CN114444864A - Block chain based hierarchical power transaction matching method in distributed power market - Google Patents

Block chain based hierarchical power transaction matching method in distributed power market Download PDF

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CN114444864A
CN114444864A CN202111600636.6A CN202111600636A CN114444864A CN 114444864 A CN114444864 A CN 114444864A CN 202111600636 A CN202111600636 A CN 202111600636A CN 114444864 A CN114444864 A CN 114444864A
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汤依水
孙耀杰
李明佳
马磊
吴煜
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Jiangsu Weiheng Intelligent Technology Co ltd
Fudan University
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Fudan University
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Abstract

The invention relates to a block chain-based hierarchical power transaction matching method in a distributed power market. The invention matches and stratifies the electric power transaction: the method comprises a first stage of negotiation bargaining taking a regional energy agent as a main body, and a second stage of clearing P2P transaction matching of users in a region under the regional energy agent, so that the difference of power supply and demand of the users in the region and the region under a distributed power market is balanced. The invention can ensure the public transparency, safety and credibility of the transaction data in the distributed power market, and provides a solution for comprehensively considering the user power declaration information and further performing transaction matching.

Description

Block chain based hierarchical power transaction matching method in distributed power market
Technical Field
The invention belongs to the technical field of energy Internet and block chain, relates to a hierarchical power transaction matching method in a distributed power market, and particularly relates to a hierarchical power transaction matching method in a distributed power market based on a block chain.
Background
The increase in the popularity of distributed photovoltaic power generation in the power grid has led to a change in the structure of the power consumption market, and traditional power consumers are becoming producers and sellers of both power consumption and power production. In 2017, the national energy agency and the national development committee issue a notice about development of market-oriented transaction points of distributed power generation, so that guidance is provided for distributed power generation transaction, and the power selling side in the power market is gradually opened. The distributed electric power marketization transaction is developed, so that the production and marketing users can exchange energy for the outside by unbalanced electric quantity after meeting the own power consumption requirements, the phenomenon of 'light abandonment' is reduced, the consumption is promoted, meanwhile, the intermediate links of the electric power transaction can be reduced by directly bargaining with other electric power users, and the power consumption benefits of each party are improved.
However, since the distributed power transaction has the characteristics of multiple orders, small scale and decentralization, in the big data era, the privacy of the account information and the transaction records of market users cannot be guaranteed, the safety factor of the central institution database is low, and once the transaction data of the users is changed by hackers, the transaction safety of both transaction parties can be directly damaged.
The block chain technology is a technical scheme which can maintain a database collectively on the premise of decentralized and trust-removing modes and ensure that data is synchronous and cannot be tampered. The block link combines computer technologies such as distributed storage, point-to-point transmission, consistency verification and encryption algorithm, the purposes of non-falsification and non-falsification of the decentralized shared account book are achieved, the characteristics of decentralized, cooperative autonomy, collective maintenance, intelligent contracts and the like are relied on, the block link is similar to the open, interconnected, shared and peer-to-peer concepts of the energy Internet, and a new solution is provided for the transaction method of the distributed power market under the energy Internet.
In addition, the transaction matching problem is a problem that needs to be considered in the user transaction in the distributed power market. At present, the electric power market mainly includes trade matching modes such as inter-area P2P trade mainly for microgrid, intra-area user P2P trade mainly for users, and point-to-microgrid trade. The microgrid is used as a transaction main body, so that the utilization rate of the collective renewable energy power generation can be improved, but the consideration of individual demands of users in the microgrid is lacked. The users in the region are used as transaction subjects, fluctuation and unpredictability of renewable energy power generation of the users can be relieved, but the surplus electric quantity can only be transacted with a power distribution network, and power utilization benefits of all parties can be reduced to a certain extent.
In the matching process, the electricity price is only taken as a single factor, trade matching is carried out according to a high-low matching principle, but with gradual opening of a lower-layer power market, when a consumer has a certain purchasing option, the consumer often takes comprehensive consideration of multiple factors such as environment friendliness, price economy, resource reliability and the like of electric energy and then preferentially purchases the electric energy based on the actual situation of the consumer.
Disclosure of Invention
The invention aims to overcome the defects of the existing method, and provides a hierarchical electric power transaction matching method in a distributed electric power market based on a block chain, so that the integral linkage between areas and users in the areas in the distributed electric power market is increased, the environment-friendly index, the economic index and the user reliability index of the users are comprehensively considered on the premise of balancing the electric power supply and demand between the areas, the transaction matching between the users is further carried out, and meanwhile, the safe and credible problems of market user information and transaction data can be solved.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a hierarchical power transaction matching method in a distributed power market based on a block chain, which comprises the following steps:
(1) dividing trading subjects under a distributed power market into power trading stages; wherein: the transaction main body comprises a regional energy source agent, and an electricity purchasing user and an electricity selling user under the regional energy source agent;
(2) the method comprises the following steps of obtaining regional energy agencies at the first stage, predicting the electric power condition of the period according to historical load demand and distributed photovoltaic power generation data in a region, and automatically calculating the electric power purchase price and selling price among the regional energy agencies, wherein: the power condition of the regional energy agent in the period refers to the total power demand or power supply of the users under the regional energy agent;
(3) and a second stage of acquiring transaction declaration information of electricity purchasing users and electricity selling users under regional energy agents, wherein: the transaction declaration information of the electricity selling user comprises an electricity selling price, electricity selling quantity, a unit carbon emission factor and a reliability index, and the environment-friendly index and the economic index of the electricity selling user are automatically calculated; the transaction declaration information of the electricity purchasing user comprises an electricity purchasing price and electricity purchasing quantity;
(4) constructing a decision matrix based on an entropy weight TOPSIS method, standardizing the decision matrix, calculating a weighted decision matrix, finally calculating a comprehensive evaluation value of the electricity selling users, and sequencing the obtained comprehensive evaluation values from high to low to form an electricity selling user queue; arranging from high to low according to a bidding price decreasing method to finally form an electricity purchasing user queue;
(5) and (4) carrying out transaction matching from high to low in sequence according to the sequence of the electricity purchasing users and the electricity selling users, and finishing the transaction clearing in the period.
In the invention, the step (2) of calculating the electric power purchase price and the selling price among regional energy agencies specifically comprises the following steps:
according to CE in the supply and demand relation of the distributed power markets、CEdThe relationship between the regional energy agents, the electricity purchase price and the selling price are determined, and the formulas (1) to (5) are shown:
when CE is presents＝CEdWhen the temperature of the water is higher than the set temperature,
Figure BDA0003431592500000021
when CE is presents＞CEdWhen the temperature of the water is higher than the set temperature,
Figure BDA0003431592500000022
Figure BDA0003431592500000023
when CE is presents＜CEdWhen the temperature of the water is higher than the set temperature,
Figure BDA0003431592500000024
Figure BDA0003431592500000031
wherein: CEsTotal supply of electricity, CE, for regional energy agents in a distributed power marketdFor the total demand of electricity, P, for regional energy agents in a distributed power marketbuyFor regional energy inter-agent electric power purchase price, PsellSelling price for regional energy agent electricity, PbfgFor selling electricity, P, of the distribution networkstgThe power price of the power distribution network is the power price of the power grid.
In the invention, the electricity purchasing price of the electricity purchasing user and the electricity selling price quoted price of the electricity selling user in the step (3) should meet the constraint of the electricity purchasing price and the electricity selling price among regional energy agencies: psell＜＜Pi＜＜PbuyIn which P isiPower quotes for user i;
calculating the reliability index of the electricity selling user as shown in a formula (6):
Figure BDA0003431592500000032
wherein: ciIs the reliability index value, C 'of the user i in the period'iIs a reliability index value e 'of the user i in the previous period'iIs the actual transaction electric quantity of the user i in the previous period, E'iBill appointment transaction for user i's transaction in previous cycleEasy electric quantity, delta is belonged to [0, 1 ]]The weighted value of the historical transaction completion degree of the user is smaller, the smaller the delta value is, the more the recent reliability of the user is biased, and otherwise, the longer the long-term power supply stability of the user is emphasized;
calculating the environmental friendliness index of the electricity selling user as shown in formula (7):
Figure BDA0003431592500000033
wherein: giFor the user i environmental friendliness index value, Q, in this cycleiUnit carbon emission factor, E, for user iiFor the user i selling electricity in this period, ESThe total electricity sales amount of all electricity sales users under regional energy agencies:
Figure BDA0003431592500000034
calculating the economic index of the electricity selling user as shown in a formula (8):
Figure BDA0003431592500000035
wherein: i isiAnd (4) the economic index value of the user i in the period.
In the invention, the method in the step (4) comprises the following steps:
constructing a decision matrix: n electricity selling users are arranged to participate in comprehensive evaluation, the comprehensive evaluation comprises 3 evaluation indexes of environment friendliness, economic benefit and reliability, and the 3 evaluation indexes form a decision matrix:
Figure BDA0003431592500000036
wherein: a. then×3An n multiplied by 3 decision matrix G is constructed according to 3 evaluation indexes of environment-friendly, economic benefit and reliability of n electricity selling usersn1Is an environmental friendliness index value of the nth electricity selling user, In2Nth electricity selling userEconomic index value of (C)n3The reliability index value of the nth electricity selling user is obtained.
Decision matrix normalization: uniformly converting the evaluation indexes in the decision matrix into respective corresponding forward indexes:
Figure BDA0003431592500000041
wherein:
Figure BDA0003431592500000042
Figure BDA0003431592500000043
Gmax＝max(G11，G21，...，Gn1)，Imax＝max(I12，I22，...，In2)，Cmax＝max(C13，C123，...，Cn3) And obtaining a new matrix after index standardization:
Figure BDA0003431592500000044
wherein: a'n×3For a new matrix obtained after standardization of the decision matrix, an1Is the value obtained by the forward conversion of the environmental-friendly index value of the nth electricity selling user, an2Is the value obtained after the forward conversion of the economic index value of the nth electricity selling user, an3The reliability index value of the nth electricity selling user is converted to the positive value.
Calculating a weighted decision matrix: for 3 evaluation indexes of environmental friendliness, economic benefit and reliability, calculating weight vectors of various parameter indexes by adopting an entropy weight method: ω ═ ω (ω)1，ω2，ω3) Multiplying the weight corresponding to each index with the forward standardized decision matrix to obtain a weighted decision matrix:
R＝(rij)n×3#(11)
wherein: r isij＝ωj×aij，i∈[1，2，...，n]；j∈[1，2，3]
Calculating a comprehensive evaluation value of each electricity selling user i:
Figure BDA0003431592500000045
wherein:
Figure BDA0003431592500000046
Figure BDA0003431592500000047
Figure BDA0003431592500000048
Figure BDA0003431592500000049
wherein: viFor the integrated evaluation value of the user i in the present period,
Figure BDA00034315925000000410
the index value in the jth column of the weighted decision matrix is the maximum value,
Figure BDA00034315925000000411
the index value in the jth column of the weighted decision matrix is the minimum value,
Figure BDA00034315925000000412
the Euclidean distance between the index values of 3 items in the weighted decision matrix of the user i and the maximum value of the corresponding column,
Figure BDA00034315925000000413
and (4) the Euclidean distance between the index values of 3 items in the weighted decision matrix and the minimum value of the corresponding column for the user i.
Forming a power selling user queue: establishing transaction matching priority from high to low according to the magnitude of the comprehensive evaluation value of each electricity selling user to form an electricity selling user queue;
forming a power purchase user queue: and establishing transaction matching priority from high to low according to the electricity purchasing price of each electricity purchasing user to form an electricity purchasing user queue.
In the invention, the method in the step (5) comprises the following steps:
according to the transaction matching priority of the electricity selling user queue and the electricity purchasing user queue obtained in the step (4), sequentially matching transaction objects in the region: when the transaction matching priorities are the same, the user reporting the transaction information preferentially performs transaction matching according to a time priority principle; surplus power or vacant power after transaction matching is according to power purchase price P between regional energy agenciesbuyAnd a selling price PsellAnd (6) carrying out allowance clearing.
The application of the block chain-based hierarchical power transaction matching method in the distributed power market in the hierarchical power transaction in the distributed power market comprises the following steps:
(1) and constructing a alliance chain multi-chain block chain network, wherein each alliance chain comprises a regional energy agent, electricity purchasing users and electricity selling users under the regional energy agent, and the alliance chain multi-chain block chain network comprises the following steps: the regional energy agent serves as a main node corresponding to the alliance chain, and the electricity purchasing user and the electricity selling user serve as slave nodes corresponding to the alliance chain;
(2) the regional energy agent submits the power demand taking the region as a main body through the corresponding main node, generates a demand declaration record and stores the data in a chaining way;
after the regional energy agent negotiation bargaining contract at the first stage in the period is triggered, reading the latest regional load demand declaration record from the blockchain network and carrying out transaction matching among the regional energy agents;
after the transaction matching is completed, generating user quotation constraint and storing uplink data;
(3) the electricity purchasing user submits electricity purchasing information through the corresponding slave node, generates an electricity purchasing declaration record and stores the data uplink;
the electricity selling users submit electricity selling information and user parameters participating in comprehensive evaluation requirements through corresponding slave nodes, electricity selling declaration records are generated, and data are linked and stored;
after a user P2P transaction matching contract in the area of the second stage in the period is triggered, reading the latest electricity purchasing declaration record and the latest electricity selling declaration record from the corresponding alliance chain network and performing transaction matching;
after the transaction matching is completed, generating a transaction bill and linking and storing the data;
(4) the electricity purchasing user and the electricity selling user obtain transaction bills from the alliance chain through corresponding slave nodes, and after signing, contract bills are generated and uplink stored;
(5) and the electricity purchasing user and the electricity selling user obtain a contract bill from the alliance chain through corresponding slave nodes, the transfer of transaction electric quantity and value is completed according to the contract bill, and the contract bill execution result is linked and stored.
The invention has the beneficial effects that:
the invention ensures the safety and credibility of the user information and the electric power transaction data under the distributed electric power market and realizes the chain link of the user transaction data under the distributed electric power market in the whole process. Meanwhile, in a transaction matching link, the invention provides a layered power transaction matching method in a distributed power market, the power transaction matching is divided into two stages, the supply and demand difference of renewable energy sources of users between domains and in the distributed power market is balanced, and in addition, the invention uniquely provides a transaction matching method based on multi-index comprehensive evaluation when the users in the region perform a P2P transaction matching stage.
Drawings
Fig. 1 is a schematic flow chart of a hierarchical power transaction matching method in a distributed power market according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart of calculating a comprehensive evaluation value of a power selling user based on the entropy weight TOPSIS method in the implementation of the present invention.
Detailed Description
The invention is further described with reference to the following figures and specific examples.
Example 1: referring to fig. 1, a hierarchical power transaction matching method in a distributed power market includes the following steps:
(1) dividing trading subjects under a distributed power market into power trading stages; the transaction body comprises a regional energy resource agent, an electricity purchasing user and an electricity selling user under the regional energy resource agent.
(2) And in the first stage, the electric power condition of the regional energy agent in the period is obtained, the electric power condition of the regional energy agent in the period is predicted according to the historical load demand and the distributed photovoltaic power generation data in the region, and the electric power purchase price and the selling price of the regional energy agent are automatically calculated, wherein the electric power condition of the regional energy agent in the period refers to the total electric power demand or electric power supply of users under the regional energy agent.
Table 1 shows the first phase regional energy broker negotiation bargaining requirement data.
TABLE 1 regional energy Agents negotiate bargaining requirement data
Regional energy agent Demand for electric power Electric power supply
The meaning of each parameter is as follows:
the regional energy agent refers to specific regional energy agent identity information in a distributed power market, the distributed power market is divided into a plurality of regional energy agents which jointly form the regional energy agents, and the regional energy agents comprise electricity selling users and electricity purchasing users in corresponding regions.
The power demand refers to specific power consumption in the area with less power production than the power consumption in the area in the period, and the specific power can be purchased by other regional energy agencies.
The power supply refers to specific power consumption in the area with power production larger than that in the area in the period, and the power can be sold to other regional energy agencies.
The specific method of the step (2) is as follows:
the electric power purchase price and the selling price among regional energy agents under three conditions are determined according to the supply and demand relation of the distributed electric power market, and the electric power purchase price and the selling price are shown in formulas (1) to (5):
when CE is presents＝CEdWhen the temperature of the water is higher than the set temperature,
Figure BDA0003431592500000061
when CE is presents＞CEdWhen the temperature of the water is higher than the set temperature,
Figure BDA0003431592500000062
Figure BDA0003431592500000071
when CE is presents＜CEdWhen the temperature of the water is higher than the set temperature,
Figure BDA0003431592500000072
Figure BDA0003431592500000073
wherein, CEsTotal supply of electricity, CE, for regional energy agents in a distributed power marketdFor the total demand of electricity, P, for regional energy agents in a distributed power marketbuyFor regional energy inter-agent electric power purchase price, PsellSelling prices for regional energy inter-agent electricity，PbfgFor selling electricity, P, of the distribution networkstgThe power price of the power distribution network is the power price of the power grid.
(3) The second stage is to obtain the transaction declaration information of regional energy agent purchasing and electricity selling users, wherein the transaction declaration information of the electricity selling users comprises electricity selling prices, electricity selling quantities, unit carbon emission factors and reliability indexes, and the environment-friendly indexes and economic indexes of the electricity selling users are automatically calculated; the transaction declaration information of the electricity purchasing user comprises an electricity purchasing price and electricity purchasing quantity.
Table 2 shows the transaction declaration information requirement data of the second-stage electricity selling user.
TABLE 2 regional energy agency trade reporting information requirement data for electricity selling user
Electricity selling user Price of electricity sold Selling electricity quantity Specific carbon emission factor Reliability index
The meaning of each parameter is as follows:
the electricity selling user refers to specific electricity selling user identity information under a regional energy agency.
The electricity selling price refers to the electricity selling price submitted by the electricity selling user.
The electricity selling amount refers to the amount of electricity which can be sold and is submitted by the electricity selling user.
The unit carbon emission factor is the amount of carbon dioxide generated by a selling electricity consumer when producing a unit amount of electricity.
The reliability index is the reliability index value obtained by the user before the period based on the past transaction completion degree.
Table 3 shows the transaction declaration information requirement data of the second-stage electricity purchasing user.
TABLE 3 regional energy agency trade declaration information requirement data for electricity purchasing users
Electricity purchasing user Price of electricity purchase Electricity purchasing quantity
The meaning of each parameter is as follows:
the electricity purchasing user refers to specific identity information of the electricity purchasing user under the regional energy agency.
The electricity purchase price refers to the electricity purchase price submitted by the electricity purchase user.
The electricity purchasing quantity refers to the required electricity quantity submitted by the electricity purchasing user.
In the step (3): the power quotation of the electricity purchasing and selling users can meet the constraints of the power purchasing price and selling price among regional energy agents: psell＜＜Pi＜＜PbuyIn which P isiPower quotes for user i;
the reliability index calculation formula of the electricity selling user is shown as a formula (6):
Figure BDA0003431592500000074
wherein, CiThe reliability index value C 'of the user i in the period'iIs a reliability index value e 'of the user i in the previous period'iIs the actual transaction electric quantity of the user i in the previous period, E'iAppointing transaction electric quantity for the transaction bill of the user i in the last period, wherein delta belongs to [0, 1 ]]The smaller the value of delta is, the more biased to the near-term reliability of the user is represented, and conversely, the more emphasized the long-term power supply stability of the user is.
The calculation formula of the environmental friendliness index of the electricity selling user is shown as formula (7):
Figure BDA0003431592500000081
wherein G isiFor the user i environmental friendliness index value, Q, in this cycleiUnit carbon emission factor, E, for user iiFor the user i selling electricity in this period, ESThe total electricity sales amount of all electricity sales users under regional energy agencies:
Figure BDA0003431592500000082
the economic index calculation formula of the electricity selling user is shown as a formula (8):
Figure BDA0003431592500000083
wherein, IiAnd (4) the economic index value of the user i in the period.
(4) Forming a power selling user queue from high to low according to the comprehensive evaluation value of the power selling users calculated based on the entropy weight TOPSIS method; and forming an electricity selling user queue from high to low according to a bidding price decreasing method.
The step (4) comprises the following steps: referring to fig. 2, the method for calculating the comprehensive evaluation value of the electricity selling user according to the TOPSIS method based on entropy weight includes the following steps:
constructing a decision matrix: n electricity selling users are arranged to participate in comprehensive evaluation, and the evaluation indexes comprise 3 evaluation indexes including environment friendliness, economic benefit and reliability, so that a decision matrix is formed:
Figure BDA0003431592500000084
decision matrix normalization: uniformly converting the evaluation indexes in the decision matrix into respective corresponding forward indexes:
Figure BDA0003431592500000085
wherein:
Figure BDA0003431592500000086
Figure BDA0003431592500000087
Gmax＝max(G11，G21，...，Gn1)，Imax＝max(I12，I22，...，In2)，Cmax＝max(C13，C123，...，Cn3) And obtaining a new matrix after index standardization:
Figure BDA0003431592500000088
calculating a weighted decision matrix: for three types of evaluation indexes, calculating weight vectors of various types of parameter indexes by adopting an entropy weight method: ω ═ ω (ω)1，ω2，ω3) Multiplying the weight corresponding to each index with the forward standardized decision matrix to obtain a weighted decision matrix:
R＝(rij)n×3#(11)
wherein: r is a radical of hydrogenij＝ωj×aij，i∈[1，2，...，n]；j∈[1，2，3]
Calculating a comprehensive evaluation value of each electricity selling user i:
Figure BDA0003431592500000091
wherein:
Figure BDA0003431592500000092
Figure BDA0003431592500000093
Figure BDA0003431592500000094
Figure BDA0003431592500000095
the step (4) comprises the following steps:
forming a power selling user queue: establishing transaction matching priority from high to low according to the magnitude of the comprehensive evaluation value of each electricity selling user to form an electricity selling user queue;
forming a power purchase user queue: and establishing transaction matching priority from high to low according to the electricity purchasing price of each electricity purchasing user to form an electricity purchasing user queue.
(5) And (4) carrying out transaction matching from high to low in sequence according to the queue sequence of the electricity purchasing and selling users to finish the transaction clearing in the period.
The specific method of the step (5) is as follows:
according to the transaction matching priority of the electricity selling user queue and the electricity purchasing user queue obtained in the step (5), sequentially matching transaction objects in the region: when the transaction matching priorities are the same, the user reporting the transaction information preferentially performs transaction matching according to a time priority principle; surplus power or vacant power after completion of transaction matching is purchased according to power purchase price P between regional energy agenciesbuyAnd a selling price PsellAnd (6) carrying out allowance clearing.
By the layered power transaction matching method in the distributed power market, overall linkage between areas and users in the areas in the distributed power market is increased conveniently, and on the premise of balancing power supply and demand between the areas, environment-friendly indexes, economic indexes and user reliability indexes of the users are considered comprehensively, so that transaction matching between the users is performed.
And simultaneously, after the regional energy agent negotiation protocol bargaining transaction matching in the first stage is completed, forming user offer constraint and storing data in an uplink mode according to the obtained electric power acquisition price and selling price between the regional energy agents.
After the user P2P completes the transaction matching in the second stage area, a transaction bill is generated and the data is linked up for storage.
The electricity purchasing and selling users obtain transaction bills from the alliance chain through corresponding slave nodes, and after signing, the contract bills are generated and are linked up for storage;
the electricity purchasing and selling users obtain the contract bills from the alliance chain through the corresponding slave nodes, the transfer of the transaction electric quantity and the value is completed according to the contract bills, and the execution result of the contract bills is linked and stored, so that the open transparency and the safety credibility of the transaction data in the distributed power market can be ensured.
Finally, the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting, and any equivalent replacement made by those skilled in the art under the technical solutions of the present specification should be covered within the scope of the claims of the present invention.

Claims (6)

1. A hierarchical power transaction matching method in a distributed power market based on a block chain is characterized by comprising the following steps:
(1) dividing trading subjects under a distributed power market into power trading stages; wherein: the transaction main body comprises a regional energy source agent, and an electricity purchasing user and an electricity selling user under the regional energy source agent;
(2) the method comprises the following steps of obtaining regional energy agencies at the first stage, predicting the electric power condition of the period according to historical load demand and distributed photovoltaic power generation data in a region, and automatically calculating the electric power purchase price and selling price among the regional energy agencies, wherein: the power condition of the regional energy agent in the period refers to the total power demand or power supply of the users under the regional energy agent;
(3) and a second stage of acquiring transaction declaration information of electricity purchasing users and electricity selling users under regional energy agents, wherein: the transaction declaration information of the electricity selling user comprises an electricity selling price, electricity selling quantity, a unit carbon emission factor and a reliability index, and the environment-friendly index and the economic index of the electricity selling user are automatically calculated; the transaction declaration information of the electricity purchasing user comprises an electricity purchasing price and electricity purchasing quantity;
(4) constructing a decision matrix based on an entropy weight TOPSIS method, standardizing the decision matrix, calculating a weighted decision matrix, finally calculating a comprehensive evaluation value of the electricity selling users, and sequencing the obtained comprehensive evaluation values from high to low to form an electricity selling user queue; arranging from high to low according to a bidding price decreasing method to finally form an electricity purchasing user queue;
(5) and carrying out transaction matching from high to low in sequence according to the queue sequence of the electricity purchasing users and the electricity selling users to finish the transaction clearing in the period.
2. The block chain-based hierarchical power transaction matching method in the distributed power market according to claim 1, wherein the calculating of the power purchase price and the selling price between regional energy agents in step (2) specifically comprises:
according to CE in the supply and demand relation of the distributed power markets、CEdThe relationship between the regional energy agents, the electricity purchase price and the selling price are determined, and the formulas (1) to (5) are shown:
when CE is presents＝CEdWhen the temperature of the water is higher than the set temperature,
Figure FDA0003431592490000011
when CE is presents＞CEdWhen the temperature of the water is higher than the set temperature,
Figure FDA0003431592490000012
Figure FDA0003431592490000013
when CE is presents＜CEdWhen the temperature of the water is higher than the set temperature,
Figure FDA0003431592490000014
Figure FDA0003431592490000015
wherein: CEsTotal electricity supply, CE, for regional energy agents in a distributed power marketdFor the total demand of electricity, P, for regional energy agents in a distributed power marketbuyFor regional energy inter-agent electric power purchase price, PsellSelling price for regional energy agent electricity, PbfgPrice of electricity sold for distribution network, PstgThe price of the power is the power price of the power distribution network.
3. The block chain-based hierarchical power transaction matching method in the distributed power market according to claim 1, wherein the electricity purchasing price of the electricity purchasing users and the electricity selling price quoted by the electricity selling users in step (3) should meet the constraints of the power purchasing price and the electricity selling price among regional energy agencies: psell＜＜Pi＜＜PbuyIn which P isiPower quotes for user i;
calculating the reliability index of the electricity selling user as shown in a formula (6):
Figure FDA0003431592490000021
wherein: ciIs the reliability index value, C 'of the user i in the period'iIs a reliability index value e 'of the user i in the previous period'iIs the actual transaction electric quantity of the user i in the previous period, E'iAnd (4) appointing transaction electric quantity for the transaction bill of the user i in the last period, wherein delta belongs to [0, 1 ]]A weighted value for the user's historical transaction completion, with a smaller delta value indicating a greater preference towards the user's near-term reliability, and conversely,the long-term power supply stability of the user is emphasized;
calculating the environmental friendliness index of the electricity selling user as shown in formula (7):
Figure FDA0003431592490000022
wherein: giFor the user i environmental friendliness index value, Q, in this cycleiUnit carbon emission factor, E, for user iiFor the user i selling electricity in this period, ESThe total electricity sales amount of all electricity sales users under regional energy agencies:
Figure FDA0003431592490000023
calculating the economic index of the electricity selling user as shown in a formula (8):
Figure FDA0003431592490000024
wherein: i isiAnd (4) the economic index value of the user i in the period.
4. The method for matching the hierarchical power transaction in the distributed power market based on the blockchain according to claim 1, wherein the method in the step (4) is as follows:
constructing a decision matrix: n electricity selling users are arranged to participate in comprehensive evaluation, the comprehensive evaluation comprises 3 evaluation indexes of environment friendliness, economic benefit and reliability, and the 3 evaluation indexes form a decision matrix:
Figure FDA0003431592490000025
wherein: a. then×3An n multiplied by 3 decision matrix G constructed according to 3 evaluation indexes of environment-friendly, economic benefit and reliability of n electricity selling usersn1Is as followsEnvironmental friendliness index values of n electricity selling users, In2Economic index value of nth electricity selling user, Cn3The reliability index value of the nth electricity selling user is as follows:
decision matrix normalization: uniformly converting the evaluation indexes in the decision matrix into respective corresponding forward indexes:
Figure FDA0003431592490000026
wherein:
Figure FDA0003431592490000027
Figure FDA0003431592490000028
Gmax＝max(G11，G21，...，Gn1)，Imax＝max(I12，I22，...，In2),Cmax＝max(C13，C123，...，Cn3) And obtaining a new matrix after index standardization:
Figure FDA0003431592490000031
wherein: a'n×3For a new matrix obtained after standardization of the decision matrix, an1Is the value obtained by the forward conversion of the environmental-friendly index value of the nth electricity selling user, an2Is the value obtained after the forward conversion of the economic index value of the nth electricity selling user, an3The value obtained after the reliability index value of the nth electricity selling user is converted in a forward mode is as follows:
calculating a weighted decision matrix: for 3 evaluation indexes of environmental friendliness, economic benefit and reliability, calculating weight vectors of various parameter indexes by adopting an entropy weight method: ω = (ω =)1，ω2，ω3) Multiplying the weight corresponding to each index with the forward standardized decision matrix to obtain a weighted decision matrix:
R＝(rij)n×3#(11)
wherein: r isij＝ωj×aij，i∈[1，2，...，n]；j∈[1，2，3]
Calculating a comprehensive evaluation value of each electricity selling user i:
Figure FDA0003431592490000032
wherein:
Figure FDA0003431592490000033
Figure FDA0003431592490000034
Figure FDA0003431592490000035
Figure FDA0003431592490000036
wherein: viFor the integrated evaluation value of the user i in the present period,
Figure FDA0003431592490000037
the maximum value in the index values in the jth column of the weighted decision matrix,
Figure FDA0003431592490000038
the index value in the jth column of the weighted decision matrix is the minimum value,
Figure FDA0003431592490000039
the Euclidean distance between the index values of 3 items in the weighted decision matrix of the user i and the maximum value of the corresponding column,
Figure FDA00034315924900000310
the Euclidean distance between 3 index values of the user i in the weighting decision matrix and the minimum value of the corresponding column;
forming a power selling user queue: establishing transaction matching priority from high to low according to the comprehensive evaluation value of each electricity selling user to form an electricity selling user queue;
forming a power purchase user queue: and establishing transaction matching priority from high to low according to the electricity purchasing price of each electricity purchasing user to form an electricity purchasing user queue.
5. The method for matching hierarchical power transactions in a distributed power market based on block chains according to claim 1, wherein the method in the step (5) is as follows:
according to the transaction matching priority of the electricity selling user queue and the electricity purchasing user queue obtained in the step (4), sequentially matching transaction objects in the region: when the transaction matching priorities are the same, the user reporting the transaction information preferentially performs transaction matching according to a time priority principle; surplus power or vacant power after completion of transaction matching is purchased according to power purchase price P between regional energy agenciesbuyAnd a selling price PsellAnd (6) carrying out allowance clearing.
6. The application of the block chain based hierarchical power transaction matching method in the distributed power market according to claim 1 in the hierarchical power transaction in the distributed power market, characterized by comprising the following steps:
(1) and constructing a alliance chain multi-chain block chain network, wherein each alliance chain consists of a regional energy agent, electricity purchasing users and electricity selling users under the regional energy agent, and the alliance chain multi-chain block chain network comprises the following steps: the regional energy agent serves as a main node corresponding to the alliance chain, and the electricity purchasing user and the electricity selling user serve as slave nodes corresponding to the alliance chain;
(2) the regional energy agent submits the power demand taking the region as a main body through the corresponding main node, generates a demand declaration record and stores the data in a chaining way;
after the regional energy agent negotiation bargaining contract at the first stage in the period is triggered, reading the latest regional load demand declaration record from the blockchain network and carrying out transaction matching among the regional energy agents;
after the transaction matching is completed, generating user quotation constraint and storing uplink data;
(3) the electricity purchasing user submits electricity purchasing information through the corresponding slave node, generates an electricity purchasing declaration record and stores the data uplink;
the electricity selling users submit electricity selling information and user parameters participating in comprehensive evaluation requirements through corresponding slave nodes, electricity selling declaration records are generated, and data are linked and stored;
after a user P2P transaction matching contract in the area of the second stage in the period is triggered, reading the latest electricity purchasing declaration record and the latest electricity selling declaration record from the corresponding alliance chain network and performing transaction matching;
after the transaction matching is completed, generating a transaction bill and uplink storing the data;
(4) the electricity purchasing user and the electricity selling user obtain a transaction bill from the alliance chain through corresponding slave nodes, and a contract bill is generated and is linked up for storage after signing;
(5) and the electricity purchasing user and the electricity selling user obtain a contract bill from the alliance chain through corresponding slave nodes, the transfer of transaction electric quantity and value is completed according to the contract bill, and the contract bill execution result is linked and stored.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114971745A (en) * 2022-07-12 2022-08-30 青岛经管易信息科技有限公司 Planting and breeding long-term market data collection statistical method and system based on block chain
CN115719238A (en) * 2022-11-08 2023-02-28 国网区块链科技(北京)有限公司 Block chain-based distributed energy demand processing method and device
CN115829276A (en) * 2022-12-05 2023-03-21 国网数字科技控股有限公司 Power transaction processing method and device based on metauniverse and electronic equipment

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114971745A (en) * 2022-07-12 2022-08-30 青岛经管易信息科技有限公司 Planting and breeding long-term market data collection statistical method and system based on block chain
CN115719238A (en) * 2022-11-08 2023-02-28 国网区块链科技(北京)有限公司 Block chain-based distributed energy demand processing method and device
CN115719238B (en) * 2022-11-08 2024-03-15 国网区块链科技(北京)有限公司 Distributed energy demand processing method and device based on block chain
CN115829276A (en) * 2022-12-05 2023-03-21 国网数字科技控股有限公司 Power transaction processing method and device based on metauniverse and electronic equipment
CN115829276B (en) * 2022-12-05 2023-10-20 国网数字科技控股有限公司 Meta universe-based power transaction processing method and device and electronic equipment

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