A smart contract transaction method and device for flexible load and a medium

Abstract

The application discloses a smart contract transaction method and device for flexible load and a medium, and the method comprises the following steps: firstly, the content of the smart contract is determined, and the income model of the new energy power generator and the heat storage type electric heating aggregator is determined according to the signed smart contract; secondly, the constraint condition of the income model is determined; finally, the income model is normalized to convert the income model into a single-target optimization problem, and the gurobi solver is used for solving. The application relies on the blockchain technology, uses the smart contract signed between the new energy power generator and the heat storage type electric heating aggregator, makes up the volatility and uncertainty of the new energy output, thereby increasing the income of the new energy power generator and the electric heating aggregator, promotes the consumption of the new energy, and has important significance for promoting the development of the local electric power transaction market and the development of the new energy and electric heating. The application provides a theoretical guidance for the behavior decision of the new energy, electric heating and other multi-market main bodies participating in the spot market.

Description

Technical Field

[0001] This invention belongs to the field of electricity market and relates to smart contract trading models in the electricity market, particularly a smart contract trading method, device and medium for flexible loads. Background Technology

[0002] In recent years, renewable energy power generation has experienced large-scale development. As renewable energy power generation technologies mature and costs continue to decline, renewable energy power generation has begun to compete in the electricity market alongside traditional power generation resources, settling accounts at market prices. However, renewable energy power generation is characterized by significant volatility and uncertainty, and the large-scale participation of renewable energy in market competition will pose numerous challenges to the electricity market bidding mechanism and the safe and reliable operation of the power grid.

[0003] At the same time, my country's flexible loads, such as electricity-heat loads, are growing rapidly. As flexible and adjustable loads with time-shifting characteristics, electricity-heat load groups have significant potential to compensate for the volatility and uncertainty of renewable energy output. Therefore, fully tapping the reserve potential of flexible loads such as electricity-heat loads to compensate for the volatility and uncertainty of renewable energy output is of great significance for promoting the development of local electricity trading markets and the development of renewable energy and electric heating. Summary of the Invention

[0004] The purpose of this invention is to address the significant volatility and uncertainty inherent in existing renewable energy power generation technologies by providing a smart contract trading method, device, and medium for flexible loads. This invention can mitigate the volatility and uncertainty of renewable energy output, while simultaneously helping to increase the revenue of renewable energy generators and electric heating aggregators.

[0005] The objective of this invention is achieved through the following technical solution: The first aspect of this invention provides a smart contract transaction method oriented towards flexible loads, comprising the following steps:

[0006] (1) Clarify the content of the smart contract and determine the revenue model of the new energy power generator and the thermal storage electric heating aggregator based on the signed smart contract.

[0007] (2) Determine the constraints of the revenue model, wherein the constraints include smart contract establishment constraints, power balance constraints, electricity consumption constraints for thermal storage electric heating and electricity price constraints;

[0008] (3) Normalize the revenue model of the new energy generator and the thermal storage electric heating aggregator to transform the revenue model into a single objective optimization problem, and solve it using the gurobi solver according to the constraints in step (2) to complete the transaction.

[0009] Optionally, the content of the smart contract includes:

[0010] (a1) All the electricity required by the electric heating aggregator is provided by the new energy power generator;

[0011] (a2) Electric heating aggregators need to provide a certain positive and negative reserve to new energy generators. When there is an error in the output of new energy generators, the aggregators should make up for the error in the predicted output of new energy generators as much as possible within the range of the positive and negative reserves provided.

[0012] (a3) Electric heating aggregators enjoy certain discounts on the electricity purchased from new energy power generators.

[0013] Optionally, the revenue models for the new energy power generator and the thermal storage electric heating aggregator in step (1) are expressed as follows:

[0014]

[0015]

[0016] Among them, F1 and F2 represent the revenue obtained by the new energy power generator and the thermal storage electric heating aggregator, respectively. w Let w be the probability of a scenario including new energy power output. This contributes to the equivalent prediction of new energy power generators at time t after smart contract adjustments under scenario w. The market-clearing electricity price at time t. and Let w represent the positive and negative power imbalance of renewable energy generators at time t. and Let c be the settlement price for the unbalanced electricity at time t. hot Here, S represents the heating price, S is the average daily heating area, and p is the electricity price discount factor stipulated in the contract. The power consumption of the thermal storage electric heating aggregator in scenario w at time t.

[0017] Optionally, the smart contract establishment constraints are expressed as follows:

[0018] F1≥F 1,before

[0019] F2≥F 2,before

[0020] Among them, F 1,before With F 2,before These are the revenues of new energy power generators and thermal storage electric heating aggregators before the signing of smart contracts.

[0021] The power balance constraint is expressed as:

[0022]

[0023]

[0024] y 1,w,t +y 2,w,t ≥1

[0025] Among them, P w,t It refers to the real-time output of new energy power generators in specific scenarios, P t a This is the predicted output from the recent market declarations by new energy power generators. and It is the positive and negative backup at time t when the smart contract is signed, y 1,w,t With y 2,w,t It is a binary variable representing the unbalanced power state, where M is a fairly large positive number;

[0026] The electrical constraints for the thermal storage electric heating system are expressed as follows:

[0027]

[0028]

[0029]

[0030]

[0031]

[0032] Where, q max Q is the maximum power consumption of a thermal storage electric heating system. max With Q min These are the upper and lower limits of the electric heating charging capacity set by thermal storage electric heating aggregators to meet the heating needs of electric heating users;

[0033] The electricity price constraint is expressed as follows:

[0034]

[0035]

[0036]

[0037] 0≤p≤1

[0038] Where, φ s With φ d This is the penalty coefficient for real-time market prices.

[0039] Optionally, step (3) includes the following sub-steps:

[0040] (3.1) The revenue models of new energy power generators and thermal storage electric heating aggregators are normalized, and their corresponding objective functions are normalized respectively:

[0041]

[0042] Among them, F i 'This refers to the normalized revenue of new energy power generators or thermal storage electric heating aggregators, min(F) i ) represents the minimum revenue for a new energy power generator or a thermal storage electric heating aggregator, max(F) i This represents the maximum revenue for new energy power generators or thermal storage electric heating aggregators.

[0043] (3.2) The normalized multi-objective function is transformed into a single-objective function, expressed as:

[0044] maxF = w1F1' + w2F2'

[0045] Where F is the transformed single-objective function, and w1 and w2 are the weights;

[0046] (3.3) Solve the single objective function in step (3.2) using the gurobi solver according to the constraints in step (2) to complete the transaction.

[0047] A second aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described smart contract transaction method for flexible workloads.

[0048] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described smart contract transaction method for flexible workloads.

[0049] The beneficial effects of this invention are that, relying on blockchain technology, it utilizes smart contracts signed between new energy power generators and thermal storage electric heating aggregators to mitigate the volatility and uncertainty of new energy output, thereby increasing the revenue of both new energy power generators and electric heating aggregators and promoting the consumption of new energy. This invention is of great significance for promoting the development of the local electricity trading market and the development of new energy and electric heating, and provides theoretical guidance for the behavioral decision-making of diverse market players such as new energy and electric heating participating in the spot market. Attached Figure Description

[0050] Figure 1 A diagram illustrating a blockchain model for electricity market transactions;

[0051] Figure 2A wind power output forecast curve;

[0052] Figure 3 A graph showing wind power output scenarios and their probability curves;

[0053] Figure 4 Electricity consumption curve for electric heating plan;

[0054] Figure 5 A graph showing the actual electricity consumption of electric heating.

[0055] Figure 6 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] The smart contract transaction method for flexible loads of the present invention includes the following steps:

[0058] (1) Clarify the content of the smart contract and determine the revenue model of the new energy generator and the thermal storage electric heating aggregator based on the signed smart contract.

[0059] In this embodiment, the electricity market transaction blockchain is as follows: Figure 1 As shown, a smart contract is signed between the new energy power generator and the thermal storage electric heating aggregator. The smart contract requires the following three points:

[0060] (a) All the electricity required by the electric heating aggregator is provided by the new energy power generator.

[0061] (b) Electric heating aggregators need to provide a certain positive and negative reserve to new energy generators. When there is an error in the output of new energy generators, the aggregators should make up for the error in the predicted output of new energy generators as much as possible within the range of the provided positive and negative reserves.

[0062] (c) Electric heating aggregators enjoy certain discounts on the electricity purchased from new energy power generators.

[0063] In this embodiment, in the day-ahead market, renewable energy generators and thermal storage electric heating aggregators submit their renewable energy output curves and electric heating electricity consumption plans based on predictions of future renewable energy output curves and the operating characteristics of thermal storage electric heating. The transaction submission information is encrypted with a private key and published on the blockchain network. In the real-time balancing market, the trading platform calculates and adjusts the thermal storage electric heating electricity consumption plan in real time based on the error between the renewable energy generator's submitted output curve and the actual output curve, combined with the smart contract signed between the renewable energy generator and the thermal storage electric heating aggregator. The balancing result is then published across the entire blockchain network. By signing the smart contract, renewable energy aggregators reduce penalties for their output errors, and electric heating aggregators reduce their electricity costs, thus achieving a win-win situation for both parties. Based on the smart contract signed between the renewable energy generator and the thermal storage electric heating aggregator, their respective profit maximization models can be obtained, expressed as:

[0064]

[0065]

[0066] Among them, F1 and F2 represent the revenue obtained by the new energy power generator and the thermal storage electric heating aggregator, respectively. w Let w be the probability of a scenario including new energy power output. This contributes to the equivalent prediction of new energy power generators at time t after smart contract adjustments under scenario w. The market-clearing electricity price at time t. and Let w represent the positive and negative power imbalance of renewable energy generators at time t. and Let c be the settlement price for the unbalanced electricity at time t. hot Here, S represents the heating price, S is the average daily heating area, and p is the electricity price discount factor stipulated in the contract. The power consumption of the thermal storage electric heating aggregator in scenario w at time t.

[0067] (2) Determine the constraints of the revenue model, including smart contract establishment constraints, power balance constraints, electricity consumption constraints for thermal storage electric heating and electricity price constraints.

[0068] Specifically, the smart contract establishment constraint is the basis for the contract signed between the thermal storage electric heating aggregator and the new energy power generator; the power balance constraint is to maintain a balance between the power of the electric heating aggregator and the power of the new energy power generator; the power consumption constraint of thermal storage electric heating prevents the power of electric heating from exceeding the upper and lower limits and meets the heating needs of heating users; the electricity price constraint is to allow the electric heating aggregator to enjoy certain discounts when purchasing electricity from the new energy power generator.

[0069] In this embodiment, the smart contract is subject to the following constraint: To incentivize both parties to enter into the smart contract, the benefits they receive after entering into the smart contract must be greater than their benefits before entering into the smart contract, i.e.:

[0070] F1≥F 1,before

[0071] F2≥F 2,before

[0072] Among them, F 1,before With F 2,before These are the revenues of new energy power generators and thermal storage electric heating aggregators before the signing of smart contracts.

[0073] In this embodiment, the power balance constraint is expressed as:

[0074]

[0075]

[0076] y 1,w,t +y 2,w,t ≥1

[0077] Among them, P w,t It refers to the real-time output of new energy power generators in specific scenarios. This is the predicted output from the recent market declarations by new energy power generators. and It is the positive and negative backup at time t when the smart contract is signed, y 1,w,t With y 2,w,t It is a binary variable representing the unbalanced power state, where M is a fairly large positive number.

[0078] Furthermore, y 1,w,t With y 2,w,t The relationship between the predicted power error and the positive and negative backups stipulated in the smart contract is as follows:

[0079] x 1,w,t =y 1,w,t ×y 2,w,t

[0080] x 2,w,t =y 1,w,t ×(1-y 2,w,t )

[0081] x 3,w,t =(1-y 1,w,t )×y 2,w,t

[0082] Where, x 1,w,t=1 indicates that the predicted power error of the renewable energy generator is within the positive and negative reserve adjustment range stipulated in the smart contract, x 2,w,t =1 indicates that the real-time output of the renewable energy generator is less than the predicted output, and the error exceeds the adjustment range of the negative reserve. 3,w,t =1 indicates that the real-time output of the renewable energy generator is greater than the predicted output, and the error exceeds the adjustment range of the positive reserve. By determining the above relationship, we can obtain:

[0083]

[0084]

[0085]

[0086]

[0087] Where, q t This refers to the electricity consumption plan submitted by the thermal storage electric heating aggregator at time t in the market before the current day.

[0088] In this embodiment, the electrical constraint for thermal storage electric heating is expressed as follows:

[0089]

[0090]

[0091]

[0092]

[0093]

[0094] Where, q max Q is the maximum power consumption of a thermal storage electric heating system. max With Q min These are the upper and lower limits of the electric heating charging capacity set by thermal storage electric heating aggregators to meet the heating needs of electric heating users.

[0095] In this embodiment, the electricity price constraint is expressed as:

[0096]

[0097]

[0098]

[0099] 0≤p≤1

[0100] Where, φ s With φ dThis is the penalty coefficient for real-time market prices.

[0101] (3) Normalize the revenue model of the new energy generator and the thermal storage electric heating aggregator to transform the revenue model into a single objective optimization problem, and solve it using the gurobi solver according to the constraints in step (2) to complete the transaction.

[0102] It should be noted that for multi-objective optimization problems, the objective function needs to be normalized first, then it needs to be transformed into a single-objective optimization problem, and then the optimization problem can be solved using the gurobi solver.

[0103] (3.1) The revenue models of new energy power generators and thermal storage electric heating aggregators are normalized, and their corresponding objective functions are normalized respectively:

[0104]

[0105] Among them, F i 'This refers to the normalized revenue of new energy power generators or thermal storage electric heating aggregators, min(F) i ) represents the minimum revenue for a new energy power generator or a thermal storage electric heating aggregator, max(F) i This represents the maximum revenue for new energy power generators or thermal storage electric heating aggregators.

[0106] Specifically, the objective function corresponding to the revenue model of new energy power generators is normalized:

[0107]

[0108] Where F1' is the normalized revenue of the new energy generator, min(F1) is the minimum revenue of the new energy generator, and max(F1) is the maximum revenue of the new energy generator.

[0109] Specifically, the objective function corresponding to the revenue model of thermal storage electric heating aggregators is normalized:

[0110]

[0111] Where F2' is the normalized revenue of the thermal storage electric heating aggregator, min(F2) is the minimum revenue of the thermal storage electric heating aggregator, and max(F2) is the maximum revenue of the thermal storage electric heating aggregator.

[0112] It should be understood that normalizing the objective functions corresponding to the revenue models of new energy power generators and thermal storage electric heating aggregators can eliminate the dimensional influence between the two objective functions, making the indicators in the two objective functions of the same order of magnitude, which facilitates the later transformation of multi-objective functions into single-objective functions.

[0113] (3.2) The normalized multi-objective function is transformed into a single-objective function, expressed as:

[0114] max F = w1F1' + w2F2'

[0115] Where F is the transformed single-objective function, and w1 and w2 are the weights, which can be determined empirically.

[0116] (3.3) Solve the single objective function in step (3.2) using the gurobi solver according to the constraints in step (2) to complete the transaction.

[0117] The profit model is optimized by solving a mixed integer programming (MIP) problem, which can be solved using Python and the gurobi solver.

[0118] It should be understood that the linear programming solver gurobi is a global optimizer that can support a variety of models, such as continuous and mixed integer linear problems, convex or constrained continuous and mixed integer quadratic problems, non-convex or constrained continuous and mixed integer quadratic problems, etc. It can solve large-scale linear problems, quadratic problems and mixed integer linear and quadratic problems, and supports multi-objective optimization.

[0119] The present invention’s smart contract transaction method for flexible workloads will be described in detail below with reference to embodiments, and the purpose and effects of the present invention will become more apparent.

[0120] Example 1

[0121] Choosing Jilin as the background location and Jilin's wind power generation enterprises as representatives of new energy power generation enterprises, their projected power output curves as reported in the market are as follows: Figure 2 As shown in the figure. Assuming the predicted power output error of wind power companies follows a normal distribution, with the standard deviation of the normal distribution being 15% of the predicted power output, the statistical error based on this prediction method is used to generate 5000 wind power output scenarios through Monte Carlo sampling. These scenarios are then reduced to 10 using a scenario clustering algorithm. The reduced wind power output and the probability of each scenario are shown in the figure. Figure 3 As shown.

[0122] Choose market-clearing electricity price Peak-valley electricity pricing: peak electricity price 0.562 yuan / kWh, valley electricity price 0.329 yuan / kWh, with a real-time market price penalty coefficient φ.s With φ d Taking 1.7 and 0.3 respectively, the heating price c hot Take 27 yuan / m 2 The maximum power consumption of thermal storage electric heating is q. max For 5MW, the upper and lower limits of electric heating charging capacity Q max With Q min We selected 31.5 MWh and 28.5 MWh respectively, with a weight of 0.5 for each.

[0123] By selecting positive and negative reserves and electricity price discount coefficients as decision variables, and using the Gurobi solver to solve the above optimization model, the electricity consumption plans of thermal storage electric heating aggregators before and after signing the smart treaty can be obtained, as follows: Figure 4 and Figure 5 As shown in the figure, by signing smart contracts, the electricity consumption plan of the thermal storage electric heating aggregator is adjusted in real time according to the output error of new energy power generation, thereby reducing the output error of new energy power generation and reducing the electricity cost of the thermal storage electric heating aggregator.

[0124] By solving the problem, we can obtain the values ​​of positive and negative reserves and the electricity price discount coefficient when the optimal solution is achieved, as shown in Table 1.

[0125] Table 1: Positive and Negative Reserve Size and Electricity Price Discount Factor

[0126]

[0127] Table 2: Changes in Revenue Before and After Signing Smart Contracts

[0128] Revenue (RMB) Before signing the contract After signing the contract Changes in earnings New energy power generators 196278.21 197196.96 +918.75 Thermal storage electric heating aggregator 20130 21049.61 +919.61

[0129] Table 2 shows the revenue changes of new energy generators and thermal storage electric heating aggregators. It can be seen that by signing smart contracts, the revenue of both new energy generators and thermal storage electric heating aggregators has increased, which is in line with the expectations of smart contract signing.

[0130] Corresponding to the aforementioned embodiments of the smart contract trading method for flexible loads, the present invention also provides embodiments of a smart contract trading apparatus for flexible loads.

[0131] See Figure 6 An electronic device provided by an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-described smart contract transaction method for flexible workloads.

[0132] Embodiments of the electronic device of the present invention can be applied to any device with data processing capabilities, such as a computer or other similar device. The device embodiment can be implemented in software, hardware, or a combination of both. Taking software implementation as an example, as a logical device, it is formed by the processor of any data processing device loading the corresponding computer program instructions from non-volatile memory into memory for execution. From a hardware perspective, such as... Figure 6 The diagram shown is a hardware structure diagram of any device with data processing capabilities, including the electronic device of this invention. Figure 6 In addition to the processor, memory, network interface, and non-volatile memory shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0133] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0134] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the present invention according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0135] This invention also provides a computer-readable storage medium storing a program thereon, which, when executed by a processor, implements the smart contract transaction method for flexible workloads described in the above embodiments.

[0136] The computer-readable storage medium can be an internal storage unit of any data processing device described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be any data processing device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data processing device, and can also be used to temporarily store data that has been output or will be output.

[0137] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for smart contract transaction oriented to flexible load, characterized in that, The method comprises the following steps: (1) determining the content of the smart contract and the revenue model of the new energy power generator and the heat storage electric heating aggregator according to the signed smart contract; the expression of the revenue model of the new energy power generator and the heat storage electric heating aggregator in the step (1) is respectively: wherein, and respectively represent the income obtained by the new energy power generator and the heat storage type electric heating aggregator, is the probability of containing new energy output scene ; is the equivalent predicted output of the new energy power generator at time t under the scene adjusted by the smart contract, is the market clearing price at time t, and is the positive and negative imbalance power of the new energy power generator at time t under the scene ; and is the settlement price of the positive and negative imbalance power at time t, is the heating price, and S is the daily average heating area, is the contract stipulated electricity price discount coefficient, is the electricity power of the heat storage type electric heating aggregator at time t under the scene ; (2) determining the constraint condition of the revenue model, wherein the constraint condition comprises a smart contract establishment constraint, a power balance constraint, a heat storage electric heating power consumption constraint and an electricity price constraint; (3) performing normalization processing on the revenue model of the new energy power generator and the heat storage electric heating aggregator to convert the revenue model into a single objective optimization problem, and solving the single objective function in the step (3.2) by using a gurobi solver according to the constraint condition in the step (2) to complete the transaction. 2.The flexible load oriented smart contract transaction method of claim 1, wherein, The content of the smart contract comprises: (a1) the required power consumption of the electric heating aggregator is provided by the new energy power generator; (a2) the electric heating aggregator needs to provide a certain positive and negative standby to the new energy power generator, and when the output of the new energy power generator has an error, the prediction output error of the new energy power generator is compensated as much as possible within the range of the provided positive and negative standby; (a3) the electric heating aggregator enjoys a certain discount on the electricity purchased from the new energy power generator. 3.The flexible load oriented smart contract transaction method of claim 1, wherein, The smart contract establishment constraint is represented as: wherein, with respectively, the income of the new energy power generator and the heat storage electric heating aggregator before signing the smart contract; The power balance constraint is represented as: wherein, is the scenario the real-time output of the new energy power producer, is the predicted output of the new energy power producer declared in the day-ahead market, and is the positive or negative reserve at time t signed by the smart contract, and is a binary variable representing the unbalanced power state, is a large positive number; The heat storage electric heating power consumption constraint is represented as: wherein, is the maximum power consumption of the electric heating, and are the upper and lower limits of the charging power of the electric heating set by the aggregator of the electric heating to meet the heating demand of the users of the electric heating, represents the power consumption plan of the aggregator of the electric heating at time t declared in the day-ahead market. The electricity price constraint is represented as: wherein, with is a real-time market price penalty factor. 4.The flexible load oriented smart contract transaction method of claim 1, wherein, The step (3) comprises the following sub-steps: (3.1) performing normalization processing on the revenue model of the new energy power generator and the heat storage electric heating aggregator, and normalizing the corresponding objective functions respectively: wherein, is the normalized revenue of the new energy power generator or the heat storage electric heating aggregator, is the minimum value of the revenue of the new energy power generator or the heat storage electric heating aggregator, is the maximum value of the revenue of the new energy power generator or the heat storage electric heating aggregator; (3.2) converting the multi-objective function after the normalization processing into a single objective function, represented as: wherein, is the transformed single-objective function, and is the weight size; (3.3) solving the single objective function in the step (3.2) by using a gurobi solver according to the constraint condition in the step (2) to complete the transaction.

5. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to realize the smart contract transaction method for flexible load in any one of claims 1-4.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to realize the smart contract transaction method for flexible load in any one of claims 1-4.

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