Rural integrated energy system multi-objective optimization method considering carbon emission cost and efficiency

Abstract

The invention relates to the field of multi-energy optimization scheduling, in particular to a rural comprehensive energy system multi-objective optimization method considering carbon emission cost and # imgabs0 # efficiency, which comprises the following steps: S1, establishing a rural comprehensive energy system; s2, establishing an electric energy supply model of a solar photo-thermal system, a gas energy supply model of a biomass pyrolysis gasification power generation system and a thermal energy supply model of a biomass biogas thermoelectric system; s3, constructing an optimization solution model of the rural comprehensive energy system; and S4, an NSGA-II algorithm is adopted to solve a Pareto solution set of the optimization solving model, the Pareto solution set comprises a plurality of parameters, and an elite gene retention strategy and rapid non-dominated sorting are introduced in the population updating process of the NSGA-II algorithm to improve the solving ability of the NSGA-II algorithm. According to the method, the technical problem that the rural comprehensive energy system optimization method mainly based on a single target cannot effectively consider the requirements of low carbon, high efficiency and economical efficiency is solved by constructing the multi-target rural comprehensive energy system optimization solution model.

Description

Technical Field

[0001] The present invention relates to the field of multi-energy optimization scheduling, specifically a method of taking into account carbon emission costs and Multi-objective optimization method for rural integrated energy system with high efficiency. Background Art

[0002] With the continuous deepening of modernization in rural areas, the improvement and upgrading of the energy structure system in rural areas has become an important direction for the development of my country's future power system. The main method for solving the energy supply problem in rural areas is relatively primitive, usually relying on the direct combustion of biomass energy. This method is single, has low energy utilization, poor flexibility, and causes serious environmental pollution, which shows that the traditional rural energy supply model is no longer suitable for today's situation. Rural areas need clean, green and efficient energy use and development paths. Therefore, the present invention analyzes and optimizes the overall energy structure by establishing a rural comprehensive energy system model that is coordinated and uniformly dispatched, focusing on the system's economy, carbon emissions, Efficiency aims to optimize the energy structure of the rural integrated energy system, reduce carbon emissions, lower energy costs, and promote the use of renewable energy.

[0003] Existing research work mainly focuses on investment costs, and the optimization method of rural comprehensive energy systems is mainly based on a single goal. For example, the Chinese patent "A Rural Comprehensive Energy Planning Resource Optimization System and Method" (application number CN202110621398.0, published on 20211022) only uses the study of the working conditions of the biogas system as a single evaluation goal. This rural comprehensive energy system optimization method with a single goal as the main focus cannot effectively take into account the requirements of low carbon, high efficiency and economy. Summary of the invention

[0004] The present invention is intended to provide a method for taking into account the carbon emission cost and The multi-objective optimization method of efficient rural integrated energy system is mainly used to solve the technical problem that the existing rural integrated energy system optimization method with a single objective as the main objective cannot effectively take into account the requirements of low carbon, high efficiency and economy.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] Solution 1:

[0007] A method that considers carbon emission costs and The multi-objective optimization method of rural integrated energy system for efficiency includes the following steps:

[0008] S1: Establish various types of equipment, including energy conversion systems and multi-energy storage facilities. The energy conversion systems include solar thermal systems, biomass pyrolysis and gasification power generation systems, and biomass biogas thermal power systems. Multi-energy storage facilities include heat storage equipment, gas storage equipment, and electricity storage equipment. The multi-energy storage facilities and energy users' electricity load demand, heat load demand, and gas load demand are coupled to form a rural comprehensive energy system.

[0009] S2: Establish the electric energy supply model of solar thermal system, the gas energy supply model of biomass pyrolysis gasification power generation system, and the thermal energy supply model of biomass biogas thermal power system;

[0010] S3: Based on the life cycle cost, carbon emission cost, Taking efficiency as the goal and power balance and equipment capacity as constraints, an optimization model for rural integrated energy system is constructed;

[0011] S4: The NSGA-Ⅱ algorithm is used to solve the Pareto solution set of the optimization solution model, which includes the volume parameters of the biogas tank and gas storage equipment, as well as the power of the biogas boiler, biogas gas turbine generator set, biomass pyrolysis gasification generator set, solar thermal system, electric heating equipment, heat storage equipment, and power storage equipment.

[0012] Preferably, in step S2, the electric energy supply model is a solar thermal system model, and the output power of the solar thermal system can be expressed as:

[0013]

[0014] in, is the output power of the generator at time t; η e is the thermal-electric conversion efficiency of the generator; is the total input thermal power of the generator at time t; E t E is the heat storage capacity of the heat storage device in the CSP power station at time t; t-1 is the heat storage capacity of the heat storage device in the CSP power station at time t-1; ρ is the energy self-loss coefficient of the heat storage device; is the charging power of the heat storage device at time t; is the charging efficiency of the heat storage device; is the heat release power of the heat storage device at time t; is the heat release efficiency of the heat storage device; Δt is the unit scheduling time.

[0015] Preferably, in step S2, the gas energy supply model is a biomass biogas thermal power system model, and the mathematical model of biogas production is:

[0016] P biog =r biog Vbiog

[0017] Where: P biog is the daily output of the biogas system, r biog is the biogas production efficiency, V biog is the biogas volume of the existing biogas digester;

[0018]

[0019] in, Indicates the thermal power output value of the biogas engine at time t; Represents the electric power output value of the biogas engine at time t; η EMT is the power generation efficiency, η HL is the heat loss coefficient of the biogas engine; is the gas power of the biogas engine, is the biogas consumption of the biogas boiler at time t; is the thermal power output of the biogas boiler; L G Indicates the calorific value of biogas, η mt represents the heating efficiency of the biogas boiler, and Δt represents the operating time of the gas energy supply model.

[0020] Preferably, in step S2, the thermal energy supply model is a biomass pyrolysis gasification power generation system model, and the output characteristic model of the biomass pyrolysis gasification power generation system is:

[0021] V f,t =m f,t β f η f

[0022]

[0023] Where: V f,t is the gas production rate of the pyrolysis gasifier; m f,t is the gasification amount of garbage at time t; β f is the gasification coefficient; η f is the pyrolysis furnace efficiency; is the power generation of the gas turbine at time t; f is the combustible gas content; Q f is the calorific value of combustion of combustible gas; is the heating power of the gas turbine; η PG is the power generation efficiency of the gas turbine; η1 is the heat loss coefficient of the gas turbine.

[0024] Preferably, the life cycle cost objective function of the rural integrated energy system in step S3 is:

[0025] Taking the static voltage stability index of the system as the objective function, the moment with the lowest stability during one day of operation is selected to evaluate the rural integrated energy system.

[0026] F A =C inv +C ma +C opt -C re

[0027] Where: F A is the life cycle cost of the rural integrated energy system, C inv is the construction cost of the rural integrated energy system, C ma is the maintenance cost of the rural integrated energy system, C opt is the operating cost of the rural integrated energy system, C re Proceeds from waste disposal for integrated rural energy systems;

[0028] The construction cost is calculated as follows:

[0029]

[0030] Where: N eq is the number of equipment types described in step S1; r is the discount rate; P i , T i , are the planned capacity, life cycle and unit capacity construction cost of the i-th type of equipment respectively;

[0031] Maintenance costs are calculated as follows:

[0032]

[0033] Where: P i,t is the output of the i-th type of equipment at time t; is the unit output maintenance cost of the i-th type of equipment;

[0034] The cost of purchased energy is calculated as follows:

[0035]

[0036] Where: c t,e is the electricity price at time t, c t,g is the gas price at time t, P grid,t is the amount of electricity purchased at time t, P gas,t is the amount of gas purchased at time t;

[0037] The calculation method of waste disposal revenue is as follows:

[0038]

[0039] Where: K is the number of garbage types; S is the total number of garbage treatment batches; m k,s is the amount of k types of garbage processed in s batches, is the unit economic benefit that can be obtained by processing k types of garbage in s batches; ω k,s Savings in transporting k types of garbage for s batches, n k,s Save the amount of k types of garbage transferred for s batches.

[0040] Preferably, the optimization solution model of the rural integrated energy system in step S3 includes a carbon emission cost objective function:

[0041]

[0042] Where: F c Carbon emission costs for rural integrated energy systems; is the carbon emission price of carbon emission source a, E a,t is the carbon emission energy of carbon emission source a at time t.

[0043] Preferably, the optimization solution model of the rural integrated energy system in step S3 includes Efficiency objective function:

[0044] η ex =E g / E p

[0045] Where: η ex Represents the rural integrated energy system Efficiency; E g Indicates that the system is being used or benefited E p Indicates the system payment or consumption

[0046] Preferably, the optimization solution model of the rural integrated energy system in step S3 includes the following constraints, and the constraint expressions are as follows:

[0047] Power balance constraints:

[0048]

[0049] Where: P e,load,t is the electrical load at time t, P h,load,t is the gas load at time t, P g,load,t is the heat load at time t; is the power generation or consumption of the biogas engine at time t, is the power generated or consumed by the solar thermal power generation equipment at time t, is the power generation or consumption of the biomass pyrolysis gasification generator set at time t, is the power generated or consumed by the electric heating equipment at time t; is the heating power of the solar thermal power generation equipment at time t, is the heating power of the electric heating equipment at time t, is the heating power of the biogas engine at time t, is the heating power of the biogas boiler at time t; is the biogas production at time t, is the gas power of the biogas boiler at time t, P is the gas power of the biogas engine at time t; grid,t is the power purchase amount of the rural integrated energy system at time t, P gas,t P is the gas purchase volume of the rural integrated energy system at time t; ESS,t is the power storage device at time t, P TSS,t is the heat storage device at time t, P TSS,t is the working power of the gas storage equipment at time t;

[0050] Equipment capacity constraints:

[0051]

[0052] Where: P i,max is the maximum operating power of the i-th type of equipment in step S2, P i,min is the minimum operating power of the i-th type equipment; P i t is the working power of the i-th type of equipment at time t in step S2; D i is the lower limit of the downward climbing power of the i-th type of equipment in step S2, B i P is the upper limit of the power of the i-th type equipment; i t+1 is the working power of the i-th type of equipment at time t+1, P i t is the working power of the i-th type of equipment at time t, and Δt is the unit time.

[0053] Preferably, in step S4, the NSGA-II algorithm steps are as follows:

[0054] S01: Initialize the system and input the parameters, light intensity, and load of each device in the rural integrated energy system in step S1;

[0055] S02: Call any function and take t=0 as the initial population P0;

[0056] S03: Calculate the fitness of each individual in population P0;

[0057]

[0058] Where: Fitness is the fitness function; f1(X), f2(X), and f3(X) are the values ​​of the three objective functions; Δ is the sum of the absolute values ​​of the relevant constraints of the individuals that do not meet the constraints; f 1，max (X) is the maximum value of the first objective function value of all individuals; f 2，max (X) represents the maximum value of the second objective function of all individuals; f 3，max (X) represents the maximum value of the third objective function of all individuals;

[0059] S04: Calculate the parent population P t Individual objective function values,parallel non-inferiority sorting;

[0060] S05: For the parent population P t Genetic operations are used to obtain the offspring population Q t ;

[0061] S06: Merge parent population P t With the offspring population Q t is the population R t , and perform non-inferiority sorting;

[0062] S07: Keep elite individuals and use them as the new parent population P t+1 ;

[0063] S08: Determine the termination condition to see whether the maximum number of iterations has been reached. If so, output the optimization result of the rural integrated energy system; otherwise, return to step S05.

[0064] Preferably, in step S4, the elite gene retention strategy and fast non-dominated sorting are introduced to optimize the NSGA-II algorithm, and the specific steps are:

[0065] S11: merge the parent population and the child population to form a new population, and then perform non-dominated sorting on the new population;

[0066] S12: Generate a new parent generation. First, put the non-dominated individuals with a Pareto level of 1 into the new parent generation set, then put the individuals with a Pareto level of 2 into the new parent generation population, and so on.

[0067] S13: If after all individuals of level k are placed in the new parent set, the number of individuals in the set is less than n, and after all individuals of level k+1 are placed in the new parent set, the number of individuals in the set is greater than n, then the crowding degree of all individuals of level k+1 is calculated and all individuals are sorted in descending order according to the crowding degree, and then all individuals with a level greater than k+1 are eliminated;

[0068] S14: Put the individuals in level k+1 into the new parent set one by one in the order arranged in step S12, until the number of individuals in the parent set is equal to n, and the remaining individuals are eliminated.

[0069] The beneficial effects of this program are as follows:

[0070] 1. This scheme considers carbon emission costs and The multi-objective optimization method of rural integrated energy system with high efficiency is used to couple various equipment in rural areas into an integrated energy system, and select the full life cycle cost, carbon emission cost, With efficiency as the objective function, a rural integrated energy system planning model is constructed. A multi-objective optimization model based on NSGA-Ⅱ algorithm is used to solve the multi-objective optimization model, and the elite gene retention strategy and fast non-dominated sorting are introduced in the population update process to improve the algorithm's solving ability, which enables the algorithm to find a better Pareto frontier solution set and provide a series of effective non-inferior solutions for rural integrated energy system decision makers to choose, thus realizing the multi-objective comprehensive optimization of various indicators of the rural energy system.

[0071] 2. This solution considers carbon emission costs and The multi-objective optimization method of the efficient rural integrated energy system includes the electricity, gas and heat energy supply model. According to the energy supply model, the variable relationship that is difficult to describe for some rural energy equipment can be described qualitatively and quantitatively, and the parameter calculation between some variables of rural energy equipment can be realized from the side. This method couples the electricity, heat and gas load demands of various equipment and energy users in rural areas into a complete integrated energy system, and realizes energy saving and efficiency improvement of the system through coordinated planning.

[0072] In summary, this solution solves the technical problem that the existing rural integrated energy system optimization method based on a single goal cannot effectively take into account the requirements of low carbon, high efficiency and economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 This is the architecture diagram of the rural integrated energy system of the present invention patent;

[0074] Figure 2 This is the flowchart of the multi-objective optimization algorithm of the rural integrated energy system of the present invention;

[0075] Figure 3 The typical daily wind power and photovoltaic characteristic curves of the present invention patent;

[0076] Figure 4 The time-of-use electricity price of the patent of this invention;

[0077] Figure 5It is the Pareto solution set of multi-objective optimization of the patent of this invention. DETAILED DESCRIPTION

[0078] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0079] The present invention first studies the energy output characteristics of energy facilities in rural areas, establishes an energy supply model for rural energy facilities, selects the full life cycle cost, carbon emission cost, With efficiency as the objective function, a rural integrated energy system planning model is constructed. An NSGA-Ⅱ algorithm is used to solve the rural integrated energy system planning model, and an elite gene retention strategy and fast non-dominated sorting are introduced in the population update process to improve the algorithm's solving ability.

[0080] The specific steps are as follows:

[0081] S1: Establish solar thermal systems, biomass pyrolysis and gasification power generation systems, biomass biogas thermal power systems for energy conversion, and heat storage equipment, gas storage equipment, and electricity storage equipment for energy storage, and couple the heat storage equipment, gas storage equipment, electricity storage equipment and energy users' electricity load demand, heat load demand, and gas load demand to form a rural comprehensive energy system; at the same time, the rural comprehensive energy system also includes biogas production facilities, biogas boilers, biogas generator sets, biomass pyrolysis and gasification generator sets, solar thermal equipment and other equipment used to support the operation of solar thermal systems, biomass pyrolysis and gasification power generation systems, and biomass biogas thermal power systems, as well as multiple energy storage facilities for energy storage. The multiple energy storage facilities include electricity storage equipment, gas storage equipment, and heat storage equipment. The specific structure is as follows: Figure 1 As shown;

[0082] S2: Study the energy output characteristics of solar thermal facilities, biomass biogas facilities, and biomass pyrolysis gasification power generation facilities in rural areas, and establish an electric energy supply model for solar thermal systems, a gas energy supply model for biomass pyrolysis gasification power generation systems, and a thermal energy supply model for biomass biogas thermal power systems;

[0083] Solar thermal system model, the output power of the solar thermal system can be expressed as:

[0084]

[0085] in, is the output power of the generator at time t; ηe is the thermal-electric conversion efficiency of the generator; is the total input thermal power of the generator at time t; E t E is the heat storage capacity of the heat storage device in the CSP power station at time t; t-1 is the heat storage capacity of the heat storage device in the CSP power station at time t-1; ρ is the energy self-loss coefficient of the heat storage device; is the charging power of the heat storage device at time t; is the charging efficiency of the heat storage device; is the heat release power of the heat storage device at time t; is the heat release efficiency of the heat storage device; Δt is the unit scheduling time;

[0086] Biomass biogas thermal power system model, the mathematical model of biogas production is:

[0087] P biog =r biog V biog

[0088] Where: P biog is the daily output of the biogas system, r biog is the biogas production efficiency, V biog is the biogas volume of the existing biogas digester;

[0089]

[0090]

[0091] in, Indicates the thermal power output value of the biogas engine at time t; Represents the electric power output value of the biogas engine at time t; η EMT is the power generation efficiency, η HL is the heat loss coefficient of the biogas engine; is the gas power of the biogas engine, is the biogas consumption of the biogas boiler at time t; is the thermal power output of the biogas boiler; L G Indicates the calorific value of biogas, η mt represents the heating efficiency of the biogas boiler, Δt represents the operating time of the gas energy supply model;

[0092] Biomass pyrolysis gasification power generation system model, the output characteristic model of the biomass pyrolysis gasification power generation system is:

[0093] V f,t =m f,t β f η f

[0094]

[0095] Where: V f,t is the gas production rate of the pyrolysis gasifier; m f,t is the gasification amount of garbage at time t; β f is the gasification coefficient; η f is the pyrolysis furnace efficiency; is the power generation of the gas turbine at time t; f is the combustible gas content; Q f is the calorific value of combustion of combustible gas; is the heating power of the gas turbine; η PG is the power generation efficiency of the gas turbine; η1 is the heat loss coefficient of the gas turbine;

[0096] S3: Taking into account the constraints of local time-of-use electricity prices, energy equipment operation, etc., the rural integrated energy system is based on the full life cycle cost, carbon emission cost, Taking efficiency as the goal and power balance and equipment capacity as constraints, an optimization model for rural integrated energy system is constructed;

[0097] 1) Life cycle cost

[0098] Taking the static voltage stability index of the system as the objective function, the moment with the lowest stability during one day of operation is selected to evaluate the rural integrated energy system.

[0099] F A =C inv +C ma +C opt -C re

[0100] Where: F A is the life cycle cost of the rural integrated energy system, C inv is the construction cost of the rural integrated energy system, C ma is the maintenance cost of the rural integrated energy system, C opt is the operating cost of the rural integrated energy system, C re Proceeds from waste disposal for integrated rural energy systems;

[0101] The construction cost is calculated as follows:

[0102]

[0103] Where: N eq is the number of equipment types described in step S1; r is the discount rate; P i , T i , are the planned capacity, life cycle and unit capacity construction cost of the i-th type of equipment respectively;

[0104] Maintenance costs are calculated as follows:

[0105]

[0106] Where: P i,t is the output of the i-th type of equipment at time t; is the unit output maintenance cost of the i-th type of equipment;

[0107] The cost of purchased energy is calculated as follows:

[0108]

[0109] Where: c t,e is the electricity price at time t, c t,g is the gas price at time t, P grid,t is the amount of electricity purchased at time t, P gas,t is the amount of gas purchased at time t;

[0110] The calculation method of waste disposal revenue is as follows:

[0111]

[0112] Where: K is the number of garbage types; S is the total number of garbage treatment batches; m k,s is the amount of k types of garbage processed in s batches, is the unit economic benefit that can be obtained by processing k types of garbage in s batches; ω k,s Savings in transporting k types of garbage for s batches, n k,s Save the amount of k types of garbage transferred for s batches;

[0113] 2) Carbon emission costs:

[0114] The rural comprehensive energy carbon emission cost model is:

[0115]

[0116] Where: F c Carbon emission costs for rural integrated energy systems; is the carbon emission price of carbon emission source a, E a,t is the carbon emission energy of carbon emission source a at time t;

[0117] 3) efficiency:

[0118] η ex =E g / E p

[0119] Where: η ex Represents the rural integrated energy system Efficiency; E g Indicates that the system is being used or benefited E p Indicates the system payment or consumption

[0120] 4) Power balance constraints:

[0121]

[0122] Where: P e,load,t is the electrical load at time t, P h,load,t is the gas load at time t, P g,load,t is the heat load at time t; is the power generation or consumption of the biogas engine at time t, is the power generated or consumed by the solar thermal power generation equipment at time t, is the power generation or consumption of the biomass pyrolysis gasification generator set at time t, is the power generated or consumed by the electric heating equipment at time t; is the heating power of the solar thermal power generation equipment at time t, is the heating power of the electric heating equipment at time t, is the heating power of the biogas engine at time t, is the heating power of the biogas boiler at time t; is the biogas production at time t, is the gas power of the biogas boiler at time t, P is the gas power of the biogas engine at time t; grid,t is the power purchase amount of the rural integrated energy system at time t, P gas,t P is the gas purchase volume of the rural integrated energy system at time t; ESS,t is the power storage device at time t, P TSS,t is the heat storage device at time t, P TSS,t is the working power of the gas storage equipment at time t;

[0123] 5) Equipment capacity constraints:

[0124]

[0125] Where: P i,max is the maximum operating power of the i-th type of equipment in step S2, P i,min is the minimum operating power of the i-th type equipment; P i t is the working power of the i-th type of equipment at time t in step S2; D i is the lower limit of the downward climbing power of the i-th type of equipment in step S2, B iP is the upper limit of the power of the i-th type equipment; i t+1 is the working power of the i-th type of equipment at time t+1, P i t is the working power of the i-th type of equipment at time t, and Δt is the unit time.

[0126] S4: The NSGA-Ⅱ algorithm is used to solve the optimization solution model, and the elite gene retention strategy and fast non-dominated sorting are introduced in the population update process of the NSGA-Ⅱ algorithm to improve the solution ability of the NSGA-Ⅱ algorithm. Multi-objective optimization method for rural integrated energy system with high efficiency.

[0127] Specifically, the NSGA-Ⅱ algorithm is used to solve the rural integrated energy system model, and the concepts of fast non-dominated sorting, congestion degree and congestion distance, and elite strategy are introduced. The solution space is divided into different non-dominated frontiers and the diversity of solutions is maintained. Compared with traditional multi-objective solution algorithms, the proposed NSGA-Ⅱ algorithm can optimize multiple conflicting objectives at the same time. It can handle constrained and unconstrained multi-objective optimization problems, has a wide range of adaptability, and can find a better Pareto frontier solution set. This enables the algorithm to provide a series of effective non-inferior solutions for decision makers of rural integrated energy systems to choose from, and realize the multi-objective comprehensive optimization of various indicators of rural energy systems.

[0128] 1) If Figure 2 As shown, the steps of NSGA-Ⅱ algorithm are as follows:

[0129] S01: Initialize the system and input the parameters, light intensity, and load of each device in the rural integrated energy system in step S1;

[0130] S02: Call any function and take t=0 as the initial population P0;

[0131] S03: Calculate the fitness of each individual in population P0;

[0132]

[0133] Where: Fitness is the fitness function; f1(X), f2(X), and f3(X) are the values ​​of the three objective functions; Δ is the sum of the absolute values ​​of the relevant constraints of the individuals that do not meet the constraints; f 1，max (X) is the maximum value of the first objective function value of all individuals; f 2，max (X) represents the maximum value of the second objective function of all individuals; f 3，max (X) represents the maximum value of the third objective function of all individuals;

[0134] S04: Calculate the parent population Pt Individual objective function values,parallel non-inferiority sorting;

[0135] S05: For the parent population P t Genetic operations are used to obtain the offspring population Q t ;

[0136] S06: Merge parent population P t With the offspring population Q t is the population R t , and perform non-inferiority sorting;

[0137] S07: Keep elite individuals and use them as the new parent population P t+1 ;

[0138] S08: Determine whether the termination condition has reached the maximum number of iterations. If so, output the optimization result of the rural integrated energy system; otherwise, return to step S05;

[0139] 2) Elite strategy of NSGA-Ⅱ algorithm

[0140] The NSGA-II algorithm introduces an elite strategy to achieve the goal of retaining excellent individuals and eliminating inferior individuals. The elite strategy expands the screening range when generating the next generation of individuals by mixing the parent and offspring individuals to form a new population. Take the example shown in the figure for analysis. In the figure, P represents the parent population, the number of individuals in it is n, and Q represents the offspring population. The specific steps are as follows:

[0141] S11: merge the parent population and the child population to form a new population, and then perform non-dominated sorting on the new population;

[0142] S12: Generate a new parent generation. First, put the non-dominated individuals with a Pareto level of 1 into the new parent generation set, then put the individuals with a Pareto level of 2 into the new parent generation population, and so on.

[0143] S13: If after all individuals of level k are placed in the new parent set, the number of individuals in the set is less than n, and after all individuals of level k+1 are placed in the new parent set, the number of individuals in the set is greater than n, then the crowding degree of all individuals of level k+1 is calculated and all individuals are sorted in descending order according to the crowding degree, and then all individuals with a level greater than k+1 are eliminated;

[0144] S14: Put the individuals in level k+1 into the new parent set one by one in the order arranged in step S12, until the number of individuals in the parent set is equal to n, and the remaining individuals are eliminated.

[0145] In this embodiment, in addition to disclosing a method that takes into account carbon emission costs and A multi-objective optimization method for rural integrated energy system with high efficiency is also disclosed. The multi-objective optimization system of rural integrated energy system with high efficiency includes:

[0146] A coupling unit is used to combine a method that considers carbon emission costs and The electric load demand, thermal load demand and gas load demand of each device and energy user mentioned in the multi-objective optimization method of efficient rural comprehensive energy system are coupled to form a rural comprehensive energy system;

[0147] Modeling unit, used to establish the electric energy supply model of solar thermal system, the gas energy supply model of biomass pyrolysis gasification power generation system, and the thermal energy supply model of biomass biogas thermal power system

[0148] Optimization unit for life cycle cost, carbon emission cost, Taking efficiency as the goal, an optimization solution model for rural integrated energy system is constructed;

[0149] The solution unit is used to solve the optimization solution model by adopting the NSGA-Ⅱ algorithm which introduces the elite gene retention strategy and the fast non-dominated sorting.

[0150] The following example analyzes the optimization configuration method. See the following description for details:

[0151] In order to study the feasibility and superiority of the optimization configuration method, the present invention conducts a simulation verification on the energy planning implementation example of a large village in central China. Figure 3 Typical day wind power and photovoltaic characteristic curves, Figure 4 This is the time-of-use electricity price curve for the area, and when the natural gas price is 1.6m3 / yuan.

[0152] The energy equipment planned in the simulation system includes biogas production facilities, biogas boilers, biogas generators, biomass pyrolysis and gasification generators, solar thermal equipment, and multi-energy storage facilities (multi-energy storage facilities include electricity storage equipment, gas storage equipment, and heat storage equipment). The parameters of each type of equipment are shown in Table 1.

[0153] Table 1 Energy supply conversion and storage facility parameters

[0154] Device Type Construction costs Maintenance costs efficiency / % Lifespan / year Biogas boiler 1 million yuan WM 153,000 yuan / MW 95 20 Biogas gas turbine generator set 9 million yuan / MW 13,000 yuan / MW 40 20 Solar thermal system 6 million yuan / MW 6,000 yuan / WM 85 15 Power storage equipment 1.2 million yuan / MW 36,000 yuan / MW 90 10 Gas storage equipment 2,000 yuan / m3 9.6 yuan / MW 95 20 Heat storage equipment 200,000 yuan / MW 20.4 yuan / MW 90 10

[0155] Table 2 Parameters of biomass utilization facilities

[0156]

[0157]

[0158] Based on the above simulation examples and parameter settings, the three objective functions are normalized and the weight distribution is set to 0.33, 0.33, and 0.34. The examples are simulated, verified, solved, and analyzed to obtain the Pareto solution set for multi-objective optimization. Figure 5 As shown in the figure, the circled data points are the points with the minimum weighted value selected by this scheme through calculating the weighted allocation ratio as the optimal solution, that is, the optimal solution of this planning scheme. Compared with the traditional genetic algorithm, the NSGA-Ⅱ algorithm does not require the setting of weights, and can obtain multiple non-inferior solutions that do not dominate each other in one operation. Decision makers can make decisions according to different conditions or calculate the optimal compromise solution as the final choice, which not only balances the economy of comprehensive energy planning and the low carbon and high efficiency of the system, but also avoids subjectivity and improves decision-making efficiency.

[0159] To verify the effectiveness of the proposed method, two scenarios are set for comparison.

[0160] Scenario 1: Not considering the carbon emission costs and efficiency, and use traditional genetic algorithms to solve;

[0161] Scenario 2: The method proposed in the present invention.

[0162] Table 3 Construction of various energy facilities under different plans

[0163] Device Type Solution 1 Solution 2 Biogas tank volume (m3) 15000 15000 Biogas boiler power (MW) 25 23 Biogas gas turbine generator power (MW) 13 12 Biomass pyrolysis gasification power generation unit power (MW) 5 5 Solar thermal system power (MW) 36 35 Electric heating equipment power (MW) 18 18 Thermal storage equipment power (MW) 62 53 Power storage equipment power (MW) 35 31 Gas storage equipment volume (m3) 1360 1100

[0164] As shown in Table 3, without considering carbon emissions and When efficiency is high, the construction capacity of various types of equipment in the rural integrated energy system is high; while in Scheme 2, when the multi-objective optimization method of the rural integrated energy system based on the NSGA-Ⅱ algorithm is used for solution, the construction capacity of various types of equipment is effectively reduced.

[0165] Table 4 Solution values ​​of the objective function of the rural integrated energy system under different schemes

[0166]

[0167] As shown in Table 4, the life cycle cost and carbon emission cost of Scheme 2 are reduced by 6.1% and 3.5% compared with Scheme 1. The efficiency was increased by 2.82%. Through comparison, it is effectively proved that the multi-objective optimization method of rural integrated energy system considering carbon emission cost and #imgpt109# efficiency described in this scheme can improve the economy, low carbon and high efficiency of rural integrated energy system.

[0168] The above is only an embodiment of the present invention, and the common knowledge such as the known specific structure and characteristics in the scheme is not described in detail here. It should be pointed out that for those skilled in the art, several deformations and improvements can be made without departing from the structure of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method that considers carbon emission costs and The multi-objective optimization method of rural integrated energy system with high efficiency is characterized by: The steps include: S1: Establish various types of equipment, including energy conversion systems and multi-energy storage facilities. The energy conversion systems include solar thermal systems, biomass pyrolysis and gasification power generation systems, and biomass biogas thermal power systems. Multi-energy storage facilities include heat storage equipment, gas storage equipment, and electricity storage equipment. The multi-energy storage facilities and energy users' electricity load demand, heat load demand, and gas load demand are coupled to form a rural comprehensive energy system. S2: Establish the electric energy supply model of solar thermal system, the gas energy supply model of biomass pyrolysis gasification power generation system, and the thermal energy supply model of biomass biogas thermal power system; S3: Based on the life cycle cost, carbon emission cost, Taking efficiency as the goal and power balance and equipment capacity as constraints, an optimization model for rural integrated energy system is constructed; S4: The NSGA-Ⅱ algorithm is used to solve the Pareto solution set of the optimization solution model, which includes the volume parameters of the biogas tank and gas storage equipment, as well as the power of the biogas boiler, biogas gas turbine generator set, biomass pyrolysis gasification generator set, solar thermal system, electric heating equipment, heat storage equipment, and power storage equipment.

2. A method according to claim 1 that takes into account carbon emission costs and The multi-objective optimization method of rural integrated energy system with high efficiency is characterized by: In step S2, the electric energy supply model is a solar thermal system model, and the output power of the solar thermal system can be expressed as: in, is the output power of the generator at time t; η e is the thermal-electric conversion efficiency of the generator; is the total input thermal power of the generator at time t; E t E is the heat storage capacity of the heat storage device in the CSP power station at time t; t-1 is the heat storage capacity of the heat storage device in the CSP power station at time t-1; ρ is the energy self-loss coefficient of the heat storage device; is the charging power of the heat storage device at time t; is the charging efficiency of the heat storage device; is the heat release power of the heat storage device at time t; is the heat release efficiency of the heat storage device; Δt is the unit scheduling time.

3. A method according to claim 1 that takes into account carbon emission costs and The multi-objective optimization method of rural integrated energy system with high efficiency is characterized by: In step S2, the gas energy supply model is a biomass biogas thermal power system model, and the mathematical model of biogas production is: P biog =r biog V biog Where: P biog is the daily output of the biogas system, r biog is the biogas production efficiency, V biog is the biogas volume of the existing biogas digester; in, Indicates the thermal power output value of the biogas engine at time t; Represents the electric power output value of the biogas engine at time t; η EMT is the power generation efficiency, η HL is the heat loss coefficient of the biogas engine; is the gas power of the biogas engine, is the biogas consumption of the biogas boiler at time t; is the thermal power output of the biogas boiler; L G Indicates the calorific value of biogas, η mt represents the heating efficiency of the biogas boiler, and Δt represents the operating time of the gas energy supply model.

4. A method according to claim 1 that takes into account carbon emission costs and The multi-objective optimization method of rural integrated energy system with high efficiency is characterized by: In step S2, the thermal energy supply model is a biomass pyrolysis gasification power generation system model, and the output characteristic model of the biomass pyrolysis gasification power generation system is: V f,t =m f,t b f or f Where: V f,t is the gas production rate of the pyrolysis gasifier; m f,t is the gasification amount of garbage at time t; β f is the gasification coefficient; η f is the pyrolysis furnace efficiency; is the power generation of the gas turbine at time t; f is the combustible gas content; Q f is the calorific value of combustion of combustible gas; is the heating power of the gas turbine; η PG is the power generation efficiency of the gas turbine; η1 is the heat loss coefficient of the gas turbine.

5. A method according to claim 1 that takes into account carbon emission costs and The multi-objective optimization method of rural integrated energy system with high efficiency is characterized by: The objective function of the life cycle cost of the rural integrated energy system in step S3 is: Taking the static voltage stability index of the system as the objective function, the moment with the lowest stability during one day of operation is selected to evaluate the rural integrated energy system. F A =C inv +C ma +C opt -C re Where: F A is the life cycle cost of the rural integrated energy system, C inv is the construction cost of the rural integrated energy system, C ma is the maintenance cost of the rural integrated energy system, C opt is the operating cost of the rural integrated energy system, C re Proceeds from waste disposal for integrated rural energy systems; The construction cost is calculated as follows: Where: N eq is the number of equipment types described in step S1; r is the discount rate; P i , T i , are the planned capacity, life cycle and unit capacity construction cost of the i-th type of equipment respectively; The maintenance cost is calculated as follows: Where: P i,t is the output of the i-th type of equipment at time t; is the unit output maintenance cost of the i-th type of equipment; The cost of purchased energy is calculated as follows: Where: c t,e is the electricity price at time t, c t,g is the gas price at time t, P grid,t is the amount of electricity purchased at time t, P gas,t is the amount of gas purchased at time t; The calculation method of waste disposal revenue is as follows: Where: K is the number of garbage types; S is the total number of garbage treatment batches; m k,s is the amount of k types of garbage processed in s batches, is the unit economic benefit that can be obtained by processing k types of garbage in s batches; ω k,s Savings in transporting k types of garbage for s batches, n k,s Save the amount of k types of garbage transferred for s batches.

6. A method according to claim 1 that takes into account carbon emission costs and The multi-objective optimization method of rural integrated energy system with high efficiency is characterized by: The optimization solution model of the rural integrated energy system in step S3 includes a carbon emission cost objective function: Where: F c Carbon emission costs for rural integrated energy systems; is the carbon emission price of carbon emission source a, E a,t is the carbon emission energy of carbon emission source a at time t.

7. A method according to claim 1 that takes into account carbon emission costs and The multi-objective optimization method of rural integrated energy system with high efficiency is characterized by: The optimization solution model of the rural integrated energy system in step S3 includes Efficiency objective function: η ex =And g / AND p Where: η ex Represents the rural integrated energy system Efficiency; E g Indicates that the system is being used or benefited E p Indicates the system payment or consumption 8. A method according to claim 3 that takes into account carbon emission costs and The multi-objective optimization method of rural integrated energy system with high efficiency is characterized by: The optimization solution model of the rural integrated energy system in step S3 includes the following constraints, and the constraint expressions are as follows: Power balance constraints: Where: P e,load,t is the electrical load at time t, P h,load,t is the gas load at time t, P g,load,t is the heat load at time t; is the power generation or consumption of the biogas engine at time t, is the power generated or consumed by the solar thermal power generation equipment at time t, is the power generation or consumption of the biomass pyrolysis gasification generator set at time t, is the power generated or consumed by the electric heating equipment at time t; is the heating power of the solar thermal power generation equipment at time t, is the heating power of the electric heating equipment at time t, is the heating power of the biogas engine at time t, is the heating power of the biogas boiler at time t; is the biogas production at time t, is the gas power of the biogas boiler at time t, P is the gas power of the biogas engine at time t; grid,t is the power purchase amount of the rural integrated energy system at time t, P gas,t P is the gas purchase volume of the rural integrated energy system at time t; ESS,t is the power storage device at time t, P TSS,t is the heat storage device at time t, P TSS,t is the working power of the gas storage equipment at time t; Equipment capacity constraints: Where: P i,max is the maximum operating power of the i-th type of equipment in step S2, P i,min is the minimum operating power of the i-th type equipment; P i t is the working power of the i-th type of equipment at time t in step S2; D i is the lower limit of the downward climbing power of the i-th type of equipment in step S2, B i P is the upper limit of the power of the i-th type equipment; i t+1 is the working power of the i-th type of equipment at time t+1, P i t is the working power of the i-th type of equipment at time t, and Δt is the unit time.

9. A method according to claim 5 that takes into account carbon emission costs and The multi-objective optimization method of rural integrated energy system with high efficiency is characterized by: In step S4, the NSGA-II algorithm steps are as follows: S01: Initialize the system and input the parameters, light intensity, and load of each device in the rural integrated energy system in step S1; S02: Call any function and take t=0 as the initial population P0; S03: Calculate the fitness of each individual in population P0; Where: Fitness is the fitness function; f1(X), f2(X), and f3(X) are the values ​​of the three objective functions; Δ is the sum of the absolute values ​​of the relevant constraints of the individuals that do not meet the constraints; f 1，max (X) is the maximum value of the first objective function value of all individuals; f 2，max (X) represents the maximum value of the second objective function of all individuals; f 3，max (X) represents the maximum value of the third objective function of all individuals; S04: Calculate the parent population P t Individual objective function values,parallel non-inferiority sorting; S05: For the parent population P t Genetic operations are used to obtain the offspring population Q t ; S06: Merge parent population P t With the offspring population Q t is the population R t , and perform non-inferiority sorting; S07: Keep elite individuals and use them as the new parent population P t+1 ; S08: Determine the termination condition to see whether the maximum number of iterations has been reached. If so, output the optimization result of the rural integrated energy system; otherwise, return to step S05.

10. A method according to claim 9 that takes into account carbon emission costs and The multi-objective optimization method of rural integrated energy system with high efficiency is characterized by: In step S4, the elite gene retention strategy and fast non-dominated sorting are introduced to optimize the NSGA-Ⅱ algorithm. The specific steps are as follows: S11: merge the parent population and the child population to form a new population, and then perform non-dominated sorting on the new population; S12: Generate a new parent generation. First, put the non-dominated individuals with a Pareto level of 1 into the new parent generation set, then put the individuals with a Pareto level of 2 into the new parent generation population, and so on. S13: If after all individuals of level k are placed in the new parent set, the number of individuals in the set is less than n, and after all individuals of level k+1 are placed in the new parent set, the number of individuals in the set is greater than n, then the crowding degree of all individuals of level k+1 is calculated and all individuals are sorted in descending order according to the crowding degree, and then all individuals with a level greater than k+1 are eliminated; S14: Put the individuals in level k+1 into the new parent set one by one in the order arranged in step S12, until the number of individuals in the parent set is equal to n, and the remaining individuals are eliminated.

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