Optimal control method, device and equipment for carbon energy flow of integrated energy system and medium

Abstract

The invention relates to the technical field of carbon energy flow control, and discloses an optimal control method, device, equipment and medium for the carbon energy flow of an integrated energy system, and the method comprises the steps: constructing a carbon energy flow model according to the energy equipment in the integrated energy system through employing a Petri net modeling technology; determining an incidence matrix according to the energy coupling relationship between the energy devices and the device operation characteristics in the carbon energy flow model, and determining the operation parameters of the energy devices according to the incidence matrix; establishing an energy-saving and carbon-reducing optimization model by taking system operation cost minimization and system carbon emission minimization as objective functions according to the carbon energy flow model and the operation parameters; and solving the energy-saving and carbon-reducing optimization model by adopting an improved PSO algorithm to obtain an optimization control strategy. According to the method, the comprehensive energy system carbon energy flow modeling based on the Petri network is adopted, and the improved PSO algorithm is adopted for solving, so that the optimal control efficiency and effect of the comprehensive energy system are effectively improved, and energy conservation and carbon reduction of the system are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon energy flow control, and in particular to a method, device, equipment and medium for optimizing control of carbon energy flow in an integrated energy system. Background Art

[0002] Under the pressure of global climate change and multiple environmental pressures, the massive emission of greenhouse gases has led to the deterioration of the environment. As energy utilization tends to low-carbon transformation, it is developing in the direction of multi-energy complementarity and energy cascade utilization. Against this background, the Integrated Energy System (IES) came into being. The integrated energy system first emerged in the field of cogeneration, focusing on the coordinated optimization of thermal power systems, and then gradually expanded and enriched, involving the coordinated complementarity of production, transmission, storage, use and conversion of multiple energy subsystems such as electricity, heat, cold, and natural gas. IES has a diversified energy coupling mode, and different energy supply strategies can be formulated for different energy needs. In order to improve the energy efficiency of each device in IES, a suitable energy supply path should be selected.

[0003] With the change of energy demand and the continuous development of energy supply system, integrated energy system has become more and more complex. For the integrated energy system with multi-energy coupling, the energy consumption and production capacity of each equipment are very diverse. The main purpose of studying the carbon flow and energy flow of the integrated energy system is to enhance the efficiency of the energy system through the interaction between the time scales of different energy processes used by each equipment in the system.

[0004] At present, there are still certain limitations in the methods for analyzing the carbon energy flow of integrated energy systems. First, the existing carbon energy flow analysis and modeling methods of integrated energy systems focus on the advantages and disadvantages of the calculation methods and the accuracy of the calculation results, but lack attention to the flow process of energy flow and carbon flow in the integrated energy system; second, most of the existing methods for measuring carbon emissions in integrated energy systems only focus on the overall carbon emissions, but lack effective methods to measure and calculate the carbon emissions of each device in the system at each time; third, most of the existing carbon energy flow analysis research on integrated energy systems focuses on the overall energy consumption, efficiency, economy, environmental protection and other aspects of the system, and lacks detailed research on the energy coupling and conversion relationship between each device in the system. These limitations of the existing methods will affect the energy-saving control effect of the system. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method, device, equipment and medium for optimizing the control of the carbon energy flow of an integrated energy system. The carbon energy flow of the integrated energy system is specifically analyzed by a carbon energy flow model based on Petri nets, and optimized control of the equipment is achieved to achieve the effect of energy saving and carbon reduction in the system.

[0006] In a first aspect, the present invention provides a method for optimizing and controlling carbon energy flow in a comprehensive energy system, the method comprising:

[0007] According to the energy equipment in the integrated energy system, the carbon energy flow model of the integrated energy system is constructed by using Petri net modeling technology;

[0008] Determine a correlation matrix according to the energy coupling relationship between various energy devices in the carbon energy flow model and the device operation characteristics, and determine the operation parameters of various energy devices according to the correlation matrix;

[0009] According to the carbon energy flow model and the operating parameters, an energy-saving and carbon-reduction optimization model is established with minimization of system operating costs and minimization of system carbon emissions as objective functions;

[0010] The improved PSO algorithm is used to solve the energy-saving and carbon-reduction optimization model to obtain an optimization control strategy, and the output of each energy device is controlled according to the optimization control strategy.

[0011] Furthermore, the step of constructing a carbon energy flow model of the integrated energy system by using Petri net modeling technology according to the energy equipment in the integrated energy system includes:

[0012] According to the heating equipment, refrigeration equipment and energy storage equipment in the integrated energy system, a carbon energy flow model based on Petri net is established. The carbon energy flow model is represented by the following seven-tuple:

[0013] S=[P,T,I,O,J,M,K]

[0014] In the formula, P represents the set of places, T represents the set of transitions, I and O represent the input function and output function respectively, J represents the set of control transition excitation functions, M represents the place capacity function, and K represents the limit on the number of tokens stored in the place;

[0015] Among them, the input function and output function are expressed by the following formulas respectively:

[0016]

[0017] In the formula, I(T i ,P j ) indicates that the i-th transition T i Points to the jth place P j , O(P j ,T i ) represents the jth place P j Pointing to the i-th transition T i , W represents electricity, N represents natural gas, Q represents heat, E represents carbon emissions, f(J i) represents the function value triggered by the i-th transition, R + represents the set of positive real numbers.

[0018] Furthermore, the step of determining the correlation matrix according to the energy coupling relationship and equipment operation characteristics between various energy devices in the carbon energy flow model includes:

[0019] According to the energy coupling relationship and equipment operation characteristics of each energy device in the carbon energy flow model, the equipment energy of the energy device is determined, wherein the equipment energy includes the gas consumption and power generation of the gas internal combustion engine, the cooling capacity of the hot water type lithium bromide refrigerator, the gas consumption of the gas hot water boiler, the power consumption of the electric refrigeration unit, the heat storage capacity of the heat storage tank and the cold storage capacity of the cold storage tank;

[0020] According to the input function, output function and equipment energy of the carbon energy flow model, an association matrix is ​​constructed, wherein the association matrix includes a preceding input matrix and a succeeding output matrix, wherein the preceding input matrix is ​​an energy consumption matrix, and the succeeding output matrix is ​​a cold energy flow matrix, a heat energy flow matrix and a carbon flow matrix.

[0021] Furthermore, the energy consumption matrix is ​​expressed by the following formula:

[0022]

[0023] The cold energy flow matrix is ​​expressed by the following formula:

[0024]

[0025] The thermal energy flow matrix is ​​expressed by the following formula:

[0026]

[0027] The carbon flow matrix is ​​expressed by the following formula:

[0028]

[0029] Where, T i represents the i-th transition, i=1,…,14,P j represents the jth place, j = 1, ..., 14, N nb Indicates the natural gas consumption of gas boiler, N ie Indicates the natural gas consumption of internal combustion engines, W e Indicates the amount of electricity purchased, W ie Indicates the power generation of the internal combustion engine, W ec Indicates the power consumption of the electric refrigeration unit, W lb Indicates the power consumption of lithium bromide unit, W k represents the power consumption of the kth water pump, k = 1, ..., 9, Q iewIndicates the heat of the cylinder jacket water in the internal combustion engine, Q nb,h Indicates the heating capacity of gas boiler, Q ie,h represents the heat capacity of the internal combustion engine, Q ec,c Indicates the cooling capacity of the electric refrigeration unit, Q lb,c Indicates the cooling capacity of the lithium bromide unit, Q hn Indicates the heat supply, Q hs Indicates the heat storage, Q he Indicates heat release, Q cn Indicates cooling capacity, Q cs Indicates the cold storage capacity, Q ce Indicates the cooling capacity, E n Indicates carbon emissions from gas boilers, E g represents carbon emissions from electricity consumption, Q c Indicates the cooling capacity of the system, Q h Indicates the system heat supply.

[0030] Furthermore, the step of establishing an energy-saving and carbon-reduction optimization model based on the carbon energy flow model and the operating parameters with minimization of system operating costs and system carbon emissions as objective functions includes:

[0031] Determine the system operation cost based on the equipment energy consumption cost, the grid electricity purchase cost and the electricity sales revenue, and determine the system carbon emissions based on the carbon emissions from natural gas combustion and the carbon emissions from equipment electricity consumption;

[0032] Taking the minimization of system operating costs and system carbon emissions as the objective function, and the equipment operating status constraints, cooling, heating and electricity power balance constraints and storage capacity constraints as constraints, an energy-saving and carbon reduction optimization model is constructed.

[0033] Furthermore, the step of taking the equipment operation status constraint, the cooling, heating and electricity power balance constraint and the storage capacity constraint as the constraint conditions comprises:

[0034] Establish equipment operation status constraints based on the timing of transition excitation, uniqueness of excitation conditions, upper and lower limits of transitions, and uniqueness of equipment switch states;

[0035] Taking energy conversion equipment as transition units and energy storage equipment as storage units, establish cooling, heating and electricity power balance constraints based on the energy supply balance of equipment units.

[0036] Establish the place capacity constraint based on the capacity of the equipment unit itself.

[0037] Furthermore, the step of using the improved PSO algorithm to solve the energy-saving and carbon-reduction optimization model to obtain an optimization control strategy includes:

[0038] Based on the linear combination of individual optimal value and group optimal value, the particle velocity is iteratively updated, and the inertia weight and learning factor are dynamically adjusted based on the number of iterations to obtain an improved PSO algorithm.

[0039] The improved PSO algorithm is used to solve the energy-saving and carbon-reduction optimization model to obtain an optimization control strategy based on equipment output control.

[0040] In a second aspect, the present invention provides an optimization control device for carbon energy flow in a comprehensive energy system, the device comprising:

[0041] The first model building module is used to build a carbon energy flow model of the integrated energy system based on the energy equipment in the integrated energy system by using Petri net modeling technology;

[0042] A correlation matrix determination module, used to determine a correlation matrix according to the energy coupling relationship between various energy devices in the carbon energy flow model and the equipment operation characteristics, and determine the operation parameters of various energy devices according to the correlation matrix;

[0043] A second model building module is used to establish an energy-saving and carbon-reduction optimization model based on the carbon energy flow model and the operating parameters, with minimization of system operating costs and minimization of system carbon emissions as objective functions;

[0044] The optimization control module is used to solve the energy-saving and carbon-reduction optimization model using an improved PSO algorithm to obtain an optimization control strategy, and control the output of each energy device according to the optimization control strategy.

[0045] In a third aspect, an embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0046] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0047] The present invention provides a method, device, equipment and medium for optimizing and controlling the carbon energy flow of an integrated energy system. Through the method, the present invention can realize the visualization of energy flow and carbon flow in the structure of the integrated energy system, realize the monitoring of the operation status of each device in the system at each time, and through the specific analysis of the carbon energy flow of the integrated energy system, can effectively improve the efficiency and effect of the system optimization control, thereby realizing energy saving and carbon reduction of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1is a flow chart of a method for optimizing the control of carbon energy flow in a comprehensive energy system according to an embodiment of the present invention;

[0049] Figure 2 is a schematic diagram of the system structure of the comprehensive energy system in an embodiment of the present invention;

[0050] Figure 3 is a schematic diagram of the carbon energy flow relationship of the comprehensive energy system in an embodiment of the present invention;

[0051] Figure 4 is a schematic diagram of a carbon energy flow model of a comprehensive energy system in an embodiment of the present invention;

[0052] Figure 5 1 is a schematic diagram of an iterative solution process of an optimization control method for carbon energy flow in a comprehensive energy system according to an embodiment of the present invention;

[0053] Figure 6 is a typical daily heat load supply diagram in the comparative test results of the embodiment of the present invention;

[0054] Figure 7 is a typical daily cooling load supply diagram in the comparative test results of the embodiment of the present invention;

[0055] Figure 8 is a typical daily load supply diagram in the comparative test results of the embodiment of the present invention;

[0056] Fig. 9 It is a structural schematic diagram of a device for optimizing the control of carbon energy flow in a comprehensive energy system according to an embodiment of the present invention;

[0057] Fig.10 It is a diagram of the internal structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] See also Figure 1 The first embodiment of the present invention provides an optimization control method for carbon energy flow in a comprehensive energy system, which includes steps S10 to S40:

[0060] Step S10, constructing a carbon energy flow model of the integrated energy system by using Petri net modeling technology according to the energy equipment in the integrated energy system;

[0061] Step S20, determining a correlation matrix according to the energy coupling relationship and equipment operation characteristics between various energy devices in the carbon energy flow model, and determining the operation parameters of various energy devices according to the correlation matrix;

[0062] Step S30, establishing an energy-saving and carbon-reduction optimization model based on the carbon energy flow model and the operating parameters, with minimization of system operating costs and minimization of system carbon emissions as objective functions;

[0063] Step S40: using an improved PSO algorithm to solve the energy-saving and carbon-reduction optimization model to obtain an optimization control strategy, and controlling the output of each energy device according to the optimization control strategy.

[0064] In the present invention, the integrated energy system refers to the use of advanced physical information technology and innovative management models in a certain area to integrate various energy sources such as coal, oil, natural gas, electricity, and thermal energy in the region to achieve coordinated planning, optimized operation, collaborative management, interactive response, and complementary assistance among various heterogeneous energy subsystems. While meeting the diversified energy needs within the system, it is necessary to effectively improve energy utilization efficiency and promote a new type of integrated energy system that promotes sustainable energy development. Specifically, the integrated energy system in this embodiment is mainly composed of heating equipment, refrigeration equipment, and energy storage equipment, and its system structure is as follows: Figure 2 As shown, the heating equipment consists of a gas internal combustion engine and a gas hot water boiler. The gas internal combustion engine mainly consumes natural gas to provide heat and electricity. The electricity produced can be used by various equipment in the energy station. The gas hot water boiler mainly burns natural gas to provide the required thermal energy for domestic hot water; the refrigeration equipment consists of a hot water type lithium bromide refrigeration unit and an electric refrigeration unit. The hot water type lithium bromide unit is connected to the gas internal combustion engine, and mainly uses the waste heat of the cylinder water generated by the gas internal combustion engine for refrigeration. The electric refrigeration unit mainly consumes electricity for cooling; the energy storage equipment consists of a heat storage tank and a cold storage tank, which plays a role in energy storage and regulation in the system; the carbon energy flow relationship of the comprehensive energy system is as follows Figure 3 shown.

[0065] Petri net is a directed graph composed of elements such as place, transition, and token. Traditional Petri net modeling often makes the model redundant and bulky, so that Petri net cannot reflect the impact of time factors on the system, that is, the traditional Petri net system is a single thread. Due to the complexity of the energy coupling of each device in the integrated energy system and the strict requirements of the switching time of each device, the traditional Petri net modeling cannot effectively characterize the energy coupling relationship between the various devices in the integrated energy system. Therefore, in this embodiment, sequential logic control variables and system hierarchical distribution definitions are added to the traditional Petri net modeling method, and the association matrix composed of the input and output functions of the system is used to simplify the energy ladder transfer and transformation of the integrated energy system, so as to realize the tracking of the system energy flow and carbon emission flow.

[0066] According to the rules of Petri net, P can only be connected to T, and T can only be connected to P, which means that the T of each device cannot be connected directly, but must be connected through an intermediate library P. In the actual system, such a library does not exist, so such a library is called a virtual library. Based on this, the carbon energy flow model of the comprehensive energy system based on Petri net can be obtained as follows: Figure 4 As shown, the orange line represents the heat flow, the blue line represents the cold energy flow, the black solid line represents the electric energy flow, and the black dotted line represents the carbon flow. The specific location change definition of the Petri net model of the integrated energy system is shown in Table 1.

[0067] Table 1 Definition of place change

[0068] symbol Place Name symbol Place Name symbol Change name symbol Change name P1 natural gas P8 Hot water distributor T1 Natural gas boiler T8 Water Pump P2 Power grid P9 Cold water manifold T2 Virtual Transformation T9 Water Pump P3 Virtual Library P10 Heat storage tank T3 internal combustion engine T10 Water Pump P4 Virtual Library P11 Heat load T4 Electric refrigeration unit T11 Water Pump P5 Virtual Library P12 Cold storage tank T5 Lithium bromide unit T12 Water Pump P6 Virtual Library P13 Cooling Load T6 Water Pump T13 Water Pump P7 Virtual Library P14 Carbon emissions T7 Water Pump T14 Water Pump

[0069] according to Figure 4 The carbon energy flow model shown uses seven tuples to represent the model:

[0070] S=[P,T,I,O,J,M,K]

[0071] Where, P = {P1, P2, ... P 14} represents the set of places, T = {T1, T2, ... T 14} represents the transition set, I and O represent the input function and output function respectively, J={J1,J2…J n} represents the set of control transition excitation functions, M={M0,M τ} represents the capacity function of the library, M0 represents the initial capacity, M τ represents the place capacity at time τ, K represents the limit on the number of tokens that can be stored in the place, and the input function and output function are defined as:

[0072]

[0073] In the formula, I(T i ,P j ) indicates that the i-th transition T i Points to the jth place P j , O(P j ,T i ) represents the jth place P j Pointing to the i-th transition T i , W represents electricity, N represents natural gas, Q represents heat, E represents carbon emissions, f(J i ) represents the function value triggered by the i-th transition, R + represents the set of positive real numbers.

[0074] The carbon energy flow model based on Petri net can well display the structural hierarchy of the integrated energy system, namely, the input layer, the operation layer, the output layer, etc. Therefore, the present invention uses the structural characteristics of Petri net to construct an association matrix, which is used to display and refine the energy conversion, energy consumption, connection coupling, carbon emissions and other characteristics between specific devices in the system, so as to obtain the detailed operation parameters of each device at each time, and then realize the energy-saving and carbon-reducing optimization operation of the system. The association matrix is ​​determined by the following steps:

[0075] According to the energy coupling relationship and equipment operation characteristics of each energy device in the carbon energy flow model, the equipment energy of the energy device is determined, wherein the equipment energy includes the gas consumption and power generation of the gas internal combustion engine, the cooling capacity of the hot water type lithium bromide refrigerator, the gas consumption of the gas hot water boiler, the power consumption of the electric refrigeration unit, the heat storage capacity of the heat storage tank and the cold storage capacity of the cold storage tank;

[0076] According to the input function, output function and equipment energy of the carbon energy flow model, an association matrix is ​​constructed, wherein the association matrix includes a preceding input matrix and a succeeding output matrix, wherein the preceding input matrix is ​​an energy consumption matrix, and the succeeding output matrix is ​​a cold energy flow matrix, a heat energy flow matrix and a carbon flow matrix.

[0077] In this embodiment, the energy coupling relationship and equipment operation characteristics of each energy device in the carbon energy flow model can be characterized by the energy generated by the equipment. Specifically, the equipment energy includes the gas consumption and power generation of the gas internal combustion engine, the cooling capacity of the hot water type lithium bromide refrigerator, the gas consumption of the gas hot water boiler, the power consumption of the electric refrigeration unit, the heat storage capacity of the heat storage tank and the cold storage capacity of the cold storage tank. Among them, the gas consumption and power generation of the gas internal combustion engine can be expressed as:

[0078]

[0079] Where: G ie Indicates the gas consumption of the gas internal combustion engine, P ieis the power generation of the gas internal combustion engine, LHV represents the calorific value of natural gas, and the value is LHV = 8600 kcal / m3 = 10 kW / m3, m ie represents the mass flow rate of the gas internal combustion engine, c p represents specific heat capacity, with a value of c p =4.182 kJ kg -1 ℃ -1 , T s and T0 represent the inlet and outlet temperatures respectively, η ieg Indicates the heating efficiency of the gas internal combustion engine, with a value of 0.67, η iep It represents the power generation efficiency of the gas internal combustion engine and takes the value of 0.335.

[0080] The hot water type lithium bromide refrigerator uses the waste heat of the cylinder water of the gas internal combustion engine to perform refrigeration. It is closely related to the gas internal combustion engine. Its cooling capacity can be expressed as:

[0081]

[0082] Where P lb Indicates the cooling capacity of hot water type lithium bromide refrigeration machine, m lb Indicates the mass flow rate of lithium bromide unit, COP lb It represents the refrigeration energy efficiency ratio of the hot water type lithium bromide refrigerator, and its value is 1.77.

[0083] The gas consumption of gas hot water boiler is expressed as:

[0084]

[0085] In the formula, G nb Indicates the gas consumption of the gas hot water boiler, P nb Indicates the heating capacity of the gas hot water boiler, η nb Indicates the heating efficiency of the gas hot water boiler, with a value of 0.96, m nb Indicates the mass flow rate of the gas hot water boiler.

[0086] The power consumption of the electric refrigeration unit is expressed as:

[0087]

[0088] In the formula, G ec Indicates the power consumption of the electric refrigeration unit, P ec Indicates the cooling capacity of the electric refrigeration unit, COP ec Indicates the refrigeration energy efficiency ratio of the electric refrigeration unit, with a value of 4, m ec represents the mass flow rate of the electric refrigeration unit, c p Represents the specific heat capacity of the refrigerant, with a value of c p =3.703 kJ kg -1℃ -1 .

[0089] The heat storage capacity of the heat storage tank and the cold storage capacity of the cold storage tank can be expressed as:

[0090] Q con,h =1000·V ch ·c p ·|T s -T0|

[0091] Q con,c =1000·V cc ·c p ·|T s -T0|

[0092] In the formula, Q con,h , Q con,c Respectively represent the heat storage capacity of the heat storage tank and the cold storage capacity of the cold storage tank, in kWh, V ch and V cc Respectively represent the volumes of the heat storage tank and the cold storage tank.

[0093] A Petri net can be represented as an m-dimensional non-negative integer vector. Its structure can also be represented by an n-row and m-column matrix, namely the correlation matrix A. The correlation matrix A is related to the input and output functions of the Petri net system. The correlation matrix A can be divided into the preceding input matrix and the subsequent output matrix. The preceding input matrix is ​​the energy consumption matrix A. p , the latter output matrix is ​​the cold energy flow matrix A c 、Thermal energy flow matrix A h , Carbon flow matrix A e , combined with the above energy expression, we can get the expression of the correlation matrix A as follows:

[0094]

[0095] Based on the above association matrix A, the energy consumption matrix A p It is expressed as:

[0096] Cold Energy Flow Matrix A c It is expressed as:

[0097]

[0098] Thermal Energy Flow Matrix A h It is expressed as:

[0099]

[0100] Carbon Flow Matrix A e It is expressed as:

[0101]

[0102] The definitions of the symbols in the correlation matrix are shown in Table 2 below.

[0103] Table 2 Incidence matrix symbol definition table

[0104] symbol variable symbol variable <![CDATA[N nb ]]> Natural gas consumption of gas boilers <![CDATA[Q ec,c ]]> Cooling capacity of electric refrigeration unit <![CDATA[N ie ]]> Natural gas consumption by internal combustion engines <![CDATA[Q lb,c ]]> Lithium bromide unit cooling capacity <![CDATA[W e ]]> Electricity Purchase <![CDATA[Q hn ]]> Heating <![CDATA[W ie ]]> Internal combustion engine power generation <![CDATA[Q hs ]]> Heat storage <![CDATA[W ec ]]> Electric refrigeration unit power consumption <![CDATA[Q he ]]> Heat release <![CDATA[W lb ]]> Power consumption of lithium bromide unit <![CDATA[Q cn ]]> Cooling capacity <![CDATA[W1~W9]]> Power consumption of each pump <![CDATA[Q cs ]]> Cooling capacity <![CDATA[Q iew ]]> Heat of cylinder jacket water in internal combustion engine <![CDATA[Q ce ]]> Cooling capacity <![CDATA[Q nb,h ]]> Gas boiler heating capacity <![CDATA[E n ]]> Carbon emissions from gas boilers <![CDATA[Q ie,h ]]> Internal combustion engine heat <![CDATA[E g ]]> Electricity consumption and carbon emissions <![CDATA[Q c ]]> System cooling capacity <![CDATA[Q h ]]> System heating capacity

[0105] Furthermore, the changes in the integrated energy system j When it is triggered at time t+τ, the number of corresponding library place identifiers will also change:

[0106]

[0107] In the formula, I(P i ,T j ) represents the total set of input objects corresponding to places and transitions, Indicates T j The input object set, Indicates T j The output object set.

[0108] According to the above correlation matrix, the detailed operating parameters of each device at each time can be obtained. Based on the equipment operating parameters, an energy-saving and carbon-reduction optimization model can be constructed to optimize the control of the equipment. The model construction steps include:

[0109] Determine the system operation cost based on the equipment energy consumption cost, the grid electricity purchase cost and the electricity sales revenue, and determine the system carbon emissions based on the carbon emissions from natural gas combustion and the carbon emissions from equipment electricity consumption;

[0110] Taking the minimization of system operating costs and system carbon emissions as the objective function, and the equipment operating status constraints, cooling, heating and electricity power balance constraints and storage capacity constraints as constraints, an energy-saving and carbon reduction optimization model is constructed.

[0111] In this embodiment, the objective function of the energy-saving and carbon-reduction optimization model of the comprehensive energy system Petri net is set to minimize the operating cost and the carbon emission:

[0112] min:C total =C fi +C g -C s

[0113] min:E=E gas +E grid

[0114] In the formula, C total represents the system operating cost, C fi represents the energy consumption cost of the equipment, C grepresents the cost of purchasing electricity from the power grid, C s represents the revenue from electricity sales, E represents the carbon emissions of the system, and E gas Indicates carbon emissions from natural gas combustion, E grid Indicates the carbon emissions from the electricity consumed by the device.

[0115] Among them, the carbon emissions generated by natural gas combustion are E gas and carbon emissions E generated by equipment electricity consumption grid The specific calculation formula is as follows:

[0116]

[0117] In the formula, G k represents the natural gas consumption of the kth device, H L Indicates lower heating value, H unhcc Indicates the carbon content per unit calorific value, O x represents the oxidation rate, f w,t represents the carbon emission factor at time t, W t Represents the electric energy consumed at time t. The carbon emission factor here is preferably a dynamic carbon emission factor.

[0118] The constraints of the energy-saving and carbon-reduction optimization model are constructed using the following steps:

[0119] Establish equipment operation status constraints based on the timing of transition excitation, uniqueness of excitation conditions, upper and lower limits of transitions, and uniqueness of equipment switch states;

[0120] Taking energy conversion equipment as transition units and energy storage equipment as storage units, establish cooling, heating and electricity power balance constraints based on the energy supply balance of equipment units.

[0121] Establish the place capacity constraint based on the capacity of the equipment unit itself.

[0122] In this embodiment, there are three main constraints in the energy-saving and carbon-reduction optimization model, including equipment operation state constraints, cooling, heating and power balance constraints and storage capacity constraints. Among them, the equipment operation state constraints include that each transition cannot exceed the upper and lower limits of the transition when it is triggered, and the switch state is unique; the transition triggering is sequential, and the corresponding token of each transition triggering will have a characteristic timestamp, and the triggering condition is unique, that is:

[0123]

[0124] In the formula, is the operating status of the i-th device at time t, J i represents the i-th transition activation function, and They represent the minimum and maximum values ​​of the transition excitation function respectively.

[0125] The balance constraint of heat, cold and electric power means that in the carbon energy flow model of the comprehensive energy system based on Petri net, the energy conversion equipment can be regarded as the transition unit, and the energy storage equipment can be regarded as the storage unit. At any time, the heat, cold and electric power of the system must meet the energy supply balance to ensure the normal operation of the system, that is:

[0126] Q ie +Q nb +Q he ≥Q h +Q hs

[0127] Q ec +Q lb +Q ce ≥Q c +Q cs

[0128] W ie +W e ≥W ec +W lb +W1+W2+…+W9

[0129] The place capacity constraint means that the device must be prevented from exceeding its own capacity, that is:

[0130]

[0131] In the formula, represents the output power of the i-th device at time t, Indicates the maximum output power of the i-th device.

[0132] For the above energy-saving and carbon-reduction optimization model, the present invention adopts an improved PSO algorithm to solve the model. The specific steps include:

[0133] Based on the linear combination of individual optimal value and group optimal value, the particle velocity is iteratively updated, and the inertia weight and learning factor are dynamically adjusted based on the number of iterations to obtain an improved PSO algorithm.

[0134] The improved PSO algorithm is used to solve the energy-saving and carbon-reduction optimization model to obtain an optimization control strategy based on equipment output control.

[0135] In this embodiment, the inertia weight and learning factor in the PSO algorithm are adaptively adjusted according to the number of iterations, and the linear combination of the individual optimal value and the group optimal value of the existing particles is used to replace the individual optimal value and the group optimal value of the particles to update the particle speed. Specifically, assume that in the D-dimensional target search space, there is a community composed of N particles, where the i-th particle is represented as a D-dimensional vector, recorded as:

[0136] X i =[x i,1 ,x i,2 ,…x i,D ]i=1,2,…N

[0137] The flying speed of the i-th particle is also a D-dimensional vector, recorded as:

[0138] V i =[v i,1 ,v i,2 ,…v i,D ]i=1,2,…N

[0139] Therefore, the improved PSO algorithm can be expressed as:

[0140]

[0141] In the formula, t represents the number of iterations, represents the position of the particle at iteration t, represents the speed of the particle in t iterations, ω', c1', c2' represent the adjusted inertia weight, the adjusted individual learning factor and the adjusted social learning factor respectively, r1 and r2 represent random numbers uniformly distributed in the interval [0,1], represents the individual optimal value of particle i in the tth iteration, represents the updated individual optimal value, represents the optimal value of the group of particle i in the tth iteration, represents the updated optimal value of the group, t max represents the maximum number of iterations, ω min and ω max Respectively represent the minimum and maximum values ​​of the inertia weight, and Respectively represent the initial value and the final value of the individual learning factor, and They represent the initial value and the final value of the social learning factor, and R represents the control factor. Among them, parameters such as the maximum number of iterations, the minimum and maximum values ​​of the inertia weight, the initial value and the final value of the learning factor, and the control factor are all preset values ​​and can be pre-set based on historical optimal values ​​or empirical values.

[0142] Based on the above formula, it can be seen that the inertia weight and the learning factor will change with the number of iterations, while the particle speed is updated using a linear combination of the individual optimal value and the group optimal value. This method of this embodiment can make the particle search range wider, thereby increasing the possibility of the algorithm obtaining a global optimal solution in the early stage of evolution, thereby improving the solution efficiency of the model.

[0143] The energy-saving and carbon-reduction optimization model is solved by the improved PSO optimization algorithm mentioned above, so as to obtain the optimization result, that is, the optimal output of the equipment, generate the optimization control strategy according to the optimization result, and generate the control instructions of the corresponding equipment based on the optimization control strategy, so as to achieve energy saving and carbon reduction of the integrated energy system. At the same time, in order to characterize the optimization effect, the optimization effect of the optimization control strategy can also be evaluated by using the lowest daily carbon emissions and the lowest daily economic cost as evaluation indicators.

[0144] The effectiveness of the optimization control method provided by the present invention is verified by comparing the solution results of different optimization algorithms. Assume that the equipment technical parameters of the energy equipment in the integrated energy system are shown in Table 3 below:

[0145] Table 3 Equipment technical parameters

[0146]

[0147] The steps of performing test calculation using the optimization control method provided by the present invention are as follows: Figure 5 As shown in the figure, firstly, the above equipment and load parameters are assigned to the places and transitions of the carbon energy flow model, and the Petri net identifier is updated. Then, the transition excitation function and the weight value of the directed arc are set, and it is judged whether the transition can be excited and whether the downstream equipment meets the constraints. If the transition excitation conditions are not met or the equipment constraints are not met, then return to the first step to check whether the parameter settings are correct, re-update the identifier for calculation, and if all are satisfied, run the improved PSO for global optimization, and control the equipment output based on the optimization results, monitor the capacity of each place in real time, and judge whether the output of each device meets the load demand. If the load demand is not met, then judge whether the place capacity reaches the limit value. If the limit value has been reached, return to the first step to check whether the parameter settings are correct and recalculate. If the place capacity limit is not reached, it means that the system is operating normally. At this time, continue to use the improved PSO algorithm for global optimization until the equipment output meets the load demand, thereby ending this optimization control.

[0148] The results of the optimization control test through the above steps are shown in Table 4 below:

[0149] Table 4 Experimental results of improved PSO algorithm

[0150]

[0151] The comparison algorithms used in this experiment include Cplex algorithm, GA algorithm and PSO algorithm. After replacing the improved PSO algorithm with the comparison algorithm, the optimization results are shown in Table 5 below:

[0152] Table 5 Comparison of optimization results of different algorithms

[0153]

[0154] It can be clearly seen from the above table that the improved PSO algorithm provided by the present invention can effectively reduce carbon emissions compared with other algorithms, while saving the economic cost of the operation of the integrated energy system.

[0155] According to the optimization solution results based on the improved PSO algorithm in this experiment, the optimal output results and operating time of each device in the above-mentioned integrated energy system on a typical summer day can be obtained, such as Figure 6 , 7 As shown in 8, according to Figure 6 It can be seen that the thermal power demand of the system is supplied by the gas internal combustion engine, gas boiler and heat storage tank. In the summer period, no heating is required at night, and only the daytime load demand needs to be met. Figure 7 It can be seen that the system's cooling power demand is met by the lithium bromide unit, the electric refrigeration unit and the cold storage tank. In the summer, cooling is required all day long. The system only turns on the electric refrigeration unit at night to provide cooling and store cold at night. During the peak cooling load period, when the lithium bromide unit and the electric refrigeration unit cannot meet the cooling power demand, the cold storage tank releases cold to meet the demand. Figure 8 It can be seen that during the peak period of electricity consumption, the system uses internal combustion engine power generation and power grid purchase to meet the electricity load demand.

[0156] The present embodiment provides an optimization control method for the carbon energy flow of an integrated energy system. Compared with traditional methods, most of them focus on the overall energy consumption and efficiency of the system, but lack detailed analysis of the specific operating status of each device in the system. The present invention uses Petri net-based carbon energy flow modeling for an integrated energy system to accurately reflect the energy flow characteristics and energy conversion process of each device in the system at each moment, and to more clearly describe the carbon emissions of each device in the system. This enables visualization of the integrated energy system. In addition, the present invention takes the lowest carbon emissions and the lowest economic cost as the objective function, fully integrates the mathematical characteristics of Petri nets into the constraints, and uses an improved PSO algorithm to perform Pe The tri-net energy-saving and carbon-reduction optimization model solution accurately reflects the economy and environmental protection of the system after optimization and solution. It can reduce energy waste while meeting the load demand, and effectively improves the optimization control effect of the integrated energy system. At the same time, the improved PSO algorithm adopted in the present invention not only satisfies the iterative changes and position updates of the inertia weight factor and the learning factor, but also obtains the average value of the optimal value of the individual group according to the linear combination method to update the particle search interval, so that the particles can search for the global optimal solution in the interval in a shorter time, effectively improving the model solution speed, and improving the efficiency of optimizing and controlling the integrated energy system, thereby realizing energy saving and carbon reduction of the integrated energy system.

[0157] See also Fig. 9Based on the same inventive concept, a second embodiment of the present invention proposes an optimization control device for carbon energy flow in a comprehensive energy system, comprising:

[0158] The first model building module 10 is used to build a carbon energy flow model of the integrated energy system by using Petri net modeling technology according to the energy equipment in the integrated energy system;

[0159] The correlation matrix determination module 20 is used to determine the correlation matrix according to the energy coupling relationship and equipment operation characteristics between various energy devices in the carbon energy flow model, and determine the operation parameters of various energy devices according to the correlation matrix;

[0160] The second model building module 30 is used to establish an energy-saving and carbon-reduction optimization model based on the carbon energy flow model and the operating parameters, with the minimization of system operating costs and the minimization of system carbon emissions as objective functions;

[0161] The optimization control module 40 is used to solve the energy-saving and carbon-reduction optimization model using an improved PSO algorithm to obtain an optimization control strategy, and control the output of each energy device according to the optimization control strategy.

[0162] The technical features and technical effects of the device for optimizing the control of carbon energy flow in an integrated energy system proposed in an embodiment of the present invention are the same as those of the method proposed in an embodiment of the present invention, and will not be described in detail here. Each module in the above-mentioned device for optimizing the control of carbon energy flow in an integrated energy system can be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0163] In addition, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0164] See also Fig.10, an internal structure diagram of a computer device in one embodiment, the computer device can specifically be a terminal or a server. The computer device includes a processor, a memory, a network interface, a display and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an optimization control method for the carbon energy flow of an integrated energy system is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0165] It can be understood by those skilled in the art that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computing device may include more or fewer components than shown in the figure, or combine certain components, or have the same component arrangement.

[0166] In addition, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0167] In summary, an optimization control method, device, equipment and medium for carbon energy flow of a comprehensive energy system proposed in an embodiment of the present invention, the method constructs a carbon energy flow model of the comprehensive energy system by using Petri net modeling technology according to the energy equipment in the comprehensive energy system; determines the association matrix according to the energy coupling relationship and equipment operation characteristics between each energy equipment in the carbon energy flow model, and determines the operation parameters of each energy equipment according to the association matrix; establishes an energy-saving and carbon reduction optimization model based on the carbon energy flow model and the operation parameters, with the minimization of system operation cost and system carbon emission as the objective function; solves the energy-saving and carbon reduction optimization model by using an improved PSO algorithm to obtain an optimization control strategy, and controls the output of each energy equipment according to the optimization control strategy. The present invention accurately reflects the energy flow characteristics and energy conversion process of each device in the system at each moment by modeling the carbon energy flow of a comprehensive energy system based on Petri nets and solving it by using an improved PSO algorithm, improves the speed of solving the global optimal solution within the interval, effectively improves the optimization control efficiency and effect of the comprehensive energy system, and thus realizes energy saving and carbon reduction of the comprehensive energy system.

[0168] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0169] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A method for optimizing the carbon energy flow of a comprehensive energy system, characterized in that: include: According to the energy equipment in the integrated energy system, the carbon energy flow model of the integrated energy system is constructed by using Petri net modeling technology; Determine a correlation matrix according to the energy coupling relationship between various energy devices in the carbon energy flow model and the device operation characteristics, and determine the operation parameters of various energy devices according to the correlation matrix; According to the carbon energy flow model and the operating parameters, an energy-saving and carbon-reduction optimization model is established with minimization of system operating costs and minimization of system carbon emissions as objective functions; The improved PSO algorithm is used to solve the energy-saving and carbon-reduction optimization model to obtain an optimization control strategy, and the output of each energy device is controlled according to the optimization control strategy.

2. The method for optimizing the carbon energy flow of a comprehensive energy system according to claim 1, characterized in that: The steps of constructing a carbon energy flow model of the integrated energy system by using Petri net modeling technology according to the energy equipment in the integrated energy system include: According to the heating equipment, refrigeration equipment and energy storage equipment in the integrated energy system, a carbon energy flow model based on Petri net is established. The carbon energy flow model is represented by the following seven-tuple: S=[P,T,I,O,J,M,K] In the formula, P represents the set of places, T represents the set of transitions, I and O represent the input function and output function respectively, J represents the set of control transition excitation functions, M represents the place capacity function, and K represents the limit on the number of tokens stored in the place; Among them, the input function and output function are expressed by the following formulas respectively: In the formula, I(T i ,P j ) indicates that the i-th transition T i Points to the jth place P j , O(P j ,T i ) represents the jth place P j Pointing to the i-th transition T i , W represents electricity, N represents natural gas, Q represents heat, E represents carbon emissions, f(J i ) represents the function value triggered by the i-th transition, R + represents the set of positive real numbers.

3. The method for optimizing and controlling carbon energy flow in a comprehensive energy system according to claim 1, characterized in that: The step of determining the correlation matrix according to the energy coupling relationship and equipment operation characteristics between various energy devices in the carbon energy flow model comprises: According to the energy coupling relationship and equipment operation characteristics of each energy device in the carbon energy flow model, the equipment energy of the energy device is determined, wherein the equipment energy includes the gas consumption and power generation of the gas internal combustion engine, the cooling capacity of the hot water type lithium bromide refrigerator, the gas consumption of the gas hot water boiler, the power consumption of the electric refrigeration unit, the heat storage capacity of the heat storage tank and the cold storage capacity of the cold storage tank; According to the input function, output function and equipment energy of the carbon energy flow model, an association matrix is ​​constructed, wherein the association matrix includes a preceding input matrix and a succeeding output matrix, wherein the preceding input matrix is ​​an energy consumption matrix, and the succeeding output matrix is ​​a cold energy flow matrix, a heat energy flow matrix and a carbon flow matrix.

4. The method for optimizing and controlling carbon energy flow in a comprehensive energy system according to claim 3, characterized in that: The energy consumption matrix is ​​expressed by the following formula: The cold energy flow matrix is ​​expressed by the following formula: The thermal energy flow matrix is ​​expressed by the following formula: The carbon flow matrix is ​​expressed by the following formula: Where, T i represents the i-th transition, i=1,…,14,P j represents the jth place, j = 1, ..., 14, N nb Indicates the natural gas consumption of gas boiler, N ie Indicates the natural gas consumption of internal combustion engines, W e Indicates the amount of electricity purchased, W ie Indicates the power generation of the internal combustion engine, W ec Indicates the power consumption of the electric refrigeration unit, W lb Indicates the power consumption of lithium bromide unit, W k represents the power consumption of the kth water pump, k = 1, ..., 9, Q iew Indicates the heat of the cylinder jacket water of the internal combustion engine, Q nb,h Indicates the heating capacity of gas boiler, Q ie,h represents the heat capacity of the internal combustion engine, Q ec,c Indicates the cooling capacity of the electric refrigeration unit, Q lb,c Indicates the cooling capacity of the lithium bromide unit, Q hn Indicates the heat supply, Q hs Indicates the heat storage, Q he Indicates heat release, Q cn Indicates cooling capacity, Q cs Indicates the cold storage capacity, Q ce Indicates the cooling capacity, E n Indicates carbon emissions from gas boilers, E g represents carbon emissions from electricity consumption, Q c Indicates the cooling capacity of the system, Q h Indicates the system heat supply.

5. The method for optimizing and controlling carbon energy flow in a comprehensive energy system according to claim 1, characterized in that: The step of establishing an energy-saving and carbon-reduction optimization model according to the carbon energy flow model and the operating parameters with minimization of system operating costs and minimization of system carbon emissions as objective functions comprises: Determine the system operation cost based on the equipment energy consumption cost, the grid electricity purchase cost and the electricity sales revenue, and determine the system carbon emissions based on the carbon emissions from natural gas combustion and the carbon emissions from equipment electricity consumption; Taking the minimization of system operating costs and system carbon emissions as the objective function, and the equipment operating status constraints, cooling, heating and electricity power balance constraints and storage capacity constraints as constraints, an energy-saving and carbon reduction optimization model is constructed.

6. The method for optimizing and controlling carbon energy flow in a comprehensive energy system according to claim 5, characterized in that: The steps of using equipment operation status constraint, cooling, heating and electricity power balance constraint and storage capacity constraint as constraint conditions include: Establish equipment operation status constraints based on the timing of transition excitation, uniqueness of excitation conditions, upper and lower limits of transitions, and uniqueness of equipment switch states; Taking energy conversion equipment as transition units and energy storage equipment as storage units, establish cooling, heating and electricity power balance constraints based on the energy supply balance of equipment units. Establish the place capacity constraint based on the capacity of the equipment unit itself.

7. The method for optimizing and controlling carbon energy flow in a comprehensive energy system according to claim 1, characterized in that: The step of using the improved PSO algorithm to solve the energy-saving and carbon-reduction optimization model to obtain the optimization control strategy includes: Based on the linear combination of individual optimal value and group optimal value, the particle velocity is iteratively updated, and the inertia weight and learning factor are dynamically adjusted based on the number of iterations to obtain an improved PSO algorithm. The improved PSO algorithm is used to solve the energy-saving and carbon-reduction optimization model to obtain an optimization control strategy based on equipment output control.

8. An optimization control device for carbon energy flow in a comprehensive energy system, characterized in that: include: The first model building module is used to build a carbon energy flow model of the integrated energy system based on the energy equipment in the integrated energy system by using Petri net modeling technology; A correlation matrix determination module, used to determine a correlation matrix according to the energy coupling relationship between various energy devices in the carbon energy flow model and the equipment operation characteristics, and determine the operation parameters of various energy devices according to the correlation matrix; A second model building module is used to establish an energy-saving and carbon-reduction optimization model based on the carbon energy flow model and the operating parameters, with minimization of system operating costs and minimization of system carbon emissions as objective functions; The optimization control module is used to solve the energy-saving and carbon-reduction optimization model using an improved PSO algorithm to obtain an optimization control strategy, and control the output of each energy device according to the optimization control strategy.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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