution price for incremental rate; and correcting deviations in the optimal solution price for incremental rate through the incremental rate consistency principle to obtain the optimal system economy. Compared with traditional architectures, this method improves the problem of uncoordinated communication between distributed generation (DG) nodes in decentralized control, solves the communication pressure and single point of failure problems in centralized control, enhances the reliability between nodes in distributed control, and ensures the security, accuracy, and traceability of data.

Description

Technical Field

[0001] This invention relates to the field of microgrid control technology, and in particular to a microgrid distributed power source collaborative control method based on blockchain technology. Background Technology

[0002] Against the backdrop of escalating energy crises and environmental pollution, electricity, as a clean energy source, has brought widespread attention to renewable energy sources such as solar and wind power. Microgrids, as a novel network structure, integrate distributed energy resources before connecting them to the main grid, and have become a promising solution. With the gradual advancement of low-carbon goals, distributed generation has gained widespread attention due to its high efficiency and environmental friendliness. However, the dispersed deployment of distributed generation equipment and its high cost when individually connected to the main grid, coupled with potential harmonic pollution and system fluctuations, make unified dispatching of distributed generation (DGs) difficult within a main grid environment. To fully utilize distributed generation, emerging microgrid technology organizes DGs within a region, enabling them to be dispatched as a whole with the main grid externally; internally, control strategies coordinate the rational dispatching of individual DGs, enhancing multi-energy complementarity and reducing operating costs. Microgrid technology, by promoting the rational utilization of DGs in a more decentralized manner, is gradually becoming an effective solution to the DG dispatching problem.

[0003] The key to microgrid implementation lies in the control methods and strategies for system operation. Due to the diverse types of distributed energy sources connected to a microgrid, their varying operating states, output characteristics, and control methods, traditional droop control suffers from voltage and frequency instability, uneven power distribution, and increasingly complex communication environments, leading to difficulties in data exchange and data security vulnerabilities. Centralized management collects internal system information and performs centralized calculations to monitor and control the entire microgrid system. However, centralized control is highly centralized and heavily reliant on communication lines; line failures can affect overall judgment, and management flaws or attacks on the central controller can lead to data leaks. Furthermore, centralized data management increases the security monitoring load on central nodes. As more devices are added to the microgrid, it inevitably negatively impacts data exchange efficiency and integrity. Decentralized management refers to each DG (Distributed Generated Energy) in the microgrid independently performing local power metering and compensation based on its own local information. However, decentralized control lacks information exchange, making it difficult to achieve system-level objectives. While distributed control achieves negotiated decision-making through communication between units, ensuring information security and accuracy remains a challenge. Meanwhile, the realization of multi-DG collaborative optimization in microgrids faces numerous technical obstacles, such as trust between interacting entities, data authenticity and privacy, the efficiency of system collaborative algorithms, and decentralized decision-making and distributed scheduling in complex systems. Solving these problems requires the integration of other technologies for support.

[0004] Blockchain technology, as a universal underlying framework, combines distributed data storage, smart contracts, consensus mechanisms, and encryption to ensure the peer-to-peer, intelligent, secure, tamper-proof, and traceable nature of the scheduling computation process. Currently, the application of blockchain technology in microgrids is mostly focused on electricity trading, addressing trust issues, reducing transaction costs, and improving efficiency. A practical Byzantine fault-tolerant consensus algorithm is employed to enhance trust among multiple energy interaction entities. A multi-timescale microgrid economic scheduling method based on smart contracts ensures the security and intelligence of the scheduling process. Some scholars have used proof-of-work mechanisms to optimize energy system operation, demonstrating the feasibility of applying blockchain to multi-energy collaboration, but this method is resource-intensive. Summary of the Invention

[0005] The purpose of this invention is to provide a microgrid distributed generation collaborative control method based on blockchain technology. This method enables collaborative autonomy among distributed generation (DG) units through peer-to-peer network communication, ensures balanced power distribution, optimizes system operating costs, and improves the efficiency, security, and reliability of system scheduling.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A microgrid distributed power source collaborative control method based on blockchain technology includes:

[0008] Construct a microgrid blockchain network, calculate the minimum total operating cost of microgrid optimization and the supply and demand relationship of system power, construct a Lagrange function, and obtain the optimal solution for the incremental rate of electricity price;

[0009] By applying the consistency principle of incremental rate, the deviation of the optimal solution electricity price of the incremental rate is corrected to obtain the optimal system economy.

[0010] Preferably, calculating the minimum total operating cost of the microgrid optimization includes:

[0011] With the goal of optimizing the active power of each controllable distributed generation (DG) in a microgrid, the minimum total operating cost of the microgrid optimization is calculated; wherein, the operating cost of each controllable DG includes fuel cost and maintenance cost, which are represented by a quadratic function of active power:

[0012] C i (P i ) = a i +b i P i +c i P i 2

[0013] In the formula, P i Controllable DG i Active power; ai b i c i C is the power generation cost coefficient; i (P i ) for DG i The cost function.

[0014] Preferably, based on the controllable DG i active power P i Calculate the minimum total operating cost of the microgrid optimization:

[0015]

[0016] In the formula, n is the number of controllable DGs.

[0017] Preferably, obtaining the supply and demand relationship of the system power includes:

[0018] Based on the incremental rate consensus algorithm, the power supply and demand relationship of the system is calculated:

[0019]

[0020] In the formula, n is the number of nodes in the blockchain network, and P T P represents the total active load of the microgrid. Grid P represents the power exchanged between the microgrid and the main grid; where P i,min ≤P i ≤P i,max P i,min and P i,max For DG i The lower and upper limits of power.

[0021] Preferably, based on the power supply and demand relationship of the system, the minimum total power generation cost of the system is calculated:

[0022]

[0023] Where E represents the electricity purchase price.

[0024] Preferably, constructing the Lagrange function includes:

[0025] Based on the minimum total power generation cost of the system and the supply and demand relationship of the system's power, the Lagrange function is constructed as follows:

[0026]

[0027] In the formula, x is a Lagrange multiplier.

[0028] Preferably, the principle of consistency of incremental rate includes:

[0029] Consistency iteration control rules are written into smart contracts for automatic execution, and the optimal economic operating state is obtained through several iterations.

[0030] Preferably, the deviation correction for the optimal solution electricity price of the incremental rate includes:

[0031] For P respectively i P Grid Taking the partial derivative with x, we determine the extrema and obtain:

[0032]

[0033] Solving the above equation, we get:

[0034] x i =2c i P i +b i =E=x *

[0035] In the formula, x * This is the optimal solution for the Lagrange function.

[0036] Preferably, the deviation correction for the optimal solution price of the incremental rate further includes: achieving the target incremental rate for each node through a consensus algorithm, that is, reaching a consensus on the target incremental rate among each node.

[0037] Preferably, the consensus algorithm further includes a JOIN protocol and a QUIT protocol, which are used to enable dynamic joining and leaving of nodes when the network is operating normally.

[0038] The beneficial effects of this invention are as follows:

[0039] (1) The blockchain-based consensus iterative control strategy provided by this invention, under the unique network routing mode of blockchain, allows each node to obtain data through neighboring nodes, and effectively coordinates each DG based on low communication to achieve optimal economic scheduling of the microgrid. Thanks to the decentralized, tamper-proof, and traceable characteristics of blockchain technology, compared with the traditional architecture, it improves the problem of communication incoordination between DGs in decentralized control, solves the problem of communication pressure and single point of failure in centralized control, enhances the trustworthiness between nodes in distributed control, and ensures the security, accuracy and traceability of data.

[0040] (2) The consensus control strategy based on the PBFT algorithm provided by this invention replaces the cumbersome iterative calculation process with a consensus process. Through three-stage verification, it enables each DG to quickly and accurately reach a consensus on the target incremental rate, achieving the optimal economic operating state. Simultaneously, the PBFT algorithm has a certain degree of fault tolerance, effectively solving the problem of malicious DG nodes. Based on the PBFT algorithm, JOIN and QUIT protocols are added, enabling dynamic joining and leaving of DG nodes, improving the algorithm's practicality and giving the PBFT network good scalability. Compared to traditional control strategies, it improves the intelligence and efficiency of scheduling calculations and reduces the impact of malicious data tampering by the main body. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of a blockchain-based microgrid control architecture in an embodiment of the present invention;

[0043] Figure 2 This is a flowchart illustrating the consistency iteration process in an embodiment of the present invention;

[0044] Figure 3 This is a flowchart of the incremental rate consensus process based on the PBFT algorithm in an embodiment of the present invention;

[0045] Figure 4 This is a flowchart of a microgrid distributed power source collaborative control method based on blockchain technology in an embodiment of the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] Microgrid control architecture using DGs (Distributed Gateways) in the microgrid as blockchain nodes, such as Figure 1As shown, this architecture is divided into five layers. The data layer, based on hash algorithms and asymmetric encryption, encrypts and distributes information such as device parameters and scheduling data to ensure data security, integrity, and traceability. The network layer encapsulates data propagation and verification mechanisms. Multiple DG nodes communicate via a peer-to-peer network to transmit relevant variable information. This point-to-point communication effectively avoids the impact of single-node failures on global scheduling. The consensus layer uses the PBFT consensus algorithm and block synchronization mechanism to enable nodes to reach consensus and synchronization on scheduling data and calculation results, enhancing mutual trust among interactive entities, achieving data sharing, and significantly improving network reliability. In the contract layer, collaborative strategies are written into smart contracts to ensure the intelligence and efficiency of scheduling calculations. In the application layer, various calling interfaces are provided for different application scenarios of multi-DG collaboration in microgrids to achieve intelligent system control.

[0049] This invention provides a distributed power source collaborative control method for microgrids based on blockchain technology. Addressing issues such as difficulty in verifying the authenticity of interactive data, challenges in protecting microgrid privacy, and low overall system security and reliability in microgrids, it designs a microgrid scheduling architecture that integrates blockchain architecture, smart contracts, consensus mechanisms, and encryption technology. Figure 1 A microgrid economic dispatch model based on the principle of incremental rate consistency was constructed. Two control strategies were designed based on a consensus iterative algorithm and a Practical Byzantine Fault-Tolerant (PBFT) consensus mechanism, respectively. These strategies were deployed in a microgrid multi-DG blockchain network to achieve multi-DG target incremental rate coordination for their own output calculations, thereby obtaining the optimal system economy and realizing coordinated economic operation of the microgrid multi-DG. The main process is as follows: Figure 4 As shown.

[0050] Specifically, it includes:

[0051] 1. Blockchain-based microgrid control strategy

[0052] The total operating cost of a microgrid mainly includes the operating costs of controllable distributed generation (DG) and uncontrollable DG. Since uncontrollable DGs mostly utilize renewable energy for power generation, they typically operate using maximum power point tracking (MPPT), and their operating costs are not considered in the optimization model. This invention aims to optimize the operating costs of each controllable DG in a microgrid. The operating costs of each controllable DG mainly consist of two parts: fuel cost and maintenance cost, represented by a quadratic function and a linear function of active power, respectively, as shown in formula (1). The minimum total operating cost of the optimized microgrid is shown in formula (2):

[0053] C i (P i ) = a i +b i P i +c i P i2 (1)

[0054]

[0055] In the formula: P i Controllable DG i Active power; a i b i c i The power generation cost coefficient is denoted by n; n is the number of controllable distributed generation (DG) generators; C is the power generation cost coefficient. i (P i ) for DG i The cost function.

[0056] The system cost is optimal when the incremental rates of all controllable distributed generation (DG) in the microgrid converge uniformly to the electricity price E. Therefore, it is essential to ensure the coordinated operation of all DGs in the system, i.e., to guarantee the consistency of their incremental rates.

[0057] 1.1 Blockchain-based Consistency Iterative Control Strategy

[0058] 1.1.1 Consensus Algorithm

[0059] A consensus algorithm refers to a method that establishes rules to enable nodes to exchange information and gradually reach a consensus on a certain indicator. That is, it satisfies equation (3):

[0060] X1 = X2 = X3 = ... = X n (3)

[0061] In the formula: X i This represents a certain index of node i.

[0062] In practical applications, since the communication process is discrete, a discrete consensus algorithm is adopted, and its k-th step iterative form is shown in formula (4):

[0063]

[0064] In the formula: i = 1, 2, ..., n, representing the number of nodes in the consensus system; k is the discrete-time exponent; d ij D is a row random matrix n The (i,j) terms; l ij L is the Laplace matrix corresponding to the system topology diagram. n The (i,j) terms.

[0065] 1.1.2 Incremental Rate Consensus Algorithm

[0066] Suppose there are n nodes in the blockchain network, and the total active power load of the microgrid is P. T The power supply and demand relationship of the system is shown in formula (1):

[0067]

[0068] In the formula: P Grid This represents the power exchanged between the microgrid and the main grid.

[0069] The power constraint is shown in equation (2):

[0070] P i,min ≤P i ≤P i,max (2)

[0071] In the formula: P i,min and P i,max For DG i The lower and upper limits of power.

[0072] Under the constraints of equations (1) and (2), the minimum total power generation cost of the system is shown in equation (3):

[0073]

[0074] In the formula: E is the electricity purchase price.

[0075] Construct the Lagrangian function to find the optimal solution, as shown in formula (4):

[0076]

[0077] In the formula: x is a Lagrange multiplier.

[0078] For P respectively i P Grid Taking the partial derivative with x to determine the extrema, we can obtain formula (5):

[0079]

[0080] Solving the equation yields formula (6):

[0081] x i =2c i P i +b i =E=x * (6)

[0082] In the formula: x * This is the optimal solution for the Lagrange function.

[0083] With x i For DG i The cost increment rate is optimal when the increment rates of all controllable DGs in the microgrid converge to the electricity price E.

[0084] Each DG incremental rate x iThe convergence criterion is as shown in formula (7):

[0085]

[0086] During the iteration process, a leader-follower mechanism is adopted, whereby the leader guides the incremental rate of the follower nodes to gradually approach the target incremental rate. The leader node update rule is shown in formula (8):

[0087]

[0088] The follower node update rule is defined as shown in formula (9):

[0089]

[0090] In the formula: x i (k) is DG i The incremental rate of the k-th iteration; ε is an adjustment coefficient that controls the convergence speed.

[0091] 1.1.3 Consistency Iterative Control Strategy

[0092] A blockchain network is constructed using each controllable distributed generation (DG) node in the microgrid. Consistency iteration control rules are written into smart contracts for automatic execution, and the optimal economic operating state is obtained through multiple iterations. The blockchain-based consistency iteration process is as follows: Figure 2 As shown. The scheduling process can be described as follows:

[0093] Step 1: Determine the target incremental rate x from the current electricity price E according to formula (10). * , will x * The initial incremental rate x of each DG i (0), cost coefficient a i b i c i Initial power P i (0), Exchange power P Grid (0), Total load demand P T Write the initial block B0.

[0094] Step 2: Each DG node reads block B0, and the leader node and follower nodes calculate and update the incremental rate x according to formulas (12) and (13) respectively. i (k), update the active power P according to formula (9). i (k), update the switching power P according to formula (5) Grid (k).

[0095] Step 3: Each DG temporarily stores the information after the kth update, encrypts it with its private key and attaches a digital signature, and broadcasts it within the network via neighbor forwarding. Once verification is successful, it is written into block B. k Distributed storage.

[0096] Step 4: Each DG node verifies whether the incremental rate x has converged according to formula (11). If it has, the iteration ends; otherwise, the iteration continues.

[0097] 1.2 Control Strategy Based on PBFT Algorithm

[0098] 1.2.1 PBFT Algorithm Control Strategy

[0099] In the above-mentioned consensus iterative control strategy, the leader guides the followers to calculate, correct, and iterate multiple times during the iteration process to achieve the target incremental rate for each node. That is, each node reaches a consensus on the target incremental rate, which can be achieved through a consensus algorithm.

[0100] In the PBFT algorithm, the master-slave node mechanism corresponds to the leader-follower mechanism mentioned above. The client sends a request to the master node, and the master node sends a consensus request to the slave nodes. The correctness of the data is guaranteed through a three-stage verification process of pre-preparation, preparation, and confirmation, so that all nodes can reach a consensus on the request. The consensus process can replace iteration, omitting the calculation and correction parts. By initiating a consensus request on the target incremental rate, the incremental rate consensus of all DG nodes can be quickly achieved.

[0101] In a PBFT network, there are n DG nodes in a view, denoted as (DG1, DG2, ..., DGn). The master node is selected by (V+1) mod n, and the others are slave nodes. V is the current view number. When the master node fails, the view is updated.

[0102] The main process of incremental rate consensus based on the PBFT algorithm is as follows, refer to the appendix. Figure 3 (Each message in the following stages contains the sender's signature):

[0103] 1) Request: Assuming DG1 is elected as the master node, the client sends a request to DG1 regarding the consensus target incremental rate. The request format is as follows:<Request,O,C,T> O represents the requested operation, C represents client information, and T is the timestamp.

[0104] 2) Pre-prepare stage: DG1 receives the request and assembles the pre-prepare message.<Pre-prepare,V=1,S=1,D=data> The message is defined as follows: `M>`, where `V` represents the current view number, `M` is the client's request message, `S` is the sequence number assigned to `M`, and `D` is the digest of `M`. DG1 broadcasts the prepared message to all DG slave nodes. Upon receiving the prepared message, the slave nodes perform signature verification, view verification, and sequence number verification. After successful verification, they accept the prepared message and enter the Prepare phase.

[0105] 3) Prepare phase: The node generates a prepare message, the format of which is...<Prepare,V=1,S=1,D=data,I> Where I represents the node number. The node broadcasts a preparation message to the entire network. When a node receives 2f+1 (including itself, where f is the tolerable number of Byzantine nodes, f = (n-1) / 3) preparation messages, it enters the Commit phase.

[0106] 4) Commit Phase: Each node generates a confirmation message, in the following format:<Commit,V=1,S=1,D=data,I> Each node broadcasts a confirmation message to the entire network. When a node receives 2f+1 confirmation messages (including its own), it executes its request, and all nodes in the network reach a consensus on the target incremental rate, update the blockchain, and reply to the client.

[0107] After all nodes reach a consensus on the incremental rate, they calculate their optimal active power according to formula (9). The optimized switching power is calculated according to formula (5), and scheduling is performed. At this point, the system's economic cost is optimal.

[0108] 1.2.2 PBFT Scalability Improvements

[0109] In microgrids, the joining and leaving of distributed generation (DG) nodes is very common. However, the PBFT algorithm has weak scalability, and node joining and leaving are generally achieved by modifying node configurations and restarting the network. To enhance its scalability, this embodiment adds the JOIN and QUIT protocols to the PBFT algorithm, enabling dynamic joining and leaving of nodes while the network is operating normally.

[0110] 1) JOIN Protocol

[0111] (1) When node Z joins, send a Request-Join. <z>A message was sent to the PBFT network requesting to join.

[0112] (2) Each node receives the message, verifies the message, votes, and sends the Pre-Join message. <z>Upon receiving the 2f+1 message from the master node, the master node verifies the message and responds with a Join message. <z>The message is sent to each slave node.

[0113] (3) After receiving the message, each node sends an End-Join. <z>Upon receiving 2f+1 valid messages, node Z successfully joins the PBFT network.

[0114] 2) QUIT protocol

[0115] (1) When node Z exits, a Request-Quit is broadcast across the entire network. <z>information.

[0116] (2) Each node receives the message, verifies the message, and votes. The node then sends the message Quit. <z>Upon receiving 2f+1 messages, the master node verifies the message and returns an End-Quit response. <z>The message is sent to each node.

[0117] (3) After each node receives the message, all nodes in the system reach a consensus on the exit of node Z, and node Z successfully exits the PBFT network.

[0118] To address the shortcomings of traditional control strategies and methods in microgrids, blockchain technology is introduced into the multi-DG collaborative control of microgrids, and two control strategies are proposed. The beneficial effects compared with previous technologies are as follows.

[0119] (1) A blockchain-based consensus iterative control strategy, under the unique network routing method of blockchain, allows each node to obtain data through neighboring nodes, effectively coordinating each DG based on low communication to achieve optimal economic dispatch of the microgrid. Thanks to the decentralized, tamper-proof, and traceable characteristics of blockchain technology, compared with the traditional architecture, it improves the problem of communication incoordination between DGs in decentralized control, solves the communication pressure and single point of failure problems in centralized control, enhances the trustworthiness between nodes in distributed control, and ensures the security, accuracy, and traceability of data.

[0120] (2) The consensus control strategy based on the PBFT algorithm replaces the cumbersome iterative calculation process with a consensus process. Through three-stage verification, each DG can quickly and accurately reach a consensus on the target incremental rate, achieving the optimal economic operating state. Simultaneously, the PBFT algorithm has a certain degree of fault tolerance, effectively addressing the problem of malicious DG nodes. Based on the PBFT algorithm, JOIN and QUIT protocols are added, enabling dynamic joining and leaving of DG nodes, improving the algorithm's practicality and giving the PBFT network good scalability. Compared to traditional control strategies, this improves the intelligence and efficiency of scheduling calculations and reduces the impact of malicious data tampering by the main body.

[0121] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.< / z> < / z> < / z> < / z> < / z> < / z> < / z>

Claims

1. A microgrid distributed power source collaborative control method based on blockchain technology, characterized in that, include: Construct a microgrid blockchain network, calculate the minimum total operating cost of microgrid optimization and the supply and demand relationship of system power, construct a Lagrange function, and obtain the optimal solution for the incremental rate of electricity price; By applying the consistency principle of incremental rate, the deviation of the optimal solution electricity price of the incremental rate is corrected to obtain the optimal system economy; Calculating the minimum total operating cost of the microgrid optimization includes: With the goal of optimizing the active power of each controllable distributed generation (DG) in a microgrid, the minimum total operating cost of the microgrid optimization is calculated; wherein, the operating cost of each controllable DG includes fuel cost and maintenance cost, which are represented by a quadratic function of active power: In the formula, To be controllable The active power; , , This is the power generation cost coefficient; for The cost function; Based on the controllability active power Calculate the minimum total operating cost of the microgrid optimization: In the formula, n is the number of controllable DGs; Obtaining the supply and demand relationship of the system power includes: Based on the incremental rate consensus algorithm, the power supply and demand relationship of the system is calculated: In the formula, n is the number of nodes in the blockchain network. The total active load of the microgrid. This represents the power exchanged between the microgrid and the main grid; among which, , and for The lower and upper limits of power; The principle of consistency in incremental rates includes: The consistency iteration control rules are written into the smart contract and executed automatically. Through several iterations, the optimal economic operating state is obtained. The deviation correction for the optimal solution electricity price of the incremental rate also includes: achieving the target incremental rate for each node through a consensus algorithm, that is, each node reaches a consensus on the target incremental rate; The consensus algorithm also includes the JOIN and QUIT protocols, which are used to enable the dynamic joining and leaving of nodes when the network is running normally. The JOIN protocol includes: (1) When node Z joins, send a Request-Join. <z> A message was sent to the PBFT network requesting to join;< / z> (2) Each node receives the message, verifies the message, votes, and sends the Pre-Join message. <z>Upon receiving the 2f+1 message from the master node, the master node verifies the message and responds with a Join message. <z> The message is sent to each slave node;< / z> < / z> (3) After receiving the message, each node sends an End-Join message. <z> Upon receiving 2f+1 valid messages, node Z successfully joins the PBFT network.< / z> The QUIT protocol includes: (1) When node Z exits, a Request-Quit is broadcast across the entire network. <z> information;< / z> (2) Each node receives the message, verifies the message, and votes. The node then sends the message Quit. <z>Upon receiving 2f+1 messages, the master node verifies the message and returns an End-Quit response. <z> Messages are sent to each node;< / z> < / z> (3) After each node receives the message, all nodes in the system reach a consensus on the exit of node Z, and node Z successfully exits the PBFT network.

2. The microgrid distributed power source collaborative control method based on blockchain technology according to claim 1, characterized in that, Based on the power supply and demand relationship of the system, calculate the minimum total power generation cost of the system: Where E represents the electricity purchase price.

3. The microgrid distributed power source collaborative control method based on blockchain technology according to claim 2, characterized in that, Constructing the Lagrange function includes: Based on the minimum total power generation cost of the system and the supply and demand relationship of the system's power, the Lagrange function is constructed as follows: In the formula, x is a Lagrange multiplier.

4. The microgrid distributed power source collaborative control method based on blockchain technology according to claim 1, characterized in that, The deviation correction for the optimal solution electricity price with incremental rate includes: To each , Taking the partial derivative with x, we determine the extrema and obtain: Solving the above equation, we get: In the formula, This is the optimal solution for the Lagrange function.

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