City-level multi-energy coupling system optimization control method oriented to electricity-carbon market

Abstract

The invention relates to the technical field of multi-energy network operation regulation and control, in particular to an electricity-carbon market-oriented city-level multi-energy coupling system optimization control method, which comprises the following steps of: establishing an electricity-gas-heat-storage multi-energy collaborative regional-level energy station model; establishing a regional energy station electrical interconnection system based on energy storage and distribution and P2G; establishing a city-level multi-energy system coupling optimization control model oriented to the electricity-carbon market; and solving based on a particle swarm algorithm to obtain an urban multi-energy station optimization control strategy. According to the method provided by the invention, carbon emission can be reduced while the urban energy consumption cost is reduced, the income of the energy station is improved, and an optimization scheme is provided for urban energy station construction and urban energy management scheduling.

Description

Technical Field

[0001] The present invention relates to the field of multi-energy network operation and control technology, and in particular to an urban-level multi-energy coupling system optimization control method for an electricity-carbon market. Background Art

[0002] In the context of the increasing challenges of the current environment and climate and the increasingly severe situation of energy conservation and emission reduction, how to achieve the improvement of energy demand and supply efficiency and low carbonization has always been a current research hotspot. Among urban users, thermal energy and electrical energy are the main forms of energy demand, and terminal electricity in cities is mainly supplied by regional distribution networks.

[0003] As a multi-energy coupling system, the integrated energy system (IES) can integrate coal, oil, natural gas, electricity, and thermal energy in the region, and realize the coordinated management and complementary assistance among various heterogeneous energy subsystems. While meeting the diversified energy demand within the system, it can participate in the peak regulation of the power grid and smooth the peak-to-valley difference, and can also output reactive power when necessary, reducing the construction and investment of large-capacity power supply points.

[0004] Integrated energy systems in cities mostly exist in the form of regional energy stations, with combined heat, cooling and power units as the main energy supply units, combined with heat pumps, chillers and gas boilers to achieve heating and cooling. With the development of the energy Internet, energy stations can share operating data and load data, and demand and supply can be adjusted dynamically at any time to optimize the operational efficiency of energy production and energy consumption. Especially in urban scenarios, the overall regulation of the city-level electricity-gas-heat-storage multi-energy system requires collaborative cooperation among multiple energy stations. The traditional top-down centralized control can no longer meet the needs of multiple entities.

[0005] At present, there are few studies on the electricity-gas interconnection model of energy, which cannot be applied to energy stations equipped with electricity-to-gas equipment, and fails to fully utilize the optimization space of multi-energy conversion. At the same time, in terms of market transactions, the dispatch model rarely considers the economic benefits of the carbon market and the peak-shaving market, weakening the positive role of the multi-energy coupling system in energy conservation and emission reduction and peak-shaving. Therefore, it is necessary to optimize the control strategy of urban multi-energy stations by combining multiple factors such as energy mutual assistance and market fluctuations. Summary of the invention

[0006] The purpose of the present invention is to overcome the defects of the existing technology and propose an urban-level multi-energy coupling system optimization control method for the electricity-carbon market. For the regional multi-energy coupling system of the current urban energy station, it can fully consider the urban energy demand and the multi-energy coordination advantages of multi-type energy complementarity, so that it can achieve greater economic benefits in the electricity-carbon market.

[0007] To achieve the above object, the present invention adopts the following specific technical solutions:

[0008] The city-level multi-energy coupling system optimization control method for the electric carbon market provided by the present invention comprises the following steps:

[0009] S1. Establish a regional energy station model for electricity-gas-heat-storage multi-energy synergy, including the regional energy station system architecture, regional cogeneration system equipment model and constraints, and regional heat-electricity-gas storage device model and constraints;

[0010] S2. Establish a regional energy station electrical interconnection system based on energy storage and distribution and P2G, including an energy station P2G model and an energy station steady-state energy network flow model;

[0011] S3. Establish an optimization control model for the city-level multi-energy coupling system for the electricity-carbon market, including energy market and carbon market trading models, energy station power demand response cost model, and optimization objective function;

[0012] S4. Solve the optimization objective function based on the particle swarm algorithm, optimize the output of the energy station's cogeneration system and the deployment of energy storage and gas storage, and supply energy to users in the region.

[0013] Further, in step S1, the regional energy station system architecture includes a municipal power grid, a municipal gas grid, a municipal heat grid, a CHP, a gas boiler, an electric chiller, an absorption chiller, an energy storage device, and electric, cold, and hot loads;

[0014] The equipment model and constraints of the regional cogeneration system are as follows:

[0015] The CHP model is expressed as:

[0016]

[0017] Where P CHP,t,g is the gas consumption of the CHP unit at time t, kW; C g is the calorific value of natural gas, which is 35.564MJ / m 3 ;q V,CHP,t is the gas consumption volume flow rate of the CHP unit at time t, m 3 / h; P CHP,t,e is the power supply power of the CHP unit at time t, kW; η GE,e Power supply efficiency for internal combustion engines; η GE,h The heating efficiency of the internal combustion engine; ε HPR is the heat-to-electricity ratio of the CHP unit; η WHB is the heat exchange efficiency of the waste heat boiler; P CHP,t,h is the heating power of the CHP unit at time t, kW;

[0018] The gas boiler model is expressed as:

[0019]

[0020] In the formula, q V,GB,t is the gas consumption volume flow rate of the gas boiler at time t, m 3 / h; η GB is the gas boiler efficiency; P GB,t,g P is the gas consumption power of the gas boiler at time t, kW; GB,t,h is the heating power of the gas boiler at time t, kW;

[0021] The electric refrigerator model is expressed as:

[0022] P EC,t,c =η COP,EC P EC,t,e ;

[0023] Where P EC,t,e is the cooling power output of the electric refrigerator at time t; η COP,EC is the cooling efficiency of the electric refrigerator; P EC,t,e is the power consumption of the electric refrigerator at time t, kW;

[0024] The absorption chiller model is expressed as:

[0025]

[0026] Where P AC,t,c is the cooling power output of the absorption refrigerator at time t; η COP,AC is the cooling efficiency of the absorption chiller; θAC is the heat distribution coefficient of the waste heat boiler input to the absorption chiller; P AC,ι,h is the input heat power of the absorption chiller at time t, kW;

[0027] The electrical and thermal power of each device meets the upper and lower power limit requirements:

[0028]

[0029] Where P i is the electrical power of device i; Q i is the thermal power of device i; P imin , P imax is the lower and upper limits of the electrical power of device i; Q imin , Q imax are the lower and upper limits of thermal power of device i.

[0030] Furthermore, in step S1, the regional-level thermal-electric-gas energy storage device model and constraints are as follows:

[0031] The storage device model is expressed as:

[0032]

[0033] Where Δt is the time period, h; W bt,t and W bt,t+1 are the electric energy stored in the storage device at time t and time t+1, kW·h; σ bt is the self-discharge rate of the storage device; η bt,c , η bt,disc are the charging and discharging efficiency of the storage device respectively; P bt,c,t , P bt,disc,t are the charging and discharging power of the storage device at time t, kW;

[0034] The thermal storage device model is expressed as:

[0035]

[0036] In the formula, Q hst,t and Q hst,t+1 is the thermal energy stored in the heat storage device at time t and time t+1, W·h; σ hst is the self-heat release rate of the heat storage device; η hst,c , η hst,disc are the charging and discharging efficiencies of the heat storage device; H hst,c,t , H hst,disc,t are the charging and discharging powers of the heat storage device at time t, kW;

[0037] The gas storage device model is expressed as:

[0038]

[0039] In the formula, and is the gas storage capacity of the gas storage device at time t and time t+1; η gsc and η gsd are the natural gas storage and release efficiency; G gsc and G gsd They are natural gas storage and release power;

[0040] Energy storage device constraints:

[0041]

[0042] Where W bt,min W is the minimum charge state of the storage device; bt,max Q is the rated charge state of the storage device; hst,min is the minimum heat storage capacity of the heat storage device; Q hst,max is the rated heat storage capacity of the heat storage device; G gs,min G is the minimum gas storage capacity of the gas storage device; gs,max is the maximum gas storage capacity of the gas storage device;

[0043] The regional energy station model is as follows:

[0044]

[0045] Where P in,t and P out,t is the import and export power of the energy station, kW; V all,t is the gas flow rate of the energy station, m 3 / h;Q all,t Heating power for energy station, kW; P all,t Power supply to the energy station, kW; α all is the heat-to-electricity ratio of the energy station; η all Provide energy efficiency for energy stations.

[0046] Furthermore, in step S2, the energy station P2G model is expressed as:

[0047]

[0048] In the formula, is the electric power input to P2G during period t; is the power of natural gas output by P2G during period t; η P2G is the conversion efficiency of P2G; V Gas (t) are the volumes of CO2 absorbed and natural gas produced by P2G during period t, respectively; is the mass of CO2 absorbed by P2G during period t; is the density of CO2.

[0049] Further, in step S2, the steady-state energy network flow model of the energy station includes a power grid flow model and a natural gas grid flow model;

[0050] The power flow model is expressed as:

[0051]

[0052] Where ΔP i , ΔQ i is the active and reactive power imbalance of node i; P i , Q i The active and reactive power injected into node i, U i , U j are the voltage amplitudes of nodes i and j respectively, G ij ,B ij is the conductance and susceptance of the branch, θ ij is the voltage phase angle difference;

[0053] The natural gas grid flow model is expressed as:

[0054]

[0055] In the formula, π m , π n are the pressures at nodes m and n respectively; s mn Indicates the flow direction of natural gas. +1 means that the natural gas flows from node m to node n, and vice versa.

[0056]

[0057] In the formula, f mn is the steady-state flow rate of the natural gas pipeline mn; K mn is the pipeline constant; f τk is the flow rate through the compressor; f m is the total flow rate in the pipeline.

[0058] Furthermore, in step S3, the energy market and carbon market transaction models are as follows:

[0059] Energy market transactions use local time-of-use electricity and gas prices, expressed as:

[0060]

[0061] In the formula, C E,n is the cost of purchasing energy from energy station n; e,t , g,t are the electricity price and natural gas price at time t respectively; P 购 、V 购 They are the electricity and natural gas purchased by the energy station respectively;

[0062] Carbon market transactions adopt a tiered carbon price, expressed as:

[0063]

[0064] In the formula, C T,n is the carbon trading cost of energy station n; λ is the carbon trading base price; l is the length of the carbon emission interval; ζ is the price growth rate.

[0065] Furthermore, in step S3, the energy station power demand response cost model is as follows:

[0066]

[0067] In the formula, C DR,n F is the electricity demand response cost of energy station n; IDR Compensation income for energy stations participating in demand response; DR,t is the demand response incentive cost; T is the number of demand response scheduling periods;

[0068]

[0069] In the formula, is the load adjustment ladder price corresponding to the i-th level; D i is the interval of the load adjustment amount at level i; L is the level of step compensation price; ΔP DR,t It is the absolute value of the load adjustment in period t.

[0070] Furthermore, in step S3, the optimization objective function is as follows:

[0071]

[0072] Where, C is the total operating cost of the urban energy station; C E,n C is the cost of purchasing energy for energy station n; T,n is the carbon trading cost of energy station n; C DR,n The cost of electricity demand response for energy stations.

[0073] The present invention can achieve the following technical effects:

[0074] The optimization control method provided by the present invention can reduce urban energy costs, reduce carbon emissions, and increase energy station revenues, providing an optimizable solution for city-level energy station construction and urban energy management and scheduling; it can target the regional multi-energy coupling system of current urban energy stations, fully consider urban energy demand and the multi-energy coordination advantages of multi-type energy complementarity, so that it can achieve greater economic benefits in the electricity-carbon market, and has theoretical and practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 is a flow chart of an optimization control method for a city-level multi-energy coupling system for an electricity-carbon market provided according to an embodiment of the present invention;

[0076] Figure 2 It is a system architecture diagram of a regional energy station provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0077] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the following description, the same modules are represented by the same reference numerals. In the case of the same reference numerals, their names and functions are also the same. Therefore, the detailed description thereof will not be repeated.

[0078] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not constitute a limitation of the present invention.

[0079] The embodiment of the present invention provides an optimization control method for a city-level multi-energy coupling system for an electric carbon market, and the process is as follows: Figure 1 As shown, the following steps are included:

[0080] S1. Establish a regional energy station model with electricity, gas, heat and storage synergy.

[0081] S11. Establish a regional energy station system architecture, including municipal power grid, municipal gas grid, municipal heat grid, CHP, gas boilers, electric chillers, absorption chillers, energy storage devices, and electricity, cooling and heating loads.

[0082] S12. Establish regional cogeneration system equipment model and constraints;

[0083] The CHP unit model is expressed as:

[0084]

[0085] Where P CHP,t,g is the gas consumption of the CHP unit at time t, kW; C g is the calorific value of natural gas, which is 35.564MJ / m 3 ;q V,CHP,t is the gas consumption volume flow rate of the CHP unit at time t, m 3 / h; P CHP,t,e is the power supply power of the CHP unit at time t, kW; η GE,e Power supply efficiency for internal combustion engines; η GE,h The heating efficiency of the internal combustion engine; ε HPR is the heat-to-electricity ratio of the CHP unit; η WHB is the heat exchange efficiency of the waste heat boiler; P CHP,t,h is the heating power of the CHP unit at time t, kW.

[0086] The gas boiler model is expressed as:

[0087]

[0088] In the formula, q V,GB,t is the gas consumption volume flow rate of the gas boiler at time t, m 3 / h; η GB is the gas boiler efficiency; P GB,t,g P is the gas consumption power of the gas boiler at time t, kW; GB,t,h is the heating power of the gas boiler at time t, kW.

[0089] The electric refrigerator model is expressed as:

[0090] P EC,t,c =η COP,EC P EC,t,e ; (3)

[0091] Where P EC,t,e is the cooling power output of the electric refrigerator at time t; η COP,EC is the cooling efficiency of the electric refrigerator; P EC,t,e is the power consumption of the refrigerator at time t, kW.

[0092] The absorption chiller model is expressed as:

[0093]

[0094] Where P AC,t,c is the cooling power output of the absorption refrigerator at time t; η COP,AC is the cooling efficiency of the absorption chiller; θAC is the heat distribution coefficient of the waste heat boiler input to the absorption chiller; P AC,ι,h is the input thermal power of the absorption chiller at time t, kW.

[0095] Regional cogeneration system equipment power constraints:

[0096] The electrical and thermal power of each device meets the upper and lower power limit requirements:

[0097]

[0098] Where P i is the electrical power of device i; Q i is the thermal power of device i; P imin , P imax is the lower and upper limits of the electrical power of device i; Q imin , Q imax are the lower and upper limits of thermal power of device i.

[0099] S13. Establish regional-level thermal, electrical, and gas energy storage device models and constraints;

[0100] The storage device model is expressed as:

[0101]

[0102] Where Δt is the time period, h; W bt,t and W bt,t+1 are the electric energy stored in the storage device at time t and time t+1, kW·h; σ bt is the self-discharge rate of the power storage device; η bt,c , η bt,disc are the charging and discharging efficiency of the storage device respectively; P bt,c,t , P bt,disc,t are the charging and discharging power of the storage device at time t, kW respectively.

[0103] The thermal storage device model is expressed as:

[0104]

[0105] In the formula, Q hst,t and Q hst,t+1 is the thermal energy stored in the heat storage device at time t and time t+1, W·h; σ hst is the self-heat release rate of the heat storage device; η hst,c , η hst,disc are the charging and discharging efficiencies of the heat storage device; H hst,c,t , H hst,disc,t are respectively the charging and discharging power of the heat storage device at time t, kW.

[0106] The gas storage device model is expressed as:

[0107]

[0108] In the formula, and is the gas storage capacity of the gas storage device at time t and time t+1; η gsc and η gsd are the natural gas storage and release efficiency; G gsc and G gsd They are natural gas storage and release power respectively.

[0109] Energy storage device constraints:

[0110]

[0111] Where W bt,min W is the minimum charge state of the storage device; bt,max Q is the rated charge state of the storage device; hst,min is the minimum heat storage capacity of the heat storage device; Q hst,max is the rated heat storage capacity of the heat storage device; G gs,min G is the minimum gas storage capacity of the gas storage device; gs,max It is the maximum gas storage capacity of the gas storage device.

[0112] S14, building a regional energy station model;

[0113] The regional energy station model is as follows:

[0114]

[0115] Where P in,t and P out,t is the import and export power of the energy station, kW; V all,t is the gas flow rate of the energy station, m 3 / h;Q all,t Heating power for energy station, kW; P all,tPower supply to the energy station, kW; α all is the heat-to-electricity ratio of the energy station; η all Provide energy efficiency for energy stations.

[0116] S2. Establish a regional energy station electrical interconnection system based on energy storage and distribution and P2G.

[0117] S21, construct the energy station P2G model;

[0118]

[0119] In the formula, is the electric power input to P2G during period t; is the power of natural gas output by P2G during period t; η P2G is the conversion efficiency of P2G; V Gas (t) are the volumes of CO2 absorbed and natural gas produced by P2G during period t, respectively; is the mass of CO2 absorbed by P2G during period t; is the density of CO2.

[0120] S22. Construct a steady-state energy network flow model for the energy station, including a power grid flow model and a natural gas grid flow model;

[0121] The power flow model is expressed as:

[0122]

[0123] Where ΔP i , ΔQ i is the active and reactive power imbalance of node i; P i , Q i The active and reactive power injected into node i, U i , U j are the voltage amplitudes of nodes i and j respectively, G ij ,B ij is the conductance and susceptance of the branch, θ ij is the voltage phase angle difference.

[0124] The natural gas grid flow model is expressed as:

[0125]

[0126] In the formula, π m , π n are the pressures at nodes m and n respectively; s mn Characterizes the flow direction of natural gas. +1 means that the natural gas flows from node m to node n, and vice versa.

[0127]

[0128] In the formula, f mn is the steady-state flow rate of the natural gas pipeline mn; K mn is the pipeline constant; f τk is the flow rate through the compressor; f m is the total flow rate in the pipeline.

[0129] S3. Establish an optimization control model for city-level multi-energy coupling systems for the electricity-carbon market.

[0130] S31. Establish energy market and carbon market trading models;

[0131] Energy market transactions use local time-of-use electricity and gas prices, expressed as:

[0132]

[0133] In the formula, C E,n is the cost of purchasing energy from energy station n; e,t , g,t are the electricity price and natural gas price at time t respectively; P 购 、V 购 They are the amount of electricity and natural gas purchased by the energy station respectively.

[0134] Carbon market transactions adopt a tiered carbon price, expressed as:

[0135]

[0136] In the formula, C T,n is the carbon trading cost of energy station n; λ is the carbon trading base price; l is the length of the carbon emission interval; ζ is the price growth rate.

[0137] S32, power demand response cost model for energy stations;

[0138]

[0139] In the formula, C DR,n F is the electricity demand response cost of energy station n; IDR Compensation income for energy stations participating in demand response; DR,t is the demand response incentive cost; T is the number of demand response scheduling periods.

[0140]

[0141] In the formula, is the load adjustment ladder price corresponding to the i-th level; D i is the interval of the load adjustment amount at level i; L is the level of step compensation price; ΔP DR,t It is the absolute value of the load adjustment in period t.

[0142] S33. Establish the optimization objective function of the city-level multi-energy coupling system for the electricity-carbon market;

[0143] The operating costs of energy stations include natural gas purchase costs, external electricity costs, and carbon emission costs. At the same time, energy stations will generate corresponding demand response benefits when participating in electricity demand response. The objective function is as follows:

[0144]

[0145] Where, C is the total operating cost of the urban energy station; C E,n C is the cost of purchasing energy for energy station n; T,n is the carbon trading cost of energy station n; C DR,n The cost of electricity demand response for energy stations.

[0146] S4. Based on the particle swarm algorithm, the optimal control strategy for urban multi-energy stations is obtained.

[0147] S41. Solve the optimization objective function of the city-level multi-energy coupling system for the electricity-carbon market based on the particle swarm algorithm;

[0148] S42. Optimize the output of the energy station's cogeneration system and the deployment of energy and gas storage to supply energy to users in the region.

[0149] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0150] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

[0151] The above specific implementations of the present invention do not constitute a limitation on the protection scope of the present invention. Any other corresponding changes and modifications made based on the technical concept of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. An optimization control method for city-level multi-energy coupling system for electric carbon market, characterized in that: The steps include: S1. Establish a regional energy station model for electricity-gas-heat-storage multi-energy synergy, including the regional energy station system architecture, regional cogeneration system equipment model and constraints, and regional heat-electricity-gas storage device model and constraints; S2. Establish a regional energy station electrical interconnection system based on energy storage and distribution and P2G, including an energy station P2G model and an energy station steady-state energy network flow model; S3. Establish an optimization control model for the city-level multi-energy coupling system for the electricity-carbon market, including energy market and carbon market trading models, energy station power demand response cost model, and optimization objective function; S4. Solve the optimization objective function based on the particle swarm algorithm, optimize the output of the energy station's cogeneration system and the deployment of energy storage and gas storage, and supply energy to users in the region.

2. The city-level multi-energy coupling system optimization control method for the electric carbon market according to claim 1 is characterized in that: In step S1, the regional energy station system architecture includes a municipal power grid, a municipal gas grid, a municipal heat grid, a CHP, a gas boiler, an electric refrigerator, an absorption refrigerator, an energy storage device, and electric, cold, and hot loads; The equipment model and constraints of the regional cogeneration system are as follows: The CHP model is expressed as: Where P CHP,t，g is the gas consumption of the CHP unit at time t, kW; C g is the calorific value of natural gas, which is 35.564MJ / m 3 ;q V,CHP,t is the gas consumption volume flow rate of the CHP unit at time t, m 3 / h; P CHP,t，e is the power supply power of the CHP unit at time t, kW; η GE,e Powering internal combustion engines efficiently; η GE,h Efficiency of providing heat to internal combustion engines; ε HPR is the heat-to-electricity ratio of the CHP unit; ηη WHB is the heat exchange efficiency of the waste heat boiler; P CHP，t，h is the heating power of the CHP unit at time t, kW; The gas boiler model is expressed as: In the formula, q V，GB，t is the gas consumption volume flow rate of the gas boiler at time t, m 3 / h; η GB is the gas boiler efficiency; P GB，t，g P is the gas consumption power of the gas boiler at time t, kW; GB，t，h is the heating power of the gas boiler at time t, kW; The electric refrigerator model is expressed as: P.S EC，t，c η COP,EC P.S EC，t，e 100. Where P EC，t，e is the cooling power output of the electric refrigerator at time t; η COP,EC is the cooling efficiency of the electric refrigerator; P EC，t，e is the power consumption of the electric refrigerator at time t, kW; The absorption chiller model is expressed as: Where P AC，t，c is the cooling power output of the absorption chiller at time t; η COP,AC is the cooling efficiency of the absorption chiller; θAC is the heat distribution coefficient of the waste heat boiler input to the absorption chiller; P AC，ι，h is the input heat power of the absorption chiller at time t, kW; The electrical and thermal power of each device meets the upper and lower power limit requirements: Where P i is the electrical power of device i; Q i is the thermal power of device i; P imin , P imax is the lower and upper limits of the electrical power of device i; Q imin , Q imax are the lower and upper limits of thermal power of device i.

3. The city-level multi-energy coupling system optimization control method for the electric carbon market according to claim 2 is characterized in that: In step S1, the regional-level thermal-electric-gas energy storage device model and constraints are as follows: The storage device model is expressed as: Where Δt is the time period, h; W bt，t and W bt,t+1 are the electric energy stored in the storage device at time t and time t+1, kW·h; σ bt is the self-discharge rate of the power storage device; η bt，c , η bt,disc are the charging and discharging efficiency of the storage device respectively; P bt，c，t , P bt，disc，t are the charging and discharging power of the storage device at time t, kW; The thermal storage device model is expressed as: Where, Qhst,t and Qhst,t +1 is the thermal energy stored in the heat storage device at time t and time t+1, W·h; σ hst is the self-heat release rate of the heat storage device; η hst,c , η hst,disc are the charging and discharging efficiencies of the heat storage device; H hst,c,t , H hst,disc，t are the charging and discharging powers of the heat storage device at time t, kW; The gas storage device model is expressed as: In the formula, and is the gas storage capacity of the gas storage device at time t and time t+1; ηηgsc and η gsd are the natural gas storage and release efficiency respectively; G gsc and G gsd They are natural gas storage and release power; Energy storage device constraints: Where W bt,min W is the minimum charge state of the storage device; bt,max Q is the rated charge state of the storage device; hst,min is the minimum heat storage capacity of the heat storage device; Q hst,max is the rated heat storage capacity of the heat storage device; G gs,min G is the minimum gas storage capacity of the gas storage device; gs，max is the maximum gas storage capacity of the gas storage device; The regional energy station model is as follows: Where P in，t and P out，t is the import and export power of the energy station, kW; V all，t is the gas flow rate of the energy station, m 3 / h;Q all，t Heating power for energy station, kW; P all，t Power supply to the energy station, kW; α all is the heat-to-electricity ratio of the energy station; η all Provide energy efficiency for energy stations.

4. The city-level multi-energy coupling system optimization control method for the electric carbon market according to claim 1 is characterized in that: In step S2, the energy station P2G model is expressed as: In the formula, is the electric power input to P2G during period t; is the power of natural gas output by P2G during period t; η P2G is the conversion efficiency of P2G; V Gas (t) are the volumes of CO2 absorbed and natural gas produced by P2G during period t, respectively; is the mass of CO2 absorbed by P2G during period t; is the density of CO2.

5. The city-level multi-energy coupling system optimization control method for the electric carbon market according to claim 4 is characterized in that: In step S2, the steady-state energy network flow model of the energy station includes a power grid flow model and a natural gas grid flow model; The power grid flow model is expressed as: Where ΔP i , ΔQ i is the active and reactive power imbalance of node i; P i , Q i The active and reactive power injected by node i, U i , U j are the voltage amplitudes of nodes i and j respectively, G ij ,B ij is the conductance and susceptance of the branch, θ ij is the voltage phase angle difference; The natural gas grid flow model is expressed as: In the formula, π m , π n are the pressures at nodes m and n respectively; s mn Indicates the flow direction of natural gas. +1 means that the natural gas flows from node m to node n, and vice versa. In the formula, f mn is the steady-state flow rate of the natural gas pipeline mn; K mn is the pipeline constant; f τk is the flow rate through the compressor; f m is the total flow rate in the pipeline.

6. The city-level multi-energy coupling system optimization control method for the electric carbon market according to claim 1 is characterized in that: In step S3, the energy market and carbon market transaction models are as follows: Energy market transactions use local time-of-use electricity and gas prices, expressed as: In the formula, C E，n is the cost of purchasing energy from energy station n; e，t , g，t are the electricity price and natural gas price at time t respectively; P 购 、V 购 They are the electricity and natural gas purchased by the energy station respectively; Carbon market transactions adopt a tiered carbon price, expressed as: In the formula, C T，n is the carbon trading cost of energy station n; λ is the carbon trading base price; l is the length of the carbon emission interval; ζ is the price growth rate.

7. The city-level multi-energy coupling system optimization control method for the electric carbon market according to claim 6 is characterized in that: In step S3, the energy station power demand response cost model is as follows: In the formula, C DR，n The electricity demand response cost for energy stations; F IDR Compensation income for energy stations participating in demand response; DR，t is the demand response incentive cost; T is the number of demand response scheduling periods; In the formula, is the load adjustment ladder price corresponding to the i-th level; D i is the interval of the load adjustment amount at level i; L is the level of step compensation price; ΔP DR，t It is the absolute value of the load adjustment in period t.

8. The city-level multi-energy coupling system optimization control method for the electric carbon market according to claim 7 is characterized in that: In step S3, the optimization objective function is as follows: Where, C is the total operating cost of the urban energy station; C E，n C is the cost of purchasing energy from energy station n; T，n is the carbon trading cost of energy station n; C DR，n The cost of electricity demand response for energy stations.

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