Method and system for determining steady-state and dynamic carbon entropy of integrated energy system based on superposition principle

Abstract

The present disclosure relates to the technical field of integrated energy system operation control, and proposes a method and system for determining the steady-state and dynamic carbon entropy of an integrated energy system based on the superposition principle. The method includes: based on the carbon emission sharing framework determined by carbon emission conservation and the superposition of non-homologous sources, a steady-state carbon entropy model and a dynamic carbon entropy model are respectively proposed for the subsystems of the integrated energy; for the subsystems of the integrated energy with a load change less than the set value and a dynamic process less than the set duration, the steady-state carbon entropy model is adopted, and carbon entropy analysis and calculation are carried out based on carbon trajectory tracking to obtain the carbon entropy distribution; for the subsystems of the integrated energy with a load change not less than the set value, a dynamic process not less than the set duration, and a time scale difference greater than the threshold, the dynamic carbon entropy model is adopted, and carbon entropy analysis and calculation are carried out based on carbon trajectory tracking to obtain the carbon entropy distribution. The carbon entropy analysis of the present disclosure can obtain more accurate carbon indicators, realize the energy consumption regulation of the integrated energy or guide the user's energy consumption.

Description

Technical Field

[0001] The present disclosure relates to the technical field of integrated energy system operation control. Specifically, it relates to a method and system for determining the steady-state and dynamic carbon entropy of an integrated energy system based on the superposition principle. Background Technique

[0002] The statements in this part merely provide background technical information related to the present disclosure and do not necessarily constitute prior art.

[0003] The research on carbon emissions in energy systems has received great attention. The macroscopic measurement of carbon emissions on the energy side is too rough to support in-depth research on low-carbonization. The role of the end-use side in energy conservation and emission reduction is becoming increasingly prominent, and carbon trajectory tracking has become an important technology for clarifying the emission reduction responsibilities of users. The integrated energy system has multiple energy sources and end-use ports. Combining with the carbon trajectory analysis method, it can effectively track the carbon intensity of ports, guide users' energy use, and give play to the initiative and potential of users in energy conservation and emission reduction.

[0004] The inventors found in the research that the existing carbon trajectory tracking theory still needs to be improved in three aspects:

[0005] 1) The carbon emission flow theory defines that "carbon flow" is attached to the energy flow, but carbon emissions do not disappear with energy loss. This theoretical framework affects the load-side attribution of loss-carrying carbon emissions. The obtained carbon index is a total index, and there is no explicit expression of the source-load carbon emission relationship, so it is impossible to distinguish the carbon emission contribution of the source to the load.

[0006] 2) The existing methods need to calculate the node carbon indexes of the whole system, and the matrix scale is large; some methods need to add virtual nodes for equivalent processing of losses, and the calculation is complex.

[0007] 3) At present, the carbon trajectory tracking method only stays in the steady state, and there is no dynamic carbon trajectory tracking method to solve more accurate carbon indexes for integrated energy systems with large load fluctuations, long dynamic processes, and large differences in time scales. Summary of the Invention

[0008] To solve the above problems, the present disclosure proposes a method and system for determining the steady-state and dynamic carbon entropy of an integrated energy system based on the superposition principle, which can determine whether to use dynamic carbon entropy analysis or steady-state carbon entropy analysis according to the operation data of each subsystem of the integrated energy system, obtain more accurate carbon indexes, and realize the energy use regulation of the integrated energy system or guide the energy use of users.

[0009] To achieve the above object, the present disclosure adopts the following technical solutions:

[0010] One or more embodiments provide a method for determining the steady-state and dynamic carbon entropy of an integrated energy system based on the superposition principle, including the following steps:

[0011] Based on the carbon emission conservation and the superposition of non-homologous components, a carbon emission allocation framework is determined, and a steady-state carbon entropy model and a dynamic carbon entropy model are respectively proposed for the subsystems of the integrated energy system;

[0012] For the subsystems of the integrated energy system where the load change is less than the set value and the dynamic process is less than the set duration, a steady-state carbon entropy model is adopted, and carbon entropy analysis and calculation are carried out based on carbon trajectory tracking to obtain the carbon entropy distribution;

[0013] For the subsystems of the integrated energy system where the load change is not less than the set value, the dynamic process is not less than the set duration, and the time scales differ by more than the threshold, a dynamic carbon entropy model is adopted, and carbon entropy analysis and calculation are carried out based on carbon trajectory tracking to obtain the carbon entropy distribution.

[0014] An integrated energy system steady-state and dynamic carbon entropy determination system based on the superposition principle includes:

[0015] A model construction module: configured to respectively propose a steady-state carbon entropy model and a dynamic carbon entropy model for the subsystems of the integrated energy system based on the carbon emission allocation framework determined by carbon emission conservation and the superposition of non-homologous components;

[0016] A carbon trajectory tracking and solving module: configured to, for the subsystems of the integrated energy system where the load change is less than the set value and the dynamic process is less than the set duration, adopt a steady-state carbon entropy model and carry out carbon entropy analysis and calculation based on carbon trajectory tracking to obtain the carbon entropy distribution;

[0017] Configured to, for the subsystems of the integrated energy system where the load change is not less than the set value, the dynamic process is not less than the set duration, and the time scales differ by more than the threshold, adopt a dynamic carbon entropy model and carry out carbon entropy analysis and calculation based on carbon trajectory tracking to obtain the carbon entropy distribution.

[0018] An electronic device includes a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the steps of the method described in the above claims are completed.

[0019] Compared with the prior art, the beneficial effects of the present disclosure are:

[0020] In the present disclosure, a reasonable carbon emission sharing framework is proposed, enabling the proposed dynamic carbon entropy analysis method to not only calculate the overall carbon index of the load, but also analyze the carbon entropy components of the load, and distinguish the carbon emission effects of different sources on the load. Under a unified framework, steady-state and dynamic carbon trajectory tracking models are respectively proposed, integrating the steady-state carbon trajectory tracking method and the dynamic carbon trajectory tracking method, which can not only meet the carbon emission analysis of subsystems with small load changes and short dynamic processes, but also meet the carbon emission analysis of subsystems with large load fluctuations, long dynamic processes, and large differences in time scales. Compared with the sole use of the steady-state carbon trajectory tracking method, the dynamic carbon entropy analysis method can more accurately define the carbon emission responsibilities of users in an integrated energy system with large load changes and long dynamic processes, and form accurate carbon indicators to guide users' energy consumption.

[0021] The advantages of the present disclosure and the advantages of additional aspects will be described in detail in the following specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings forming a part of this disclosure are used to provide a further understanding of the present disclosure. The schematic embodiments and descriptions thereof of the present disclosure are used to explain the present disclosure and do not constitute a limitation to the present disclosure.

[0023] Figure 1 is the carbon emission sharing framework constructed in Embodiment 1 of the present disclosure;

[0024] Figure 2 is the schematic diagram of the thermal system structure in Embodiment 1 of the present disclosure;

[0025] Figure 3 is the schematic diagram of the superposition characteristics of the natural gas system in Embodiment 1 of the present disclosure;

[0026] Figure 4 is the schematic diagram of the change in the pipeline storage of the natural gas system in Embodiment 1 of the present disclosure;

[0027] Figure 5 is the schematic diagram of the superposition characteristics of the thermal system in Embodiment 1 of the present disclosure;

[0028] FIG. 6(a) is the schematic diagram of the ports of a single-input single-output coupling device in Embodiment 1 of the present disclosure;

[0029] FIG. 6(b) is the schematic diagram of the CHP port in Embodiment 1 of the present disclosure;

[0030] Figure 7 is the carbon entropy analysis calculation flow chart of the integrated energy system in Embodiment 1 of the present disclosure;

[0031] Figure 8 is the carbon intensity of the power system nodes in the simulation example of Embodiment 1 of the present disclosure;

[0032] Figure 9is the carbon entropy of the load node of the power system in the simulation example of Embodiment 1 of the present disclosure;

[0033] Figure 10 is the result comparison of the carbon intensity of the natural gas system in the simulation example of Embodiment 1 of the present disclosure using the dynamic and steady-state tracking methods;

[0034] Figure 11 is the result comparison of the carbon intensity of the thermal system in the simulation example of Embodiment 1 of the present disclosure using the dynamic and steady-state tracking methods;

[0035] Figure 12 is the schematic diagram of the carbon entropy and power components of the load node of the natural gas system in the simulation example of Embodiment 1 of the present disclosure;

[0036] Figure 13 is the schematic diagram of the total load carbon entropy and power components of the nodes of the thermal system in the simulation example of Embodiment 1 of the present disclosure. Detailed implementation manners

[0037] The present disclosure will be further described below in conjunction with the drawings and embodiments.

[0038] It should be noted that the following detailed descriptions are all exemplary and are intended to provide further explanations of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present disclosure belongs.

[0039] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof. It should be noted that, without conflict, the various embodiments and features in the present disclosure can be combined with each other. The embodiments will be described in detail below in conjunction with the drawings.

[0040] Technical term explanations

[0041] Integrated Energy System (IES): An integrated energy system refers to an energy system within a certain area that utilizes advanced physical information technology and innovative management models to integrate various energies such as coal, oil, natural gas, electric energy, and heat energy within the area, achieving coordinated planning, optimized operation, collaborative management, interactive response, and mutual complementarity among multiple heterogeneous energy subsystems. While meeting the diversified energy demand within the system, it effectively improves energy utilization efficiency and promotes the sustainable development of energy. A new type of integrated energy system. In the integrated energy system model, it generally includes a power system model, a thermal system model, and a natural gas system model. Among them, the power system has the smallest time constant, at the millisecond and microsecond levels; the thermal system and the natural gas system have longer time constants, at the minute and hour levels. The integrated energy system model in this embodiment separately models the steady-state model and the dynamic model to study the system operation conditions at different time scales.

[0042] Carbon Emission Flow Theory: This theory is a relatively systematic carbon trajectory tracking theory at present and can be applied in the integrated energy system. This theory defines carbon emission flow as a virtual network flow that depends on the energy flow. Without considering transmission losses, based on the known DC power flow data, the carbon emissions of the energy production links are allocated to the energy consumption side, forming specific carbon index definitions and calculation methods.

[0043] Embodiment 1

[0044] In the technical solutions disclosed in one or more embodiments, as Figures 1 - 13 shown, the method for determining the steady-state and dynamic carbon entropy of an integrated energy system based on the superposition principle includes the following steps:

[0045] Step 1: Based on the carbon emission sharing framework determined by carbon emission conservation and the superposition of non-homologous sources, respectively propose a steady-state carbon entropy model and a dynamic carbon entropy model for the subsystems of the integrated energy.

[0046] Step 2: For the subsystems of the integrated energy with a load change less than the set value and a dynamic process less than the set duration, use the steady-state carbon entropy model to perform carbon entropy analysis and calculation based on carbon trajectory tracking to obtain the carbon entropy distribution.

[0047] Step 3: For the subsystems of the integrated energy with a load change not less than the set value, a dynamic process not less than the set duration, and a time scale difference greater than the threshold, use the dynamic carbon entropy model to perform carbon entropy analysis and calculation based on carbon trajectory tracking to obtain the carbon entropy distribution.

[0048] [[ID=2,3]]According to the obtained carbon entropy distribution, realize the energy consumption regulation of the integrated energy or guide the user's energy consumption.

[0049] Specifically, the subsystems of integrated energy include the power system, the thermal system, the natural gas system, and the coupling system. Different types of carbon entropy analysis methods are selected for different analysis requirements to analyze the integrated energy system. Among them, for the electric-thermal integrated energy system with a fast dynamic process and a small load change, the power system carbon entropy analysis method, the steady-state carbon entropy analysis method of the thermal system, and the carbon entropy model of the coupling equipment are used for analysis; for the electric-thermal-gas integrated energy system with a long dynamic process and a large load change, the power system carbon entropy method, the dynamic carbon entropy analysis method of the thermal system, the dynamic carbon entropy analysis method of the natural gas system, and the carbon entropy model of the coupling equipment are used for carbon emission analysis.

[0050] In this embodiment, a reasonable carbon emission allocation framework is proposed, so that the proposed dynamic carbon entropy analysis method can not only calculate the overall carbon index of the load, but also analyze the carbon entropy composition of the load, and distinguish the carbon emission effects of different sources on the load. Under the unified framework, the steady-state and dynamic carbon trajectory tracking models are respectively proposed, and the steady-state carbon trajectory tracking method and the dynamic carbon trajectory tracking method are integrated, which can not only meet the carbon emission analysis of subsystems with small load changes and short dynamic processes, but also meet the carbon emission analysis of subsystems with large load fluctuations, long dynamic processes, and large differences in time scales. Compared with the separate use of the steady-state carbon trajectory tracking method, the dynamic carbon entropy analysis method can more accurately define the carbon emission responsibility of users in the integrated energy system with large load changes and long dynamic processes, and form accurate carbon indicators to guide users' energy use.

[0051] Furthermore, when constructing the steady-state carbon entropy model and the dynamic carbon entropy model, the power and carbon emissions are directly separated from the source side, the power and carbon emission transfer relationships between different sources and the load are clarified, and the carbon emissions carried by energy and losses are reasonably allocated. The method of adding virtual nodes to handle losses can be avoided, so as not to increase the computational complexity. The formed carbon trajectory tracking method can analyze the carbon emission sources of the load and obtain more refined carbon emission indicators to guide users' energy use.

[0052] In step 1, the carbon emission allocation framework is specifically as follows: the carbon emissions transferred from the carbon source to the carbon load include the carbon entropy flow transferred along with the energy transmission and the carbon entropy production carried by the energy loss; the carbon entropy transfer relationship between the source and the load is further established by establishing the energy relationship between the source and the load; based on the additivity of carbon entropy from different sources, the total carbon entropy index at the load is calculated.

[0053] According to the conservation of carbon emissions, the total carbon emissions corresponding to the energy output of the carbon source are equal to the total carbon emissions corresponding to the energy received by the carbon load. Therefore, the key to tracking carbon entropy lies in tracking the energy output relationship between the source and the load. After clarifying the power flow output relationship of a single source to a single load, the carbon entropy transfer relationship of a single source to a single load is determined, and finally the total carbon entropy of energy use is obtained by summing them up using the additivity of carbon entropy.

[0054] During the process of carbon emissions transferring from the source to the load, the carbon entropy increase ds cIt includes two parts of carbon entropy change, as shown in the following formula:

[0055] ds c = ds cf + ds cg (1)

[0056] In the formula: s cf is the "carbon entropy flow", which is the carbon entropy "transferred" by the energy flow carrying the fixed carbon emission intensity.

[0057] Such as Figure 1 carbon entropy flows a and b in cg s Figure 1 is the "carbon entropy production", which is the carbon entropy generated by irreversible factors in the energy flow. Such as

[0058] carbon entropy productions a and b in

[0059]

[0060] In the actual energy flow process, the irreversible factor is the energy loss during transmission. The sum of the carbon entropy flow and the carbon entropy production is the carbon entropy transferred by the energy flow from the source to the load; the sum of carbon entropy a and carbon entropy b is the total carbon entropy transferred during the load energy consumption process. is the power transmitted from the source node w of the energy system to the node v of the energy system; s c,wv is the power transferred from the source node w of the energy system to the node v of the energy system; e w is the carbon intensity of the source node w of the energy system; s c,v is the total carbon entropy transferred to the node v of the energy system under the action of multiple sources; V sr is the set of all energy source (i.e., carbon source) nodes of the energy system.

[0061] According to the additivity of carbon entropy, the carbon entropy theory can independently analyze the role of a certain carbon source while obtaining the total carbon index. Different from the method of calculating the carbon index by fusing and recursively calculating each node one by one in other theories, the carbon entropy model proposed in this embodiment can directly separate the power and carbon emissions from the source side, clarify the power and carbon entropy transfer relationships of different sources to the load, and then superimpose the carbon emissions on the load side to form an explicit expression of the carbon emission relationship between the carbon source and the carbon load.

[0062] In step 1, for the natural gas system, according to the proposed carbon emission sharing framework, the constructed dynamic carbon entropy model is as follows:

[0063] (1) Natural gas system dynamic carbon entropy model: The gas flow mass flow rate G at the end of the gas network pipeline n, the gas pressure p0 at the head end of the pipeline is expressed as the superposition of multiple step functions. The mass flow rate G0(s) of the gas flow at the head end of the gas network pipeline and the gas pressure p n (s) at the end of the pipeline are expressed as the superposition of a finite number of step responses. Only considering the gas flow component from the gas source node to the load node, the gas flow relationship between the source and load in the natural gas system is constructed, and then the dynamic power flow tracking model of the natural gas system is established. According to the power flow tracking model, considering the gas flow in the pipeline storage and distinguishing the gas flow supply relationship of different gas sources (including pipeline storage) to the gas load, the dynamic carbon entropy model of the natural gas system is established. The specific model construction process is as follows:

[0064] In the natural gas system, the gas flow is driven by pressure and transmitted along the pipeline, satisfying the mass conservation equation (1.1) and the momentum conservation equation (1.2):

[0065]

[0066]

[0067] Where: ρ, v, and p are the density, velocity, and pressure of natural gas respectively; g is the acceleration due to gravity; λ, θ, and D are the friction coefficient, inclination angle, and inner diameter of the natural gas network pipeline respectively; t and x are the time and space coordinates respectively.

[0068] Simplify equation (1.2) and transform it to the complex frequency domain to solve the transmission characteristic equation of the gas network pipeline, as shown in equation (1.3).

[0069]

[0070] Where: G0, G n are the mass flow rates of the gas flow at the head and end of the gas network pipeline; p0, p n are the gas pressures at the head and end of the pipeline; A3(s), B3(s), C3(s), D3(s) are the elements in the transfer function matrix of the gas network pipeline.

[0071] In this embodiment, in order to reduce the difficulty of solving the inverse Laplace transform of G0(s) and p n (s), the mass flow rate G n of the gas flow at the end of the gas network pipeline and the gas pressure p0 at the head end of the pipeline are expressed as the superposition of multiple step functions. The mass flow rate G0(s) of the gas flow at the head end of the gas network pipeline and the gas pressure p n (s) at the end of the pipeline are expressed as the superposition of a finite number of step responses:

[0072]

[0073] Among them, in formula 1.4, the superscripts G and p are used to distinguish the relevant quantities of the gas flow mass flow rate and gas pressure. Where: q G 、 are the serial number and number of the step change amount of the gas flow mass flow at the end of the gas network pipeline respectively; q p and are the serial number and number of the step change amount of the natural gas pressure at the head of the pipeline respectively; are the q G -th and q p -th amplitudes of the step change amounts respectively; is the starting time of the q G -th and q p -th step change amounts; ε(t) is the step function.

[0074] In this embodiment, the unit step change amounts of G n , p0 are used as the excitation to simplify the unit step response matrix of G0, p n .

[0075] First, the elements in the unit step response matrix are transformed into the form of a step response added with multiple inertia links. Taking the formula A3(s) / s as an example, it is as follows:

[0076]

[0077] In the formula: δ is the number of inertia links; τ is the amplitude of the step and inertia links and the time constant of the inertia link.

[0078] The smaller the time constant, the faster the dynamic response. The dynamic response of the natural gas system is relatively slow. Therefore, the sum of multiple inertia links with a sufficiently small time constant is simplified to one inertia link

[26] :

[0079]

[0080] In the formula: i is the serial number of the inertia link; τ set is the appropriately sized time constant set.

[0081] Then formula (1.5) can be simplified to:

[0082]

[0083] Then the unit step response matrix of G0, p n is simplified to formula (1.8), and G0(s), p n (s) can be expressed as the superposition of a finite number of step responses, as shown in formula (1.9).

[0084]

[0085]

[0086] In the formula: H(s) is the simplified G0, pn Unit step response matrix; h1(s), h2(s), h3(s), and h4(s) are the simplified unit step response functions of A3(s) / s, B3(s) / s, C3(s) / s, and D3(s) / s respectively. Respectively the q G th, the q p th product of the step change amplitude and phase shift, which can change the amplitude and phase of the unit step response.

[0087] This method in this embodiment not only reduces the difficulty of inverse Laplace transform and improves the calculation efficiency, but also obtains the unit step response matrix of G0, p n . H(s) is determined by the pipeline characteristics and remains unchanged during the calculation. When the flow rate at the end of the gas network pipeline changes, the gas flow response at the head of the gas network can be directly calculated, and the state quantity can be calculated by superimposing the response quantities.

[0088] As can be seen from the gas network pipeline modeling method, the pipeline two-port model is shown in Equation (1.10).

[0089]

[0090] Where: sH(s) is the simplified pipeline transfer function matrix.

[0091] Adjusting the input and output of the pipeline two-port model gives:

[0092]

[0093] According to Equation (1.11), establish the two-port models of all pipelines in the gas network, as shown in Equations (1.12) and (1.13).

[0094]

[0095]

[0096] Where: G0(s), G n (s) are the mass flow rate vectors at the head and end of the natural gas system pipeline respectively; O1(s) to O4(s) are the diagonal matrices of the elements in the pipeline transfer function; A p is the inflow incidence matrix of the natural gas system node pipeline. When the gas flow enters the gas network pipeline b from the gas network node w, the element a p in the matrix A p,wb =1, otherwise it is 0; A n is the outflow incidence matrix of the natural gas system node pipeline. When the gas flow exits the gas network node w from the gas network pipeline b, the element a n in the matrix A n,wb =1, otherwise it is 0; p N(s) is the pressure vector of the nodes in the natural gas system; n gb is the number of pipelines in the natural gas system.

[0097] Using the incidence matrix, the mass flow rate of the injected gas at the nodes in the gas network (hereinafter simply referred to as the gas network node flow) is expressed as Equation (1.14), and then the relationship between the node flow and the node pressure can be obtained as shown in Equation (1.15).

[0098]

[0099]

[0100] In the formula: G N (s) is the gas network node flow vector.

[0101] The state quantities of the known quantities in the natural gas system are generally the pressures of the gas source nodes and the gas flows of the intermediate nodes and load nodes. According to the known situation of the state quantities, Equation (1.15) is adjusted. At the same time, in order to make the inverse Laplace transform of the unknown quantity proceed smoothly, the simplified method of the pipeline step response matrix is used to process the system step response matrix, and the power flow model of the natural gas system based on the superposition characteristic can be obtained:

[0102]

[0103] In the formula: is the vector of the unknown node flow and air pressure in the gas network; is the simplified unit step response matrix of the natural gas system; is the vector of the product of the amplitude and phase shift of the known node air pressure and flow change in the gas network; σ is the serial number of all known quantities.

[0104] From Equation (1.16) and Figure 3 it can be seen that the node flow of the gas source can be expressed as the superposition of multiple step responses, remains unchanged. When the load changes, the amplitude, phase of the change amount and can be used to calculate the response change amount of the gas source node flow in the natural gas system, and the overall response amount of the gas source is calculated by superimposing the response change amounts.

[0105] Constructing a power flow tracking model based on the superposition characteristic of the natural gas system is the dynamic carbon entropy tracking model of the natural gas system, as shown in Equation (1.17).

[0106]

[0107] In the formula: G sc (s) is the gas source node flow component matrix; is the block matrix of, and the matrix dimensions are n gs ×ngs , n gs ×(n gn - n gs ), where n gs is the number of gas network gas source nodes, and n gn is the number of gas network nodes.

[0108] Using the inverse Laplace transform to convert G sc (s) into the time domain, the source-load action relationship of the gas network at any time section can be obtained. Among them, the sum of the gas flow action components of the gas source nodes on the nodes with known gas pressure states (gas source nodes) is 0. Therefore, only the gas flow components of the gas source nodes on the load nodes need to be considered during carbon trajectory tracking.

[0109] In this embodiment, the carbon emission index calculation of the natural gas system: According to the analytical source-load power flow relationship provided by the dynamic power flow tracking model of the natural gas system, combined with the carbon intensities of the gas source nodes and the pipeline storage, by superimposing the carbon entropy components transferred from the gas source and the pipeline storage to the gas load, the carbon emission index of the gas load is calculated. The specific calculation process of the dynamic carbon entropy analysis of the natural gas system is as follows:

[0110] As Figure 4 shown, when the gas load increases, the gas flow mass flow rate of the load node is supplied by two parts: the consumption of the pipeline storage G pk- and the input of the gas source G sc ; when the gas load decreases, the mass flow rate input by the gas source simultaneously supports the load and the increase of the pipeline storage G pk+ .

[0111] Based on the source-load action relationship of the gas network obtained from Equation (1.17), calculate the carbon entropy index and node carbon intensity of the gas network, that is, the dynamic carbon entropy analysis calculation method of the natural gas system. The steps are as follows:

[0112] Step1: Initialization:

[0113] Initialize the total capacity of the gas network pipeline storage at time t = 0 The carbon intensity of the pipeline storage The carbon intensity of the gas source f The proportion γ of the gas flow supplied by the gas source f to the gas load j in the total gas load, where f,j,t The calculation formula of is:

[32] is:

[0114] [[ID=—56]]

[0115] In the formula: Γ G is the set of all pipelines in the natural gas system; π is the pi; Z is the gas compression coefficient; p 1,b,t , p n,b,t are the inlet and outlet pressures of pipeline b in the gas network at time t.

[0116] Step 2: Calculate the gas load change during the interval duration at the set time interval, and calculate the gas load carbon entropy and the carbon intensity of the gas load node;

[0117] Specifically, set the time interval for updating the pipeline inventory data as Δt, determine the change trend of the gas load j during the time period t to (t + Δt), and calculate the gas load carbon entropy and the carbon intensity of the gas load node. If the gas load increases, the carbon entropy of the gas load j at time t Consists of two parts: the pipeline inventory carbon entropy and the gas source carbon entropy, as shown in Equation (1.19); if the load decreases or remains unchanged, then use the gas source supply gas flow ratio γ at time t - 1 f,j,t-1 Calculate the gas load carbon entropy, as shown in Equation (1.20). The carbon intensity of the gas load node [[ID=​9]] As shown in Equation (1.21).

[0118]

[0119]

[0120]

[0121] In the formula: Q is the calorific value of natural gas; I is the set of gas sources; Is the mass flow component of the gas source f acting on the gas load j at time t; Is the gas flow mass flow and the consumed pipeline inventory of the gas load j at time t.

[0122] Step 3: Update the pipeline inventory capacity and the pipeline inventory carbon intensity according to the gas load change amount at the set time interval.

[0123] Update the pipeline inventory capacity at time t + Δt Pipeline inventory carbon intensity As shown in Equations (1.22) - (1.23).

[0124]

[0125]

[0126] In the formula: Ω is the set of gas loads; Is the pipeline inventory generated by the gas source f when the mass flow of the gas load j decreases at time t.

[0127] Step 4: Repeat Step 1 - 3 until the calculation of carbon indicators for all times is completed.

[0128] During the calculation process, the calculation accuracy of the carbon entropy of the natural gas system and the node carbon indicators can be improved by reducing Δt; to avoid the preset γ f,j,0 、 There is a large difference from the actual value. A section of historical data can be added before time 0 to improve the accuracy of solving the carbon index of the natural gas system.

[0129] The dynamic power flow tracing of the natural gas system provides an analytical source-load power flow relationship for carbon entropy analysis. By combining the carbon intensities of the gas source nodes and pipeline storage, after further clarifying the source-load carbon entropy relationship, the carbon emission index of the gas load is calculated by superimposing the carbon entropy components transferred from the gas source and pipeline storage to the gas load.

[0130] In step 1, for the thermal system, according to the proposed carbon emission sharing framework, a dynamic carbon entropy model and a steady-state carbon entropy model are respectively constructed. Among them, the construction and solution process of the dynamic carbon entropy model are as follows:

[0131] (2) The dynamic carbon trajectory tracing model of the thermal system, that is, the dynamic carbon entropy model of the thermal system: The temperature at the head of the pipeline is expressed as the superposition of step excitations, and the temperature at the end of the heat network pipeline is expressed as the superposition of step responses. According to the proportional relationship between heat and temperature at the node, the temperature action relationship between the source node and the non-source node is expressed as the heat supply relationship between the source node and the non-source node; based on the carbon entropy superposition characteristics of the thermal system, the carbon intensity relationship between the steady-state heat network source node and the non-source node is derived, the carbon entropy relationship between the source node and the load node is determined, and the dynamic carbon entropy model of the thermal system is established considering the heat delay.

[0132] In this embodiment, constructing the thermal system pipeline model includes heat conduction with forced convection and heat loss during transmission.

[0133] The heat in the heat network is transferred as the heat transfer medium flows and is affected by the pipe-environment heat exchange, satisfying the equation shown in Equation (2.1).

[0134]

[0135] In the formula: T is the relative temperature between the heat medium in the pipeline and the environment; m is the mass flow rate of the heat medium, and water is generally used as the heat medium in engineering; c and ρ′ are the specific heat capacity and density of water; μ is the thermal conductivity of the heat network pipeline; γ0 is the radial heat diffusion coefficient of the water flow; A′ is the cross-sectional area of the heat network pipeline.

[0136] The first and second terms in Equation (2.1) represent heat conduction with forced convection, the third term represents static heat conduction inside the water flow, and the fourth term represents heat loss during transmission. Since the thermal conductivity of water is extremely low, the third term of static heat conduction inside the water flow is usually ignored, and Equation (2.1) is simplified to Equation (2.2).

[0137]

[0138] Converting Equation (2.2) to the complex frequency domain and solving the first-order linear homogeneous differential equation gives:

[0139]

[0140] In the formula: T0 and T n are the temperatures at the head and end of the heat network pipeline respectively, and l′ is the length of the heat network pipeline; e -α and e -sβ are the heat loss factor and time delay factor of the heat network pipeline.

[0141] Express the temperature at the head of the pipeline as the superposition of step excitations:

[0142]

[0143] In the formula: q H and are the serial number and number of the step change amount of the temperature at the head of the heat network pipeline; is the q H th amplitude of the step change amount; is the q H th starting time of the step change amount.

[0144] Substitute Equation (2.4) into Equation (2.3), and the temperature at the end of the heat network pipeline can be obtained as:

[0145]

[0146] In the formula: H′(s) is the unit step response of the temperature at the end of the heat network pipeline; is the product of the amplitude and phase shift of the q H th step change amount.

[0147] It can be seen from Equation (2.5) that the temperature at the end of the heat network pipeline can be expressed as the superposition of step responses. When the temperature at the head of the pipeline changes, the temperature at the end of the pipeline can be calculated by superimposing the response quantities.

[0148] According to the superposition principle of the thermal system, the node temperatures of the quality-regulated heat supply network and the regenerative heat network both satisfy the following formula:

[0149] M out T(s) = J(s)T(s), J(s) = M in B(s)A np (2.6)

[0150] In the formula: M in and M out are the mass flow matrices of the water flowing into and out of the nodes in a single heat network (heat supply network or regenerative heat network); T(s) is the node temperature vector of the heat network; J(s) is the temperature conversion matrix of the heat network; is the diagonal matrix of the transmission characteristics of the heat network pipeline, where b′ is the serial number of the heat network pipeline and n hb is the number of heat network pipelines; Anp is the associated matrix of the pipeline at the heat network node. When the head end of the heat network pipeline b′ is the heat network node w′, a np,b′w′ = 1; otherwise it is 0.

[0151] Equation (2.6) is partitioned according to the source and non-source nodes as shown in Equation (2.7):

[0152]

[0153] In the formula, is the mass flow rate matrix of the water flowing out from the heat network source node and non-source node; T sr (s), T ns (s) are the temperature vectors of the heat network source node and non-source node; J 11 (s), J 12 (s), J 21 (s), J 22 (s) are the partitioned matrices of J(s).

[0154] When T sr (s) is expressed as Equation (2.8), it can be seen from Equation (2.7) that the temperature of the non-source node can be expressed as Equation (2.9):

[0155]

[0156]

[0157] In the formula: ζ sr (s) is the vector of the product of the amplitude and phase shift of the heat source temperature change; ζ sr,r (s) is the product of the amplitude and phase shift of the r-th heat source temperature change; is the unit step response matrix of the heat network.

[0158] The superposition characteristic of the heat network is as Figure 5 shown. The temperature of the non-source node of the heat network can be expressed as the superposition of multiple step responses. The unit step response matrix of the heat network remains unchanged. When the heat source temperature changes, the response change of the non-source node temperature can be calculated through the amplitude, phase and of the change amount, and the total response of the non-source node temperature can be calculated as a whole by superposing the response change amounts.

[0159] The supply heat network and the regenerative heat network satisfy Equation (2.10) at the heat source and Equation (2.11) at the load.

[0160]

[0161]

[0162] In the formula: The temperature vectors of the source nodes of the heating network and the heat regeneration network respectively; The temperature vectors of the non-source nodes of the heating network and the heat regeneration network respectively, calculated by Equation (2.9); ΔT L (s) is the load temperature drop vector; ΔT sc (s) is the heat source temperature rise vector.

[0163] Furthermore, considering the transmission delay, a dynamic power flow tracking model of the heat network is established, and a method for solving the total heat transferred from the source node to the non-source node using a lossless heat transfer model. In this embodiment, the "lossless heat transfer model" calculates the heat power distribution at the heat network load without heat loss, including the heat power actually transferred to the heat network nodes through lossy heat transfer and the heat power lost in the pipeline.

[0164] The temperature distributions under dynamic lossless heat transfer of the heating network and the heat regeneration network are as follows:

[0165]

[0166] In the formula: T noloss,ns (s) is the temperature component matrix of the non-source nodes of the heat network under lossless heat transfer, and the elements of each column are the temperatures of the non-source nodes under the action of a single source node; is the unit step response matrix of the heat network under lossless heat transfer; J noloss22 (s), J noloss21 (s) is the sub-block matrix of the heat network temperature conversion matrix J noloss (s), and the sub-block rule is the same as that of J(s) in Equation (2.7). Among them, J noloss (s) is obtained under the condition of only considering the heat transfer delay of the pipeline in Equation (2.6), that is

[0167]

[0168] In the heat network with quality regulation, the heat at the node is proportional to the temperature, so the temperature action relationship between the source node and the non-source node can represent the heat supply relationship between the source node and the non-source node.

[0169] According to the temperature action relationship between the source node and the non-source node under lossless heat transfer of the dynamic thermal system obtained by Equation (2.12), combined with the carbon intensity relationship between the source node and the non-source node of the steady-state heat network based on the carbon entropy superposition characteristic of the thermal system, the carbon intensity relationship between the source node of the heating network and the non-source node of the heat regeneration network in the dynamic thermal system is established, as shown in Equation (2.13):

[0170]

[0171] In the formula: is the carbon intensity vector of the source node of the heating network and the non-source node of the heat regeneration network; K S 、KR is the carbon entropy transfer matrix of the heat supply network and the heat regeneration network. The non-zero elements in the matrix are the ratios of the total heat including heat loss transmitted from the source node to the non-source node to the actual heat transmitted to the non-source node, and the carbon emissions carried by the transmission loss are allocated to the non-source nodes; are the temperature vectors of the non-heat source nodes of the heat supply network and the heat regeneration network under heat transfer with losses respectively; are the temperature component matrices of the non-heat source nodes of the heat supply network and the heat regeneration network under heat transfer without losses respectively.

[0172] Due to the carbon emission conservation at the heat source, the relationship between the carbon intensity of the heat network source node and the heat source carbon intensity can be further obtained as shown in Equation (2.14).

[0173]

[0174]

[0175] In the formula: and φ in are the diagonal matrices of the heat power flowing out of the heat supply network source node, the diagonal matrix of the heat power flowing out of the non-source nodes of the heat regeneration network, and the diagonal matrix of the heat power injected by the heat source respectively; the heat source carbon intensity e in is a known quantity. The carbon intensities of the remaining nodes in the heat network can be obtained through Equations (2.13) and (2.15).

[0176] Heat load carbon entropy can be obtained from Equation (2.16):

[0177]

[0178] In the formula: is the heat load distribution matrix, and n hl is the number of heat network load nodes; n ns is the number of non-source nodes in the heat network; when a heat load j′ is connected to the non-source node w′ of the heat network, otherwise it is 0.

[0179] (3) For the thermal system, the steady-state carbon entropy model is: decouple the heat network into sub-networks supplied by a single heat source. The heat provided by the single heat source is evenly allocated according to the pipeline mass flow rate after being injected into the pipeline; the temperature vector T of the non-source nodes of the single heat source ns is directly obtained through the product of the transformation matrix J and the source node temperature vector T sr which can distinguish the temperature effects of different sources on the non-source nodes and further distinguish the heat effects of different sources on the non-source nodes. Use the heat transfer model without losses to calculate the total heat transferred from the source node to the non-source node, and establish the carbon entropy relationship between the source node and the non-source node. Establish the carbon emission relationship between the heat supply network and the heat regeneration network through the carbon intensity relationship, and finally obtain the carbon intensity relationship between the heat source node and the load node to establish the steady-state carbon entropy model of the thermal system.

[0180] The carbon entropy of the thermal system consists of two parts: carbon entropy flow and carbon entropy generation. In this embodiment, for a relatively small-scale thermal system, the superposition principle of the thermal system is used to model and solve the carbon entropy model of the steady-state system of mass regulation, and the total carbon entropy transferred from the heat source to the load is directly calculated.

[0181] The meaning of the superposition principle of the thermal system is as follows: According to the mass flow direction and network topology, the heat network is decoupled into sub-networks supplied by a single heat source. The heat provided by a single heat source is evenly distributed according to the mass flow rate of the pipeline after being injected into the pipeline; the temperature components of multiple heat sources acting on the same node can be superimposed.

[0182] The heat supply network and the regenerative heat network of the thermal system can be separated. As Figure 2 shown, the pipelines and nodes pointing from the heat source to the load form the heat supply network; the pipelines and nodes pointing from the load to the heat source form the regenerative heat network. The heat in the heat supply network is transmitted from all heat supply network source nodes to the non-source nodes of the heat supply network (including intermediate nodes and load nodes); the heat in the regenerative heat network is transmitted from all regenerative heat network source nodes to the non-source nodes of the regenerative heat network

[0183] The temperature vector T of the non-source nodes in a single heat network (heat supply network or regenerative heat network) ns can be directly obtained by multiplying the transformation matrix J and the temperature vector T of the source nodes sr , as shown in Equations (3.1) and (3.2).

[0184]

[0185]

[0186] In the formula: M out,sr and M out,ns are the mass flow rate matrices of the heat medium flowing out of the heat network source nodes and the non-source nodes of the heat network respectively; J 11 , J 12 , J 21 , J 22 are the sub-block matrices of the matrix J; k is the conversion coefficient matrix. This method is applicable to both the heat supply network and the regenerative heat network.

[0187] According to the superposition principle of the thermal system, the total thermal power provided by the source nodes to the non-source nodes in the heat network can be calculated by the lossless heat transfer model. It should be noted that in this embodiment, the "lossless heat transfer model" calculates the thermal power distribution at the heat network load without heat loss, which includes the thermal power actually transferred to the heat network nodes through lossy heat transfer and the thermal power lost in the pipeline. The conversion equation of the lossless heat transfer model of the heat supply network is shown in Equation (3.3).

[0188]

[0189] In the formula: is the node temperature component matrix in the lossless heat transfer model; of; is the lossless conversion coefficient matrix of the heat supply network, obtained when the heat loss coefficient is zero; is the known steady-state temperature diagonal matrix under lossy heat transfer; is the mass flow rate matrix of the heat medium flowing out of the non-source nodes of the heat supply network; and are the transformation matrices in the lossless heat transfer model

[0190] In the heat supply network, the total heat power provided by the heat source node k′ to the node i′ is shown in Equation (3.4).

[0191]

[0192] Equation (3.4) analyzes the heat component transferred from a single heat source to the load node, and the carbon entropy transferred during this process is shown in Equation (3.5).

[0193]

[0194] In the formula: c is the specific heat capacity of water; is the mass flow rate of the water flowing out of the non-source node i′ of the heat supply network, is the temperature component of the lossless heat transfer of the heat source node k′ of the heat supply network acting on the non-source node i′ of the heat supply network; is the carbon intensity of the heat source node k′ of the heat supply network.

[0195] The carbon entropy of the non-source node i′ of the heat supply network under the action of multiple heat sources is shown in Equation (3.6).

[0196]

[0197] Define the carbon intensity of the non-source node i′ of the heat supply network as the carbon entropy transferred when unit heat power is output from the non-source node i′ of the heat supply network, as shown in Equation (3.7):

[0198]

[0199] In the formula: is the heat power of the non-source node i′ of the heat supply network in lossy heat transfer; is the actual temperature of the non-source node i′ of the heat supply network in lossy heat transfer, and this variable is a known quantity when the power flow is known.

[0200] Substitute the superposition principle formula (3.2) into formulas (3.5)-(3.7) and extend it to the dimension of the entire thermal system, and the carbon entropy of the system nodes can be obtained as shown in formula (3.8), and the carbon intensity of the nodes can be obtained as shown in formula (3.9).

[0201]

[0202]

[0203] In the formula: is the carbon entropy vector of the node under the action of the heat supply network source node k′ ; is under the combined action of the carbon entropy vector of the node; and are respectively and the carbon intensity vectors of; are respectively the diagonal matrix of the mass flow rate of the outflow water of the set , the diagonal matrix of the steady-state temperature of the set S2 under known lossy heat transfer, and the diagonal matrix of the steady-state temperature of the set under known lossy heat transfer;

[0204] is the lossless conversion coefficient matrix of the heat supply network temperature; K S is defined as the carbon entropy transfer matrix of the heat supply network. When is known, this formula can obtain

[0205] The regenerative heat network is a reverse-flow network of the heat medium after the source and load of the heat supply network are exchanged, as shown in Figure 2 . Use the same method to establish the carbon entropy model of the regenerative heat network as shown in (3.***)-(3.***).

[0206]

[0207]

[0208] The variable definitions in the formula are similar to those of the heat supply network: is the lossless conversion coefficient of the temperature in the regenerative heat network, and the calculation method is the same as that of ; K S is defined as the carbon entropy transfer matrix of the heat supply network of the thermal system.

[0209] On the load side, the carbon intensities of the thermal systems and are equal, as shown in formula (3.12).

[0210]

[0211]

[0212] Where: is carbon intensity vector of is carbon intensity vector of ; K R is the carbon entropy transfer matrix of the feedwater heating network in the thermal system.

[0213] Up to this point, can be deduced from However, in actual calculations, [[ID=2s]]is an unknown quantity, while the carbon intensity vector e of the external heat source in is a known quantity. It is easy to know that there is carbon emission conservation at the heat source. As shown in Equation (3.14), can be calculated through the carbon intensity of the external heat source as shown in Equation (3.15).

[0214]

[0215]

[0216] Where: and φ in are respectively the diagonal matrix of the heat power flowing out of the set the diagonal matrix of the heat power flowing out of the set and the diagonal matrix of the heat power injected by the external heat source. According to Equation (3.15), is obtained, and then the carbon intensity of all system nodes can be calculated. The carbon entropy at the heat load in the thermal system can be obtained from Equation (3.16):

[0217]

[0218] Where: is the heat load distribution matrix, where is the element of the matrix φ L d' is the number of heat loads. When the heat load α' is connected to the heating network node β', otherwise it is 0.

[0219] In step 1, for the power system, a steady-state carbon entropy model is constructed according to the proposed carbon emission sharing framework.

[0220] (4) Steady-state carbon entropy model of the power system: The total carbon entropy transferred to grid node j with power in the power system includes two parts, namely the carbon entropy flow carried by the actual received power of grid node j and the carbon entropy production transferred by transmission losses. The current tracing method is used to determine the source-load relationship of power, and the total carbon entropy transferred by each power source to the grid node is solved respectively. The specific modeling process is as follows:

[0221] Based on the known steady-state power flow of the power system, the current correlation matrix A=(a ij ) n×n is established between the power source nodes and the grid nodes, where n is the number of nodes in the power system, and the expression of the element a ij is:

[0222]

[0223] In the formula and are the current phasors flowing through the grid branch ij and the grid node j respectively; Ω i is the set of power system branches ij with the end node j.

[0224] By expressing the power source current as a matrix according to the injection node, the current component of a single power source acting on the grid node can be obtained as:

[0225]

[0226] In the formula: I N is the matrix of the current components of the grid nodes under the separate action of each power source node, which is an n×g matrix, where g is the number of generators; is the injection current vector of all grid nodes under the separate action of the power source node k; I G is an n×g power source current matrix, where the k-th column vector is the current vector flowing out of the power source node k.

[0227] Given the current component of the power source node acting on the grid node, the power component provided by the power source node to the grid node can be directly obtained as shown in Equation (4.3). The carbon entropy corresponding to the transportation and transfer of this part of the active power is the carbon entropy flow s cf .

[0228]

[0229] In the formula: is the complex power provided by the power source node k to the grid node j; is the conjugate of the current of the power source node k acting on the grid node j; is the full voltage of the grid node j.

[0230] Assume Starting from the power source k, flowing through the path l to the node j, the full voltage loss on the b-th branch in the path l is Then the total loss power from the power source node k to the grid node j is:

[0231]

[0232] Where: Ψ kj is the set of all current flow paths from power source k to grid node j; is the current component of the total current acting on grid node j by power source node k on path l; is the total loss voltage between power source node k and grid node j. The carbon entropy corresponding to the transfer of active power loss in this part is equal to the carbon entropy production s cg . The total complex power provided by power source node k to grid node j is and The sum, as shown in Equation (4.5).

[0233]

[0234] Where: is the voltage of power source node k.

[0235] The carbon entropy analysis method for the power system is as follows:

[0236] Given that the carbon intensity of power source k is The carbon entropy transferred by the active part in Equation (4.5) is the total carbon entropy s transferred from power source node k to grid node j c,j,k , as shown in Equation (4.6). Then, the carbon entropy s of grid node j under the action of all power source nodes in the system c,j As shown in Equation (4.7).

[0237]

[0238]

[0239] Where: is the set of all power sources.

[0240] The carbon intensity e of grid node j j is defined as the carbon entropy transferred when unit power is output from grid node j. Define the node carbon intensity as the carbon entropy transferred when unit power is transmitted by this node, as shown in Equation (4.8).

[0241]

[0242] Where: P j is the power injected into grid node j.

[0243] Generalizing Equations (4.6)-(4.8) to the full system dimension, we get:

[0244]

[0245]

[0246] In the formula: is the node carbon entropy vector of the power grid nodes under the action of the power source node k; is the node carbon entropy vector of the power grid nodes under the action of all power source nodes; e N is the carbon intensity vector of the power grid nodes; e G , P N , U G are respectively the carbon intensity vector of the power source node, the active power injection matrix of the power grid nodes, and the diagonal voltage matrix of the power source nodes; K E is defined as the carbon entropy transfer matrix of the power system.

[0247] The carbon intensity of the power grid nodes can be directly calculated through Equation (4.10). Given the load power moment distribution matrix P L , the carbon entropy at the electrical load is as shown in Equation (4.11).

[0248]

[0249] In the formula: is the electrical load distribution matrix, where is an element of the matrix P L , d is the number of electrical loads. When the electrical load α is connected to the power grid node β, otherwise it is 0.

[0250] (5) Carbon entropy model of coupling equipment: According to the fact that the total carbon emissions remain unchanged while the energy quantity of the input and output of the coupling equipment changes, the carbon entropy relationship between the input and output ends of the coupling equipment is established based on power conservation and carbon emission conservation.

[0251] Energy flows in the pipeline, and the transmission loss increases with the distance, resulting in an increase in the carbon entropy of the energy flow received at the end. Heterogeneous energy depends on the coupling equipment to achieve energy conversion. The effects of the loss and conversion ability of the coupling equipment on carbon entropy are all concentrated in the conversion efficiency η, which is independent of the transmission distance. Regarding the coupling equipment as a node, the energy quantity of the input and output of the coupling equipment changes but the total carbon emissions remain unchanged. When η is low, the carbon entropy of the unit energy flow after conversion increases, and the port carbon intensity is high; when η is high, the carbon entropy of the unit energy flow after conversion decreases, and the port carbon intensity is low.

[0252] (5.1) Single-input single-output coupling equipment

[0253] The schematic diagram of the input and output of the equipment is shown in Figure 6(a), P in , P out are the input and output powers of the coupling equipment; e in , e out are the carbon intensities of the input and output energies. Its power satisfies Equation (5.0), and the carbon entropy conversion is as shown in Equation (5.1).

[0254] Pout = ηP in (5.0)

[0255]

[0256] where is the carbon entropy at the input and output of the coupling device.

[0257] After flowing through the node of the coupling device, the carbon intensity is as shown in Equation (5.2), where: 1η is the carbon entropy transfer coefficient of the single-input and single-output device.

[0258] e out = e in / η (5.2)

[0259] The models of heat pump (HP) and gas boiler (GB) can be established according to the above method.

[0260] (5.2) Single-input and multi-output coupling device

[0261] Taking the combined heat and power (CHP) device as an example to model the single-input and multi-output coupling device, its input-output schematic diagram is shown in Figure 6(b), P out,e 、P out,h are the electrical and thermal powers output by the CHP device; e out,e 、e out,h are the carbon intensities of the electrical energy and thermal energy output by the CHP device; η e 、η h are the power generation and heat production efficiency coefficients of the CHP device.

[0262] It is known that the device satisfies power conservation and carbon emission conservation, as shown in Equation (5.3), and after rearrangement, Equation (5.4) can be obtained.

[0263]

[0264] e in = e out,e η e + e out,h η h (5.4)

[0265] where: e out,e and e out,h are unknowns. Assuming that the carbon entropy of the CHP device for generating unit power of electricity and heat is the same, i.e., e out,e = e out,h , then Equation (5.5) can be obtained, and 1 / (η e + η h ) is the carbon entropy transfer coefficient of the single-input and multi-output device.

[0266] e out,e = e out,h = e in / (η e + η h ) (5.5)

[0267] The entire integrated energy system is an organic whole. Through the established carbon entropy models above, the flowchart for carbon entropy analysis and calculation is as Figure 7 shown. In the figure, the CHP equipment and GB are the load nodes of the natural gas system, the HP is the load node of the power system, and the CHP equipment is the source node of the power system. The CHP equipment, GB, and HP are the source nodes of the thermal system. There is a sequential relationship in the calculation process. Specifically, for the integrated energy system, the process of carbon emission analysis using carbon trajectory tracking is as follows:

[0268] Step S1: Obtain the dynamic power flow of the integrated energy system;

[0269] Step S2: Obtain the established dynamic carbon entropy model of the natural gas system. Combining with the carbon intensity of the gas source, calculate the carbon entropy of the natural gas system load and the carbon intensity of the load nodes, that is, obtain the carbon intensity of the CHP equipment and GB equipment;

[0270] Step S3: Obtain the established steady-state carbon entropy model of the power system. Combining with the carbon intensity of the power source and the carbon intensity of the natural gas system load nodes, calculate the carbon entropy of the power system load nodes and the carbon intensity of the load nodes, that is, obtain the carbon intensity of the HP equipment;

[0271] Step S4: According to the scale of the thermal system, select either the steady-state carbon entropy model or the dynamic carbon entropy model for the thermal system. Combining with the calculated carbon entropy of the natural gas system load nodes and the carbon intensity of the power system load nodes, calculate the carbon intensity of the heat supply network source nodes and non-source nodes;

[0272] Step S5: According to the obtained carbon entropy and carbon intensity of the natural gas system load nodes, the carbon entropy and carbon intensity of the power system load nodes, and the carbon intensity of the heat supply network source nodes and non-source nodes, obtain the carbon entropy distribution of the load nodes of the integrated energy system.

[0273] To illustrate the effect of the method in this embodiment, a simulation experiment was conducted as follows.

[0274] This method takes the integrated energy system in a certain area of Jilin Province as an example to establish a dynamic power flow and carbon entropy model.

[0275] The carbon intensity of the power system nodes is as Figure 8 shown, and the carbon entropy of the power system load nodes is as Figure 9As shown, the load at Node 4 is powered by a coal-fired unit, and its node carbon intensity and load carbon entropy are relatively high; since the load at Node 2 is powered by a wind turbine, its carbon entropy is 0; the carbon intensities of load nodes 6 and 7 are not much different, but due to the difference in the electrical load capacity, the difference in their carbon entropy is relatively obvious.

[0276] The node carbon intensity data of the natural gas system is shown in the figure. The solid line is the node carbon intensity obtained by using the dynamic carbon entropy analysis method proposed in this method, and the dashed line is the node carbon intensity obtained by using the steady-state carbon trajectory tracking method. There is a large change in the carbon intensity of gas load nodes 3 and 5 at 1 h because the gas supply ratio γ of the gas source at the node at t = 0 f,j,0 differs greatly from the gas supply ratio after 1 h, and the gas load of load nodes 3 and 5 remains unchanged within 0 - 1 h. Therefore, γ f,j,0 is used to calculate the load carbon entropy during this period; there is a peak in the carbon intensity of this node during the subsequent dynamic process because the pipe storage carbon intensity in the initial state of the system is relatively high. When the load uses the gas flow in the pipe storage, the carbon entropy increases. The dynamic carbon intensity of Node 1 remains unchanged because the gas flow rate of the load at this node remains unchanged, and the two gas sources have been supplying gas to the load at a fixed ratio.

[0277] From Figure 10 it can be seen that using the steady-state carbon trajectory tracking method to solve the node carbon intensity of the dynamic natural gas system will produce a large error, with a maximum error of 53.947%. This is because the carbon entropy method proposed in this method can allocate the source transfer carbon entropy to the load according to the gas flow response of the source to the load, while the steady-state carbon trajectory tracking method cannot do this.

[0278] The carbon entropy and power components of the load nodes in the natural gas system are shown in Figure 12 . Using the dynamic carbon entropy analysis method of the natural gas system proposed in this method, the carbon entropy sources of each gas load can be distinguished. The figure includes: the carbon entropy transferred from gas source 4 to the gas load node; the carbon entropy transferred from gas source 7 to the gas load node; the carbon entropy transferred from the pipe storage to the gas load node. From Figure 12 it can be seen that the carbon entropy source at load node 3 is mainly gas source 4. The carbon entropy calculated by using the steady-state method differs greatly from the dynamic method, with an average error of 35.638% and a maximum error of 44.584%. The maximum difference in carbon emissions per hour is 4.617 tCO2.

[0279] The node carbon intensity data of the thermal system is as Figure 11 shown. It can be seen from the figure that there is also a large gap in calculating the carbon intensity by using the steady-state carbon trajectory tracking method in the dynamic system. The reason for this phenomenon is different from that of the natural gas system. This is because there is a long-time lag in the heat transfer process in the thermal system, and the steady-state carbon trajectory tracking method directly corresponds the heat input at the current moment to the load output at that moment during the power flow tracking, without considering that this part of the heat has not yet been transferred to the load.

[0280] The carbon entropy and power components of the total load at the nodes of the thermal system are shown in Figure 13 . The dynamic carbon entropy analysis method of the thermal system proposed by this method can distinguish the sources of carbon entropy at the heat load nodes. The figure includes: the carbon entropy transferred from heat source node 1 to the heat load node; the carbon entropy transferred from heat source node 47 to the heat load node; the carbon entropy transferred from heat source node 53 to the heat load node. From Figure 13 the figure, it can be seen that the main source of carbon entropy at heat load node 18 is heat source node 1, and the effect of heat source node 53 is the smallest, both originating from the integration of heat at the regenerative network nodes. The carbon entropy obtained by using the steady-state carbon trajectory tracking method has a large difference from the dynamic carbon entropy method. The average error is 3.452%, the maximum error is 23.041%, the maximum carbon emission difference per hour is 38.833 kgCO2, and the cumulative carbon entropy difference in 12 hours is 90.978 kgCO2.

[0281] The dynamic carbon entropy analysis method proposed in this embodiment can not only calculate the overall carbon index of the load, but also analyze the carbon entropy components of the load and distinguish the carbon emission effects of different sources on the load. In addition, compared with the separate use of the steady-state carbon trajectory tracking method, the integrated dynamic carbon entropy analysis method can more accurately define the carbon emission responsibilities of users in an integrated energy system with large load changes and long dynamic processes, and form accurate carbon indicators to guide user energy consumption.

[0282] Embodiment 2

[0283] Based on Embodiment 1, a steady-state and dynamic carbon entropy determination system for an integrated energy system based on the superposition principle is provided in this embodiment, including:

[0284] Model construction module: configured to propose a steady-state carbon entropy model and a dynamic carbon entropy model for the subsystems of the integrated energy respectively based on the carbon emission sharing framework determined by carbon emission conservation and the superposition of non-homologous sources;

[0285] Carbon trajectory tracking and solving module: configured to perform carbon entropy analysis and calculation based on carbon trajectory tracking using the steady-state carbon entropy model for the subsystems of the integrated energy with load changes less than the set value and dynamic processes less than the set duration to obtain the carbon entropy distribution;

[0286] The carbon trajectory tracking and solving module is also configured to perform carbon entropy analysis and calculation based on carbon trajectory tracking using the dynamic carbon entropy model for the subsystems of the integrated energy with load changes not less than the set value, dynamic processes not less than the set duration, and time scales differing by more than the threshold to obtain the carbon entropy distribution.

[0287] Furthermore, the carbon trajectory tracking and solving module further includes:

[0288] Data acquisition module: configured to obtain the dynamic power flow of the integrated energy system;

[0289] Natural gas system carbon entropy model solving module: configured to obtain the constructed dynamic carbon entropy model of the natural gas system, and combine with the carbon intensity of the gas source to calculate the carbon entropy of the natural gas system load and the carbon intensity of the load node, that is, obtain the carbon intensity of the CHP equipment and the GB equipment;

[0290] Power system carbon entropy model solving module: configured to obtain the constructed steady-state carbon entropy model of the power system, and combine with the carbon intensity of the power source and the carbon intensity of the natural gas system load node to calculate the carbon entropy of the power system load node and the carbon intensity of the load node, that is, obtain the carbon intensity of the HP equipment;

[0291] Thermal system carbon entropy model solving module: configured to select a steady-state carbon entropy model or a dynamic carbon entropy model for the thermal system according to the scale of the thermal system, and combine with the calculated carbon entropy of the natural gas system load node and the carbon intensity of the power system load node to calculate the carbon intensity of the heat supply network source node and the non-source node;

[0292] Carbon entropy distribution determination module: configured to obtain the carbon entropy distribution of the load nodes of the integrated energy system according to the carbon entropy and carbon intensity of the natural gas system load nodes obtained, the carbon entropy and carbon intensity of the power system load nodes, and the carbon intensity of the heat supply network source nodes and non-source nodes.

[0293] It should be noted here that each module in this embodiment corresponds to each step in Embodiment 1, and its specific implementation process is the same, so it will not be repeated here.

[0294] Embodiment 3

[0295] Based on Embodiment 1, this embodiment provides an electronic device, including a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the steps described in the method of Embodiment 1 are completed.

[0296] The above are only the preferred embodiments of the present disclosure and are not used to limit the present disclosure. For those skilled in the art, the present disclosure can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present disclosure shall be included within the protection scope of the present disclosure.

[0297] Although the specific implementation manners of the present disclosure are described above in conjunction with the accompanying drawings, it is not a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications or deformations that can be made without creative labor on the basis of the technical solutions of the present disclosure are still within the protection scope of the present disclosure.

Claims

1. A method for determining the steady-state and dynamic carbon entropy of an integrated energy system based on the superposition principle, characterized in that, It includes the following steps: Based on the carbon emission sharing framework determined by carbon emission conservation and the superposition of non-homologous components, a steady-state carbon entropy model and a dynamic carbon entropy model are respectively proposed for the subsystems of the integrated energy system; For the subsystems of the integrated energy system where the load change is less than the set value and the dynamic process is less than the set duration, the steady-state carbon entropy model is adopted, and carbon entropy analysis and calculation are carried out based on carbon trajectory tracking to obtain the carbon entropy distribution; For the subsystems of the integrated energy system where the load change is not less than the set value, the dynamic process is not less than the set duration, and the time scale difference is greater than the threshold, the dynamic carbon entropy model is adopted, and carbon entropy analysis and calculation are carried out based on carbon trajectory tracking to obtain the carbon entropy distribution; The steady-state carbon entropy model of the power system is as follows: In the power system, the total carbon entropy transferred to the grid nodes along with the power includes two parts, namely, the carbon entropy flow carried by the actual received power of the grid nodes and the carbon entropy production transferred by the transmission loss. The current tracing method is used to determine the source-load relationship of the power, and the total carbon entropy transferred by each power source to the grid nodes is solved separately; The total carbon entropy transferred to the grid nodes along with the power includes two parts, namely, the carbon entropy flow carried by the actual received power of the grid nodes and the carbon entropy production transferred by the transmission loss. The current tracing method is used to determine the source-load relationship of the power, and the total carbon entropy transferred by each power source to the grid nodes is solved separately; The total carbon entropy transferred to the grid nodes along with the power includes two parts, namely, the carbon entropy flow carried by the actual received power of the grid nodes and the carbon entropy production transferred by the transmission loss. The current tracing method is used to determine the source-load relationship of the power, and the total carbon entropy transferred by each power source to the grid nodes is solved separately; The coupled equipment carbon entropy model is as follows: According to the fact that the total carbon emission remains unchanged while the energy quantity input and output of the coupled equipment change, the carbon entropy relationship between the input end and the output end of the coupled equipment is established based on power conservation and carbon emission conservation; For the integrated energy system, the carbon emission analysis using carbon trajectory tracking is as follows: Step S1: Obtain the dynamic power flow of the integrated energy system; Step S2: Obtain the constructed dynamic carbon entropy model of the natural gas system, and combine it with the carbon intensity of the gas source to calculate the carbon entropy of the natural gas system load and the carbon intensity of the load node; Step S3: Obtain the constructed steady-state carbon entropy model of the power system, and combine it with the carbon intensity of the power source and the carbon intensity of the load node of the natural gas system to calculate the carbon entropy of the power system load node and the carbon intensity of the load node; Step S4: According to the scale of the thermal system, select the steady-state carbon entropy model or the dynamic carbon entropy model for the thermal system, and combine the calculated carbon entropy of the load node of the natural gas system and the carbon intensity of the load node of the power system to calculate the carbon intensity of the source node and the non-source node of the heat supply network; Step S5: According to the obtained carbon entropy and carbon intensity of the load node of the natural gas system, the carbon entropy and carbon intensity of the load node of the power system, and the carbon intensity of the source node and the non-source node of the heat supply network, obtain the carbon entropy distribution of the load node of the integrated energy system.

2. The steady-state and dynamic carbon entropy determination method for the integrated energy system based on the superposition principle according to claim 1, characterized in that: The subsystems of the integrated energy system include a power system, a thermal system, a natural gas system, and a coupling system; The steady-state carbon entropy model is adopted for carbon emission analysis of the power system and the coupling system; The dynamic carbon entropy model is adopted for carbon emission analysis of the natural gas system; For the thermal system, the steady-state carbon entropy model or the dynamic carbon entropy model is selected according to the load change, the duration of the dynamic process, and the magnitude of the time scale difference.

3. The steady-state and dynamic carbon entropy determination method for an integrated energy system based on the superposition principle according to claim 1, characterized in that: When constructing the steady-state carbon trajectory tracking model and the dynamic carbon trajectory tracking model, power and carbon emissions are directly separated from the source side, the power and carbon emission transfer relationships of different sources to the load are clarified, and the carbon emissions carried by energy and losses are reasonably shared.

4. The method for determining the steady-state and dynamic carbon entropy of an integrated energy system based on the superposition principle according to claim 1, wherein: For the natural gas system, the dynamic carbon entropy model is as follows: The mass flow rate of the gas flow at the end of the gas network pipeline and the gas pressure at the head of the pipeline are expressed as the superposition of multiple step functions. The mass flow rate of the gas flow at the head of the gas network pipeline and the gas pressure at the end of the pipeline are expressed as the superposition of a finite number of step responses. Only considering the gas flow component from the gas source node to the load node, a relationship between the gas network source and load of the natural gas system considering the load change and gas source change is constructed to form a dynamic power flow tracking model of the natural gas system. According to the power flow tracking model, considering the natural gas stored in the pipeline and distinguishing the gas supply relationship of different gas sources to the gas load, a dynamic carbon entropy model of the natural gas system is established.

5. The method for determining the steady-state and dynamic carbon entropy of an integrated energy system based on the superposition principle according to claim 1, characterized in that: For the thermal system, the dynamic carbon entropy model is as follows: The temperature at the head of the pipeline is expressed as the superposition of step excitations, and the temperature at the end of the heat network pipeline is expressed as the superposition of step responses. According to the fact that the heat quantity at the node is proportional to the temperature, the temperature interaction relationship between the source node and the non-source node is expressed as the heat supply relationship between the source node and the non-source node. Based on the carbon entropy superposition characteristics of the thermal system, the carbon intensity relationship between the steady-state heat network source node and the non-source node is deduced to determine the carbon entropy relationship between the source node and the load node.

6. The steady-state and dynamic carbon entropy determination method for an integrated energy system based on the superposition principle according to claim 1, characterized in that: For the thermal system, the steady-state carbon entropy model is as follows: decouple the heat network into sub-networks supplied by a single heat source, and the heat provided by the single heat source is evenly distributed according to the pipeline mass flow rate after being injected into the pipeline; the temperature vector of non-source nodes of a single heat source is directly obtained through the transformation matrix and the temperature vector of source nodes by taking the product. By distinguishing the temperature effects of different sources on non-source nodes and the heat effects of different sources on non-source nodes, calculate the total heat transferred from source nodes to non-source nodes, establish the carbon entropy relationship between source nodes and non-source nodes, establish the carbon emission relationship between the heat supply network and the heat regeneration network through the carbon intensity relationship, and finally obtain the carbon intensity relationship between the heat source nodes and the load nodes to establish the steady-state carbon entropy model of the thermal system.

7. A steady-state and dynamic carbon entropy determination system for an integrated energy system based on the superposition principle, characterized in that Including: Model construction module: Configured to propose a steady-state carbon entropy model and a dynamic carbon entropy model for the subsystems of the integrated energy respectively based on the carbon emission sharing framework determined by carbon emission conservation and the superposition of non-homogeneous sources. Carbon trajectory tracking and solving module: Configured to, for the subsystems of the integrated energy with a load change less than the set value and a dynamic process less than the set duration, use the steady-state carbon entropy model to perform carbon entropy analysis and calculation based on carbon trajectory tracking to obtain the carbon entropy distribution. The carbon trajectory tracking and solving module is also configured to, for the subsystems of the integrated energy with a load change not less than the set value, a dynamic process not less than the set duration, and a time scale difference greater than the threshold, use the dynamic carbon entropy model to perform carbon entropy analysis and calculation based on carbon trajectory tracking to obtain the carbon entropy distribution. The steady-state carbon entropy model of the power system is as follows: In the power system, the total carbon entropy transferred to the grid nodes along with the power includes two parts, namely, the carbon entropy flow carried by the actual received power of the grid nodes and the carbon entropy production transferred by the transmission loss. The current tracing method is used to determine the source-load relationship of the power, and the total carbon entropy transferred by each power source to the grid nodes is solved separately; The total carbon entropy transferred to the grid nodes along with the power includes two parts, namely, the carbon entropy flow carried by the actual received power of the grid nodes and the carbon entropy production transferred by the transmission loss. The current tracing method is used to determine the source-load relationship of the power, and the total carbon entropy transferred by each power source to the grid nodes is solved separately; The total carbon entropy transferred to the grid nodes along with the power includes two parts, namely, the carbon entropy flow carried by the actual received power of the grid nodes and the carbon entropy production transferred by the transmission loss. The current tracing method is used to determine the source-load relationship of the power, and the total carbon entropy transferred by each power source to the grid nodes is solved separately; The carbon entropy model of the coupling device is as follows: According to the fact that the total carbon emission remains unchanged while the energy quantity input and output of the coupling device change, the carbon entropy relationship between the input end and the output end of the coupling device is established based on power conservation and carbon emission conservation. The carbon trajectory tracking and solving module also includes: Data acquisition module: Configured to obtain the dynamic power flow of the integrated energy system. Natural gas system carbon entropy model solving module: Configured to obtain the constructed dynamic carbon entropy model of the natural gas system, and combine it with the carbon intensity of the gas source to calculate the carbon entropy of the natural gas system load and the carbon intensity of the load node. Power system carbon entropy model solving module: Configured to obtain the constructed steady-state carbon entropy model of the power system, and combine it with the carbon intensity of the power source and the carbon intensity of the load node of the natural gas system to calculate the carbon entropy of the load node of the power system and the carbon intensity of the load node. Thermal system carbon entropy model solving module: Configured to, according to the scale of the thermal system, select whether to use the steady-state carbon entropy model or the dynamic carbon entropy model for the thermal system, and combine the calculated carbon entropy of the load node of the natural gas system and the carbon intensity of the load node of the power system to calculate the carbon intensity of the heat network source node and the non-source node. Carbon entropy distribution determination module: Configured to obtain the carbon entropy distribution of the load nodes in the integrated energy system based on the carbon entropy and carbon intensity of the load nodes in the natural gas system, the carbon entropy and carbon intensity of the load nodes in the power system, and the carbon intensity of the heat supply network source nodes and non-source nodes.

8. An electronic device, characterized in that, It includes a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the steps described in the method according to any one of claims 1-7 are completed.

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