Multi-park collaborative integrated energy system optimization method considering stepped carbon transaction

Abstract

The invention relates to the field of comprehensive energy system optimization, in particular to a multi-park collaborative comprehensive energy system optimization method considering stepped carbon transactions. The method comprises the following steps: firstly, acquiring multi-source data including energy prices, carbon transaction rules, equipment parameters and park interaction limits; constructing a target function with the purpose of minimizing the total operation cost of the system, wherein the target function covers the energy purchasing cost, the stepped carbon transaction cost and the energy abandoning punishment cost; designing composite constraint conditions of energy balance, node constraint, multi-energy conversion, energy storage dynamics, carbon emission segmented pricing and inter-park energy interaction; and finally, solving and generating a multi-park cooperative operation scheme through a state optimization algorithm. According to the invention, through integration of a stepped carbon transaction mechanism and multi-park dynamic energy flow allocation, the problems of extensive carbon cost accounting and insufficient consideration of an inter-park energy complementation strategy in an existing method are solved, and collaborative optimization of economical efficiency and low-carbon performance is realized.

Description

Technical Field

[0001] This invention relates to the field of integrated energy operation optimization in industrial parks, and specifically to an optimization method for a multi-park collaborative integrated energy system that considers tiered carbon trading. Background Technology

[0002] Current integrated energy system optimization methods mostly focus on the internal scheduling of a single industrial park, failing to adequately support the coordinated operation of multiple parks and thus not fully realizing the potential for regional energy complementarity. Existing carbon trading models often employ fixed carbon price mechanisms, which are ill-suited to the tiered emission control requirements of high-emission scenarios and cannot effectively incentivize deep emission reductions. Furthermore, traditional methods lack refined modeling of the dynamic interactions of multiple energy flows such as electricity, heat, and hydrogen across industrial parks, limiting the overall flexibility and economic efficiency of the system. There is an urgent need for a collaborative optimization method that balances the segmented cost characteristics of tiered carbon trading with the energy mutual support capabilities of multiple industrial parks to improve system operational efficiency and low-carbon levels. Summary of the Invention

[0003] To address the shortcomings of existing optimization methods for integrated energy systems in industrial parks, such as inefficient carbon cost accounting and insufficient consideration of inter-park energy complementarity strategies, a new optimization method for multi-park collaborative integrated energy systems that considers tiered carbon trading is proposed. This method is characterized by the following steps:

[0004] S1: Acquire data from the multi-park integrated energy system and establish a set of known parameters;

[0005] The known parameter set includes energy price parameters, carbon trading parameters, penalty cost parameters, node parameters, combined heat and power unit parameters, gas boiler unit parameters, electrolyzer unit parameters, methane reactor unit parameters, hydrogen fuel cell unit parameters, gas turbine unit parameters, energy storage system parameters, carbon emission parameters, maximum interaction volume of the park, wind power generation capacity limit, photovoltaic power generation capacity limit, maximum gas purchase volume of the park, and electricity demand.

[0006] S2: Construct the objective function of an optimization model for a multi-park collaborative integrated energy system considering tiered carbon trading;

[0007] The objective function is the total operating cost of the system, including energy purchase cost, carbon emission cost, and energy curtailment penalty cost, and its expression is:

[0008]

[0009] In the formula, F represents the operating cost of system a; Energy purchase cost for system a; For the carbon emission cost of system a; The cost of energy forfeiture penalty for system a;

[0010] S3: Design constraints for a multi-park collaborative integrated energy system optimization model considering tiered carbon trading;

[0011] The constraints include balance constraints, node constraints, energy conversion constraints, energy storage constraints, carbon emission constraints, and energy interaction constraints.

[0012] S4: Use state optimization algorithms to solve the optimization model of the multi-park collaborative integrated energy system considering tiered carbon trading, and generate the operation scheme of the multi-park collaborative integrated energy system.

[0013] Preferably, the energy price parameters mentioned in step S1 include electricity purchase price and gas purchase price; carbon trading parameters include carbon trading base price, carbon emission range length and price growth rate; penalty cost parameters include unit penalty cost for wind curtailment and unit penalty cost for solar curtailment; node parameters include maximum active power transmitted by transmission lines, minimum active power transmitted by transmission lines, maximum pressure of natural gas pipeline network nodes, minimum pressure of natural gas pipeline network nodes, and transmission coefficient of natural gas pipeline network nodes.

[0014] The parameters of a combined heat and power (CHP) unit include the heat conversion efficiency, the electrical conversion efficiency, the upper limit of natural gas input, the lower limit of natural gas input, the upper limit of ramp rate, the lower limit of ramp rate, the upper limit of heat-to-power ratio, and the lower limit of heat-to-power ratio.

[0015] Gas-fired boiler unit parameters include gas-fired boiler conversion efficiency, upper limit of gas consumption, and lower limit of gas consumption.

[0016] Electrolytic cell unit parameters include electrolytic cell unit conversion efficiency, upper limit of electrolytic cell unit electrical energy input, lower limit of electrolytic cell unit electrical energy input, upper limit of electrolytic cell unit ramp rate, and lower limit of electrolytic cell unit ramp rate;

[0017] The parameters of the methane reactor unit include the methane reactor conversion efficiency, the upper limit of hydrogen input to the methane reactor, the lower limit of hydrogen input to the methane reactor, the upper limit of ramp-up to the methane reactor, and the lower limit of ramp-up to the methane reactor.

[0018] The parameters of a hydrogen fuel cell unit include the hydrogen fuel cell conversion efficiency, the upper limit of hydrogen input to the hydrogen fuel cell, the lower limit of hydrogen input to the hydrogen fuel cell, the upper limit of ramp rate, the lower limit of ramp rate, the upper limit of the thermoelectric ratio, and the lower limit of the thermoelectric ratio.

[0019] Gas turbine unit parameters include upper limit of gas turbine power generation, lower limit of gas turbine power generation, upper limit of gas turbine ramp rate, and lower limit of gas turbine ramp rate.

[0020] The parameters of an energy storage system include the maximum power of a single charge of the energy storage device, the maximum power of a single discharge of the energy storage device, the charging efficiency of the energy storage device, the discharge efficiency of the energy storage device, the upper limit of the capacity of the energy storage device, and the lower limit of the capacity of the energy storage device.

[0021] Carbon emission parameters include carbon emission allowances per unit of electricity purchase, carbon emission allowances per unit of natural gas consumption, carbon emission calculation parameters for electricity purchase, carbon emission calculation parameters for gas-fired boilers and combined heat and power units, and carbon emission calculation parameters for gas turbines.

[0022] Preferably, the energy purchase cost expression in step S2 is:

[0023]

[0024] In the formula, α t,a Let β be the electricity purchase price at time t for system a; t,a Let a be the gas purchase price at time t for system a; Purchase electricity for system at time a and time t; Let a be the gas purchase amount at time t for system a;

[0025] The carbon emission cost expression is:

[0026]

[0027] In the formula, λ is the base price for carbon trading; l is the length of the carbon emission range; and α is the price growth rate. Let a be the carbon emission trading amount at time t in system a;

[0028] The expression for the cost of energy curtailment penalty is:

[0029]

[0030] In the formula, The unit penalty cost for wind curtailment in system a; The penalty cost per unit of light wasted for system a; Let the amount of air curtailed be system a at time t; Let be the amount of light discarded by system a at time t.

[0031] Preferably, the balance constraints mentioned in step S3 include natural gas quantity balance constraints, electrical power balance constraints, thermal power balance constraints, and hydrogen energy power balance constraints.

[0032] The expression for the natural gas supply balance constraint is:

[0033]

[0034] In the formula, Let a be the gas injection rate of the energy storage device at time t in system a; Let a be the gas extraction rate of the energy storage device at time t in system a; Let a be the amount of gas produced by the methane reactor at time t. Let a be the natural gas demand of users at time t in system a; Let a be the gas consumption of the gas boiler in system a at time t; Let a be the gas consumption of the gas boiler at time t in system a; Let a be the maximum gas purchase volume at time t for system a; Let a be the gas consumption of the combined heat and power (CHP) system at time t.

[0035] The expression for the power balance constraint is:

[0036]

[0037] In the formula, Let a be the power supply of the wind turbine at time t in system a; Let a be the power supply of the cogeneration unit at time t in system a; Let be the power supplied by the hydrogen fuel cell at time a and t. Let be the power generation capacity of the gas turbine at time a and t in the system; Let a be the charging power of the energy storage device at time a and t. Let a be the discharge power of the energy storage device at time a and t. Let be the power consumed by the electrolytic cell at time a and t in the system; Let the system be the user's power demand at time a and t; Let the power of photoelectric generation of system a at time t be denoted as ; Let the wind power generation capacity be the system's output at time a and t. Let be the power generation capacity of the gas turbine at time a and t in the system;

[0038] The expression for the thermal power balance constraint is:

[0039]

[0040] In the formula, Let the system be the heat load at time a and t. Heat is injected into the energy storage device at time t for system a; Let the heat released by the energy storage device at time t be the heat of system a. Let the combined heat and power unit output thermal power at time a and t be the system. Let a be the thermal power of the cogeneration unit at time t. Let be the thermal power of the gas-fired boiler at time a and t in the system; Let the system be the heat load at time a and t.

[0041] The hydrogen power balance constraint expression is:

[0042]

[0043] In the formula, Let a be the amount of hydrogen injected into the hydrogen storage device at time t in system a; Let a be the amount of hydrogen collected by the hydrogen storage device at time t in system a; The hydrogen gas input to the hydrogen fuel cell is given at time a and t.

[0044] The energy conversion constraints include combined heat and power (CHP) operation constraints, gas boiler operation constraints, gas turbine operation constraints, wind and solar power operation constraints, electrolyzer operation constraints, methane reactor operation constraints, and hydrogen fuel cell operation constraints.

[0045] The operating constraint expression for combined heat and power (CHP) is:

[0046]

[0047] In the formula, The power conversion efficiency of the combined heat and power unit in system a; The thermal energy conversion efficiency of the combined heat and power unit in system a; The lower limit of natural gas input for system a cogeneration unit; The upper limit of natural gas input for system a cogeneration unit; This represents the lower limit of ramp rate for the combined heat and power unit (CHP) in system a. This represents the maximum ramp rate for the combined heat and power unit (CHP) in system a. This represents the upper limit of the heat-to-power ratio for the combined heat and power unit (CHP) in system a. This represents the lower limit of the heat-to-power ratio for the combined heat and power unit in system a. For system a, the gas consumption of the cogeneration unit at time t+1;

[0048] The operating constraint expression for a gas-fired boiler is:

[0049]

[0050] In the formula, The energy conversion efficiency of the gas-fired boiler in system a; This represents the lower limit of gas consumption for the gas-fired boiler in system a. This represents the upper limit of gas consumption for the gas-fired boiler in system a. Let be the gas consumption of the gas boiler in system a at time t+1;

[0051] The gas turbine operating constraint expression is:

[0052]

[0053] In the formula, This represents the upper limit of power generation from the gas turbine in system a. This represents the lower limit of gas turbine power generation for system a; For the gas turbine ramping limit of system a, This represents the lower limit of the ramp rate for the gas turbine in system a. Let be the gas turbine power generation of system a at time t+1;

[0054] The operating constraint expressions for wind and solar power are:

[0055]

[0056] In the formula, This represents the upper limit of the power generation capacity of wind turbine unit a in system a; This represents the upper limit of the power generation capacity of the photovoltaic generator unit in system a;

[0057] The operating constraint expression for the electrolytic cell is:

[0058]

[0059]

[0060] In the formula, The energy conversion efficiency of the electrolytic cell in system a; The upper limit of the electrical energy input to the electrolytic cell of system a; The upper limit of the electrical energy input to the electrolytic cell of system a; This represents the maximum ramp rate for electrolytic cell a in system a. This represents the lower limit of the ramp rate for electrolytic cell a in system a; Let be the power generation of the electrolyzer in system a at time t+1;

[0061] The operating constraints for the methane reactor are expressed as follows:

[0062]

[0063] In the formula, The energy conversion efficiency of the methane reactor in system a; This represents the upper limit of hydrogen input to the methane reactor in system a; This is the lower limit of hydrogen input to the methane reactor in system a; Let t+1 be the amount of hydrogen input to the methane reactor in system a.

[0064] The operating constraints for hydrogen fuel cells are expressed as follows:

[0065]

[0066] In the formula, The energy conversion efficiency of system a hydrogen fuel cell; This represents the upper limit of hydrogen input to system a hydrogen fuel cell; This is the lower limit of hydrogen input for system a hydrogen fuel cell; This represents the upper limit of hydrogen ramp-up for system a hydrogen fuel cell; This represents the lower limit of hydrogen ramp-up for system a hydrogen fuel cell; This represents the upper limit of the hydrogen-to-thermal-electric ratio in system a's hydrogen fuel cell; This represents the lower limit of the hydrogen thermoelectric ratio in system a hydrogen fuel cell; Let be the hydrogen input to the hydrogen fuel cell at time t+1 for system a.

[0067] The energy storage constraint expression is:

[0068]

[0069] In the formula, The maximum power of a single charge / discharge cycle for the energy storage device in system a; The variable is used to charge the energy storage device of system a; The energy release variable of the energy storage device in system a; Let t be the output power of the energy storage device in system a at time t; The charging efficiency of the energy storage device in system a; The energy release efficiency of the energy storage device in system a; Let a be the capacity of the energy storage device at time t in system a; This represents the upper limit of the energy storage device capacity in system a; This represents the upper limit of the energy storage device capacity in system a;

[0070] The carbon emission constraints include carbon emission quota constraints, carbon emission quantity constraints, and carbon emission rights trading quota constraints.

[0071] The carbon emission quota constraint expression is:

[0072]

[0073] In the formula, For system a carbon emission quota; Purchase carbon emission allowances for electricity for system a; For the carbon emission quota of the combined heat and power unit in system a; Carbon emission quotas for system a gas-fired boiler; For system a, the carbon emission quota for the gas turbine; χ e Provide carbon emission credits per unit of electricity; χ g Carbon emission credits per unit volume of gas consumed;

[0074] The carbon emission constraint expression is:

[0075]

[0076] In the formula, This represents the actual carbon emissions of system a. The actual carbon emissions of system a for purchasing electricity; This represents the actual carbon emissions of the gas turbine in system a. The actual carbon emissions of the gas-fired boiler and combined heat and power unit in system a; The amount of carbon dioxide absorbed by the methane reactor in system a; Let a be the output power of the gas-fired boiler and cogeneration unit in system a; a1, b1, and c1 are the carbon emission calculation coefficients for electricity purchase; a2, b2, and c2 are the carbon emission calculation coefficients for the gas-fired boiler and cogeneration unit; and a3, b3, and c3 are the carbon emission calculation coefficients for the gas turbine. The carbon dioxide absorption coefficient of the methane reactor;

[0077] The carbon emission trading quota constraint expression is:

[0078]

[0079] In the formula, For system a, the carbon emission trading volume;

[0080] The energy interaction constraint expression is:

[0081]

[0082] In the formula, The energy transferred from system a to system a'; For system a' to receive energy supplied by system a; The upper limit of energy that system a can supply to system a'; The upper limit of the energy supplied by system a to system a' is the energy that system a receives.

[0083] Preferably, the specific steps of the global swarm optimization algorithm described in step S4 are as follows:

[0084] S401: Set the population size n and the maximum number of iterations T. max Initial temperature T0, cooling rate CR, initial variability λ i and initial variable asynchronous length δ i The population random initialization formula is as follows:

[0085] x i =r i ×(ub-lb)+lb

[0086] In the formula, x i For population location; r i ub represents the population location; lb represents the upper bound of the problem space; and lb represents the lower bound of the problem space.

[0087] S402: Using the objective function described in S2 as the fitness function, calculate the fitness value and the root mean square (RMS) of the fitness values ​​for each initial population. f The calculation formula is:

[0088]

[0089] In the formula, RMS f The root mean square (RMS) of fitness values f n is the population size; f(x) i ) represents the population location x i Fitness value at that time;

[0090] Based on each fitness value and root mean square (RMS) f Based on the differences, the population displacement step size coefficient and displacement direction coefficient are calculated using the following formula:

[0091] d i =RMS f -f(x i )

[0092]

[0093] In the formula, d i c is the population displacement step size coefficient; i This is the population displacement direction coefficient;

[0094] S403: Generate random number r i The population position is updated iteratively by combining the population displacement direction coefficient. The population position update formula is:

[0095]

[0096] In the formula, x i ' represents the updated population position; α represents the state parameter; r i x is a random number in the range [0,1]. r For random population locations;

[0097] S404: Population location selection based on adaptive simulated annealing, the annealing temperature update formula is:

[0098] Te i =Te in ×CR

[0099] In the formula, Te i The updated annealing temperature; Te in CR represents the initial annealing temperature;

[0100] If the updated population position fitness value is less than the previous population position, then the updated population position replaces the previous population position.

[0101] If the updated population position fitness value is greater than the previous population position, the following process is applied for selection:

[0102] Δf=f(xi ')-f(x i )

[0103] In the formula, f(x') i ) represents the updated population fitness value; Δf represents the change in population fitness value before and after the update;

[0104] The new position is accepted with probability P, calculated using the formula:

[0105]

[0106] In the formula, P a For the probability of acceptance;

[0107] S405: Population position updates are perturbed through adaptive mutation. The mutation rate and mutation length are dynamically adjusted according to the current iteration. The calculation formula is as follows:

[0108]

[0109] In the formula, λ' Ti λ is the mutation rate at the Tth iteration; in T represents the initial mutation rate; i λ is the current iteration number; fi This represents the minimum variability; δ' Ti δ is the variable asynchronous length at the Tth iteration; in The initial variable asynchronous length; δ fi This is the minimum value of the variable asynchronous length;

[0110] The random perturbation added during population position updates is based on the mutation rate, and the calculation formula is as follows:

[0111] Δx i =δ' Ti ·N(0,1)

[0112]

[0113] In the formula, Δx i The disturbance is random; N(0,1) is a standard normal distribution. The location of the mutated population;

[0114] S406: Determine if the current iteration number satisfies Ti≥T max If the condition is met, the algorithm terminates the iteration and outputs the optimal solution as the optimization result; otherwise, it returns to S402 to continue the iteration.

[0115] Beneficial effects:

[0116] 1. By calculating carbon emission costs in stages through a tiered carbon trading mechanism and combining it with multi-park energy purchase strategies for coordinated optimization, the applicability of optimization methods under complex carbon trading policies can be improved.

[0117] 2. By leveraging the strong constraint effect of tiered carbon pricing on high-emission zones, we can incentivize the priority scheduling of low-carbon equipment, effectively reducing the total carbon emissions of the system.

[0118] 3. Break through the limitations of energy islands in the industrial park, establish a cross-park mutual assistance mechanism for electricity, heat and hydrogen energy, and improve the renewable energy consumption rate and equipment utilization rate. Attached Figure Description

[0119] Figure 1 A flowchart of an optimization method for a multi-park collaborative integrated energy system considering tiered carbon trading;

[0120] Figure 2 Flowchart for solving the state optimization algorithm;

[0121] Figure 3 Energy purchase prices for different time periods in each park system;

[0122] Figure 4 The energy load of each park system during different time periods;

[0123] Figure 5 Purchase energy for the park system at different times in different scenarios. Detailed Implementation

[0124] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0125] This invention provides an optimization method for a multi-park collaborative integrated energy system considering tiered carbon trading, specifically as follows: Figure 1 As shown, it includes the following steps:

[0126] S1: Acquire data from the multi-park integrated energy system and establish a set of known parameters;

[0127] The known parameter set includes energy price parameters, carbon trading parameters, penalty cost parameters, node parameters, combined heat and power unit parameters, gas boiler unit parameters, electrolyzer unit parameters, methane reactor unit parameters, hydrogen fuel cell unit parameters, gas turbine unit parameters, energy storage system parameters, carbon emission parameters, maximum interaction volume of the park, wind power generation capacity limit, photovoltaic power generation capacity limit, maximum gas purchase volume of the park, and electricity demand.

[0128] S2: Construct the objective function of an optimization model for a multi-park collaborative integrated energy system considering tiered carbon trading;

[0129] The objective function is the total operating cost of the system, including energy purchase cost, carbon emission cost, and energy curtailment penalty cost, and its expression is:

[0130]

[0131] In the formula, F represents the operating cost of system a; Energy purchase cost for system a; For the carbon emission cost of system a; The cost of energy forfeiture penalty for system a;

[0132] S3: Design constraints for a multi-park collaborative integrated energy system optimization model considering tiered carbon trading;

[0133] The constraints include balance constraints, node constraints, energy conversion constraints, energy storage constraints, carbon emission constraints, and energy interaction constraints.

[0134] S4: Use state optimization algorithms to solve the optimization model of the multi-park integrated energy system considering tiered carbon trading, and generate the operation scheme of the multi-park integrated energy system.

[0135] The multi-park collaborative integrated energy system operation plan includes:

[0136] In one embodiment, the energy price parameters mentioned in step S1 include electricity purchase price and gas purchase price; carbon trading parameters include carbon trading base price, carbon emission range length and price growth rate; penalty cost parameters include unit penalty cost for wind curtailment and unit penalty cost for solar curtailment; node parameters include maximum active power transmitted by transmission lines, minimum active power transmitted by transmission lines, maximum pressure of natural gas pipeline network nodes, minimum pressure of natural gas pipeline network nodes, and transmission coefficient of natural gas pipeline network nodes;

[0137] The parameters of a combined heat and power (CHP) unit include the heat conversion efficiency, the electrical conversion efficiency, the upper limit of natural gas input, the lower limit of natural gas input, the upper limit of ramp rate, the lower limit of ramp rate, the upper limit of heat-to-power ratio, and the lower limit of heat-to-power ratio.

[0138] Gas-fired boiler unit parameters include gas-fired boiler conversion efficiency, upper limit of gas consumption, and lower limit of gas consumption.

[0139] Electrolytic cell unit parameters include electrolytic cell unit conversion efficiency, upper limit of electrolytic cell unit electrical energy input, lower limit of electrolytic cell unit electrical energy input, upper limit of electrolytic cell unit ramp rate, and lower limit of electrolytic cell unit ramp rate;

[0140] The parameters of the methane reactor unit include the methane reactor conversion efficiency, the upper limit of hydrogen input to the methane reactor, the lower limit of hydrogen input to the methane reactor, the upper limit of ramp-up to the methane reactor, and the lower limit of ramp-up to the methane reactor.

[0141] The parameters of a hydrogen fuel cell unit include the hydrogen fuel cell conversion efficiency, the upper limit of hydrogen input to the hydrogen fuel cell, the lower limit of hydrogen input to the hydrogen fuel cell, the upper limit of ramp rate, the lower limit of ramp rate, the upper limit of the thermoelectric ratio, and the lower limit of the thermoelectric ratio.

[0142] Gas turbine unit parameters include upper limit of gas turbine power generation, lower limit of gas turbine power generation, upper limit of gas turbine ramp rate, and lower limit of gas turbine ramp rate.

[0143] The parameters of an energy storage system include the maximum power of a single charge of the energy storage device, the maximum power of a single discharge of the energy storage device, the charging efficiency of the energy storage device, the discharge efficiency of the energy storage device, the upper limit of the capacity of the energy storage device, and the lower limit of the capacity of the energy storage device.

[0144] Carbon emission parameters include carbon emission allowances per unit of electricity purchase, carbon emission allowances per unit of natural gas consumption, carbon emission calculation parameters for electricity purchase, carbon emission calculation parameters for gas-fired boilers and combined heat and power units, and carbon emission calculation parameters for gas turbines.

[0145] In one embodiment, the energy purchase cost expression in step S2 is:

[0146]

[0147] In the formula, α t,a Let β be the electricity purchase price at time t for system a; t,a Let a be the gas purchase price at time t for system a; Purchase electricity for system at time a and time t; Let a be the gas purchase amount at time t for system a;

[0148] The carbon emission cost expression is:

[0149]

[0150] In the formula, λ is the base price for carbon trading; l is the length of the carbon emission range; and α is the price growth rate. Let a be the carbon emission trading amount at time t in system a;

[0151] The expression for the cost of energy curtailment penalty is:

[0152]

[0153] In the formula, The unit penalty cost for wind curtailment in system a; The penalty cost per unit of light wasted for system a; Let the amount of air curtailed be system a at time t; Let be the amount of light discarded by system a at time t.

[0154] In one embodiment, the balance constraints mentioned in step S3 include natural gas quantity balance constraints, electrical power balance constraints, thermal power balance constraints, and hydrogen energy power balance constraints.

[0155] The expression for the natural gas supply balance constraint is:

[0156]

[0157] In the formula, Let a be the gas injection rate of the energy storage device at time t in system a; Let a be the gas extraction rate of the energy storage device at time t in system a; Let a be the amount of gas produced by the methane reactor at time t. Let a be the natural gas demand of users at time t in system a; Let a be the gas consumption of the gas boiler in system a at time t; Let a be the gas consumption of the gas boiler at time t in system a; Let a be the maximum gas purchase volume at time t for system a; Let a be the gas consumption of the combined heat and power (CHP) system at time t.

[0158] The expression for the power balance constraint is:

[0159]

[0160] In the formula, Let a be the power supply of the wind turbine at time t in system a; Let a be the power supply of the cogeneration unit at time t in system a; Let be the power supplied by the hydrogen fuel cell at time a and t. Let be the power generation capacity of the gas turbine at time a and t in the system; Let a be the charging power of the energy storage device at time a and t. Let a be the discharge power of the energy storage device at time a and t. Let be the power consumed by the electrolytic cell at time a and t in the system; Let the system be the user's power demand at time a and t; Let the power of photoelectric generation of system a at time t be denoted as ; Let the wind power generation capacity be the system's output at time a and t. Let be the power generation capacity of the gas turbine at time a and t in the system;

[0161] The expression for the thermal power balance constraint is:

[0162]

[0163] In the formula, Let the system be the heat load at time a and t. Heat is injected into the energy storage device at time t for system a; Let the heat released by the energy storage device at time t be the heat of system a. Let the combined heat and power unit output thermal power at time a and t be the system. Let a be the thermal power of the cogeneration unit at time t. Let a be the thermal power of the gas-fired boiler at time t. Let the system be the heat load at time a and t.

[0164] The hydrogen power balance constraint expression is:

[0165]

[0166] In the formula, Let a be the amount of hydrogen injected into the hydrogen storage device at time t in system a; Let a be the amount of hydrogen collected by the hydrogen storage device at time t in system a; The hydrogen gas input to the hydrogen fuel cell is given at time a and t.

[0167] The energy conversion constraints include combined heat and power (CHP) operation constraints, gas boiler operation constraints, gas turbine operation constraints, wind and solar power operation constraints, electrolyzer operation constraints, methane reactor operation constraints, and hydrogen fuel cell operation constraints.

[0168] The operating constraint expression for combined heat and power (CHP) is:

[0169]

[0170] In the formula, The power conversion efficiency of the combined heat and power unit in system a; The thermal energy conversion efficiency of the combined heat and power unit in system a; The lower limit of natural gas input for system a cogeneration unit; The upper limit of natural gas input for system a cogeneration unit; This represents the lower limit of ramp rate for the combined heat and power unit (CHP) in system a. This represents the maximum ramp rate for the combined heat and power unit (CHP) in system a. This represents the upper limit of the heat-to-power ratio for the combined heat and power unit (CHP) in system a. This represents the lower limit of the heat-to-power ratio for the combined heat and power unit in system a. For system a, the gas consumption of the cogeneration unit at time t+1;

[0171] The operating constraint expression for a gas-fired boiler is:

[0172]

[0173] In the formula, The energy conversion efficiency of the gas-fired boiler in system a; This represents the lower limit of gas consumption for the gas-fired boiler in system a. This represents the upper limit of gas consumption for the gas-fired boiler in system a. Let be the gas consumption of the gas boiler in system a at time t+1;

[0174] The gas turbine operating constraint expression is:

[0175]

[0176] In the formula, This represents the upper limit of power generation from the gas turbine in system a. This represents the lower limit of gas turbine power generation for system a; For the gas turbine ramping limit of system a, This represents the lower limit of the ramp rate for the gas turbine in system a. Let be the gas turbine power generation of system a at time t+1;

[0177] The operating constraint expressions for wind and solar power are:

[0178]

[0179] In the formula, This represents the upper limit of the power generation capacity of wind turbine unit a in system a; This represents the upper limit of the power generation capacity of the photovoltaic generator unit in system a;

[0180] The operating constraint expression for the electrolytic cell is:

[0181]

[0182] In the formula, The energy conversion efficiency of the electrolytic cell in system a; The upper limit of the electrical energy input to the electrolytic cell of system a; The upper limit of the electrical energy input to the electrolytic cell of system a; This represents the maximum ramp rate for electrolytic cell a in system a. This represents the lower limit of the ramp rate for electrolytic cell a in system a; Let be the power generation of the electrolyzer in system a at time t+1;

[0183] The operating constraints for the methane reactor are expressed as follows:

[0184]

[0185] In the formula, The energy conversion efficiency of the methane reactor in system a; This represents the upper limit of hydrogen input to the methane reactor in system a; This is the lower limit of hydrogen input to the methane reactor in system a; Let t+1 be the amount of hydrogen input to the methane reactor in system a.

[0186] The operating constraints for hydrogen fuel cells are expressed as follows:

[0187]

[0188] In the formula, The energy conversion efficiency of system a hydrogen fuel cell; This represents the upper limit of hydrogen input to system a hydrogen fuel cell; This is the lower limit of hydrogen input for system a hydrogen fuel cell; This represents the upper limit of hydrogen ramp-up for system a hydrogen fuel cell; This represents the lower limit of hydrogen ramp-up for system a hydrogen fuel cell; This represents the upper limit of the hydrogen-to-thermal-electric ratio in system a's hydrogen fuel cell; This represents the lower limit of the hydrogen thermoelectric ratio in system a hydrogen fuel cell; Let be the hydrogen input to the hydrogen fuel cell at time t+1 for system a.

[0189] The energy storage constraint expression is:

[0190]

[0191] In the formula, The maximum power of a single charge / discharge cycle for the energy storage device in system a; The variable is used to charge the energy storage device of system a; The energy release variable of the energy storage device in system a; Let t be the output power of the energy storage device in system a at time t; The charging efficiency of the energy storage device in system a; The energy release efficiency of the energy storage device in system a; Let a be the capacity of the energy storage device at time t in system a; This represents the upper limit of the energy storage device capacity in system a; This represents the upper limit of the energy storage device capacity in system a;

[0192] The carbon emission constraints include carbon emission quota constraints, carbon emission quantity constraints, and carbon emission rights trading quota constraints.

[0193] The carbon emission quota constraint expression is:

[0194]

[0195] In the formula, For system a carbon emission quota; Purchase carbon emission allowances for electricity for system a; For the carbon emission quota of the combined heat and power unit in system a; Carbon emission quotas for system a gas-fired boiler; For system a, the carbon emission quota for the gas turbine; χ e Provide carbon emission credits per unit of electricity; χ g Carbon emission credits per unit volume of gas consumed;

[0196] The carbon emission constraint expression is:

[0197]

[0198]

[0199] In the formula, This represents the actual carbon emissions of system a. The actual carbon emissions of system a for purchasing electricity; This represents the actual carbon emissions of the gas turbine in system a. The actual carbon emissions of the gas-fired boiler and combined heat and power unit in system a; The amount of carbon dioxide absorbed by the methane reactor in system a; Let a be the output power of the gas-fired boiler and cogeneration unit in system a; a1, b1, and c1 are the carbon emission calculation coefficients for electricity purchase; a2, b2, and c2 are the carbon emission calculation coefficients for the gas-fired boiler and cogeneration unit; and a3, b3, and c3 are the carbon emission calculation coefficients for the gas turbine. The carbon dioxide absorption coefficient of the methane reactor;

[0200] The carbon emission trading quota constraint expression is:

[0201]

[0202] In the formula, For system a, the carbon emission trading volume;

[0203] The energy interaction constraint expression is:

[0204]

[0205] In the formula, The energy transferred from system a to system a'; For system a' to receive energy supplied by system a; The upper limit of energy that system a can supply to system a'; The upper limit of the energy supplied by system a to system a' is the energy that system a receives.

[0206] In one embodiment, the overall swarm optimization algorithm flow described in step S4 is as follows: Figure 2 As shown, the specific steps are as follows:

[0207] S401: Set the population size n and the maximum number of iterations T. max Initial temperature T0, cooling rate CR, initial variability λi and initial variable asynchronous length δ i The population random initialization formula is as follows:

[0208] x i =r i ×(ub-lb)+lb

[0209] In the formula, x i For population location; r i ub represents the population location; lb represents the upper bound of the problem space; and lb represents the lower bound of the problem space.

[0210] S402: Using the objective function described in S2 as the fitness function, calculate the fitness value and the root mean square (RMS) of the fitness values ​​for each initial population. f The calculation formula is:

[0211]

[0212] In the formula, RMS f The root mean square (RMS) of fitness values f n is the population size; f(x) i ) represents the population location x i Fitness value at that time;

[0213] Based on each fitness value and root mean square (RMS) f Based on the differences, the population displacement step size coefficient and displacement direction coefficient are calculated using the following formula:

[0214] d i =RMS f -f(x i )

[0215]

[0216] In the formula, d i c is the population displacement step size coefficient; i This is the population displacement direction coefficient;

[0217] S403: Generate random number r i The population position is updated iteratively by combining the population displacement direction coefficient. The population position update formula is:

[0218]

[0219] In the formula, x i ' represents the updated population position; α represents the state parameter; r i x is a random number in the range [0,1]. r For random population locations;

[0220] S404: Population location selection based on adaptive simulated annealing, the annealing temperature update formula is:

[0221] Te i =Te in ×CR

[0222] In the formula, Te i The updated annealing temperature; Te in CR represents the initial annealing temperature;

[0223] If the updated population position fitness value is less than the previous population position, then the updated population position replaces the previous population position.

[0224] If the updated population position fitness value is greater than the previous population position, the following process is applied for selection:

[0225] Δf=f(x i ')-f(x i )

[0226] In the formula, f(x') i ) represents the updated population fitness value; Δf represents the change in population fitness value before and after the update;

[0227] The new position is accepted with probability P, calculated using the formula:

[0228]

[0229] In the formula, P a For the probability of acceptance;

[0230] S405: Population position updates are perturbed through adaptive mutation. The mutation rate and mutation length are dynamically adjusted according to the current iteration. The calculation formula is as follows:

[0231]

[0232] In the formula, λ' Ti λ is the mutation rate at the Tth iteration; in T is the initial mutation rate; i λ is the current iteration number; fi This represents the minimum variability; δ' Ti δ is the variable asynchronous length at the Tth iteration; in The initial variable asynchronous length; δ fi This is the minimum value of the variable asynchronous length;

[0233] The random perturbation added during population position updates is based on the mutation rate, and the calculation formula is as follows:

[0234] Δx i =δ' Ti ·N(0,1)

[0235]

[0236] In the formula, Δx i The disturbance is random; N(0,1) is a standard normal distribution. The location of the mutated population;

[0237] S406: Determine if the current iteration number satisfies Ti≥T max If the condition is met, the algorithm terminates the iteration and outputs the optimal solution as the optimization result; otherwise, it returns to S402 to continue the iteration.

[0238] In a specific embodiment, the multi-park collaborative integrated energy system optimization method considering tiered carbon trading, as described in this invention, is used to optimize a domestic integrated energy system. The system comprises three parks, and the energy conversion terminal configurations of the parks are shown in Table 1. Park A1 contains four gas turbines, while parks A2 and A3 each contain two gas turbines. Parks A1 and A3 have abundant wind and solar resources and are equipped with corresponding wind and solar power units to utilize these resources; Park A2 lacks wind and solar resources and has not installed wind and solar power units, nor has it configured hydrogen fuel cells or hydrogen storage devices. The electricity and gas purchase prices and equipment specifications are the same in energy systems A1, A2, and A3. The system energy purchase price is as follows: Figure 3 As shown, the energy load of each park at different times is as follows: Figure 4 As shown in Table 2, the parameters of the energy system equipment are as follows.

[0239] Table 1 Configuration of Energy Conversion Terminals in the Park

[0240]

[0241] Table 2 Equipment Operating Parameters

[0242]

[0243] To verify the superiority of the proposed multi-park collaborative operation optimization method, scenario 1 and scenario 2 were set up for optimization. In scenario 1, each park operates independently and energy interaction is not allowed, while in scenario 2, energy interaction between parks is allowed. At time t, the maximum energy interaction is 15% of the energy load.

[0244] Table 3 shows the carbon emissions and operating costs of each park under scenarios 1 and 2, obtained using the state optimization algorithm. As can be seen from Table 3, when the systems operate individually, system A2 has the highest carbon emissions and operating costs, while system A3 has the lowest. The carbon emissions and operating costs of the system in scenario 2 are lower than the sum of the carbon emissions and operating costs of the three systems in scenario 1. The carbon emissions and operating costs of the system in scenario 2 are 244.5t and 11.4 × 10⁻⁶ tons, respectively. 4Yuan, a reduction of 85t and 3.9×10 compared to scenario 1. 4 The figures were RMB, a decrease of 25.8% and 25.6% year-on-year. This demonstrates that energy exchange and coordinated development among multiple industrial parks can significantly reduce system carbon emissions and operating costs.

[0245] Table 3 System Carbon Emissions and Operating Costs

[0246]

[0247] Taking the A2 system as an example, this paper analyzes the energy purchase capacity of the park at different times under different scenarios, such as... Figure 5 As shown. From Figure 5 It is evident that when A2 participates in inter-system energy flow interaction, a significant amount of renewable energy generation flows from A1 and A3 into A2, effectively resolving the dilemma of high fossil fuel power generation and a lack of clean energy in A2. Simultaneously, this substantial energy exchange reduces the amount of electricity A2 purchases from the upper-level grid, effectively lowering system operating costs. Compared to scenario 1, A2's electricity purchases in scenario 2 decreased by 7237.7 kW, a year-on-year reduction of 60.4%. As shown in Figure (b), the gas purchase volume in scenario 2 is less than in scenario 1, decreasing by 608.9 × 10⁻⁶ kW. 4 m 3 The decrease was 3.4% year-on-year. The smaller decrease in gas purchases is because the system's heat load supply mainly relies on combined heat and power units and gas-fired boilers, which require a large supply of gas. At the same time, in energy interaction, system A2 needs to provide heat energy to A1 and A3 to reduce the operating costs and carbon emissions of A1 and A3. Therefore, compared with scenario 1, the decrease in gas purchases for A2 in scenario 2 is relatively small.

[0248] The above descriptions are merely embodiments of this specification and are not intended to limit the scope of this specification. Various modifications and variations can be made to the embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the embodiments of this specification should be included within the scope of the claims of the embodiments of this specification.

Claims

1. A method for optimizing a multi-park integrated energy system considering tiered carbon trading, characterized in that, Includes the following steps: S1: Acquire data from the multi-park integrated energy system and establish a set of known parameters; The known parameter set includes energy price parameters, carbon trading parameters, penalty cost parameters, node parameters, combined heat and power unit parameters, gas boiler unit parameters, electrolyzer unit parameters, methane reactor unit parameters, hydrogen fuel cell unit parameters, gas turbine unit parameters, energy storage system parameters, carbon emission parameters, maximum interaction volume of the park, wind power generation capacity limit, photovoltaic power generation capacity limit, maximum gas purchase volume of the park, and electricity demand. S2: Construct the objective function of an optimization model for a multi-park collaborative integrated energy system considering tiered carbon trading; The objective function is the total operating cost of the system, including energy purchase cost, carbon emission cost, and energy curtailment penalty cost, and its expression is: In the formula, F represents the operating cost of system a; Energy purchase cost for system a; For the carbon emission cost of system a; The cost of energy forfeiture penalty for system a; S3: Design constraints for a multi-park collaborative integrated energy system optimization model considering tiered carbon trading; The constraints include balance constraints, node constraints, energy conversion constraints, energy storage constraints, carbon emission constraints, and energy interaction constraints. S4: Use state optimization algorithms to solve the optimization model of the multi-park collaborative integrated energy system considering tiered carbon trading, and generate the operation scheme of the multi-park collaborative integrated energy system.

2. The method for optimizing a multi-park integrated energy system considering tiered carbon trading as described in claim 1, characterized in that, The energy price parameters mentioned in step S1 include electricity purchase price and gas purchase price; carbon trading parameters include carbon trading base price, carbon emission range length and price growth rate; penalty cost parameters include unit penalty cost for wind curtailment and unit penalty cost for solar curtailment; node parameters include maximum active power transmitted by transmission lines, minimum active power transmitted by transmission lines, maximum pressure of natural gas pipeline network nodes, minimum pressure of natural gas pipeline network nodes, and transmission coefficient of natural gas pipeline network nodes. The parameters of a combined heat and power (CHP) unit include the heat conversion efficiency, the electrical conversion efficiency, the upper limit of natural gas input, the lower limit of natural gas input, the upper limit of ramp rate, the lower limit of ramp rate, the upper limit of heat-to-power ratio, and the lower limit of heat-to-power ratio. Gas-fired boiler unit parameters include gas-fired boiler conversion efficiency, upper limit of gas consumption, and lower limit of gas consumption. Electrolytic cell unit parameters include electrolytic cell unit conversion efficiency, upper limit of electrolytic cell unit electrical energy input, lower limit of electrolytic cell unit electrical energy input, upper limit of electrolytic cell unit ramp rate, and lower limit of electrolytic cell unit ramp rate; The parameters of the methane reactor unit include the methane reactor conversion efficiency, the upper limit of hydrogen input to the methane reactor, the lower limit of hydrogen input to the methane reactor, the upper limit of ramp-up to the methane reactor, and the lower limit of ramp-up to the methane reactor. The parameters of a hydrogen fuel cell unit include the hydrogen fuel cell conversion efficiency, the upper limit of hydrogen input to the hydrogen fuel cell, the lower limit of hydrogen input to the hydrogen fuel cell, the upper limit of ramp rate, the lower limit of ramp rate, the upper limit of the thermoelectric ratio, and the lower limit of the thermoelectric ratio. Gas turbine unit parameters include upper limit of gas turbine power generation, lower limit of gas turbine power generation, upper limit of gas turbine ramp rate, and lower limit of gas turbine ramp rate. The parameters of an energy storage system include the maximum power of a single charge of the energy storage device, the maximum power of a single discharge of the energy storage device, the charging efficiency of the energy storage device, the discharge efficiency of the energy storage device, the upper limit of the capacity of the energy storage device, and the lower limit of the capacity of the energy storage device. Carbon emission parameters include carbon emission allowances per unit of electricity purchase, carbon emission allowances per unit of natural gas consumption, carbon emission calculation parameters for electricity purchase, carbon emission calculation parameters for gas-fired boilers and combined heat and power units, and carbon emission calculation parameters for gas turbines.

3. The method for optimizing a multi-park integrated energy system considering tiered carbon trading as described in claim 1, characterized in that, The energy purchase cost expression mentioned in step S2 is: In the formula, α t,a Let β be the electricity purchase price at time t for system a; t,a Let a be the gas purchase price at time t for system a; Purchase electricity for system at time a and time t; Let a be the gas purchase amount at time t for system a; The carbon emission cost expression is: In the formula, λ is the base price for carbon trading; l is the length of the carbon emission range; and α is the price growth rate. Let a be the carbon emission trading amount at time t in system a; The expression for the cost of energy curtailment penalty is: In the formula, The unit penalty cost for wind curtailment in system a; The penalty cost per unit of light wasted for system a; Let the amount of air curtailed be system a at time t; Let be the amount of light discarded by system a at time t.

4. The method for optimizing a multi-park integrated energy system considering tiered carbon trading as described in claim 1, characterized in that, The balance constraints mentioned in step S3 include natural gas quantity balance constraints, electrical power balance constraints, thermal power balance constraints, and hydrogen energy power balance constraints. The expression for the natural gas supply balance constraint is: In the formula, Let a be the gas injection rate of the energy storage device at time t in system a; Let a be the gas extraction rate of the energy storage device at time t in system a; Let a be the amount of gas produced by the methane reactor at time t. Let a be the natural gas demand of users at time t in system a; Let a be the gas consumption of the gas boiler in system a at time t; Let a be the gas consumption of the gas boiler at time t in system a; Let a be the maximum gas purchase volume at time t for system a; Let a be the gas consumption of the combined heat and power (CHP) system at time t. The expression for the power balance constraint is: In the formula, Let a be the power supply of the wind turbine at time t in system a; Let a be the power supply of the cogeneration unit at time t in system a; Let be the power supplied by the hydrogen fuel cell at time a and t. Let be the power generation capacity of the gas turbine at time a and t in the system; Let a be the charging power of the energy storage device at time a and t. Let a be the discharge power of the energy storage device at time a and t. Let be the power consumed by the electrolytic cell at time a and t in the system; Let the system be the user's power demand at time a and t; Let the power of photoelectric generation of system a at time t be denoted as ; Let the wind power generation capacity be the system's output at time a and t. Let be the power generation capacity of the gas turbine at time a and t in the system; The expression for the thermal power balance constraint is: In the formula, Let the system be the heat load at time a and t. Heat is injected into the energy storage device of system a at time t; Let the heat released by the energy storage device at time t be the heat of system a. Let the combined heat and power unit output thermal power at time a and t be the system. Let be the thermal power of the hydrogen fuel cell at time a and t. Let a be the thermal power of the gas-fired boiler at time t. Let the system be the heat load at time a and t. The hydrogen power balance constraint expression is: In the formula, Let a be the amount of hydrogen injected into the hydrogen storage device at time t in system a; Let a be the amount of hydrogen collected by the hydrogen storage device at time t in system a; The hydrogen gas input to the hydrogen fuel cell is given at time a and t. The hydrogen produced by the electrolyzer at time t in system a; The hydrogen gas input to the methane reactor at time a and t is the system. The energy conversion constraints include combined heat and power (CHP) operation constraints, gas boiler operation constraints, gas turbine operation constraints, wind and solar power operation constraints, electrolyzer operation constraints, methane reactor operation constraints, and hydrogen fuel cell operation constraints. The operating constraint expression for combined heat and power (CHP) is: In the formula, The power conversion efficiency of the combined heat and power unit in system a; The thermal energy conversion efficiency of the combined heat and power unit in system a; The lower limit of natural gas input for system a cogeneration unit; The upper limit of natural gas input for system a cogeneration unit; This represents the lower limit of ramp rate for the combined heat and power unit (CHP) in system a. This represents the maximum ramp rate for the combined heat and power unit (CHP) in system a. This represents the upper limit of the heat-to-power ratio for the combined heat and power unit (CHP) in system a. This represents the lower limit of the heat-to-power ratio for the combined heat and power unit in system a. For system a, the gas consumption of the cogeneration unit at time t+1; The operating constraint expression for a gas-fired boiler is: In the formula, The energy conversion efficiency of the gas-fired boiler in system a; This represents the lower limit of gas consumption for the gas-fired boiler in system a. This represents the upper limit of gas consumption for the gas-fired boiler in system a. Let be the gas consumption of the gas boiler in system a at time t+1; The lower limit of ramp rate for gas-fired boiler in system a; The ramp-up limit for gas-fired boiler in system a; The gas turbine operating constraint expression is: In the formula, This represents the upper limit of power generation from the gas turbine in system a. This represents the lower limit of gas turbine power generation for system a; For the gas turbine ramping limit of system a, This represents the lower limit of the ramp rate for the gas turbine in system a. Let be the gas turbine power generation of system a at time t+1; The operating constraint expressions for wind and solar power are: In the formula, This represents the upper limit of the power generation capacity of wind turbine unit a in system a; This represents the upper limit of the power generation capacity of the photovoltaic generator unit in system a; The operating constraint expression for the electrolytic cell is: In the formula, The energy conversion efficiency of the electrolytic cell in system a; The upper limit of the electrical energy input to the electrolytic cell of system a; The upper limit of the electrical energy input to the electrolytic cell of system a; This represents the maximum ramp rate for electrolytic cell a in system a. This represents the lower limit of the ramp rate for electrolytic cell a in system a; Let be the power generation of the electrolyzer in system a at time t+1; The operating constraints for the methane reactor are expressed as follows: In the formula, The energy conversion efficiency of the methane reactor in system a; This represents the upper limit of hydrogen input to the methane reactor in system a; This is the lower limit of hydrogen input to the methane reactor in system a; Let t+1 be the amount of hydrogen input to the methane reactor in system a. This represents the lower limit of the ramp-up limit for the methane reactor in system a. This represents the upper limit of the ramp-up for the methane reactor in system a. The operating constraints for hydrogen fuel cells are expressed as follows: In the formula, The electrical energy conversion efficiency of the hydrogen fuel cell in system a; The thermal energy conversion efficiency of the hydrogen fuel cell in system a; This represents the upper limit of hydrogen input to system a hydrogen fuel cell; This is the lower limit of hydrogen input for system a hydrogen fuel cell; This represents the upper limit of hydrogen ramp-up for system a hydrogen fuel cell; This represents the lower limit of hydrogen ramp-up for system a hydrogen fuel cell; This represents the upper limit of the hydrogen-to-thermal-electric ratio in system a's hydrogen fuel cell; This represents the lower limit of the hydrogen thermoelectric ratio in system a hydrogen fuel cell; Let be the hydrogen input to the hydrogen fuel cell at time t+1 for system a. The energy storage constraint expression is: In the formula, The maximum power of a single charge / discharge cycle for the energy storage device in system a; The variable is used to charge the energy storage device of system a; The energy release variable of the energy storage device in system a; Let t be the output power of the energy storage device in system a at time t; The charging efficiency of the energy storage device in system a; The energy release efficiency of the energy storage device in system a; Let a be the capacity of the energy storage device at time t in system a; Let be the capacity of the energy storage device in system a at time t-1; This represents the upper limit of the energy storage device capacity in system a; This represents the upper limit of the energy storage device capacity in system a; The carbon emission constraints include carbon emission quota constraints, carbon emission quantity constraints, and carbon emission rights trading quota constraints. The carbon emission quota constraint expression is: In the formula, For system a carbon emission quota; Purchase carbon emission allowances for electricity for system a; For the carbon emission quota of the combined heat and power unit in system a; Carbon emission quotas for system a gas-fired boiler; For system a, the carbon emission quota for the gas turbine; χ e Provide carbon emission credits per unit of electricity; χ g Carbon emission credits per unit volume of gas consumed; The carbon emission constraint expression is: In the formula, This represents the actual carbon emissions of system a. The actual carbon emissions of system a for purchasing electricity; This represents the actual carbon emissions of the gas turbine in system a. The actual carbon emissions of the gas-fired boiler and combined heat and power unit in system a; The amount of carbon dioxide absorbed by the methane reactor in system a; Let a be the output power of the gas-fired boiler and cogeneration unit in system a; a1, b1, and c1 are the carbon emission calculation coefficients for electricity purchase; a2, b2, and c2 are the carbon emission calculation coefficients for the gas-fired boiler and cogeneration unit; and a3, b3, and c3 are the carbon emission calculation coefficients for the gas turbine. The carbon dioxide absorption coefficient of the methane reactor; The carbon emission trading quota constraint expression is: In the formula, For system a, the carbon emission trading volume; The energy interaction constraint expression is: In the formula, The energy transferred from system a to system a'; For system a' to receive energy supplied by system a; The upper limit of energy that system a can supply to system a'; The upper limit of the energy supplied by system a to system a' is the energy that system a receives.

5. The method for optimizing a multi-park integrated energy system considering tiered carbon trading as described in claim 1, characterized in that, The specific steps of the global swarm optimization algorithm described in step S4 are as follows: S401: Set the population size n and the maximum number of iterations T. max Initial temperature T0, cooling rate CR, initial variability λ i and initial variable asynchronous length δ i The population random initialization formula is as follows: x i =r i ×(ub-lb)+lb In the formula, x i For population location; r i ub represents the population location; lb represents the upper bound of the problem space; and lb represents the lower bound of the problem space. S402: Using the objective function described in S2 as the fitness function, calculate the fitness value and the root mean square (RMS) of the fitness values ​​for each initial population. f The calculation formula is: In the formula, RMS f The root mean square (RMS) of fitness values f n is the population size; f(x) i ) represents the population location x i Fitness value at that time; Based on each fitness value and root mean square (RMS) f Based on the differences, the population displacement step size coefficient and displacement direction coefficient are calculated using the following formula: d i =RMS f -f(x i ) In the formula, d i c is the population displacement step size coefficient; i This is the population displacement direction coefficient; S403: Generate random number r i The population position is updated iteratively by combining the population displacement direction coefficient. The population position update formula is: In the formula, x i ' represents the updated population position; α represents the state parameter; r i x is a random number in the range [0,1]. r For random population locations; S404: Population location selection based on adaptive simulated annealing, the annealing temperature update formula is: yourself i =You in ×CR In the formula, Te i For the updated annealing temperature; Te in CR represents the initial annealing temperature; If the updated population position fitness value is less than the previous population position, then the updated population position replaces the previous population position; if the updated population position fitness value is greater than the previous population position, then the following process is applied for selection: Δf=f(x i ')-f(x i ) In the formula, f(x) i ') represents the updated population fitness value; Δf represents the change in population fitness before and after the update; The new position is accepted with probability P, calculated using the formula: In the formula, P a For the probability of acceptance; S405: Population position updates are perturbed through adaptive mutation. The mutation rate and mutation length are dynamically adjusted according to the current iteration. The calculation formula is as follows: In the formula, λ' Ti λ is the mutation rate at the Tth iteration; in T represents the initial mutation rate; i λ is the current iteration number; fi This represents the minimum variability. δ' Ti δ is the variable asynchronous length at the Tth iteration; in The initial variable asynchronous length; δ fi This is the minimum value of the variable asynchronous length; The random perturbation added during population position updates is based on the mutation rate, and the calculation formula is as follows: Δx i =δ' Ti ·N(0,1) In the formula, Δx i The disturbance is random; N(0,1) is a standard normal distribution. The location of the mutated population; S406: Determine if the current iteration number satisfies Ti≥T max If the condition is met, the algorithm terminates the iteration and outputs the optimal solution as the optimization result; otherwise, it returns to S402 to continue the iteration.

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