Multi-energy coupling system regulation optimization method based on improved non-dominated genetic algorithm

Abstract

This invention discloses a method for regulating and optimizing multi-energy coupled systems based on an improved non-dominated genetic algorithm, relating to the field of multi-energy coupled system regulation. The method includes determining the objective function based on a simulation model of the multi-energy coupled system, obtaining the objective function space based on the parent controlled variables and the objective function, sorting and classifying the parent controlled variables using a non-dominated genetic algorithm, and improving the non-dominated genetic algorithm by adding a mechanism for dynamically selecting the optimal solution. Specifically, different methods are used to generate offspring controlled variables based on the number of parent controlled variables included in the highest level. This allows the search for the optimal solution to proceed in the direction of the selected possible optimal solution, thereby accelerating the convergence speed and achieving real-time regulation of the multi-energy coupled system.

Description

Technical Field

[0001] This invention relates to the field of regulation of multi-energy coupled systems, and in particular to a method for regulating and optimizing multi-energy coupled systems based on an improved non-dominated genetic algorithm. Background Technology

[0002] A multi-energy coupled system is a comprehensive energy system comprising multiple coupled energy units such as cooling, heating, wind power generation, and photovoltaic power generation. To achieve the regulation and optimization of a multi-energy coupled system, a mathematical model needs to be established, and multi-objective optimization solutions need to be applied to determine the regulation method. Existing technologies typically use non-dominated genetic algorithms for multi-objective optimization solutions. However, non-dominated genetic algorithms require generating a large number of random solutions and undergoing multiple iterations, resulting in slow convergence speeds. This makes it difficult to meet the real-time requirements of multi-objective optimization solutions and thus hinders real-time regulation of multi-energy coupled systems. Summary of the Invention

[0003] The purpose of this invention is to provide a method for regulating and optimizing multi-energy coupled systems based on an improved non-dominated genetic algorithm. When determining the regulation mode of a multi-energy coupled system using the non-dominated genetic algorithm, after selecting the possible directions of the optimal solution of the objective function, the optimal solution is searched in the direction of the optimal solution, thereby accelerating the convergence speed and realizing real-time regulation of the multi-energy coupled system.

[0004] To address the aforementioned technical problems, this invention provides a method for regulating and optimizing multi-energy coupled systems based on an improved non-dominated genetic algorithm, comprising:

[0005] Establish a simulation model of the multi-energy coupled system and determine the objective function that the multi-energy coupled system must satisfy when it is controlled under the simulation model;

[0006] By inputting N sets of parent controlled variables into the objective function, an objective function space including N sets of objective function values ​​is obtained. The parent controlled variables are the adjustable variables included in the objective function.

[0007] The parental controlled variables in each group are sorted in ascending order and classified into levels according to the magnitude of the objective function values ​​of the parental controlled variables in each group using a non-dominated genetic algorithm.

[0008] When the number of parental controlled variables in the highest level is less than the first preset number, N groups of the parental controlled variables are randomly selected for crossbreeding and mutation to generate a second preset number of offspring controlled variables.

[0009] When the number of parental controlled variables in the highest rank is not less than the first preset number, the parental controlled variables in the highest rank are selected for crossbreeding and mutation to generate the second preset number of offspring controlled variables.

[0010] According to the preset selection rules, the optimal controlled variable is selected from each of the parent controlled variables and each of the child controlled variables, and the multi-energy coupling system is regulated according to the objective function value corresponding to the optimal controlled variable.

[0011] Preferably, a simulation model of the multi-energy coupled system is established, including:

[0012] A mathematical simulation model is established that conforms to the preset overall constraints of the multi-energy coupling system and the preset constraints of each energy unit included in the multi-energy coupling system.

[0013] Preferably, the overall constraint condition of the preset multi-energy coupling system is:

[0014]

[0015] in, M represents the electricity purchase rate of the multi-energy coupled system from the main grid. Source (t) represents the gas purchase rate from the multi-energy coupling system to the main gas supply system, P EL (t), P HL (t), P CL (t) and M GL (t) represents, in order, the electrical load, thermal load, cooling load power, and natural gas load demand rate of the multi-energy coupled system. and The output / input power, P, corresponds to the electrical storage, thermal storage, and cold storage of the multi-energy coupling system, respectively. PV (t), P WT (t), P EB (t), P CHP (t), P EC (t), P GB (t) and P AC (t) Combined with its appendices E / H / C, these represent the electrical output power / thermal output power / cooling energy consumption output power of the photovoltaic power generation device, wind turbine power generation device, electric boiler, combined heat and power device, electric chiller, natural gas boiler, and absorption chiller in the multi-energy coupling system, respectively. M GB,G (t) represents the natural gas transmission rate of the natural gas boiler in the multi-energy coupling system, M CHP,G (t) represents the natural gas transmission rate of the cogeneration unit in the multi-energy coupling system.

[0016] Preferably, the objective function that the multi-energy coupled system must satisfy when controlled under the simulation model includes:

[0017] Determine the first objective function and the second objective function that the multi-energy coupled system must satisfy when it is controlled under the simulation model;

[0018] Wherein, the first objective function is a function of the total comprehensive energy supply cost that needs to be satisfied when regulating the multi-energy coupling system, and the second objective function is a function of the carbon emissions that need to be satisfied when regulating the multi-energy coupling system;

[0019] The first objective function is in, This represents the cost of purchasing electricity from the main grid for the multi-energy coupling system. In the formula, EP(t) represents the real-time electricity price. The rate at which the multi-energy coupling system purchases electricity from the main grid is denoted as t, where t is the current time period and T is the total time period. This represents the cost of purchasing natural gas from the natural gas network for the multi-energy coupling system. In the formula, c represents the fixed natural gas price, and M Source (t) represents the gas purchase rate from the multi-energy coupling system to the main gas transmission system; This represents the cost of purchasing carbon emission permits for the multi-energy coupling system. In the formula, CP(t) represents the price of segmented tradable carbon emission permits, and CF(t) represents the system carbon emissions;

[0020] The second objective function is Among them CF GB (t) represents the carbon emissions of the natural gas boiler in the multi-energy coupling system, CF CHP (t) represents the carbon emissions of the cogeneration power plant in the multi-energy coupling system.

[0021] Preferably, determining the first objective function and the second objective function that the multi-energy coupled system must satisfy when controlled under the simulation model includes:

[0022] A first objective function and a second objective function are determined to be satisfied when the multi-energy coupled system is controlled under the simulation model. Both the first objective function and the second objective function must meet preset objective function constraints, wherein the objective function constraints are:

[0023]

[0024] Among them, M Source (t) represents the gas purchase rate from the multi-energy coupling system to the main gas transmission system.

[0025] M Source,min (t) and M Source,max (t) represents the preset minimum gas purchase rate and the preset maximum gas purchase rate, respectively; PEB,E (t) represents the electrical output power of the electric boiler in the multi-energy coupling system, P EB,E,min (t) and P EB,E,max (t) represent the preset minimum and maximum electrical output power of the electric boiler, respectively; M CHP,G (t) represents the natural gas transmission rate of the cogeneration unit in the multi-energy coupling system, M CHP,G,min (t) and M CHP,G,max (t) represents the preset minimum natural gas transmission rate and the preset maximum natural gas transmission rate of the cogeneration unit, respectively; P EC,E (t) represents the electrical output power of the electric chiller in the multi-energy coupling system, P EC,E,min (t) and P EC,E,max (t) represent the preset minimum and maximum electrical output power of the electric chiller, respectively; M GB,G (t) represents the natural gas transmission rate of the natural gas boiler in the multi-energy coupling system, M GB,G,min (t) and M GB,G,max (t) represents the preset minimum natural gas transmission rate and the preset maximum natural gas transmission rate of the natural gas boiler, respectively; P AC,H (t) represents the thermal energy output power of the absorption chiller in the multi-energy coupling system, P AC,H,max (t) represents the preset maximum thermal energy output power; The input efficiency of the storage device in the multi-energy coupling system. S(t) represents the output efficiency of the storage device, S(t) represents the real-time state of the storage device, t represents the time period at the current moment, and T represents the total number of time periods included in the preset time interval.

[0026] Preferably, a non-dominated genetic algorithm is used to sort and classify the parental controlled variables in each group in ascending order according to the magnitude of the objective function values ​​of the parental controlled variables, including:

[0027] According to preset rules, target controlled variables are selected from the parent controlled variables of each group. Before all target controlled variables are traversed, the following operations are performed on each target controlled variable:

[0028] For all undetermined controlled variables, it is determined whether the undetermined controlled variable dominates the target controlled variable, wherein the undetermined controlled variable is the other parent controlled variable in each group of parent controlled variables besides the target controlled variable;

[0029] When the undetermined controlled variable dominates the target controlled variable, the dominated set of the target controlled variable is added to the undetermined controlled variable, and the dominated number of the target controlled variable is incremented by one;

[0030] When the undetermined controlled variable does not dominate the target controlled variable, proceed to the step of selecting the target controlled variable from each group of parent controlled variables according to a preset rule;

[0031] The parent controlled variables are sorted and ranked according to the number of controlled variables from smallest to largest.

[0032] Preferably, the first preset quantity is N / 2, where N is the total number of the parent controlled variables.

[0033] Preferably, after sorting and classifying the parental controlled variables in each group in ascending order according to the objective function values ​​of the parental controlled variables in each group using a non-dominated genetic algorithm, the method further includes:

[0034] The crowding degree of each parent controlled variable is determined based on the objective function value of each parent controlled variable.

[0035] Preferably, determining the crowding degree of each of the parent controlled variables based on the objective function values ​​of each parent controlled variable includes:

[0036] Perform the following operation sequentially on each of the aforementioned levels: sort all parent controlled variables in the current level in ascending order according to the values ​​of the objective function, from highest to lowest. Then, the crowding degree of the i-th parent controlled variable in the current level is:

[0037]

[0038] Among them, Crowd X(t) Let X(t) represent the crowding level, where i is a positive integer between 1 and the total number of parent controlled variables included in the current level. i+1 Let X(t) be the value of the objective function for the (i+1)th parent controlled variable. i-1 Let X(t) be the value of the objective function for the (i-1)th parent controlled variable. max Let X(t) be the largest objective function value in the current level. min The objective function value is the smallest among the current levels.

[0039] Preferably, the optimal controlled variable is selected from the parent controlled variables and the child controlled variables according to a preset selection rule, including:

[0040] The top three predetermined number of controlled variables in the parent and child generations of controlled variables are selected as the optimal controlled variables. When the controlled variables are ranked in the same order, the controlled variable with the highest crowding is selected as the optimal controlled variable.

[0041] In summary, this invention provides a method for regulating and optimizing a multi-energy coupled system based on an improved non-dominated genetic algorithm. The method includes determining the objective function based on a mathematical simulation model of the multi-energy coupled system, obtaining the objective function space based on the parent controlled variables and the objective function, sorting and classifying the parent controlled variables using a non-dominated genetic algorithm, and improving the non-dominated genetic algorithm by adding a mechanism for dynamically selecting the optimal solution. Specifically, different methods for generating offspring controlled variables are selected based on the number of parent controlled variables in the highest level. This allows the search for the optimal solution to proceed in the direction of the selected possible optimal solution, thereby accelerating the convergence speed and enabling real-time regulation of the multi-energy coupled system. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the prior art and embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 The first flowchart of the multi-energy coupled system regulation and optimization method based on the improved non-dominated genetic algorithm provided in this application;

[0044] Figure 2 A schematic diagram of the structure of the multi-energy coupled system in the multi-energy coupled system regulation and optimization method based on the improved non-dominated genetic algorithm provided in this application;

[0045] Figure 3 A flowchart illustrating the non-dominated sorting process in the multi-energy coupled system regulation and optimization method based on the improved non-dominated genetic algorithm provided in this application;

[0046] Figure 4 The second flowchart of the multi-energy coupled system regulation and optimization method based on the improved non-dominated genetic algorithm provided in this application;

[0047] Figure 5 The third flowchart of the multi-energy coupled system regulation optimization method based on the improved non-dominated genetic algorithm provided in this application. Detailed Implementation

[0048] The core of this invention is to provide a method for regulating and optimizing multi-energy coupled systems based on an improved non-dominated genetic algorithm. When determining the regulation mode of a multi-energy coupled system using the non-dominated genetic algorithm, after selecting the possible directions of the optimal solution of the objective function, the optimal solution is searched in the direction of the optimal solution, thereby accelerating the convergence speed and realizing real-time regulation of the multi-energy coupled system.

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Please refer to Figure 1 , Figure 1 The first flowchart of the multi-energy coupled system regulation optimization method based on the improved non-dominated genetic algorithm provided in this application includes:

[0051] S1: Establish a simulation model of the multi-energy coupled system and determine the objective function that the multi-energy coupled system must satisfy when it is controlled under the simulation model;

[0052] Establishing simulation models for multi-energy coupled systems and performing multi-objective regulation and optimization based on these models is one direction for improving the overall performance of multi-energy coupled systems. Therefore, this application first determines the simulation model of the multi-energy coupled system and the objective functions that must be satisfied when regulating the multi-energy coupled system under the simulation model. Multiple objective functions can be used (for example, the carbon emissions and total cost of comprehensive energy supply of the multi-energy coupled system can be used as objective functions simultaneously). This application does not impose any particular limitations on the specific method used to establish the simulation model of the multi-energy coupled system, and the objective functions can also be set according to actual regulation requirements.

[0053] For example, please refer to Figure 2 , Figure 2 This is a schematic diagram of the structure of the multi-energy coupled system in the multi-energy coupled system regulation and optimization method based on the improved non-dominated genetic algorithm provided in this application. Figure 2 The multi-energy coupling system includes photovoltaic power generation devices, wind turbines, electric chillers, electric boilers, natural gas boilers, absorption chillers, combined heat and power devices, electric storage devices, thermal storage devices, and cold storage devices. When performing multi-objective regulation and optimization on the above-mentioned multi-energy coupling system, the total cost of comprehensive energy supply and carbon emissions are the objectives. After determining the final objective function value through the regulation and optimization method provided in this application, the carbon emissions can be minimized when the multi-energy coupling system operates at the total cost of comprehensive energy supply in the final solution, or the total cost of comprehensive energy supply can be minimized when the multi-energy coupling system operates at the carbon emissions in the final solution.

[0054] S2: Input the N sets of parent controlled variables into the objective function to obtain the objective function space including the N sets of objective function values. The parent controlled variables are the adjustable variables included in the objective function.

[0055] The variables involved in regulating a multi-energy coupled system include input variables, decision variables, and controlled variables. Figure 2 Taking a multi-energy coupled system as an example, the method for determining each variable is explained as follows:

[0056] (1) The multi-energy coupled system includes eight input variables in each time period, namely the electrical load P EL (t), heat load P HL (t), cooling load P CL (t), Natural Gas Load M GL (t), the real-time electricity price EP(t) in the market, the load state of the energy storage device S(t), and the wind energy E at the current moment. WT (t) and the light energy E at the current moment PV (t);

[0057] (2) The control variables of a multi-energy coupled system include the following three types: the input natural gas quantity M of the cogeneration unit. CHP,G (t) Input electrical power P of the electric boiler EB,E (t) and the input heat power P of the absorption chiller AC,H (t).

[0058] At the same time, it is also necessary to set lower limit values ​​for each regulation variable [M]. CHP,G,min (t),P EB,E,min [(t),0], setting upper limits for each control variable [M] CHP,G,max (t),P EB,E,max (t),P AC,H,max (t)].

[0059] Then, corresponding decision variables are generated for each control variable. The formulas for the decision variables are as follows:

[0060] CV = CV min +rand×(CV max -CV min );

[0061] Where CV is the decision variable, and rand represents a random number between 0 and 1.

[0062] Each control variable generates N sets of random decision variables. Once the initial population is established, it can be represented as follows:

[0063]

[0064] (3) The controlled variables of a multi-energy coupled system mainly include the following six types: the rate of electricity purchase from the main grid. The rate M for purchasing natural gas from the main natural gas network source Input status B of storage devicein (t), Output state B of the storage device out (t), storage device input / output power and the input / output of storage devices

[0065] The specific method for determining the controlled variables is as follows:

[0066] For storage devices, no output or input is performed when the output or input conditions are not met. These conditions can be expressed by the following formula:

[0067]

[0068] Where S(t) represents the real-time state of the storage device.

[0069] After satisfying the output and input conditions of the above formula, the following judgments are made on each energy unit in the multi-energy coupling system:

[0070] For a specific input / output condition of an electrical storage device, the following two sets of formulas must be satisfied simultaneously:

[0071]

[0072]

[0073] Among them, P PV (t) represents the output power of the photovoltaic power generation device, P WT (t) represents the output electrical power of the wind turbine, P EB (t) represents the input electrical power of the electric boiler, B ES,in (t) represents the state parameters of the energy storage device, and when the parameter is 1, it is in the energy storage state. ES,out (t) represents the state parameter of the electrical storage device, and when the parameter is 1, it is in the power supply state. Output power to power electrical storage devices Input power for storing energy in an electrical storage device.

[0074] The specific input and output conditions of the cold storage device must meet the following requirements:

[0075]

[0076]

[0077] Among them, B CS,in (t) represents the state parameters of the cold storage device, and when the parameter is 1, it is in the energy storage state. CS,out (t) represents the state parameters of the cold storage device, and when the parameter is 1, it is in the power supply state. Output power that supplies power to cold storage devices.

[0078] The specific input and output conditions of the thermal storage device must meet the following requirements:

[0079]

[0080]

[0081] Among them, P GB (t) represents the thermal power output of the gas-fired boiler, B HS,in (t) represents the state parameters of the thermal storage device, and when the state parameter is 1, it is in the energy storage state. HS,out (t) represents the state parameters of the thermal storage device, and when the state parameter is 1, it is in the power supply state. Output power that supplies energy to the thermal storage device.

[0082] The rate of electricity purchased from the main grid by multi-energy coupled systems It can be represented as:

[0083]

[0084] Among them, P EB (t) represents the input electrical power of the electric boiler, P PV (t) represents the output power of the photovoltaic power generation device, P WT (t) represents the output electrical power of the wind turbine.

[0085] The amount of natural gas purchased from the natural gas network by a multi-energy coupled system can be expressed as:

[0086] M Source (t)=M GL (t)+M GB,G (t)+M CHP,G (t);

[0087] Among them, M GL (t) represents the natural gas load demand rate of the multi-energy coupled system, M GB,G (t) represents the natural gas transmission rate of the natural gas boiler, M CHP,G (t) represents the natural gas transmission rate of the combined heat and power (CHP) unit.

[0088] The objective function of a multi-energy coupled system can be expressed as:

[0089]

[0090] Based on the input variables and N sets of randomly generated decision variables, N sets of parent controlled variables can be obtained. Inputting these N sets of parent controlled variables into the objective function of the multi-energy coupled system yields N sets of objective function values. These N sets of objective function values ​​form the objective function space, which can be represented as:

[0091]

[0092] S3: Using a non-dominated genetic algorithm, the parent generation controlled variables of each group are sorted in ascending order and classified into levels according to the magnitude of the objective function values ​​of the parent generation controlled variables;

[0093] S4: When the number of parental controlled variables in the highest level is less than the first preset number, randomly select N parental controlled variables for crossbreeding and mutation to generate the second preset number of offspring controlled variables.

[0094] S5: When the number of parental controlled variables in the highest rank is not less than the first preset number, select the parental controlled variables in the highest rank for crossbreeding and mutation and generate the second preset number of offspring controlled variables.

[0095] To regulate a multi-energy coupled system, multiple objective functions need to be solved. Existing techniques typically use non-genetic algorithms and require generating numerous random solutions through multiple iterations, resulting in slow convergence speeds and difficulty meeting the real-time requirements of optimization, thus hindering real-time regulation of the multi-energy coupled system. Therefore, this application improves upon the existing non-dominated genetic algorithm by incorporating a dynamic optimal solution selection mechanism. This mechanism, after selecting the direction of the optimal solution, changes the method from generating random solutions to searching for the optimal solution in that direction, thereby accelerating convergence and enabling real-time regulation of the multi-energy coupled system. It also avoids the problem of getting trapped in local optima caused by selecting only superior individuals for hybridization, as in existing techniques. Furthermore, this application combines a simulation model obtained by modeling the multi-energy coupled system using two-dimensional nonlinear system units to improve the accuracy of the mathematical description of the multi-energy coupled system and solve the optimization scheduling problem of multi-energy coupled systems.

[0096] Please refer to Figure 4 , Figure 4 The second flowchart of the multi-energy coupled system regulation and optimization method based on the improved non-dominated genetic algorithm provided in this application is as follows: the initial population is the parent generation controlled variables of each group. The initial iteration number gen is 1. After the parent generation controlled variables are sorted by the non-dominated genetic algorithm, the parent generation controlled variables are dynamically selected for crossbreeding and mutation in an improved manner. Finally, the parent generation controlled variables and offspring generation controlled variables are merged to select the optimal controlled variable. The above steps are repeated until the number of iterations reaches the preset maximum number of iterations.

[0097] Specifically, since the optimal solution (i.e., the optimal controlled variable in this application) needs to be generated based on the parent generation controlled variables, in order to determine the direction of the optimal solution, this application uses a non-dominated genetic algorithm to sort and rank the parent generation controlled variables in ascending order according to the value of the objective function of each group of parent generation controlled variables. Then, based on the number of parent generation controlled variables included in the highest rank, different methods are selected to generate offspring controlled variables. For example, the first preset number is set to N / 2, where N is the total number of parent generation controlled variables. The highest rank is called F1. If the number of parent generation controlled variables included in F1 is less than N / 2, then parent generation controlled variables are randomly selected for crossbreeding and mutation to generate offspring controlled variables; if the number of parent generation controlled variables included in F1 is not less than N / 2, then parent generation controlled variables are selected directionally from F1 for crossbreeding and mutation to generate offspring controlled variables. Please refer to [reference needed]. Figure 5 , Figure 5 The third flowchart of the multi-energy coupled system regulation optimization method based on the improved non-dominated genetic algorithm provided in this application.

[0098] The process of randomly selecting two controlled variables from the parent generation for crossbreeding variation is as follows:

[0099] Each parent controlled variable corresponds to a set of regulatory variables [M] CHP,G,p ,P EB,E,p ,P AC,H,p The process involves combining two randomly selected parental individuals by altering their controlled variables to create a new individual. All controlled variables in this new individual are derived from the parents, and the combination is random. The offspring randomly inherit a portion of the genes from their parents.

[0100]

[0101] Here, r is a random number between 0 and 1, the purpose of which is to select the inheritance mode of a certain gene in the offspring.

[0102] During hybridization, a mutation occurs in one of the genes of an individual, generating a random gene to replace the mutated gene. The probability of this mutation is... Generate a random number j between 0 and 1. If j is less than 1, then... This proves that the individual's gene has mutated, generating a random gene to replace it, where n CV To control the number of variables.

[0103] For example, if the second preset quantity is set to 4N, hybridization and mutation will stop after 4N sets of offspring controlled variables are generated based on the hybridization and mutation of the parent controlled variables. This process imitates the evolutionary logic of most organisms in nature, that is, a large number of offspring individuals are generated, and after selection, a large number of individuals will be eliminated, so that the population can be maintained at a certain number.

[0104] S6: Select the optimal controlled variable from each parent and child controlled variable according to the preset selection rules, and regulate the multi-energy coupled system according to the objective function value corresponding to the optimal controlled variable.

[0105] After generating a second preset number of controlled variables for offspring using a dynamic selection method, the optimal controlled variable is selected from each parent and offspring controlled variable according to a preset selection rule. This application does not specifically limit the preset selection rule. For example, after the total number of parent and offspring controlled variables reaches 4N and the objective function value corresponding to each group of offspring controlled variables is determined, these controlled variables are quickly non-dominated and sorted based on the objective function value, and the top N groups of controlled variables are selected to form a new population. The selection rule can be to compare the ranks of each group of controlled variables, with controlled variables at higher ranks being preferred over those at lower ranks. In addition, for controlled variables at the same rank, their crowding can be further compared, with controlled variables with higher crowding being preferred over those with lower crowding. This application does not specifically limit the method for determining crowding.

[0106] In summary, this application provides a method for regulating and optimizing multi-energy coupled systems based on an improved non-dominated genetic algorithm. The method includes determining the objective function based on a mathematical simulation model of the multi-energy coupled system, obtaining the objective function space based on the parent controlled variables and the objective function, sorting and classifying the parent controlled variables using a non-dominated genetic algorithm, and improving the non-dominated genetic algorithm by adding a mechanism for dynamically selecting the optimal solution. Specifically, different methods for generating offspring controlled variables are selected based on the number of parent controlled variables in the highest level. This allows the search for the optimal solution to proceed in the direction of the selected possible optimal solution, thereby accelerating the convergence speed and enabling real-time regulation of the multi-energy coupled system.

[0107] Based on the above embodiments:

[0108] As a preferred embodiment, a simulation model of a multi-energy coupled system is established, including:

[0109] Establish a simulation model that conforms to the preset overall constraints of the multi-energy coupling system and the preset constraints of each energy unit included in the multi-energy coupling system.

[0110] A multi-energy coupled system consists of multiple mutually coupled energy units. When establishing a simulation model of a multi-energy coupled system, it is necessary to consider both the overall multi-energy coupled system and the constraints of each energy unit. Therefore, the simulation model established in this application needs to simultaneously satisfy the preset overall constraints of the multi-energy coupled system and the preset constraints of each energy unit.

[0111] Specifically, the pre-defined overall constraints for the multi-energy coupled system are as follows:

[0112]

[0113] in, M represents the rate at which the multi-energy coupled system purchases electricity from the main grid. Source (t) represents the gas purchase rate from the multi-energy coupled system to the main gas supply system, P EL (t), P HL (t), P CL (t) and M GL (t) represents the electrical load, thermal load, cooling load power, and natural gas load demand rate of the multi-energy coupled system, in that order. and The output / input power, P, represents the power output for electrical storage, thermal storage, and cold storage in a multi-energy coupled system, respectively. PV (t), P WT (t), P EB (t), P CHP (t), P EC (t), P GB (t) and P AC (t) Combined with its appendices E / H / C, these represent the electrical output power / thermal output power / cooling energy consumption output power of the photovoltaic power generation device, wind turbine power generation device, electric boiler, combined heat and power device, electric chiller, natural gas boiler, and absorption chiller in the multi-energy coupling system, respectively. M GB,G (t) represents the natural gas transfer rate of the natural gas boiler in the multi-energy coupling system, M CHP,G (t) represents the natural gas transmission rate of the cogeneration unit in the multi-energy coupling system.

[0114] Specifically, the pre-defined energy unit constraints that a multi-energy coupled system must satisfy are as follows:

[0115] (1) Constraints on tradable carbon emission rights

[0116] The carbon emission rights purchase price model can be represented as a piecewise function, divided into three stages. First, if the actual carbon emissions are less than the specified allowance, the multi-energy coupled system can profit by selling carbon emission rights. Second, if the actual carbon emissions exceed the specified allowance, the multi-energy coupled system will incur the cost of purchasing carbon emission rights. Finally, if the actual carbon emissions exceed the specified allowance and exceed the penalty allowance, the multi-energy coupled system will not only need to purchase carbon emission rights but also pay a higher penalty.

[0117]

[0118] Where u and f are the designated quota and the quota within the penalty period, respectively; ξ s ξb and ξ f The prices are listed in order: selling price, purchase price, and penalty price.

[0119] (2) Constraints of energy storage devices

[0120] The energy storage and release process of an energy storage device can be represented by the following formula, and the current energy storage state of the energy storage device is always determined by the storage device and energy usage at the previous moment.

[0121]

[0122] Where, η in and η out These represent the actual energy input efficiency and the actual energy output efficiency, respectively.

[0123] Considering that energy storage devices cannot simultaneously input and output energy, the following constraints apply:

[0124]

[0125] Since the above equation introduces a quadratic constraint into the optimization model, it can be transformed into a linear constraint by introducing a binary index, as follows:

[0126]

[0127] Among them, B in (t) and B out (t) is a binary index, when the energy storage device inputs B in (t) is 1, when the energy storage device outputs B out (t) is 1.

[0128] (3) Renewable Energy Constraints

[0129] The renewable energy sources in the multi-energy coupling system involved in this application include photovoltaic power generation devices and wind turbine power generation devices. Both of these power generation devices are related to weather factors and are uncertain. They can only generate electricity when the power generation requirements are met. In order to protect the equipment, they will also stop generating electricity when the wind energy exceeds certain constraints.

[0130]

[0131]

[0132] Among them, E PV (t) represents the real-time light energy, E WT (t) represents real-time wind energy, u PV For the minimum light energy required for photovoltaic power generation, m PV η represents the maximum solar energy generated by photovoltaic power generation. PVThe efficiency of converting light energy into electrical energy, u WT The minimum wind energy required for a wind turbine power generation device, m WT η represents the maximum wind energy of a wind turbine generator. WT This refers to the efficiency of converting wind energy into electrical energy.

[0133] (4) Constraints of cogeneration units

[0134] The coupling relationship between the electrical output power and the thermal output power of a combined heat and power (CHP) unit is as follows:

[0135]

[0136] Where, η CHP,a and η CHP,b Both are efficiency parameters for converting natural gas energy into the total output energy of a combined heat and power (CHP) unit. β is the proportional coefficient of the heat energy produced when the CHP unit produces electricity, which is determined according to the actual situation of the CHP unit.

[0137] The combined heat and power (CHP) unit lacks carbon capture facilities; its corresponding carbon emissions are the same as those for supplying natural gas.

[0138] CF CHP (t)=gM CHP,G (t)△t;

[0139] Where g is a parameter corresponding to the carbon emissions calculated using natural gas energy, which is set according to the local natural gas quality.

[0140] (5) Electric boiler constraint

[0141] An electric boiler is a device that converts excess electricity generated from renewable energy sources into heat energy when the electrical load is low and the heat load is high. It can also reduce solar and wind curtailment to some extent when energy storage devices reach their limits. Specific constraints are as follows:

[0142]

[0143] Where, η EB The thermal energy conversion efficiency of an electric boiler.

[0144] (6) Natural gas boiler constraints

[0145] Natural gas boilers can generate heat energy quickly and efficiently, but their specific constraints are as follows:

[0146]

[0147] Where, η GB,a and η GB,b This refers to the energy conversion efficiency parameter of a natural gas boiler.

[0148] (7) Constraints of electric refrigeration equipment

[0149] Electric refrigeration devices can convert electrical energy into refrigeration energy, subject to the following constraints:

[0150] P EC,C (t)=η EC P EC,E (t);

[0151] Where, η EC This refers to the energy conversion efficiency parameter of an electric refrigeration device.

[0152] (8) Constraints of absorption refrigeration units

[0153] Absorption refrigeration devices can convert thermal energy into refrigeration energy, with the following specific constraints:

[0154] P AC,C (t)=η AC P AC,H (t);

[0155] Where, η AC This refers to the energy conversion efficiency parameter of an absorption refrigeration unit.

[0156] As a preferred embodiment, the objective function that the multi-energy coupled system must satisfy when controlled under a simulation model is determined, including:

[0157] Determine the first and second objective functions that the multi-energy coupled system must satisfy when it is controlled under the simulation model;

[0158] The first objective function is a function of the total comprehensive energy supply cost that needs to be satisfied when regulating the multi-energy coupled system, and the second objective function is a function of the carbon emissions that need to be satisfied when regulating the multi-energy coupled system.

[0159] The first objective function is: in, This represents the cost of purchasing electricity from the main grid for a multi-energy coupled system. In the formula, EP(t) represents the real-time electricity price. The rate at which the multi-energy coupled system purchases electricity from the main grid is t, where t is the current time period and T is the total time period. This represents the cost of purchasing natural gas from the natural gas network for a multi-energy coupled system. In the formula, c represents the fixed natural gas price, and M Source (t) represents the gas purchase rate from the multi-energy coupling system to the main gas transmission system;

[0160] This indicates the cost of purchasing carbon emission permits for multi-energy coupled systems. In the formula, CP(t) represents the price of segmented tradable carbon emission permits, and CF(t) represents the system carbon emissions;

[0161] The second objective function is Among them CF GB (t) represents the carbon emissions of the natural gas boiler in the multi-energy coupled system, CF CHP (t) represents the carbon emissions of a combined heat and power plant in a multi-energy coupled system.

[0162] In this embodiment, the objectives of regulating the multi-energy coupling system include two aspects: the total cost of integrated energy supply and carbon emissions. The specific mathematical expressions for these two aspects are described above. The ultimate goal of this application is to obtain the solution set of each energy unit in the multi-energy coupling system when both the total cost of integrated energy supply and carbon emissions are simultaneously optimal. After obtaining the optimal controlled variables according to preset selection rules, these optimal controlled variables constitute the Pareto front. The Pareto front is a solution set, where all solutions enable the multi-energy coupling system to operate optimally. However, optimality here does not mean that both the total cost of integrated energy supply and carbon emissions are simultaneously optimal, but rather that the carbon emissions are minimized under this total cost of integrated energy supply, or the total cost of integrated energy supply is minimized under this carbon emissions. A single solution includes the operating parameters of the natural gas supply of the cogeneration unit, the power consumption of the electric boiler, and the heat absorption power of the absorption chiller for all time periods t within a preset time interval (e.g., one day). Based on these three operating parameters, the operating parameters of the remaining energy units can be obtained according to the operating logic of the simulation model of the multi-energy coupling system.

[0163] Furthermore, when determining the first and second objective functions that the multi-energy coupled system must satisfy when being controlled under the simulation model, it is also necessary to ensure that both the first and second objective functions meet the preset objective function constraints, whereby the objective function constraints are:

[0164]

[0165] Among them, M Source (t) represents the gas purchase rate from the multi-energy coupled system to the main gas supply system, M Source,min (t) and M Source,max (t) represents the preset minimum gas purchase rate and the preset maximum gas purchase rate, respectively; P EB,E (t) represents the electrical output power of the electric boiler in the multi-energy coupling system, P. EB,E,min (t) and P EB,E,max (t) represents the preset minimum and maximum electrical output power of the electric boiler, respectively; M CHP,G (t) represents the natural gas transfer rate of the cogeneration unit in the multi-energy coupling system, M CHP,G,min(t) and M CHP,G,max (t) represents the preset minimum natural gas transmission rate and the preset maximum natural gas transmission rate of the cogeneration unit, respectively; P EC,E (t) represents the electrical output power of the electric chiller in the multi-energy coupled system, P. EC,E,min (t) and P EC,E,max (t) represents the preset minimum and maximum electrical output power of the electric chiller, respectively; M GB,G (t) represents the natural gas transfer rate of the natural gas boiler in the multi-energy coupling system, M GB,G,min (t) and M GB,G,max (t) represents the preset minimum natural gas transmission rate and the preset maximum natural gas transmission rate of the natural gas boiler, respectively; P AC,H (t) represents the thermal energy output power of the absorption chiller in the multi-energy coupling system, P AC,H,max (t) represents the preset maximum thermal energy output power; For the input efficiency of storage devices in multi-energy coupled systems, S(t) represents the output efficiency of the storage device, S(t) represents the real-time state of the storage device, t represents the time period at the current moment, and T represents the total number of time periods included in the preset time interval.

[0166] As a preferred embodiment, a non-dominated genetic algorithm is used to sort and rank the parental controlled variables of each group in ascending order according to the magnitude of the objective function values ​​of the parental controlled variables, including:

[0167] Select target controlled variables from each set of parent controlled variables according to preset rules. Before traversing all target controlled variables, perform the following operations on each target controlled variable:

[0168] For all undetermined controlled variables, determine whether the undetermined controlled variables dominate the target controlled variable. The undetermined controlled variables are the other parent controlled variables in each group besides the target controlled variable.

[0169] When the undetermined controlled variable dominates the target controlled variable, the dominated set of the target controlled variable is added to the undetermined controlled variable, and the dominated number of the target controlled variable is incremented by one;

[0170] When the undetermined controlled variable does not dominate the target controlled variable, proceed to the step of selecting the target controlled variable from each group of parent controlled variables according to preset rules;

[0171] The parent controlled variables are sorted and ranked according to the number of controlled variables from least to most.

[0172] Please refer to Figure 3 , Figure 3This application provides a flowchart of the non-dominated sorting process in a multi-energy coupled system regulation and optimization method based on an improved non-dominated genetic algorithm. The non-dominated sorting is illustrated using an example where the objective function is the total cost of integrated energy supply F(t) and carbon emissions CF(t).

[0173] (1) First, select the i-th parent controlled variable p from each group of parent controlled variables, which has its corresponding objective function [F(t)]. p ,CF(t) p ];

[0174] (2) Select a parent controlled variable q from the remaining parent controlled variables, and its corresponding objective function is [F(t)]. q ,CF(t) q ];

[0175] (3) Make the following judgment:

[0176] F(t) q ≤F(t) p &CF(t) q ≤CF(t) p ;

[0177] If the above conditions are met, then q is said to dominate p; if the above conditions are not met, then q does not dominate p; if p also does not dominate q, then p and q have a non-dominant relationship.

[0178] If q dominates p, then the dominance number of p is n. p +1, and at the same time add q to the dominated set p.domination=[q1,q2...]. Then select a new parent controlled variable as q and repeat the above step (3) until all parent controlled variables are traversed.

[0179] If q does not dominate p, then select a new parent controlled variable q and perform the judgment in step (3) above until all parent controlled variables are traversed.

[0180] (4) Select a new parent controlled variable p and perform steps (2) and (3) until all parent controlled variables have been traversed.

[0181] (5) n p Parent controlled variables with a value of 0 are placed in the first level F1.

[0182] (6) The remaining parent controlled variables n p -1, then n at this time p Parent controlled variables with a value of 0 are placed in the next level F2, and so on, until all parent controlled variables are placed in their corresponding levels, or the level cap is reached.

[0183] Furthermore, to further ensure the accuracy of the final determination of the optimal controlled variables, after sorting and classifying the parent controlled variables in ascending order according to the objective function values ​​of each parent group using a non-dominated genetic algorithm, the crowding degree of each parent controlled variable is determined based on its objective function value. The specific implementation process for determining each parent controlled variable is as follows:

[0184] Perform the following operation sequentially on each level: sort all parent controlled variables in the current level in ascending order according to the objective function value from highest to lowest. Then, the crowding degree of the i-th parent controlled variable in the current level is:

[0185]

[0186] Among them, Crowd X(t) Let X(t) represent the crowding level, where i is a positive integer between 1 and the total number of parent controlled variables included in the current level. i+1 Let X(t) be the value of the objective function for the (i+1)th parent controlled variable. i-1 Let X(t) be the value of the objective function for the (i-1)th parent controlled variable. max Let X(t) be the maximum objective function value in the current level. min The objective function value is the minimum value in the current level.

[0187] Correspondingly, the process of selecting the optimal controlled variable from each parent and child generation controlled variable according to preset selection rules is as follows:

[0188] The top three controlled variables in the parent and child generations are selected as the optimal controlled variables. When the controlled variables are ranked in the same order, the controlled variable with the highest crowding is selected as the optimal controlled variable.

[0189] First, compare the Pareto priority (rank) of the controlled variables. Individuals at higher ranks are generally better than those at lower ranks. Within the same rank, they satisfy a non-dominated relationship, meaning that no single individual's objective function solution is better than any other individual in the same rank. The crowding degree is compared here because within the same rank, the controlled variables are non-dominated. A controlled variable with high crowding degree indicates that there are no other controlled variables of the same rank nearby (the Euclidean distance between points in the two-dimensional space formed by the two objective functions), while a controlled variable with low crowding degree indicates that there are other individuals of the same rank nearby. Choosing a controlled variable with high crowding degree is to obtain a more complete solution set. Therefore, a controlled variable with higher crowding degree is generally better than a controlled variable with lower crowding degree within the same rank, thus enabling a better way to regulate the multi-energy coupled system.

[0190] This application also provides a multi-energy coupled system regulation and optimization system based on an improved non-dominated genetic algorithm, the system comprising:

[0191] The objective function determination unit is used to establish the simulation model of the multi-energy coupled system and determine the objective function that the multi-energy coupled system must satisfy when it is controlled under the simulation model.

[0192] The objective function space determination unit is used to input N sets of parent controlled variables into the objective function to obtain an objective function space including N sets of objective function values. The parent controlled variables are the adjustable variables included in the objective function.

[0193] The rank division unit is used to sort and rank the parent control variables of each group in ascending order according to the magnitude of the objective function value of the parent control variables in each group using a non-dominated genetic algorithm.

[0194] The first generation controlled variable determination unit is used to randomly select N sets of parent controlled variables for hybridization and mutation to generate a second preset number of sets of offspring controlled variables when the number of parent controlled variables in the highest level is less than the first preset number.

[0195] The second generation controlled variable determination unit is used to select the parent controlled variable in the highest ranking for crossbreeding and mutation and generate a second preset number of generation controlled variables when the number of parent controlled variables in the highest ranking is not less than the first preset number.

[0196] The control mode determination unit is used to select the optimal controlled variable from each parent generation and each child generation controlled variable according to a preset selection rule, and to control the multi-energy coupled system according to the objective function value corresponding to the optimal controlled variable.

[0197] For a detailed description of the multi-energy coupled system regulation and optimization system based on the improved non-dominated genetic algorithm, please refer to the above-described embodiments of the multi-energy coupled system regulation and optimization method based on the improved non-dominated genetic algorithm. This application will not repeat the details here.

[0198] Based on the above embodiments:

[0199] In a preferred embodiment, the objective function determination unit includes:

[0200] The simulation model building unit is used to build a mathematical simulation model that conforms to the preset overall constraints of the multi-energy coupled system and the preset constraints of each energy unit included in the multi-energy coupled system.

[0201] The objective function determination sub-unit is used to determine the objective function that the multi-energy coupled system must satisfy when it is controlled under the simulation model.

[0202] As a preferred embodiment, the overall constraint conditions of the multi-energy coupled system are preset as follows:

[0203]

[0204] in, M represents the rate at which the multi-energy coupled system purchases electricity from the main grid. Source (t) represents the gas purchase rate from the multi-energy coupled system to the main gas supply system, P EL (t), P HL (t), P CL (t) and M GL (t) represents the electrical load, thermal load, cooling load power, and natural gas load demand rate of the multi-energy coupled system, in that order. and The output / input power, P, represents the power output for electrical storage, thermal storage, and cold storage in a multi-energy coupled system, respectively. PV (t), P WT (t), P EB (t), P CHP (t), P EC (t), P GB (t) and P AC (t) Combined with its appendices E / H / C, these represent the electrical output power / thermal output power / cooling energy consumption output power of the photovoltaic power generation device, wind turbine power generation device, electric boiler, combined heat and power device, electric chiller, natural gas boiler, and absorption chiller in the multi-energy coupling system, respectively. M GB,G (t) represents the natural gas transfer rate of the natural gas boiler in the multi-energy coupling system, M CHP,G (t) represents the natural gas transmission rate of the cogeneration unit in the multi-energy coupling system.

[0205] In a preferred embodiment, the objective function determination sub-unit is specifically used to determine the first objective function and the second objective function that the multi-energy coupled system must satisfy when it is controlled under the simulation model;

[0206] The first objective function is a function of the total comprehensive energy supply cost that needs to be satisfied when regulating the multi-energy coupled system, and the second objective function is a function of the carbon emissions that need to be satisfied when regulating the multi-energy coupled system.

[0207] The first objective function is: in, This represents the cost of purchasing electricity from the main grid for a multi-energy coupled system. In the formula, EP(t) represents the real-time electricity price. The rate at which the multi-energy coupled system purchases electricity from the main grid is t, where t is the current time period and T is the total time period. This represents the cost of purchasing natural gas from the natural gas network for a multi-energy coupled system. In the formula, c represents the fixed natural gas price, and M Source (t) represents the gas purchase rate from the multi-energy coupling system to the main gas transmission system; This indicates the cost of purchasing carbon emission permits for multi-energy coupled systems. In the formula, CP(t) represents the price of segmented tradable carbon emission permits, and CF(t) represents the system carbon emissions;

[0208] The second objective function is Among them CF GB (t) represents the carbon emissions of the natural gas boiler in the multi-energy coupled system, CF CHP (t) represents the carbon emissions of a combined heat and power plant in a multi-energy coupled system.

[0209] As a preferred embodiment, the first objective function and the second objective function that the multi-energy coupled system must satisfy when controlled under the simulation model are determined, including:

[0210] The first and second objective functions that the multi-energy coupled system must satisfy when controlled under the simulation model are determined, and both the first and second objective functions must meet the preset objective function constraints. The objective function constraints are as follows:

[0211]

[0212] Among them, M Source (t) represents the gas purchase rate from the multi-energy coupled system to the main gas supply system, M Source,min (t) and M Source,max (t) represents the preset minimum gas purchase rate and the preset maximum gas purchase rate, respectively; P EB,E (t) represents the electrical output power of the electric boiler in the multi-energy coupling system, P. EB,E,min (t) and P EB,E,max (t) represents the preset minimum and maximum electrical output power of the electric boiler, respectively; M CHP,G (t) represents the natural gas transfer rate of the cogeneration unit in the multi-energy coupling system, M CHP,G,min (t) and M CHP,G,max (t) represents the preset minimum natural gas transmission rate and the preset maximum natural gas transmission rate of the cogeneration unit, respectively; P EC,E (t) represents the electrical output power of the electric chiller in the multi-energy coupled system, P. EC,E,min (t) and P EC,E,max (t) represents the preset minimum and maximum electrical output power of the electric chiller, respectively; M GB,G (t) represents the natural gas transfer rate of the natural gas boiler in the multi-energy coupling system, M GB,G,min (t) and M GB,G,max(t) represents the preset minimum natural gas transmission rate and the preset maximum natural gas transmission rate of the natural gas boiler, respectively; P AC,H (t) represents the thermal energy output power of the absorption chiller in the multi-energy coupling system, P AC,H,max (t) represents the preset maximum thermal energy output power; For the input efficiency of storage devices in multi-energy coupled systems, S(t) represents the output efficiency of the storage device, S(t) represents the real-time state of the storage device, t represents the time period at the current moment, and T represents the total number of time periods included in the preset time interval.

[0213] As a preferred embodiment, the grading unit includes:

[0214] The target controlled variable selection unit is used to select the target controlled variable from each group of parent controlled variables according to preset rules. When not all target controlled variables have been traversed, the following operation is performed on each target controlled variable:

[0215] The dominance judgment unit is used to determine whether the undetermined controlled variable dominates the target controlled variable for all undetermined controlled variables, wherein the undetermined controlled variables are the other parent controlled variables in each group of parent controlled variables besides the target controlled variable;

[0216] The domination unit is used to add the dominated set of the target controlled variable to the undetermined controlled variable and increment the dominated number of the target controlled variable by one when the undetermined controlled variable dominates the target controlled variable.

[0217] The non-dominated unit is used to enter the step of selecting the target controlled variable from each group of parent controlled variables according to preset rules when the undetermined controlled variable does not dominate the target controlled variable;

[0218] The rank determination unit is used to sort and rank each parent controlled variable according to the order of the number of controlled variables from least to most.

[0219] In a preferred embodiment, the first preset quantity is N / 2, where N is the total number of parent controlled variables.

[0220] As a preferred embodiment, it also includes:

[0221] The crowding determination unit is used to determine the crowding degree of each parent control variable based on the objective function value of each parent control variable after sorting and classifying the parent control variables in ascending order according to the objective function value of each parent control variable by a non-dominated genetic algorithm.

[0222] In a preferred embodiment, the congestion determination unit is specifically used for:

[0223] Perform the following operation sequentially on each level: sort all parent controlled variables in the current level in ascending order according to the objective function value from highest to lowest. Then, the crowding degree of the i-th parent controlled variable in the current level is:

[0224]

[0225] Among them, Crowd X(t) Let X(t) represent the crowding level, where i is a positive integer between 1 and the total number of parent controlled variables included in the current level. i+1 Let X(t) be the value of the objective function for the (i+1)th parent controlled variable. i-1 Let X(t) be the value of the objective function for the (i-1)th parent controlled variable. max Let X(t) be the maximum objective function value in the current level. min The objective function value is the minimum value in the current level.

[0226] In a preferred embodiment, the control method determination unit includes:

[0227] The optimal controlled variable selection unit is used to select the third preset number of controlled variables with the highest ranking among the parent and child controlled variables as the optimal controlled variables, and when the ranking of controlled variables is consistent, the controlled variable with the highest crowding degree is selected as the optimal controlled variable.

[0228] The control unit is used to control the multi-energy coupled system according to the objective function value corresponding to the optimal controlled variable.

[0229] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0230] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0231] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-energy coupling system regulation optimization method based on an improved non-dominated genetic algorithm, characterized in that, include: Establish a simulation model of the multi-energy coupled system and determine the objective function that the multi-energy coupled system must satisfy when it is controlled under the simulation model; By inputting N sets of parent controlled variables into the objective function, an objective function space including N sets of objective function values ​​is obtained. The parent controlled variables are the adjustable variables included in the objective function. The parental controlled variables in each group are sorted in ascending order and classified into levels according to the magnitude of the objective function values ​​of the parental controlled variables in each group using a non-dominated genetic algorithm. When the number of parental controlled variables in the highest level is less than the first preset number, N groups of the parental controlled variables are randomly selected for crossbreeding and mutation to generate a second preset number of offspring controlled variables. When the number of parental controlled variables in the highest rank is not less than the first preset number, the parental controlled variables in the highest rank are selected for crossbreeding and mutation to generate the second preset number of offspring controlled variables. According to the preset selection rules, the optimal controlled variable is selected from each of the parent generation controlled variables and each of the child generation controlled variables, and the multi-energy coupling system is regulated according to the objective function value corresponding to the optimal controlled variable. The simulation model of the multi-energy coupled system includes: A mathematical simulation model is established that conforms to the preset overall constraints of the multi-energy coupling system and the preset constraints of each energy unit included in the multi-energy coupling system.

2. The improved non-dominated sorting genetic algorithm based multi-ability coupled system regulation optimization method of claim 1, wherein, The overall constraint conditions of the preset multi-energy coupling system are as follows: ； in, The electricity purchase rate of the multi-energy coupled system from the main grid. The gas purchase rate from the multi-energy coupling system to the main gas supply system is [missing information]. , , and The following are the electrical load, thermal load, cooling load power, and natural gas load demand rate of the multi-energy coupled system, in that order. , and The output / input power corresponding to the electrical storage, thermal storage, and cold storage of the multi-energy coupling system are, in order. , , , , , and The labels E / H / C respectively represent the electrical output power / thermal output power / cooling energy consumption output power of the photovoltaic power generation device, wind turbine power generation device, electric boiler, combined heat and power device, electric chiller, natural gas boiler and absorption chiller in the multi-energy coupling system. This indicates the natural gas transmission rate of the natural gas boiler in the multi-energy coupling system. This indicates the natural gas transmission rate of the cogeneration unit in the multi-energy coupling system.

3. The method for regulating and optimizing multi-energy coupled systems based on an improved non-dominated genetic algorithm as described in claim 1, characterized in that, The objective function that the multi-energy coupled system must satisfy when controlled under the simulation model includes: Determine the first objective function and the second objective function that the multi-energy coupled system must satisfy when it is controlled under the simulation model; Wherein, the first objective function is a function of the total comprehensive energy supply cost that needs to be satisfied when regulating the multi-energy coupling system, and the second objective function is a function of the carbon emissions that need to be satisfied when regulating the multi-energy coupling system; The first objective function is ,in, This represents the cost of purchasing electricity from the main grid for the multi-energy coupling system. In the formula Indicates real-time electricity price. The rate at which the multi-energy coupling system purchases electricity from the main grid is denoted as t, where t is the current time period and T is the total time period. This represents the cost of purchasing natural gas from the natural gas network for the multi-energy coupling system. In the formula Indicates a fixed natural gas price. The gas purchase rate from the multi-energy coupling system to the main gas transmission system; This represents the cost of purchasing carbon emission permits for the multi-energy coupling system. In the formula This indicates the price of segmented tradable carbon emission permits. Indicates the system's carbon emissions; The second objective function is ,in This represents the carbon emissions of the natural gas boiler in the multi-energy coupling system. This indicates the carbon emissions of the combined heat and power plant in the multi-energy coupling system.

4. The improved non-dominated sorting genetic algorithm based multi-ability coupled system regulation optimization method of claim 3, wherein, The first objective function and the second objective function that the multi-energy coupled system must satisfy when controlled under the simulation model are determined, including: A first objective function and a second objective function are determined to be satisfied when the multi-energy coupled system is controlled under the simulation model. Both the first objective function and the second objective function must meet preset objective function constraints, wherein the objective function constraints are: ； in, The gas purchase rate from the multi-energy coupling system to the main gas supply system is [missing information]. and These are the preset minimum gas purchase rate and the preset maximum gas purchase rate, respectively. The electrical output power of the electric boiler in the multi-energy coupling system is [missing information]. and These are the preset minimum electrical output power and the preset maximum electrical output power of the electric boiler, respectively. The natural gas transmission rate of the cogeneration unit in the multi-energy coupling system is given. and These are the preset minimum natural gas transmission rate and the preset maximum natural gas transmission rate of the combined heat and power unit, respectively. The electrical output power of the electric chiller in the multi-energy coupling system is given. and These are the preset minimum and preset maximum electrical output power of the electric chiller, respectively. The natural gas transmission rate of the natural gas boiler in the multi-energy coupling system is given. and These are the preset minimum natural gas transmission rate and the preset maximum natural gas transmission rate of the natural gas boiler, respectively. The thermal energy output power of the absorption chiller in the multi-energy coupling system is [missing information]. The preset maximum thermal output power; The input efficiency of the storage device in the multi-energy coupling system. The output efficiency of the storage device. The real-time state of the storage device is t, where t is the time period at the current moment, and T is the total number of time periods included in the preset time interval.

5. The method for regulating and optimizing multi-energy coupled systems based on an improved non-dominated genetic algorithm as described in claim 1, characterized in that, The parental controlled variables in each group are sorted in ascending order and classified into ranks according to the magnitude of the objective function values ​​of the parental controlled variables using a non-dominated genetic algorithm, including: According to preset rules, target controlled variables are selected from the parent controlled variables of each group. Before all target controlled variables are traversed, the following operations are performed on each target controlled variable: For all undetermined controlled variables, it is determined whether the undetermined controlled variable dominates the target controlled variable, wherein the undetermined controlled variable is the other parent controlled variable in each group of parent controlled variables besides the target controlled variable; When the undetermined controlled variable dominates the target controlled variable, the dominated set of the target controlled variable is added to the undetermined controlled variable, and the dominated number of the target controlled variable is incremented by one; When the undetermined controlled variable does not dominate the target controlled variable, proceed to the step of selecting the target controlled variable from each group of parent controlled variables according to a preset rule; The parent controlled variables are sorted and ranked according to the number of controlled variables from smallest to largest.

6. The method for regulating and optimizing multi-energy coupled systems based on an improved non-dominated genetic algorithm as described in claim 1, characterized in that, The first preset quantity is N / 2, where N is the total number of groups of the parent controlled variables.

7. The method for regulating and optimizing multipotential coupled systems based on an improved non-dominated genetic algorithm as described in any one of claims 1 to 6, characterized in that, After sorting and classifying the parental controlled variables in ascending order according to the objective function values ​​of the parental controlled variables in each group using a non-dominated genetic algorithm, the process also includes: The crowding degree of each parent controlled variable is determined based on the objective function value of each parent controlled variable.

8. The method for regulating and optimizing multi-energy coupled systems based on an improved non-dominated genetic algorithm as described in claim 7, characterized in that, The crowding degree of each parent controlled variable is determined based on the objective function value of each parent controlled variable, including: Perform the following operation sequentially on each of the aforementioned levels: sort all parent controlled variables in the current level in ascending order according to the values ​​of the objective function, from highest to lowest. Then, the crowding degree of the i-th parent controlled variable in the current level is: ； in, The crowding level is represented by i, which is a positive integer between 1 and the total number of parent controlled variables included in the current level. Let the objective function of the (i+1)th parent controlled variable take values. Let the objective function of the (i-1)th parent controlled variable take values. The objective function value is the largest in the current level. The objective function value is the smallest among the current levels.

9. The method for regulating and optimizing multi-energy coupled systems based on an improved non-dominated genetic algorithm as described in claim 7, characterized in that, According to preset selection rules, the optimal controlled variable is selected from each of the parent controlled variables and each of the child controlled variables, including: The top three predetermined number of controlled variables in the parent and child generations of controlled variables are selected as the optimal controlled variables. When the controlled variables are ranked in the same order, the controlled variable with the highest crowding is selected as the optimal controlled variable.

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