CN116402210A - Multi-objective optimization method, system, equipment and medium for comprehensive energy system - Google Patents

Abstract

The invention provides a multi-objective optimization method, a system, equipment and a medium for an integrated energy system, which comprise the following steps: acquiring installed capacity data of renewable energy sources in an electric-gas interconnection comprehensive energy system and related parameters of system operation; based on the installed capacity data of the renewable energy sources and related parameters of system operation, solving a multi-objective optimal configuration model which is built in advance and comprises electric conversion equipment and multi-class energy storage equipment by adopting a genetic algorithm to obtain an optimal configuration solution set; and selecting an optimal configuration result of the electric-gas interconnection comprehensive energy system from the optimal configuration solution set based on the optimization demand. According to the invention, the multi-objective optimal configuration model comprising the electric conversion equipment and the multi-type energy storage equipment is established, so that the collaborative planning of the electric conversion equipment and the multi-type energy storage equipment is performed, and a more comprehensive optimal configuration scheme which is more in line with the actual situation of the comprehensive energy system can be provided.

Description

A multi-objective optimization method, system, device and medium for integrated energy system

Technical Field

The present invention belongs to the field of integrated energy system optimization, and specifically relates to a multi-objective optimization method for an electricity-gas interconnected integrated energy system based on electricity-to-gas conversion.

Background Art

With the gradual intensification of the fossil energy crisis and environmental pollution problems, it is urgent to complete the energy transformation and build a modern energy system. Due to the obvious intermittent, uncertain and anti-peak characteristics of wind power generation, the phenomenon of wind abandonment is serious. In recent years, with the introduction of the concept of integrated energy system, its mutual coupling with renewable energy will greatly improve energy utilization efficiency and reduce pollution emissions. Among them, the power grid and the natural gas grid are the two most important large-scale transmission carriers in the current energy field, so the coupling relationship between the two has always received widespread attention. Natural gas can usually be converted into electrical energy with the help of equipment such as gas turbines, and power-to-gas technology can convert electrical energy into natural gas, thereby realizing the two-way flow of energy between the power grid and the natural gas grid, and further deepening the coupling of the electric-gas integrated energy system, and realizing the two-way coupling of the two systems together with gas turbines, providing a new solution for absorbing renewable energy such as wind power. At the same time, the addition of energy storage devices can usually improve the utilization rate of renewable energy and improve the quality of electricity, which plays an important supporting role in the operation of the integrated energy system.

There have been a lot of studies on the problem of absorbing renewable energy in integrated energy systems. At present, the modeling of integrated energy systems includes a variety of equipment, and the research focuses of integrated energy system models based on different equipment are different. For example, the optimization and scheduling of integrated energy systems including power-to-gas equipment mainly analyzes the economic benefits of absorbing wind power to the system, while the research on integrated energy systems including multiple energy storage equipment mainly tends to consume wind power using electric and thermal energy storage equipment, considering the economy and feasibility of different energy storage equipment configurations in the system. At present, most of the existing technologies unilaterally consider the optimal configuration of power-to-gas or multiple types of energy storage equipment, but in actual production work, power-to-gas and multiple types of energy storage equipment in the electric-gas interconnected integrated energy system often only consider the configuration scheme of power-to-gas or multiple types of energy storage equipment. In addition, when optimizing the configuration of integrated energy systems, there are usually multi-objective optimization solutions. In such a process, there is often a problem that the global optimal solution cannot be quickly converged, so that the optimization efficiency needs to be improved.

Summary of the invention

In order to overcome the above-mentioned deficiencies of the prior art, the present invention proposes a multi-objective optimization method for an integrated energy system, comprising:

Obtain installed capacity data of renewable energy and system operation related parameters in the electricity-gas interconnected integrated energy system;

Based on the installed capacity data of the renewable energy and system operation related parameters, a genetic algorithm is used to solve a pre-built multi-objective optimization configuration model including power-to-gas equipment and multiple types of energy storage equipment to obtain an optimization configuration solution set;

Selecting the optimal configuration result of the electricity-gas interconnected integrated energy system from the optimal configuration solution set based on the optimization requirements;

Among them, the multi-objective optimization model is constructed on the basis of maximizing the renewable energy consumption of the electricity-gas interconnected integrated energy system with the minimum system economic cost and the minimum _CO2 emissions.

Preferably, the construction of the multi-objective optimization configuration model including the power-to-gas equipment and multiple types of energy storage equipment includes:

Constructing an electric-gas interconnected comprehensive energy system model including power-to-gas equipment and multiple types of energy storage equipment based on multiple constraints, wherein the electric-gas interconnected comprehensive energy system model including power-to-gas equipment and multiple types of energy storage equipment includes one or more of the following power system models, natural gas system models, coupling device models, and multiple types of energy storage equipment models;

A multi-objective optimization configuration function is constructed with the objectives of minimizing the economic cost, maximizing the consumption of renewable energy and minimizing CO ₂ emissions of the electric-gas interconnected comprehensive energy system model including the electric-to-gas equipment and various types of energy storage equipment;

A multi-objective optimization configuration model including power-to-gas equipment and various types of energy storage equipment is constructed based on the power-to-gas interconnected comprehensive energy system model including power-to-gas equipment and various types of energy storage equipment and the multi-objective optimization configuration function.

Preferably, the construction of an electric-gas interconnected integrated energy system model including power-to-gas equipment and multiple types of energy storage equipment based on multiple constraints includes:

Constructing the power system model based on power balance constraints, unit output constraints, node voltage constraints, and branch power flow constraints;

The natural gas system model is constructed based on the gas source output constraint, the natural gas pipeline operation constraint, the pipe village operation constraint, the gas storage tank operation constraint, the compressor operation constraint, and the node flow balance constraint;

Constructing the coupling device model based on the gas turbine output constraint and the power-to-gas device output constraint;

Constructing the multiple types of energy storage device models based on the operation constraints of the electric storage device, the operation constraints of the heat storage device, and the operation constraints of the cold storage device;

An electric-gas interconnected comprehensive energy system model including power-to-gas equipment and multiple types of energy storage equipment is constructed using the electric power system model, the natural gas system model, the coupling device model and the multiple types of energy storage equipment models.

Preferably, the calculation formula corresponding to the multi-objective optimization configuration function is as follows:

minF ₁ =F _inv +F _ope

为t时段系统对于风电机组j的计划接纳风电功率；

为t时段系统对于光电机组j的计划接纳光电功率；

为风电机组j的理想功率；

为光电机组j的理想功率；F₃为CO₂排放量；

为综合能源系统在t时刻从电网购入的电功率；

为在综合能源系统t时刻从气网购入的气功率；α_e为购电CO₂排放系数；α_gas为购气CO₂排放系数。Where: _F1 is the economic cost of the system; _Finv is the investment cost; _Fope is the operating cost; _F2 is the renewable energy consumption rate; T is the dispatch period; _NW is the total number of wind turbines in the system; _NV is the total number of photovoltaic generators in the system;

The planned wind power accepted by the system for wind turbine j during period t;

The system plans to receive photovoltaic power for photovoltaic group j during period t;

is the ideal power of wind turbine j;

is the ideal power of photovoltaic group j; F ₃ is the CO ₂ emission;

is the electric power purchased by the integrated energy system from the power grid at time t;

is the gas power purchased from the gas grid at time t in the integrated energy system; _αe is the _CO2 emission coefficient for purchased electricity; _αgas is the _CO2 emission coefficient for purchased gas.

Preferably, the calculation formula of the investment cost _Finv is as follows:

The calculation formula of the operating cost _Fope is as follows:

为t时刻从电网购电的电价；J_G为天然气价格；P_out,i为设备i在t时段的输出功率；β_i为设备i的单位运行维护费用。Where: γ _i is the installation cost per unit capacity of equipment i; C _i is the installed capacity of equipment i; I is the total number of equipment in the integrated energy system; α is the annual interest rate; Yi _i is the operating life of equipment i; T is the scheduling period;

is the electricity price purchased from the power grid at time t; J _G is the natural gas price; P _out,i is the output power of device i in period t; β _i is the unit operation and maintenance cost of device i.

Preferably, based on the installed capacity data of the renewable energy and system operation related parameters, a genetic algorithm is used to solve a pre-built multi-objective optimization configuration model including power-to-gas equipment and multiple types of energy storage equipment to obtain an optimization configuration solution set, including:

Based on the installed capacity data of the renewable energy and system operation related parameters, determining that the parent population individuals in the genetic algorithm based on the adaptive elite retention strategy are the power-to-gas equipment capacity and the multi-type energy storage equipment capacity, and the fitness value of each individual in the population is the multi-objective optimization configuration function value;

The genetic algorithm based on the adaptive elite retention strategy is used to solve the multi-objective optimization configuration function to obtain an optimization configuration solution set.

Preferably, the multi-objective optimization configuration function is solved by using a genetic algorithm based on an adaptive elite retention strategy to obtain an optimization configuration solution set, including:

Step S1: Initialize the population, set the population size, number of iterations, basic crossover probability and basic mutation probability;

Step S2: randomly generate a parent population P, each individual in the parent population P represents the capacity of the power-to-gas equipment and the capacity of multiple types of energy storage equipment, and calculate the fitness value of each individual in the parent population P, wherein the fitness value represents the value of the multi-objective optimization configuration function;

Step S3: selecting the parent population P by a genetic algorithm, and performing adaptive crossover and mutation on the parent population P based on the individual fitness value, the basic crossover probability and the basic mutation probability to generate a child population Q;

Step S4: Mix the parent population P and the child population Q to obtain a new population R, and then perform fast non-dominated sorting on the new population R to obtain a non-dominated dominant population sequence;

Step S5: Based on the fitness value, a reference point selection strategy is adopted to select the population R to obtain the population Y as the parent population of the next iteration;

Step S6: Based on the non-dominated dominant population sequence, the adaptive elite retention strategy is used to select the dominant individuals in the population R and add them to the population Y as the parent population for the next iteration;

Step S7: Determine whether the number of iterations has been reached. If so, obtain the power-to-gas equipment capacity, multi-type energy storage equipment capacity and multi-objective optimization configuration function value corresponding to each individual as the optimization configuration solution set and end. Otherwise, return to step S3.

Preferably, the performing adaptive crossover mutation on the parent population P based on the individual fitness value, the basic crossover probability and the basic mutation probability includes:

Determine an adaptive crossover probability and an adaptive mutation probability based on the individual fitness value, the basic crossover probability and the basic mutation probability;

Adaptive crossover and mutation are performed on the parent population P based on the adaptive crossover probability and the adaptive mutation probability.

Preferably, the calculation formula of the adaptive crossover probability is as follows:

The calculation formula of the adaptive mutation probability is as follows:

In the formula: _pc is the adaptive crossover probability; _k1 is the first basic crossover probability; _k2 is the second basic crossover probability; _pm is the adaptive mutation probability; _k3 is the first basic mutation probability; _k4 is the second basic mutation probability; _fm is the fitness value of the individual to be mutated; _fm is the maximum fitness value that the population can accept; _fc is the larger fitness value of the two individuals to be crossed; _fmin is the minimum fitness value of all individuals in the population; the first basic crossover probability is less than the second basic crossover probability; the first basic mutation probability is less than the second basic mutation probability.

Preferably, the calculation formula of the adaptive elite retention strategy is as follows:

In the formula: _Ne is the number of elite retained individuals; _fi is the fitness value of the i-th population individual; N is the number of individuals in the population; _fb is the fitness value of the best individual in the population.

Preferably, the system operation related parameters include one or more of the following: photovoltaic power prediction value, load prediction value, wind power prediction value, electricity price list for different time periods, power-to-gas equipment parameters, parameters of various types of energy storage equipment, cogeneration equipment operating parameters, gas boiler operating parameters, absorption chiller operating parameters, gas tank operating parameters, and compressor operating parameters.

Based on the same inventive concept, the present invention also provides a multi-objective optimization system for an electricity-gas interconnected integrated energy system based on electricity-to-gas conversion, comprising:

Data acquisition module: used to obtain installed capacity data of renewable energy and system operation related parameters in the electricity-gas interconnected integrated energy system;

A solution module: used to solve a pre-built multi-objective optimization configuration model including power-to-gas equipment and multiple types of energy storage equipment using a genetic algorithm based on the installed capacity data of the renewable energy and system operation related parameters, to obtain an optimization configuration solution set;

Optimal configuration result acquisition module: selects the optimal configuration result of the electricity-gas interconnected integrated energy system from the optimal configuration solution set based on the optimization requirements;

Among them, the multi-objective optimization model is constructed on the basis of maximizing the renewable energy consumption of the electricity-gas interconnected integrated energy system with the minimum system economic cost and the minimum _CO2 emissions.

Preferably, the construction of a multi-objective optimization configuration model including power-to-gas equipment and multiple types of energy storage equipment in the solution module includes:

Constructing an electric-gas interconnected comprehensive energy system model including power-to-gas equipment and multiple types of energy storage equipment based on multiple constraints, wherein the electric-gas interconnected comprehensive energy system model including power-to-gas equipment and multiple types of energy storage equipment includes one or more of the following power system models, natural gas system models, coupling device models, and multiple types of energy storage equipment models;

A multi-objective optimization configuration function is constructed with the objectives of minimizing the economic cost, maximizing the consumption of renewable energy and minimizing CO ₂ emissions of the electric-gas interconnected integrated energy system model including the electric-to-gas equipment and various types of energy storage equipment;

Based on the electric-gas interconnected comprehensive energy system model including the power-to-gas equipment and various types of energy storage equipment and the multi-objective optimization configuration function, a multi-objective optimization configuration model including the power-to-gas equipment and various types of energy storage equipment is constructed.

Preferably, the solution module constructs an electric-gas interconnected integrated energy system model including electric-to-gas equipment and multiple types of energy storage equipment based on multiple constraints, including:

Constructing the power system model based on power balance constraints, unit output constraints, node voltage constraints, and branch power flow constraints;

The natural gas system model is constructed based on the gas source output constraint, the natural gas pipeline operation constraint, the pipe village operation constraint, the gas storage tank operation constraint, the compressor operation constraint, and the node flow balance constraint;

Constructing the coupling device model based on the gas turbine output constraint and the power-to-gas device output constraint;

Constructing the multiple types of energy storage device models based on the operation constraints of the electric storage device, the operation constraints of the heat storage device, and the operation constraints of the cold storage device;

An electric-gas interconnected comprehensive energy system model including power-to-gas equipment and multiple types of energy storage equipment is constructed using the electric power system model, the natural gas system model, the coupling device model and the multiple types of energy storage equipment models.

Preferably, the calculation formula corresponding to the multi-objective optimization configuration function in the solution module is as follows:

minF ₁ =F _inv +F _ope

为t时段系统对于风电机组j的计划接纳风电功率；

为t时段系统对于光电机组j的计划接纳光电功率；

为风电机组j的理想功率；

为光电机组j的理想功率；F₃为CO₂排放量；

为综合能源系统在t时刻从电网购入的电功率；

为在综合能源系统t时刻从气网购入的气功率；α_e为购电CO₂排放系数；α_gas为购气CO₂排放系数。Where: _F1 is the economic cost of the system; _Finv is the investment cost; _Fope is the operating cost; _F2 is the renewable energy consumption rate; T is the dispatch period; _NW is the total number of wind turbines in the system; _NV is the total number of photovoltaic generators in the system;

The planned wind power accepted by the system for wind turbine j during period t;

The system plans to receive photovoltaic power for photovoltaic group j during period t;

is the ideal power of wind turbine j;

is the ideal power of photovoltaic group j; F ₃ is the CO ₂ emission;

is the electric power purchased by the integrated energy system from the power grid at time t;

is the gas power purchased from the gas grid at time t in the integrated energy system; _αe is the _CO2 emission coefficient for purchased electricity; _αgas is the _CO2 emission coefficient for purchased gas.

Preferably, the calculation formula of the investment cost _Finv in the solution module is as follows:

The calculation formula of the operating cost _Fope is as follows:

为t时刻从电网购电的电价；J_G为天然气价格；P_out,i为设备i在t时段的输出功率；β_i为设备i的单位运行维护费用。Where: γ _i is the installation cost per unit capacity of equipment i; C _i is the installed capacity of equipment i; I is the total number of equipment in the integrated energy system; α is the annual interest rate; Yi _i is the operating life of equipment i; T is the scheduling period;

is the electricity price purchased from the power grid at time t; J _G is the natural gas price; P _out,i is the output power of device i in period t; β _i is the unit operation and maintenance cost of device i.

Preferably, the solution module is specifically used for:

Based on the installed capacity data of the renewable energy and system operation related parameters, determining that the parent population individuals in the genetic algorithm based on the adaptive elite retention strategy are the power-to-gas equipment capacity and the multi-type energy storage equipment capacity, and the fitness value of each individual in the population is the multi-objective optimization configuration function value;

The genetic algorithm based on the adaptive elite retention strategy is used to solve the multi-objective optimization configuration function to obtain an optimization configuration solution set.

Preferably, the solution module uses a genetic algorithm based on an adaptive elite retention strategy to solve the multi-objective optimization configuration function to obtain an optimization configuration solution set, including:

Step S1: Initialize the population, set the population size, number of iterations, basic crossover probability and basic mutation probability;

Step S2: randomly generate a parent population P, each individual in the parent population P represents the capacity of the power-to-gas equipment and the capacity of multiple types of energy storage equipment, and calculate the fitness value of each individual in the parent population P, wherein the fitness value represents the value of the multi-objective optimization configuration function;

Step S3: selecting the parent population P by a genetic algorithm, and performing adaptive crossover and mutation on the parent population P based on the individual fitness value, the basic crossover probability and the basic mutation probability to generate a child population Q;

Step S4: Mix the parent population P and the child population Q to obtain a new population R, and then perform fast non-dominated sorting on the new population R to obtain a non-dominated dominant population sequence;

Step S5: Based on the fitness value, a reference point selection strategy is adopted to select the population R to obtain a population Y as the parent population for the next iteration;

Step S6: Based on the non-dominated dominant population sequence, the adaptive elite retention strategy is used to select the dominant individuals in the population R and add them to the population Y as the parent population for the next iteration;

Step S7: Determine whether the number of iterations has been reached. If so, obtain the power-to-gas equipment capacity, multi-type energy storage equipment capacity and multi-objective optimization configuration function value corresponding to each individual as the optimization configuration solution set and end. Otherwise, return to step S3.

Preferably, the solution module performs adaptive crossover and mutation on the parent population P based on the individual fitness value, the basic crossover probability and the basic mutation probability, including:

Determine an adaptive crossover probability and an adaptive mutation probability based on the individual fitness value, the basic crossover probability and the basic mutation probability;

Adaptive crossover and mutation are performed on the parent population P based on the adaptive crossover probability and the adaptive mutation probability.

Preferably, the calculation formula of the adaptive crossover probability in the solution module is as follows:

The calculation formula of the adaptive mutation probability is as follows:

In the formula: _pc is the adaptive crossover probability; _k1 is the first basic crossover probability; _k2 is the second basic crossover probability; _pm is the adaptive mutation probability; _k3 is the first basic mutation probability; _k4 is the second basic mutation probability; _fm is the fitness value of the individual to be mutated; _fm is the maximum fitness value that the population can accept; _fc is the larger fitness value of the two individuals to be crossed; _fmin is the minimum fitness value of all individuals in the population; the first basic crossover probability is less than the second basic crossover probability; the first basic mutation probability is less than the second basic mutation probability.

Preferably, the calculation formula of the adaptive elite retention strategy in the solution module is as follows:

In the formula: _Ne is the number of elite retained individuals; _fi is the fitness value of the i-th population individual; N is the number of individuals in the population; _fb is the fitness value of the best individual in the population.

Preferably, the system operation related parameters in the data acquisition module include one or more of the following: photovoltaic power prediction value, load prediction value, wind power prediction value, electricity price list for different time periods, power-to-gas equipment parameters, parameters of various types of energy storage equipment, cogeneration equipment operating parameters, gas boiler operating parameters, absorption chiller operating parameters, gas tank operating parameters, and compressor operating parameters.

Compared with the closest prior art, the present invention has the following beneficial effects:

The present invention provides a multi-objective optimization method, system, device and medium for an integrated energy system, including: obtaining installed capacity data of renewable energy and system operation related parameters in an electricity-gas interconnected integrated energy system; based on the installed capacity data of renewable energy and system operation related parameters, using a genetic algorithm to solve a pre-constructed multi-objective optimization configuration model including power-to-gas equipment and multiple types of energy storage equipment to obtain an optimized configuration solution set; based on optimization requirements, selecting the optimal configuration result of the electricity-gas interconnected integrated energy system from the optimized configuration solution set. The present invention establishes a multi-objective optimization configuration model including power-to-gas equipment and multiple types of energy storage equipment, performs coordinated planning of power-to-gas equipment and multiple types of energy storage equipment, and can provide a more comprehensive optimization configuration solution that is more in line with the actual situation of the integrated energy system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic flow chart of a multi-objective optimization method for an integrated energy system according to a specific embodiment of the present invention;

FIG2 is a diagram of typical daily solar radiation intensity and wind speed according to a specific embodiment of the present invention;

FIG3 is a load diagram of a comprehensive energy system according to a specific embodiment of the present invention;

FIG4 is a structural diagram of a comprehensive energy system according to a specific embodiment of the present invention;

FIG5 is a flow chart of a genetic algorithm according to a specific embodiment of the present invention;

FIG6 is an optimal solution set diagram for a specific embodiment of the present invention;

FIG7 is a comparison chart of annual _CO2 emissions according to a specific embodiment of the present invention;

FIG8 is a comparison diagram of renewable energy consumption rates according to a specific embodiment of the present invention;

FIG9 is a schematic diagram of a multi-objective optimization system for an integrated energy system provided by the present invention.

DETAILED DESCRIPTION

The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

Embodiment 1:

The present invention provides a multi-objective optimization method for an integrated energy system. Specifically, FIG1 is a flow chart of a multi-objective optimization method for an integrated energy system provided in an embodiment of the present application, and as shown in the figure, the method includes the following steps:

Step 1: Obtain the installed capacity data of renewable energy and system operation related parameters in the electricity-gas interconnected integrated energy system;

Step 2: Based on the installed capacity data of the renewable energy and system operation related parameters, a pre-built multi-objective optimization configuration model including power-to-gas equipment and multiple types of energy storage equipment is solved using a genetic algorithm to obtain an optimization configuration solution set;

Step 3: Selecting the optimal configuration result of the electricity-gas interconnected integrated energy system from the optimal configuration solution set based on the optimization requirements;

Among them, the multi-objective optimization model is constructed on the basis of maximizing the renewable energy consumption of the electricity-gas interconnected integrated energy system with the minimum system economic cost and the minimum _CO2 emissions.

In step 1, the installed capacity data of renewable energy units in the electricity-gas interconnected integrated energy system and system operation related parameters are obtained. In the present invention, the optimization configuration is mainly calculated and planned based on the installed capacity of the renewable energy units. The renewable energy units can be wind turbines, photovoltaic generators, hydroelectric generators or ocean energy generators. In the embodiments of the present disclosure, the renewable energy units are: photovoltaic generators and wind turbines. In the present disclosure, the installed capacity data of renewable energy units in the electricity-gas interconnected integrated energy system are obtained, including: 9 groups of data for 3 scenarios, as shown in Table 1. The above 3 scenarios are: a scenario considering only power-to-gas equipment, a scenario considering only energy storage equipment, and a scenario considering power-to-gas equipment and energy storage equipment.

Table 1

Acquiring system operation related parameters includes the operating parameters of each device in the system and data such as the electricity price of the system operation. Specifically, in the disclosed embodiment, one or more of the following are included: photovoltaic power prediction value, load prediction value, wind power prediction value, electricity price table for different time periods, power-to-gas equipment parameters, multi-type energy storage equipment parameters, cogeneration equipment operating parameters, gas boiler operating parameters, absorption chiller operating parameters, gas storage tank operating parameters, compressor operating parameters.

In the disclosed embodiment, the photovoltaic power prediction value is taken into account the time difference of solar radiation intensity and load. One day is selected as a typical day from the acquired historical data according to the required data characteristics. The solar radiation intensity and load conditions of the typical day represent the corresponding photovoltaic power prediction value. The load prediction value and wind power prediction value are obtained from the typical day in the historical data. Specifically, the photovoltaic power prediction value and wind power prediction value obtained in this embodiment are shown in Figure 2, and the load prediction value is shown in Figure 3. The electricity price table for different periods of the day, the existing equipment parameters of the integrated energy system, and the parameters of power-to-gas and multiple types of energy storage equipment are obtained. The above-mentioned acquired data are shown in Table 2, Table 3 and Table 4, where Table 2 is the electricity price table for different periods of the day, Table 3 is the existing equipment parameters of the integrated energy system, and Table 4 is the parameters of power-to-gas and multiple types of energy storage equipment.

Table 2

Table 3

Table 4

Step 2:

Specifically, based on the installed capacity data of the renewable energy and system operation related parameters, a genetic algorithm is used to solve a pre-built multi-objective optimization configuration model including power-to-gas equipment and multiple types of energy storage equipment to obtain an optimization configuration solution set.

In the embodiment of the present disclosure, the construction of the above-mentioned pre-built multi-objective optimization configuration model including power-to-gas equipment and multiple types of energy storage equipment includes: constructing an electric-gas interconnected comprehensive energy system model including power-to-gas equipment and multiple types of energy storage equipment based on multiple constraints. As shown in Figure 4, it is a schematic diagram of the electric-gas interconnected comprehensive energy system model established in the embodiment of the present disclosure. Specifically, it includes one or more of the following models: power system model, natural gas system model, coupling device model and multiple types of energy storage equipment model.

Specifically, in the embodiment of the present disclosure, the power system model, the natural gas system model, the coupling device model and the multi-type energy storage device model are the following formulas:

The power network model includes: node power active and reactive balance equations, unit output constraints, node voltage constraints, and branch power flow constraints;

(1) Power balance equation

为节点i的发电机无功功率；P_i ^wind为风电场注入的有功功率；V_i为节点i的电压幅值；V_j为节点j的电压幅值；θ_ij为节点i和节点j的电压相角差；G_ij为节点ij导纳矩阵的实部；B_ij为节点ij导纳矩阵的虚部；j∈ⁱ表示所有与节点i直接连接的节点j。Where: P _i ^cle is the active power of the generator at node i;

is the reactive power of the generator at node i; _Piwind is the active power injected by the wind farm _{; Vi} ^is the voltage amplitude at node i; _Vj is the voltage amplitude at node j; _θij is the voltage phase difference between node i and node j; _Gij is the real part of the admittance matrix of node ij; _Bij is the imaginary part of the admittance matrix of node ij; j∈ ⁱ represents all nodes j directly connected to node i.

(2) Unit output constraints

为t时刻机组i的有功出力；

为机组i的有功出力上限；

为机组i的有功出力下限；

为t时刻机组i的无功出力；

为机组i的无功出力下限；

为机组i的无功出力上限；

为t时刻风电机组i的有功出力；

为风电机组i的有功出力上限；

为风电机组i的有功出力下限。Where:

is the active power output of unit i at time t;

is the upper limit of active output of unit i;

is the lower limit of active output of unit i;

is the reactive power output of unit i at time t;

is the lower limit of reactive power output of unit i;

is the upper limit of reactive power output of unit i;

is the active power output of wind turbine i at time t;

is the upper limit of active output of wind turbine i;

is the lower limit of active power output of wind turbine i.

(3) Node voltage constraints

U _i,min ≤U _i,t ≤U _i,max

Where: U _i,t is the voltage of node i at time t; U _i,min is the lower limit of the voltage of node i; U _i,max is the upper limit of the voltage of node i.

(4) Branch flow constraints

|P _kl,t |≤P _kl,max

Where: _Pkl,t is the power flow value of branch kl at time t; _Pkl,max is the upper limit of the power flow of branch kl.

The natural gas network model includes: gas source, natural gas pipeline, pipeline storage, gas storage tank, compressor, and node flow balance;

(1) Gas source

S _i,min ≤S _i,t ≤S _i,max

Where: S _i,t is the natural gas supply of node i in the natural gas network at time t; S _i,max is the upper limit of gas production of the gas well; S _i,min is the lower limit of gas production of the gas well.

(2) Natural gas pipeline

为天然气管道节点i的压力上限。Where: F _ij is the pipeline flow rate of pipeline ij; C _ij is a constant related to the length, radius, temperature, gas density, compression factor, etc. of pipeline ij; π _i is the pressure of natural gas pipeline node i; π _j is the pressure of natural gas pipeline node j; sgn(π _i ,π _j ) is a sign function, indicating the natural gas flow direction. When the pressure of node i is greater than the pressure of node j, its value is 1, otherwise it is -1; π _i is the lower limit of the pressure of natural gas pipeline node i;

is the upper limit of the pressure at the natural gas pipeline node i.

(3) Custody

表示管道ij的平均压力；

为t时刻管道ij的进气量；

为t时刻管道ij的出气量。Where: _Lij,t is the pipe storage of pipeline ij at time t; _Lij,t-1 is the pipe storage of pipeline ij at time t-1; _Cij is a constant related to the length, radius, temperature, gas density, compression factor, etc. of pipeline ij;

represents the average pressure in pipeline ij;

is the air intake volume of pipeline ij at time t;

is the gas output of pipe ij at time t.

(4) Gas tank

为t时刻储气罐j的天然气注入流量；

为t时刻储气罐j的天然气输出流量；S_S,j,max为储气罐j存储容量的上限；S_S,j,min为储气罐j存储容量的下限；

为储气罐j天然气注入流量的上限；

为储气罐j天然气输出流量的上限。Where: S _S,j,t is the storage capacity of gas tank j at time t;

is the natural gas injection flow of gas tank j at time t;

is the natural gas output flow of gas tank j at time t; S _S,j,max is the upper limit of the storage capacity of gas tank j; S _S,j,min is the lower limit of the storage capacity of gas tank j;

The upper limit of the natural gas injection flow rate of gas storage tank j;

It is the upper limit of the natural gas output flow rate of gas storage tank j.

(5) Compressor

P _com =H _com (0.7479×10 ^-5 )

Where: H _com is the power required by the compressor; F _ij is the flow rate through the compressor; B is a constant; π _i is the pressure at the natural gas pipeline node i; π _j is the pressure at the natural gas pipeline node j; P _com is the electrical load of the electric drive compressor.

(6) Node traffic balance

为t时刻储气罐j的天然气注入流量；

为t时刻储气罐j的天然气输出流量；

为t时刻管道ij的进气量；

为t时刻管道ij的出气量；Q_P2G,j,t为t时刻电转气设备j转换得到的天然气流量；Q_GT,j,t为t时刻燃气轮机j消耗的天然气流量；Q_com,j,t为t时刻压缩机j消耗的天然气流量；Q_L,j,t为t时刻节点j的天然气负荷。Where: Q _N,j,t is the natural gas flow of node j in the natural gas network at time t; i∈j represents all nodes connected to node j;

is the natural gas injection flow of gas tank j at time t;

is the natural gas output flow of gas tank j at time t;

is the air intake volume of pipeline ij at time t;

is the gas output of pipeline ij at time t; Q _P2G,j,t is the natural gas flow converted by power-to-gas device j at time t; Q _GT,j,t is the natural gas flow consumed by gas turbine j at time t; Q _com,j,t is the natural gas flow consumed by compressor j at time t; Q _L,j,t is the natural gas load of node j at time t.

Multi-type energy storage equipment models include:

(1) Energy storage equipment

Where: W _ES,t is the energy stored in the battery at time t; μ _ES is the loss rate of the battery; P _ch,t is the charging power of the battery at time t; P _dis,t is the discharging power of the battery at time t; W _ES,t-1 is the energy stored in the battery at time t-1; λ _ES,ch is the charging efficiency of the battery; λ _ES,dis is the discharging efficiency of the battery; Δt is the scheduling time interval.

(2) Heat storage equipment

In the formula: W _HS,t is the energy stored in the heat storage device at time t; μ _HS is the loss rate of the heat storage device; H _ch,t is the charging power of the heat storage device at time t; H _dis,t is the heat release power of the heat storage device at time t; λ _HS,ch is the charging efficiency of the heat storage device; λ _HS,dis is the heat release efficiency of the heat storage device; W _HS,t-1 is the energy stored in the heat storage device at time t-1; Δt is the scheduling time interval.

(3) Cold storage equipment

为蓄冷设备t时刻储存的能量；

为蓄冷设备t-1时刻储存的能量；；μ_CS为蓄冷设备的损耗率；

为蓄冷设备t时刻的充冷功率；

为蓄冷设备t时刻的放冷功率；λ_CS,ch为蓄冷设备的充冷效率；λ_CS,dis为蓄冷设备的放冷效率；Δt为调度时间间隔。Where:

The energy stored in the cold storage device at time t;

is the energy stored in the cold storage device at time t-1; μ _CS is the loss rate of the cold storage device;

is the cooling power of the cold storage equipment at time t;

is the cooling power of the cold storage equipment at time t; λ _CS,ch is the cooling efficiency of the cold storage equipment; λ _CS,dis is the cooling efficiency of the cold storage equipment; Δt is the scheduling time interval.

The above coupling device models include:

(1) Gas turbine model:

为燃机轮机机组g时间t消耗的天然气；β_g为燃机轮机机组g燃气系数；

为燃机轮机机组g时间t产生的电功率；GHV为天然气高热值；

为燃机轮机机组g输出电功率最小值；

为燃机轮机机组g输出电功率最大值；Ω_T为调度时间间隔；N_GAS为燃机轮机机组总数。Where:

is the natural gas consumed by the gas turbine unit g in time t; β _g is the gas coefficient of the gas turbine unit g;

is the electric power generated by the gas turbine unit g in time t; GHV is the high calorific value of natural gas;

is the minimum output power of the gas turbine unit g;

is the maximum output power of gas turbine unit g; Ω _T is the scheduling time interval; N _GAS is the total number of gas turbine units.

(2) Power-to-gas equipment model:

为电转气设备o时间t产气量；

为电转气设备o时间t消耗天然气功率；GHV为天然气高热值；

为电转气设备o转换效率系数；

为电转气设备o最小消耗天然气功率；

为电转气设备o最大消耗天然气功率；Ω_T为调度时间间隔；N_P2G为电转气设备总数。Where:

is the gas production of the power-to-gas equipment in time o;

is the natural gas power consumed by the power-to-gas equipment in time o; GHV is the higher calorific value of natural gas;

is the conversion efficiency coefficient of the power-to-gas equipment;

Minimum natural gas power consumption for power-to-gas equipment;

is the maximum natural gas power consumed by the power-to-gas equipment o; Ω _T is the scheduling time interval; _NP2G is the total number of power-to-gas equipment.

Afterwards, based on the above-established electric-gas interconnected integrated energy system model including power-to-gas equipment and various types of energy storage equipment, a multi-objective optimization configuration function is constructed with the goal of minimizing the economic cost of the system, maximizing the consumption of renewable energy, and minimizing _CO2 emissions. The multi-objective optimization configuration function includes at least one or more of the following objective functions: the optimal objective function of the economic cost of the system, the maximum objective function of the consumption rate of renewable energy, and the minimum objective function of _CO2 emissions.

Specifically, in the embodiment of the present disclosure, the calculation formula of the optimal objective function of the system economic cost is as follows:

minF ₁ =F _inv +F _ope

F _ope = F _be + F _OM

F _OM = β _i P _out,i

为综合能源系统在t时刻从电网购入的电功率；

为在综合能源系统t时刻从气网购入的气功率；

为t时刻从电网购电的电价；J_G为天然气价格；P_out,i为设备i在t时段的输出功率；β_i为设备i的单位运行维护费用。Where: _F1 is the economic cost of the system; _Finv is the investment cost; _Fope is the operating cost; _Fbe is the energy purchase cost; _FOM is the system maintenance cost; _Ci is the installed capacity of equipment i; _γi is the installation cost per unit capacity of equipment i; α is the annual interest rate, which is 6% in this paper; _Yi is the operating life of equipment i; T is the scheduling period;

is the electric power purchased by the integrated energy system from the power grid at time t;

is the gas power purchased from the gas grid at time t in the integrated energy system;

is the electricity price purchased from the power grid at time t; J _G is the natural gas price; P _out,i is the output power of device i in period t; β _i is the unit operation and maintenance cost of device i.

The calculation formula of the maximum objective function of renewable energy consumption rate is as follows:

为t时段系统对于风电机组j的计划接纳风电功率；

为t时段系统对于光电机组j的计划接纳光电功率；

为风电机组j的理想功率；

为光电机组j的理想功率；T为调度周期；N_W为风电机组的总个数；N_V为光电机组的总个数。Where: F ₂ is the renewable energy consumption rate;

The planned wind power accepted by the system for wind turbine j during period t;

The system plans to receive photovoltaic power for photovoltaic group j during period t;

is the ideal power of wind turbine j;

is the ideal power of photovoltaic generator set j; T is the scheduling period; N _W is the total number of wind turbines; _NV is the total number of photovoltaic generator sets.

The calculation formula of the minimum objective function of _CO2 emissions is as follows:

为综合能源系统在t时刻从电网购入的电功率；

为在综合能源系统t时刻从气网购入的气功率；α_e为购电CO₂排放系数；α_gas为购气CO₂排放系数。Where: F ₃ is CO ₂ emissions;

is the electric power purchased by the integrated energy system from the power grid at time t;

is the gas power purchased from the gas grid at time t in the integrated energy system; _αe is the _CO2 emission coefficient for purchased electricity; _αgas is the _CO2 emission coefficient for purchased gas.

Finally, based on the electric-gas interconnected comprehensive energy system model including the power-to-gas equipment and various types of energy storage equipment and the multi-objective optimization configuration function, a multi-objective optimization configuration model including the power-to-gas equipment and various types of energy storage equipment is constructed.

For the multi-objective optimization configuration model established above, a genetic algorithm based on an adaptive elite retention strategy is used to solve the multi-objective optimization configuration function to obtain an optimized configuration solution set. In the present invention, a genetic algorithm is improved based on an adaptive elite retention strategy and adaptive crossover mutation, and is applied to the solution of the optimization model, which can increase the convergence speed and further improve the optimization efficiency.

Specifically, in the disclosed embodiment, based on the installed capacity data of the renewable energy and the system operation related parameters, the parent population individuals in the genetic algorithm based on the adaptive elite retention strategy are determined to be the power-to-gas equipment capacity and the multi-type energy storage equipment capacity, and the fitness value of each individual in the population is the multi-objective optimization configuration function value. In the disclosed embodiment, as shown in FIG5 , it is a flow chart of the genetic algorithm, and the genetic algorithm based on the adaptive elite retention strategy solves the multi-objective optimization configuration function to obtain the optimized configuration solution set, which specifically includes:

Step S1 population initialization: set the population size to 100, the maximum number of iterations to 100, _k1 to 0.5, _k2 to 0.9, _k3 to .01, and _k4 to 0.1.

Step S2: randomly generate a parent population P, each individual in the parent population P represents the capacity of the power-to-gas device and the capacity of the multi-type energy storage device, and calculate the fitness value of each individual in the parent population P, wherein the fitness value represents the objective function;

Step S3: Generate a progeny population Q by selecting a genetic algorithm and performing an adaptive crossover and mutation operation based on the individual fitness value, the basic crossover probability and the basic mutation probability. The calculation formulas for the adaptive crossover probability and the adaptive mutation probability in the adaptive crossover and mutation operation are:

Adaptive crossover, mutation:

In the formula: p _c is the adaptive crossover probability; k ₁ and k ₂ are the basic crossover probabilities, and k ₂ > k ₁ ; f _m is the maximum fitness value that the population can accept; f _c is the larger fitness value of the two individuals to be crossed; f _min is the minimum fitness value of all individuals in the population; p _m is the adaptive mutation probability; k ₃ and k ₄ are the basic mutation probabilities, and k ₄ > k ₃ ; f _m is the fitness value of the individual to be mutated. In the adaptive crossover probability, setting k ₂ > k ₁ and k ₄ > k ₃ can make the crossover probability and mutation probability produce adaptive changes near the k ₂ and k ₄ values based on the fitness value, so that the evolutionary direction of the individuals in the population changes towards the optimal direction, thereby improving the optimization efficiency of the algorithm.

Step S4: Mix the parent population P and the child population Q to obtain a new population R, and then perform fast non-dominated sorting on the new population R to obtain a non-dominated dominant population sequence;

Step S5: Based on the fitness value, a reference point selection strategy is adopted to select the population R to obtain the population Y as the parent population of the next iteration;

Step S6: Based on the non-dominated dominant population sequence, an adaptive elite retention strategy is used to screen out dominant individuals in the population R and add them to the population Y as the parent population for the next iteration.

The expression of the adaptive elite retention strategy is as follows:

Where: _Ne is the number of elite retained individuals; N is the number of individuals in the population; _fb is the fitness value of the best individual in the population. Compared with the ordinary elite retention strategy, the adaptive elite retention strategy has the advantage of being able to dynamically change with the fitness value, preserve the best individuals in the population, improve the accuracy of the algorithm, and also improve the optimization efficiency.

Step S7: Determine whether the preset number of iterations has been reached. If so, output the power-to-gas equipment capacity, multi-type energy storage equipment capacity and objective function value corresponding to each individual as the optimal configuration solution set. Otherwise, return to step S3.

Based on the installed capacity data of renewable energy and system operation related parameters obtained from the above different scenarios, the multi-objective optimization configuration function is solved by a genetic algorithm based on an adaptive elite retention strategy, and the optimal configuration solution set is obtained as shown in Figure 6. In the present invention, the output after the solution is the optimization solution set, and the final optimization configuration is selected based on demand. Specifically, in the disclosed embodiment, the optimal optimization configuration is selected based on different objective functions. Figure 7 is a data diagram of _CO2 emissions corresponding to the optimal solution of 9 groups of data, and Figure 8 is a diagram of renewable energy consumption rate corresponding to the optimal solution of the above 9 groups of data. Comparative analysis of the simulation results from the two aspects of annual _CO2 emissions and renewable energy consumption rate can find that the use of power-to-gas equipment and multi-energy storage equipment can greatly improve the renewable energy consumption rate and reduce the _annual CO2 emissions. The mutual conversion and storage of multiple energies in the integrated energy system greatly reduces the abandonment of wind and solar power, thereby improving energy utilization and reducing economic costs.

Table 5

The present invention proposes a multi-objective optimization method for an integrated energy system based on power-to-gas. For the integrated energy system with power-to-gas interconnection, a power system model, a natural gas system model, an energy storage device model, and a coupling device model that meet multiple constraints are established. Then, a multi-objective optimization model with the minimum economic cost of the system, the maximum renewable energy consumption, and the minimum CO2 emission is established, and an improved genetic algorithm is used to solve it. Compared with the existing methods, the coordinated planning of power-to-gas equipment and various types of energy storage equipment is carried out, and multiple objectives of the integrated energy system are optimized, which is more in line with the actual situation of the integrated energy system and improves the economy and environmental performance of the system. As shown in Table 5, it can be seen that the genetic algorithm is better at obtaining the optimal solution. Compared with the unimproved genetic algorithm (NSGA-Ⅲ), the genetic algorithm based on the adaptive elite retention strategy used in this technical solution has a better ability to obtain the optimal solution.

Embodiment 2:

Based on the same inventive concept, the present invention also provides a multi-objective optimization system for an electricity-gas interconnected integrated energy system based on electricity-to-gas conversion. The system structure is shown in FIG9 , and includes:

Data acquisition module: used to obtain installed capacity data of renewable energy and system operation related parameters in the electricity-gas interconnected integrated energy system;

A solution module: used to solve a pre-built multi-objective optimization configuration model including power-to-gas equipment and multiple types of energy storage equipment using a genetic algorithm based on the installed capacity data of the renewable energy and system operation related parameters, to obtain an optimization configuration solution set;

Optimal configuration result acquisition module: selects the optimal configuration result of the electricity-gas interconnected integrated energy system from the optimal configuration solution set based on the optimization requirements;

Among them, the multi-objective optimization model is constructed on the basis of maximizing the renewable energy consumption of the electricity-gas interconnected integrated energy system with the minimum system economic cost and the minimum _CO2 emissions.

Preferably, the construction of a multi-objective optimization configuration model including power-to-gas equipment and multiple types of energy storage equipment in the solution module includes:

Constructing an electric-gas interconnected comprehensive energy system model including power-to-gas equipment and multiple types of energy storage equipment based on multiple constraints, wherein the electric-gas interconnected comprehensive energy system model including power-to-gas equipment and multiple types of energy storage equipment includes one or more of the following power system models, natural gas system models, coupling device models, and multiple types of energy storage equipment models;

A multi-objective optimization configuration function is constructed with the objectives of minimizing the economic cost, maximizing the consumption of renewable energy and minimizing CO ₂ emissions of the electric-gas interconnected comprehensive energy system model including the electric-to-gas equipment and various types of energy storage equipment;

A multi-objective optimization configuration model including power-to-gas equipment and various types of energy storage equipment is constructed based on the power-to-gas interconnected comprehensive energy system model including power-to-gas equipment and various types of energy storage equipment and the multi-objective optimization configuration function.

Preferably, the solution module constructs an electric-gas interconnected integrated energy system model including electric-to-gas equipment and multiple types of energy storage equipment based on multiple constraints, including:

Constructing the power system model based on power balance constraints, unit output constraints, node voltage constraints, and branch power flow constraints;

The natural gas system model is constructed based on the gas source output constraint, the natural gas pipeline operation constraint, the pipe village operation constraint, the gas storage tank operation constraint, the compressor operation constraint, and the node flow balance constraint;

Constructing the coupling device model based on the gas turbine output constraint and the power-to-gas device output constraint;

Constructing the multiple types of energy storage device models based on the operation constraints of the electric storage device, the operation constraints of the heat storage device, and the operation constraints of the cold storage device;

An electric-gas interconnected comprehensive energy system model including power-to-gas equipment and multiple types of energy storage equipment is constructed using the electric power system model, the natural gas system model, the coupling device model and the multiple types of energy storage equipment models.

Preferably, the calculation formula corresponding to the multi-objective optimization configuration function in the solution module is as follows:

minF ₁ =F _inv +F _ope

为t时段系统对于风电机组j的计划接纳风电功率；

为t时段系统对于光电机组j的计划接纳光电功率；

为风电机组j的理想功率；

为光电机组j的理想功率；F₃为CO₂排放量；

为综合能源系统在t时刻从电网购入的电功率；

为在综合能源系统t时刻从气网购入的气功率；α_e为购电CO₂排放系数；α_gas为购气CO₂排放系数。Where: _F1 is the economic cost of the system; _Finv is the investment cost; _Fope is the operating cost; _F2 is the renewable energy consumption rate; T is the dispatch period; _NW is the total number of wind turbines in the system; _NV is the total number of photovoltaic generators in the system;

The planned wind power accepted by the system for wind turbine j during period t;

The system plans to receive photovoltaic power for photovoltaic group j during period t;

is the ideal power of wind turbine j;

is the ideal power of photovoltaic group j; F ₃ is the CO ₂ emission;

is the electric power purchased by the integrated energy system from the power grid at time t;

is the gas power purchased from the gas grid at time t in the integrated energy system; _αe is the _CO2 emission coefficient for purchased electricity; _αgas is the _CO2 emission coefficient for purchased gas.

Preferably, the calculation formula of the investment cost _Finv in the solution module is as follows:

The calculation formula of the operating cost _Fope is as follows:

为t时刻从电网购电的电价；J_G为天然气价格；P_out,i为设备i在t时段的输出功率；β_i为设备i的单位运行维护费用。Where: γ _i is the installation cost per unit capacity of equipment i; C _i is the installed capacity of equipment i; I is the total number of equipment in the integrated energy system; α is the annual interest rate; Yi _i is the operating life of equipment i; T is the scheduling period;

is the electricity price purchased from the power grid at time t; J _G is the natural gas price; P _out,i is the output power of device i in period t; β _i is the unit operation and maintenance cost of device i.

Preferably, the solution module is specifically used for:

Based on the installed capacity data of the renewable energy and system operation related parameters, determining that the parent population individuals in the genetic algorithm based on the adaptive elite retention strategy are the power-to-gas equipment capacity and the multi-type energy storage equipment capacity, and the fitness value of each individual in the population is the multi-objective optimization configuration function value;

The genetic algorithm based on the adaptive elite retention strategy is used to solve the multi-objective optimization configuration function to obtain an optimization configuration solution set.

Preferably, the solution module adopts a genetic algorithm based on an adaptive elite retention strategy to solve the multi-objective optimization configuration function to obtain an optimization configuration solution set, including:

Step S1: Initialize the population, set the population size, number of iterations, basic crossover probability and basic mutation probability;

Step S2: randomly generate a parent population P, each individual in the parent population P represents the capacity of the power-to-gas equipment and the capacity of multiple types of energy storage equipment, and calculate the fitness value of each individual in the parent population P, wherein the fitness value represents the value of the multi-objective optimization configuration function;

Step S3: selecting the parent population P by a genetic algorithm, and performing adaptive crossover and mutation on the parent population P based on the individual fitness value, the basic crossover probability and the basic mutation probability to generate a child population Q;

Step S4: Mix the parent population P and the child population Q to obtain a new population R, and then perform fast non-dominated sorting on the new population R to obtain a non-dominated dominant population sequence;

Step S5: Based on the fitness value, a reference point selection strategy is adopted to select the population R to obtain the population Y as the parent population of the next iteration;

Step S6: Based on the non-dominated dominant population sequence, the adaptive elite retention strategy is used to select the dominant individuals in the population R and add them to the population Y as the parent population for the next iteration;

Step S7: Determine whether the number of iterations has been reached. If so, obtain the power-to-gas equipment capacity, multi-type energy storage equipment capacity and multi-objective optimization configuration function value corresponding to each individual as the optimization configuration solution set and end. Otherwise, return to step S3.

Preferably, the solution module performs adaptive crossover and mutation on the parent population P based on the individual fitness value, the basic crossover probability and the basic mutation probability, including:

Determine an adaptive crossover probability and an adaptive mutation probability based on the individual fitness value, the basic crossover probability and the basic mutation probability;

Adaptive crossover and mutation are performed on the parent population P based on the adaptive crossover probability and the adaptive mutation probability.

Preferably, the calculation formula of the adaptive crossover probability in the solution module is as follows:

The calculation formula of the adaptive mutation probability is as follows:

In the formula: _pc is the adaptive crossover probability; _k1 is the first basic crossover probability; _k2 is the second basic crossover probability; _pm is the adaptive mutation probability; _k3 is the first basic mutation probability; _k4 is the second basic mutation probability; _fm is the fitness value of the individual to be mutated; _fm is the maximum fitness value that the population can accept; _fc is the larger fitness value of the two individuals to be crossed; _fmin is the minimum fitness value of all individuals in the population; the first basic crossover probability is less than the second basic crossover probability; the first basic mutation probability is less than the second basic mutation probability.

Preferably, the calculation formula of the adaptive elite retention strategy in the solution module is as follows:

In the formula: _Ne is the number of elite retained individuals; _fi is the fitness value of the i-th population individual; N is the number of individuals in the population; _fb is the fitness value of the best individual in the population.

Preferably, the system operation related parameters in the data acquisition module include one or more of the following: photovoltaic power prediction value, load prediction value, wind power prediction value, electricity price list for different time periods, power-to-gas equipment parameters, parameters of various types of energy storage equipment, cogeneration equipment operating parameters, gas boiler operating parameters, absorption chiller operating parameters, gas tank operating parameters, and compressor operating parameters.

The present invention solves the optimal configuration scheme of the integrated energy system including power-to-gas equipment and multiple types of energy storage equipment through a data acquisition module, a solution module and an optimal configuration result acquisition module. By establishing a multi-objective optimization configuration model including power-to-gas equipment and multiple types of energy storage equipment, the coordinated planning of power-to-gas equipment and multiple types of energy storage equipment is carried out, which can provide a more comprehensive optimization configuration scheme that is more in line with the actual situation of the integrated energy system. In addition, the solution module adopts an improved genetic algorithm based on an adaptive elite retention strategy and adaptive crossover mutation, which further improves the optimization efficiency in the solution process.

Embodiment 3:

Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a multi-objective optimization method for an integrated energy system in the above embodiment.

Embodiment 4:

Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of a multi-objective optimization method for an integrated energy system in the above embodiment.

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

The present invention is described with reference to the flowchart and/or block diagram of the method, device (system), and computer program product according to the embodiment of the present invention. It should be understood that each process and/or box in the flowchart and/or block diagram, as well as the combination of the process and/or box in the flowchart and/or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

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 its protection scope. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims to be approved.

Claims (24)

1. A multi-objective optimization method for an integrated energy system, comprising: Obtain installed capacity data of renewable energy and system operation related parameters in the electricity-gas interconnected integrated energy system; Based on the installed capacity data of the renewable energy and system operation related parameters, a genetic algorithm is used to solve a pre-built multi-objective optimization configuration model including power-to-gas equipment and multiple types of energy storage equipment to obtain an optimization configuration solution set; Selecting the optimal configuration result of the electricity-gas interconnected integrated energy system from the optimal configuration solution set based on the optimization requirements; Among them, the multi-objective optimization model is constructed on the basis of maximizing the renewable energy consumption of the electricity-gas interconnected integrated energy system with the minimum system economic cost and the minimum _CO2 emissions. 2. The method according to claim 1, characterized in that the construction of the multi-objective optimization configuration model including the power-to-gas equipment and multiple types of energy storage equipment comprises: Constructing an electric-gas interconnected comprehensive energy system model including power-to-gas equipment and multiple types of energy storage equipment based on multiple constraints, wherein the electric-gas interconnected comprehensive energy system model including power-to-gas equipment and multiple types of energy storage equipment includes one or more of the following power system models, natural gas system models, coupling device models, and multiple types of energy storage equipment models; A multi-objective optimization configuration function is constructed with the objectives of minimizing the economic cost, maximizing the consumption of renewable energy and minimizing CO ₂ emissions of the electric-gas interconnected comprehensive energy system model including the electric-to-gas equipment and various types of energy storage equipment; A multi-objective optimization configuration model including power-to-gas equipment and various types of energy storage equipment is constructed based on the power-to-gas interconnected comprehensive energy system model including power-to-gas equipment and various types of energy storage equipment and the multi-objective optimization configuration function. 3. The method according to claim 2, characterized in that the construction of an electric-gas interconnected integrated energy system model including power-to-gas equipment and multiple types of energy storage equipment based on multiple constraints comprises: Constructing the power system model based on power balance constraints, unit output constraints, node voltage constraints, and branch power flow constraints; The natural gas system model is constructed based on the gas source output constraint, the natural gas pipeline operation constraint, the pipe village operation constraint, the gas storage tank operation constraint, the compressor operation constraint, and the node flow balance constraint; Constructing the coupling device model based on the gas turbine output constraint and the power-to-gas device output constraint; Constructing the multiple types of energy storage device models based on the operation constraints of the electric storage device, the operation constraints of the heat storage device, and the operation constraints of the cold storage device; An electric-gas interconnected comprehensive energy system model including power-to-gas equipment and multiple types of energy storage equipment is constructed using the electric power system model, the natural gas system model, the coupling device model and the multiple types of energy storage equipment models. 4. The method according to claim 2, characterized in that the calculation formula corresponding to the multi-objective optimization configuration function is as follows: minF ₁ =F _inv +F _ope

Where: _F1 is the economic cost of the system; _Finv is the investment cost; _Fope is the operating cost; _F2 is the renewable energy consumption rate; T is the dispatch period; _NW is the total number of wind turbines in the system; _NV is the total number of photovoltaic generators in the system;

The planned wind power accepted by the system for wind turbine j during period t;

The system plans to receive photovoltaic power for photovoltaic group j during period t;

is the ideal power of wind turbine j;

is the ideal power of photovoltaic group j; F ₃ is the CO ₂ emission;

is the electric power purchased by the integrated energy system from the power grid at time t;

is the gas power purchased from the gas grid at time t in the integrated energy system; _αe is the _CO2 emission coefficient for purchased electricity; _αgas is the _CO2 emission coefficient for purchased gas. 5. The method according to claim 4, characterized in that the calculation formula of the investment cost _Finv is as follows:

The calculation formula of the operating cost _Fope is as follows:

Where: γ _i is the installation cost per unit capacity of equipment i; C _i is the installed capacity of equipment i; I is the total number of equipment in the integrated energy system; α is the annual interest rate; Yi _i is the operating life of equipment i; T is the scheduling period;

is the electricity price purchased from the power grid at time t; J _G is the natural gas price; P _out,i is the output power of device i in period t; β _i is the unit operation and maintenance cost of device i. 6. The method according to claim 2, characterized in that, based on the installed capacity data of the renewable energy and system operation related parameters, a genetic algorithm is used to solve a pre-built multi-objective optimization configuration model including power-to-gas equipment and multiple types of energy storage equipment to obtain an optimization configuration solution set, including: Based on the installed capacity data of the renewable energy and system operation related parameters, determining that the parent population individuals in the genetic algorithm based on the adaptive elite retention strategy are the power-to-gas equipment capacity and the multi-type energy storage equipment capacity, and the fitness value of each individual in the population is the multi-objective optimization configuration function value; The genetic algorithm based on the adaptive elite retention strategy is used to solve the multi-objective optimization configuration function to obtain an optimization configuration solution set. 7. The method according to claim 6, characterized in that the use of a genetic algorithm based on an adaptive elite retention strategy to solve the multi-objective optimization configuration function to obtain an optimization configuration solution set comprises: Step S1: Initialize the population, set the population size, number of iterations, basic crossover probability and basic mutation probability; Step S2: randomly generate a parent population P, each individual in the parent population P represents the capacity of the power-to-gas equipment and the capacity of multiple types of energy storage equipment, and calculate the fitness value of each individual in the parent population P, wherein the fitness value represents the value of the multi-objective optimization configuration function; Step S3: selecting the parent population P by a genetic algorithm, and performing adaptive crossover and mutation on the parent population P based on the individual fitness value, the basic crossover probability and the basic mutation probability to generate a child population Q; Step S4: Mix the parent population P and the child population Q to obtain a new population R, and then perform fast non-dominated sorting on the new population R to obtain a non-dominated dominant population sequence; Step S5: Based on the fitness value, a reference point selection strategy is adopted to select the population R to obtain the population Y as the parent population of the next iteration; Step S6: Based on the non-dominated dominant population sequence, the adaptive elite retention strategy is used to select the dominant individuals in the population R and add them to the population Y as the parent population for the next iteration; Step S7: Determine whether the number of iterations has been reached. If so, obtain the power-to-gas equipment capacity, multi-type energy storage equipment capacity and multi-objective optimization configuration function value corresponding to each individual as the optimization configuration solution set and end. Otherwise, return to step S3. 8. The method according to claim 7, characterized in that the step of performing adaptive crossover and mutation on the parent population P based on the individual fitness value, the basic crossover probability and the basic mutation probability comprises: Determine an adaptive crossover probability and an adaptive mutation probability based on the individual fitness value, the basic crossover probability and the basic mutation probability; Adaptive crossover and mutation are performed on the parent population P based on the adaptive crossover probability and the adaptive mutation probability. 9. The method according to claim 8, characterized in that the calculation formula of the adaptive crossover probability is as follows:

The calculation formula of the adaptive mutation probability is as follows:

In the formula: _pc is the adaptive crossover probability; _k1 is the first basic crossover probability; _k2 is the second basic crossover probability; _pm is the adaptive mutation probability; _k3 is the first basic mutation probability; _k4 is the second basic mutation probability; _fm is the fitness value of the individual to be mutated; _fm is the maximum fitness value that the population can accept; _fc is the larger fitness value of the two individuals to be crossed; _fmin is the minimum fitness value of all individuals in the population; the first basic crossover probability is less than the second basic crossover probability; the first basic mutation probability is less than the second basic mutation probability. 10. The method according to claim 7, characterized in that the calculation formula of the adaptive elite retention strategy is as follows:

In the formula: _Ne is the number of elite retained individuals; _fi is the fitness value of the i-th population individual; N is the number of individuals in the population; _fb is the fitness value of the best individual in the population. 11. The method according to claim 1 is characterized in that the system operation related parameters include one or more of the following: photovoltaic power prediction value, load prediction value, wind power prediction value, electricity price table for different time periods, power-to-gas equipment parameters, multi-type energy storage equipment parameters, cogeneration equipment operating parameters, gas boiler operating parameters, absorption chiller operating parameters, gas tank operating parameters, compressor operating parameters. 12. A multi-objective optimization system for an electricity-gas interconnected integrated energy system based on electricity-to-gas conversion, characterized by comprising: Data acquisition module: used to obtain installed capacity data of renewable energy and system operation related parameters in the electricity-gas interconnected integrated energy system; A solution module: used to solve a pre-built multi-objective optimization configuration model including power-to-gas equipment and multiple types of energy storage equipment using a genetic algorithm based on the installed capacity data of the renewable energy and system operation related parameters to obtain an optimization configuration solution set; An optimal configuration result acquisition module is used to select the optimal configuration result of the electricity-gas interconnected integrated energy system from the optimal configuration solution set based on optimization requirements; Among them, the multi-objective optimization model is constructed on the basis of maximizing the renewable energy consumption of the electricity-gas interconnected integrated energy system with the minimum system economic cost and the minimum _CO2 emissions. 13. The system according to claim 12, characterized in that the construction of a multi-objective optimization configuration model including power-to-gas equipment and multiple types of energy storage equipment in the solution module comprises: Constructing an electric-gas interconnected comprehensive energy system model including power-to-gas equipment and multiple types of energy storage equipment based on multiple constraints, wherein the electric-gas interconnected comprehensive energy system model including power-to-gas equipment and multiple types of energy storage equipment includes one or more of the following power system models, natural gas system models, coupling device models, and multiple types of energy storage equipment models; A multi-objective optimization configuration function is constructed with the objectives of minimizing the economic cost, maximizing the consumption of renewable energy and minimizing CO ₂ emissions of the electric-gas interconnected comprehensive energy system model including the electric-to-gas equipment and various types of energy storage equipment; A multi-objective optimization configuration model including power-to-gas equipment and various types of energy storage equipment is constructed based on the power-to-gas interconnected comprehensive energy system model including power-to-gas equipment and various types of energy storage equipment and the multi-objective optimization configuration function. 14. The system according to claim 13, characterized in that the solution module constructs an electric-gas interconnected comprehensive energy system model including power-to-gas equipment and multiple types of energy storage equipment based on multiple constraints, including: Constructing the power system model based on power balance constraints, unit output constraints, node voltage constraints, and branch power flow constraints; The natural gas system model is constructed based on the gas source output constraint, the natural gas pipeline operation constraint, the pipe village operation constraint, the gas storage tank operation constraint, the compressor operation constraint, and the node flow balance constraint; Constructing the coupling device model based on the gas turbine output constraint and the power-to-gas device output constraint; Constructing the multiple types of energy storage device models based on the operation constraints of the electric storage device, the operation constraints of the heat storage device, and the operation constraints of the cold storage device; An electric-gas interconnected comprehensive energy system model including power-to-gas equipment and multiple types of energy storage equipment is constructed using the electric power system model, the natural gas system model, the coupling device model and the multiple types of energy storage equipment models. 15. The system according to claim 13, characterized in that the calculation formula corresponding to the multi-objective optimization configuration function in the solution module is as follows: minF ₁ =F _inv +F _ope

Where: _F1 is the economic cost of the system; _Finv is the investment cost; _Fope is the operating cost; _F2 is the renewable energy consumption rate; T is the dispatch period; _NW is the total number of wind turbines in the system; _NV is the total number of photovoltaic generators in the system;

The planned wind power accepted by the system for wind turbine j during period t;

The system plans to receive photovoltaic power for photovoltaic group j during period t;

is the ideal power of wind turbine j;

is the ideal power of photovoltaic group j; F ₃ is the CO ₂ emission;

is the electric power purchased by the integrated energy system from the power grid at time t;

is the gas power purchased from the gas grid at time t in the integrated energy system; _αe is the _CO2 emission coefficient for purchased electricity; _αgas is the _CO2 emission coefficient for purchased gas. 16. The system according to claim 15, characterized in that the calculation formula of the investment cost _Finv in the solution module is as follows:

The calculation formula of the operating cost _Fope is as follows:

Where: γ _i is the installation cost per unit capacity of equipment i; C _i is the installed capacity of equipment i; I is the total number of equipment in the integrated energy system; α is the annual interest rate; Yi _i is the operating life of equipment i; T is the scheduling period;

is the electricity price purchased from the power grid at time t; J _G is the natural gas price; P _out,i is the output power of device i in period t; β _i is the unit operation and maintenance cost of device i. 17. The system according to claim 13, wherein the solution module is specifically used for: Based on the installed capacity data of the renewable energy and system operation related parameters, determining that the parent population individuals in the genetic algorithm based on the adaptive elite retention strategy are the power-to-gas equipment capacity and the multi-type energy storage equipment capacity, and the fitness value of each individual in the population is the multi-objective optimization configuration function value; The genetic algorithm based on the adaptive elite retention strategy is used to solve the multi-objective optimization configuration function to obtain an optimization configuration solution set. 18. The system according to claim 17, characterized in that the solution module uses a genetic algorithm based on an adaptive elite retention strategy to solve the multi-objective optimization configuration function to obtain an optimization configuration solution set, including: Step S1: Initialize the population, set the population size, number of iterations, basic crossover probability and basic mutation probability; Step S2: randomly generate a parent population P, each individual in the parent population P represents the capacity of the power-to-gas equipment and the capacity of multiple types of energy storage equipment, and calculate the fitness value of each individual in the parent population P, wherein the fitness value represents the value of the multi-objective optimization configuration function; Step S3: selecting the parent population P by a genetic algorithm, and performing adaptive crossover and mutation on the parent population P based on the individual fitness value, the basic crossover probability and the basic mutation probability to generate a child population Q; Step S4: Mix the parent population P and the child population Q to obtain a new population R, and then perform fast non-dominated sorting on the new population R to obtain a non-dominated dominant population sequence; Step S5: Based on the fitness value, a reference point selection strategy is adopted to select the population R to obtain the population Y as the parent population of the next iteration; Step S6: Based on the non-dominated dominant population sequence, the adaptive elite retention strategy is used to select the dominant individuals in the population R and add them to the population Y as the parent population for the next iteration; Step S7: Determine whether the number of iterations has been reached. If so, obtain the power-to-gas equipment capacity, multi-type energy storage equipment capacity and multi-objective optimization configuration function value corresponding to each individual as the optimization configuration solution set and end. Otherwise, return to step S3. 19. The system according to claim 18, wherein the solution module performs adaptive crossover and mutation on the parent population P based on the individual fitness value, the basic crossover probability and the basic mutation probability, comprising: Determine an adaptive crossover probability and an adaptive mutation probability based on the individual fitness value, the basic crossover probability and the basic mutation probability; Adaptive crossover and mutation are performed on the parent population P based on the adaptive crossover probability and the adaptive mutation probability. 20. The system according to claim 13, characterized in that the calculation formula of the adaptive crossover probability in the solution module is as follows:

The calculation formula of the adaptive mutation probability is as follows:

In the formula: _pc is the adaptive crossover probability; _k1 is the first basic crossover probability; _k2 is the second basic crossover probability; _pm is the adaptive mutation probability; _k3 is the first basic mutation probability; _k4 is the second basic mutation probability; _fm is the fitness value of the individual to be mutated; _fm is the maximum fitness value that the population can accept; _fc is the larger fitness value of the two individuals to be crossed; _fmin is the minimum fitness value of all individuals in the population; the first basic crossover probability is less than the second basic crossover probability; the first basic mutation probability is less than the second basic mutation probability. 21. The system according to claim 13, characterized in that the calculation formula of the adaptive elite retention strategy in the solution module is as follows:

In the formula: _Ne is the number of elite retained individuals; _fi is the fitness value of the i-th population individual; N is the number of individuals in the population; _fb is the fitness value of the best individual in the population. 22. The system according to claim 12 is characterized in that the system operation related parameters in the data acquisition module include one or more of the following: photovoltaic power prediction value, load prediction value, wind power prediction value, electricity price list for different time periods, power-to-gas equipment parameters, parameters of multiple types of energy storage equipment, cogeneration equipment operating parameters, gas boiler operating parameters, absorption chiller operating parameters, gas tank operating parameters, and compressor operating parameters. 23. A computer device, comprising: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, a multi-objective optimization method for an integrated energy system as described in any one of claims 1 to 11 is implemented. 24. A computer-readable storage medium, characterized in that a computer program is stored thereon, and when the computer program is executed, it implements a multi-objective optimization method for an integrated energy system as described in any one of claims 1 to 11.

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