Collaborative carbon reduction method for source and load subjects of integrated energy system based on blockchain technology

Abstract

The application is suitable for the technical field of low-carbon scheduling of integrated energy systems, and provides a source-load main body collaborative carbon reduction method of an integrated energy system based on a blockchain technology, which uses the blockchain for power authentication, tracks different power type consumption in a power market, and records carbon emissions generated by user power consumption. Not only can the user actively participate in personal-level carbon trading under the guidance and incentive of the market mechanism and adjust the power utilization strategy, but also can drive source-side coordination of multi-energy complementation and increase the carbon emission reduction effort. Thus, a complete emission reduction system from the energy production source to the terminal consumption is constructed, and a collaborative emission reduction effect of the supply and demand sides is realized. In addition, through the DPoS consensus mechanism based on the credit threshold, the nodes can be encouraged to actively participate in the consensus election, the fairness of the consensus election is ensured, and the abnormal nodes are effectively handled.

Description

Technical Field

[0001] This invention belongs to the field of low-carbon dispatching technology for integrated energy systems, and particularly relates to a method for collaborative carbon reduction of source and load entities in integrated energy systems based on blockchain technology. Background Technology

[0002] To achieve the "dual carbon" goals as soon as possible, it is urgent to analyze the current new situation of energy conservation, carbon reduction, and green development, and to propose new ideas for energy conservation and carbon reduction. When carbon reduction and emission reduction only focus on the source side of electricity, there are certain obstacles to the transmission of electricity information between supply and demand, and the impact of carbon emission reduction considering only one source is still limited.

[0003] Globally, energy structure transformation and carbon emission reduction have become crucial aspects of sustainable development. Global demand for renewable energy is constantly increasing, with renewable and clean energy sources such as wind and solar power accounting for a growing share. However, their power generation is significantly affected by factors such as geographical location and climate. To ensure the reliable operation of power systems, stable backup power sources are needed. While traditional thermal power can produce stable electricity, it generates greenhouse gases such as carbon dioxide and methane.

[0004] Therefore, this invention proposes a method for coordinated carbon reduction of source and load entities in an integrated energy system based on blockchain technology. Summary of the Invention

[0005] The purpose of this invention is to provide a method for coordinated carbon reduction of source and load entities in an integrated energy system based on blockchain technology, aiming to solve the problems mentioned in the background art.

[0006] The present invention is implemented as follows: a method for coordinated carbon reduction of source and load entities in an integrated energy system based on blockchain technology, comprising the following steps:

[0007] Step 1: Establish a source-load collaborative carbon reduction architecture based on blockchain technology, analyze the impact of blockchain on electricity consumption and prices, and establish a collaborative carbon reduction coefficient to quantify the effectiveness of introducing blockchain technology;

[0008] Step 2: Establish a collaborative carbon reduction node model based on blockchain technology, including source-side objective function and constraints, and load-side objective function and constraints;

[0009] Step 3: Establish a multi-layered game-theoretic transaction model based on blockchain technology, and adopt a DPoS consensus mechanism based on credit threshold to improve the activity of the decentralized network and quickly eliminate abnormal nodes.

[0010] This invention provides a blockchain-based integrated energy system source-load collaborative carbon reduction method. This method utilizes blockchain for electricity authentication, tracks different types of electricity consumption in the electricity market, and records carbon emissions generated by user electricity consumption. This not only enables users to actively participate in personal-level carbon trading and adjust their electricity consumption strategies under the guidance and incentive of market mechanisms, but also drives source-side coordination and multi-energy complementarity, increasing carbon emission reduction efforts. This constructs a complete emission reduction system from energy production sources to end-user consumption, achieving a synergistic emission reduction effect on both the supply and demand sides. Furthermore, through a DPoS consensus mechanism based on a credit threshold, nodes can be incentivized to actively participate in consensus elections, ensuring the fairness of the consensus election and effectively handling abnormal nodes. Attached Figure Description

[0011] Figure 1 A source-load collaborative carbon reduction architecture based on blockchain technology;

[0012] Figure 2 It is a multi-layered game interaction process based on blockchain technology;

[0013] Figure 3 For the consensus node election process;

[0014] Figure 4 To increase user preference for green electricity;

[0015] Figure 5 A comparison of green electricity transactions within a single day;

[0016] Figure 6 A comparison of thermal power transactions within a single day;

[0017] Figure 7 A comparison of new energy power transactions within a single day;

[0018] Figure 8 A comparison of the percentage of abnormal nodes;

[0019] Figure 9 For comparing node activity. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0021] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0022] like Figure 1 As shown, an embodiment of the present invention provides a method for coordinated carbon reduction of source and load entities in an integrated energy system based on blockchain technology, comprising the following steps:

[0023] Step 1: Establish a source-load collaborative carbon reduction architecture based on blockchain technology, analyze the impact of blockchain on electricity consumption and prices, and establish a collaborative carbon reduction coefficient to quantify the effectiveness of introducing blockchain technology.

[0024] Step 1.1: Establish a source-load collaborative carbon reduction architecture based on blockchain technology;

[0025] A power generation alliance is formed among power suppliers on the source side. A carbon trading mechanism is implemented to encourage cooperation between thermal power producers and renewable energy power producers, prioritizing the dispatch of new energy power and promoting complementary energy dispatch between thermal and renewable power. The power generation alliance participates in carbon trading as the main body, making decisions aimed at maximizing the alliance's benefits and generating electricity prices for different types of electricity.

[0026] Blockchain's traceability capability enables the identification of green electricity in the market, establishing corresponding green electricity prices and promoting the consumption of renewable energy. Before the introduction of blockchain technology, distinguishing renewable electricity from other types of electricity in the market was a challenge, as the lack of specific recognition of its environmental attributes could lead to it being treated as ordinary electricity. However, with the introduction of blockchain, there is a better solution to improve the homogenization of electricity. Electricity is clearly divided into ordinary electricity and green electricity, each with different prices. Through electricity trading supported by blockchain technology, electricity users can actively choose the type of electricity they want. Blockchain can also accurately measure carbon emissions related to thermal power consumption and quantify the carbon reduction achieved by using renewable energy. This real-time tracking and measurement of carbon emissions from energy consumption allows users to intuitively understand the environmental impact of their electricity consumption, change their electricity consumption habits, and actively participate in personal carbon trading. When users choose to use green electricity according to their preferences, the source side can capture their consumption trends of green electricity to help them make better power supply strategies.

[0027] Step 1.2: Analyze the impact of blockchain on electricity consumption and prices;

[0028] The proportion of new energy power generators participating in collaborative carbon reduction through blockchain is expressed as... , This indicates that the power system does not adopt a source-load collaborative carbon reduction strategy based on blockchain technology. This indicates that all thermal power suppliers and half of the renewable energy suppliers in the power system will participate in collaborative carbon reduction strategies by going on-chain. This indicates that all renewable energy power supplier nodes on the source side adopt blockchain technology. Before the introduction of blockchain, the power generation alliance consisted of J thermal power suppliers, K wind power suppliers, and M solar power suppliers. The number of wind and solar power nodes on the blockchain are represented as M1 and K1, respectively, and the number of nodes not on the blockchain are represented as M2 and K2, respectively, with M1 + M2 = M and K1 + K2 = K. Given that this invention focuses on the overall benefits and carbon emission reduction of the power generation alliance, while ensuring sufficient reserve capacity of thermal power in the power system, the power generation alliance will formulate electricity pricing strategies to promote renewable energy consumption and reduce carbon emissions.

[0029] The electricity consumption cycle is divided into T time periods, and each trading period is marked as follows: During these periods, the total power supply from the source side to the electricity market, for different power types, is expressed as follows:

[0030] (1);

[0031] In the formula, , , These represent the total supply of thermal power, wind power, and solar power during time period t, respectively. The electricity provided to the j-th thermal power plant and These represent the total electricity provided by all wind power and solar power companies participating in the blockchain-based collaborative carbon reduction strategy; and These represent the total electricity generated by all wind power and solar power companies that did not participate in the collaborative carbon reduction strategy.

[0032] (2);

[0033] In the formula, This represents the amount of electricity provided by the m-th on-chain wind power company. This represents the amount of electricity provided by the k-th on-chain optical e-commerce platform; This represents the amount of electricity provided by the m-th wind power provider that is not on the blockchain; This represents the electricity provided by the k-th unchained optical e-commerce platform.

[0034] This electricity serves N customers, users The type of electricity consumed is selected through blockchain. The amount of thermal power consumed by the nth user can be represented as... The amount of electricity purchased from the j-th thermal power plant is Therefore, the total thermal power consumed in time period t is expressed as .

[0035] Because renewable energy generators have different strategies for participating in collaborative blockchain initiatives, the renewable energy power consumed by the nth user can be represented as:

[0036] (3);

[0037] In the formula, and These represent the total wind power and solar power consumed by the nth user at time t, respectively. and This indicates that the consumed electricity comes from the on-chain wind and solar power grids. and This indicates that the consumed electricity comes from wind and solar power sources that are not connected to the blockchain.

[0038] (4);

[0039] (5);

[0040] In the formula, This indicates that the consumed wind power comes from the m-th wind power e-commerce platform on the blockchain; This indicates that the consumed optical power comes from the kth optical e-commerce device on the blockchain; This indicates that the consumed wind power comes from the m-th wind power e-commerce platform that has not been certified on the blockchain; This indicates that the consumed photoelectric power comes from the kth photoelectric device that is not on the blockchain.

[0041] Therefore, the total wind power consumed during time period t is... and photoelectric power Represented as:

[0042] (6);

[0043] In the formula, and These represent the total, blockchain-verified wind and solar power consumed during time period t. and These represent the total unverified wind and solar power consumed during time period t.

[0044] In the electricity market, because renewable energy suppliers have not applied blockchain technology, they lack the credibility to prove the environmental value of their renewable electricity. Although the electricity source is renewable, this type of electricity is still considered ordinary electricity with carbon emissions. Therefore, ordinary electricity supply... and ordinary electricity consumption The expressions are as follows:

[0045] (7);

[0046] Therefore, green electricity supply and green electricity consumption They can be expressed as follows:

[0047] (8);

[0048] Step 1.3: Establish a synergistic carbon reduction coefficient;

[0049] Define a collaborative carbon reduction coefficient to measure the effectiveness of introducing blockchain technology. Defined as follows:

[0050] (9);

[0051] In the formula, , , and The four assessment indicators are thermal power consumption, new energy power consumption, alliance revenue, and carbon emissions, all of which are normalized ratios of the differences before and after the introduction of blockchain. , , and The weighting coefficients for each indicator are set as follows: thermal power consumption, new energy power consumption, alliance revenue, and carbon emissions are 0.2, 0.2, 0.3, and 0.3, respectively.

[0052] (10);

[0053] In the formula, , and These are respectively represented as new energy power consumption, wind power consumption, and photovoltaic power consumption; and The figures show the thermal power consumption before and after the introduction of blockchain, respectively. and The figures represent the consumption of new energy power before and after the introduction of blockchain technology. and The revenue of the alliance before and after the introduction of blockchain technology; and Carbon emissions before and after the introduction of blockchain; synergistic carbon reduction coefficient. The larger the value, the better the collaborative carbon reduction model performed on that day after the introduction of blockchain.

[0054] Step 2: Establish a collaborative carbon reduction node model based on blockchain technology;

[0055] Step 2.1: Source-side objective function;

[0056] The source side consists of a power generation alliance composed of thermal power generators and distributed renewable energy power suppliers. Under the background of promoting the consumption of renewable energy power, the carbon trading costs of the power generation alliance decrease, resulting in the highest overall benefit for the alliance, rather than the optimal benefit for any single power generator within the alliance. The objective utility function is shown in the following equation:

[0057] (11);

[0058] In the formula, The electricity sales revenue of the power generation alliance; The cost of electricity supply for the power generation consortium; The carbon trading costs for the power generation consortium; The willingness of the power generation alliance to reduce carbon emissions; To maximize the utility function of the power supply alliance.

[0059] (12);

[0060] In the formula, and Let t represent the green electricity price and the regular electricity price, respectively.

[0061] (13);

[0062] In the formula, , , These are the operation and maintenance costs for thermal power, solar power, and wind power, respectively.

[0063] (14);

[0064] In the formula, The price per unit of carbon traded on the source side. and These represent the carbon emissions and free carbon allowances of the power generation alliance at time t, respectively.

[0065] (15);

[0066] In the formula, This represents the carbon emission coefficient per unit of electricity generated by a thermal power unit. The average carbon emission coefficient for the region; t represents the carbon emission adjustment factor for the power generation alliance at time t, and this adjustment factor is related to the proportion of green electricity supply.

[0067] (16);

[0068] In the formula, Free carbon credits for the MomentPower Generation Alliance Carbon allowance for each unit of electricity supplied.

[0069] Step 2.2: Source-side constraints;

[0070] (17);

[0071] In the formula, Let be the power supply of the j-th thermal power unit during time period t; and These represent the maximum and minimum values ​​of thermal power supply during the time period t, respectively. Let t be the power supply of the m-th wind turbine during time period t; This represents the maximum power supply of the wind turbine. The power supply for the k-th photoelectric generator during time period t; Maximum power supply for the photovoltaic unit .

[0072] The supply of conventional and green electricity in the electricity market meets the constraints, which are as follows:

[0073] (18);

[0074] In the formula, and These are the minimum and maximum values ​​for normal electricity supply, respectively; and These represent the minimum and maximum values ​​of green electricity supply, respectively.

[0075] Step 2.3: Objective function on the load side;

[0076] The user-side objective utility function is:

[0077] (19);

[0078] In the formula, Let be the utility value function of a user's electricity consumption over time period t (n time periods). This refers to the individual electricity purchase cost for the nth user during time period t. The individual carbon trading cost for the nth user in time period t; Maximize the user's utility function.

[0079] (20);

[0080] In the formula, and These represent the green electricity consumption and conventional electricity consumption during the nth user's time period t, respectively.

[0081] (twenty one);

[0082] In the formula, The price per unit of carbon in personal carbon trading; This represents the actual carbon emissions during the nth user's time period t. This refers to the free carbon credits obtained by the user during the nth user's time period t.

[0083] (twenty two);

[0084] In the formula, is the adjustment factor for personal electricity consumption carbon emissions at time t, which is related to personal electricity consumption.

[0085] (twenty three);

[0086] In the formula, Carbon allowances allocated to a region.

[0087] Step 2.4: Load-side constraints;

[0088] The user's total power consumption should not exceed the total power supply from the source side, as shown in the following formula:

[0089] (twenty four);

[0090] Each user's electricity consumption must not exceed its maximum limit.

[0091] (25);

[0092] In the formula, and These are the minimum and maximum battery consumption values ​​for user n during time period t, respectively. and It is divided into the maximum value of ordinary electricity consumption and green electricity consumption for user n time period t.

[0093] Step 3: Establish a multi-layered game-theoretic transaction model based on blockchain technology, and adopt a DPoS consensus mechanism based on credit threshold to improve the activity of the decentralized network and quickly eliminate abnormal nodes;

[0094] Step 3.1: Construction of a multi-layered game theory transaction model;

[0095] Within the power generation alliance, new energy power suppliers and thermal power suppliers engage in cooperative bargaining. Each power supplier, based on its own energy characteristics, actively participates in price negotiations for each time period, prioritizing the dispatch of green electricity and maximizing carbon emission reduction, with equation (11) as the objective function.

[0096] An evolutionary game is conducted among electricity users to find an evolutionary equilibrium solution through multiple iterations. At the same time, the preference of electricity user groups for green electricity in different time periods is analyzed.

[0097] Considering the sequential competition between power suppliers and users, this can be constructed as a master-slave game model, where the generation alliance acts as the leader and the demand side as the follower. The generation alliance sets the optimal electricity price, while users make the optimal response to the alliance's pricing strategy, determining the optimal electricity consumption strategy. The electricity market responds to supply and demand balance through dynamic pricing.

[0098] Step 3.2: Solving the multi-layer game model based on blockchain technology;

[0099] Smart contracts require a multi-layered game-theoretic incentive mechanism for execution. The smart contract execution process comprises three phases: node admission, game-solving, and transaction settlement. The multi-layered game-theoretic interaction process based on blockchain technology is as follows: Figure 2 As shown. The steps to solve the game theory problem are as follows:

[0100] 1) Set initial values ​​for regular electricity prices, green electricity prices, and user policies;

[0101] 2) The internal cooperation game of the power generation alliance, on the basis of prioritizing the dispatch of green electricity and reducing the carbon emissions of the power system, uses equation (11) as the objective function to carry out Nash bargaining, and outputs the first ordinary electricity price and green electricity price on the blockchain smart trading platform;

[0102] 3) Based on the prices of the two types of electricity released by the power generation alliance, the electricity user group uses Equation (19) as the objective function to solve the group evolution game, analyzes and obtains the user group's preference for green electricity during the time period, and determines the user group's optimal electricity consumption strategy.

[0103] 4) The power generation alliance will conduct a new round of price negotiations based on information such as user groups' preference for green electricity, user demand for both types of electricity, and the supply and demand balance of green electricity.

[0104] 5) Iterate through steps 3) and 4) until the calculations no longer change, indicating that the user side has found the optimal consumption strategy and the power generation alliance has found the optimal price for ordinary electricity and green electricity.

[0105] Step 3.3: Consensus Mechanism Design;

[0106] The consensus value of the i-th node in the t-th election It consists of two parts: credit score and vote score, and is calculated as follows:

[0107] (26);

[0108] In the formula: and These are the credit value and vote value of the i-th node in the t-th election, respectively. and These are the weighting coefficients for credit score and vote score, respectively.

[0109] In the designed consensus mechanism, the credit score takes into account the transaction quality score. Credit loss and credit gain Therefore, the credit value of the i-th node in the t-th election. Represented as:

[0110] (27);

[0111] In the formula, , , and It is divided into the credit value of the i-th node in the previous round, the transaction quality value in the previous transaction period, the credit gain value, and the credit decay value.

[0112] Transaction quality value in the previous trading session The definition is as follows:

[0113] (28);

[0114] In the formula, and These are the set reward and penalty coefficients, respectively. This represents the actual energy value of the i-th node during the previous voting period. The reported planned energy transaction value.

[0115] For credit decay value If a node remains inactive for an extended period during a consensus node election cycle, failing to participate in consensus voting more than three times, its credit score will decline. The definition is as follows:

[0116] (29);

[0117] In the formula, k1 represents the rate at which the credit value decays, and k1 represents the number of times the node did not participate in the consensus node election.

[0118] Credit gain Credit gain refers to the increase in a node's credit score during a transaction period. A node's credit score increases when it is elected as a consensus node more than three times within an election cycle. This credit gain gives the node an advantage in subsequent elections. The definition is as follows:

[0119] (30);

[0120] In the formula, K1 represents the credit value growth rate, and K2 represents the number of times a consensus node has been elected. At the end of each consensus node election cycle, both K1 and K2 are reset to 0. Credit information from the last trading session of the previous election cycle is directly transferred to the next election cycle. Figure 3 This is a flowchart for the election of consensus nodes.

[0121] As a preferred embodiment of the present invention, to demonstrate the synergistic carbon reduction performance of this method, three scenarios are set up for comparative analysis:

[0122] Scene 1: The power system does not use blockchain and only performs carbon reduction for a single source.

[0123] Scene 2: By introducing blockchain technology, the power system adopts a collaborative carbon reduction strategy, but only one thermal power generation node, three wind power generation nodes, and three solar power generation nodes participate in the collaborative strategy on the source side.

[0124] Scene 3: In the power system, all nodes use blockchain.

[0125] Figure 4 The graph shows the green electricity preference for different time periods, obtained from the internal evolutionary game among user groups in scenarios 2 and 3. As can be seen from the graph, the green electricity preference of daytime users is lower than at night. This is because the electricity load of the power system is higher during the day than at night. The power generation alliance, as the leader in the game, formulates power supply strategies to coordinate supply and demand, guiding users to smooth peak and off-peak periods. Users, as followers in the game, are influenced by personal carbon trading mechanisms and time-of-use pricing incentives. During the day, they control their electricity consumption or purchase thermal power, changing their electricity usage habits and behaviors, shifting some load to the low-price hours at night, resulting in a lower willingness to trade green electricity during the day compared to nighttime.

[0126] The trading of different types of electricity under three scenarios is analyzed and compared, such as... Figure 5 , Figure 6 and Figure 7 As shown in the diagram, in Scenario 1, the power system has not incorporated blockchain, and the electricity consumption on the demand side is homogeneous, with users only having ordinary electricity needs. Scenario 1 shows a significant difference in thermal power trading volume compared to Scenario 2 and Scenario 3, with the total daily thermal power trading volume exceeding that of renewable energy, indicating that the power system is more reliant on thermal power. With the increasing application of blockchain and collaborative carbon reduction strategies, thermal power trading volume is gradually decreasing, green electricity trading volume is gradually increasing, and consequently, renewable energy trading volume is also gradually increasing.

[0127] Table 1 Key performance indicators for daily alliance operation under three scenarios

[0128]

[0129] As shown in Table 1 above, the thermal power trading volume in scenarios 2 and 3 decreased by 41.02% and 62.99% respectively compared to scenario 1; the new energy power trading volume increased by 73.97% and 118.27% respectively; and the power generation alliance revenue increased by 35.73% and 64.64% respectively. The increase in new energy power consumption reduced the power system's dependence on thermal power, thereby reducing the carbon trading cost of the power generation alliance and relatively improving the overall economic efficiency. Assuming that the initial value of the collaborative carbon reduction coefficient was 0 before the introduction of blockchain technology, that is, all indicators were 0, the normalized values ​​of each indicator in scenarios 2 and 3 were calculated according to Table 1. According to equations (9) and (10), the collaborative carbon reduction coefficients of scenarios 2 and 3 were calculated to be 0.48 and 0.74 respectively. This demonstrates the effectiveness of introducing blockchain technology, and scenario 3 performs better than scenario 2.

[0130] To demonstrate the performance of the consensus mechanism proposed in this invention, a comparative experiment was conducted with the traditional DPoS consensus mechanism:

[0131] like Figure 8 As shown, the proportion of malicious nodes in the initial stage of the traditional DPoS consensus mechanism is significantly higher than that of this method. Although the proportion of abnormal nodes generally decreases with each consensus round, it eventually remains at around 25%. In contrast, the consensus mechanism of this method performs even better, with the proportion of abnormal nodes never exceeding 40%, and the proportion of abnormal nodes gradually decreases and stabilizes below 10% as the number of consensus rounds increases.

[0132] according to Figure 9 As can be seen, after 30 rounds of voting, the percentage of participants in the traditional DPoS consensus mechanism remains between 45% and 60%. This is because, in the DPoS consensus mechanism, a node's voting behavior has little impact on its own interests; therefore, node participation in the election is random, and there is a lack of motivation to participate. In contrast, in the consensus mechanism of this method, after 30 rounds of simulated consensus elections, the percentage of participants ultimately remains around 80%.

[0133] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for coordinated carbon reduction of source and load entities in an integrated energy system based on blockchain technology, characterized in that: Includes the following steps: Step 1: Establish a source-load collaborative carbon reduction architecture based on blockchain technology, analyze the impact of blockchain on electricity consumption and prices, and establish a collaborative carbon reduction coefficient to quantify the effectiveness of introducing blockchain technology; Step 2: Establish a collaborative carbon reduction node model based on blockchain technology, including source-side objective function and constraints, and load-side objective function and constraints; Step 3: Establish a multi-layered game-theoretic transaction model based on blockchain technology, and adopt a DPoS consensus mechanism based on credit threshold to improve the activity of the decentralized network and quickly eliminate abnormal nodes; Step 1 includes the following specific steps: Step 1.1: Establish a source-load collaborative carbon reduction architecture based on blockchain technology; A power generation alliance is formed among power suppliers on the source side. The carbon trading mechanism is implemented to encourage cooperation between thermal power suppliers and renewable energy power suppliers, prioritize the dispatch of new energy power, and promote the complementary dispatch of energy between thermal power and new energy power. The power generation alliance participates in carbon trading as the main body, makes decisions aimed at maximizing the alliance's benefits, and generates electricity prices for different types of electricity. The traceability of blockchain is used to identify and price green electricity, enabling users to actively choose the type of electricity and understand the carbon emissions of their electricity consumption in real time. Step 1.2: Analyze the impact of blockchain on electricity consumption and prices; The proportion of new energy power generators participating in collaborative carbon reduction through blockchain is expressed as... , This indicates that the power system does not adopt a source-load collaborative carbon reduction strategy based on blockchain technology. This indicates that all thermal power suppliers and half of the renewable energy suppliers in the power system will participate in collaborative carbon reduction strategies by going on-chain. This indicates that all new energy power supplier nodes on the source side adopt blockchain technology; The electricity consumption cycle is divided into T time periods, and each trading period is marked as... Define the total supply of thermal power, wind power, and solar power during time period t, and distinguish between electricity that is certified by the blockchain and electricity that is not certified; define the various types of electricity consumed by user n during time period t; Step 1.3: Establish a synergistic carbon reduction coefficient; Define a collaborative carbon reduction coefficient to measure the effectiveness of introducing blockchain technology. Defined as follows: （9）； In the formula, , , and The four assessment indicators are thermal power consumption, new energy power consumption, alliance revenue and carbon emissions, which are all normalized ratios of the differences before and after the introduction of blockchain. , , and The weighting coefficients for each indicator are set as follows: thermal power consumption, new energy power consumption, alliance revenue, and carbon emissions are 0.2, 0.2, 0.3, and 0.3, respectively.

2. The method for coordinated carbon reduction of integrated energy system source and load entities based on blockchain technology according to claim 1, characterized in that, In step 1.2, electricity is supplied from the source side to the electricity market, and the total electricity supply for different electricity types is expressed as follows: （1）； In the formula, , , These represent the total supply of thermal power, wind power, and solar power during time period t, respectively. The amount of electricity provided to the j-th thermal power plant; and These represent the total electricity provided by all wind power and solar power companies participating in the blockchain-based collaborative carbon reduction strategy; and These represent the total electricity provided by all wind power and solar power companies that did not participate in the collaborative carbon reduction strategy; （2）； In the formula, This represents the amount of electricity provided by the m-th on-chain wind power company. This represents the amount of electricity provided by the k-th on-chain optical e-commerce platform; This represents the amount of electricity provided by the m-th wind power provider that is not on the blockchain; This represents the electricity provided by the k-th unchained optical e-commerce platform; This electricity serves N customers, users The type of electricity consumed is selected through blockchain; the amount of thermal power consumed by the nth user is represented as... The amount of electricity purchased from the j-th thermal power plant is Therefore, the total thermal power consumed in time period t is expressed as ; The amount of renewable energy power consumed by the nth user is represented as: （3）； In the formula, and These represent the total wind power and solar power consumed by the nth user at time t, respectively. and This indicates that the consumed electricity comes from the on-chain wind and solar power grids. and This indicates that the consumed electricity comes from wind and solar power sources that are not on-chain. （4）； （5）； In the formula, This indicates that the consumed wind power comes from the m-th wind power e-commerce platform on the blockchain; This indicates that the consumed optical power comes from the kth optical e-commerce device on the blockchain; This indicates that the consumed wind power comes from the m-th wind power e-commerce platform that has not been certified on the blockchain; This indicates that the consumed optical power comes from the kth optical network that is not yet on-chain; Therefore, the total wind power consumed during time period t is... and photoelectric power Represented as: （6）； In the formula, and These represent the total, blockchain-verified wind and solar power consumed during time period t. and These represent the total unverified wind and solar power consumed during time period t; Treating renewable energy suppliers as ordinary electricity with carbon emissions, therefore, ordinary electricity supply and ordinary electricity consumption The expressions are as follows: （7）； Therefore, green electricity supply and green electricity consumption They are expressed as follows: （8）。 3. The method for coordinated carbon reduction of integrated energy system source and load entities based on blockchain technology according to claim 2, characterized in that, In step 1.3, , , and The calculation formula is as follows: （10）； In the formula, , and These are respectively represented as new energy power consumption, wind power consumption, and photovoltaic power consumption; and The figures show the thermal power consumption before and after the introduction of blockchain, respectively. and The figures represent the consumption of new energy power before and after the introduction of blockchain technology. and The revenue of the alliance before and after the introduction of blockchain technology; and Carbon emissions before and after the introduction of blockchain; synergistic carbon reduction coefficient. The larger the value, the better the collaborative carbon reduction model performed on that day after the introduction of blockchain.

4. The method for coordinated carbon reduction of integrated energy system source and load entities based on blockchain technology according to claim 3, characterized in that, In step 2, the source-side objective function is the objective utility function that maximizes the power generation consortium, as shown in the following equation: （11）； In the formula, The electricity sales revenue of the power generation alliance; The cost of electricity supply for the power generation consortium; The carbon trading costs for the power generation consortium; The willingness of the power generation alliance to reduce carbon emissions; Maximize the utility function of the power supply alliance; （12）； In the formula, and Let be the green electricity price and the conventional electricity price at time t, respectively; （13）； In the formula, , , These are the operation and maintenance costs for thermal power, solar power, and wind power, respectively. （14）； In the formula, The price per unit of carbon traded on the source side. and These represent the carbon emissions and free carbon credits of the power generation alliance at time t, respectively. （15）； In the formula, This represents the carbon emission coefficient per unit of electricity generated by a thermal power unit. The average carbon emission coefficient for the region; The adjustment factor for carbon emissions of the power generation alliance at time t is related to the proportion of green electricity supply. （16）； In the formula, Free carbon credits for the MomentPower Generation Alliance Carbon allowance for each unit of electricity supplied; The source-side constraints are as follows: （17）； In the formula, Let be the power supply of the j-th thermal power unit during time period t; and These represent the maximum and minimum values ​​of thermal power supply during the time period t, respectively. Let t be the power supply of the m-th wind turbine during time period t; This represents the maximum power supply of the wind turbine. The power supply for the k-th photoelectric generator during time period t; Maximum power supply for the photovoltaic unit ; The supply of conventional and green electricity in the electricity market meets the constraints, which are as follows: （18）； In the formula, and These are the minimum and maximum values ​​for normal electricity supply, respectively; and These represent the minimum and maximum values ​​of green electricity supply, respectively.

5. The method for coordinated carbon reduction of source and load entities in an integrated energy system based on blockchain technology according to claim 4, characterized in that, In step 2, the load-side objective function is the user-side objective utility function, as shown in the following equation: （19）； In the formula, Let be the utility value function of a user's electricity consumption over time period t (n time periods). This refers to the individual electricity purchase cost for the nth user during time period t. The individual carbon trading cost for the nth user in time period t; Maximize the user's utility function; （20）； In the formula, and These represent the green electricity consumption and conventional electricity consumption for the nth user's time period t, respectively. （21）； In the formula, The price per unit of carbon in personal carbon trading; This represents the actual carbon emissions during the nth user's time period t. The free carbon credits obtained by the user in the nth user time period t; （22）； In the formula, is the adjustment factor for personal electricity consumption carbon emissions at time t, which is related to personal electricity consumption. （23）； In the formula, Carbon quotas allocated to regions; The load-side constraints are as follows: The user's total power consumption should not exceed the total power supply from the source side, as shown in the following formula: （24）； Each user's electricity consumption must not exceed its maximum limit: （25）； In the formula, and These are the minimum and maximum battery consumption values ​​for user n during time period t, respectively. and It is divided into the maximum value of ordinary electricity consumption and green electricity consumption for user n time period t.

6. The method for coordinated carbon reduction of source and load entities in an integrated energy system based on blockchain technology according to claim 5, characterized in that, Step 3 includes the following specific steps: Step 3.1: Construct a multi-layered game-theoretic transaction model, including: cooperative game between new energy power suppliers and thermal power suppliers within the power generation alliance; evolutionary game between power users; and constructing the behavior between the power generation alliance and users into a master-slave game model with the power generation alliance as the leader and users as the subordinates. Step 3.2: Execute an incentive mechanism based on multi-level game theory through smart contracts; Step 3.3: Design a DPoS consensus mechanism based on a credit threshold, where the consensus value of the i-th node in the t-th election is... It consists of two parts: credit score and vote score, and is calculated as follows: （26）； In the formula: and These are the credit value and vote value of the i-th node in the t-th election, respectively. and These are the weighting coefficients for credit score and vote score, respectively.

7. The method for coordinated carbon reduction of source and load entities in an integrated energy system based on blockchain technology according to claim 6, characterized in that, In step 3.2, the game-solving steps include: 1) Set initial values ​​for regular electricity prices, green electricity prices, and user policies; 2) The internal cooperation game of the power generation alliance, on the basis of prioritizing the dispatch of green electricity and reducing the carbon emissions of the power system, uses equation (11) as the objective function to carry out Nash bargaining, and outputs the first ordinary electricity price and green electricity price on the blockchain smart trading platform; 3) Based on the prices of the two types of electricity released by the power generation alliance, the electricity user group uses Equation (19) as the objective function to solve the group evolution game, analyzes and obtains the user group's preference for green electricity during the time period, and determines the user group's optimal electricity consumption strategy. 4) The power generation alliance will conduct a new round of price negotiations based on information such as user groups' preference for green electricity, user demand for both types of electricity, and the supply and demand balance of green electricity. 5) Iterate through steps 3) and 4) until the calculations no longer change, indicating that the user side has found the optimal consumption strategy and the power generation alliance has found the optimal price for ordinary electricity and green electricity.

8. The method for coordinated carbon reduction of source and load entities in an integrated energy system based on blockchain technology according to claim 7, characterized in that, In step 3.3, the credit score in the designed consensus mechanism considers the transaction quality score. Credit loss and credit gain Therefore, the credit value of the i-th node in the t-th election. Represented as: （27）； In the formula, , , and It is divided into the credit value of the i-th node in the previous round, the transaction quality value in the previous transaction period, the credit gain value, and the credit decay value; Transaction quality value in the previous trading session The definition is as follows: （28）； In the formula, and These are the set reward and penalty coefficients, respectively. This represents the actual energy value of the i-th node during the previous voting period. The reported planned energy transaction value; For credit decay value If a node remains inactive for an extended period during a consensus node election cycle, failing to participate in consensus voting more than three times, its credit score will decline. The definition is as follows: （29）； In the formula, The credit value decay rate is represented by k1, which is the number of times the user did not participate in the consensus node election. Credit gain This refers to the increase in a node's credit value during a transaction period. When a node is elected as a consensus node more than three times in an election cycle, its credit value will increase. The definition is as follows: （30）； In the formula, k1 represents the credit value growth rate, and k2 represents the number of times a consensus node has been elected. After each consensus node election cycle ends, the counts of k1 and k2 are reset to 0. Credit information from the last trading session of the previous election cycle is directly transferred to the next election cycle.

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