Optimized operation method, device, electronic device and storage medium of multi-energy system

Abstract

The present application discloses an optimized operation method, device, electronic device and storage medium for a multi-energy system, and relates to the technical field of optimized operation of carbon emissions. The method includes: determining the carbon quota demand data corresponding to each carbon emission participant in the multi-energy system; constructing a corresponding production energy consumption model for each carbon emission participant based on the carbon quota demand data of each carbon emission participant; constructing an optimal clearing model corresponding to each energy market party with the goal of maximizing the benefits of each energy market party in the multi-energy system; optimizing the production energy consumption model and the optimal clearing model based on pre-established decision constraints to obtain an optimized operation strategy for the multi-energy system. The technical solution provided by the present application can guide the energy production and consumption activities of a regional multi-energy system under a multi-energy market system, and can obtain a more cost-effective and efficient optimized operation strategy for a multi-energy system.

Description

Technical Field

[0001] The present application relates to the technical field of carbon emission optimization operation, and in particular to an optimization operation method, device, electronic device and storage medium for a multi-energy system. Background Art

[0002] Intensifying global warming has drawn global attention to greenhouse gas emissions, particularly carbon dioxide emissions from industrial activities. Many countries and regions have chosen to establish carbon markets, leveraging market mechanisms to control carbon emissions and promote the transition to a low-carbon economy. Currently, fossil fuel power generation and certain energy-intensive industrial sectors have become the primary trading entities within carbon market mechanisms. For example, electricity production and industries such as steel, paper, and glass are required to participate in the EU Emissions Trading System (ETS). These carbon market participants are also significant energy consumers, making them crucial trading entities within regional multi-energy systems. For example, gas-fired power plants not only purchase fuel from the natural gas system to meet their power generation needs but also sell electricity to the power system. This production model forces gas-fired power plants to participate simultaneously in the electricity, natural gas, and carbon markets, presenting them with complex optimization decision-making. This complexity arises not only at the operational level but also at the transaction level, where energy production and the purchase of carbon allowances interact with each other. Furthermore, with the increasing adoption of carbon capture, utilization, and storage (CCUS) equipment, the coupled relationship between energy and carbon emissions is deepening. In the existing carbon emission optimization operation technology, only the performance and technical details are focused on, and the carbon market and economic factors are not fully considered, which is not conducive to further guiding the production strategies, consumption strategies and carbon quota purchase / sale strategies of market players in the market environment. Summary of the Invention

[0003] The present application provides a method, device, electronic device and storage medium for optimizing the operation of a multi-energy system, which can guide the energy production and consumption activities of a regional multi-energy system under a multi-energy market system, and can obtain a lower-cost and more effective optimization operation strategy for the multi-energy system.

[0004] In a first aspect, the present application provides a method for optimizing the operation of a multi-energy system, the method comprising:

[0005] Determine the carbon quota demand data corresponding to each carbon emission participant in the multi-energy system;

[0006] Constructing a corresponding production energy consumption model for each carbon emission participant based on the carbon quota demand data of each carbon emission participant;

[0007] Taking maximizing the revenue of each energy market party in the multi-energy system as the goal, constructing an optimal clearing model corresponding to each energy market party;

[0008] The production energy consumption model and the optimal clearing model are optimized based on pre-established decision constraints to obtain an optimized operation strategy for the multi-energy system.

[0009] In a second aspect, the present application provides an optimized operation device for a multi-energy system, the device comprising:

[0010] A demand data determination module is used to determine the carbon quota demand data corresponding to each carbon emission participant in the multi-energy system;

[0011] A first model building module is configured to build a corresponding production energy consumption model for each carbon emission participant based on the carbon quota demand data of each carbon emission participant;

[0012] A second model building module is configured to build an optimal clearing model corresponding to each energy market party with the goal of maximizing the revenue of each energy market party in the multi-energy system;

[0013] The optimization strategy determination module is used to optimize the production energy consumption model and the optimal clearing model based on pre-established decision constraints to obtain the optimized operation strategy of the multi-energy system.

[0014] In a third aspect, the present application provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for optimizing the operation of the multi-energy system described in any embodiment of the present application.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a processor to implement the optimized operation method of the multi-energy system described in any embodiment of the present application when executed.

[0016] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the optimized operation method of the multi-energy system described in any embodiment of the present application.

[0017] In order to address the defects of the existing technology in the background technology, an embodiment of the present application provides an optimized operation method for a multi-energy system, and the execution of this method can bring the following beneficial effects: This application fully considers the impact of carbon quotas on energy producers and consumers, and not only constructs an optimized operation model for carbon capture, utilization and storage operators, but also comprehensively considers the demands of various stakeholders in the electricity market, natural gas market and carbon market environment, so that this application can guide the energy production and consumption activities of regional multi-energy systems under the multi-energy market system, and can obtain a lower-cost and more effective optimized operation strategy for multi-energy systems.

[0018] It should be noted that the above-mentioned computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer-readable storage medium may be packaged together with the processor of the multi-energy system's optimized operation device, or may be packaged separately from the processor of the multi-energy system's optimized operation device, and this application does not limit this.

[0019] The descriptions of the second, third, ..., and fifth aspects of this application can refer to the detailed description of the first aspect; and the beneficial effects of the descriptions of the second, third, ..., and fifth aspects can refer to the analysis of the beneficial effects of the first aspect, which will not be repeated here.

[0020] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description.

[0021] It is understandable that before using the technical solutions disclosed in the embodiments of this application, the type, scope of use, and usage scenarios of the personal information involved in this application should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0023] Figure 1 A flow chart of a method for optimizing the operation of a multi-energy system provided in an embodiment of the present application;

[0024] Figure 2 A schematic diagram of energy flow in a regional multi-energy system provided in an embodiment of the present application;

[0025] Figure 3A schematic structural diagram of an optimized operation device for a multi-energy system provided in an embodiment of the present application;

[0026] Figure 4 It is a block diagram of an electronic device used to implement an optimized operation method of a multi-energy system in an embodiment of the present application. DETAILED DESCRIPTION

[0027] To make the purpose, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0028] It should be noted that the terms "first," "second," "target," and "original" in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or apparatus.

[0029] Figure 1 This is a flow chart of a method for optimizing the operation of a multi-energy system provided in an embodiment of the present application. This embodiment can be applied to developing an optimized operation strategy for a multi-energy system based on carbon emissions data in a multi-energy market environment system. The method for optimizing the operation of a multi-energy system provided in this embodiment can be executed by an optimized operation device for a multi-energy system provided in an embodiment of the present application. This device can be implemented in software and / or hardware and integrated into an electronic device that executes the method.

[0030] See also Figure 1 The method of this embodiment includes but is not limited to the following steps:

[0031] S110: Determine the carbon quota demand data corresponding to each carbon emission participant in the multi-energy system.

[0032] A multi-energy system refers to a multi-energy market environment system that couples electricity, natural gas, and coal (i.e., electricity-gas-carbon). Energy market participants can include the electricity market, natural gas market, and coal market, among others. Carbon emission participants refer to market participants that emit carbon within the multi-energy system. These participants can include users, fuel-fired power generators, and energy suppliers. A multi-energy system also includes carbon capture operators, such as carbon capture, utilization, and storage (CCUS) operators. Market clearing is performed by electricity market operators, natural gas market operators, and carbon market operators. All carbon emission participants purchase carbon allowances equal to their emissions per hour to avoid penalties for excessive emissions at the end of a carbon emission cycle. Therefore, carbon market operators and carbon capture, utilization, and storage (CCUS) operators can provide carbon allowances to the market, influencing the market price of carbon. This example does not consider the free allocation of carbon allowances; it is assumed that all carbon allowances must be purchased at the clearing price.

[0033] Users in a multi-energy system are divided into two categories: residential and industrial. Assuming residential users only consume electricity and have no direct carbon emissions, they are not subject to carbon quotas. Industrial users, however, require electricity and natural gas for their production processes and must purchase carbon quotas equal to the carbon emissions from burning the natural gas.

[0034] Based on the fuels used, gas-fired power generators in a multi-energy system can be divided into two categories: gas-fired generators (which own gas-fired generators) and coal-fired generators (which own coal-fired generators). Gas-fired generators purchase natural gas in the natural gas market to generate electricity and sell it on the electricity market. Coal-fired generators use coal to produce electricity and do not participate in the natural gas market. Both gas-fired and coal-fired generators are subject to carbon emission constraints.

[0035] The upstream energy supplier (i.e., natural gas station) in the multi-energy system can be regarded as a natural gas supplier and is the main source of natural gas in the natural gas market. CCUS consists of three parts: a carbon capture system (CC), a power-to-hydrogen system (P2H), and a power-to-gas system (P2G). There are three common types of CC: pre-combustion capture, post-combustion capture, and oxygen-fuel carbon capture. In this embodiment, it is assumed that CC is a post-combustion capture type, so it directly captures the carbon dioxide produced by the combustion of the generator. When only P2H is operating, the collected carbon emissions will be sealed.

[0036] S120: Construct a corresponding production energy consumption model for each carbon emission participant based on the carbon quota demand data of each carbon emission participant.

[0037] In an optional embodiment, when the carbon emission participant is an industrial user, the production energy consumption model is the optimal production model; based on the carbon quota demand data of each carbon emission participant, a corresponding production energy consumption model is constructed for each carbon emission participant, including: first, calculating the carbon emission factor of the industrial user's combustion of natural gas by the following formula (1):

[0038]

[0039] Where, δ gas represents the carbon emission factor for burning natural gas; and m C represent the relative molecular masses of carbon dioxide and carbon respectively; Carbon content of natural gas receiving basis; OF gas represents the carbon oxidation rate; Indicates the ratio of hydrogen doping.

[0040] Then, based on the carbon quota demand data and carbon emission factors, the energy use limit of industrial users is calculated using the following formula (2):

[0041]

[0042] Where, δ gas represents the carbon emission factor for burning natural gas; represents the carbon quota purchased by industrial user k in period t, i.e., the carbon quota demand data; represents the natural gas consumed by industrial user k in period t, i.e., the energy usage limit; is the dual multiplier of the equation; Indicates the number of elements in the optimization time period set.

[0043] Finally, the optimal production model is obtained according to the energy use limit and the efficiency mathematical model of industrial users through the following formulas (3)-(5).

[0044]

[0045] Formula (3) represents the optimal production model for maximizing the efficiency of industrial users, which is equal to the utility generated by electricity and gas consumption minus the cost of purchasing electricity, gas and carbon emissions. Formulas (4) and (5) indicate that industrial users' electricity and gas consumption needs to be within a reasonable range. represents the quadratic coefficient and linear coefficient of the electricity consumption utility function of industrial user k in period t, represents the quadratic coefficient and linear coefficient of the gas consumption utility function of industrial user k in period t, represents the amount of electricity and natural gas consumed by industrial user k during period t, represents the carbon quota purchased by industrial user k in period t, represents the node electricity price at the power network node i where the industrial user k is located during the t period, represents the node gas price at the natural gas network node j where industrial user k is located during period t, represents the carbon quota price in period t, They represent the lower and upper limits of electricity consumption of industrial user k in period t, are the dual multipliers corresponding to the lower and upper bound constraints of power consumption of industrial user k, They represent the lower and upper limits of natural gas consumption by industrial user k during period t, are the dual multipliers corresponding to the lower and upper constraints of gas consumption of industrial user k in period t.

[0046] In another optional embodiment, when the carbon emission participant is a fuel energy power generation party, the production energy consumption model is the optimal power generation model; based on the carbon quota demand data of each carbon emission participant, a corresponding production energy consumption model is constructed for each carbon emission participant, including: constructing a first relationship between the carbon emissions and energy consumption of the fuel energy power generation party through the following formulas (9) and (14), and constructing a second relationship between the energy consumption and power generation of the fuel energy power generation party through the following formulas (7) and (12). Calculate the target energy consumption based on the carbon quota demand data and the first relationship (i.e., formulas (9) and (14)), the target energy consumption includes: gas consumption and coal consumption. Calculate the target power generation based on the target energy consumption and the second relationship (i.e., formulas (7) and (12)). Construct the optimal power generation model based on the target energy consumption, target power generation, carbon quota demand data, and the first profit mathematical model of the fuel energy power generation party.

[0047] Energy-fired power generation is divided into gas-fired power generation and coal-fired power generation. The optimal power generation model for gas-fired power generation is as follows:

[0048]

[0049] Formula (6) represents the optimal power generation model for maximizing the profit of gas-fired power generators, which is equal to the electricity sales revenue minus the gas purchase cost and carbon emission cost. Formula (7) represents the relationship between gas consumption and power generation. Formula (8) represents that the gas consumption of gas-fired power generators needs to be within a reasonable range. Formula (9) represents the relationship between the carbon emissions of gas-fired power generators and gas consumption. Formula (10) represents that the ramp rate of the gas-fired units of gas-fired power generators needs to be within a reasonable range. In the formula, represents the gas consumption of gas power generator k during period t, represents the amount of electricity produced by gas power generator k during period t, represents the amount of electricity produced by gas power generator k during the period t-1, represents the carbon quota purchased by gas power generator k in period t. The coefficients of the linear term and the constant term in formula (7) are and is the dual multiplier of constraint formula (7), They represent the lower and upper limits of gas consumption of gas power generator k in period t, are the dual multipliers corresponding to the lower and upper constraints of gas consumption of gas power plant k, is the dual multiplier of constraint formula (9), is the maximum ramp rate of the gas generator k, are the dual multipliers of the downward and upward ramp rate constraints of gas generator k, Indicates the number of elements in the optimization time period set. represents the node electricity price at the power network node i where the industrial user k is located during the t period, represents the node gas price at the natural gas network node j where industrial user k is located during period t, represents the carbon quota price in period t, δ gas Represents the carbon emission factor for burning natural gas.

[0050] The optimal power generation model for coal-fired power generators is as follows:

[0051]

[0052] Formula (11) represents the optimal power generation model for coal-fired power plants to maximize their profits, which is equal to the sum of electricity sales revenue minus coal purchase costs and carbon emission costs. Formula (12) represents the relationship between coal consumption and electricity produced by coal-fired power plants. Formula (13) indicates that the power generation of coal-fired power plants needs to be within a reasonable range. Formula (14) represents the relationship between carbon emissions and coal consumption of coal-fired power plants. Formula (15) indicates that the ramp rate of coal-fired units of coal-fired power plants needs to be within a reasonable range. Where, represents the amount of electricity produced by coal-fired power plant k during period t, represents the amount of electricity produced by coal-fired power producer k during the period t-1, represents the coal consumption of coal-fired power plant k in period t, κ coal represents the unit price of coal, represents the carbon quota purchased by coal-fired power producer k in period t, represents the node electricity price at the power network node i where the industrial user k is located during the t period, represents the carbon quota price in period t. The coefficients of the linear term and the constant term in formula (12) are and is the dual multiplier of constraint formula (12), They represent the lower and upper limits of the power generation of coal-fired power plant k in period t, are the dual multipliers corresponding to the lower and upper constraints of the power generation of coal-fired power plant k, δ coal represents the carbon emission factor of coal combustion, is the dual multiplier of constraint formula (14), is the maximum ramp rate of the coal-fired generator k, are the dual multipliers of the downward and upward ramp rate constraints of coal-fired generator k, respectively.

[0053] In another optional embodiment, when the carbon emission participant is an energy supplier (such as a natural gas supplier), the production energy consumption model is the optimal energy supply model; a corresponding production energy consumption model is constructed for each carbon emission participant based on the carbon quota demand data of each carbon emission participant, including: calculating the energy production limit of the energy supplier through formula (17) based on the carbon quota demand data; and constructing the optimal energy supply model based on the energy production limit and the second profit mathematical model of the energy supplier.

[0054] The optimal gas supply model of a natural gas supplier is as follows:

[0055]

[0056] Formula (16) represents the optimal energy supply model for maximizing the profit of the natural gas supplier, which is equal to the gas sales revenue minus the gas production cost. Formula (17) indicates that the natural gas production volume of the natural gas supplier needs to be within a reasonable range. In the formula, represents the node gas price at the natural gas network node j where industrial user k is located during period t, represents the amount of natural gas produced by natural gas supplier k in period t, i.e., the energy production limit, represents the unit production cost of natural gas supplier k, They represent the lower and upper limits of natural gas production by natural gas supplier k in period t, are the dual multipliers corresponding to the lower and upper constraints of gas production of natural gas supplier k, respectively.

[0057] In another optional embodiment, when the multi-energy system also includes a carbon capture operator (such as CCUS), the production energy consumption model is the optimal operating model; the optimal operating model is constructed for the carbon capture operator in the following manner: determining the carbon quota sales data based on the carbon quota demand data of each carbon emission participant; determining the amount of electricity consumed by the carbon capture operator when capturing carbon emissions; determining the amount of natural gas produced by the carbon capture operator using the captured carbon emissions; and constructing the optimal operating model based on the carbon quota sales data, electricity consumption, natural gas volume and the carbon capture operator's third profit mathematical model.

[0058] The carbon dioxide produced by combustion is first captured by CC and then used to produce methane (CO2+4H2→CH4+2H2O) or stored in a carbon tank (carbon storage, CS). The optimal operating model of CCUS purchases electricity and sells natural gas and carbon quotas is as follows:

[0059]

[0060]

[0061]

[0062] Formula (18) represents the optimal operating model for maximizing the profit of a carbon capture operator (such as CCUS), which is equal to the revenue from the sale of natural gas minus the cost of electricity consumption, plus the revenue from the sale of carbon quotas, minus the expenditure on purchasing carbon dioxide from external sources, and minus the cost of carbon dioxide storage. Formula (19) shows that the electricity consumed by the carbon capture, utilization and storage operator is equal to the electricity consumed by CC, the electricity consumed by P2H, and the electricity consumed by P2G. Formula (20) shows that the amount of natural gas sold is equal to the amount produced by P2G and the amount of hydrogen produced by P2H converted into natural gas. Formulas (21)-(23) show that the electricity consumed by CC, P2H, and P2G must be within a reasonable range. Formula (24) shows the relationship between the amount of carbon dioxide stored by CC and the amount of electricity consumed. Formula (25) shows the relationship between the amount of hydrogen generated by P2H and the amount of electricity consumed. Formula (26) shows the relationship between the amount of natural gas generated by P2G and the amount of electricity consumed. Formula (27) shows the relationship between the amount of carbon dioxide and hydrogen consumed by P2G. Formula (28) indicates that part of the hydrogen generated by P2H is supplied to the internal P2G equipment, and the other part is sold directly to the outside. Formula (29) shows the relationship between the carbon dioxide consumption of the P2G equipment and the natural gas production. Formula (30) shows that the carbon dioxide consumption of the P2G equipment needs to be within a reasonable range. Formula (31) shows that the carbon dioxide consumption of the P2G equipment is the sum of the carbon dioxide captured from combustion power generation and the carbon dioxide purchased from the outside. Formula (32) shows that the amount of carbon quotas sold by the carbon capture, utilization and storage operator needs to be within a reasonable range. Formula (33) shows that the amount of carbon dioxide captured by the carbon capture system is equal to the sum of the carbon dioxide captured from combustion power generation and the carbon dioxide stored. Formula (34) shows that the amount of carbon dioxide captured from combustion power generation needs to be within a reasonable range. Formula (35) shows that the amount of carbon dioxide stored needs to be within a reasonable range. Formula (36) shows that the amount of carbon quotas sold by the carbon capture, utilization and storage operator is equal to the amount of carbon dioxide captured by the carbon capture system. Equation (37) indicates that the amount of carbon dioxide purchased from external sources by CCS operators must be within a reasonable range. Equation (38) indicates that the carbon emissions from coal-fired and gas-fired units must be greater than the amount of carbon captured by CC. Equations (39)-(40) indicate that the amount of hydrogen generated by P2H and supplied to P2G equipment must be within a reasonable range, as must the amount of hydrogen sold directly to external sources.

[0063] Where, represents the node electricity price at the power network node i where the industrial user k is located during the t period, represents the node gas price at the natural gas network node j where industrial user k is located during period t, represents the carbon quota price in period t, represents the amount of electricity consumed by CCS operator k during period t, represents the amount of natural gas sold by CCS operator k during period t, represents the amount of carbon quota sold by CCS operator k in period t, represents the amount of carbon dioxide purchased from the outside by the CCS operator k during period t, is the unit price of carbon dioxide, represents the amount of CO2 stored by CCS operator k during period t, κ cs is the unit cost of CO2 storage, They represent the electricity consumed by CC, electricity consumed by P2H and electricity consumed by P2G of CCS operator k in period t respectively. are the dual multipliers of constraint formulas (19) and (20) respectively. represents the amount of natural gas produced by P2G of CCS operator k in period t, γ represents the heat conversion coefficient from hydrogen to natural gas, represents the portion of hydrogen produced by P2H during period t by CCS operator k. are the lower and upper limits of electricity consumption of CC by CCS operator k, are the dual multipliers of the lower and upper constraints on CC electricity consumption by CCS operator k in period t, respectively. are the lower and upper limits of the electricity consumed by the P2G of the CCS operator k, are the dual multipliers of the lower and upper constraints on the P2G electricity consumption of CCS operator k in period t, respectively. are the lower and upper limits of electricity consumption for P2H of CCS operator k, are the dual multipliers of the lower and upper constraints on the electricity consumption of the P2H of the CCS operator k in period t, respectively. are the carbon captured by the CCS operator k in period t, represents the energy consumption coefficient of CC in CCS operator k, are the energy consumption coefficients of P2H and P2G in CCS operator k, are the dual multipliers of constraint formulas (24) and (28) respectively. Indicates the relative molecular mass of hydrogen. represents the amount of hydrogen supplied to P2G by the P2H of CCS operator k, represents the amount of hydrogen sold directly by the P2H of carbon capture, utilization and storage operator k, represents the total hydrogen production of CCS operator k in period t. represents the carbon dioxide consumption of the P2G of CCS operator k in period t. is the dual multiplier of equality constraint (31), is the dual multiplier of the constrained inequalities (30) and (32). represents the amount of carbon dioxide captured by operator k from combustion power generation during period t, is the dual multiplier of constraint formula (31), is the dual multiplier of constraint formula (33), They are also dual multipliers. and represent the relative molecular masses of carbon dioxide and hydrogen respectively.

[0064] S130. With the goal of maximizing the benefits of each energy market party in the multi-energy system, an optimal clearing model corresponding to each energy market party is constructed.

[0065] Specifically, with the goal of maximizing the revenue of each energy market participant in the multi-energy system, an optimal clearing model is constructed for each energy market participant. This includes: determining the net injected power of each energy market participant at the current network node in the energy network; and constructing an optimal clearing model that maximizes the revenue of each energy market participant based on the net injected power and the current energy price of each energy market participant at the current network node. Energy market participants can include electricity, natural gas, and coal markets, among others.

[0066] In an optional embodiment, for the electricity market, an optimal clearing model is constructed with the goal of maximizing electricity sales revenue to clear the market while trying to meet the needs of all parties in the electricity market. The specific model is as follows:

[0067]

[0068] Formula (41) represents the optimal clearing model for maximizing electricity sales revenue. Formula (42) represents the relationship between the net injected power on the grid bus and the branch power flow. Formula (43) indicates that the net injected power on the grid bus is equal to the electricity consumed by residential users, industrial users, and carbon capture, utilization, and storage operators minus the electricity produced by gas-fired units and coal-fired units. Formula (44) indicates that the power flow of the grid line needs to be within a reasonable range. In the formula, the set V e represents the set of power grid buses, ε e represents the set of power grid lines, represents the net injected power on the grid bus m, represents the node electricity price on the grid bus m during period t, θ m,t / θ n,t Indicates the voltage phase on the grid busbars m and n during period t, B mn represents the susceptance on line mn, Indicates the maximum active power on the grid line mn, is the dual multiplier of the power flow constraint, is the dual multiplier of the equality constraint. represents the electric energy consumed by residential user k on the grid bus m during period t, represents the amount of electricity consumed by industrial user k on the grid bus m during period t, represents the amount of electricity consumed by CCS operator k on grid bus m during period t, It represents the amount of electricity produced by coal-fired power generator k on the grid bus m during the period t. It represents the amount of electricity produced by gas power generator k on grid bus m during period t.

[0069] In another optional embodiment, for the natural gas market, an optimal clearing model is constructed with the goal of maximizing revenue from natural gas sales to clear the market while trying to meet the needs of all parties in the natural gas market. The specific model is as follows:

[0070]

[0071] Formula (45) represents the optimal clearing model with the goal of maximizing the revenue from selling natural gas. Formula (46) indicates that the net injection flow at the natural gas network node is equal to the flow from the pipeline and the compressor. The net injection flow at the natural gas network node is also equal to the consumption of industrial users and gas units minus the production of natural gas suppliers and CCUS. Formula (47) represents the constraint between natural gas flow and gas pressure. Formula (48) indicates that the flow in the natural gas pipeline needs to be controlled within a reasonable range. Formula (49) indicates that the pressure at the natural gas pipeline node needs to be controlled within a reasonable range. Formula (50) indicates that the gas pressure at both ends of the compressor needs to be controlled within a reasonable range. Formula (51) indicates that the natural gas flow through the compressor needs to be controlled within a reasonable range. In the formula, the set V g represents a collection of gas network nodes, represents the node set with m as the end point, represents the node set starting from m, represents the set of flows from compressor c to node m, represents the set of nodes m flowing out to compressor c, ε g represents a collection of natural gas pipelines, represents the node gas price of natural gas network node m at time t, represents the net injection flow at the natural gas network node m, f mn,t represents the natural gas flow between the weather network pipelines mn at time t, Indicates the compression ratio of the compressor, is the dual multiplier of the equality constraint. m,t and Π n,t represents the pressure of nodes m and n during period t, S mn represents the maximum flow on pipe mn, Both are dual multipliers of the second-order cone relaxation constraints of the Weymouth equation. are dual multipliers of inequality constraints, Ω com represents a collection of compressors, are the lower and upper limits of the compressor pressure safety factor, represent the minimum and maximum pressures on node m, respectively. represents the flow rate through compressor c during period t, represents the maximum flow rate through compressor c during period t, Indicates the pressure at the inlet of compressor C, Indicates the pressure at the outlet of compressor C.

[0072] In another optional embodiment, for the coal market, carbon quotas are sold with the goal of maximizing carbon quota revenue to construct an optimal carbon market clearing model. The specific model is as follows:

[0073]

[0074]

[0075] Formula (52) indicates that the natural gas market operator is responsible for selling carbon quotas with the goal of maximizing carbon quota revenue. Formula (53) indicates that the total amount of carbon quotas sold needs to be controlled within a reasonable range. Formula (54) indicates that the carbon quotas sold at each moment can only be greater than zero, indicating sales rather than purchases. Formula (55) indicates that the number of carbon quotas purchased by industrial users, coal-fired units, and gas-fired units is equal to the sum of the carbon quotas sold by CCUS and the natural gas market. Where, represents the carbon quota price in period t, represents the number of carbon allowances sold in the natural gas market, Represent the lower and upper limits of the number of carbon quotas sold, Both represent the dual multipliers of the inequality constraints, Represents the dual multiplier of the equality constraint. Ω ind represents the set of industrial users, Ω gc represents the set of coal-fired power producers, Ω gt represents the set of gas power generators, Ωccus Represents the collection of CCS operators. represents the carbon quota purchased by industrial user k in period t, represents the carbon quota purchased by coal-fired power producer k in period t, represents the carbon quota purchased by gas power producer k during period t, represents the amount of carbon quota sold by CCS operator k in period t, Indicates the number of carbon allowances sold in the natural gas market.

[0076] Optionally, constraints need to be placed on electricity prices, natural gas prices, and carbon prices to avoid unreasonable prices.

[0077] S140. Optimize the production energy consumption model and the optimal clearing model based on pre-established decision constraints to obtain an optimized operation strategy for the multi-energy system.

[0078] In an embodiment of the present application, the model in the above steps can observe multiple interdependent constraints, which couple the decision variables of different market entities. Therefore, the set of available strategies of market entities depends not only on their own feasible domain, but also on the strategies adopted by other entities. The market equilibrium problem is expressed here as a generalized Nash equilibrium problem. In order to find the market equilibrium solution, based on the Karush-Kuhn-Tucker (KTT) condition, the convex optimization problem set composed of the above optimization models is converted into a complementary model, and the production energy consumption model and the optimal clearing model are optimized to obtain the optimal operation strategy of the multi-energy system.

[0079] Specifically, decision constraints are determined by: identifying decision variables that establish dependencies between each carbon emission participant and each energy market player; and determining decision constraints for the multi-energy system, such as KKT conditions, based on the decision variables, the feasible domain corresponding to each carbon emission participant, and the feasible domain corresponding to each energy market player. Based on preset calculation formulas, KKT conditions can be calculated for the optimal production model for industrial users, the optimal power generation model for gas-fired power generators, the optimal power generation model for coal-fired power generators, the optimal gas supply model for natural gas suppliers, the optimal operation model for CCUS, the optimal clearing model for the electricity market, the optimal clearing model for the natural gas market, and the optimal clearing model for the carbon market.

[0080] Among them, the KKT conditions of the optimal production model of industrial users (i.e., formulas (3)-(5)) are as follows:

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088] Where, represents the node electricity price at the power network node i where the industrial user k is located during the t period, represents the quadratic coefficient and linear coefficient of the electricity consumption utility function of industrial user k in period t, represents the quadratic coefficient and linear coefficient of the gas consumption utility function of industrial user k in period t, represents the amount of electricity and natural gas consumed by industrial user k during period t, represents the node gas price at the natural gas network node j where industrial user k is located during period t, represents the carbon quota price in period t, They represent the lower and upper limits of electricity consumption of industrial user k in period t, are the dual multipliers corresponding to the lower and upper bound constraints of power consumption of industrial user k, They represent the lower and upper limits of natural gas consumption by industrial user k during period t, are the dual multipliers corresponding to the lower and upper constraints of gas consumption of industrial user k in period t, is the dual multiplier of formula (2), δ gas Represents the carbon emission factor for burning natural gas.

[0089] The KKT conditions of the optimal power generation model (i.e., formulas (6)-(10)) for gas-fired power generators are as follows:

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098] Where, represents the node electricity price at the power network node i where the industrial user k is located during the t period, represents the amount of electricity produced by gas power generator k during period t, is the dual multiplier of constraint formula (7), are the dual multipliers corresponding to the lower and upper constraints of gas consumption of gas generator k in period t, are the dual multipliers of the downward and upward ramp rate constraints of gas generator k in period t, are the dual multipliers of the downward and upward ramp rate constraints of gas generator k in the t+1 period, represents the node gas price at the natural gas network node j where user k is located during period t, δ gas represents the carbon emission factor for burning natural gas, is the dual multiplier of constraint formula (9), represents the carbon quota price in period t, It is the dual multiplier of formula (2). are the dual multipliers corresponding to the lower and upper constraints of gas consumption of gas generator k in period t, are the dual multipliers of the downward and upward ramp rate constraints of gas generator k in period t, represents the amount of electricity produced by gas power generator k during period t, represents the amount of electricity produced by gas power generator k during the period t-1, is the maximum ramp rate of the units of gas-fired generator k. Indicates the number of elements in the optimization time period set.

[0099] The technical solution provided by this embodiment determines the carbon quota demand data corresponding to each carbon emission participant in the multi-energy system; constructs a corresponding production energy consumption model for each carbon emission participant based on the carbon quota demand data of each carbon emission participant; takes maximizing the benefits of each energy market party in the multi-energy system as the goal, constructs the optimal clearing model corresponding to each energy market party; optimizes the production energy consumption model and the optimal clearing model based on pre-established decision constraints to obtain the optimized operation strategy of the multi-energy system. This application fully considers the impact of carbon quotas on energy producers and consumers, and not only constructs an optimized operation model for carbon capture, utilization and storage operators, but also comprehensively considers the demands of various stakeholders in the electricity market, natural gas market and carbon market environments, so that this application can guide the energy production and consumption activities of regional multi-energy systems under the multi-energy market system, and can obtain a lower-cost and more effective optimized operation strategy for multi-energy systems.

[0100] In a specific application scenario embodiment, Figure 2 This diagram shows the energy flow of a regional multi-energy system, which includes one residential and two industrial users. Electricity is supplied by a coal-fired generator and a gas-fired generator. Natural gas is supplied by a gas supplier and a carbon capture, utilization, and storage operator.

[0101] In order to analyze the impact of carbon quotas on regional multi-energy systems, six case settings are considered: (1) there is no upper limit on the carbon emission quotas available from the carbon market operator (CMO); (2) the upper limit of the carbon emission quotas available from the CMO is set to 90% of the carbon emission quotas in the optimization result in (1); (3) the upper limit of the carbon emission quotas available from the CMO is set to 80% of the carbon emission quotas in (1); (4) the upper limit of the carbon emission quotas available from the CMO is set to 70% of the carbon emission quotas in (1); (5) the upper limit of the carbon emission quotas available from the CMO is set to 60% of the carbon emission quotas in (1); and (6) the upper limit of the carbon emission quotas available from the CMO is set to 50% of the carbon emission quotas in (1). The sum of the available carbon emission quotas considered in (1) is regarded as the maximum carbon emission quota that the CMO should allocate to the emitter, which is 319.78 tons of carbon dioxide in this case.

[0102] Table 1 summarizes the benefits accruing to market participants under different carbon market settings. It can be observed that as the available allowances from the CMO decrease, both residential and industrial users experience a decrease in utility. Social welfare in both energy markets also decreases. For the electricity market, this is primarily due to rising electricity prices. Meanwhile, in the natural gas market, despite stable natural gas prices, rising carbon prices indirectly increase the cost of natural gas consumption, reducing demand for natural gas. Furthermore, in the test cases, both coal-fired and gas-fired generators experience consistently negative profitability. The profits earned by coal-fired and gas-fired generators in the energy market without the cost of carbon allowances are annotated in parentheses. It can be seen that, without the need to purchase carbon allowances, coal-fired generators maintain positive profits due to their lower unit production costs. As electricity prices gradually rise, gas-fired generators also achieve positive profits. This difference in net profit is due to the lack of free carbon allowances. In practice, the CMO can adjust the profitability or loss of fossil fuel generators by controlling the number of free carbon allowances allocated to generators. As for CCUS, it is barely profitable in the carbon market when there are abundant allowances available. However, when the available carbon allowances decrease to a certain level, CCUS profitability increases dramatically. The marginal costs associated with carbon capture and storage are not only relatively high but also correlated with electricity prices. Electricity prices, in turn, rise with carbon prices. Therefore, only when carbon allowances reach a relatively high scarcity level will rising carbon prices further boost CCUS profitability.

[0103] Table 1 Profitability of various stakeholders in the market

[0104]

[0105] Figure 3 A schematic diagram of the structure of an optimized operation device for a multi-energy system provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the apparatus 300 may include:

[0106] The demand data determination module 310 is used to determine the carbon quota demand data corresponding to each carbon emission participant in the multi-energy system;

[0107] A first model building module 320 is configured to build a corresponding production energy consumption model for each carbon emission participant based on the carbon quota demand data of each carbon emission participant;

[0108] A second model building module 330 is configured to build an optimal clearing model corresponding to each energy market party with the goal of maximizing the revenue of each energy market party in the multi-energy system;

[0109] The optimization strategy determination module 340 is used to optimize the production energy consumption model and the optimal clearing model based on pre-established decision constraints to obtain an optimized operation strategy for the multi-energy system.

[0110] In one embodiment, when the carbon emission participant is an industrial user, the production energy consumption model is an optimal production model;

[0111] Furthermore, the above-mentioned first model construction module 320 can be specifically used to: determine the carbon emission factor of the industrial user; calculate the energy usage limit of the industrial user based on the carbon quota demand data and the carbon emission factor; and determine the optimal production model based on the energy usage limit and the efficiency mathematical model of the industrial user.

[0112] In one embodiment, when the carbon emission participant is a fuel-fired power generation party, the production energy consumption model is an optimal power generation model;

[0113] Furthermore, the above-mentioned first model construction module 320 can also be specifically used to: construct a first relationship between the carbon emissions and energy consumption of the fuel energy power generation party, and construct a second relationship between the energy consumption and power generation of the fuel energy power generation party; calculate the target energy consumption based on the carbon quota demand data and the first relationship, and calculate the target power generation based on the target energy consumption and the second relationship; construct the optimal power generation model based on the target energy consumption, the target power generation, the carbon quota demand data and the first profit mathematical model of the fuel energy power generation party.

[0114] In one embodiment, when the carbon emission participant is an energy supplier, the production energy consumption model is an optimal energy supply model;

[0115] Furthermore, the above-mentioned first model construction module 320 can also be specifically used to: calculate the energy production limit of the energy supplier based on the carbon quota demand data; and construct the optimal energy supply model based on the energy production limit and the second profit mathematical model of the energy supplier.

[0116] In one embodiment, when the multi-energy system also includes a carbon capture operator, the production energy consumption model is the optimal operation model; the optimal operation model is constructed for the carbon capture operator in the following manner: determining the carbon quota sales data based on the carbon quota demand data of each carbon emission participant; determining the amount of electricity consumed by the carbon capture operator when capturing carbon emissions; determining the amount of natural gas produced by the carbon capture operator using the captured carbon emissions; and constructing the optimal operation model based on the carbon quota sales data, the electricity consumption, the amount of natural gas and the third profit mathematical model of the carbon capture operator.

[0117] Furthermore, the above-mentioned second model construction module 330 can be specifically used to: determine the net injected power of each energy market party at the current network node in the energy network; and construct the optimal clearing model corresponding to the maximization of the profit of each energy market party based on the net injected power and the current energy price of each energy market party at the current network node.

[0118] In one embodiment, the decision constraints are determined in the following manner: determining a decision variable with a dependency relationship between each carbon emission participant and each energy market party; and determining the decision constraints of the multi-energy system based on the decision variables, the feasible domain corresponding to each carbon emission participant, and the feasible domain corresponding to each energy market party.

[0119] The optimized operation device of the multi-energy system provided in this embodiment can be applied to the optimized operation method of the multi-energy system provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0120] Figure 4 It is a block diagram of an electronic device for implementing an optimized operation method of a multi-energy system of an embodiment of the present application. Electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0121] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0122] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0123] The processor 11 may be any general-purpose and / or specialized processing component with processing and computing capabilities. Examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors for running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the optimized operation method of the multi-energy system.

[0124] In some embodiments, the method for optimizing the operation of a multi-energy system may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for optimizing the operation of a multi-energy system described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for optimizing the operation of a multi-energy system in any other appropriate manner (e.g., by means of firmware).

[0125] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0126] Computer programs for implementing the methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0127] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, device or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0128] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and the input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0129] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0130] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0131] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. For example, those skilled in the art can use the various forms of processes shown above, reorder, add, or delete steps; and can perform the steps described in the present application in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present application can be achieved, and this document does not limit them here.

[0132] The above specific embodiments do not limit the scope of protection of this application. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A method for optimizing the operation of a multi-energy system, characterized in that: The method comprises: Determine the carbon quota demand data corresponding to each carbon emission participant in the multi-energy system; Constructing a corresponding production energy consumption model for each carbon emission participant based on the carbon quota demand data of each carbon emission participant; Taking maximizing the revenue of each energy market party in the multi-energy system as the goal, constructing an optimal clearing model corresponding to each energy market party; Optimizing the production energy consumption model and the optimal clearing model based on pre-established decision constraints to obtain an optimized operation strategy for the multi-energy system; The objective of maximizing the revenue of each energy market party in the multi-energy system is to construct an optimal clearing model corresponding to each energy market party, including: For a current network node in the energy network, determining a net injected power of each energy market party on the current network node; Constructing the optimal clearing model corresponding to maximizing the revenue of each energy market party based on the net injected power and the current energy price of each energy market party at the current network node; The decision constraints are determined as follows: Determining a decision variable having a dependency relationship between each carbon emission participant and each energy market party; The decision constraints of the multi-energy system are determined according to the decision variables, the feasible domain corresponding to each carbon emission participant and the feasible domain corresponding to each energy market party.

2. The method for optimizing operation of a multi-energy system according to claim 1, characterized in that: When the carbon emission participant is an industrial user, the production energy consumption model is the optimal production model; and constructing a corresponding production energy consumption model for each carbon emission participant based on the carbon quota demand data of each carbon emission participant includes: Determining the carbon emission factors of the industrial users; Calculating the energy usage limit of the industrial user based on the carbon quota demand data and the carbon emission factor; The optimal production model is determined according to the energy usage limit and the efficiency mathematical model of the industrial user.

3. The method for optimizing operation of a multi-energy system according to claim 1, characterized in that: When the carbon emission participant is a fuel-fired power generation party, the production energy consumption model is an optimal power generation model; the corresponding production energy consumption model is constructed for each carbon emission participant based on the carbon quota demand data of each carbon emission participant, including: Establishing a first relationship between carbon emissions and energy consumption of the fuel energy power generation party, and establishing a second relationship between energy consumption and power generation of the fuel energy power generation party; Calculating a target energy consumption based on the carbon quota demand data and the first relationship, and calculating a target power generation based on the target energy consumption and the second relationship; The optimal power generation model is constructed based on the target energy consumption, the target power generation, the carbon quota demand data and the first profit mathematical model of the fuel energy power generation party.

4. The method for optimizing operation of a multi-energy system according to claim 1, characterized in that: When the carbon emission participant is an energy supplier, the production energy consumption model is an optimal energy supply model; and constructing a corresponding production energy consumption model for each carbon emission participant based on the carbon quota demand data of each carbon emission participant includes: Calculating the energy production limit of the energy supplier based on the carbon quota demand data; The optimal energy supply model is constructed based on the energy production limit and the second profit mathematical model of the energy supplier.

5. The method for optimizing operation of a multi-energy system according to claim 1, characterized in that: When the multi-energy system also includes a carbon capture operator, the production energy model is an optimal operation model; the optimal operation model is constructed for the carbon capture operator in the following manner: Determining carbon quota sales data based on the carbon quota demand data of each carbon emission participant; Determining the amount of electricity consumed by the carbon capture operator when capturing carbon emissions; Determining the amount of natural gas produced by the carbon capture operator from the captured carbon emissions; The optimal operation model is constructed based on the carbon quota sales data, the electricity consumption, the natural gas volume, and a third profit mathematical model of the carbon capture operator.

6. An optimized operation device for a multi-energy system, characterized in that: The device comprises: A demand data determination module is used to determine the carbon quota demand data corresponding to each carbon emission participant in the multi-energy system; A first model building module is configured to build a corresponding production energy consumption model for each carbon emission participant based on the carbon quota demand data of each carbon emission participant; A second model building module is configured to build an optimal clearing model corresponding to each energy market party with the goal of maximizing the revenue of each energy market party in the multi-energy system; an optimization strategy determination module, configured to optimize the production energy consumption model and the optimal clearing model based on pre-established decision constraints to obtain an optimized operation strategy for the multi-energy system; The second model building module is specifically configured to determine, for a current network node in the energy network, the net injected power of each energy market party at the current network node; and construct, based on the net injected power and the current energy price of each energy market party at the current network node, the optimal clearing model corresponding to maximizing the revenue of each energy market party; The decision constraints are determined in the following manner: determining decision variables with a dependency relationship between each carbon emission participant and each energy market party; and determining the decision constraints of the multi-energy system based on the decision variables, the feasible domain corresponding to each carbon emission participant, and the feasible domain corresponding to each energy market party.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executed by the at least one processor, and the computer program is executed by the at least one processor so as to enable the at least one processor to execute the method for optimizing operation of the multi-energy system according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for optimizing the operation of the multi-energy system according to any one of claims 1 to 5 when executed.

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