Hierarchical Power Transaction Matching Method under Blockchain-Based Distributed Power Market

Abstract

The present invention relates to a hierarchical power trading matching method in a distributed power market based on blockchain. The present invention hierarchizes power trading matching: the first stage is the negotiation and bargaining with regional energy agents as the main body, and the second stage is the clearing of P2P trading matching among users within the region under the regional energy agent, balancing the differences in power supply and demand between regions and within regions in the distributed power market. In the stage of P2P trading matching among users within the region, according to the power declaration information of power selling users, on the basis of considering environmental friendliness, economy and user reliability, the comprehensive evaluation value of power selling users is obtained through multi-index comprehensive evaluation, and trading matching is carried out with power purchasing users according to the size of the comprehensive evaluation value, meeting the needs of users to select electric energy from multiple factors. The present invention can ensure the openness, transparency, security and credibility of trading data in the distributed power market, and at the same time provide a solution for comprehensively considering the power declaration information of users and then carrying out trading matching.

Description

Technical Field

[0001] The present invention belongs to the technical fields of the energy Internet and blockchain, and relates to a hierarchical power trading matching method in a distributed power market, and particularly relates to a hierarchical power trading matching method in a distributed power market based on blockchain. Background Art

[0002] The increasing popularity of distributed photovoltaic power generation in the power grid has changed the structure of the power consumption market. Traditional power consumers are becoming prosumers who can not only consume electricity but also produce electricity. In 2017, the National Energy Administration and the National Development and Reform Commission of China issued the "Notice on Carrying out Pilot Projects for Market-based Transactions of Distributed Generation", which provides guidance for distributed generation transactions and gradually opens up the power selling side in the power market. Conducting market-based distributed power transactions allows prosumer users to exchange unbalanced electricity externally after meeting their own electricity consumption needs, reducing the phenomenon of "abandoned light", promoting consumption, and at the same time directly bargaining with other power users can reduce the intermediate links in power transactions and improve the electricity consumption benefits of all parties.

[0003] However, due to the characteristics of distributed power transactions, such as a large number of orders, small scale, and decentralization, in the big data era, the privacy of the account information and transaction records of market users cannot be guaranteed, and the security coefficient of the central institutional database is low. Once the user transaction data is hacked and tampered with, it will directly damage the transaction security of both parties.

[0004] Blockchain technology refers to a technical solution that can collectively maintain a database in a decentralized and trustless manner and ensure data synchronization and immutability. Blockchain combines computer technologies such as distributed storage, peer-to-peer transmission, consistency verification, and encryption algorithms, and realizes the immutability and non-forgery of a decentralized shared ledger. With its characteristics of decentralization, collaborative autonomy, collective maintenance, and smart contracts, it is similar to the concepts of openness, interconnection, sharing, and peer-to-peer in the energy Internet, providing a new solution idea for the transaction method in the distributed power market under the energy Internet.

[0005] In addition, the transaction matching problem is an important issue to be considered in user transactions in a distributed power market. At present, the main transaction matching methods in the power market include inter-regional P2P transactions with microgrids as the main body, intra-regional user P2P transactions with users as the main body, and point-to-microgrid transactions. Taking the microgrid as the transaction main body can improve the utilization rate of collective renewable energy power generation, but it lacks consideration for the personalized needs of users within the microgrid. Taking intra-regional users as the transaction main body can alleviate the fluctuations and unpredictability of user renewable energy power generation, but the surplus and deficit electricity can only be traded with the distribution network, which will reduce the electricity consumption benefits of all parties to a certain extent.

[0006] And during the matching process, only the electricity price is regarded as a single factor, and trading matching is carried out according to the principle of high-low matching. However, with the gradual opening of the lower-level electricity market, when consumers have certain purchasing options, they often make a comprehensive consideration of multiple factors such as the environmental friendliness, price economy, and resource reliability of electric energy based on their actual situations, and then choose to purchase electric energy preferentially. Summary of the Invention

[0007] The purpose of the present invention is to overcome the deficiencies of the existing methods, and propose a hierarchical power trading matching method under a distributed power market based on blockchain, so as to increase the overall linkage between users in different regions and within the same region under the distributed power market. On the premise of balancing the power supply and demand between regions, comprehensively consider the environmental friendliness index, economic index, and user reliability index of users, and then conduct trading matching between users. At the same time, it can solve the problems of the security and credibility of market user information and transaction data.

[0008] To achieve the above purpose, the present invention adopts the following technical solutions:

[0009] A hierarchical power trading matching method under a distributed power market based on blockchain proposed by the present invention includes the following steps:

[0010] (1) Divide the stages for trading entities to participate in power trading under the distributed power market; where: the trading entities include regional energy agents, as well as power purchase users and power sale users under the regional energy agents;

[0011] (2) In the first stage, obtain regional energy agents, and predict the power situation in this cycle according to the historical load demand and distributed photovoltaic power generation data within the region, and automatically calculate the power purchase price and sale price between regional energy agents, where: the power situation of regional energy agents in this cycle refers to the total power demand or power supply of users under the regional energy agents;

[0012] (3) In the second stage, obtain the transaction declaration information of power purchase users and power sale users under the regional energy agents, where: the transaction declaration information of power sale users includes the power sale price, power sale volume, unit carbon emission factor, and reliability index, and automatically calculate the environmental friendliness index and economic index of power sale users; the transaction declaration information of power purchase users includes the power purchase price and power purchase volume;

[0013] (4) Based on the entropy weight TOPSIS method, construct a decision matrix, standardize the decision matrix, calculate the weighted decision matrix, and finally calculate the comprehensive evaluation value of power sale users. Sort the obtained comprehensive evaluation values from high to low to form a queue of power sale users; arrange them from high to low according to the decreasing method of bidding price to finally form a queue of power purchase users;

[0014] (5) Perform transaction matching in order of the power purchase users and power sale users queues from high to low to complete the transaction clearing for this cycle.

[0015] In the present invention, the calculation of the power purchase price and the power sale price between regional energy agents in step (2) specifically is:

[0016] Based on the relationship between CE s and CE d in the distributed power market supply and demand relationship, determine the power purchase price and the power sale price between regional energy agents, as shown in formulas (1)-(5):

[0017] When CE s = CE d ,

[0018] When CE s > CE d ,

[0019]

[0020] When CE s < CE d ,

[0021]

[0022] Wherein: CE s is the total power supply of the regional energy agent in the distributed power market, CE d is the total power demand of the regional energy agent in the distributed power market, P buy is the power purchase price between regional energy agents, P sell is the power sale price between regional energy agents, P bfg is the power sale price of the distribution network, P stg is the grid connection price of the distribution network.

[0023] In the present invention, the power purchase price of the power purchase user and the power sale price quotation of the power sale user in step (3) should satisfy the constraints of the power purchase price and the power sale price between regional energy agents: P sell << P i << P buy , where P i is the power quotation of user i;

[0024] The calculation of the reliability index of the power sale user is as shown in formula (6):

[0025]

[0026] Where: C i is the reliability index value of user i in this cycle, C′ i is the reliability index value of user i in the previous period, e′ i is the actual transaction amount of user i in the previous cycle, E′ i is the agreed transaction amount of user i in the transaction bill of the previous period, δ∈[0,1] is the weight value of the user's historical transaction completion degree, the smaller the δ value, the more biased towards the user's recent reliability, and vice versa, more emphasis is placed on the user's long-term power supply stability;

[0027] The calculation of the environmental friendliness index of electricity sellers is shown in formula (7):

[0028]

[0029] Where: G i is the environmental friendliness index value of user i in this cycle, Q i is the unit carbon emission factor corresponding to user i, E i is the electricity sold by user i in this cycle, E S The total electricity sales of all electricity users under the regional energy agency:

[0030] The economic indicators of electricity sellers are calculated as shown in formula (8):

[0031]

[0032] Where: I i is the economic index value of user i in this period.

[0033] In the present invention, the method of step (4) is:

[0034] Constructing a decision matrix: n electricity sellers are assumed to participate in the comprehensive evaluation, which includes three evaluation indicators: environmental friendliness, economic benefits and reliability. These three evaluation indicators form a decision matrix:

[0035]

[0036] Among them: A n×3 G is an n×3 decision matrix constructed based on the three evaluation indicators of environmental friendliness, economic benefits and reliability of n electricity sales users. n1 is the environmental friendliness index value of the nth electricity seller, I n2 The economic index value of the nth electricity sales user, C n3 is the reliability index value of the nth electricity sales user.

[0037] Decision matrix standardization: The evaluation indicators in the decision matrix are uniformly converted into their corresponding positive indicators: Among them: G max = max(G 11 , G 21 ,..., G n1 ), I max = max(I 12 , I 22 ,..., I n2 ), C max = max(C 13 , C 123 ,..., C n3 ), to obtain a new matrix after index standardization:

[0038]

[0039] Among them: A' n×3 is the new matrix obtained after standardizing the decision matrix, a n1 is the value obtained after positive transformation of the environmental friendliness index value of the nth electricity selling user, a n2 is the value obtained after positive transformation of the economic index value of the nth electricity selling user, a n3 is the value obtained after positive transformation of the reliability index value of the nth electricity selling user.

[0040] Calculate the weighted decision matrix: For the three evaluation indicators of environmental friendliness, economic benefits, and reliability, the entropy weight method is used to calculate the weight vector of various parameter indicators: ω = (ω1, ω2, ω3), and multiply the weight corresponding to each indicator by the positive standardized decision matrix to obtain the weighted decision matrix:

[0041] R = (r ij ) n×3 #(11)

[0042] Among them: r ij = ω j × a ij , i ∈ [1, 2,..., n]; j ∈ [1, 2, 3]

[0043] Calculate the comprehensive evaluation value of each electricity selling user i:

[0044]

[0045] Among them:

[0046]

[0047]

[0048]

[0049]

[0050] where: V i is the comprehensive evaluation value of user i in this period, is the maximum value among the index values in the j-th column of the weighted decision matrix, is the minimum value among the index values in the j-th column of the weighted decision matrix, is the Euclidean distance between the three index values of user i in the weighted decision matrix and the maximum value in the corresponding column, is the Euclidean distance between the three index values of user i in the weighted decision matrix and the minimum value in the corresponding column.

[0051] Form the electricity selling user queue: According to the comprehensive evaluation values of each electricity selling user, formulate the trading matching priority from high to low to form the electricity selling user queue;

[0052] Form the electricity purchasing user queue: According to the electricity purchasing prices of each electricity purchasing user, formulate the trading matching priority from high to low to form the electricity purchasing user queue.

[0053] In the present invention, the method described in step (5) is as follows:

[0054] According to the trading matching priorities of the electricity selling user queue and the electricity purchasing user queue obtained in step (4), sequentially match the trading objects in the region: When the trading matching priorities are the same, according to the time priority principle, the user who declares the trading information first is given priority for trading matching; After the trading matching is completed, the surplus electricity or the vacant electricity is settled according to the electricity purchase price P buy and the selling price P sell of the regional energy agents.

[0055] The application of the hierarchical electricity trading matching method based on blockchain in the distributed electricity market in the present invention in the hierarchical electricity trading in the distributed electricity market includes the following steps:

[0056] (1) Construct a consortium chain multi-chain blockchain network. Each consortium chain consists of regional energy agents, electricity purchasing users and electricity selling users under the regional energy agents. Among them: The regional energy agent is the main node of the corresponding consortium chain, and the electricity purchasing user and the electricity selling user are the slave nodes of the corresponding consortium chain;

[0057] (2) The regional energy agent submits the electricity demand with the region as the main body through the corresponding main node, generates a demand declaration record, and stores the data on the chain;

[0058] After the negotiation and bargaining contract of the regional energy agent in the first stage of the period is triggered, read the latest regional load demand declaration records in the blockchain network and conduct trading matching among the regional energy agents;

[0059] After the transaction matching is completed, generate user offer constraints and store the data on the blockchain.

[0060] (3) The electricity purchasing user submits electricity purchasing information through the corresponding slave node, generates an electricity purchasing declaration record, and stores the data on the blockchain.

[0061] The electricity selling user submits electricity selling information and user parameters for participating in the comprehensive evaluation through the corresponding slave node, generates an electricity selling declaration record, and stores the data on the blockchain.

[0062] After the P2P transaction matching contract of the users within the region in the second stage of the cycle is triggered, read the latest electricity purchasing declaration record and electricity selling declaration record from the corresponding alliance chain network and conduct transaction matching.

[0063] After the transaction matching is completed, generate a transaction bill and store the data on the blockchain.

[0064] (4) The electricity purchasing user and the electricity selling user obtain the transaction bill from the alliance chain through the corresponding slave node, sign it to generate a contract bill and store it on the blockchain.

[0065] (5) The electricity purchasing user and the electricity selling user obtain the contract bill from the alliance chain through the corresponding slave node, complete the transfer of transaction electricity quantity and value according to the contract bill, and store the execution result of the contract bill on the blockchain.

[0066] The beneficial effects of the present invention are as follows:

[0067] The present invention ensures the security and credibility of user information and power transaction data in the distributed power market, and realizes the whole-process blockchain storage of user transaction data in the distributed power market. At the same time, in the transaction matching link, the present invention proposes a hierarchical power transaction matching method in the distributed power market, divides the power transaction matching into two stages, balances the supply and demand differences of renewable energy among users between regions and within regions in the distributed power market, and in the P2P transaction matching stage of users within the region, the present invention uniquely proposes a transaction matching method based on multi-index comprehensive evaluation. This method comprehensively considers the user's environmental friendliness index, economic index, and user reliability index, and conducts transaction matching with the electricity purchasing user according to the size of the comprehensive evaluation value, rather than only using the electricity price as a single factor for transaction matching, meeting the user's need to select electric energy based on multiple factors. Description of the Drawings

[0068] Figure 1 is a schematic flow chart of the hierarchical power transaction matching method in the distributed power market in the embodiment of the present invention.

[0069] Figure 2 is a schematic flow chart of calculating the comprehensive evaluation value of electricity selling users based on the entropy weight TOPSIS method in the embodiment of the present invention. Detailed Embodiments

[0070] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0071] Embodiment 1: As shown in Figure 1 A hierarchical power trading matching method in a distributed power market includes the following steps:

[0072] (1) Divide the power trading stages of trading entities in the distributed power market; wherein, the trading entities include regional energy agents and power purchase users and power sale users under the regional energy agents.

[0073] (2) In the first stage, obtain the power situation of this cycle predicted by the regional energy agent according to the historical load demand and distributed photovoltaic power generation data in the region, and automatically calculate the power purchase price and sale price between regional energy agents. Among them, the power situation of the regional energy agent in this cycle refers to the overall power demand or power supply of the users under the regional energy agent.

[0074] Table 1 shows the data of the negotiation requirements of regional energy agents in the first stage.

[0075] Table 1 Data of negotiation requirements of regional energy agents

[0076] Regional energy agent Electricity demand Electricity supply

[0077] The meanings of the respective parameters are as follows:

[0078] The regional energy agent refers to the specific regional energy agent identity information in the distributed power market. The distributed power market is divided into multiple regional energy agents, and the regional energy agent includes power sale users and power purchase users in the corresponding region.

[0079] Power demand refers to the specific electricity quantity when the power production in the region is less than the power consumption in the region in this cycle. This part of the electricity quantity can be purchased from other regional energy agents.

[0080] Power supply refers to the specific electricity quantity when the power production in the region is greater than the power consumption in the region in this cycle. This part of the electricity quantity can be sold to other regional energy agents.

[0081] The specific method of step (2) is as follows:

[0082] Determine the power purchase price and sale price between regional energy agents in three cases according to the supply and demand relationship in the distributed power market, as shown in formulas (1)-(5):

[0083] When CE s = CE d ,

[0084] When CE s > CE d ,

[0085]

[0086] When CE s < CE d ,

[0087]

[0088] Among them, CE s is the total power supply of the regional energy agent in the distributed power market, and CE d is the total power demand of the regional energy agent in the distributed power market. P buy is the power purchase price among regional energy agents, and P sell is the power selling price among regional energy agents. P bfg is the power selling price of the distribution network, and P stg is the grid connection price of the distribution network.

[0089] (3) In the second stage, obtain the transaction declaration information of power purchase and selling users under the regional energy agent. Among them, the transaction declaration information of the power selling user includes the power selling price, the power selling volume, the unit carbon emission factor, and the reliability index, and automatically calculate the environmental friendliness index and the economic index of the power selling user; the transaction declaration information of the power purchase user includes the power purchase price and the power purchase volume.

[0090] Table 2 shows the data requirements for the transaction declaration information of power selling users in the second stage.

[0091] Table 2 Data Requirements for Transaction Declaration Information of Power Selling Users under Regional Energy Agents

[0092] Power selling user Power selling price Power selling volume Unit carbon emission factor Reliability index

[0093] The meanings of the parameters are as follows:

[0094] The power selling user refers to the specific power selling user identity information under the regional energy agent.

[0095] The power selling price refers to the power selling price submitted by the power selling user.

[0096] The power selling volume refers to the power volume that can be sold submitted by the power selling user.

[0097] The unit carbon emission factor refers to the amount of carbon dioxide generated when the power selling user produces unit power.

[0098] The reliability index refers to the reliability index value obtained by the user based on the previous transaction completion degree in this cycle.

[0099] Table 3 shows the transaction information reporting requirements for electricity purchasers in the second phase.

[0100] Table 3 Data required for transaction declaration information of power purchasers under regional energy agencies

[0101] Power purchasing user Power purchasing price Power purchasing volume

[0102] The meanings of each parameter are as follows:

[0103] Electricity purchasers refer to the specific identity information of electricity purchasers under the regional energy agency.

[0104] The electricity purchase price refers to the electricity purchase price submitted by the electricity purchasing user.

[0105] The amount of electricity purchased refers to the amount of electricity demand submitted by the electricity purchasing user.

[0106] In step (3): the power quotations of power purchasers and sellers shall satisfy the constraints of power purchase price and sales price between regional energy agents: P sell <<P i <<P buy , where P i Provide electricity quotation for user i;

[0107] The calculation formula of the reliability index of the electricity sales user is shown in formula (6):

[0108]

[0109] Among them, C i is the reliability index value of user i in this cycle, C′ i is the reliability index value of user i in the previous period, e′ i is the actual transaction amount of user i in the previous cycle, E′ i is the transaction amount agreed upon by user i in the transaction bill of the previous period, δ∈[0,1] is the weight value of the user’s historical transaction completion degree, and the smaller the δ value is, the more it is biased towards the user’s recent reliability, and vice versa, it pays more attention to the user’s long-term power supply stability.

[0110] The calculation formula of the environmental friendliness index of electricity sellers is shown in formula (7):

[0111]

[0112] Among them, G i is the environmental friendliness index value of user i in this cycle, Q i is the unit carbon emission factor corresponding to user i, E i is the electricity sold by user i in this cycle, E STotal electricity sales volume of all electricity sales users under the regional energy agent:

[0113] The economic index calculation formula of electricity sales users is shown in formula (8):

[0114]

[0115] Among them, I i is the economic index value of user i in this period.

[0116] (4) According to the comprehensive evaluation values of electricity sales users calculated by the entropy weight TOPSIS method, form an electricity sales user queue in descending order; form an electricity sales user queue in descending order according to the decreasing method of bidding price.

[0117] The step (4) includes: combining Figure 2 As shown, calculating the comprehensive evaluation value of electricity sales users according to the entropy weight TOPSIS method includes the following steps:

[0118] Construct a decision matrix: Suppose there are n electricity sales users participating in the comprehensive evaluation, including 3 evaluation indicators: environmental friendliness, economic benefits, and reliability, and form a decision matrix:

[0119]

[0120] Standardize the decision matrix: Uniformly convert the evaluation indicators in the decision matrix into their corresponding positive indicators: Among them: G max = max(G 11 , G 21 ,..., G n1 ), I max = max(I 12 , I 22 ,..., I n2 ), C max = max(C 13 , C 123 ,..., C n3 ), and obtain the new matrix after index standardization:

[0121]

[0122] Calculate the weighted decision matrix: For the three types of evaluation indicators, use the entropy weight method to calculate the weight vector of various parameter indicators: ω = (ω1, ω2, ω3), and multiply the weight corresponding to each indicator by the positive standardized decision matrix to obtain the weighted decision matrix:

[0123] R = (r ij )n×3 #(11)

[0124] where: r ij = ω j × a ij , i ∈ [1, 2,..., n]; j ∈ [1, 2, 3]

[0125] Calculate the comprehensive evaluation value of each electricity-selling user i:

[0126]

[0127] where:

[0128]

[0129]

[0130]

[0131]

[0132] The steps of (4) include:

[0133] Form an electricity-selling user queue: According to the comprehensive evaluation values of each electricity-selling user, determine the trading matching priorities from high to low to form an electricity-selling user queue;

[0134] Form an electricity-purchasing user queue: According to the electricity-purchasing prices of each electricity-purchasing user, determine the trading matching priorities from high to low to form an electricity-purchasing user queue.

[0135] (5) Perform trading matching in descending order according to the order of the electricity-purchasing and -selling user queues to complete the trading clearing of this cycle.

[0136] The specific method of the steps of (5) is:

[0137] According to the trading matching priorities of the electricity-selling user queue and the electricity-purchasing user queue obtained in step (5), sequentially match the trading objects in the region: When the trading matching priorities are the same, according to the principle of first come, first served, the user who declares the trading information first is given priority for trading matching; After the trading matching is completed, the excess electricity or the vacant electricity is cleared according to the electricity purchase price P buy and the selling price P sell for surplus settlement.

[0138] Through the above hierarchical electricity trading matching method in the distributed power market, it is convenient to increase the overall linkage between users in different regions and within the region in the distributed power market. On the premise of balancing the power supply and demand between regions, the environmental friendliness index, economic index, and user reliability index of users are comprehensively considered, and then trading matching between users is carried out.

[0139] After the negotiation agreement bargaining transaction matching of the regional energy agents in the first stage is completed, according to the obtained electricity purchase price and selling price among the regional energy agents, user quotation constraints are formed and the data is stored on the chain.

[0140] After the P2P transaction matching of the users within the region in the second stage is completed, a transaction bill is generated and the data is stored on the chain.

[0141] The electricity purchase and selling users obtain the transaction bill from the alliance chain through the corresponding slave nodes, sign it to generate a contract bill and store it on the chain;

[0142] The electricity purchase and selling users obtain the contract bill from the alliance chain through the corresponding slave nodes, complete the transfer of the transaction electricity quantity and value according to the contract bill, and store the execution result of the contract bill on the chain, which can ensure the openness, transparency, security and credibility of the transaction data in the distributed power market.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Any equivalent replacements made by those skilled in the art under the technical solutions of this specification shall be covered within the scope of the claims of the present invention.

Claims

1. A hierarchical power trading matching method under a blockchain-based distributed power market, characterized in that It includes the following steps: (1) Divide the stages for trading entities to participate in power trading in the distributed power market; where: the trading entities include regional energy agents, as well as power purchase users and power sale users under the regional energy agents; (2) In the first stage, obtain the regional energy agents, predict the power situation in this cycle based on historical load demand and distributed photovoltaic power generation data within the region, and automatically calculate the power purchase price and sale price among regional energy agents; where: the power situation of the regional energy agents in this cycle refers to the overall power demand or power supply of the users under the regional energy agents; The calculation of the power purchase price and sale price among regional energy agents is specifically as follows: Based on the relationship between CE in the supply-demand relationship of the distributed power market s and CE d The electricity purchase price and selling price between regional energy agents are determined as shown in formulas (1)-(5): When CE s = CE d , When CE s > CE d then When CE s <CE d is true Among them: CE s is the total power supply of the regional energy agent in the distributed power market, CE d is the total power demand of the regional energy agent in the distributed power market, P buy is the power purchase price among regional energy agents, P sell is the power selling price among regional energy agents, P bfg is the power selling price of the distribution network, P stg is the grid connection price of the distribution network; (3) In the second stage, obtain the transaction declaration information of electricity purchase users and electricity sale users under the regional energy agent. Among them, the transaction declaration information of electricity sale users includes the electricity sale price, electricity sale volume, unit carbon emission factor, and reliability index, and automatically calculate the environmental friendliness index and economic index of electricity sale users; the transaction declaration information of electricity purchase users includes the electricity purchase price and electricity purchase volume; the electricity purchase price of the electricity purchase user and the electricity sale price of the electricity sale user shall satisfy the constraints of the electricity purchase price and sale price among regional energy agents: P sell <<P i <<P buy , where P i is the electricity price offer of user i; Calculation of the reliability index of power sale users, as shown in formula (6): Where: C i is the reliability index value of user i in this cycle, C i ′ is the reliability index value of user i in the previous cycle, e i ′ is the actual transaction power of user i in the previous cycle, E i ′ is the agreed transaction amount of user i in the transaction bill of the previous period, δ∈[0,1] is the weight value of the user's historical transaction completion degree. The smaller the δ value, the more biased it is towards the user's recent reliability. On the contrary, it pays more attention to the user's long-term power supply stability. Calculation of the environmental friendliness index of power sale users, as shown in formula (7): Where: G i is the environmental friendliness index value of user i in this period, Q i is the unit carbon emission factor corresponding to user i, E i is the electricity sales volume of user i in this period, E S is the total electricity sales volume of all electricity sales users under the regional energy agent: Calculation of the economic index of power sale users, as shown in formula (8): Where: I i is the economic index value of user i in this period; (4) Based on the entropy weight TOPSIS method, construct a decision matrix, standardize the decision matrix, calculate the weighted decision matrix, and finally calculate the comprehensive evaluation value of power sale users. Sort the obtained comprehensive evaluation values from high to low to form a queue of power sale users; arrange them from high to low according to the decreasing method of bidding prices to finally form a queue of power purchase users; Construct a decision matrix: Suppose there are n power sale users participating in the comprehensive evaluation. The comprehensive evaluation includes 3 evaluation indicators: environmental friendliness, economic benefits, and reliability. These 3 evaluation indicators form a decision matrix: Where: A n×3 is an n×3 decision matrix constructed based on three evaluation indicators of environmental friendliness, economic benefits, and reliability for n electricity selling users, G n1 is the value of the environmental friendliness indicator for the nth electricity selling user, I n2 the value of the economic benefit indicator for the nth electricity selling user, C n3 is the value of the reliability indicator for the nth electricity selling user: Decision matrix standardization: Uniformly convert the evaluation indicators in the decision matrix into their corresponding positive indicators: Where: G max = max(G 11 , G 21 , …, G n1 ), I max = max(I 12 , I 22 , …, I n2 ), C max = max(C 13 , C 123 , …, C n3 ), to obtain the new matrix after index standardization: Among them: A′ n×3 is the new matrix obtained after standardizing the decision matrix, and a n1 is the value obtained after positive transformation of the environmental friendliness index value of the nth electricity selling user, and a n2 is the value obtained after positive transformation of the economic index value of the nth electricity selling user, and a n3 is the value obtained after positive transformation of the reliability index value of the nth electricity selling user; Calculate the weighted decision matrix: For the 3 evaluation indicators of environmental friendliness, economic benefits, and reliability, use the entropy weight method to calculate the weight vector of various parameter indicators: ω = (ω1, ω2, ω3), and multiply the weight corresponding to each indicator by the positive normalized decision matrix to obtain the weighted decision matrix: R＝(r ij ) n×3 (11) where: r ij = ω j × a ij , i ∈ [1, 2, …, n]; j ∈ [1, 2, 3] Calculate the comprehensive evaluation value of each power sale user i: Where: Where: V i is the comprehensive evaluation value of user i in this cycle, is the maximum value among the index values in the j-th column of the weighted decision matrix, is the minimum value among the index values in the j-th column of the weighted decision matrix, is the Euclidean distance between the three index values of user i in the weighted decision matrix and the maximum value in the corresponding column, is the Euclidean distance between the three index values of user i in the weighted decision matrix and the minimum value in the corresponding column; Form a queue of power sale users: According to the size of the comprehensive evaluation values of each power sale user, formulate the trading matching priority from high to low to form a queue of power sale users; Form a queue of power purchase users: According to the size of the power purchase prices of each power purchase user, formulate the trading matching priority from high to low to form a queue of power purchase users; (5) Conduct transaction matching in descending order according to the queue order of electricity purchasing users and electricity selling users to complete the transaction clearing of this cycle; according to the transaction matching priorities of the electricity selling user queue and the electricity purchasing user queue obtained in step (4), sequentially match the trading objects in the region: when the transaction matching priorities are the same, according to the principle of first come, first served, the user who declares the transaction information first is given priority for transaction matching; after the transaction matching is completed, the surplus electricity or the vacant electricity is cleared according to the electricity purchase price P buy and the selling price P sell for balance settlement.

2. Application of the hierarchical power trading matching method based on blockchain in distributed power markets described in claim 1 in hierarchical power trading in distributed power markets, characterized in that It includes the following steps: (1) Construct a consortium chain multi-chain blockchain network. Each consortium chain is composed of regional energy agents, power purchase users and power sale users under the regional energy agents; where: the regional energy agent serves as the main node of the corresponding consortium chain, and the power purchase user and power sale serve as the slave nodes of the corresponding consortium chain; (2) The regional energy agent submits the region-based power demand through the corresponding main node, generates a demand declaration record, and stores the data on the chain; After the negotiation and bargaining contract of the regional energy agent in the first stage of the cycle is triggered, read the latest regional load demand declaration records from the blockchain network and perform trading matching among regional energy agents; After the trading matching is completed, generate user quotation constraints and store the data on the chain; (3) The power purchase user submits power purchase information through the corresponding slave node, generates a power purchase declaration record, and stores the data on the chain; The power sale user submits power sale information and user parameters required for participating in the comprehensive evaluation through the corresponding slave node, generates a power sale declaration record, and stores the data on the chain; After the P2P trading matching contract of users in the region in the second stage within the cycle is triggered, read the latest electricity purchase declaration record and electricity sale declaration record from the corresponding alliance chain network and conduct trading matching; After the trading matching is completed, generate a trading bill and store the data on the chain; (4) The electricity purchase user and the electricity sale user obtain the trading bill from the alliance chain through the corresponding slave node, sign it and then generate a contract bill and store it on the chain; (5) The electricity purchase user and the electricity sale user obtain the contract bill from the alliance chain through the corresponding slave node, complete the transfer of the trading electricity quantity and value according to the contract bill, and store the execution result of the contract bill on the chain.

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