Electrical coupling system seasonal configuration optimization method based on carbon transaction mechanism

Abstract

The invention provides an electrical coupling system seasonal configuration optimization method based on a carbon transaction mechanism, and relates to the technical field of energy systems, and the method comprises the steps: obtaining a mathematical model of a comprehensive energy system; a first objective function is obtained based on the annual investment cost, the electricity purchasing cost and the gas purchasing cost, carbon emission is obtained based on the carbon emission intensity and the gas purchasing power rate of the gas turbine and the gas boiler, and the total emission amount is obtained by weighting the carbon emission amount of the gas turbine and the gas boiler and the carbon emission amount of photovoltaic distribution; acquiring a second objective function based on the total emission amount and the carbon emission amount, and acquiring a total cost function based on the first objective function and the second objective function; and obtaining a constraint condition, and carrying out iterative optimization on the total cost function based on the constraint condition and the mathematical model to obtain an optimization result. By considering the complementarity of different systems in seasonal characteristics, a more accurate configuration result can be obtained, and the environmental cost and the economical efficiency achieve the optimal effect.

Description

Technical Field

[0001] The present invention relates to the technical field of energy systems, and in particular to a seasonal configuration optimization method for an electrical coupling system based on a carbon trading mechanism. Background Art

[0002] In recent years, with the continuous increase of extreme climate, the rational optimization of energy utilization is an important research issue to solve the future development of energy supply. The integrated energy system provides a solution to this problem, that is, the use of energy technologies such as gas turbines to ensure energy security and connect the power grid with the natural gas pipeline network. The integrated energy system planning is to achieve the purpose of efficient utilization through various energy conversion devices.

[0003] However, most of the existing integrated energy systems are planned, constructed and operated separately, with few ways for different forms of energy to operate together. In addition, in the early stages of planning, most of them basically use the lowest investment cost and operating cost targets as optimization options, and do not consider the role of environmental costs in the overall energy system planning.

[0004] Therefore, it is urgent to provide a solution to improve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a seasonal configuration optimization method for an electrical coupling system based on a carbon trading mechanism, so as to improve the problems in the above-mentioned background technology.

[0006] The present invention provides a seasonal configuration optimization method for an electrical coupling system based on a carbon trading mechanism, comprising the following steps:

[0007] Obtain the mathematical model of the integrated energy system based on the mathematical models of gas turbine equipment, gas boiler equipment, energy storage equipment, absorption chiller equipment, electric chiller equipment, cold storage equipment, solar power generation equipment, and P2G equipment;

[0008] The first objective function is obtained based on the annual investment cost, electricity purchase cost and gas purchase cost, the carbon emissions are obtained based on the carbon emission intensity of gas turbines and gas boilers and the gas purchase power, and the total emissions are obtained by weighting the carbon emissions of gas turbines and gas boilers and the carbon emissions of photovoltaic allocations;

[0009] Acquire a second objective function based on the total emissions and the carbon emissions, and acquire a total cost function based on the first objective function and the second objective function;

[0010] Constraints are obtained based on the gas turbine electric power limit, the gas boiler electric power limit, the electric storage limit of the electric storage device, and the heat storage limit of the heat storage device, and the total cost function is iteratively optimized based on the constraints and the mathematical model to obtain an optimization result.

[0011] The beneficial effect of the seasonal configuration optimization method of an electrical coupling system based on a carbon trading mechanism provided by the present invention is that, firstly, each energy supply device is modeled, and the characteristics and parameters of each energy supply device are analyzed. Considering the diversity of energy input, the model has better coordination and practicality; secondly, a multi-objective planning optimization model for an integrated energy system is established. The model adopts the output structure of each device to achieve the optimal effect of the environmental cost and economy of the system.

[0012] Optionally, the mathematical model of the gas turbine equipment includes an efficiency model, a power generation model, and a waste heat output power model, wherein:

[0013] The expression of the efficiency model is:

[0014] F G,i,t =λP G,i,t Δt / η Gi

[0015]

[0016]

[0017] Among them, F G,i,t is the gas consumption of the i-th gas generator at time t, P G,i,t is the power output of the i-th gas generator at time t; λ is the gas consumption coefficient; Δt is the time interval; η Gi is the comprehensive efficiency of the gas generator; η Gi.e , η Gi.h are electricity generation efficiency and thermal efficiency respectively; η Gi.en , η Gi.hn is the rated power generation efficiency and rated thermal efficiency; a i 、b i 、c i d i is the efficiency coefficient of the i-th gas generator; a j 、b j 、c j is the thermal efficiency coefficient of the gas generator; P′ G,i,t is the rated output power of the i-th gas generator at time t;

[0018] The expression of the power generation model is:

[0019] P GT =η ci. e·V g ·q g ;

[0020] Among them, η Gi.eis the power generation efficiency of the gas turbine; V g is the natural gas consumption rate; q g is the calorific value of natural gas;

[0021] The expression of the waste heat output power model is:

[0022] Q GT =η Gih ·V q ·q g ;

[0023] Among them, η Gi.h is the heating efficiency of the gas turbine.

[0024] Optionally, the mathematical model of the gas boiler equipment is:

[0025] Q gb =η gb ·V g ·q g ;

[0026] Among them, Q gb is the operating efficiency of the gas boiler; η gb is the heat generation efficiency of the gas boiler; V g is the natural gas consumption rate; q g The calorific value of natural gas;

[0027] The mathematical model of the P2G device is:

[0028] p gt =η p2g p et ;

[0029] Among them, p gt is the gas production power; η p2g is the gas production efficiency; p et is the power consumption;

[0030] The mathematical model of the absorption refrigeration equipment is:

[0031] Q ac =η ac p ac.h ;

[0032] Among them, Q ac is the output cooling power; ac is the refrigeration efficiency; p ac.h is the input thermal power;

[0033] The mathematical model of the electric refrigerator device is:

[0034]

[0035] in, is the cooling power output of the electric refrigerator; is the input electrical power; ec is the electric refrigeration efficiency;

[0036] The mathematical model of the energy storage device is:

[0037]

[0038] in, is the stored energy at time t; μ e is the self-discharge rate of the energy storage device; is the stored energy at time t-1; P in.e (t), P out.e (t) is the charging and discharging power of the power storage device; η ine , η out.e The charging and discharging efficiency of the power storage device;

[0039] The mathematical model of the cold storage equipment is:

[0040]

[0041] Among them, μ c is the cold storage energy loss rate; P is the cold storage energy in period t-1; in.c (t), P out.c (t) is the input and output cooling power of the cold storage equipment; η inc , η out.c Input and output cold power efficiency for cold storage equipment;

[0042] The mathematical model of the solar power generation equipment is:

[0043]

[0044] Among them, P PV The light intensity is G ING Output power at G STC is the light intensity under standard test conditions; P STC is the maximum output power of the solar power generation system under standard conditions; k is the power temperature coefficient; T c is the solar cell temperature; T r is the reference temperature.

[0045] Optionally, when obtaining the first objective function based on the annual investment cost, the electricity purchase cost and the gas purchase cost,

[0046] The mathematical expression of the first objective function is:

[0047] F J =C CCHP+C E +C G ;

[0048] Among them, C CCHP is the annual investment cost of the equipment; C E is the electricity purchase cost; C G F is the gas purchase cost; J is the first objective function;

[0049] The mathematical expression of the annual investment cost is:

[0050] C CCHP =∑ i∈Ωgt R(C inf.gt P eap.gt +C inf.gb P eap.gb +C inf.ec Q eap.ec +C inf.ac Q eap.ac );

[0051] Where Ωgt is the gas turbine, gas boiler unit, and refrigeration unit; R is the investment capital recovery rate; P eap.gt , P eap.gb are the rated power of gas turbine and gas boiler units respectively; Q eap.ec , Q eap.ac are the rated cooling power of the electric refrigerator and the absorption refrigerator respectively; C inf.gt , C inf.gb , C inf.ec , C inf.ac are the operating costs of the gas turbine, gas boiler, electric chiller, and absorption chiller at rated power, respectively;

[0052] The mathematical expression of the electricity purchase cost is:

[0053]

[0054] Among them, T is a system operation cycle; C e.t P is the power purchase price and power purchase power of the system in period t, e.t is the power purchased by the system during period t;

[0055] The gas purchase cost C G The mathematical expression is:

[0056]

[0057] Among them, C g P is the power purchase price and power purchase power of the system in period t, g.t is the gas purchasing power of the system during period t.

[0058] Optionally, the process of obtaining carbon emissions based on carbon emission intensity and gas purchase power of gas turbines and gas boilers includes:

[0059]

[0060] Among them, E G is the carbon emissions of gas turbines and gas boilers; g is the carbon emission intensity per unit active output of gas turbines and gas boilers; P Ωgt Purchase gas power for gas turbines and gas boilers.

[0061] Optionally, when the carbon emissions of the weighted gas turbines and gas boilers and the carbon emissions of the photovoltaic allocation are used to obtain the total emissions,

[0062] The carbon emission amount of the gas turbine and gas boiler is expressed as:

[0063]

[0064] Where D1 is the carbon emission amount allocated to gas turbines and gas boilers, η g is the carbon emission allocation coefficient per unit gas consumption of gas turbines and gas boilers; P Ωgt Purchase gas power for gas turbines and gas boilers;

[0065] The expression of the carbon emission amount allocated by photovoltaic power generation is:

[0066]

[0067] Among them, D2 is the carbon emission amount allocated for photovoltaics, η P is the carbon emission allocation coefficient of photovoltaic active power; P P Indicates the photovoltaic output active power;

[0068] The expression of the total emission is:

[0069] D = D1 + D2;

[0070] Where D is the total emissions.

[0071] Optionally, when the second objective function is obtained based on the total emissions and the carbon emissions, the expression of the second objective function is:

[0072] F H =P co2 (E G -D);

[0073] Among them, P co2 is the carbon trading price; F H is the second objective function; E Gis the carbon emission of gas turbines and gas boilers; D is the total emission.

[0074] Optionally, when a total cost function is obtained based on the first objective function and the second objective function, a mathematical expression of the total cost function is:

[0075] C z =min(F J -F H );

[0076] Among them, C z represents the total cost function, F J represents the first objective function, F H represents the second objective function.

[0077] Optionally, when the constraint conditions are obtained based on the gas turbine electric power limit, the gas boiler electric power limit, the electric storage limit of the electric storage device, and the heat storage limit of the heat storage device, the constraint conditions are composed of the output constraint conditions of each device and the power balance constraint conditions, wherein:

[0078] The output constraints of each device are:

[0079]

[0080] in, is the gas turbine electrical power limit; The electric power limit of gas boiler; The power storage limit for power storage equipment; Heat storage limit for heat storage equipment; P is the cold storage limit of cold storage equipment; gt , They represent the electric power value of the gas turbine, the electric power value of the gas boiler, the electric storage value of the electric storage device, the heat storage value of the heat storage device, and the cold storage value of the cold storage device at time t respectively;

[0081] The power balance constraints are:

[0082]

[0083] P gt =P gt.g +P p2g.g -P gb.g ;

[0084]

[0085] Among them, P e The amount of electrical load delivered by the system to the user side; Purchase electricity for the power grid; P gt.e is the power generation of the gas turbine; P pv.e is the photovoltaic power generation; P is the power consumed by the electric refrigerator; h The heat load delivered by the system to the user side; P is the heat of the gas turbine; gb.h Heating for gas boiler; P p2g.h The heat generated by the gas produced by the P2G equipment through the gas boiler; Q ac The heat consumed by the absorption refrigeration system; P gt P is the gas load delivered by the system to the user side; gt.g The direct gas supply from natural gas pipeline; P p2g.g P is the amount of natural gas synthesized by the P2G device; gb.g is the gas consumption of gas boiler; P c The cooling load delivered by the system to the user side; P is the cooling power output of the electric refrigerator; ac Output cooling power for absorption chiller.

[0086] Optionally, when the total cost function is iteratively optimized to obtain the optimization result, the model is solved by using the simulated annealing improved particle swarm algorithm, including:

[0087] Initialize the particle positions and velocities of the basic particle swarm, calculate the total cost function of each particle in the swarm, and update the particle velocity, individual extreme value and global extreme value based on the minimization of the total cost function;

[0088] Combine the simulated annealing algorithm to perform domain search, and update the individual extreme value of the simulated annealing algorithm particles according to the search results. The best choice is to update the global extreme value of the particles to determine whether the termination condition is met. If not, return to combine the simulated annealing algorithm to perform domain search. If the termination condition is met, directly output the optimal solution.

[0089] Among them, the expression for updating the speed of particles based on the total cost function minimization is:

[0090]

[0091] Among them, ω represents the inertia weight, c1 represents the first acceleration coefficient, c2 represents the second acceleration coefficient, r1 and r2 are random numbers between [0.1]. represents the best position experienced by the d-th dimension component of the position vector of particle i at time t, represents the d-th dimension of the position vector of particle i at time t, represents the d-th dimension component of the best position experienced by the particle swarm in the solution space at time t, represents the d-th dimension of the velocity vector of particle i at time t+1, Represents the d-th dimension of the flight velocity vector of particle i at time t. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 It is a flow chart of a seasonal configuration optimization method of an electrical coupling system based on a carbon trading mechanism provided by the present invention;

[0093] Figure 2 It is a basic network architecture diagram of the electrical coupling integrated energy system provided by the present invention;

[0094] Figure 3 The present invention provides a simulated annealing improved particle swarm optimization solution flow chart. DETAILED DESCRIPTION

[0095] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be understood by people with general skills in the field to which the present invention belongs. "Including" and similar words used in this article mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0096] The embodiment of the present invention provides a seasonal configuration optimization method for an electrical coupling system based on a carbon trading mechanism, see Figure 1 , including the following steps:

[0097] S1. Obtain the mathematical model of the integrated energy system based on the mathematical models of gas turbine equipment, gas boiler equipment, energy storage equipment, absorption refrigeration equipment, electric refrigeration equipment, cold storage equipment, solar power generation equipment, and P2G equipment;

[0098] S2. Obtain the first objective function based on the annual investment cost, electricity purchase cost and gas purchase cost, obtain the carbon emissions based on the carbon emission intensity of gas turbines and gas boilers and the gas purchase power, and obtain the total emissions by weighting the carbon emissions of gas turbines and gas boilers and the carbon emissions of photovoltaic allocations;

[0099] S3, obtaining a second objective function based on the total emissions and the carbon emissions, and obtaining a total cost function based on the first objective function and the second objective function;

[0100] S4. Obtain constraint conditions based on the gas turbine electric power limit, the gas boiler electric power limit, the electric storage limit of the electric storage device, and the heat storage limit of the heat storage device, and iteratively optimize the total cost function based on the constraint conditions and the mathematical model to obtain an optimization result.

[0101] In some embodiments, when executing step S1, refer to Figure 2 , establish mathematical models of gas-fired power generation equipment, power-to-gas devices, gas-fired boiler equipment, energy storage equipment, refrigeration equipment, and solar power generation equipment. The mathematical models of gas turbine equipment include efficiency models, power generation models, and waste heat output power models.

[0102] The expression of the efficiency model is:

[0103] F G,i,t =λP G,t Δt / η Gi

[0104]

[0105] Among them, F G,i,t is the gas consumption of the i-th gas generator at time t, P G,i,t is the power output of the i-th gas generator at time t; λ is the gas consumption coefficient; Δt is the time interval; η Gi is the comprehensive efficiency of the gas generator; η Gi.e , η Gi.h are electricity generation efficiency and thermal efficiency respectively; η Gi.en , η Gi.hn is the rated power generation efficiency and rated thermal efficiency; a i 、b i 、c i d i is the efficiency coefficient of the i-th gas generator; a j 、b j 、c j is the thermal efficiency coefficient of the gas generator; P′ G，i,t is the rated output power of the i-th gas generator at time t;

[0106] The expression of the power generation model is:

[0107] P GT =η Gi.e ·V g ·q g ;

[0108] Among them, η Gi.e is the power generation efficiency of the gas turbine; V g is the natural gas consumption rate; q g is the calorific value of natural gas;

[0109] The expression of the waste heat output power model is:

[0110] Q GT =η Gi.h ·V g ·q g ;

[0111] Among them, η Gi.h is the heating efficiency of the gas turbine.

[0112] In some embodiments, when executing step S1, the mathematical model of the gas boiler equipment is:

[0113] Q gb =η gb ·V g ·q g ;

[0114] Among them, Q gb is the operating efficiency of the gas boiler; V g is the natural gas consumption rate; q g The calorific value of natural gas;

[0115] The mathematical model of the P2G device is:

[0116] p gt =η p2g p et ;

[0117] Among them, p gt is the gas production power; η p2g is the gas production efficiency; p et is the power consumption;

[0118] The mathematical model of the absorption refrigeration equipment is:

[0119] Q ac =η ac p ac.h ;

[0120] Among them, Q ac is the output cooling power; ac is the refrigeration efficiency; p ac.h is the input thermal power;

[0121] The mathematical model of the electric refrigerator device is:

[0122]

[0123] in, is the cooling power output of the electric refrigerator; is the input electrical power; ec is the electric refrigeration efficiency;

[0124] The mathematical model of the energy storage device is:

[0125]

[0126] Among them, μ e is the self-discharge rate of the energy storage device; at t-1, P is the charging and discharging energy of the segment; in.e (t), P out.e (t) is the charging and discharging power of the power storage device; η ine , η out.e The charging and discharging efficiency of the power storage device;

[0127] The mathematical model of the cold storage equipment is:

[0128]

[0129] Among them, μ c is the cold storage energy loss rate; P is the cold storage energy in period t-1; in.c (t), P out.c (t) Input and output cooling power for cold storage equipment;

[0130] The mathematical model of the solar power generation equipment is:

[0131]

[0132] Among them, P PV The light intensity is G ING The output power at STC is the maximum output power of the solar power generation system under standard conditions; k is the power temperature coefficient; T c is the solar cell temperature; T r is the reference temperature.

[0133] In fact, when executing step S1 to obtain the mathematical model of the integrated energy system, the basic working process of the integrated energy system is: first, the energy generated by the new energy equipment is consumed. If there is excess energy, it is stored in the energy storage device; if the energy generated by the new energy equipment does not meet the requirements of the system, the energy in the energy storage device is used; if it still cannot meet the user's requirements, internal combustion engine power generation, power purchase from the power grid or supplementary combustion from a supplementary boiler will be considered.

[0134] In some embodiments, when executing step S2 to obtain the first objective function based on the annual investment cost, the electricity purchase cost and the gas purchase cost, the mathematical expression of the first objective function is:

[0135] F J =C CCHP +C E +CG ;

[0136] Among them, C CCHP is the annual investment cost of the equipment; C E is the electricity purchase cost; C G is the gas purchase cost, F J is the first objective function;

[0137] The mathematical expression of the annual investment cost is:

[0138] C CCHP =Σ i∈Ωgt R(C inf.gt P eap.gt +C inf.gb P eap.gb +C inf.ec Q eap.ec +C inf.ac Q eap.ac );

[0139] Where Ωgt is the gas turbine, gas boiler unit, and refrigeration unit; R is the investment capital recovery rate; P eap.gt Yes, P eap.gb are the rated power of gas turbine and gas boiler units respectively; Q eap.ec Yes, Q eap.ac are the rated cooling power of the electric refrigerator and absorption refrigerator respectively; C inf.gt Yes, C inf.gb Yes, C inf.ec Yes, C inf.ac are the operating costs of the rated powers of the gas turbine, gas boiler, electric chiller, and absorption chiller respectively;

[0140] R is the investment capital recovery rate; P eap.gt , P eap.gb are the rated power of gas turbine and gas boiler units respectively; Q eap.ec , Q eap.ac are the rated cooling power of the electric refrigerator and the absorption refrigerator respectively; C inf.gt , C inf.gb , C inf.ec , C inf.ac are the operating costs of the gas turbine, gas boiler, electric chiller, and absorption chiller at rated power, respectively;

[0141] The mathematical expression of the electricity purchase cost is:

[0142]

[0143] Among them, T is a system operation cycle; C e.t P is the power purchase price and power purchase power of the system in period t, e.tis the power purchased by the system during period t;

[0144] The gas purchase cost C G The mathematical expression is:

[0145]

[0146] Among them, C g P is the power purchase price and power purchase power of the system in period t, g.t is the gas purchasing power of the system during period t.

[0147] In some embodiments, the process of obtaining carbon emissions based on the carbon emission intensity and gas purchase power of gas turbines and gas boilers in step S2 includes:

[0148]

[0149] Among them, E G is the carbon emissions of gas turbines and gas boilers; g is the carbon emission intensity per unit active output of gas turbines and gas boilers; P Ωgt Purchase gas power for gas turbines and gas boilers.

[0150] In fact, in the traditional single power system, the initial allocation method of carbon emission rights is based on the baseline method, in which carbon emissions are proportional to active power, and the carbon trading model of CCER (China Certified Emission Reduction) is incorporated into carbon benefits.

[0151] In some embodiments, when performing step S2 to weight the carbon emissions of gas turbines and gas boilers and the carbon emissions of photovoltaic allocations to obtain the total emissions,

[0152] The carbon emission amount of the gas turbine and gas boiler is expressed as:

[0153]

[0154] Where D1 is the carbon emission amount allocated to gas turbines and gas boilers, η g The carbon emission allocation coefficient per unit gas consumption power of gas turbine and gas boiler;

[0155] The expression of the carbon emission amount allocated by photovoltaic power generation is:

[0156]

[0157] Among them, D2 is the carbon emission amount allocated for photovoltaics, η P is the carbon emission allocation coefficient of photovoltaic active power; P P Indicates the photovoltaic output active power;

[0158] The expression of the total emission is:

[0159] D = D1 + D2;

[0160] Where D is the total emissions.

[0161] In some embodiments, when executing step S3 to obtain a second objective function based on the total emissions and the carbon emissions, the expression of the second objective function is:

[0162] FH=P co2 (E G -D);

[0163] Among them, P co2 is the carbon trading price; F H is the second objective function; E G is the carbon emission of gas turbines and gas boilers; D is the total emission.

[0164] Specifically, the present invention takes the average transaction price of 50 yuan / ton in the past 7 working days based on the carbon trading price of Shanghai Environment and Energy Exchange.

[0165] In fact, when the carbon emissions of carbon-emitting equipment exceed their share of solar power generation, carbon emission fees will be incurred; if it is less than the allocated amount, it will bring carbon dioxide benefits. If the solar power generation capacity reaches a certain level, it can generate considerable carbon income, thereby offsetting the operating expenses of gas turbines and gas boilers, and promoting the healthy development of clean energy companies.

[0166] In some embodiments, when executing step S3 to obtain a total cost function based on the first objective function and the second objective function, the mathematical expression of the total cost function is:

[0167] C z =min(F J -F H );

[0168] Among them, C z represents the total cost function, F J represents the first objective function, F H represents the second objective function.

[0169] In some embodiments, when executing step S4, the constraint conditions are obtained based on the gas turbine electric power limit, the gas boiler electric power limit, the electric storage limit of the electric storage device, and the heat storage limit of the heat storage device, the constraint conditions are composed of the output constraint conditions of each device and the power balance constraint conditions, wherein:

[0170] The output constraints of each device are:

[0171]

[0172] in, is the gas turbine electrical power limit; The electric power limit of gas boiler; The power storage limit for power storage equipment; Heat storage limit for heat storage equipment; P is the cold storage limit of cold storage equipment; gt , They represent the electric power value of the gas turbine, the electric power value of the gas boiler, the electric storage value of the electric storage device, the heat storage value of the heat storage device, and the cold storage value of the cold storage device at time t respectively;

[0173] The power balance constraints are:

[0174]

[0175] P gt =P gt.g +P p2g.g -P gb.g ;

[0176]

[0177] Among them, P e The amount of electrical load delivered by the system to the user side; Purchase electricity for the power grid; P gt.e is the power generation of the gas turbine; P pv.e is the photovoltaic power generation; P is the power consumed by the electric refrigerator; h The heat load delivered by the system to the user side; P is the heat of the gas turbine; gb.h Heating for gas boiler; P p2g.h The heat generated by the gas produced by the P2G equipment through the gas boiler; Q ac The heat consumed by the absorption refrigeration system; P gt P is the gas load delivered by the system to the user side; gt.g The direct gas supply from natural gas pipeline; P p2g.g P is the amount of natural gas synthesized by the P2G device; gb.g is the gas consumption of gas boiler; P c The cooling load delivered by the system to the user side; P is the cooling power output of the electric refrigerator; ac Output cooling power for absorption chiller.

[0178] In some embodiments, when executing step S4 to iteratively optimize the total cost function to obtain the optimization result, the model is solved by using the simulated annealing improved particle swarm algorithm, including:

[0179] S4-1, randomly initialize the positions and velocities of particles in the elementary particle group;

[0180] S4-2, set the initial temperature t0 = f max -f min , f max and f min are the maximum and minimum fitness of the initial population respectively;

[0181] S4-3, set the particle's pBest to the current position, and set gBest to the position of the best particle in the particle swarm;

[0182] S4-4, determine whether the algorithm convergence criterion is met. If so, go to S4-11; otherwise, go to the next step S4-5;

[0183] S4-5. Update the particle's velocity and position based on the minimization of the total cost function. The expression is:

[0184]

[0185] Among them, ω represents the inertia weight, c1 represents the first acceleration coefficient, c2 represents the second acceleration coefficient, r1 and r2 are random numbers between [0.1]. represents the best position experienced by the d-th dimension component of the position vector of particle i at time t, represents the d-th dimension of the position vector of particle i at time t, represents the d-th dimension of the position vector of particle i at time t+1; represents the d-th dimension component of the best position experienced by the particle swarm in the solution space at time t, represents the d-th dimension of the velocity vector of particle i at time t+1, Represents the d-th dimension of the flight velocity vector of particle i at time t.

[0186] S4-6, if the fitness of the particle is better than the fitness of pBest, pBest is set as the current position; otherwise, execute the next step; if the fitness of the particle is better than the fitness of gBest, gBest is set as the current position; otherwise, execute the next step S4-7;

[0187] S4-7, randomly generate a new position for the particle, and calculate the fitness difference △C between the new and old positions;

[0188] S4-8, if △C<0, the particle enters a new position; otherwise, execute the next step S4-9;

[0189] S4-9. Generate a random number r between (0, 1). If r < min[1, exp(-ΔC / t)], the particle enters the new position and S4-6 is executed again.

[0190] S4-10. Perform the annealing operation:

[0191] T = 0.9t;

[0192] S4-11. Determine whether the algorithm convergence criterion is satisfied. If satisfied, execute S4-12; otherwise, go to S4-5.

[0193] S4-12. Output gBest and the algorithm runs to an end.

[0194] Specifically, when performing step S4 to iteratively optimize the total cost function based on the constraint conditions and the mathematical model to obtain the optimization result, three typical working days of the cooling, heating, and power load demands and the photovoltaic power generation in the target area in spring and autumn, summer, and winter are used as representatives to simulate the load demand and power supply data. At the same time, the investment and operation costs of the system and the carbon trading costs on an annual basis are analyzed to determine the electricity purchase, sale prices, and natural gas prices in the power market. Through the example simulation, the optimal configuration of the system's energy supply equipment and energy transmission lines is obtained to obtain the optimization result.

[0195] Actually, the load differences between winter and summer are the most obvious. In summer, there is almost no need for heat energy supply, while in winter, the demand for cold energy is less. The loads in spring and autumn are relatively stable, being between winter and summer. Therefore, the target area is divided into three typical scenarios: spring and autumn, summer, and winter, and typical days in each scenario are selected for operation simulation.

[0196] In some embodiments, refer to Figure 2 , the P2G device is a technology that can convert electrical energy into natural gas. This technology is generally called P2G (power-to-gas, P2G). The P2G device can use CO2 to synthesize methane, and the methane enters the natural gas network and is converted into electricity and heat through a gas turbine. The electrical energy directly supplies energy to the user side through the power grid to jointly meet the user's electrical load demand with the power network. The heat generated by the gas turbine power generation is transferred to the user side through the waste heat recovery system and the gas boiler through the heat network to meet the user's heat load demand, and the remaining heat is converted into cold energy by an absorption chiller, and the electrical chiller converts electrical energy and cold energy into cold energy together to meet the user's requirement for cold. Using the P2G conversion technology, renewable clean energy, such as solar energy, is converted into natural gas, thus realizing the energy coupling between the power grid and the natural gas network.

[0197] In some embodiments, refer to Figure 3First, the particle position and velocity of the basic particle swarm are initialized, the objective function of each particle in the swarm is calculated (i.e., the total cost function, which is obtained by combining the first objective function and the second objective function), and the individual extreme value and the global extreme value of the particle are updated based on the minimization of the objective function. The simulated annealing algorithm is combined to perform a domain search, and the individual extreme value of the simulated annealing algorithm particle is updated according to the search results. The global extreme value of the particle is updated optimally, and it is judged whether the termination condition is met. If not, it returns to the simulated annealing algorithm for domain search. If the termination condition is met, the optimal solution is directly output.

[0198] Although the embodiments of the present invention are described in detail above, it is obvious to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as described in the claims. Moreover, the present invention described herein may have other embodiments and may be implemented or realized in a variety of ways.

Claims

1. A seasonal configuration optimization method for electrical coupling system based on carbon trading mechanism, characterized in that: include: Obtain the mathematical model of the integrated energy system based on the mathematical models of gas turbine equipment, gas boiler equipment, energy storage equipment, absorption chiller equipment, electric chiller equipment, cold storage equipment, solar power generation equipment, and P2G equipment; The first objective function is obtained based on the annual investment cost, electricity purchase cost and gas purchase cost. The carbon emissions are obtained based on the carbon emission intensity of gas turbines and gas boilers and the gas purchase power. The carbon emissions of gas turbines and gas boilers and the carbon emissions of photovoltaic allocation are weighted to obtain the total emissions. Acquire a second objective function based on the total emissions and the carbon emissions, and acquire a total cost function based on the first objective function and the second objective function; Constraints are obtained based on the gas turbine electric power limit, the gas boiler electric power limit, the electric storage limit of the electric storage device, and the heat storage limit of the heat storage device, and the total cost function is iteratively optimized based on the constraints and the mathematical model to obtain an optimization result.

2. The seasonal configuration optimization method of an electrical coupling system based on a carbon trading mechanism according to claim 1 is characterized in that: The mathematical model of gas turbine equipment includes efficiency model, power generation model and waste heat output power model, among which: The expression of the efficiency model is: F G，i，t =λP G，i，t Δt / h Gi Among them, F G，i，t is the gas consumption of the i-th gas generator at time t, P G，i，t is the power output of the i-th gas generator at time t; λ is the gas consumption coefficient; Δt is the time interval; η Gi is the comprehensive efficiency of the gas generator; η Gi.e , η Gi.h are electricity generation efficiency and thermal efficiency respectively; η Gi.en , η Gi.hn is the rated power generation efficiency and rated thermal efficiency; a i , b i 、c i d i is the efficiency coefficient of the i-th gas generator; a j , b j 、c j is the thermal efficiency coefficient of the gas generator; P′ G，i，t is the rated output power of the i-th gas generator at time t; The expression of the power generation model is: P GT =η Gi.e ·V g ·q g ; Among them, η Gi.e is the power generation efficiency of the gas turbine; V g is the natural gas consumption rate; q g is the calorific value of natural gas; The expression of the waste heat output power model is: Q GT =the Gi.h ·V g ·g g ; Among them, η Gi.h is the heating efficiency of the gas turbine.

3. The seasonal configuration optimization method of an electrical coupling system based on a carbon trading mechanism according to claim 1 is characterized in that: The mathematical model of the gas boiler equipment is: Q gb =η gb ·V g ·q g ; Among them, Q gb is the operating efficiency of the gas boiler; η gb is the heat generation efficiency of the gas boiler; V g is the natural gas consumption rate; q g The calorific value of natural gas; The mathematical model of the P2G device is: p gt Hη p2g p et 100. Among them, p gt is the gas production power; η p2g is the gas production efficiency; p et is the power consumption; The mathematical model of the absorption refrigeration equipment is: Q ac =η ac p ac.h ; Among them, Q ac is the output cooling power; ac is the refrigeration efficiency; p ac.h is the input thermal power; The mathematical model of the electric refrigerator device is: in, is the cooling power output of the electric refrigerator; is the input electrical power; ec is the efficiency of electric refrigeration; The mathematical model of the energy storage device is: in, is the stored energy at time t; μ e is the self-discharge rate of the energy storage device; is the stored energy at time t-1; P in.e (t), P out.e (t) is the charging and discharging power of the power storage device; η ine , η out.e The charging and discharging efficiency of the power storage device; The mathematical model of the cold storage equipment is: Among them, μ c is the cold storage energy loss rate; P is the cold storage energy in period t-1; in.c (t), P out.c (t) is the input and output cooling power of the cold storage equipment; η inc , η out.c Input and output cold power efficiency for cold storage equipment; The mathematical model of the solar power generation equipment is: Among them, P PV The light intensity is G ING Output power at G STC is the light intensity under standard test conditions; P STC is the maximum output power of the solar power generation system under standard conditions; k is the power temperature coefficient; T c is the solar cell temperature; T r is the reference temperature.

4. The seasonal configuration optimization method of an electrical coupling system based on a carbon trading mechanism according to claim 1 is characterized in that: When the first objective function is obtained based on the annual investment cost, electricity purchase cost and gas purchase cost, The mathematical expression of the first objective function is: F J =C CCHP +C E +C G ; Among them, C CCHP is the annual investment cost of the equipment; C E is the electricity purchase cost; C G F is the gas purchase cost; J is the first objective function; The mathematical expression of the annual investment cost is: C CCHP =∑ i∈Ωgt R(C inf.gt P eap.gt +C inf.gb P eap.gb +C inf.ec Q eap.ec +C inf.ac Q eap.ac ); Where Ωgt is the gas turbine, gas boiler unit, and refrigeration unit; R is the investment capital recovery rate; P eap.gt , P eap.gb are the rated power of gas turbine and gas boiler units respectively; Q eap.ec , Q eap.ac are the rated cooling power of the electric refrigerator and the absorption refrigerator respectively; C inf.gt , C inf.gb , C inf，ec , C inf.ac are the operating costs of the gas turbine, gas boiler, electric chiller, and absorption chiller at rated power, respectively; The mathematical expression of the electricity purchase cost is: Among them, T is a system operation cycle; C e.t P is the power purchase price and power purchase power of the system in period t, e.t is the power purchased by the system during period t; The gas purchase cost C G The mathematical expression is: Among them, C g P is the power purchase price and power purchase power of the system in period t, g.t is the gas purchasing power of the system during period t.

5. The seasonal configuration optimization method of an electrical coupling system based on a carbon trading mechanism according to claim 1 is characterized in that: The process of obtaining carbon emissions based on carbon emission intensity and gas purchase power of gas turbines and gas boilers includes: Among them, E G is the carbon emissions of gas turbines and gas boilers; δ g P is the carbon emission intensity per unit active output of gas turbines and gas boilers; Ωgt Purchase gas power for gas turbines and gas boilers.

6. The seasonal configuration optimization method of an electrical coupling system based on a carbon trading mechanism according to claim 1 is characterized in that: When the carbon emissions of the weighted gas turbines and gas boilers and the carbon emissions of the photovoltaic allocation are used to obtain the total emissions, The carbon emission amount of the gas turbine and gas boiler is expressed as: Among them, D1 is the carbon emission amount allocated to gas turbines and gas boilers, η g P is the carbon emission allocation coefficient per unit gas consumption of gas turbine and gas boiler; Ωgt Purchasing gas power for gas turbines and gas boilers; The expression of the carbon emission amount allocated by photovoltaic power generation is: Among them, D2 is the carbon emission amount allocated by photovoltaic power generation, η P is the carbon emission allocation coefficient of photovoltaic active power; P P Indicates the photovoltaic output active power; The expression of the total emission is: D = D1 + D2; Where D is the total emissions.

7. The seasonal configuration optimization method of an electrical coupling system based on a carbon trading mechanism according to claim 1 is characterized in that: When the second objective function is obtained based on the total emissions and the carbon emissions, the expression of the second objective function is: F H =P co2 (E G -D); Among them, P co2 is the carbon trading price; F H is the second objective function; E G is the carbon emission of gas turbines and gas boilers; D is the total emission.

8. The seasonal configuration optimization method of an electrical coupling system based on a carbon trading mechanism according to claim 1 is characterized in that: When the total cost function is obtained based on the first objective function and the second objective function, the mathematical expression of the total cost function is: C z =min(F J -F H ); Among them, C z represents the total cost function, F J represents the first objective function, F H represents the second objective function.

9. The seasonal configuration optimization method of an electrical coupling system based on a carbon trading mechanism according to claim 1 is characterized in that: When the constraint conditions are obtained based on the gas turbine electric power limit, the gas boiler electric power limit, the electric storage limit of the electric storage device, and the heat storage limit of the heat storage device, the constraint conditions are composed of the output constraint conditions of each device and the power balance constraint conditions, wherein: The output constraints of each device are: in, is the gas turbine electrical power limit; The electric power limit of gas boiler; The power storage limit for power storage equipment; Heat storage limit for heat storage equipment; P is the cold storage limit of cold storage equipment; gt , They represent the electric power value of the gas turbine, the electric power value of the gas boiler, the electric storage value of the electric storage device, the heat storage value of the heat storage device, and the cold storage value of the cold storage device at time t respectively; The power balance constraints are: P gt =P gt.g +P p2g.g -P gb.g ; Among them, P e The amount of electrical load delivered by the system to the user side; Purchase electricity for the power grid; P gt.e is the power generation of the gas turbine; P pv.e is the photovoltaic power generation; P is the power consumed by the electric refrigerator; h The heat load delivered by the system to the user side; P is the heat of the gas turbine; gb.h Heating for gas boiler; P p2g.h The heat generated by the gas produced by the P2G equipment through the gas boiler; Q ac The heat consumed by the absorption refrigeration system; P gt P is the gas load delivered by the system to the user side; gt.g The direct gas supply from natural gas pipeline; P p2g.g P is the amount of natural gas synthesized by the P2G device; gb.g is the gas consumption of gas boiler; P c The cooling load delivered by the system to the user side; P is the cooling power output of the electric refrigerator; ac Output cooling power for absorption chiller.

10. The seasonal configuration optimization method of electrical coupling system based on carbon trading mechanism according to claim 1 is characterized in that: When the total cost function is iterated to obtain the optimization result, the simulated annealing improved particle swarm algorithm is used to solve the model, including: Initialize the particle positions and velocities of the basic particle swarm, calculate the total cost function of each particle in the swarm, and update the particle velocity, individual extreme value and global extreme value based on the minimization of the total cost function; Combine the simulated annealing algorithm to perform domain search, and update the individual extreme value of the simulated annealing algorithm particles according to the search results. The best choice is to update the global extreme value of the particles to determine whether the termination condition is met. If not, return to combine the simulated annealing algorithm to perform domain search. If the termination condition is met, directly output the optimal solution. Among them, the expression for updating the speed of particles based on the total cost function minimization is: Among them, ω represents the inertia weight, c1 represents the first acceleration coefficient, c2 represents the second acceleration coefficient, r1 and r2 are random numbers between [0.1]. represents the best position experienced by the d-th dimension component of the position vector of particle i at time t, represents the d-th dimension of the position vector of particle i at time t, represents the d-th dimension component of the best position experienced by the particle swarm in the solution space at time t, represents the d-th dimension of the velocity vector of particle i at time t+1, Represents the d-th dimension of the flight velocity vector of particle i at time t.

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