A blockchain-based multi-micronet cooperative economic dispatch method

Abstract

The application provides a kind of multi-microgrid collaborative economic dispatching method based on blockchain, comprising: based on microgrid group multi-group architecture model, construct microgrid group dispatching architecture model based on blockchain;Based on objective function submodel, constraint condition submodel and incremental cost submodel, an economic dispatching model is constructed;Wherein, economic dispatching model includes: microgrid layer economic dispatching model and microgrid group layer economic dispatching model;Based on microgrid group dispatching architecture model, intra-group scheduling is carried out on microgrid layer economic dispatching model, and inter-group scheduling is carried out on microgrid group layer economic dispatching model, to obtain multi-microgrid collaborative economic dispatching result.The application constructs microgrid group system distributed dispatching architecture, designs system economic dispatching model with comprehensive economic and environmental protection targets, and proposes a kind of intra-group optimization and inter-group optimization economic dispatching method based on blockchain.In the case of ensuring good expansibility, the economic efficiency and fault tolerance of microgrid group are improved.

Description

Technical Field

[0001] This invention belongs to the field of multi-microgrid collaborative economic scheduling technology, and in particular relates to a blockchain-based multi-microgrid collaborative economic scheduling method. Background Technology

[0002] Against the backdrop of escalating energy crises and environmental pollution, my country is vigorously promoting green and low-carbon development. Microgrid clusters, composed of microgrids (MG) as units, have attracted widespread attention due to their higher stability and better energy efficiency. However, with the expansion of microgrid clusters, their network topology and control structures are becoming increasingly complex, and the operating entities are becoming more diverse. This places higher demands on subgrid privacy maintenance, interest balancing, and system security. How to achieve multi-microgrid collaborative optimization and improve the security, economy, and reliability of microgrid cluster systems has become an urgent problem to be solved.

[0003] Current research on the collaborative optimization of microgrid systems mainly falls into the following categories: Designing a collaborative control framework for microgrids to centrally manage control signals and optimize scheduling, ensuring efficient collaborative operation of the entire microgrid. Centralized control methods have been widely applied to microgrid collaborative optimization, but centralized scheduling relies on a central controller collecting global data for calculations, resulting in high communication requirements, heavy computational burden, and difficulty in guaranteeing the data privacy and interests of each microgrid. Designing a decentralized decomposition and coordination algorithm for microgrids, using decentralized autonomy to protect the data privacy of sub-microgrids, and achieving voltage stability and power sharing based on a decentralized control architecture. However, in decentralized control, the information of each unit is incomplete, making it difficult to achieve system-level optimization goals. Based on distributed control, a hierarchical control scheme for microgrids based on multi-agent consensus relies on information interaction among agents to complete distributed decision-making. Designing different distributed collaborative control methods to achieve automatic power allocation and power sharing among microgrid clusters. While distributed control can overcome the shortcomings of centralized control, it cannot verify the correctness of data and cannot guarantee the safety and reliability of the system. Meanwhile, the realization of collaborative optimization in microgrid cluster systems faces numerous institutional and technical obstacles, such as the authenticity and privacy of multi-microgrid data, trust among interacting entities, balancing the interests of multiple parties, the efficiency of system collaboration algorithms, and distributed decision-making in complex systems. Solving these problems requires the integration of other technologies for support. Summary of the Invention

[0004] To address the aforementioned technical issues, this invention proposes a blockchain-based multi-microgrid collaborative economic scheduling method, which improves the economy and fault tolerance of microgrid clusters while ensuring good scalability.

[0005] To achieve the above objectives, this invention provides a blockchain-based multi-micronet collaborative economic scheduling method, comprising:

[0006] Based on the microgrid multi-group architecture model, a blockchain-based microgrid scheduling architecture model is constructed.

[0007] An economic dispatch model is constructed based on the objective function sub-model, constraint condition sub-model, and incremental cost sub-model; wherein, the economic dispatch model includes: a microgrid layer economic dispatch model and a microgrid group layer economic dispatch model;

[0008] Based on the microgrid group scheduling architecture model, intra-group scheduling is performed on the microgrid layer economic scheduling model, and inter-group scheduling is performed on the microgrid group layer economic scheduling model to obtain multi-microgrid collaborative economic scheduling results.

[0009] Optionally, the microgrid scheduling architecture model includes: a data layer, a network layer, a consensus layer, a contract layer, and an application layer;

[0010] The data layer, network layer, consensus layer, contract layer, and application layer are interconnected in sequence.

[0011] The data layer is used for multi-micronet data sharing, encryption, and storage;

[0012] The network layer is used for multi-micronet data interaction and verification;

[0013] The consensus layer is used to achieve data consensus and synchronization across multiple micronets;

[0014] The contract layer is used to set the system operation rules;

[0015] The application layer is used to provide the calling interface.

[0016] Optionally, the objective function sub-model includes: a sub-model for the minimum operating cost of the microgrid cluster and a sub-model for the minimum carbon trading cost;

[0017] The expression for the minimum operating cost sub-model is:

[0018]

[0019] Where: F1 is the minimum operating cost, m is the number of microgrids in the microgrid group, n is the number of distributed power sources in the microgrid, and C(P) ij Let P be the operating cost of distributed generation j in microgrid i. ij For distributed power generation, δ ij This represents the cost coefficient for the operation and maintenance of distributed power sources.

[0020] The expression for the minimum carbon trading cost sub-model is as follows:

[0021]

[0022] Where: F2 is the minimum carbon trading cost, Ai For the carbon emission allowance of microgrid i, B i Let τ be the carbon emissions of microgrid i, and τ be the carbon trading price.

[0023] The expression for the objective function sub-model is:

[0024] minF total =ωF1+(1-ω)F2

[0025] Among them, F total ω represents the total cost of microgrid operation, carbon trading, and electricity trading, and ω is a weighting coefficient reflecting the importance of the objective, where ω ≥ 0.

[0026] Optionally, the constraint sub-model includes: a power constraint sub-model and a supply-demand balance constraint sub-model;

[0027] The expression for the power constraint sub-model is:

[0028] P ij,min ≤P ij ≤P ij,max

[0029] Among them, P ij,min P ij,max To limit the extreme values ​​of output power for distributed power sources, P ij Power generation capacity of distributed power sources;

[0030] The expression for the supply-demand balance constraint sub-model is:

[0031]

[0032] in, Let i be the load power of microgrid i. Let represent the power loss of microgrid i, D represent the load power identifier, and L represent the power loss identifier.

[0033] Optionally, the incremental cost sub-model includes: a microgrid layer incremental cost sub-model and a microgrid group layer incremental cost sub-model;

[0034] The expression for the incremental cost sub-model of the microgrid layer is as follows:

[0035] λ ij =ω(2a) ij P ij +b ij +δ ij )+(1-ω)τ(v ij -θ)

[0036] Where, λ ij For the incremental cost of micro-source j in microgrid i, a ij b ijAll are distributed generation cost coefficients, v ij Let be the carbon emission coefficient of distributed power source j in microgrid i;

[0037] The expression for the microgrid group layer incremental cost sub-model is as follows:

[0038]

[0039] Where, λ i Let d be the incremental cost of microgrid i, t be the discrete-time exponent, and d be the incremental cost of microgrid i. iz For the row random matrix D of the communication network n The (i,z) terms, λ z Let ξ be the incremental cost of the microgrid z, and ξ' be the convergence coefficient.

[0040] Optionally, intra-group scheduling of the microgrid layer economic dispatch model includes:

[0041] 1.1. The DCs within the group in the microgrid layer are divided into DCs consensus and DCs verification;

[0042] 1.2. In the microgrid layer, each DC node writes the initial parameters into its local initial block;

[0043] 1.3. After each DC node encrypts the data using its private key, it sends a data consensus transaction request to the consensus DCs, and the consensus DCs begin performing the PBFT algorithm;

[0044] 1.4. Upon receiving the request, the DC master node assembles the transaction hash and disconnection list into a Prepare message packet and broadcasts it to the adjacent consensus DCs. Consensus DCs that receive the Prepare message packet determine whether the disconnection list is empty. If it is not empty, it removes the adjacent consensus DC from the disconnection list, updates the Prepare message packet, and forwards it to the adjacent consensus DC. If it is empty, it indicates that the message has reached all DCs, and the message is no longer forwarded.

[0045] 1.5. The verification DCs verify transaction data blocks and synchronize the ledger within the group;

[0046] 1.6. After synchronizing the group ledger, each DC node contains the latest global transaction data. Each DC node calculates the power difference based on the latest global transaction data, updates its own incremental cost and output power, caches the updated data in its local block, signs it, requests data consensus from the consensus DC, and writes the data into the group ledger after consensus is reached.

[0047] 1.7. Perform a convergence judgment on the incremental cost sub-model of the microgrid layer for each DC node. If convergence is not satisfied, return to step 1.6. If convergence is satisfied, trigger inter-group adjustment.

[0048] Optionally, after receiving the Prepare message packet, the consensus DC performs a three-phase consensus:

[0049] Pre-Prepare phase: Verify the legality of the Prepare message packet; the consensus DC first obtains matching transactions from the local transaction pool based on the transaction hash. If no matching transaction is found, it requests the missing transaction from the master node; the consensus DC writes the transaction data into the block for temporary storage. After completing the verification and execution of the transaction, it broadcasts the signature packet to other consensus DCs.

[0050] Prepare phase: After receiving the signature packet, each consensus DC verifies its legality. When the cached signature packets reach a preset number, a Commit packet is broadcast to other consensus DCs.

[0051] Commit Phase: After receiving the Commit packet, the consensus DC verifies its legality. When the number of caching Commit packets reaches a preset amount, the block containing the transaction data temporarily stored in the Pre-Prepare phase is written into the local group ledger storage.

[0052] Optionally, the convergence judgment of the microgrid layer incremental cost sub-model for each DC node is based on the following expression:

[0053]

[0054] Where, λ j DC j The incremental cost is n, where n is the number of DCs and ε is the convergence index.

[0055] Optionally, inter-group scheduling of the microgrid group-level economic scheduling model includes:

[0056] 2.1. Each DC node obtains the micronet consistency incremental cost from its local group ledger, achieves inter-group consensus through the RPBFT algorithm, and writes it into the inter-group ledger;

[0057] 2.2. Each DC node reads the incremental cost of each microgrid from the inter-group ledger, adjusts the inter-group incremental cost difference and updates the power according to the microgrid group layer incremental cost sub-model, and writes the updated data into the inter-group ledger after inter-group consensus.

[0058] 2.3. Verify whether there is a difference in incremental cost between micronets of each DC node; if there is, return to execute step 2.2; if not, terminate the execution and obtain the multi-micronet collaborative economic scheduling result.

[0059] Optionally, the expression for verifying whether there is a difference in incremental costs between the micronets of each DC node is:

[0060] λ1=…=λi =…=λ m

[0061] Where λ1 is the final incremental cost of microgrid 1, λ m Let m be the final incremental cost of the microgrid.

[0062] Compared with the prior art, the present invention has the following advantages and technical effects:

[0063] This invention integrates smart contracts, consensus mechanisms, group architecture, and encryption technology to construct a distributed scheduling architecture for microgrid cluster systems. It designs a system economic scheduling model that combines economic and environmental objectives, and proposes a blockchain-based intra-group and inter-group optimization economic scheduling method. While ensuring good scalability, it improves the economy and fault tolerance of microgrid clusters. Attached Figure Description

[0064] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0065] Figure 1 This is a schematic diagram of the system scheduling process according to an embodiment of the present invention;

[0066] Figure 2 This is a schematic diagram of a microgrid group multi-group architecture model according to an embodiment of the present invention;

[0067] Figure 3 This is a schematic diagram of a blockchain-based microgrid group scheduling architecture according to an embodiment of the present invention;

[0068] Figure 4 This is a schematic diagram of the intra-group scheduling process according to an embodiment of the present invention;

[0069] Figure 5 This is a schematic diagram of the inter-group scheduling process according to an embodiment of the present invention. Detailed Implementation

[0070] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0071] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0072] Example

[0073] This invention provides a blockchain-based multi-micronet collaborative economic scheduling method, comprising:

[0074] Based on the microgrid multi-group architecture model, a blockchain-based microgrid scheduling architecture model is constructed.

[0075] An economic dispatch model is constructed based on the objective function sub-model, constraint condition sub-model, and incremental cost sub-model; wherein, the economic dispatch model includes: a microgrid layer economic dispatch model and a microgrid group layer economic dispatch model;

[0076] Based on the microgrid group scheduling architecture model, intra-group scheduling is performed on the microgrid layer economic scheduling model, and inter-group scheduling is performed on the microgrid group layer economic scheduling model to obtain multi-microgrid collaborative economic scheduling results.

[0077] This invention integrates smart contracts, consensus mechanisms, group architecture, and encryption technology to construct a distributed scheduling architecture for microgrid cluster systems. It designs a system economic scheduling model that combines economic and environmental objectives, and proposes a blockchain-based intra-group and inter-group optimization economic scheduling method. While ensuring good scalability, it improves the economy and fault tolerance of microgrid clusters.

[0078] To address the challenges of subnet privacy protection, difficulty in verifying the authenticity of interactive data, and low overall system security and reliability in microgrid systems, a distributed scheduling and operation architecture for microgrids integrating group architecture, smart contracts, consensus mechanisms, and encryption technologies was designed. An optimization method for decentralized collaborative autonomous decision-making in microgrids based on blockchain was also proposed.

[0079] The economic and environmental objectives are normalized using a linear weighting method, and the weight coefficients are evaluated according to the importance of the objectives to determine the system's economic scheduling model.

[0080] In a microgrid cluster, each microgrid is treated as a distributed generation (DG) group. Based on the principle of consistency of incremental costs among multiple DGs, intra-group optimization and inter-group optimization methods are further planned using a single-chain multi-ledger model.

[0081] The micronet cluster consists of multiple sub-micronets, each of which is a DG group. Each DG controller (DC) acts as a node to construct the blockchain network. Based on the FISCO BCOS group architecture, each DC maintains an intra-group ledger and an inter-group ledger, implementing a single-chain multi-ledger operation and storage mechanism. The system scheduling flowchart is as follows: Figure 1 As shown.

[0082] Blockchain's characteristics of distributed storage, decentralization, transparency, tamper-proofing, and traceability can effectively solve the data interaction problem of distributed energy terminals. Through a consensus algorithm involving multiple pairwise interactions, it obtains the required data and completes calculations, resolving the data bias problem in droop control while ensuring the security and robustness of the entire system. The advantages of a blockchain-based distributed energy droop control strategy are:

[0083] (1) In the blockchain network, each subject adopts asymmetric encryption technology to prevent data tampering and fraudulent transactions. The sender encrypts the digest of the transaction information with a private key, and the receiver verifies the sender's identity and the authenticity of the data with a public key, thus forming a trust basis between the interactive subjects and ensuring the security and reliability of the data.

[0084] (2) In a blockchain network, stakeholders collaborate on a large scale and efficiently without relying on a centralized organization through a consensus mechanism. Stakeholders simultaneously share system operation and transaction information, optimizing resource allocation. Effective consensus among stakeholders is a prerequisite for the micro-network group system to propose optimized operation strategies.

[0085] (3) By setting up unit servers for each micronet entity to access the blockchain network, based on peer-to-peer (P2P) transmission, the entities can achieve full interconnection of information, share data verification and calculation work, make decisions equally, and jointly participate in the operation and maintenance of the micronet group blockchain network.

[0086] (4) Through the rPBFT consensus algorithm and smart contract technology, the coordinated scheduling of distributed energy sources is carried out through inter-node interaction, which can ensure the effective recording of each scheduling data and eliminate the hidden dangers caused by malicious node behavior. The traceability of blockchain technology can save the scheduling data of each distributed energy source in the system, and provide a guarantee that the data is authentic and verifiable.

[0087] (5) Based on the group architecture concept of FISCO BCOS, data isolation and confidentiality on the same chain are achieved through a single-chain multi-ledger approach. Block consensus, transaction processing, and data storage are isolated between different groups. Through group architecture technology, the privacy of multiple parties is guaranteed, the interests of multiple parties are maximized, and the system's operational complexity and management costs are reduced.

[0088] Using distributed generation (DG) as nodes, groups are established within the microgrid based on different collaborative relationships. The multi-group architecture model of a microgrid is as follows: Figure 2As shown, DG1 and DG2 in microgrid 1 form group 1 and maintain ledger 1. DG3 and DG4 in microgrid 2 form group 2 and maintain ledger 2. DG1-DG4 belong to the same microgrid group, join group 0, and maintain ledger 0. Groups 0, 1, and 2 share public network services, but each group has an independent ledger and transaction execution environment. During a transaction, if DG1 in group 1 receives a transaction request, group 1 internally reaches a consensus on the transaction and data and stores it in ledger 1. Other groups are unaware of and cannot see this transaction, ensuring the system's privacy requirements. Simultaneously, each group can formulate smart contracts according to its own needs to maximize its own interests. Applying the group architecture from blockchain technology to microgrid groups results in a multi-group architecture for microgrids.

[0089] Based on the microgroup multi-group architecture model, a blockchain-based microgroup system scheduling architecture is built and deployed, such as... Figure 3 As shown, it comprises a data layer, network layer, consensus layer, contract layer, and application layer. It achieves global data sharing; the network layer encapsulates message propagation protocols and data verification mechanisms, enabling information exchange based on a P2P peer-to-peer network. Its decentralized topology avoids the impact of single-point failures on overall operation; the consensus layer uses the rPBFT consensus mechanism to ensure that all nodes reach effective consensus on scheduling information and operation results, significantly improving network reliability; the contract layer uses smart contracts to formulate system operation rules, ensuring that the scheduling calculation process is efficient, secure, and transparent; and the application layer provides various callable interfaces for microgrid application scenarios, thereby achieving intelligent system control.

[0090] 1. Economic Scheduling Model for Microgrid Cluster Systems

[0091] The economic dispatch problem of power systems is typically a mathematical problem to solve. Under the conditions of energy balance and relevant operational constraints, the power generation capacity of generator units is planned to ensure the economical operation of the entire microgrid cluster. A cost function is constructed by integrating the operating costs and carbon trading costs of the microgrid cluster, and a mathematical model is established by combining relevant constraints. In this embodiment, the economic dispatch model of the microgrid cluster system includes a microgrid-level economic dispatch model and a microgrid cluster-level economic dispatch model. The objective functions and constraints in the microgrid-level and microgrid cluster-level economic dispatch models are the same, but they differ in their requirements for incremental cost consistency.

[0092] 1.1 Objective Function:

[0093] (1) Minimum operating cost F1

[0094]

[0095] C(P ij ) = a ij P ij 2 +bij P ij +c ij (2)

[0096] Where: m is the number of microgrids in the microgrid group, n is the number of distributed power sources in the microgrid, and C(P) ij Let P be the operating cost of distributed generation j in microgrid i. ij For distributed power generation, δ ij a represents the cost coefficient for the operation and maintenance of distributed power sources. ij b ij c ij This represents the cost coefficient for distributed power generation.

[0097] (2) Minimum carbon trading cost F2

[0098] For a given scheduling cycle, considering the difference between the actual carbon emissions of the integrated microgrid cluster and the carbon emission allowance, and based on the carbon trading price, the minimum carbon trading cost function is constructed as follows: (This function involves selling or buying carbon emission rights).

[0099]

[0100] In the formula: A i Let θ represent the carbon emission allowance for microgrid i, and θ represent the carbon trading allowance per unit of power generation.

[0101]

[0102] In the formula: B i v represents the carbon emissions of microgrid i. ij Let be the carbon emission coefficient of distributed power source j in microgrid i.

[0103] The carbon trading fee is

[0104]

[0105] In the formula: τ is the carbon trading price.

[0106] By aggregating multi-objective functions using the linear weighted sum method, weight coefficients are assigned according to the importance of the objectives, and the aggregation is transformed into a single-objective solution. The aggregated multi-objective objective function is shown in formula (6):

[0107] minF total =ωF1+(1-ω)F2 (6)

[0108] In the formula: F total ω represents the total cost of microgrid operation, carbon trading, and electricity trading, and ω is a weighting coefficient reflecting the importance of the objective, where ω ≥ 0.

[0109] 1.2 Constraints:

[0110] (1) Power constraint:

[0111] P ij,min ≤P ij ≤P ij,max (7)

[0112] In the formula: P ij,min P ij,max Limit the extreme values ​​of output power for distributed power sources.

[0113] (2) Supply and demand balance constraints:

[0114]

[0115] In the formula: Let i be the load power of microgrid i; Let be the power loss of microgrid i, which can be solved by formula (9).

[0116] P L =αP D (9)

[0117] In the formula: α is the loss factor, which is generally 3%-6%.

[0118] 1.3 Incremental cost consistency:

[0119] Incremental cost refers to the change in power generation cost caused by a change in the output power of a micro-source. Under the constraints, each micro-source has the same incremental cost at its optimal operating point, minimizing the total system cost. Therefore, incremental cost is chosen as a consistent variable to solve the economic optimization problem.

[0120] (1) Microgrid layer

[0121] At the microgrid layer, the incremental cost of each micro-source is defined as:

[0122] λ ij =ω(2a) ij P ij +b ij +δ ij )+(1-ω)τ(v ij -θ) (10)

[0123] In the formula: λ ij For the incremental cost of micro-source j in microgrid i, a ij b ij All are distributed generation cost coefficients, v ij Let λ be the carbon emission coefficient of distributed power source j in microgrid i; when the incremental costs of each microsource are equal, the final solution λ of microgrid i is obtained. i At this time, the output power is

[0124]

[0125] The discrete consensus algorithm is used to iteratively solve for the optimal incremental cost solution. The convergence direction of incremental cost is adjusted by the power supply and demand difference in the microgrid. The iterative form of incremental cost consensus in microgrid i can be expressed as:

[0126]

[0127]

[0128]

[0129] In the formula: i = 1, 2, ..., n, representing the number of nodes in the consensus system; t is the discrete-time exponent; d jk Communication network row random matrix D n The (j,k) term, ξ is the convergence coefficient.

[0130] The convergence criterion is as shown in formula (15):

[0131]

[0132] Where, λ j DC j The incremental cost is n, where n is the number of DCs and ε is the convergence index.

[0133] (2) Microgrid cluster

[0134] Formula (16) indicates that there is a difference in incremental costs between microgrids. Formula (17) is used to adjust the difference and solve for the incremental cost of inter-grid consistency.

[0135] λ1=…=λ i =…=λ m (16)

[0136]

[0137]

[0138] In the formula: ξ' is the convergence coefficient, λ1 is the final incremental cost of microgrid 1, and λ m Let m be the final incremental cost of the microgrid.

[0139] 2. An Economic Scheduling Method for Microgrid Cluster Systems Based on RPBFT Consensus Algorithm

[0140] Under the economic dispatch model, specific dispatch methods are deployed. Based on the hierarchical model of economic dispatch, the specific dispatch methods are divided into intra-group dispatch and inter-group dispatch. Intra-group optimization executes the economic dispatch model of the microgrid layer, while inter-group optimization executes the economic dispatch model of the microgrid group layer. The system dispatch flowchart is as follows: Figure 1As shown, a micronet cluster consists of multiple sub-micronets, each of which is a DG group. Each DG controller (DC) acts as a node to construct the blockchain network. Based on the FISCO BCOS group architecture, each DC maintains an intra-group ledger and an inter-group ledger, implementing a single-chain multi-ledger operation and storage mechanism.

[0141] 3.1. Intra-group optimization

[0142] In a single micro-network, each DC within the group maintains its own group ledger. The consensus network of the single micro-network consists of DCs within the group, which are divided into consensus DCs and validator DCs based on the RPBFT algorithm, and their roles are periodically adjusted. A master DC is elected from the consensus DCs. The master DC has equal status with other DCs and only receives information first. The group scheduling flowchart is as follows. Figure 4 The steps are described below.

[0143] Step 1: Each DC node writes the initial parameters into its local initial block.

[0144] Step 2: After each DC node encrypts the data using its private key, it sends a data consensus transaction request to the consensus DCs, and the consensus DCs begin executing the PBFT algorithm.

[0145] Step 3: The DC master node receives the request first, assembles the transaction hash and disconnection list into a Prepare message packet, and broadcasts it to adjacent consensus DCs. Consensus DCs receiving the Prepare packet check if the disconnection list is empty. If it is not empty, it removes the adjacent consensus DC from the disconnection list, updates the Prepare message packet, and forwards it to the adjacent consensus DC; if it is empty, it indicates that the message has reached all DCs, and the message is no longer forwarded. (The following message broadcasting method is the same and will not be repeated.)

[0146] The consensus DC receives the Prepare packet and begins the three-phase consensus process:

[0147] Pre-Prepare Phase: Verifying the validity of the Prepare package. The consensus DC prioritizes retrieving matching transactions from its local transaction pool based on the transaction hash. If no matching transaction is found, it requests the missing transaction from the master node. The consensus DC writes the transaction data into a block temporary storage. After completing the verification and execution of the transaction, it broadcasts the signature package to other consensus DCs.

[0148] Prepare phase: After receiving the signature packet, each consensus DC verifies its legitimacy. When the number of cached signature packets reaches 2f+1 (f is the number of Byzantine nodes), it broadcasts the Commit packet to other consensus DCs.

[0149] Commit Phase: After receiving the Commit packet, the consensus DC verifies its legality. When the number of cached Commit packets reaches 2f+1, the block containing the transaction data temporarily stored in the Pre-Prepare phase is written into the local group ledger storage.

[0150] Step 4: Verify DCs to verify transaction data blocks and synchronize the group ledger.

[0151] Step 5: At this point, each DC node contains the latest global transaction data. Based on this, each DC node calculates the power difference and updates its own incremental cost and output power. The updated data is cached in the local block, signed, and then requests data consensus from the consensus DC (refer to steps 2-4 for the consensus process). After successful consensus, the data is written to the group's ledger.

[0152] Step 6: Verify equation (15) at each node. If it is not satisfied, proceed to step 5. If it is satisfied, trigger inter-group adjustment.

[0153] 3.2. Between-group optimization

[0154] In a micronet cluster, each DC node maintains not only its own micronet's intra-group ledger but also a synchronized inter-group ledger. The basic scheduling flowchart for the inter-group ledger based on the RPBFT consensus algorithm is shown below. Figure 5 The steps are described below.

[0155] Step 1: Each DC node obtains the micronet consistency incremental cost from its local group ledger, achieves inter-group consensus through the RPBFT algorithm, and writes it into the inter-group ledger.

[0156] Step 2: Each DC node reads the incremental cost of each micronet from the inter-group ledger, adjusts the difference in incremental cost between groups and updates the power according to (17), and writes the updated data into the inter-group ledger after the inter-group consensus is successful.

[0157] Step 3: Verify equation (16) at each node. If it is not satisfied, proceed to step 2. If it is satisfied, terminate the execution. The incremental cost of all micro-sources in the system is consistent, and the system is running at the optimal economic point.

[0158] This embodiment integrates smart contracts, consensus mechanisms, group architecture, and encryption technology to construct a distributed scheduling architecture for a microgrid cluster system. It designs a system economic scheduling model that combines economic and environmental objectives, and proposes a blockchain-based intra-group and inter-group optimization economic scheduling method. While ensuring good scalability, it improves the economy and fault tolerance of the microgrid cluster. The technical problems to be solved are as follows:

[0159] (1) There are risks such as incompleteness, tampering, and forgery in the data interaction process of multiple microgrids. By designing a distributed scheduling architecture for the microgrid group system, the data layer includes technologies such as asymmetric encryption and hash functions to ensure the traceability and tamper-proofness of the data. Information such as equipment parameters, energy consumption data, and operation data are distributed and stored. Data consensus is achieved among nodes, and data interaction records are traceable, ensuring the security and reliability of data storage and interaction.

[0160] (2) The data in a multi-micronet system is diverse, and the authenticity of the interactive information is difficult to verify. The network structure of a multi-micronet system is complex, and the failure of a single node may affect the operation of the system. By designing a distributed scheduling architecture for the micronet group system, a message propagation protocol and data verification mechanism are encapsulated at the network layer. Information interaction is based on a P2P peer-to-peer network. Its decentralized topology avoids the impact of a single point of failure on the overall operation.

[0161] (3) In blockchain networks with complex structures, the application of consensus mechanisms faces numerous challenges. For example, the Proof-of-Work mechanism consumes a large amount of computing power; the Proof-of-Stake mechanism suffers from low fairness due to the concentration of stake; and BFT-type algorithms such as Byzantine Fault Tolerance (BFT) and Practical Byzantine Fault Tolerance (PBFT) have complexity related to the node size and weak scalability. Therefore, the consensus layer of the distributed scheduling architecture of the microgrid system is based on the rPBFT consensus mechanism of FISCO BCOS to ensure that all nodes reach an effective consensus on scheduling information and running results, thereby significantly improving network reliability.

[0162] (4) Traditional control schemes for multi-micronet systems often fail to balance the interests of multiple stakeholders. This patent, through FISCOBCOS group architecture technology, ensures the privacy of multiple stakeholders, maximizes the satisfaction of their interests, and reduces the system's operational complexity and management costs.

[0163] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1.A blockchain-based multi-microgrid cooperative economic dispatch method, characterized in that, The application relates to a micro-grid group scheduling architecture model based on a block chain. The economic scheduling model comprises a micro-grid layer economic scheduling model and a micro-grid group layer economic scheduling model. The micro-grid group scheduling architecture model is used to perform group scheduling on the micro-grid layer economic scheduling model and group scheduling on the micro-grid group layer economic scheduling model, so as to obtain a multi-micro-grid collaborative economic scheduling result. The micro-grid layer incremental cost sub-model has an expression as follows: The micro-grid group layer incremental cost sub-model has an expression as follows: The group scheduling on the micro-grid layer economic scheduling model comprises the following steps. wherein, is the incremental cost of micro-source j in micro-grid i, are the distributed generation cost coefficients, is the carbon emission coefficient of distributed generator j in micro-grid i; 1-1. The DCs in the micro-grid layer are divided into consensus DCs and verification DCs; wherein, is the incremental cost for microgrid i, is the discrete-time index, is the row stochastic matrix for the communication network is the (i, z) entry of, is the incremental cost for microgrid z, is the convergence coefficient; 1-2. Each DC node in the micro-grid layer writes initial parameters into a local initial block; 1-3. After each DC node encrypts data by using a private key, the data consensus transaction request is sent to the consensus DCs, and the consensus DCs start the PBFT algorithm; 1-4. The DC master node firstly receives the request, assembles a transaction hash and a disconnection list into a Prepare message package, and broadcasts the Prepare message package to adjacent consensus DCs; the consensus DCs receiving the Prepare message package judge whether the disconnection list is empty; if not, the adjacent consensus DCs are removed from the disconnection list, the Prepare message package is updated and forwarded to adjacent consensus DCs; if the disconnection list is empty, it indicates that the message has reached all DCs, and the message is not forwarded any more; 1-5. The verification DCs verify the transaction data block and synchronize the group account book; 1-6. After synchronizing the group account book, each DC node contains the latest global transaction data, each DC node calculates the power difference according to the latest global transaction data, updates the incremental cost and output power, and caches the updated data to a local block, signs the data and requests data consensus from the consensus DCs, and writes the data into the group account book after the data consensus; 1-7. The convergence of the micro-grid layer incremental cost sub-model of each DC node is judged; if the convergence is not satisfied, the step 1.6 is executed; if the convergence is satisfied, group adjustment is triggered; The group scheduling on the micro-grid group layer economic scheduling model comprises the following steps. 2-1. Each DC node obtains the consistency incremental cost of the micro-grid from the local group account book, reaches group consensus through the RPBFT algorithm, and writes the group consensus into the group account book; 2-2. Each DC node reads the incremental cost of each micro-grid from the group account book, adjusts and updates the power according to the micro-grid group layer incremental cost sub-model, and writes the updated data into the group account book after the group consensus; 2-3. The incremental cost difference between the micro-grids of each DC node is verified; if the incremental cost difference exists, the step 2.2 is executed; if the incremental cost difference does not exist, the execution is terminated, and the multi-micro-grid collaborative economic scheduling result is obtained. ​ ​ 2.The blockchain-based multi-micronet cooperative economic dispatching method according to claim 1, characterized in that, The micro-grid group scheduling architecture model comprises a data layer, a network layer, a consensus layer, a contract layer and an application layer; The data layer, the network layer, the consensus layer, the contract layer and the application layer are sequentially connected with each other; The data layer is configured to perform multi-micro-grid data sharing, encryption and storage; The network layer is configured to perform multi-micro-grid data interaction and verification; The consensus layer is configured to realize multi-micro-grid data consensus and synchronization; The contract layer is configured to set system operation rules; The application layer is configured to provide a calling interface. 3.The blockchain-based multi-micronet cooperative economic dispatching method of claim 1, wherein, The target function sub-model comprises a minimum operation cost sub-model of the micro-grid group and a minimum carbon trading cost sub-model; The expression of the minimum operation cost sub-model is: Wherein: is the minimum operating cost, m is the number of microgrids in the microgrid group, n is the number of distributed power sources in the microgrid, is the operating cost of the microgrid distributed power source , is the distributed power generation power, is the distributed power operation and maintenance cost coefficient; The expression of the minimum carbon trading cost sub-model is: wherein: , is the carbon emission quota of microgrid i, is the carbon emission of microgrid i, is the carbon trading price; The expression of the target function sub-model is: wherein, is the total cost of microgrid cluster operation, carbon trading and electricity trading, is the weight coefficient reflecting the importance of the target, . 4.The blockchain-based multi-micronet cooperative economic dispatching method of claim 1, wherein, The constraint condition sub-model comprises a power constraint sub-model and a supply-demand balance constraint sub-model; The expression of the power constraint sub-model is: wherein, is an extreme value of the output power of the distributed power supply, is the generated power of the distributed power supply; The expression of the supply-demand balance constraint sub-model is: wherein, is the load power of the microgrid i, is the loss power of the microgrid i, D is the load power identifier, L is the loss power identifier. 5.The blockchain-based multi-micronet cooperative economic dispatching method of claim 1, wherein, After the consensus DC receives the Prepare message package, three-stage consensus is performed: Pre-Prepare stage: verifying the legality of the Prepare message package; the consensus DC acquires a hit transaction from a local transaction pool according to a transaction hash in priority, and requests a missing transaction from a master node if there is no hit; the consensus DC writes transaction data into a block temporary storage, and broadcasts a signature package to other consensus DCs after verifying and executing the transaction; Prepare stage: each consensus DC verifies the legality of the signature package after receiving the signature package, and broadcasts a Commit package to other consensus DCs when the cache of the signature package reaches a preset number; Commit stage: the consensus DC verifies the legality of the Commit package after receiving the Commit package, and writes the block of the Pre-Prepare stage temporary transaction data into a local group ledger storage when the cache of the Commit package reaches a preset number. 6.The blockchain-based multi-micronet cooperative economic dispatching method of claim 1, wherein, The expression for judging the convergence of the micro-grid layer incremental cost sub-model of each DC node is: wherein is the incremental cost, is the incremental cost, n is the number of DCs, is the convergence indicator. 7.The blockchain-based multi-micronet cooperative economic dispatching method of claim 1, wherein, The expression for verifying whether there is a difference in the incremental cost between the micro-grids of each DC node is: wherein, is the final incremental cost of the microgrid 1, is the final incremental cost of the microgrid m.

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