Park integrated energy system scheduling optimization method and device based on energy-carbon coupling

Abstract

The invention relates to a park integrated energy system scheduling optimization method and device based on energy-carbon coupling, and the method specifically comprises the steps: systematically analyzing the coupling relation between energy flow and carbon flow, and recognizing the coupling node and carbon flow density path of each energy conversion device in a multi-energy system; based on the coupling analysis, establishing an energy-carbon joint optimization model, designing a multi-objective optimization function including system economy and environmental protection, and quantifying a carbon emission path and an energy efficiency level under different energy configuration strategies through a multi-scene analysis method; and solving the energy-carbon coupling model by applying an efficient solving algorithm to realize optimization of low-carbon economic dispatching of the multi-energy system. According to the energy-carbon coupling modeling and optimization method provided by the invention, the carbon emission distribution among the multi-energy equipment can be accurately quantified, the economy and environmental protection benefits of the system are comprehensively considered, and a new way is provided for the optimization of a multi-energy coexisting low-carbon park comprehensive energy system.

Description

Technical Field

[0001] The present invention relates to the field of integrated energy systems and carbon emission management, and in particular to a method and device for optimizing the scheduling of an integrated energy system in a park based on energy-carbon coupling. Background Art

[0002] The low-carbon transformation of the economy and energy system has ushered in huge challenges, and the construction of smart low-carbon parks has become an important means to improve energy efficiency and reduce carbon emissions.

[0003] Existing research has mostly focused on optimizing the scheduling of a single energy form or modeling carbon emission flows, and has not yet fully revealed the coupling mechanism between energy flow and carbon flow in a multi-energy system. With the widespread application of integrated energy systems, multiple energy forms such as electricity, heat, cooling, and gas interact and convert in the same system, and the coupling relationship between energy flow and carbon flow has become increasingly complex. The current modeling method makes it difficult to achieve joint modeling of energy flow and carbon flow, and it is impossible to accurately quantify the carbon emission distribution path of various energy equipment during the energy conversion process, resulting in the system facing problems such as low energy efficiency and insufficient carbon emission control when optimizing scheduling. Summary of the invention

[0004] The present invention provides a method and device for optimizing the scheduling of a park integrated energy system based on energy-carbon coupling. The method uses an energy hub as the core to build a unified modeling framework for energy flow and carbon flow, analyzes the coupling nodes and carbon flow density relationships between multiple energy conversion devices, and realizes the joint modeling of energy flow and carbon flow. The method proposed in the present invention is applicable to a low-carbon park integrated energy system with multiple energy sources, which can significantly improve the system energy efficiency and reduce carbon emissions.

[0005] The present invention solves the above technical problems in the following way: A method for optimizing the scheduling of a comprehensive energy system in a park based on energy-carbon coupling, comprising the following steps:

[0006] Based on the relationship between the energy flow conversion path and carbon flow density of each energy conversion equipment in the park, a carbon emission flow model for each energy conversion equipment is established;

[0007] Based on the carbon emission flow model of each energy conversion equipment, an energy-carbon coupling model is established;

[0008] Based on the energy-carbon coupling model, an objective function is established with the minimization of operating costs and carbon emissions as the optimization goals;

[0009] Establish constraints based on the operating conditions of energy conversion equipment in the park;

[0010] Based on the constraints and the objective function, the energy-carbon coupling model is solved to obtain the scheduling strategy of the park.

[0011] Preferably, the carbon emission flow model of each energy conversion device is established based on the relationship between the energy flow conversion path and the carbon flow density of each energy conversion device in the park, including:

[0012] Model the gas turbine as follows:

[0013] The power consumed by burning natural gas and the power generated are expressed as:

[0014] P EGT η EGT,e =P EGT,e

[0015] P EGT η EGT,h =P EGT,h

[0016] The relationship between the carbon flow density at the input port and the output port of the gas turbine is expressed as:

[0017] ρ EGT η EGT =ρ EGT,e

[0018] ρ EGT η EGT,h =ρ EGT,h

[0019] Among them, P EGT is the gas power consumed by the gas turbine, P EGT,e and P EGT,h is the power generation and heat generation of the gas turbine, ρ EGT is the carbon flow density at the gas turbine input port, ρ EGT,e is the carbon flow density at the gas turbine electrical load output port, ρ EGT,h is the carbon flow density at the heat load output port of the gas turbine, η EGT,e and η EGT,h The power generation efficiency and heat generation efficiency of the gas turbine;

[0020] Model the gas boiler as follows:

[0021] The energy conversion relationship of a gas boiler that converts the internal energy of gas into thermal energy is as follows:

[0022] P GHB η GHB =P GHB,h

[0023] The relationship between the carbon flow density at the input port and the output port of the gas boiler is expressed as:

[0024] ρ GHB η GHB =ρ GHB,h

[0025] Among them, P GHB is the gas consumption power of the gas boiler, P GHB,h is the heating power of the gas boiler, ρ GHB is the carbon flow density at the gas boiler input port, ρ GHB,h is the carbon flow density at the gas turbine output port, η GHB Energy conversion efficiency of gas boiler;

[0026] Model the electric boiler as follows:

[0027] The energy conversion relationship of an electric boiler converting electrical energy into thermal energy is expressed as:

[0028] P EHB η EHB =P EHB,h

[0029] The relationship between the carbon flow density at the input port and the output port of the electric boiler is expressed as:

[0030] ρ EHB η EHB =ρ EHB,h

[0031] Among them, P EHB is the power consumption of the electric boiler, P EHB,h is the heating power of the electric boiler, ρ EHB is the carbon flow density at the input port of the electric boiler, ρ EHB,h is the carbon flow density at the output port of the electric boiler, η EHB Energy efficiency of electric boiler;

[0032] Model the electric refrigerator as follows:

[0033] The cooling power of an electric refrigerator is expressed as:

[0034] P EC η EC =P EC,c

[0035] The relationship between the carbon flow density at the input port and the output port of the electric refrigerator is expressed as:

[0036] ρ EC η EC =ρ EC,c

[0037] Where P EC , P EC,c Respectively represent the power consumption and cooling power of the electric refrigerator; ρ EC , EC,c Respectively represent the carbon flow density at the input port and output port of the electric refrigerator; η EC is the refrigeration performance coefficient of the electric refrigerator;

[0038] Model the absorption chiller as follows

[0039] The power conversion between the input port and the output port of the absorption system is expressed as:

[0040] P AC η AC =P AC,c

[0041] The relationship between the carbon flow density at the input port and the output port of the absorption chiller is expressed as:

[0042] ρ AC η AC =ρ AC,c

[0043] Among them, η AC is the operating performance coefficient of the absorption chiller, referred to as the energy efficiency ratio, which represents the ratio of the cooling capacity to the heat consumption of the absorption chiller when it is working, ρ AC , AC,c represent the carbon flow density at the input port and output port of the absorption chiller respectively;

[0044] The carbon capture device is modeled as a device that consumes only electricity, enriches CO2, and transports it to the P2G system. It consumes electricity in the integrated energy system and does not generate cooling, heating, electricity, or gas loads. Its energy hub model is characterized by 1 input and 0 output. The corresponding quantity at the input end of the coupling matrix is ​​represented by 1, which means that all the electricity input to the CCS device is used.

[0045] The electric hydrogen production system is modeled as follows

[0046] The power of the electric hydrogen production system and the hydrogen production power are expressed as:

[0047] P P2G η P2G =P P2G,g

[0048] The relationship between the carbon flow density of the input port and the output port of the electric hydrogen production system is expressed as:

[0049] ρ P2G η P2G =ρ P2G,g

[0050] Among them, P P2G is the power consumed by electrolysis of water, P P2G,g is the gas production power of the electric hydrogen production system, ρ P2G is the carbon flow density at the input port of the electric hydrogen production system, ρ P2G,g is the carbon flow density at the output port of the electric hydrogen production system, η P2GThe energy conversion efficiency of the electric hydrogen production system;

[0051] The modeling of the CCHP unit is as follows:

[0052] The relationship between the power consumed by burning natural gas and the power generation, heat generation, and cooling power can be expressed as:

[0053] P CCHP η CCHP,e =P CCHP,e

[0054] P CCHP η CCHP,h =P CCHP,h

[0055] P CCHP η CCHP,c =P CCHP,c

[0056] The relationship between the carbon flow density at the input port of the CCHP unit and the carbon flow density of the output electric load, the carbon flow density of the output heat load, and the carbon flow density of the output cold load are expressed as follows:

[0057] ρ CCHP η CCHP,e =ρ CCHP,e

[0058] ρ CCHP η CCHP,h =ρ CCHP,h

[0059] ρ CCHP η CCHP,c =ρ CCHP,c

[0060] Among them, P CCHP is the gas power consumed by the cogeneration unit, P CCHP,e , P CCHP,h , P CCHP,c are the power generation power, heating power and cooling power of CCHP respectively, ρ CCHP,e , CCHP,h , CCHP,c are the carbon flow density of CCHP output electric load, carbon flow density of output heat load and carbon flow density of output cooling load, respectively, η CCHP,e , η CCHP,h , η CCHP,c They are the unit's power generation efficiency, heat generation efficiency and cooling efficiency respectively;

[0061] The energy storage device is modeled as follows:

[0062]

[0063] Where: i∈{ES,HS,CS,GS}; SOC i (t) and SOC i (t0) represents the energy stored at time t and t0, including electricity, heat, cold and gas; δ i It represents the self-consumption rate of energy storage, in % / h; Δt represents the time span from t0 to t; P i ch and P i dis Respectively represent the charging and discharging power of the energy storage device; and Respectively represent the charging and discharging efficiency of the energy storage device.

[0064] Preferably, the energy-carbon coupling model is established based on the carbon emission flow model of each energy conversion device, including:

[0065] Define each type of internal energy conversion device in the energy hub of the park's integrated energy system as a corresponding energy conversion unit, and use the energy conversion unit as the framework of the energy-carbon coupling model;

[0066] Establish the system's energy and carbon source balance conversion and transfer relationship, the expression is as follows:

[0067] F=ρ in E in =ρ out E out

[0068]

[0069] Where F is carbon emissions, ρ in and ρ out are the carbon flow densities at the input and output ports of the energy conversion device, E in and E out represent energy input and energy output respectively, ρ i is the carbon flow density at the output port of a certain load, E i is the energy at a certain load output port, n is the number of multiple energy flows in the integrated energy system, and i is the i-th energy flow / energy in the integrated energy system.

[0070] Based on the input port elements and output port elements of the energy hub, the system input vector and output vector are established as follows:

[0071] The input vector P is represented as:

[0072]

[0073] Where P grid is the input power of the grid, P pvis the input power of photovoltaic power generation, P wt is the input power of wind power generation, P gas is the input power of the natural gas grid, ρ i,grid is the carbon flow density of the power grid, ρ i,gas is the carbon flow density of the natural gas grid;

[0074] The output vector L can be expressed as:

[0075]

[0076] Where P el , P hl , P cl , P gl are the output power of electrical load, heating load, cooling load and gas load respectively, ρ o,el , o,hl , o,cl , o,gl are the carbon flow densities of electricity, heat, cooling and gas loads respectively;

[0077] Based on the system's energy and carbon source balance conversion and transfer relationship, as well as the input vector and output vector, an energy-carbon coupling model is established, as follows:

[0078] C=C 15 C 14 C 13 ...C3C2C1

[0079]

[0080] In the formula, C j is the coupling matrix of the carbon emission flow model of energy conversion equipment in each branch of the energy hub, j∈{1,2,3...14,15}, C mn Here, m∈{1,2,3...7,8}, n∈{1,2,3,4,5,6} are the elements in the energy-carbon coupling matrix C of the comprehensive energy system.

[0081] Preferably, the objective function based on the energy-carbon coupling model is established with the minimization of operating costs and carbon emissions as the optimization goal, specifically including:

[0082] The objective function of minimizing the system operation cost is established. The system operation cost includes the power purchase cost, gas purchase cost, operation and maintenance cost of each equipment, and the abandoned energy cost of photovoltaic generators and wind turbines. The expression is as follows:

[0083] min C=C ele +C gas +C om +C loss

[0084]

[0085]

[0086]

[0087]

[0088] In the formula, C represents the system operating cost; C ele Represents the cost of purchasing electricity; C gas Represents the gas purchase cost; C om Represents the equipment operation and maintenance cost; C loss represents the energy abandonment cost; e , g , solar , wind are electricity purchase price, gas purchase price, solar abandonment price, and wind abandonment price respectively; om,i represents the operation and maintenance coefficient of the i-th equipment; P i (t) They represent the electricity purchase amount, gas purchase amount, output power of the ith device, abandoned solar power, and abandoned wind power at time t respectively;

[0089] The objective function of minimizing system carbon emissions is established, and the expression is as follows:

[0090]

[0091] Where P el,t , P hl,t , P cl,t , P gl,t are the output power of electrical load, heating load, cooling load and gas load at time t, respectively, o,el,t , o,hl,t , o,cl,t , o,gl,t are the carbon flow densities of electricity, heat, cooling and gas loads at time t respectively.

[0092] Preferably, the constraint conditions are established according to the operating conditions of the energy conversion equipment in the park, including:

[0093] Set up the power constraints for the energy conversion equipment as follows:

[0094] P i,min ≤P i (t)≤P i,max

[0095] Where P i,min , P i,max are the upper and lower limits of the output of the i-th device respectively;

[0096] Set up energy storage device constraints as follows:

[0097]

[0098] In the formula, SOC i,min , SOC i,max , SOC i (0) are the upper and lower limits of energy storage of energy storage device i; (0) are the energy storage status of energy storage element at time t;

[0099] Set up the ramp rate constraints for the energy conversion equipment as follows:

[0100] |ΔP i |≤P i,climb

[0101] Where ΔP i represents the output change of the i-th device; P i,climb is the equipment ramp rate;

[0102] The power constraints for purchasing electricity, selling electricity, purchasing gas and selling gas between the system and the upper-level power grid and gas grid are as follows:

[0103]

[0104]

[0105]

[0106]

[0107] In the formula, and are the maximum allowable power purchase and sales between the integrated energy system and the upper power grid, and They are the maximum permissible gas purchase and gas sales power between the integrated energy system and the upper-level gas grid;

[0108] Constraints are set for the carbon flow density of the load-side electricity, cooling, gas, and heat loads as follows:

[0109] 0<ρ o,el,t <ρ o,el,max

[0110] 0<ρ o,cl,t <ρ o,cl,max

[0111] 0<ρ o,hl,t <ρ o,hl,max

[0112] 0<ρ o,gl,t <ρ o,gl,max

[0113] In the formula, ρ o,el,max , o,cl,max , o,hl,max , o,gl,max are the upper limits of carbon flow density for electricity, cooling, heating and gas loads respectively;

[0114] According to the system's carbon emission target, constraints are set on the carbon flow density of electricity, heat, cooling and gas loads as follows:

[0115]

[0116] In the formula, E k,max is the upper limit of carbon emissions of equipment k. Ω is the set of energy conversion equipment in the park; ρ k,t is the carbon flow density of the energy generated during the operation of equipment k; P k,t is the power of device k at time t.

[0117] Preferably, the energy-carbon coupling model is solved based on the constraint conditions and the objective function to obtain the scheduling strategy of the park, including:

[0118] Set the particle swarm optimization algorithm parameters and randomly generate the initial population as the initial solution of the objective function;

[0119] Calculate the fitness value of each solution according to the objective function, find the particle with the best fitness value in the entire particle swarm, and use its position and fitness value as the global optimal position and fitness value;

[0120] According to the update rules, the particle speed and position are updated to obtain the optimal position; the particle position corresponds to the specific value of the decision variables in the energy-carbon coupling model, while the speed represents the movement trend of these decision variables in the search space.

[0121] Until the set termination condition is reached, the global optimal position and fitness value are output to obtain the best scheduling strategy.

[0122] The present invention also provides a park comprehensive energy system scheduling optimization device based on energy-carbon coupling, comprising:

[0123] The carbon emission flow model building module of energy conversion equipment is used to build the carbon emission flow model of each energy conversion equipment based on the relationship between the energy flow conversion path and carbon flow density of each energy conversion equipment in the park;

[0124] The energy-carbon coupling model building module is used to build an energy-carbon coupling model based on the carbon emission flow model of each energy conversion equipment;

[0125] The optimization target establishment module is used to establish an objective function with the optimization target of minimizing operating costs and carbon emissions based on the energy-carbon coupling model;

[0126] The constraint condition establishment module is used to establish constraint conditions according to the operating conditions of the energy conversion equipment in the park;

[0127] The scheduling strategy generation module is used to solve the energy-carbon coupling model based on constraint conditions and objective functions to obtain the scheduling strategy of the park.

[0128] Preferably, the carbon emission flow model of each energy conversion device is established based on the relationship between the energy flow conversion path and the carbon flow density of each energy conversion device in the park, including:

[0129] Model the gas turbine as follows:

[0130] The power consumed by burning natural gas and the power generated are expressed as:

[0131] P EGT η EGT,e =P EGT,e

[0132] P EGT η EGT,h =P EGT,h

[0133] The relationship between the carbon flow density at the input port and the output port of the gas turbine is expressed as:

[0134] ρ EGT η EGT =ρ EGT,e

[0135] ρ EGT η EGT,h =ρ EGT,h

[0136] Among them, P EGT is the gas power consumed by the gas turbine, P EGT,e and P EGT,h is the power generation and heat generation of the gas turbine, ρ EGT is the carbon flow density at the gas turbine input port, ρ EGT,e is the carbon flow density at the gas turbine electrical load output port, ρ EGT,h is the carbon flow density at the heat load output port of the gas turbine, η EGT,e and η EGT,h The power generation efficiency and heat generation efficiency of the gas turbine;

[0137] Model the gas boiler as follows:

[0138] The energy conversion relationship of a gas boiler that converts the internal energy of gas into thermal energy is as follows:

[0139] P GHB η GHB =P GHB,h

[0140] The relationship between the carbon flow density at the input port and the output port of the gas boiler is expressed as:

[0141] ρ GHB η GHB =ρ GHB,h

[0142] Among them, P GHB is the gas consumption power of the gas boiler, P GHB,h is the heating power of the gas boiler, ρ GHB is the carbon flow density at the gas boiler input port, ρ GHB,h is the carbon flow density at the gas turbine output port, η GHB Energy conversion efficiency of gas boiler;

[0143] Model the electric boiler as follows:

[0144] The energy conversion relationship of an electric boiler converting electrical energy into thermal energy is expressed as:

[0145] P EHB η EHB =P EHB,h

[0146] The relationship between the carbon flow density at the input port and the output port of the electric boiler is expressed as:

[0147] ρ EHB η EHB =ρ EHB,h

[0148] Among them, P EHB is the power consumption of the electric boiler, P EHB,h is the heating power of the electric boiler, ρ EHB is the carbon flow density at the input port of the electric boiler, ρ EHB,h is the carbon flow density at the output port of the electric boiler, η EHB Energy efficiency of electric boiler;

[0149] Model the electric refrigerator as follows:

[0150] The cooling power of an electric refrigerator is expressed as:

[0151] P EC η EC =P EC,c

[0152] The relationship between the carbon flow density at the input port and the output port of the electric refrigerator is expressed as:

[0153] ρ EC η EC =ρ EC,c

[0154] Where P EC , P EC,c Respectively represent the power consumption and cooling power of the electric refrigerator; ρ EC , EC,c Respectively represent the carbon flow density at the input port and output port of the electric refrigerator; η EC is the refrigeration performance coefficient of the electric refrigerator;

[0155] Model the absorption chiller as follows

[0156] The power conversion between the input port and the output port of the absorption system is expressed as:

[0157] P AC η AC =P AC,c

[0158] The relationship between the carbon flow density at the input port and the output port of the absorption chiller is expressed as:

[0159] ρ AC η AC =ρ AC,c

[0160] Among them, η AC is the operating performance coefficient of the absorption chiller, referred to as the energy efficiency ratio, which represents the ratio of the cooling capacity to the heat consumption of the absorption chiller when it is working, ρ AC , AC,c represent the carbon flow density at the input port and output port of the absorption chiller respectively;

[0161] When modeling the carbon capture device, its energy hub model is characterized by 1 input and 0 output. The corresponding quantity at the input end of the coupling matrix is ​​represented by 1, that is, all the electrical energy input to the CCS device is used.

[0162] The electric hydrogen production system is modeled as follows

[0163] The power of the electric hydrogen production system and the hydrogen production power are expressed as:

[0164] P P2G η P2G =P P2G,g

[0165] The relationship between the carbon flow density of the input port and the output port of the electric hydrogen production system is expressed as:

[0166] ρ P2G η P2G =ρ P2G,g

[0167] Among them, P P2G is the power consumed by electrolysis of water, P P2G,g is the gas production power of the electric hydrogen production system, ρP2G is the carbon flow density at the input port of the electric hydrogen production system, ρ P2G,g is the carbon flow density at the output port of the electric hydrogen production system, η P2G The energy conversion efficiency of the electric hydrogen production system;

[0168] The modeling of the CCHP unit is as follows:

[0169] The relationship between the power consumed by burning natural gas and the power generation, heat generation, and cooling power can be expressed as:

[0170] P CCHP η CCHP,e =P CCHP,e

[0171] P CCHP η CCHP,h =P CCHP,h

[0172] P CCHP η CCHP,c =P CCHP,c

[0173] The relationship between the carbon flow density at the input port of the CCHP unit and the carbon flow density of the output electric load, the carbon flow density of the output heat load, and the carbon flow density of the output cold load are expressed as follows:

[0174] ρ CCHP η CCHP,e =ρ CCHP,e

[0175] ρ CCHP η CCHP,h =ρ CCHP,h

[0176] ρ CCHP η CCHP,c =ρ CCHP,c

[0177] Among them, P CCHP is the gas power consumed by the cogeneration unit, P CCHP,e , P CCHP,h , P CCHP,c are the power generation power, heating power and cooling power of CCHP respectively, ρ CCHP,e , CCHP,h , CCHP,c are the carbon flow density of CCHP output electric load, carbon flow density of output heat load and carbon flow density of output cooling load, respectively, η CCHP,e , η CCHP,h , η CCHP,c They are the unit's power generation efficiency, heat generation efficiency and cooling efficiency respectively;

[0178] The energy storage device is modeled as follows:

[0179]

[0180] Where: i∈{ES,HS,CS,GS}; SOC i (t) and SOC i (t0) represents the energy stored at time t and t0, including electricity, heat, cold and gas; δ i It represents the self-consumption rate of energy storage, in % / h; Δt represents the time span from t0 to t; P i ch and P i dis Respectively represent the charging and discharging power of the energy storage device; and Respectively represent the charging and discharging efficiency of the energy storage device.

[0181] Preferably, the energy-carbon coupling model is established based on the carbon emission flow model of each energy conversion device, including:

[0182] Each type of internal energy conversion device in the energy hub of the park's integrated energy system is defined as a corresponding energy conversion unit.

[0183] Establish the system's energy and carbon source balance conversion and transfer relationship, the expression is as follows:

[0184] F=ρ in E in =ρ out E out

[0185]

[0186] Where F is carbon emissions, ρ in and ρ out are the carbon flow densities at the input and output ports of the energy conversion device, E in and E out represent energy input and energy output respectively, ρ i is the carbon flow density at the output port of a certain load, E i is the energy at a certain load output port, n is the number of multiple energy flows in the integrated energy system, and i is the i-th energy flow / energy in the integrated energy system.

[0187] Based on the input port elements and output port elements of the energy hub, the system input vector and output vector are established as follows:

[0188] The input vector P is represented as:

[0189]

[0190] Where P grid is the input power of the grid, P pv is the input power of photovoltaic power generation, P wt is the input power of wind power generation, P gas is the input power of the natural gas grid, ρ i,grid is the carbon flow density of the power grid, ρ i,gas is the carbon flow density of the natural gas grid;

[0191] The output vector L can be expressed as:

[0192]

[0193] Where P el , P hl , P cl , P gl are the output power of electrical load, heating load, cooling load and gas load respectively, ρ o,el , o,hl , o,cl , o,gl are the carbon flow densities of electricity, heat, cooling and gas loads respectively;

[0194] Based on the system's energy and carbon source balance conversion and transfer relationship, as well as the input vector and output vector, an energy-carbon coupling model is established, as follows:

[0195] C=C 15 C 14 C 13 ...C3C2C1

[0196]

[0197] In the formula, C j is the coupling matrix of the carbon emission flow model of energy conversion equipment in each branch of the energy hub, j∈{1,2,3...14,15}, C mn Here, m∈{1,2,3...7,8}, n∈{1,2,3,4,5,6} are the elements in the energy-carbon coupling matrix C of the comprehensive energy system.

[0198] Preferably, the objective function based on the energy-carbon coupling model is established with the minimization of operating costs and carbon emissions as the optimization goal, specifically including:

[0199] The objective function of minimizing the system operation cost is established. The system operation cost includes the power purchase cost, gas purchase cost, operation and maintenance cost of each equipment, and the abandoned energy cost of photovoltaic generators and wind turbines. The expression is as follows:

[0200] min C=C ele +C gas +C om +Closs

[0201]

[0202]

[0203]

[0204]

[0205] In the formula, C represents the system operating cost; C ele Represents the cost of purchasing electricity; C gas Represents the gas purchase cost; C om Represents the equipment operation and maintenance cost; C loss represents the energy abandonment cost; e , g , solar , wind are electricity purchase price, gas purchase price, solar abandonment price, and wind abandonment price respectively; om,i represents the operation and maintenance coefficient of the i-th equipment; P i (t) They represent the electricity purchase amount, gas purchase amount, output power of the ith device, abandoned solar power, and abandoned wind power at time t respectively;

[0206] The objective function of minimizing system carbon emissions is established, and the expression is as follows:

[0207]

[0208] Where P el,t , P hl,t , P cl,t , P gl,t are the output power of electrical load, heating load, cooling load and gas load at time t, respectively, o,el,t , o,hl,t , o,cl,t , o,gl,t are the carbon flow densities of electricity, heat, cooling and gas loads at time t respectively.

[0209] Preferably, the constraint conditions are established according to the operating conditions of the energy conversion equipment in the park, including:

[0210] Set up the power constraints for the energy conversion equipment as follows:

[0211] P i,min ≤P i (t)≤P i,max

[0212] Where P i,min , P i,maxare the upper and lower limits of the output of the i-th device respectively;

[0213] Set up energy storage device constraints as follows:

[0214]

[0215] In the formula, SOC i,min , SOC i,max , SOC i (0) are the upper and lower limits of energy storage of energy storage device i; (0) are the energy storage status of energy storage element at time t;

[0216] Set up the ramp rate constraints for the energy conversion equipment as follows:

[0217] |ΔP i |≤P i,climb

[0218] Where ΔP i represents the output change of the i-th device; P i,climb is the equipment ramp rate;

[0219] The power constraints for purchasing electricity, selling electricity, purchasing gas and selling gas between the system and the upper-level power grid and gas grid are as follows:

[0220]

[0221]

[0222]

[0223]

[0224] In the formula, and are the maximum allowable power purchase and sales between the integrated energy system and the upper power grid, and They are the maximum permissible gas purchase and gas sales power between the integrated energy system and the upper-level gas grid;

[0225] Constraints are set for the carbon flow density of the load-side electricity, cooling, gas, and heat loads as follows:

[0226] 0<ρ o,el,t <ρ o,el,max

[0227] 0<ρ o,cl,t <ρ o,cl,max

[0228] 0<ρ o,hl,t <ρ o,hl,max

[0229] 0<ρ o,gl,t<ρ o,gl,max

[0230] In the formula, ρ o,el,max , o,cl,max , o,hl,max , o,gl,max are the upper limits of carbon flow density for electricity, cooling, heating and gas loads respectively;

[0231] According to the system's carbon emission target, constraints are set on the carbon flow density of electricity, heat, cooling and gas loads as follows:

[0232]

[0233] In the formula, E k,max is the upper limit of carbon emissions of equipment k. Ω is the set of energy conversion equipment in the park; ρ k,t is the carbon flow density of the energy generated during the operation of equipment k; P k,t is the power of device k at time t.

[0234] Preferably, the energy-carbon coupling model is solved based on the constraint conditions and the objective function to obtain the scheduling strategy of the park, including:

[0235] Set the particle swarm optimization algorithm parameters and randomly generate the initial population as the initial solution of the objective function;

[0236] Calculate the fitness value of each solution according to the objective function, find the particle with the best fitness value in the entire particle swarm, and use its position and fitness value as the global optimal position and fitness value;

[0237] According to the update rules, the particle speed and position are updated to obtain the optimal position.

[0238] Until the set termination condition is reached, the global optimal position and fitness value are output to obtain the best scheduling strategy.

[0239] The present invention also provides a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for optimizing the scheduling of a comprehensive energy system in a park based on energy-carbon coupling as described above are implemented.

[0240] The present invention also provides an electronic device, comprising a memory and a processor: the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the park integrated energy system scheduling optimization method based on energy-carbon coupling as described above are implemented.

[0241] The beneficial effects of the present invention are: improving energy utilization efficiency and reducing energy waste; reducing carbon emissions and meeting the requirements of sustainable development; enhancing system stability and reliability; promoting sustainable development of the park; and having wide applicability and being easy to promote and apply.

[0242] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0243] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0244] Figure 1 A flow chart of a method for optimizing the scheduling of a comprehensive energy system in a park based on energy-carbon coupling provided in Example 1;

[0245] Figure 2 Schematic diagram of photovoltaic and wind power output and load conditions of various embodiments and comparative examples applied to a certain park in North China;

[0246] Figure 3 This is a schematic diagram of the external electricity purchase price and external gas purchase price of the park;

[0247] Figure 4 This is a schematic diagram of the carbon flow density of the upper-level power grid and gas grid that interact with a certain park in North China;

[0248] Figure 5 The park power grid operation curve after optimization in Example 1;

[0249] Figure 6 The park heating network operation curve after optimization in Example 1;

[0250] Figure 7 The park cooling network operation curve after optimization in Example 1;

[0251] Figure 8 The park gas network operation curve after optimization in Example 1;

[0252] Fig. 9 This is the park carbon emission curve after optimization in Example 1;

[0253] Fig.10 A module diagram of a park integrated energy system scheduling optimization device based on energy-carbon coupling provided in Example 2. DETAILED DESCRIPTION

[0254] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0255] The following examples and comparative examples are all simulated and verified in a park in North China. The photovoltaic and wind power output and load demand of the park are as follows: Figure 2 As shown in the figure, the external electricity and gas purchase prices of the park are as follows: Figure 3 As shown in Figure 2, the carbon flow density of the upper power grid and gas grid is as follows: Figure 4 The parameters of various energy conversion equipment in the park are shown in Table 1.

[0256] Table 1 Parameters of various energy conversion equipment in the park

[0257]

[0258] Example 1

[0259] like Figure 1 As shown, this embodiment provides a method for optimizing the scheduling of a park integrated energy system based on energy-carbon coupling, comprising the following steps:

[0260] S1. Based on the relationship between the energy flow conversion path and carbon flow density of each energy conversion equipment in the park, a carbon emission flow model for each energy conversion equipment is established, as follows:

[0261] 1.1 Model the gas turbine as follows:

[0262] The power consumed by burning natural gas and the power generated are expressed as:

[0263] P EGT η EGT,e =P EGT,e

[0264] P EGT η EGT,h =P EGT,h

[0265] The relationship between the carbon flow density at the input port and the output port of the gas turbine is expressed as:

[0266] ρ EGT η EGT =ρ EGT,e

[0267] ρ EGT η EGT,h =ρ EGT,h

[0268] Among them, P EGT is the gas power consumed by the gas turbine, P EGT,e and P EGT,h is the power generation and heat generation of the gas turbine, ρEGT is the carbon flow density at the gas turbine input port, ρ EGT,e is the carbon flow density at the gas turbine electrical load output port, ρ EGT,h is the carbon flow density at the heat load output port of the gas turbine, η EGT,e and η EGT,h The power generation efficiency and heat generation efficiency of the gas turbine;

[0269] 1.2 Model the gas boiler as follows:

[0270] The energy conversion relationship of a gas boiler that converts the internal energy of gas into thermal energy is as follows:

[0271] P GHB η GHB =P GHB,h

[0272] The relationship between the carbon flow density at the input port and the output port of the gas boiler is expressed as:

[0273] ρ GHB η GHB =ρ GHB,h

[0274] Among them, P GHB is the gas consumption power of the gas boiler, P GHB,h is the heating power of the gas boiler, ρ GHB is the carbon flow density at the gas boiler input port, ρ GHB,h is the carbon flow density at the gas turbine output port, η GHB Energy conversion efficiency of gas boiler;

[0275] 1.3 Model the electric boiler as follows:

[0276] The energy conversion relationship of an electric boiler converting electrical energy into thermal energy is expressed as:

[0277] P EHB η EHB =P EHB,h

[0278] The relationship between the carbon flow density at the input port and the output port of the electric boiler is expressed as:

[0279] ρ EHB η EHB =ρ EHB,h

[0280] Among them, P EHB is the power consumption of the electric boiler, P EHB,h is the heating power of the electric boiler, ρ EHB is the carbon flow density at the input port of the electric boiler, ρ EHB,h is the carbon flow density at the output port of the electric boiler, η EHBEnergy efficiency of electric boiler;

[0281] 1.4 Model the electric refrigerator as follows:

[0282] The cooling power of an electric refrigerator is expressed as:

[0283] P EC η EC =P EC,c

[0284] The relationship between the carbon flow density at the input port and the output port of the electric refrigerator is expressed as:

[0285] ρ EC η EC =ρ EC,c

[0286] Where P EC , P EC,c Respectively represent the power consumption and cooling power of the electric refrigerator; ρ EC , EC,c Respectively represent the carbon flow density at the input port and output port of the electric refrigerator; η EC is the refrigeration performance coefficient of the electric refrigerator;

[0287] 1.5 Model the absorption chiller as follows

[0288] The power conversion between the input port and the output port of the absorption system is expressed as:

[0289] P AC η AC =P AC,c

[0290] The relationship between the carbon flow density at the input port and the output port of the absorption chiller is expressed as:

[0291] ρ AC η AC =ρ AC,c

[0292] Among them, η AC is the operating performance coefficient of the absorption chiller, referred to as the energy efficiency ratio, which represents the ratio of the cooling capacity to the heat consumption of the absorption chiller when it is working, ρ AC , AC,c represent the carbon flow density at the input port and output port of the absorption chiller respectively;

[0293] 1.6 Modeling of carbon capture device, which only consumes electricity, enriches CO2, and transports it to the P2G system. It plays the role of consuming electricity in the integrated energy system and does not generate cold, heat, electricity, or gas loads. Its energy hub model is characterized by 1 input and 0 output. The corresponding quantity at the input end of the coupling matrix is ​​represented by 1, that is, all the electricity input to the CCS device is used.

[0294] 1.7 Modeling of the electric hydrogen production system is as follows

[0295] The power of the electric hydrogen production system and the hydrogen production power are expressed as:

[0296] P P2G η P2G =P P2G,g

[0297] The relationship between the carbon flow density of the input port and the output port of the electric hydrogen production system is expressed as:

[0298] ρ P2G η P2G =ρ P2G,g

[0299] Among them, P P2G is the power consumed by electrolysis of water, P P2G,g is the gas production power of the electric hydrogen production system, ρ P2G is the carbon flow density at the input port of the electric hydrogen production system, ρ P2G,g is the carbon flow density at the output port of the electric hydrogen production system, η P2G The energy conversion efficiency of the electric hydrogen production system;

[0300] 1.8 Modeling of CCHP unit is as follows:

[0301] The relationship between the power consumed by burning natural gas and the power generation, heat generation, and cooling power can be expressed as:

[0302] P CCHP η CCHP,e =P CCHP,e

[0303] P CCHP η CCHP,h =P CCHP,h

[0304] P CCHP η CCHP,c =P CCHP,c

[0305] The relationship between the carbon flow density at the input port of the CCHP unit and the carbon flow density of the output electric load, the carbon flow density of the output heat load, and the carbon flow density of the output cold load are expressed as follows:

[0306] ρCCHP η CCHP,e =ρ CCHP,e

[0307] ρ CCHP η CCHP,h =ρ CCHP,h

[0308] ρ CCHP η CCHP,c =ρ CCHP,c

[0309] Among them, P CCHP is the gas power consumed by the cogeneration unit, P CCHP,e , P CCHP,h , P CCHP,c are the power generation power, heating power and cooling power of CCHP respectively, ρ CCHP,e , CCHP,h , CCHP,c are the carbon flow density of CCHP output electric load, carbon flow density of output heat load and carbon flow density of output cooling load, respectively, η CCHP,e , η CCHP,h , η CCHP,c They are the unit's power generation efficiency, heat generation efficiency and cooling efficiency respectively;

[0310] 1.9 Energy storage device modeling, as follows:

[0311]

[0312] Where: i∈{ES,HS,CS,GS}; SOC i (t) and SOC i (t0) represents the energy stored at time t and t0, including electricity, heat, cold and gas; δ i It represents the self-consumption rate of energy storage, in % / h; Δt represents the time span from t0 to t; P i ch and P i dis Respectively represent the charging and discharging power of the energy storage device; and Respectively represent the charging and discharging efficiency of the energy storage device.

[0313] S2. Establish an energy-carbon coupling model based on the carbon emission flow model of each energy conversion device;

[0314] 2.1 Each type of internal energy conversion device in the energy hub of the park's integrated energy system is defined as a corresponding energy conversion unit, and the energy conversion unit is used as the framework of the energy-carbon coupling model; the energy conversion devices inside the energy hub can be divided into four categories: single input and single output, single input and multiple output, multiple input and single output, and multiple input and multiple output. Each energy conversion device is now defined as an energy conversion unit (Energy Conversion Unit, ECU). Among them, the special ECU includes an energy flow aggregation point and an energy flow separation point. The energy flow aggregation point contains multiple energy inputs and is aggregated into a single energy flow output through this point. The energy flow separation point decomposes a single input energy flow into multiple energy flows of the same type and sends them to the output end.

[0315] 2.2 For energy conversion equipment, all carbon emissions related to input energy should be allocated to output energy, and the total carbon flow density at the input port is equal to the total carbon flow density at the output port; at the same time, for multi-output conversion equipment, it also conforms to the carbon emission balance expression.

[0316] The energy and carbon source balance conversion and transfer relationship of the system is established in turn, and the expression is as follows:

[0317] F=ρ in E in =ρ out E out

[0318]

[0319] Where F is carbon emissions, ρ in and ρ out are the carbon flow densities at the input and output ports of the energy conversion device, E in and E out represent energy input and energy output respectively, ρ i is the carbon flow density at the output port of a certain load, E i is the energy at a certain load output port, n is the number of multiple energy flows in the integrated energy system, and i is the i-th energy flow / energy in the integrated energy system.

[0320] The core of energy hub modeling is to solve the coupling matrix that reflects the conversion and transfer relationship between multiple energy flows and carbon flows between the input and output ports of the system. To establish an energy hub model for a comprehensive energy system, it is necessary to solve the system input vector, output vector, and the coupling matrix that reflects the conversion relationship between multiple energy flows at the input and output ports.

[0321] Based on the input port elements and output port elements of the energy hub, the system input vector and output vector are established as follows:

[0322] The input vector P is represented as:

[0323]

[0324] Where P grid is the input power of the grid, P pv is the input power of photovoltaic power generation, P wt is the input power of wind power generation, P gas is the input power of the natural gas grid, ρ i,grid is the carbon flow density of the power grid, ρ i,gas is the carbon flow density of the natural gas grid;

[0325] The output vector L can be expressed as:

[0326]

[0327] Where P el , P hl , P cl , P gl are the output power of electrical load, heating load, cooling load and gas load respectively, ρ o,el , o,hl , o,cl , o,gl are the carbon flow densities of electricity, heat, cooling and gas loads respectively;

[0328] 2.3 Based on the system's energy and carbon source balance conversion and transfer relationship, as well as the input vector and output vector, an energy-carbon coupling model is established, as follows:

[0329] C=C 15 C 14 C 13 ...C3C2C1

[0330]

[0331] In the formula, C j is the coupling matrix of the carbon emission flow model of the energy conversion equipment in each branch of the energy hub, j∈{1,2,3...14,15}, C mn Here are the elements m∈{1,2,3...7,8}, n∈{1,2,3,4,5,6} in the energy-carbon coupling matrix C of the comprehensive energy system.

[0332] S3. Based on the energy-carbon coupling model, establish an objective function with the minimization of operating costs and carbon emissions as the optimization goals;

[0333] The resources on the power generation side are coordinated by optimizing the scheduling among the electricity, heat, cooling and gas systems in the integrated energy system. While meeting the energy demand on the load side and the various operating constraints in the system, the energy storage devices are optimized and demand-side management is performed. The system is optimized and scheduled with the goal of minimizing the operating costs and carbon emissions in the integrated energy system.

[0334] 3.1 Establish the objective function of minimizing the system operation cost. The system operation cost includes the system's electricity purchase cost, gas purchase cost, operation and maintenance cost of each equipment, and the abandoned energy cost of photovoltaic generators and wind turbines. The expression is as follows:

[0335] min C=C ele +C gas +C om +C loss

[0336]

[0337]

[0338]

[0339]

[0340] In the formula, C represents the system operating cost; C ele Represents the cost of purchasing electricity; C gas Represents the gas purchase cost; C om Represents the equipment operation and maintenance cost; C loss represents the energy abandonment cost; e , g , solar , wind are electricity purchase price, gas purchase price, solar abandonment price, and wind abandonment price respectively; om,i represents the operation and maintenance coefficient of the i-th equipment; P i (t) They represent the electricity purchase amount, gas purchase amount, output power of the ith device, abandoned solar power, and abandoned wind power at time t respectively;

[0341] 3.2 Establish the objective function of minimizing system carbon emissions, the expression is as follows:

[0342]

[0343] Where P el,t , P hl,t , P cl,t , P gl,t are the output power of electrical load, heating load, cooling load and gas load at time t, respectively, o,el,t , o,hl,t , o,cl,t , o,gl,t are the carbon flow densities of electricity, heat, cooling and gas loads at time t respectively.

[0344] S4. Establish constraints based on the operating conditions of the energy conversion equipment in the park;

[0345] 4.1 Establish power constraints for energy conversion equipment as follows:

[0346] P i,min ≤P i (t)≤P i,max

[0347] Where P i,min , P i,max are the upper and lower limits of the output of the i-th device respectively;

[0348] 4.2 Establish energy storage device constraints as follows:

[0349]

[0350] In the formula, SOC i,min , SOC i,max , SOC i (0) are the upper and lower limits of energy storage of energy storage device i; (0) are the energy storage status of energy storage element at time t;

[0351] 4.3 Set up the ramp rate constraints for energy conversion equipment as follows:

[0352] |ΔP i |≤P i,climb

[0353] Where ΔP i represents the output change of the i-th device; P i,climb is the equipment ramp rate;

[0354] 4.4 The power constraints of the system and the upper-level power grid and gas grid for purchasing electricity, selling electricity, purchasing gas and selling gas are as follows:

[0355]

[0356]

[0357]

[0358]

[0359] In the formula, and are the maximum allowable power purchase and sales between the integrated energy system and the upper power grid, and They are the maximum permissible gas purchase and gas sales power between the integrated energy system and the upper-level gas grid;

[0360] 4.5 Set constraints on the carbon flow density of the load end electricity, cooling, gas and heat loads as follows:

[0361] 0<ρ o,el,t <ρ o,el,max

[0362] 0<ρ o,cl,t <ρ o,cl,max

[0363] 0<ρ o,hl,t <ρ o,hl,max

[0364] 0<ρ o,gl,t <ρ o,gl,max

[0365] In the formula, ρ o,el,max , o,cl,max , o,hl,max , o,gl,max are the upper limits of carbon flow density for electricity, cooling, heating and gas loads respectively;

[0366] 4.6 According to the system's carbon emission target, set constraints on the carbon flow density of electricity, heat, cooling and gas loads as follows:

[0367]

[0368] In the formula, E k,max is the upper limit of carbon emissions of equipment k. Ω is the set of energy conversion equipment in the park; ρ k,t is the carbon flow density of the energy generated during the operation of equipment k; P k,t is the power of device k at time t.

[0369] S5. Based on the constraints and the objective function, the energy-carbon coupling model is solved to obtain the scheduling strategy of the park, which is as follows:

[0370] 5.1 Set the particle swarm optimization algorithm parameters, including the particle swarm size N, the maximum number of iterations T max , the value range of weight ω is [0.4, 0.9], and the values ​​of learning factors c1 and c2 are [0, 4]; the positions and velocities of N particles are initially randomly generated as the initial solution of the objective function; the positions of the particles correspond to the specific values ​​of the decision variables in the energy-carbon coupling model, while the velocities represent the movement trends of these decision variables in the search space.

[0371] 5.2 Calculate the fitness value of each solution according to the objective function, find the particle with the best fitness value in the entire particle swarm, and use its position and fitness value as the global optimal position and fitness value;

[0372] 5.3 Update the particle speed and position according to the update rules to obtain the optimal position;

[0373] For each particle, update its velocity according to the following formula:

[0374]

[0375] in, is the velocity of particle i at the tth iteration; w is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers in the interval [0,1]; is the individual optimal historical position of particle i; is the global optimal position; is the position of particle i at the tth iteration.

[0376] Update the particle's position based on the updated velocity:

[0377]

[0378] The position is updated based on the velocity, which determines the direction and step size of the particle's movement in the search space. The updated position represents the potential solution of the particle in the new iteration.

[0379] 5.4 Until the maximum number of iterations T is reached max , output the global optimal position and fitness value to obtain the best scheduling strategy.

[0380] If the maximum number of iterations has not been reached, return to step 5.2 to continue iterating.

[0381] In the process of continued iteration, the particle swarm will continuously update the individual optimal and global optimal positions, and adjust the speed and position of the particles to further search for better solutions.

[0382] Comparative Example 1

[0383] This comparative example provides another method for optimizing the scheduling of a comprehensive energy system in a park based on energy-carbon coupling, the steps of which are basically the same as those of Example 1, except that:

[0384] S3 only sets the objective function of minimizing the system operation cost.

[0385] Comparative Example 2

[0386] This comparative example provides another method for optimizing the scheduling of a comprehensive energy system in a park based on energy-carbon coupling, the steps of which are basically the same as those of Example 1, except that:

[0387] S3 only sets the objective function of minimizing the system’s carbon emissions.

[0388] Through the optimization methods provided in Example 1, Comparative Example 1 and Comparative Example 2, the optimization results of the park system are shown in Table 2.

[0389] Table 2 Optimization results of various embodiments and comparative examples

[0390] Total system operating cost / yuan Equipment operation and maintenance costs Electricity purchase cost / yuan Gas purchase cost / yuan Carbon emissions / kg Comparative Example 1 53882 5508.9 38978.5 9394.6 85421 Comparative Example 2 62669 6725.1 46857.5 9086.4 83115 Example 1 58967 5991.1 44514.8 8461.1 84093

[0391] As shown in Table 2, the optimization scheduling method based on energy-carbon coupling modeling proposed in Example 1 can reduce the carbon emissions of the system by 1,328 kg while increasing the total operating cost of the system by 5,058 yuan. Although the carbon emissions of Comparative Example 2 are 978 kg lower than those of Example 1, the total operating cost of the system is 3,702 yuan higher than that of Example 1.

[0392] Among them, after the park comprehensive energy system is operated according to the optimized dispatching strategy provided in Example 1, the power grid operation curve is as follows: Figure 6 As shown in the figure, the power load demand of the industrial park is stable at 4000-6000kW·h. From 0 to 7 o'clock, the photovoltaic power generation output is 0, and more electricity is purchased from the superior power grid at this time; from 8 to 18 o'clock, the photovoltaic power generation output gradually increases, and at the same time, the demand for power load also gradually increases, and the purchase of power from the superior power grid also gradually increases; from 19 to 23 o'clock, the power load demand becomes smaller, the photovoltaic power generation output is 0, and the purchase of power from the superior power grid is stable at about 2500kW·h. It can be seen from the figure that through multi-objective optimization under the optimal operation mode of low-carbon economy, the load curve is optimized compared with the load curve under the optimal operation mode of economy based on electricity and heat, and part of the load during the peak period of electricity consumption is transferred to the low period of electricity consumption, which plays the role of "peak shaving and valley filling" of the power load.

[0393] The heat network operation curve is as follows Figure 7 As shown in the figure, the time period from 6 to 18 o'clock is the peak period of heat consumption. It can be seen from the figure that through multi-objective optimization under the low-carbon economic optimal operation mode, the load curve is smoother than the load curve under the economic optimal operation mode with electricity as the heat source. The heat storage tank stores heat during the low heat consumption period and releases heat during the peak heat consumption period. At certain moments, the heat production of CCHP is slightly higher than the heat load of the industrial park. This is because part of the waste heat in CCHP will be absorbed by the absorption chiller and eventually converted into cooling load.

[0394] The cold network operation curve is as follows Figure 8 As shown in the figure, the cooling load demand of the industrial park fluctuates in the range of 1000-1800kW·h. It can be seen from the figure that through multi-objective optimization under the low-carbon economic optimal operation mode, the load curve is more stable than the load curve under the economic optimal operation mode with electricity as the heat source. The cold storage tank stores cold during the low-cold period and releases cold during the peak cold period, which can alleviate the peak pressure of cold use to a certain extent.

[0395] Gas network operation curve Fig. 9As shown in the figure, the gas load demand of the industrial park is stable at 2000-3000kW·h. It can be seen from the figure that through multi-objective optimization under the low-carbon economic optimal operation mode, the load curve is smoother than the load curve under the economic optimal operation mode with electricity as the heat. At some moments, the gas purchase volume of the upper gas network and the P2G gas production are slightly higher than the gas load of the industrial park. This is because a certain amount of gas load is used as input for CCHP, gas turbines and gas boilers.

[0396] The carbon emissions of electricity, heating, cooling, and gas loads and the total carbon emissions of IES are as follows: Fig.10 As shown in the figure, the carbon emissions of the electric load are relatively stable as a whole, with small fluctuations. The carbon emissions remain at a low level because the proportion of clean energy used in the power grid is relatively high. The carbon emissions of the heat load have a significant increasing trend from 6 to 18 o'clock, especially reaching a higher peak at noon, because the heating demand or hot water demand of the building increases, and the heat source is mainly gas, and carbon emissions will increase significantly with the increase in demand. The carbon emissions of the cooling load fluctuate greatly during the daytime and reach a peak at noon, which is related to the cooling demand caused by the temperature rise. The carbon emissions of the gas load are low and change smoothly because the gas load demand is relatively stable in the system. The total carbon emissions of IES show a significant upward trend from 12 to 18 o'clock, especially the high demand for heat load and cooling load during this period, resulting in an increase in system carbon emissions. The total carbon emissions during the morning and evening trough period are significantly reduced, indicating that the load demand is reduced at night and the equipment operating load in the system is low, thereby reducing carbon emissions.

[0397] Example 2

[0398] This embodiment provides a park comprehensive energy system scheduling optimization device based on energy-carbon coupling, including:

[0399] The carbon emission flow model building module of energy conversion equipment is used to build the carbon emission flow model of each energy conversion equipment based on the relationship between the energy flow conversion path and carbon flow density of each energy conversion equipment in the park;

[0400] The energy-carbon coupling model building module is used to build an energy-carbon coupling model based on the carbon emission flow model of each energy conversion equipment;

[0401] The optimization target establishment module is used to establish an objective function with the optimization target of minimizing operating costs and carbon emissions based on the energy-carbon coupling model;

[0402] The constraint condition establishment module is used to establish constraint conditions according to the operating conditions of the energy conversion equipment in the park;

[0403] The scheduling strategy generation module is used to solve the energy-carbon coupling model based on constraint conditions and objective functions to obtain the scheduling strategy of the park.

[0404] Example 3

[0405] The present invention also provides a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for optimizing the scheduling of a comprehensive energy system in a park based on energy-carbon coupling as described in Example 1 are implemented.

[0406] Example 4

[0407] The present invention also provides an electronic device, comprising a memory and a processor: the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the park integrated energy system scheduling optimization method based on energy-carbon coupling as described in Example 1 are implemented.

[0408] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0409] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0410] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0411] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

Claims

1. A method for optimizing the scheduling of a comprehensive energy system in a park based on energy-carbon coupling, characterized in that: The following steps are involved: Based on the relationship between the energy flow conversion path and carbon flow density of each energy conversion equipment in the park, a carbon emission flow model for each energy conversion equipment is established; Based on the carbon emission flow model of each energy conversion equipment, an energy-carbon coupling model is established; Based on the energy-carbon coupling model, an objective function is established with the minimization of operating costs and carbon emissions as the optimization goals; Establish constraints based on the operating conditions of energy conversion equipment in the park; Based on the constraints and the objective function, the energy-carbon coupling model is solved to obtain the scheduling strategy of the park.

2. According to claim 1, a method for optimizing the scheduling of a comprehensive energy system in a park based on energy-carbon coupling, characterized in that: The carbon emission flow model of each energy conversion device is established based on the relationship between the energy flow conversion path and the carbon flow density of each energy conversion device in the park, including: Model the gas turbine as follows: The power consumed by burning natural gas and the power generated are expressed as: P.S EGT η EGT,e JP EGT,e P.S EGT η EGT,h JP EGT,h The relationship between the carbon flow density at the input port and the output port of the gas turbine is expressed as: r EGT or EGT =ρ EGT,e r EGT or EGT,h =ρ EGT,h Among them, P EGT is the gas power consumed by the gas turbine, P EGT,e and P EGT,h is the power generation and heat generation of the gas turbine, ρ EGT is the carbon flow density at the gas turbine input port, ρ EGT,e is the carbon flow density at the gas turbine electrical load output port, ρ EGT,h is the carbon flow density at the heat load output port of the gas turbine, η EGT,e and η EGT,h The power generation efficiency and heat generation efficiency of the gas turbine; Model the gas boiler as follows: The energy conversion relationship of a gas boiler that converts the internal energy of gas into thermal energy is as follows: P.S GHB η GHB JP GHB,h The relationship between the carbon flow density at the input port and the output port of the gas boiler is expressed as: r GHB or GHB =ρ GHB,h Among them, P GHB is the gas consumption power of the gas boiler, P GHB,h is the heating power of the gas boiler, ρ GHB is the carbon flow density at the gas boiler input port, ρ GHB,h is the carbon flow density at the gas turbine output port, η GHB Energy conversion efficiency of gas boiler; Model the electric boiler as follows: The energy conversion relationship of an electric boiler converting electrical energy into thermal energy is expressed as: P.S EHB η EHB JP EHB,h The relationship between the carbon flow density at the input port and the output port of the electric boiler is expressed as: r EHB or EHB =ρ EHB,h Among them, P EHB is the power consumption of the electric boiler, P EHB,h is the heating power of the electric boiler, ρ EHB is the carbon flow density at the input port of the electric boiler, ρ EHB,h is the carbon flow density at the output port of the electric boiler, η EHB Energy efficiency of electric boiler; Model the electric refrigerator as follows: The cooling power of an electric refrigerator is expressed as: P.S EC η EC JP EC,c The relationship between the carbon flow density at the input port and the output port of the electric refrigerator is expressed as: r EC or EC =ρ EC,c Where P EC , P EC,c Respectively represent the power consumption and cooling power of the electric refrigerator; ρ EC , EC,c Respectively represent the carbon flow density at the input port and output port of the electric refrigerator; η EC is the refrigeration performance coefficient of the electric refrigerator; Model the absorption chiller as follows The power conversion between the input port and the output port of the absorption system is expressed as: P.S AC η AC JP AC,c The relationship between the carbon flow density at the input port and the output port of the absorption chiller is expressed as: r AC or AC =ρ AC,c Among them, η AC is the operating performance coefficient of the absorption chiller, referred to as the energy efficiency ratio, which represents the ratio of the cooling capacity to the heat consumption of the absorption chiller when it is working, ρ AC , AC,c represent the carbon flow density at the input port and output port of the absorption chiller respectively; The carbon capture device is modeled, and its energy hub model is characterized by 1 input and 0 output. The corresponding quantity at the input of the coupling matrix is ​​represented by 1, that is, all the electrical energy input to the CCS device is used; The electric hydrogen production system is modeled as follows The power of the electric hydrogen production system and the hydrogen production power are expressed as: P.S P2G η P2G JP P2G,g The relationship between the carbon flow density of the input port and the output port of the electric hydrogen production system is expressed as: r P2G or P2G =ρ P2G,g Among them, P P2G is the power consumed by electrolysis of water, P P2G,g is the gas production power of the electric hydrogen production system, ρ P2G is the carbon flow density at the input port of the electric hydrogen production system, ρ P2G,g is the carbon flow density at the output port of the electric hydrogen production system, η P2G The energy conversion efficiency of the electric hydrogen production system; The modeling of the CCHP unit is as follows: The relationship between the power consumed by burning natural gas and the power generation, heat generation, and cooling power can be expressed as: P.S CCHP η CCHP,e JP CCHP,e P.S CCHP η CCHP,h JP CCHP,h P.S CCHP η CCHP,c JP CCHP,c The relationship between the carbon flow density at the input port of the CCHP unit and the carbon flow density of the output electric load, the carbon flow density of the output heat load, and the carbon flow density of the output cold load are expressed as follows: r CCHP or CCHP,e =ρ CCHP,e r CCHP or CCHP,h =ρ CCHP,h r CCHP or CCHP,c =ρ CCHP,c Among them, P CCHP is the gas power consumed by the cogeneration unit, P CCHP,e , P CCHP,h , P CCHP,c are the power generation power, heating power and cooling power of CCHP respectively, ρ CCHP,e , CCHP,h , CCHP,c are the carbon flow density of CCHP output electric load, carbon flow density of output heat load and carbon flow density of output cooling load, respectively, η CCHP,e , η CCHP,h , η CCHP,c They are the unit's power generation efficiency, heat generation efficiency and cooling efficiency respectively; The energy storage device is modeled as follows: Where: i∈{ES,HS,CS,GS}; SOC i (t) and SOC i (t0) represents the energy stored at time t and t0, including electricity, heat, cold and gas; δ i represents the self-consumption rate of energy storage; Δt represents the time span from t0 to t; and Respectively represent the charging and discharging power of the energy storage device; and Respectively represent the charging and discharging efficiency of the energy storage device.

3. According to claim 1, a method for optimizing the scheduling of a comprehensive energy system in a park based on energy-carbon coupling, characterized in that: The energy-carbon coupling model is established based on the carbon emission flow model of each energy conversion device, including: Each type of internal energy conversion device in the energy hub of the park integrated energy system is defined as a corresponding energy conversion unit; Establish the system's energy and carbon source balance conversion and transfer relationship, the expression is as follows: F=ρ in E in =ρ out E out Where F is carbon emissions, ρ in and ρ out are the carbon flow densities at the input and output ports of the energy conversion device, E in and E out represent energy input and energy output respectively, ρ i is the carbon flow density at the output port of a certain load, E i is the energy of a certain load output port, n is the number of multi-energy flows in the integrated energy system, and i is the i-th energy flow / energy in the integrated energy system; Based on the input port elements and output port elements of the energy hub, the system input vector and output vector are established as follows: The input vector P is represented as: Where P grid is the input power of the grid, P pv is the input power of photovoltaic power generation, P wt is the input power of wind power generation, P gas is the input power of the natural gas grid, ρ i,grid is the carbon flow density of the power grid, ρ i,gas is the carbon flow density of the natural gas grid; The output vector L can be expressed as: Where P el , P hl , P cl , P gl are the output power of electrical load, heating load, cooling load and gas load respectively, ρ o,el , o,hl , o,cl , o,gl are the carbon flow densities of electricity, heat, cooling and gas loads respectively; Based on the system's energy and carbon source balance conversion and transfer relationship, as well as the input vector and output vector, an energy-carbon coupling model is established, as follows: C=C 15 C 14 C 13 ...C3C2C1 In the formula, C j is the coupling matrix of the carbon emission flow model of energy conversion equipment in each branch of the energy hub, j∈{1,2,3...14,15}, C mn Here, m∈{1,2,3...7,8}, n∈{1,2,3,4,5,6} are the elements in the energy-carbon coupling matrix C of the comprehensive energy system.

4. According to claim 1, a method for optimizing the scheduling of a comprehensive energy system in a park based on energy-carbon coupling, characterized in that: Based on the energy-carbon coupling model, an objective function is established with the minimization of operating costs and carbon emissions as the optimization goals, specifically including: The objective function of minimizing the system operation cost is established. The system operation cost includes the power purchase cost, gas purchase cost, operation and maintenance cost of each equipment, and the abandoned energy cost of photovoltaic generators and wind turbines. The expression is as follows: minC=C ele +C gas +C om +C loss In the formula, C represents the system operating cost; C ele Represents the cost of purchasing electricity; C gas Represents the gas purchase cost; C om Represents the equipment operation and maintenance cost; C loss represents the energy abandonment cost; e , g , solar , wind are electricity purchase price, gas purchase price, solar abandonment price, and wind abandonment price respectively; om,i represents the operation and maintenance coefficient of the i-th equipment; P i (t) They represent the electricity purchase amount, gas purchase amount, output power of the ith device, abandoned solar power, and abandoned wind power at time t respectively; The objective function of minimizing system carbon emissions is established, and the expression is as follows: Where P el,t , P hl,t , P cl,t , P gl,t are the output power of electrical load, heating load, cooling load and gas load at time t, respectively, o,el,t , o,hl,t , o,cl,t , o,gl,t are the carbon flow densities of electricity, heat, cooling and gas loads at time t respectively.

5. According to claim 1, a method for optimizing the scheduling of a comprehensive energy system in a park based on energy-carbon coupling, characterized in that: The constraint conditions are set according to the operating conditions of the energy conversion equipment in the park, including: Set up the power constraints for the energy conversion equipment as follows: P i,min ≤P i (t)≤P i,max Where P i,min , P i,max are the upper and lower limits of the output of the i-th device respectively; Set up energy storage device constraints as follows: In the formula, SOC i,min , SOC i,max , SOC i (0) are the upper and lower limits of energy storage of energy storage device i; (0) are the energy storage status of energy storage element at time t; Set up the ramp rate constraints for the energy conversion equipment as follows: |ΔP i |≤P i,climb Where ΔP i represents the output change of the i-th device; P i,climb is the equipment ramp rate; The power constraints for purchasing electricity, selling electricity, purchasing gas and selling gas between the system and the upper-level power grid and gas grid are as follows: In the formula, and are the maximum allowable power purchase and sales between the integrated energy system and the upper power grid, and They are the maximum permissible gas purchase and gas sales power between the integrated energy system and the upper-level gas grid; Constraints are set for the carbon flow density of the load-side electricity, cooling, gas, and heat loads as follows: 0<ρ o,el,t <p o,el,max 0<ρ o,cl,t <p o,cl,max 0<ρ o,hl,t <p o,hl,max 0<ρ o,gl,t <p o,gl,max In the formula, ρ o,e l ,max , o,c l ,max , o, hl ,max , o,g l ,max are the upper limits of carbon flow density for electricity, cooling, heating and gas loads respectively; According to the system's carbon emission target, constraints are set on the carbon flow density of electricity, heat, cooling and gas loads as follows: In the formula, E k,max is the upper limit of carbon emissions of equipment k; Ω is the set of energy conversion equipment in the park; ρ k,t is the carbon flow density of the energy generated during the operation of equipment k; P k,t is the power of device k at time t.

6. According to claim 1, a method for optimizing the scheduling of a comprehensive energy system in a park based on energy-carbon coupling, characterized in that: Based on the constraint conditions and the objective function, the energy-carbon coupling model is solved to obtain the park scheduling strategy, including: Set the particle swarm optimization algorithm parameters and randomly generate the initial population as the initial solution of the objective function; Calculate the fitness value of each solution according to the objective function, find the particle with the best fitness value in the entire particle swarm, and use its position and fitness value as the global optimal position and fitness value; According to the update rules, the particle speed and position are updated to obtain the optimal position; Until the set termination condition is reached, the global optimal position and fitness value are output to obtain the best scheduling strategy.

7. A park integrated energy system scheduling optimization device based on energy-carbon coupling, comprising: The carbon emission flow model building module of energy conversion equipment is used to build the carbon emission flow model of each energy conversion equipment based on the relationship between the energy flow conversion path and carbon flow density of each energy conversion equipment in the park; The energy-carbon coupling model building module is used to build an energy-carbon coupling model based on the carbon emission flow model of each energy conversion equipment; The optimization target establishment module is used to establish an objective function with the optimization target of minimizing operating costs and carbon emissions based on the energy-carbon coupling model; The constraint condition establishment module is used to establish constraint conditions according to the operating conditions of the energy conversion equipment in the park; The scheduling strategy generation module is used to solve the energy-carbon coupling model based on constraint conditions and objective functions to obtain the scheduling strategy of the park.

8. According to claim 7, a park integrated energy system scheduling optimization system based on energy-carbon coupling is characterized in that: The carbon emission flow model of each energy conversion device is established based on the relationship between the energy flow conversion path and the carbon flow density of each energy conversion device in the park, including: Model the gas turbine as follows: The power consumed by burning natural gas and the power generated are expressed as: P.S EGT η EGT,e JP EGT,e P.S EGT η EGT,h JP EGT,h The relationship between the carbon flow density at the input port and the output port of the gas turbine is expressed as: r EGT or EGT =ρ EGT,e r EGT or EGT,h =ρ EGT,h Among them, P EGT is the gas power consumed by the gas turbine, P EGT,e and P EGT,h is the power generation and heat generation of the gas turbine, ρ EGT is the carbon flow density at the gas turbine input port, ρ EGT,e is the carbon flow density at the gas turbine electrical load output port, ρ EGT,h is the carbon flow density at the heat load output port of the gas turbine, η EGT,e and η EGT,h The power generation efficiency and heat generation efficiency of the gas turbine; Model the gas boiler as follows: The energy conversion relationship of a gas boiler that converts the internal energy of gas into thermal energy is as follows: P.S GHB η GHB JP GHB,h The relationship between the carbon flow density at the input port and the output port of the gas boiler is expressed as: r GHB or GHB =ρ GHB,h Among them, P GHB is the gas consumption power of the gas boiler, P GHB,h is the heating power of the gas boiler, ρ GHB is the carbon flow density at the gas boiler input port, ρ GHB,h is the carbon flow density at the gas turbine output port, η GHB Energy conversion efficiency of gas boiler; Model the electric boiler as follows: The energy conversion relationship of an electric boiler converting electrical energy into thermal energy is expressed as: P.S EHB η EHB JP EHB,h The relationship between the carbon flow density at the input port and the output port of the electric boiler is expressed as: r EHB or EHB =ρ EHB,h Among them, P EHB is the power consumption of the electric boiler, P EHB,h is the heating power of the electric boiler, ρ EHB is the carbon flow density at the input port of the electric boiler, ρ EHB,h is the carbon flow density at the output port of the electric boiler, η EHB Energy efficiency of electric boiler; Model the electric refrigerator as follows: The cooling power of an electric refrigerator is expressed as: P.S EC η EC JP EC,c The relationship between the carbon flow density at the input port and the output port of the electric refrigerator is expressed as: r EC or EC =ρ EC,c Where P EC , P EC,c Respectively represent the power consumption and cooling power of the electric refrigerator; ρ EC , EC,c Respectively represent the carbon flow density at the input port and output port of the electric refrigerator; η EC is the refrigeration performance coefficient of the electric refrigerator; Model the absorption chiller as follows The power conversion between the input port and the output port of the absorption system is expressed as: P.S AC η AC JP AC,c The relationship between the carbon flow density at the input port and the output port of the absorption chiller is expressed as: r AC or AC =ρ AC,c Among them, η AC is the operating performance coefficient of the absorption chiller, referred to as the energy efficiency ratio, which represents the ratio of the cooling capacity to the heat consumption of the absorption chiller when it is working, ρ AC , AC,c represent the carbon flow density at the input port and output port of the absorption chiller respectively; The carbon capture device is modeled, and its energy hub model is characterized by 1 input and 0 output. The corresponding quantity at the input of the coupling matrix is ​​represented by 1, that is, all the electrical energy input to the CCS device is used; The electric hydrogen production system is modeled as follows The power of the electric hydrogen production system and the hydrogen production power are expressed as: P.S P2G η P2G JP P2G,g The relationship between the carbon flow density of the input port and the output port of the electric hydrogen production system is expressed as: r P2G or P2G =ρ P2G,g Among them, P P2G is the power consumed by electrolysis of water, P P2G,g is the gas production power of the electric hydrogen production system, ρ P2G is the carbon flow density at the input port of the electric hydrogen production system, ρ P2G,g is the carbon flow density at the output port of the electric hydrogen production system, η P2G The energy conversion efficiency of the electric hydrogen production system; The modeling of the CCHP unit is as follows: The relationship between the power consumed by burning natural gas and the power generation, heat generation, and cooling power can be expressed as: P.S CCHP η CCHP,e JP CCHP,e P.S CCHP η CCHP,h JP CCHP,h P.S CCHP η CCHP,c JP CCHP,c The relationship between the carbon flow density at the input port of the CCHP unit and the carbon flow density of the output electric load, the carbon flow density of the output heat load, and the carbon flow density of the output cold load are expressed as follows: r CCHP or CCHP,e =ρ CCHP,e r CCHP or CCHP,h =ρ CCHP,h r CCHP or CCHP,c =ρ CCHP,c Among them, P CCHP is the gas power consumed by the cogeneration unit, P CCHP,e , P CCHP,h , P CCHP,c are the power generation power, heating power and cooling power of CCHP respectively, ρ CCHP,e , CCHP,h , CCHP,c are the carbon flow density of CCHP output electric load, carbon flow density of output heat load and carbon flow density of output cooling load, respectively, η CCHP,e , η CCHP,h , η CCHP,c They are the unit's power generation efficiency, heat generation efficiency and cooling efficiency respectively; The energy storage device is modeled as follows: Where: i∈{ES,HS,CS,GS}; SOC i (t) and SOC i (t0) represents the energy stored at time t and t0, including electricity, heat, cold and gas; δ i represents the self-consumption rate of energy storage; Δt represents the time span from t0 to t; and Respectively represent the charging and discharging power of the energy storage device; and Respectively represent the charging and discharging efficiency of the energy storage device.

9. The park integrated energy system scheduling optimization system based on energy-carbon coupling according to claim 8 is characterized in that: The energy-carbon coupling model is established based on the carbon emission flow model of each energy conversion device, including: Each type of internal energy conversion device in the energy hub of the park integrated energy system is defined as a corresponding energy conversion unit; Establish the system's energy and carbon source balance conversion and transfer relationship, the expression is as follows: F=ρ in E in =ρ out E out Where F is carbon emissions, ρ in and ρ out are the carbon flow densities at the input and output ports of the energy conversion device, E in and E out represent energy input and energy output respectively, ρ i is the carbon flow density at the output port of a certain load, E i is the energy of a certain load output port, n is the number of multi-energy flows in the integrated energy system, and i is the i-th energy flow / energy in the integrated energy system; Based on the input port elements and output port elements of the energy hub, the system input vector and output vector are established as follows: The input vector P is represented as: Where P grid is the input power of the grid, P pv is the input power of photovoltaic power generation, P wt is the input power of wind power generation, P gas is the input power of the natural gas grid, ρ i,grid is the carbon flow density of the power grid, ρ i,gas is the carbon flow density of the natural gas grid; The output vector L can be expressed as: Where P el , P hl , P cl , P gl are the output power of electrical load, heating load, cooling load and gas load respectively, ρ o,el , o,hl , o,cl , o,gl are the carbon flow densities of electricity, heat, cooling and gas loads respectively; Based on the system's energy and carbon source balance conversion and transfer relationship, as well as the input vector and output vector, an energy-carbon coupling model is established, as follows: C=C 15 C 14 C 13 ...C3C2C1 In the formula, C j is the coupling matrix of the carbon emission flow model of energy conversion equipment in each branch of the energy hub, j∈{1,2,3...14,15}, C mn Here, m∈{1,2,3...7,8}, n∈{1,2,3,4,5,6} are the elements in the energy-carbon coupling matrix C of the comprehensive energy system.

10. A park integrated energy system scheduling optimization system based on energy-carbon coupling according to claim 8, characterized in that: Based on the energy-carbon coupling model, an objective function is established with the minimization of operating costs and carbon emissions as the optimization goals, specifically including: The objective function of minimizing the system operation cost is established. The system operation cost includes the power purchase cost, gas purchase cost, operation and maintenance cost of each equipment, and the abandoned energy cost of photovoltaic generators and wind turbines. The expression is as follows: minC=C ele +C gas +C om +C loss In the formula, C represents the system operating cost; C ele Represents the cost of purchasing electricity; C gas Represents the gas purchase cost; C om Represents the equipment operation and maintenance cost; C loss represents the energy abandonment cost; e , g , solar , wind are electricity purchase price, gas purchase price, solar abandonment price, and wind abandonment price respectively; om,i represents the operation and maintenance coefficient of the i-th equipment; P i (t) They represent the electricity purchase amount, gas purchase amount, output power of the ith device, abandoned solar power, and abandoned wind power at time t respectively; The objective function of minimizing system carbon emissions is established, and the expression is as follows: Where P el,t , P hl,t , P cl,t , P gl,t are the output power of electrical load, heating load, cooling load and gas load at time t, respectively, o,el,t , o,hl,t , o,cl,t , o,gl,t are the carbon flow densities of electricity, heat, cooling and gas loads at time t respectively.

11. The park integrated energy system scheduling optimization system based on energy-carbon coupling according to claim 8 is characterized in that: The constraint conditions are set according to the operating conditions of the energy conversion equipment in the park, including: Set up the power constraints for the energy conversion equipment as follows: P i,min ≤P i (t)≤P i,max Where P i,min , P i,max are the upper and lower limits of the output of the i-th device respectively; Set up energy storage device constraints as follows: In the formula, SOC i,min , SOC i,max , SOC i (0) are the upper and lower limits of energy storage of energy storage device i; (0) are the energy storage status of energy storage element at time t; Set up the ramp rate constraints for the energy conversion equipment as follows: |ΔP i |≤P i,climb Where ΔP i represents the output change of the i-th device; P i,climb is the equipment ramp rate; The power constraints for purchasing electricity, selling electricity, purchasing gas and selling gas between the system and the upper-level power grid and gas grid are as follows: In the formula, and are the maximum allowable power purchase and sales between the integrated energy system and the upper power grid, and They are the maximum permissible gas purchase and gas sales power between the integrated energy system and the upper-level gas grid; Constraints are set for the carbon flow density of the load-side electricity, cooling, gas, and heat loads as follows: 0<ρ o,el,t <p o,el,max 0<ρ o,cl,t <p o,cl,max 0<ρ o,hl,t <p o,hl,max 0<ρ o,gl,t <p o,gl,max In the formula, ρ o,el,max , o,cl,max , o,hl,max , o,gl,max are the upper limits of carbon flow density for electricity, cooling, heating and gas loads respectively; According to the system's carbon emission target, constraints are set on the carbon flow density of electricity, heat, cooling and gas loads as follows: In the formula, E k,max is the upper limit of carbon emissions of equipment k; Ω is the set of energy conversion equipment in the park; ρ k,t is the carbon flow density of the energy generated during the operation of equipment k; P k,t is the power of device k at time t.

12. The park integrated energy system scheduling optimization system based on energy-carbon coupling according to claim 8 is characterized in that: Based on the constraint conditions and the objective function, the energy-carbon coupling model is solved to obtain the park scheduling strategy, including: Set the particle swarm optimization algorithm parameters and randomly generate the initial population as the initial solution of the objective function; Calculate the fitness value of each solution according to the objective function, find the particle with the best fitness value in the entire particle swarm, and use its position and fitness value as the global optimal position and fitness value; According to the update rules, the particle speed and position are updated to obtain the optimal position; Until the set termination condition is reached, the global optimal position and fitness value are output to obtain the best scheduling strategy.

13. A computer storage medium, wherein the computer readable storage medium stores a computer program, characterized in that: When the computer program is executed by the processor, the steps of the method for optimizing the scheduling of a comprehensive energy system in a park based on energy-carbon coupling are implemented as described in any one of claims 1 to 6.

14. An electronic device, characterized in that: It includes a memory and a processor: the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the park integrated energy system scheduling optimization method based on energy-carbon coupling as described in any one of claims 1 to 6 are implemented.

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