Parameter optimization method of ladder-type carbon trading mechanism based on improved particle swarm optimization algorithm

Abstract

The present invention provides a method for optimizing parameters of a ladder-type carbon trading mechanism based on an improved particle swarm algorithm. The method first obtains information and operation data of a park comprehensive energy system, then establishes a model and constraints of equipment for the park comprehensive energy system, and establishes a ladder-type carbon trading model. The low-carbon optimization scheduling process of the park comprehensive energy system is then encapsulated as a fitness function whose input is the carbon trading mechanism parameters and whose output is the carbon emissions of the system; finally, the improved particle swarm algorithm is introduced to optimize the fitness function, and the result information after the algorithm optimization is output. The present invention verifies the effectiveness and rationality of the model and method in giving full play to the role of the ladder-type carbon trading mechanism in the park comprehensive energy system through example analysis, and can provide a solution for how to reasonably and effectively formulate parameters of the ladder-type carbon trading mechanism in the park comprehensive energy system.

Description

Technical Field

[0001] The present invention relates to a ladder-type carbon trading mechanism, and in particular to a ladder-type carbon trading mechanism parameter optimization method based on an improved particle swarm algorithm. Background Art

[0002] Efficient, safe, low-carbon and clean energy utilization technology is the mainstream direction of current energy development and an objective requirement for sustainable development in the world. In the park's integrated energy system, the flexible complementarity of various energy forms such as electricity, gas and heat can meet the diversified load requirements and realize energy cascade utilization. The energy industry accounts for a large proportion of carbon emissions and is the main force in energy conservation and emission reduction. In order to ensure the sustainable development of the system, it is necessary to introduce carbon trading to take into account the economy and low-carbon environmental protection of the park's integrated energy system. Carbon trading mechanisms are mainly divided into two types: ladder type and traditional type. Compared with the traditional type, the ladder type carbon trading mechanism has stricter control over carbon emissions. For the ladder type carbon trading mechanism, the final result of its cost is closely related to the three parameters of the selected carbon trading base price, the length of the carbon emission interval and the carbon trading price growth rate. However, at present, how to determine reasonable and effective carbon trading mechanism parameters needs further study.

[0003] Therefore, when introducing a tiered carbon trading mechanism into the park's comprehensive energy system, it is important to determine a reasonable carbon trading base price, carbon emission range length, and price growth rate based on the park's actual conditions, in order to give full play to the role of the mechanism and maximize the realization of low-carbon emission reduction targets. Summary of the invention

[0004] The purpose of the present invention is to provide a method for optimizing the parameters of a ladder-type carbon trading mechanism based on an improved particle swarm algorithm. The method is based on the actual situation of the park's comprehensive energy system, takes the optimization scheduling process of the entire park's comprehensive energy system as the fitness function, and uses the improved particle swarm algorithm to determine reasonable parameters of the ladder-type carbon trading mechanism, thereby giving full play to the role of the carbon trading mechanism, reducing the system's carbon emissions, and improving the low-carbon nature of the system's operation.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is as follows.

[0006] A method for optimizing parameters of a ladder-type carbon trading mechanism based on an improved particle swarm algorithm comprises the following steps:

[0007] (1) Collect information and operation data of the park's integrated energy system, including the topological architecture of the park's integrated energy system, the capacity and efficiency of the park's integrated energy system equipment, the carbon emission coefficient of gas turbines, the carbon emission coefficient of gas boilers, the carbon emission coefficient of the equivalent electricity purchased by the superior, the operation cost of the park's integrated energy system, the safe operation constraints of the park's integrated energy system, and the information on electricity, heat and gas loads;

[0008] (2) Establish mathematical models of the park's integrated energy system equipment, including P2G equipment models, gas turbine models, gas boiler models, battery models, gas storage tank models, and heat storage tank models;

[0009] (3) Establish a ladder-type carbon trading model, including the free carbon emission quota model of the park's comprehensive energy system and the actual carbon emission model of the park's comprehensive energy system, and a ladder-type carbon emission trading model. The mathematical expression of the ladder-type carbon trading cost is obtained as follows:

[0010]

[0011] Where: is the step-by-step carbon trading cost; λ is the carbon trading base price; l is the length of the carbon emission interval; σ is the carbon trading price growth rate; EX p The carbon emission rights trading amount of the park's comprehensive energy system;

[0012] (4) Based on the system operation cost of the park's comprehensive energy system C run and carbon trading costs The minimum overall cost is the low-carbon economic optimization scheduling target, and the low-carbon economic optimization scheduling process of the park's comprehensive energy system is encapsulated as a fitness function. The input of the fitness function includes three step-type carbon trading mechanism parameters: carbon trading base price, carbon emission interval length, and carbon trading price growth rate. The output is the carbon emissions of the park's comprehensive energy system.

[0013] (5) An improved particle swarm algorithm is introduced to optimize the fitness function in step (4), including adjusting the inertia weight in the particle swarm algorithm using S-type functions, trigonometric functions, and random functions as carriers and optimizing the acceleration factor in the particle swarm algorithm based on trigonometric functions. The optimization process includes taking the maximum number of iterations as the termination condition and iteratively optimizing the fitness function with the goal of minimizing the system carbon emissions. The optimization result information includes the carbon trading base price, the length of the carbon emission range, the carbon trading price growth rate, and the carbon emissions of the park's comprehensive energy system.

[0014] Furthermore, step (2) establishes a park integrated energy system equipment model including the following models:

[0015] (2a) The mathematical model of P2G device model is as follows:

[0016]

[0017] Where: are the output and input of the P2G device at time t; η p2g is the P2G conversion efficiency;

[0018] (2b) The mathematical model of the gas turbine is as follows:

[0019]

[0020] Where: are the output power, output heat and input gas volume of the gas turbine at time t respectively; η gte , η gth They are gas-to-electricity efficiency and gas-to-heat efficiency of gas turbines respectively;

[0021] (2c) The mathematical model of the gas boiler is as follows:

[0022]

[0023] Where: is the output and input of the gas boiler at time t, η gb For gas boiler conversion efficiency;

[0024] (2d) The electricity / gas / heat storage model includes three types of energy storage devices: batteries, heat storage tanks, and gas tanks. They are processed using a unified general model, including storage energy balance constraints, storage energy upper and lower limit constraints, storage energy cycle equal amount constraints, and charging and discharging energy power constraints. Its mathematical expression is as follows:

[0025]

[0026] In the formula: x represents the energy type, which can be electricity, heat or gas; is the storage energy of the energy storage system in unit time period t; are the charging and discharging power of the energy storage system respectively; η xchar , η xdis are the charging and discharging efficiency of the energy storage system respectively; are the upper and lower limits of the storage energy of the energy storage system respectively; The storage energies at the start and end of one-day scheduling of the energy storage system are required to return to the original value after one-day scheduling; are the upper limits of charging and discharging power of the energy storage system; n x is a 0-1 variable to ensure that the energy storage system does not charge or discharge energy at the same time in each time period. x When it is 1, it stores energy, and when it is 0, it releases energy.

[0027] Furthermore, step (3) establishes a ladder-type carbon trading model including the following sub-models:

[0028] (3a) The mathematical expression of the carbon emission quota model is as follows:

[0029]

[0030] Where: EX PIESo 、EX eo,buy 、EX gto 、EX gbo They are the carbon emission quotas of the park's integrated energy system, electricity purchased from higher authorities, gas turbines, and gas boilers; μ e , μ h are the carbon emission quotas for generating unit electrical power and unit thermal power respectively; δ e,h It is the electrical and thermal power conversion parameter; are the power purchase amount from the upper level, the input power of the gas turbine, and the input power of the gas boiler at time t; η gte , η gth , η gb are the gas-to-electricity efficiency and gas-to-heat efficiency of the gas turbine and the conversion efficiency of the gas boiler, respectively, and T is the scheduling period;

[0031] (3b) The mathematical expression of the actual carbon emission model is as follows:

[0032]

[0033] Where: EX PIES 、EX e,buy 、EX gt 、EX gb They are the actual carbon emissions of the park's integrated energy system, electricity purchased from higher authorities, gas turbines, and gas boilers; e , h are the actual carbon emissions per unit of electrical power and per unit of thermal power respectively;

[0034] (3c) The calculation of the carbon emission rights trading amount actually participating in the carbon trading market in the ladder-type carbon emission trading model is as follows:

[0035] EX p =EX PIESo -EX PIES

[0036] Where: EX p 、EX PIESo 、EX PIES They are the carbon emission rights trading amount, carbon emission rights quota and actual carbon emissions of the park's comprehensive energy system.

[0037] Furthermore, step (4) specifically includes the following process:

[0038] (4a) Determine the objective function and comprehensively consider the system operation cost C of the park's integrated energy system run , Carbon trading costs The low-carbon economic optimization scheduling target with the minimum comprehensive operating cost C is constructed, and its expression is as follows:

[0039]

[0040] The system operating cost is as follows:

[0041]

[0042] Where: C buy C is the energy purchase cost; save Equipment maintenance costs; is the amount of electricity and gas purchased in unit time period t; α t , β t are the electricity price and gas price in period t respectively; ψ gt , gb , p2g , e , h , g They are the unit maintenance costs of gas turbines, gas boilers, P2G equipment, batteries, heat storage tanks, and gas storage tanks; are the input power of gas turbine, gas boiler and P2G equipment at time t respectively; is the discharge power of the battery in unit time period t; is the deflation power of the gas tank in unit time period t; The heat release power of the heat storage tank in unit time period t;

[0043] The carbon trading cost is as follows:

[0044] EX p =EX PIESo -EX PIES

[0045]

[0046] Where: is the step-type carbon trading cost; λ is the carbon trading base price; l is the length of the carbon emission interval; σ is the carbon trading price growth rate;

[0047] (4b) Constraints

[0048] A. The power balance constraints for electricity, gas and heat are as follows:

[0049]

[0050] Where: are the gas, heat and electricity load powers of the park's integrated energy system at time t, respectively; They are the electricity and gas purchased at time t respectively; is the wind power participating in the dispatch at time t; is the input power of the P2G device at time t; are the gas storage and gas release powers of the gas tank at time t respectively; are the heat storage and heat release powers of the heat storage tank at time t respectively; are the charging and discharging power of the battery at time t; η p2g The energy conversion efficiency of the P2G device;

[0051] B. Wind power output constraints

[0052]

[0053] Where: Indicates the upper limit of wind power output power;

[0054] C. P2G device constraints

[0055]

[0056] Where: P p2gn is the rated power of the P2G device;

[0057] D. Gas turbine output and climbing constraints

[0058]

[0059] Where: P gtn is the rated power of the gas turbine; are the upper and lower limits of the gas turbine ramp rate;

[0060] E. Gas boiler output and climbing constraints

[0061]

[0062] Where: P gbn is the rated power of the gas boiler; It is the upper and lower limits of the gas boiler ramp rate;

[0063] F. Power / gas / heat storage constraints

[0064] The three types of energy storage devices, batteries, heat storage tanks, and gas storage tanks, can be processed using a unified general model, including storage energy balance constraints, storage energy upper and lower limit constraints, storage energy cycle equal amount constraints, and charging and discharging power constraints:

[0065]

[0066] In the formula: x represents the energy type, which can be electricity, heat or gas; is the storage energy of the energy storage system in unit time period t; are the charging and discharging power of the energy storage system respectively; η xchar , η xdis are the charging and discharging efficiency of the energy storage system respectively; are the upper and lower limits of the storage energy of the energy storage system respectively; The storage energies at the start and end of one-day scheduling of the energy storage system are required to return to the original value after one-day scheduling; are the upper limits of charging and discharging power of the energy storage system; n x is a 0-1 variable to ensure that the energy storage system does not charge or discharge energy at the same time in each time period. x When it is 1, it stores energy, and when it is 0, it releases energy;

[0067] G. Constraints on purchasing electricity and gas

[0068] The park's integrated energy system is connected to the external power network and natural gas network, and its energy exchange range needs to be constrained. The constraints are as follows:

[0069]

[0070] Where: They are the upper and lower limits of the system's purchased power in unit time period t, respectively; They are respectively the upper and lower limits of the system's purchased natural gas power.

[0071] Furthermore, step (5) introduces an improved particle swarm algorithm to optimize the fitness function described in step (4), as follows:

[0072] (5a) Inertia weight improvement

[0073] Using S-type function, trigonometric function and random function as carriers, ω is optimized and adjusted. The calculation expression of the optimization process is as follows:

[0074]

[0075] Where: ω is the inertia weight; iter represents the current number of iterations; Iter_max represents the maximum number of iterations; rand is a random number uniformly distributed between [0,1], k is a constant, and k=30;

[0076] (5b) Improvement of acceleration factor

[0077] The acceleration factor is improved based on trigonometric functions, and nonlinear adjustments are also performed, including the following calculations:

[0078]

[0079]

[0080] Where: c1 and c2 are acceleration factors; iter represents the current number of iterations; Iter_max represents the maximum number of iterations; the value range of c1 and c2 is [0.5, 2];

[0081] (5c) Improved particle swarm algorithm optimization process

[0082] The operation process of the improved particle swarm algorithm is as follows:

[0083] Step 1: Initialize the algorithm, set the population size m, particle dimension d, acceleration factors c1, c2, and the maximum and minimum flight speeds V of the particles. max and V min , maximum number of iterations Iter_max;

[0084] Step 2: Randomly initialize the velocity and position of the particle within the specified search range;

[0085] Step 3: Calculate the inertia weight and acceleration factor of the particle before the next iteration based on the improved calculation formula of inertia weight and acceleration factor;

[0086] Step 4: Calculate the speed and position of the particle;

[0087] Step 5: According to the set fitness function, calculate the fitness value f of each particle i , and according to f i Find the individual extreme value P best and the global extremum G best , and compare it with the individual extreme value P best Compare, if the result is better than the individual extreme value, then P best =f i ;

[0088] Step 6: Set the individual extreme value P of each particle best and the global extreme value G in the population best Compare, if the result is better than the global extreme value, then G best =P best ;

[0089] Step 7: Determine whether the termination condition described in step (5) is met. If the termination condition is met, the algorithm stops and enters Step 8. If the termination condition is not met, it returns to Step 3.

[0090] Step8: Output the global optimal value.

[0091] Beneficial effects: Compared with the prior art, the ladder-type carbon trading mechanism parameter optimization method based on the improved particle swarm algorithm described in the present invention has the following four essential features and significant improvements:

[0092] (1) Using intelligent algorithms to find the best solution eliminates the need to manually formulate parameters for a tiered carbon trading mechanism;

[0093] (2) Based on the specific conditions of the park's integrated energy system, the improved particle swarm algorithm was used to find the optimal solution, which improved the rationality of the parameters of the ladder-type carbon trading mechanism;

[0094] (3) Reasonable formulation of parameters can give full play to the effectiveness of the carbon trading mechanism;

[0095] (4) The effective role of the carbon trading mechanism has reduced the carbon emissions of the park’s comprehensive energy system and improved the low-carbon nature of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 Flow chart for implementing the present invention;

[0097] Figure 2 This is a calculation example structure diagram of the park integrated energy system in the present invention;

[0098] Figure 3 This is a graph showing the predicted output data of electricity, gas, heat load and wind power in the embodiment;

[0099] Figure 4 This is the relationship diagram between the carbon trading base price and the system carbon emissions;

[0100] Figure 5 It is the relationship diagram between the length of carbon emission interval and the carbon emission of the system;

[0101] Figure 6 This is the relationship between the carbon trading price growth rate and the system carbon emissions;

[0102] Figure 7 To improve the particle swarm algorithm optimization iteration graph. DETAILED DESCRIPTION

[0103] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments of the specification, but the protection scope of the present invention is not limited to the embodiments.

[0104] A parameter optimization method for ladder-type carbon trading mechanism based on improved particle swarm algorithm. Figure 1 As shown, the following steps are included:

[0105] (1) Collecting data and information on the park's integrated energy system

[0106] Collect data and information on the park's comprehensive energy system, including the capacity and efficiency of the park's comprehensive energy system equipment, the carbon emission coefficient of gas turbines, the carbon emission coefficient of gas boilers, the equivalent carbon emission coefficient of electricity purchased from higher-level authorities, the park's comprehensive energy system topology, the park's comprehensive energy system operating costs, the park's comprehensive energy system safe operation constraints, and electricity, heat and gas load information.

[0107] (2) Establishing a comprehensive energy system equipment model for the park

[0108] A. P2G device model

[0109] The mathematical model of P2G equipment is as follows:

[0110]

[0111] Where: are the output and input of the P2G device at time t; η p2g is the P2G conversion efficiency.

[0112] B. Gas Turbine Model

[0113] The mathematical model of the gas turbine is as follows:

[0114]

[0115] Where: are the output power, output heat and input gas volume of the gas turbine at time t respectively; η gte , η gth They are gas-to-electricity efficiency and gas-to-heat efficiency of gas turbines respectively.

[0116] C. Gas boiler model

[0117] The mathematical model of gas boiler is as follows:

[0118]

[0119] Where: is the output and input of the gas boiler at time t; η gb For gas boiler conversion efficiency.

[0120] D. Electricity / Gas / Heat Storage Model

[0121] The three types of energy storage devices, batteries, heat storage tanks, and gas storage tanks, can be processed using a unified general model, including storage energy balance constraints, storage energy upper and lower limit constraints, storage energy cycle equal amount constraints, and charging and discharging power constraints:

[0122]

[0123] In the formula: x represents the energy type, which can be electricity, heat or gas; is the storage energy of the energy storage system in unit time period t; are the charging and discharging power of the energy storage system respectively; η xchar , η xdis are the charging and discharging efficiency of the energy storage system respectively; are the upper and lower limits of the storage energy of the energy storage system respectively; The storage energies at the start and end of one-day scheduling of the energy storage system are required to return to the original value after one-day scheduling; are the upper limits of charging and discharging power of the energy storage system; n x is a 0-1 variable to ensure that the energy storage system does not charge or discharge energy at the same time in each time period. x When it is 1, it stores energy, and when it is 0, it releases energy.

[0124] (3) Establishing a ladder-type carbon trading model

[0125] A. Carbon emission quota model

[0126] The carbon emission quota model is as follows:

[0127]

[0128] Where: EX PIESo 、EX eo,buy 、EX gto 、EX gbo They are the carbon emission quotas of the park's integrated energy system, electricity purchased from higher authorities, gas turbines, and gas boilers; μ e , μ h are the carbon emission quotas for generating unit electrical power and unit thermal power respectively; δ e,h It is the electrical and thermal power conversion parameter; are the power purchase amount from the upper level, the input power of the gas turbine, and the input power of the gas boiler at time t; η gte , η gth , η gb They are the gas-to-electricity efficiency and gas-to-heat efficiency of the gas turbine and the conversion efficiency of the gas boiler. T is the scheduling period.

[0129] B. Actual Carbon Emission Model

[0130] The actual carbon emission model is as follows:

[0131]

[0132] Where: EX PIES 、EX e,buy 、EX gt 、EXgb They are the actual carbon emissions of the park's integrated energy system, electricity purchased from higher authorities, gas turbines, and gas boilers; e , h They are the actual carbon emissions per unit of electrical power and per unit of thermal power respectively.

[0133] C. Ladder-type carbon emissions trading model

[0134] After calculating the carbon emission quota and actual carbon emissions of the park’s comprehensive energy system, the actual carbon emission trading amount in the carbon trading market can be obtained.

[0135] EX p =EX PIESo -EX PIES

[0136] Where: EX p It is the carbon emission rights trading amount of the park’s comprehensive energy system.

[0137] Compared with the existing carbon trading pricing mechanism, in order to further limit carbon emissions, the present invention adopts a tiered pricing mechanism. The tiered pricing mechanism divides multiple purchase intervals. As the number of carbon emission quotas to be purchased increases, the purchase price of the corresponding interval increases. The tiered carbon trading cost is:

[0138]

[0139] Where: is the step-type carbon trading cost; λ is the carbon trading base price; l is the length of the carbon emission interval; σ is the carbon trading price growth rate.

[0140] (5) Encapsulate the low-carbon economic optimization scheduling process of the park's comprehensive energy system, including the equipment model and the step-type carbon trading model, with the goal of reducing operating costs and carbon emissions, as a fitness function

[0141] A. Objective Function

[0142] Comprehensively consider the system operation cost C of the park's comprehensive energy system run , Carbon trading costs The low-carbon economic optimization scheduling target with the minimum comprehensive operating cost C is constructed:

[0143]

[0144] A1. System operating costs

[0145]

[0146] Where: C buy C is the energy purchase cost; save Equipment maintenance costs; is the amount of electricity and gas purchased in unit time period t; α t , β t are the electricity price and gas price in period t respectively; ψ gt , gb , p2g , e , h , g They are the unit maintenance costs of gas turbines, gas boilers, P2G equipment, batteries, heat storage tanks, and gas storage tanks; are the input power of gas turbine, gas boiler and P2G equipment at time t respectively; is the discharge power of the battery in unit time period t; is the deflation power of the gas tank in unit time period t; The heat release power of the heat storage tank in unit time period t.

[0147] A2. Carbon trading costs

[0148] EX p =EX PIESo -EX PIES

[0149] Where: EX p 、EX PIESo 、EX PIES They are the carbon emission rights trading amount, carbon emission rights quota and actual carbon emissions of the park's comprehensive energy system.

[0150]

[0151] Where: is the step-type carbon trading cost; λ is the carbon trading base price; l is the length of the carbon emission interval; σ is the carbon trading price growth rate.

[0152] B. Constraints

[0153] B1. Power balance constraints

[0154] The power balance constraints for electricity, gas and heat are as follows:

[0155]

[0156] Where: are the gas, heat and electricity load powers of the park's integrated energy system at time t, respectively; They are the electricity and gas purchased at time t respectively; is the wind power participating in the dispatch at time t; is the input power of the P2G device at time t; are the gas storage and gas release powers of the gas tank at time t respectively; are the heat storage and heat release powers of the heat storage tank at time t respectively; are the charging and discharging power of the battery at time t; η p2g is the energy conversion efficiency of the P2G device.

[0157] B2. Wind power output constraints

[0158]

[0159] Where: Indicates the upper limit of wind power output power.

[0160] B3. P2G device constraints

[0161]

[0162] Where: P p2gn is the rated power of the P2G device.

[0163] B4. Gas turbine output and climbing constraints

[0164]

[0165] Where: P gtn is the rated power of the gas turbine; These are the upper and lower limits of the gas turbine ramp rate.

[0166] B5. Gas boiler output and climbing constraints

[0167]

[0168] Where: P gbn is the rated power of the gas boiler; It is the upper and lower limits of the gas boiler climbing rate.

[0169] B6. Electricity / gas / heat storage constraints

[0170] The three types of energy storage devices, batteries, heat storage tanks, and gas storage tanks, can be processed using a unified general model, including storage energy balance constraints, storage energy upper and lower limit constraints, storage energy cycle equal amount constraints, and charging and discharging power constraints:

[0171]

[0172] In the formula: x represents the energy type, which can be electricity, heat or gas; is the storage energy of the energy storage system in unit time period t; are the charging and discharging power of the energy storage system respectively; η xchar , η xdis are the charging and discharging efficiency of the energy storage system respectively; are the upper and lower limits of the storage energy of the energy storage system respectively; The storage energies at the start and end of one-day scheduling of the energy storage system are required to return to the original value after one-day scheduling; are the upper limits of charging and discharging power of the energy storage system; n x is a 0-1 variable to ensure that the energy storage system does not charge or discharge energy at the same time in each time period. x When it is 1, it stores energy, and when it is 0, it releases energy.

[0173] B7. Constraints on purchasing electricity and gas

[0174] The park's integrated energy system is connected to the external power grid and natural gas grid, and its energy exchange range needs to be restricted:

[0175]

[0176] Where: P e max , P e min They are the upper and lower limits of the system's purchased power in unit time period t, respectively; They are respectively the upper and lower limits of the system's purchased natural gas power.

[0177] Based on this, the present invention establishes a low-carbon economic dispatch model for the park's comprehensive energy system.

[0178] Due to the different actual conditions of different parks, the corresponding carbon emissions are very different, but there is no universal method to formulate the corresponding carbon trading mechanism parameters according to the specific conditions of the system. Therefore, step (4) encapsulates the optimization scheduling process of the park's comprehensive energy system into a fitness function, which is convenient for the next step to introduce the improved particle swarm algorithm to optimize the carbon trading mechanism parameters. Among them, the input of the fitness function is the carbon trading base price, the length of the carbon emission interval, and the carbon trading price growth rate, and the output is the system carbon emissions.

[0179] (5) An improved particle swarm algorithm is introduced to optimize the fitness function described in step (4). The specific process is as follows:

[0180] A. Inertia weight improvement

[0181] In order to improve the search speed and search accuracy of the particle swarm algorithm in space, an S-type-trigonometric function particle swarm algorithm is introduced. The improvement direction is: using S-type function, trigonometric function, and random function as carriers, ω is optimized and adjusted to further improve the overall optimization ability of the algorithm. After this method is improved, the algorithm converges slowly in the early stage, which helps to expand the search range; in the middle and late stages, ω decreases rapidly, which is conducive to group learning, thereby quickly converging to the global optimal solution.

[0182]

[0183] Where: ω is the inertia weight; iter represents the current number of iterations; Iter_max represents the maximum number of iterations; rand is a random number uniformly distributed between [0,1], k is a constant, and k=30.

[0184] B. Improvement of acceleration factor

[0185] The acceleration factor is improved based on trigonometric functions, and nonlinear adjustments are also performed to improve the search ability of the algorithm.

[0186]

[0187]

[0188] Where: c1 and c2 are acceleration factors; iter represents the current number of iterations; Iter_max represents the maximum number of iterations; the value range of c1 and c2 is [0.5, 2].

[0189] C. Improved particle swarm algorithm optimization process

[0190] The operation process of the improved particle swarm algorithm is as follows:

[0191] Step 1: Initialize the algorithm, set the population size m, particle dimension d, acceleration factors c1, c2, and the maximum and minimum flight speeds V of the particles. max and V min , maximum number of iterations Iter_max.

[0192] Step 2: Randomly initialize the velocity and position of the particle within the specified search range;

[0193] Step 3: Calculate the inertia weight and acceleration factor of the particle before the next iteration according to the improved formula;

[0194] Step 4: Calculate the speed and position of the particle;

[0195] Step 5: According to the set fitness function, calculate the fitness value f of each particle i , and according to f i Find the individual extreme value P best and the global extremum G best . And compare it with the individual extreme value P best Compare, if the result is better than the individual extreme value, then P best =f i ;

[0196] Step 6: Set the individual extreme value P of each particlebest and the global extreme value G in the population best Compare, if the result is better than the global extreme value, then G best =P best ;

[0197] Step 7: Determine whether the termination condition is met (reaching the preset maximum number of iterations). If the termination condition is met, the algorithm stops the operation and enters Step 8. If the termination condition is not met, it returns to Step 3.

[0198] Step8: Output the global optimal value.

[0199] (6) Output the optimization result information of step (5)

[0200] In step (5), after optimizing the fitness function mentioned in step (4) with the goal of minimizing the system carbon emissions using the improved particle swarm algorithm, the corresponding algorithm optimization result information is output, and the optimization result information includes the carbon trading base price, the carbon emissions interval length, the carbon trading price growth rate and the system carbon emissions.

[0201] Example

[0202] The structure diagram of the park comprehensive energy system involved in the present invention is as follows Figure 2 As shown, a scheduling cycle is 24 hours a day. Figure 3 is the forecast value of wind power output and electricity, heat and gas load demand within a park's integrated energy system, assuming that the output fluctuation error of renewable wind turbines is ±10%. Based on the calorific value, carbon content and efficiency of each carbon emission link of the fuel, the unit carbon emission of each carbon emission link is determined as follows: the equivalent carbon emission of purchased electricity δ e is 1.08kg / (kW·h), and the carbon emissions of gas boilers δ h is 0.065kg / MJ. After converting the electricity into heat (conversion coefficient ξ e,h Taking 6MJ / (kW·h)), the equivalent heat supply of the gas turbine is close to that of the gas boiler, and its carbon emissions are also 0.065kg / MJ. In addition, the invention uses the average carbon emissions of various industries as a benchmark to comprehensively determine the carbon emission quota per unit of electrical power generated. e is 0.728kg / (kW·h), and the carbon emission quota per unit thermal power is ξ h It is 0.102kg / MJ. In addition, the equipment capacity, conversion efficiency and other information involved in the park's comprehensive energy system are shown in Tables 1 and 2.

[0203] Table 1 Energy conversion equipment parameters

[0204]

[0205] Table 2 Energy storage equipment parameters

[0206]

[0207] The main results and analysis of optimizing the parameters of the step-by-step carbon trading mechanism using the improved particle swarm algorithm in this invention are as follows:

[0208] A. Parameter Impact Analysis

[0209] According to the formula for calculating the cost of step-by-step carbon trading, the mechanism mainly includes three parameters: carbon trading base price, carbon emission interval length, and carbon trading price growth rate. The following analyzes the impact of these three parameters on the step-by-step carbon trading mechanism.

[0210] A1. Analysis of the impact of carbon trading base price

[0211] At this time, the carbon emission interval length is set to 2000kg, and the carbon trading price growth rate is set to 0.25.

[0212] Depend on Figure 4 It can be seen that when the carbon trading base price is less than 0.9 yuan / kg, as the carbon trading base price increases, the greater the proportion of carbon emission costs in the objective function, the stronger the constraint of the carbon trading mechanism on carbon emissions, thus forcing the system to reduce carbon emissions to reduce carbon trading costs, so the system carbon emissions gradually decrease. When the carbon trading base price is greater than 0.9 yuan / kg, although the carbon trading base price continues to increase, in the park's comprehensive energy system, the output of each device has basically stabilized, and the overall carbon emissions of the system have also stabilized. At this time, the impact of changes in the carbon trading base price on the system's carbon emission level is relatively weak. At this time, the system's minimum carbon emissions are 15053.652kg.

[0213] A2. Analysis of the impact of carbon emission interval length

[0214] At this time, the carbon trading base price is set to 0.252 yuan / kg, and the carbon trading price growth rate is set to 0.25.

[0215] Depend on Figure 5It can be seen that when the length of the carbon emission interval is in the range of [500,1300] kg, because the interval length is relatively small, the system carbon emissions are mostly traded at high-step prices in the carbon trading market. At this time, the corresponding carbon trading cost is high, and the constraints of the carbon trading mechanism in the system are strengthened, so carbon emissions are reduced. When the carbon emission interval length is in the range of (1300,2300]kg, the interval length becomes larger, but because of the inherent electricity, gas and other load conditions in the park's comprehensive energy system, the amount of system carbon emissions traded at high-tier prices in the carbon trading market becomes smaller, so the corresponding carbon trading cost also becomes smaller. The gradual reduction in carbon trading costs has led to a decrease in its proportion in the objective function, and the constraints of the carbon trading mechanism have weakened, so the system's carbon emissions are increasing. When the carbon emission interval length is in the range of (2300,3000]kg, the system's carbon emissions are mostly traded in the carbon trading market at the base price or the first-tier price. At this time, the ladder-type carbon trading situation is close to the traditional carbon trading, and the size of the system's carbon emissions is less affected by the change in the interval length, so the carbon emission level is relatively stable. At this time, the system's minimum carbon emissions are 17948.725kg.

[0216] A3. Analysis of the impact of price growth rate

[0217] At this time, the carbon trading base price is set at 0.252 yuan / kg, and the carbon emission interval length is 2000kg.

[0218] Depend on Figure 6 It can be seen that when the price growth rate is less than 0.7, the carbon trading cost gradually increases with the increase of the price growth rate, causing its proportion in the objective function to gradually increase, and the carbon trading mechanism also strengthens the carbon emission constraints on the system. In order to reduce the carbon emissions of the system to achieve the purpose of reducing carbon trading costs, during the optimization and scheduling process, the system will adjust the output of each internal equipment and select gas equipment with a lower emission coefficient to supply energy. At the same time, due to the load conditions of electricity, heat and other loads in the park's comprehensive energy system, when the price growth rate is greater than 0.7, the output of each device in the system gradually reaches a relative stability, and the carbon emission level also reaches a relative stability. At this time, the minimum carbon emission of the system is 17946.487kg.

[0219] Based on the above analysis of the parameters of the step-type carbon trading mechanism, it can be concluded that for the carbon trading base price, when the base price is greater than 0.9 yuan / kg, the system's carbon emissions reach the minimum. At this time, the increase in the base price can no longer reduce the carbon emissions of the park's comprehensive energy system, but will only increase the system's comprehensive operating costs. For the length of the carbon emission interval, when the interval length is less than or equal to 1300kg, the system's carbon emissions are the smallest, and at this time the step-type carbon trading mechanism has a strong constraint on the system's carbon emissions; and when the interval length is greater than 2300kg, the system's carbon emissions are the largest, and at this time the mechanism has a poor constraint on the system's carbon emissions. For the price growth rate, when the price growth rate is greater than 0.7, the system's carbon emissions are less affected by changes in the price growth rate. At this time, the system's carbon emissions are low and reach a relatively stable state. Therefore, the reasonable setting of the carbon trading base price, the length of the carbon emission interval, and the price growth rate can effectively achieve the system's low-carbon goals.

[0220] B. Optimizing carbon trading mechanism parameters based on improved particle swarm algorithm

[0221] Figure 7 To improve the particle swarm algorithm optimization process. The ladder-type carbon trading mechanism is introduced into the park's comprehensive energy system, and the improved particle swarm algorithm is used to optimize the parameters of the mechanism. The final parameter optimization results are: the carbon trading base price is determined to be 0.853 yuan / kg, the carbon emission interval length is determined to be 553.512kg, the price growth rate is determined to be 0.823, and the corresponding system carbon emissions are 13510.183kg. The carbon emissions of this system are reduced by 1543.469kg compared to the minimum carbon emissions in A1 - 15053.652kg, 4438.542kg compared to the minimum carbon emissions in A2 - 17948.725kg, and 4436.304kg compared to the minimum carbon emissions in A3 - 17946.487kg. In addition, the optimization characteristics of the algorithm are used to make the carbon trading mechanism parameters adaptive to the actual situation of the park, eliminating the trouble of manually setting parameters and improving work efficiency. The superiority of the ladder-type carbon trading mechanism parameter optimization method based on the improved particle swarm algorithm in the low-carbon optimization scheduling of the park's integrated energy system has been verified.

[0222] This invention takes ladder-type carbon trading as the research object, introduces intelligent algorithms, and proposes a ladder-type carbon trading mechanism parameter optimization method based on an improved particle swarm algorithm. At the same time, the superiority of the proposed method is analyzed and verified with the park's integrated energy system as the background. The simulation results of the example of the invention show that: (1) the improved particle swarm algorithm is used to optimize according to the actual situation of the park, eliminating the trouble of manually formulating the parameters of the ladder-type carbon trading mechanism, and at the same time improving the rationality of the formulation of the parameters of the ladder-type carbon trading mechanism; (2) the rational formulation of parameters can give full play to the effectiveness of the carbon trading mechanism, reduce the carbon emissions of the park's integrated energy system, and improve the low-carbon nature of the system.

[0223] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing parameters of a ladder-type carbon trading mechanism based on an improved particle swarm algorithm, characterized in that: The following steps are involved: (1) Collect information and operation data of the park's integrated energy system, including the topological architecture of the park's integrated energy system, the capacity and efficiency of the park's integrated energy system equipment, the carbon emission coefficient of gas turbines, the carbon emission coefficient of gas boilers, the carbon emission coefficient of the equivalent electricity purchased by the superior, the operation cost of the park's integrated energy system, the safe operation constraints of the park's integrated energy system, and the information on electricity, heat and gas loads; (2) Establish a mathematical model of the park's integrated energy system equipment, including a P2G equipment model, a gas turbine model, a gas boiler model, a battery model, a gas storage tank model, and a heat storage tank model, as follows: (2a) The mathematical model of P2G device model is as follows: Where: are the output and input of the P2G device at time t; η p2g is the P2G conversion efficiency; (2b) The mathematical model of the gas turbine is as follows: Where: They are the output power, output heat and input gas volume of the gas turbine at time t respectively; η gte , η gth They are gas-to-electricity efficiency and gas-to-heat efficiency of gas turbines respectively; (2c) The mathematical model of the gas boiler is as follows: Where: is the output and input of the gas boiler at time t, η gb For gas boiler conversion efficiency; (2d) The electricity / gas / heat storage model includes three types of energy storage devices: batteries, heat storage tanks, and gas tanks. They are processed using a unified general model, including storage energy balance constraints, storage energy upper and lower limit constraints, storage energy cycle equal amount constraints, and charging and discharging energy power constraints. Its mathematical expression is as follows: In the formula: x represents the energy type, which can be electricity, heat or gas; is the storage energy of the energy storage system in unit time period t; are the charging and discharging power of the energy storage system respectively; η xchar , η xdis are the charging and discharging efficiency of the energy storage system respectively; are the upper and lower limits of the storage energy of the energy storage system respectively; The storage energy of the energy storage system at the start and end of one-day scheduling is required to return to the original value after one-day scheduling. are the upper limits of charging and discharging power of the energy storage system; n x is a 0-1 variable to ensure that the energy storage system does not charge or discharge energy at the same time in each time period. x When it is 1, it stores energy, and when it is 0, it releases energy; (3) Establish a ladder-type carbon trading model, including the free carbon emission quota model of the park's comprehensive energy system and the actual carbon emission model of the park's comprehensive energy system, and a ladder-type carbon emission trading model. The mathematical expression of the ladder-type carbon trading cost is obtained as follows: Where: is the step-by-step carbon trading cost; λ is the carbon trading base price; l is the length of the carbon emission interval; σ is the carbon trading price growth rate; EX p The carbon emission rights trading amount of the park's comprehensive energy system; Furthermore, the ladder-type carbon trading model includes the following sub-models: (3a) The mathematical expression of the carbon emission quota model is as follows: Where: EX PIESo 、EX eo,buy 、EX gto 、EX gbo They are the carbon emission quotas of the park's integrated energy system, electricity purchased from higher authorities, gas turbines, and gas boilers; μ e , μ h are the carbon emission quotas for generating unit electrical power and unit thermal power respectively; δ e,h It is the electrical and thermal power conversion parameter; They are the power purchased by the upper level, the gas turbine input power, and the gas boiler input power at time t respectively; η gte , η gth , η gb are the gas-to-electricity efficiency and gas-to-heat efficiency of the gas turbine and the conversion efficiency of the gas boiler, respectively, and T is the scheduling period; (3b) The mathematical expression of the actual carbon emission model is as follows: Where: EX PIES 、EX e,buy 、EX gt 、EX gb They are the actual carbon emissions of the park's integrated energy system, electricity purchased from higher authorities, gas turbines, and gas boilers; e , h are the actual carbon emissions per unit of electrical power and per unit of thermal power respectively; (3c) The calculation of the carbon emission rights trading amount actually participating in the carbon trading market in the ladder-type carbon emission trading model is as follows: EX p =EX PIESo -EX PIES Where: EX p 、EX PIESo 、EX PIES They are the carbon emission rights trading amount, carbon emission rights quota, and actual carbon emissions of the park’s comprehensive energy system; (4) Based on the system operation cost of the park's comprehensive energy system C run and carbon trading costs The minimum overall cost is the low-carbon economic optimization scheduling target, and the low-carbon economic optimization scheduling process of the park's comprehensive energy system is encapsulated as a fitness function. The input of the fitness function includes three step-type carbon trading mechanism parameters: carbon trading base price, carbon emission interval length, and carbon trading price growth rate. The output is the carbon emissions of the park's comprehensive energy system. The specific process includes the following: (4a) Determine the objective function and comprehensively consider the system operation cost C of the park's integrated energy system run , Carbon trading costs The low-carbon economic optimization scheduling target with the minimum comprehensive operating cost C is constructed, and its expression is as follows: The system operating cost is as follows: Where: C buy C is the energy purchase cost; save Equipment maintenance costs; is the amount of electricity and gas purchased in unit time period t; α t , β t are the electricity price and gas price in period t respectively; ψ gt , gb , p2g , e , h , g They are the unit maintenance costs of gas turbines, gas boilers, P2G equipment, batteries, heat storage tanks, and gas storage tanks; are the input power of gas turbine, gas boiler and P2G equipment at time t respectively; is the discharge power of the battery in unit time period t; is the deflation power of the gas tank in unit time period t; The heat release power of the heat storage tank in unit time period t; The carbon trading cost is as follows: EX p =EX PIESo -EX PIES Where: is the step-type carbon trading cost; λ is the carbon trading base price; l is the length of the carbon emission interval; σ is the carbon trading price growth rate; (4b) Constraints A. The power balance constraints for electricity, gas and heat are as follows: Where: are the gas, heat and electricity load powers of the park's integrated energy system at time t, respectively; They are the electricity and gas purchased at time t respectively; is the wind power participating in the dispatch at time t; is the input power of the P2G device at time t; are the gas storage and gas release powers of the gas tank at time t respectively; are the heat storage and heat release powers of the heat storage tank at time t respectively; are the charging and discharging power of the battery at time t; η p2g The energy conversion efficiency of the P2G device; B. Wind power output constraints Where: Indicates the upper limit of wind power output power; C. P2G device constraints Where: P p2gn is the rated power of the P2G device; D. Gas turbine output and climbing constraints Where: P gtn is the rated power of the gas turbine; are the upper and lower limits of the gas turbine ramp rate; E. Gas boiler output and climbing constraints Where: P gbn is the rated power of the gas boiler; It is the upper and lower limits of the gas boiler ramp rate; F. Power / gas / heat storage constraints The three types of energy storage devices, batteries, heat storage tanks, and gas storage tanks, can be processed using a unified general model, including storage energy balance constraints, storage energy upper and lower limit constraints, storage energy cycle equal constraints, and charging and discharging power constraints: In the formula: x represents the energy type, which can be electricity, heat or gas; is the storage energy of the energy storage system in unit time period t; are the charging and discharging power of the energy storage system respectively; η xchar , η xdis are the charging and discharging efficiency of the energy storage system respectively; are the upper and lower limits of the storage energy of the energy storage system respectively; The storage energy of the energy storage system at the start and end of one-day scheduling is required to return to the original value after one-day scheduling. are the upper limits of charging and discharging power of the energy storage system; n x is a 0-1 variable to ensure that the energy storage system does not charge or discharge energy at the same time in each time period. x When it is 1, it stores energy, and when it is 0, it releases energy; G. Constraints on purchasing electricity and gas The park's integrated energy system is connected to the external power network and natural gas network, and its energy exchange range needs to be constrained. The constraints are as follows: Where: They are the upper and lower limits of the system's purchased power in unit time period t, respectively; They are the upper and lower limits of the system's purchased natural gas power respectively; (5) An improved particle swarm algorithm is introduced to optimize the fitness function in step (4), including adjusting the inertia weight in the particle swarm algorithm with S-type function, trigonometric function and random function as carriers and optimizing the acceleration factor in the particle swarm algorithm with trigonometric function as support. The optimization process includes iterative optimization of the fitness function with the maximum number of iterations as the termination condition and the lowest system carbon emissions as the goal; the optimization result information includes the carbon trading base price, the length of the carbon emission interval, the carbon trading price growth rate and the carbon emissions of the park's comprehensive energy system, as follows: (5a) Inertia weight improvement Using S-type function, trigonometric function and random function as carriers, ω is optimized and adjusted. The calculation expression of the optimization process is as follows: Where: ω is the inertia weight; iter represents the current number of iterations; Iter_max represents the maximum number of iterations; rand is a random number uniformly distributed between [0,1], k is a constant, and k=30; (5b) Improvement of acceleration factor The acceleration factor is improved based on trigonometric functions, and nonlinear adjustments are also performed, including the following calculations: Where: c1 and c2 are acceleration factors; iter represents the current number of iterations; Iter_max represents the maximum number of iterations; the value range of c1 and c2 is [0.5, 2]; (5c) Improved particle swarm algorithm optimization process The operation process of the improved particle swarm algorithm is as follows: Step 1: Initialize the algorithm, set the population size m, particle dimension d, acceleration factors c1, c2, and the maximum and minimum flight speeds V of the particles. max and V min , maximum number of iterations Iter_max; Step 2: Randomly initialize the velocity and position of the particle within the specified search range; Step 3: Calculate the inertia weight and acceleration factor of the particle before the next iteration based on the improved calculation formula of inertia weight and acceleration factor; Step 4: Calculate the speed and position of the particle; Step 5: According to the set fitness function, calculate the fitness value f of each particle i , and according to f i Find the individual extreme value P best and the global extremum G best , and compare it with the individual extreme value P best Compare, if the result is better than the individual extreme value, then P best =f i ; Step 6: Set the individual extreme value P of each particle best and the global extreme value G in the population best Compare, if the result is better than the global extreme value, then G best =P best ; Step 7: Determine whether the termination condition described in step (5) is met. If the termination condition is met, the algorithm stops and enters Step 8. If the termination condition is not met, it returns to Step 3. Step8: Output the global optimal value.

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