REP-based DG-GC joint transaction method

Abstract

The invention discloses a DG-GC joint transaction method based on REP, and the method comprises the steps: constructing a DG-GC joint transaction framework of an energy block chain based on a retail power supplier; according to the DG-GC joint transaction framework, a joint transaction mechanism and a transaction process are designed, and the joint transaction mechanism comprises the joint of DG transaction and GC transaction; according to the joint transaction mechanism and the transaction process, establishing an optimization model taking economical efficiency, low-carbon property and electricity purchase satisfaction as objective functions; and solving the objective function by using a bacterial community chemotaxis algorithm to obtain a target value of the optimization model. According to the method, the transaction of the distributed power generation and the green certificate is comprehensively considered, the blank of separate processing of the DG transaction and the GC transaction in the prior art is filled, the joint optimization of the two transactions is realized, and the practicability and the accuracy of the transaction model are enhanced; through the optimization model, the problem that multiple targets such as economical efficiency, low-carbon property and electricity purchase satisfaction cannot be optimized at the same time in the prior art is solved.

Description

Technical Field

[0001] The present invention belongs to the field of energy blockchain environment technology, and in particular to a DG-GC joint transaction method based on REP. Background Art

[0002] In the energy blockchain environment, the joint trading of distributed generation (DG) and green certificates (GC) is seen as one of the potential ways to support the development of the future power market. Existing research and practice mainly focus on modeling, decision-making, models, mechanisms, scheduling, planning and architecture design. For example, some studies have established transaction aggregation and circulation models based on the issuance characteristics and transaction process of GCs, while other studies have established bilateral reserve market trading mechanisms with a focus on renewable energy consumption. In addition, there are studies that explore the optimal decision-making model for pricing and alliance selection based on cooperative game theory, as well as the distributed power trading model and physical architecture designed under the assumption that the power sales market is fully liberalized. These studies and practices provide a theoretical basis and practical guidance for the joint trading of DG-GC in the energy blockchain environment.

[0003] Although existing technologies have made some progress in DG and GC transactions, they rarely consider the mechanism relationship and modeling analysis between DG and GC companies and users in various transaction scenarios. Especially in the context of carbon emission quotas (CEA) and renewable energy quotas (RPS), existing studies often separate DG transactions and GC transactions, lacking a comprehensive consideration of the interaction and integration benefits between these transactions. In addition, existing technologies are also insufficient in dealing with the comprehensive optimization of green electricity, traditional energy and GC, and fail to fully consider the role and responsibility of retail electric providers (REP) in these transactions. Therefore, existing technologies fail to provide a comprehensive framework to simultaneously optimize economic efficiency, low carbon and electricity purchase satisfaction, especially when considering CEA and RPS requirements. These problems limit the potential and effectiveness of existing technologies in realizing DG-GC joint multilateral transactions in the energy blockchain environment. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a DG-GC joint transaction method based on REP to solve the problems existing in the above prior art.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a DG-GC joint transaction method based on REP, comprising:

[0006] Build a DG-GC joint transaction framework for energy blockchain based on retail electricity suppliers;

[0007] According to the DG-GC joint transaction framework, a joint transaction mechanism and transaction process are designed, wherein the joint transaction mechanism includes the combination of DG transaction and GC transaction;

[0008] According to the joint trading mechanism and trading process, an optimization model with economy, low carbon and electricity purchase satisfaction as objective functions is established;

[0009] The objective function is solved using a bacterial community chemotaxis algorithm to obtain the target value of the optimization model.

[0010] Preferably, the DG-GC joint transaction framework for building the energy blockchain includes:

[0011] Includes generators, sales representatives, power companies, government entities, and electricity consumers as key players;

[0012] According to the major players, transactions between retail electricity suppliers, distributed generation and green certificates are completed through an electronic trading platform using smart contracts supported by blockchain technology.

[0013] Preferably, the design of the joint transaction mechanism and transaction process includes:

[0014] Transactions are protected using the merkle tree data structure of smart contracts to achieve multilateral declaration and multilateral response transaction mechanisms for day-ahead and real-time markets.

[0015] Preferably, establishing an optimization model with economy, low carbon and electricity purchase satisfaction as objective functions includes:

[0016] According to the constraints, a multi-objective optimization transaction model of electricity purchase cost, low carbon and electricity purchase satisfaction is constructed;

[0017] The constraints include: energy balance, energy trading, network transmission and energy equipment.

[0018] Preferably, solving the objective function using a bacterial community chemotaxis algorithm comprises:

[0019] Set system parameters according to the required calculation accuracy;

[0020] According to the system parameters, a new direction of bacterial movement and a time and a position of the bacteria moving in the new direction are calculated;

[0021] According to the new direction, time and position of bacterial movement, the position of the center coordinates is sought and the system parameters are updated.

[0022] Preferably, in the DG-GC joint transaction framework, both the DG scheme and REP consider purchasing green certificates with carbon emission quotas and purchasing green certificates with renewable energy quotas.

[0023] Preferably, the method further comprises verifying the effectiveness of the bacterial community chemotaxis algorithm and the optimization model through examples.

[0024] In a second aspect, the present invention further discloses a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method described in the first aspect when executed by a processor.

[0025] In a third aspect, the present invention further discloses a computer program product, comprising a computer program, which implements the steps of the method described in the first aspect when executed by a processor.

[0026] Compared with the prior art, the present invention has the following advantages and technical effects:

[0027] The present invention provides a DG-GC joint transaction method based on REP. Firstly, a DG-GC joint transaction framework of an energy blockchain is constructed based on retail power suppliers. Secondly, according to the DG-GC joint transaction framework, a joint transaction mechanism and a transaction process are designed, wherein the joint transaction mechanism includes a combination of DG transaction and GC transaction. Then, according to the joint transaction mechanism and the transaction process, an optimization model with economy, low carbon and electricity purchase satisfaction as objective functions is established. Finally, a bacterial community chemotaxis algorithm is used to solve the objective function to obtain the target value of the optimization model.

[0028] The present invention establishes an energy blockchain DG-GC joint transaction framework with retail power suppliers as the core, comprehensively considers the transactions of distributed generation and green certificates, fills the gap in the existing technology of separate processing of DG and GC transactions, and realizes the joint optimization of the two transactions; the present invention enhances the practicality and accuracy of the transaction model through the joint transaction mechanism and process; the present invention solves the problem of the failure to simultaneously optimize multiple objectives such as economy, low carbon and electricity purchase satisfaction in the existing technology through the optimization model, and provides a more comprehensive optimization perspective. The present invention adopts the bacterial community chemotaxis algorithm to solve the target value of the optimization model, improves the solution efficiency and accuracy, and solves the limitations that may exist in the algorithm in the existing technology, such as the problem of being easily trapped in the local optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0030] Figure 1This is a schematic diagram of a DG-GC joint transaction framework according to an embodiment of the present invention;

[0031] Figure 2 A schematic diagram of an operation optimization strategy for a DG scenario according to an embodiment of the present invention;

[0032] Figure 3 This is a schematic diagram of the DG-GC joint transaction process of the energy blockchain based on REP in an embodiment of the present invention;

[0033] Figure 4 Schematic diagram of the optimization model solving process of an embodiment of the present invention. DETAILED DESCRIPTION

[0034] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0035] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0036] Embodiment 1

[0037] like Figure 1 As shown, this embodiment provides a DG-GC joint transaction method based on REP, including:

[0038] S1. Build a DG-GC joint transaction framework for energy blockchain based on retail electricity suppliers;

[0039] Furthermore, the DG-GC joint transaction framework for building the energy blockchain includes:

[0040] Includes generators, sales representatives, power companies, government entities, and electricity consumers as key players;

[0041] According to the major players, transactions between retail electricity suppliers, distributed generation and green certificates are completed through an electronic trading platform using smart contracts supported by blockchain technology.

[0042] Specifically, blockchain provides a secure and reliable technical framework, establishes a safe and stable trading environment for multilateral transactions, and thus promotes the healthy development of the power market. Construct a DG-GC joint transaction framework within the blockchain environment, the main participants of which include power generators, sales representatives, power companies (EPCs), government entities, and power consumers. Power generation companies include both GC entities and DG entities. REP acts as an intermediary for DG-GC joint transactions and assumes the role of RP, while EPC and government agencies are responsible for supervising market operations. Power users purchase electricity on demand. Transactions between GC, REP, and DG are conducted through an electronic trading platform that utilizes smart contracts supported by blockchain technology. This platform includes various modules such as market trading plan management, member management, transaction settlement, transaction information release, transaction management, and contract management. This embodiment adopts a smart contract transaction process and code based on a Merkle tree.

[0043] The DG scenario created by CER and distributed green power in this embodiment mainly includes photovoltaics, wind turbines (WTs), gas turbines (GTs), energy storage systems (ESS), and GC scenarios constructed by centralized photovoltaic systems and onshore wind power generation. This analysis is based on user consumption needs and uses smart contracts to promote a series of transactions involving traditional energy, distributed green power, DG and CEA through REP. Specifically, in order to complete the CER task, DG must purchase GC directly or indirectly through REP to execute CEA. In this context, GC is mainly used to absorb centralized photovoltaics and onshore wind power. In addition, REP needs to participate in both DG and GC market transactions at the same time to meet the collective needs of DG, GC and consumers and achieve balance. In addition, REP also has the task of fulfilling RPS. If it fails to complete, it will be punished according to the penalties outlined in equation (1).

[0044]

[0045] Among them, μ total is the total penalty and breach cost; μ 1 , μ 2 , μ 3 The default cost includes the penalty fee for REP failing to meet the RPS requirements and the penalty fee for CEA failing to meet the quota requirements; M total , M done They are the total term of the electricity package and the number of months already executed; They are the electricity package fee and default coefficient for the execution month respectively; is the annual electricity sales; It is the penalty margin for REP’s unfinished absorption task; is the amount of electricity corresponding to the amount of GC purchased by REP; α is the percentage of renewable energy consumption quota; λ is the GC carbon emission factor, which is 0.58tCO2 / MWh. 1 ,ξ 2 ,ξ 3 ,ξ 4 ,ξ 5 It is the minimum and maximum liquidated damages, the minimum and maximum penalties, and the penalty for failure to comply with the RPS; is the average transaction price of CEA in the previous month; Ψ is the penalty factor for failure to complete the CEA payment task. According to the "temporary work" provisions of the "Carbon Emission Trading Management Regulations", the penalty factor range is 5 to 10, and the value of this embodiment is 8; U 1 , U 2 , I are and the set of i.

[0046] This example focuses on modeling and simulating DG-GC joint transactions from the perspective of representatives. Several key issues need to be addressed:

[0047] REP involves various trading options such as CEA, RPS, green power and conventional energy, and DG and GC transactions occur in different power markets. It is crucial to clarify the relationship between REP and these markets, as well as the factors that affect transactions.

[0048] In DG-GC transactions, customers transfer their power purchase rights to representatives. In order to optimize the objective function of REP in the spot and medium- and long-term markets, it is necessary to design DG planning scenarios based on customer power loads and determine the optimal equipment capacity for power market participation.

[0049] S2. Design a joint transaction mechanism and transaction process according to the DG-GC joint transaction framework, wherein the joint transaction mechanism includes the combination of DG transaction and GC transaction;

[0050] Furthermore, the design of the joint transaction mechanism and transaction process includes:

[0051] Transactions are protected using the merkle tree data structure of smart contracts to achieve multilateral declaration and multilateral response transaction mechanisms for day-ahead and real-time markets.

[0052] Specifically, the multilateral transaction mechanism in the energy blockchain, as a distributed database, has an important feature in the formation of smart contracts. Based on the demand and supply information of the multilateral market, the execution is completed by forming smart contracts.

[0053] S201, DG-GC joint transaction characteristics;

[0054] Feature 1: In addition to signing medium- and long-term contracts with DG, REP participates in the day-ahead and real-time markets for the remaining proportion of electricity purchased, and participates in GC transactions.

[0055] Feature 2: Quotations are made through centralized bidding and continuous bidding. After being processed and encrypted, they form a smart contract together with the public key.

[0056] Feature 3: After the public key is decrypted, verification and information proofreading are completed through platform consistency, auction function, security and other steps.

[0057] Feature 4: Multilateral transactions are automatically settled.

[0058] Specifically, Figure 2 The operational optimization strategy for the DG scenario is shown. The strategy emphasizes customer power load demand, renewable energy on-grid electricity price, time-of-use electricity price, and market price factors. It aims to promote ESS peak-valley arbitrage, alleviate renewable energy fluctuations, and increase the revenue of park operators. In addition, based on the principle of clean energy priority, this embodiment formulates a renewable energy priority output strategy that meets the user's power load demand in the energy consumption scenario, and adopts a renewable energy surplus power feed-in mode. In the ESS operation mode, it is necessary to compare the current electricity price (on-grid electricity price and market price) with the sum of the time-of-use electricity price, and determine the charging and discharging strategy at each moment based on the peak-valley arbitrage and fluctuation smoothing functions of energy storage for renewable energy.

[0059] S202, joint transaction process;

[0060] The core of energy blockchain is to establish smart contracts and then conduct decentralized electricity transactions. In the DG market, a "multilateral declaration, multilateral response" transaction mechanism can be adopted to allow REPs to conduct "many-to-many" transaction declarations and transaction matching, that is, one DG can sell electricity to multiple REPs, and one REP can purchase electricity from multiple DGs. During the transaction, REP and DG match the group of trading parties with the smallest absolute value of the relative difference between the two parties' quotations in the manner of "transaction price priority and reporting time priority", and use the average of the quotations of both parties as the clearing price. In the DG wholesale market, there are the following regulations:

[0061] (i) The power purchase quotation is valid only when the REP quotation is greater than the power sales quotation.

[0062] (ii) When one party of a transaction has the same quotation, the transaction will be matched according to the time when the information was reported.

[0063] (iii) The transaction order cannot be changed after it is confirmed by both REP and DG.

[0064] (iiii). If the transaction matching data is found to be invalid, the transaction matching will be automatically cancelled, the price sequence of the intersection matching will be returned, the declaration information will be modified and the matching will be waited for.

[0065] In the GC market, DG companies based on CER will calculate CEA according to the unit type and key parameter values ​​of different units, and will be distributed free of charge by the ecological environment bureaus of different provinces and cities. If the carbon quota is less than the carbon emission, GC needs to be purchased to offset the difference; in addition, if the REP cannot meet the agreed RPS, GC needs to be purchased to complete the quota requirements. GC clearing price The transaction is determined according to the price ranking and supply and demand quantity of the market buyers and sellers s and z. The DG-GC joint transaction process based on the REP energy blockchain is as follows: Figure 3 shown.

[0066] S3. Based on the joint trading mechanism and trading process, establish an optimization model with economy, low carbon and electricity purchase satisfaction as the objective function;

[0067] Furthermore, the optimization model with economy, low carbon and electricity purchase satisfaction as the objective function is established, including:

[0068] According to the constraints, a multi-objective optimization transaction model of electricity purchase cost, low carbon and electricity purchase satisfaction is constructed;

[0069] The constraints include: energy balance, energy trading, network transmission and energy equipment.

[0070] Specifically, a multi-objective optimization model of DG-GC joint trading is established based on REP. The purpose of optimization is to minimize the annual purchase cost of REP, maximize the low-carbon performance, and maximize the satisfaction of electricity purchase.

[0071] Objective function 1:

[0072] The annual purchase cost of REP mainly includes the purchase cost of distributed power and the purchase cost of GC. The purchase cost of distributed power includes the cost of green power and conventional energy, and the GC cost includes the cost of GC purchased by REP itself to meet the RPS requirements and the cost of GC purchased by DG to offset CEA. In addition, the risk cost of power purchase should also be included. Objective function 1 is as follows:

[0073] min C=C 1 +C 2 +C 3 +μ total +ψ risk (2)

[0074] Among them, minC is the annual purchase cost of REP, C 1 is the distributed power procurement cost, C 2The cost of GC purchased by REP itself to meet RPS requirements, C 3 To offset the cost of GC purchased by CEA, ψ risk The risk cost of purchasing electricity.

[0075] Distributed power trading builds a mathematical model based on different trading products and trading market types, such as equations (3)-(4):

[0076]

[0077] in, Represents the purchase cost in the medium and long term market and the spot market respectively; Respectively represent the green electricity and conventional energy electricity under medium- and long-term contracts; Respectively represent the electricity prices of green electricity and conventional energy under medium- and long-term contracts; They represent the day-ahead green power, day-ahead conventional power and real-time power purchased by s at time t respectively; N s represents the number of s; N, M represent the sets of the number of s and c respectively; Δt represents the time interval.

[0078]

[0079] in, Represents the GC price purchased by s at time t to offset RPS.

[0080]

[0081] in, Represents the GC price purchased by s at time t to offset CEA.

[0082]

[0083] Among them, A represents the real-time market risk aversion coefficient, ε represents the error value in the Gaussian distribution, and φ represents the proportion of real-time market electricity purchases to the total electricity purchases.

[0084] Objective Function II:

[0085] The highest low-carbon performance of REP includes the green electricity purchased from GC and distributed power trading, where the grid CO2 factor corresponding to green electricity is 0.61. Objective function II is as follows:

[0086]

[0087] Objective function III:

[0088] maxκ=κ 1 +κ 2 (9)

[0089]

[0090]

[0091] Where P load Indicates the user's electrical load; Indicates the amount of electricity purchased from the market at time t; They represent the grid-connected power and the renewable power at time t; Q gas,t , η gas Respectively represent gas consumption and GT electric-to-heat conversion coefficient; Φ represents the proportion of economic efficiency to satisfaction, Respectively represent the retail price and wholesale price of each trading product of DG.

[0092] Constraints:

[0093] The constraints of power balance mainly include the total power input to the wth DG station, the transmission power difference between the starting node and the ending node, and the unbalanced power, as shown in formulas (12)-(14):

[0094]

[0095] Among them, ΔIP w,c Insufficient power, ΔEP w,c is excess power, P g,t is the power of device g at time t, P l,t is the transmission power of line l; p(l) and q(l) are the end node and start node of line l respectively.

[0096] The natural gas balance constraints mainly include the natural gas pipeline injection flow at node j, the flow at upstream and downstream nodes, and the natural gas load at node j. The model is shown in formula (15):

[0097]

[0098] Wherein, J represents the set of j.

[0099] DG-GC transaction constraints:

[0100] (1) Trading electricity price constraints

[0101]

[0102] Among them, P guide (t) represents the guidance price of DG transaction at time t; τ, Respectively represent the real-time quotation fluctuation coefficients of electricity sales and electricity purchases; They represent s as the minimum and maximum value of the GC purchase quotation that satisfies RPS and s as the minimum and maximum value of the GC purchase quotation that offsets the CEA deficiency.

[0103] (2) Transaction Clearance Constraints

[0104]

[0105] Among them, E s,n,t , E c,m,t Respectively represent the clearing amount of s and c at time t; N c Indicates the number of c; They represent the GC trading volume and the total tradable GC volume at time t respectively.

[0106] (3) Transaction time constraints

[0107] t clean ≤t clean,max (20)

[0109] Among them, t clean , t clean,max They represent the correction time and cut-off time for transaction clearing respectively.

[0110] Network transmission constraints:

[0111] (1) Power network constraints

[0112]

[0113] Among them, V q,t 、V q,min 、V q,max Respectively represent the voltage, minimum voltage and maximum voltage at the q node; They represent the active power, minimum power and maximum power at node q respectively; Respectively represent the reactive power, minimum power and maximum power at node q; Δ q , Δ p Represent the voltage phases at the q and p nodes respectively.

[0114] (2) Natural gas grid constraints

[0115]

[0116] in, Indicates pipeline flow rate; represents a symbolic variable, representing the flow direction of the airflow; ρ oj,t Indicates the transmission coefficient of the pipeline; PA o,t ,PA j,tRepresent the pressure amplitude of nodes o and j respectively.

[0117] (3) Distribution line capacity margin constraints

[0118]

[0119] Among them, TC pq , Respectively represent the current transmission capacity, limit transmission capacity and maximum transmission capacity margin of nodes p and q; Ω pq , They represent the compensation factor of useless power and the power transfer factor at the node respectively; They represent the amount of electricity purchased and sold by s and c at time t respectively; R represents the set of p and q.

[0120] (4) Energy trading network constraints

[0121] This embodiment is based on a P2P energy trading network based on Bloom filtering, connecting various block nodes to achieve effective communication of information between buyers and sellers. The model is shown in formula (24).

[0122]

[0123] Where, Υ represents the length of the Bloom filter; ω represents the number of hash functions; represents the number of inserted elements; Π represents the false alarm rate.

[0124] Equipment operation constraints

[0125] (1) Power generation equipment constraints

[0126]

[0127] (2) Energy storage battery constraints

[0128]

[0129] in, They represent the charging and discharging power of ESS at time t respectively; represents the installed capacity of ESS; dis', cha' represent the maximum charge and discharge efficiency of ESS respectively; SOC(t), SOC min , SOC max They represent the state of charge, minimum charge and maximum charge of ESS at time t respectively.

[0130] S4. Solving the objective function using a bacterial community chemotaxis algorithm to obtain a target value of the optimization model.

[0131] Furthermore, solving the objective function using the bacterial community chemotaxis algorithm includes:

[0132] Set system parameters according to the required calculation accuracy;

[0133] According to the system parameters, a new direction of bacterial movement and a time and a position of the bacteria moving in the new direction are calculated;

[0134] According to the new direction, time and position of bacterial movement, the position of the center coordinates is sought and the system parameters are updated.

[0135] Specifically, there are a large number of integer and continuous variables in the DG-GC joint trading model, involving numerous discrete, random and uncertain factors. Therefore, the bacterial colony chemotaxis algorithm (BCC) in the intelligent population optimization algorithm is used to solve the model. The BCC algorithm is a random population optimization algorithm formed by establishing a mode of information transmission between bacterial populations. Compared with other population optimization algorithms, BCC bacteria are affected by both individual information and group information, and maintain high performance in both speed and accuracy in nonlinear combinatorial optimization problems, which enhances the global optimization ability of the algorithm.

[0136] (1)BCC

[0137] 1) Set system parameters and give the expected calculation accuracy.

[0138]

[0139] Among them, T 0 represents the minimum average moving time; l represents the convergence accuracy; represents the gradient parameter; ∠Δ represents the deflection angle.

[0140] 2) Set the bacterial movement speed v BCC , which is assumed to be a constant.

[0141] 3) Calculate the new movement direction of the bacteria, that is, the direction of movement of the bacteria in n BCC The moving direction of the dimensional space can be (n-1) BCC dimensional angle vector.

[0142] 4) Calculate the movement time and position of bacteria in the new direction.

[0143] 5) Find a location with better center coordinates. When bacteria adjust their movement state, they must sense their surroundings before moving to a new location each time, looking for other bacteria with better locations. If possible, the bacteria will move to the center of these bacteria with better locations.

[0144] 6) Improved method: Each time the bacterial group moves one step, the state of the bacteria in the worst position is changed so that it moves to the vicinity of the best bacterial position before the group moves.

[0145]

[0146] in, Indicates the worst state of bacteria; Respectively represent the worst and best positions of bacteria; Crand() obeys uniform distribution between (0-2).

[0147] 7) Update parameters. When the set conditions are met, the bacteria end the optimization process, otherwise the bacteria determine the next move based on the existing information and update various parameters.

[0148] (2)VIKOR algorithm

[0149] VIKOR is a multi-objective decision-making method and is considered to be an effective method to select the optimal solution in the Pareto solution set.

[0150] The trading optimization model takes energy demand, random renewable energy generation forecast, energy price and equipment technical parameters as input data. The model solution process is as follows Figure 4 shown.

[0151] (1) System initialization. Input system parameters: equipment operating status, predicted load, predicted sunshine, predicted wind speed, quotation and other data.

[0152] (2) Initialization: Set the population generation number and generate the initial population.

[0153] (3) Simulation: Calculate the target values ​​of REP electricity purchase cost, electricity purchase satisfaction, and energy efficiency based on the joint trading mechanism.

[0154] (4) Bacteria operation: Use the calculation accuracy and bacterial position status to sort the individuals and select the best state.

[0155] (5) Termination condition: When the termination condition is met, output the optimal transaction quantity and price, otherwise return to step 4.

[0156] (6) Select the optimal solution. Use the VIKOR method to screen the Pareto solution set to determine the best solution.

[0157] Furthermore, in the DG-GC joint trading framework, both the DG scheme and REP consider purchasing green certificates with carbon emission quotas and purchasing green certificates with renewable energy quotas.

[0158] Embodiment 2

[0159] This embodiment further discloses a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in the first embodiment are implemented.

[0160] Embodiment 3

[0161] This embodiment also discloses a computer program product, including a computer program, which implements the steps of the method described in the first embodiment when executed by a processor.

[0162] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A DG-GC joint transaction method based on REP, characterized in that: The following steps are involved: Build a DG-GC joint transaction framework for energy blockchain based on retail electricity suppliers; According to the DG-GC joint transaction framework, a joint transaction mechanism and transaction process are designed, wherein the joint transaction mechanism includes the combination of DG transaction and GC transaction; According to the joint trading mechanism and trading process, an optimization model with economy, low carbon and electricity purchase satisfaction as objective functions is established; The objective function is solved using a bacterial community chemotaxis algorithm to obtain the target value of the optimization model.

2. The method according to claim 1, characterized in that The DG-GC joint transaction framework for building the energy blockchain includes: Includes generators, sales representatives, power companies, government entities, and electricity consumers as key players; According to the major players, transactions between retail electricity suppliers, distributed generation and green certificates are completed through an electronic trading platform using smart contracts supported by blockchain technology.

3. The method according to claim 1, characterized in that Designing joint transaction mechanisms and transaction processes includes: Transactions are protected using the merkle tree data structure of smart contracts to achieve multilateral declaration and multilateral response transaction mechanisms for day-ahead and real-time markets.

4. The method according to claim 1, characterized in that: The optimization model with economy, low carbon and electricity purchase satisfaction as the objective function includes: According to the constraints, a multi-objective optimization transaction model of electricity purchase cost, low carbon and electricity purchase satisfaction is constructed; The constraints include: energy balance, energy trading, network transmission and energy equipment.

5. The method according to claim 1, characterized in that Solving the objective function using the bacterial community chemotaxis algorithm includes: Set system parameters according to the required calculation accuracy; According to the system parameters, a new direction of bacterial movement and a time and a position of the bacteria moving in the new direction are calculated; According to the new direction, time and position of bacterial movement, the position of the center coordinates is sought and the system parameters are updated.

6. The method according to claim 1, characterized in that In the DG-GC joint trading framework, both the DG scheme and REP consider purchasing green certificates with carbon emission quotas and purchasing green certificates with renewable energy quotas.

7. The method according to claim 1, characterized in that It also includes verifying the effectiveness of the bacterial community chemotaxis algorithm and the optimization model through examples.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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