Method for modeling carbon conversion process of heating system and low-carbon optimization operation scheduling decision

Abstract

The present application relates to a kind of carbon conversion process modeling and low carbon optimization operation scheduling decision-making method for heating system, it includes: determining the nature of heating system and carbon emission boundary;Carbon conversion process model is constructed based on process flow modeling software;The model of heating system is identified and corrected;Parameter optimization is carried out to heating system;Carbon flow footprint in process flow and between equipment is simulated and calculated layer by layer;According to optimization demand, different levels of total expected income target function and constraint condition are established;Low carbon optimization operation scheduling decision-making model is constructed by process flow model, constraint condition and target function;According to real-time scheduling command, each heat source unit heating mode is optimized in real time based on optimization scheduling decision-making model, and thermal power output is scheduled, different heat source heating mode and carbon emission quota are fully considered, carbon flow footprint is shown, parameter optimization of heat source unit and real-time optimization of thermal load are realized, so that optimization scheduling result considers economy and environmental protection.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of low-carbon intelligent regulation and control of urban central heating systems, and particularly relates to a method for modeling carbon conversion processes of a heating system and low-carbon optimal operation scheduling decision-making. BACKGROUND

[0002] Extreme weather caused by the greenhouse effect is becoming increasingly serious, and at the same time, in order to achieve the goal of global temperature rise of 1.5℃, China formally proposed the goal of reaching the peak of carbon emissions by 2030 and achieving carbon neutrality by 2060 at the 75th United Nations General Assembly, prompting the formal opening of the national carbon trading market, and the gradual improvement of the carbon trading system.

[0003] At present, the National Development and Reform Commission has issued relevant policies for carbon emission accounting, and various provinces have responded by introducing a series of carbon dioxide emission accounting requirements. The emission factor method, the measurement method and the mass balance method are the three most widely used methods. The emission factor method is suitable for rough macro calculation of specific regions, such as the country, province and city. The measurement method is based on measuring carbon emission data from emission sources, and the calculation is relatively accurate, which is suitable for key carbon emission source industries such as thermal power plants and steel plants. However, the above two methods are based on the idea of total input and total output of carbon emissions, and cannot distinguish the differences between various equipment, and the requirements for detection equipment are relatively strict. The mass balance method can calculate carbon emissions based on specific equipment and process flow, which is beneficial to comparing the advantages and disadvantages of different equipment and process flow, but it is not intuitive. The power generation industry, as the main source of carbon emissions, has a complex system, and it is difficult to accurately and intuitively calculate carbon emissions and carbon flow footprint. Although the "Provincial Greenhouse Gas Inventory Compilation Guide (Trial)" proposes a calculation method that is divided by department, fuel type and equipment, it is difficult to implement in detail, and the emission factor method is still used uniformly. This method ignores factors such as regional energy quality differences and unit combustion efficiency differences, which is not conducive to the long-term development of the carbon trading market.

[0004] The heating system is closely related to the power generation industry, and the resulting carbon emissions need to be controlled. It is of utmost importance to find an effective method for calculating carbon emissions of different equipment, process flow and heating and power generation modes, and based on this method, low-carbon optimal operation scheduling can be carried out in an orderly manner. The current operation scheduling method at the heat source side is still based on artificial decision-making, which is simple but cannot balance economic efficiency and environmental protection. With the introduction of the "double carbon" policy and the concept of urban brain, the advantages of single heat source, multi-heat source low-carbon real-time optimal scheduling decision-making will gradually emerge.

[0005] Therefore, based on the above technical problems, a method for modeling carbon conversion processes of a heating system and low-carbon optimal operation scheduling decision-making is needed. SUMMARY

[0006] The application aims to provide a method for modeling carbon conversion process of a heating system and making low-carbon optimization operation scheduling decisions.

[0007] To solve the above technical problems, the application provides a method for modeling carbon conversion process of a heating system and making low-carbon optimization operation scheduling decisions, comprising the following steps.

[0008] determining the properties of the heating system and the carbon emission boundary;

[0009] constructing carbon conversion process models of devices and process flows of single heat source, multiple heat sources and heat networks based on process flow modeling software;

[0010] carrying out identification correction on the heating system model;

[0011] optimizing parameters of the heating system;

[0012] simulating and calculating carbon flow footprints among devices and in process flows layer by layer;

[0013] establishing total expected benefit target functions and constraint conditions of different levels according to optimization requirements;

[0014] constructing a low-carbon optimization operation scheduling decision model through the process flow model, constraint conditions and target functions;

[0015] based on the optimization scheduling decision model, optimizing the heating mode of each heat source unit and scheduling the thermal power output in real time according to real-time scheduling commands.

[0016] Further, the method for determining the properties of the heating system comprises the following steps.

[0017] analyzing the heating mode type according to the heat balance diagram or the DCS operation interface of the heating system to determine the properties of the heating system;

[0018] The method for determining the carbon emission boundary of the heating system comprises the following steps.

[0019] determining the properties of the heating system, determining the carbon emission sources according to the properties of the heating system, and determining the carbon emission boundary of the heating system, i.e. carbon inflow factors and carbon outflow factors;

[0020] The carbon inflow factors include combustion of fossil fuels, purchased power, purchased heat, purchased steam and other carbon-containing substances.

[0021] The carbon outflow factors include external power supply, external heat supply, external steam supply, carbon capture and other carbon-containing products.

[0022] For specific devices inside the heating system, the factors for considering carbon emissions include carbon inflow, carbon outflow factors and device performance factors.

[0023] Further, the method for constructing a carbon conversion process model of a single heat source, multiple heat sources and a heat network by the process flow modeling software comprises:

[0024] determining the process flow modeling software according to the nature of the heating system;

[0025] determining the modeling object according to the actual demand, and the modeling object is divided into a single heat source, multiple heat sources and a heat network;

[0026] the carbon conversion process modeling of the single heat source, multiple heat sources and heat network comprises: equipment-level modeling and process flow modeling, namely

[0027] equipment-level modeling: modeling the equipment inside the heating system, considering the carbon emissions generated after the material flow passes through the equipment, and splitting the equipment placed in the process flow black box, comprehensively considering the nature factors of the equipment, and simulating and calculating the carbon emissions;

[0028] process flow modeling: modeling the process flow of the heating system, regarding the process flow as a black box, and simulating and calculating the carbon emissions of the entire system under different heating modes from a macro perspective;

[0029] the single heat source modeling process based on the process flow modeling software comprises:

[0030] modeling the equipment according to the nature factors of the heating equipment, determining the input and output parameters of different equipment of the heating system and the initial values of the input parameters under the rated working condition, and constructing the equipment model; based on the equipment model, connecting the steam-water flow process, and constructing the carbon conversion model of the entire process flow, combining macro modeling and detailed modeling;

[0031] the input and output parameters are:

[0032] wherein, X is an input parameter; Y is an output parameter; i is the i th input parameter of the heat source unit; j is the j th output parameter of the heat source unit; and m is the m th heat source.

[0033] the modeling object is a single heat source system, and the single heat source system internally comprises different unit types, and the unit is composed of multiple equipment;

[0034] multiple single heat source systems constitute a multiple heat source system, all heat sources are included in the heating pipe network to constitute the entire heat network, and the carbon emissions of the multiple heat sources and the heat network are simulated and analyzed by means of step-by-step modeling, namely

[0035] multiple heat source modeling: determining the categories and nature of each heat source in the multiple heat sources of the central heating system, and modeling one by one according to the single heat source modeling mode;

[0036] heat network modeling: after the multiple heat source modeling is completed, the pipe and pump are connected to construct the entire heat network model.

[0037] Further, the method for identifying and correcting the heating system model comprises:

[0038] Based on the operation data of the distributed control system, the parameter data sample set of each working condition relationship model is established, and the equipment model of the heating system unit is identified and corrected:

[0039] The operation data of the DCS system is cleaned by a data cleaning algorithm to determine the correct parameter data sample set, and the parameter curve of the equipment model is corrected:

[0040] N / ND=f(M / MD)

[0041] Where N is the parameter to be corrected; ND is the nominal value of the corrected parameter; M is the flow rate through the equipment; and MD is the nominal value of the flow rate through the equipment.

[0042] Based on the parameter data sample set, the model of each equipment is identified and corrected, and the unit process flow model strictly consistent with the actual operation condition of the heating system is constructed.

[0043] Further, the method for optimizing the parameters of the heating system comprises:

[0044] According to the input parameter X i , the control variable is used to adjust the parameter range [MinX i ,MaxX i ], and the sensitivity analysis of the output parameter Y j is carried out;

[0045] Determine the relationship between a single input parameter and an output parameter, and obtain the optimal optimized adjustment parameter range according to the sensitivity analysis curve;

[0046] Y i =f(X i ,(X1,X2,......,X i-1 ,X i+1 ,......));

[0047] Where, set (X1, X2,..., X i-1 , X i+1 ,...) input variables as constant values;

[0048] According to the optimization result, the single equipment and single unit of the single heat source of the heating system are overall debugged, and the lowest carbon emission is ensured on the basis of considering the thermal power output.

[0049] Further, the method for simulating and calculating the carbon flow footprint between the equipment and the process flow layer by layer comprises:

[0050] Model identification ensures to establish a device and process flow model strictly in accordance with the actual operation, to simulate the device model, and to obtain the carbon emission of the specific device;

[0051] Simulate the entire process flow model, obtain the carbon flow footprint of all devices in the process flow, and obtain the overall carbon emission of the entire heating system;

[0052] Combine macro simulation calculation and detailed simulation calculation.

[0053] Further, the method for establishing different levels of total expected revenue target functions and constraint conditions according to optimization requirements comprises:

[0054] Determine the optimization level, which includes the expected revenue target functions of different heating modes in each heat source system and the total expected revenue target functions of the heating modes of each heat source system;

[0055] The expected revenue target functions of different heating modes in each heat source system include:

[0056]

[0057] Wherein, P k is the electric load of the Kth unit, with the unit of MW; H k is the thermal load of the Kth unit, with the unit of MW; k is the kth unit of the heat source; n is the number of units in the heat source; b k is the unit electric load conversion cost coefficient; c k is the unit thermal load conversion cost coefficient; m k is the unit carbon emission and carbon quota conversion cost coefficient; C outk is the CO2 emission of the unit, with the unit of t;

[0058] The total expected revenue of the heating modes of each heat source system includes:

[0059]

[0060] Wherein, g is the gth heat source; m is the number of heat sources; s g is the heat source thermal load conversion cost coefficient; H g is the thermal load of the gth heat source, with the unit of MW; q g is the carbon emission and carbon quota conversion cost coefficient; C outg is the heating conversion CO2 emission, with the unit of t;

[0061] When the operation scheduling period is E, the target functions of the two levels are:

[0062]

[0063] Wherein, t is the tth time period of the Eth operation scheduling cycle, unit is h; b kt is the electricity load conversion cost coefficient of the unit in the tth time period; P Kt is the power generation in the tth time period, unit is MW; c kt is the heat load conversion cost coefficient of the unit in the tth time period; H Kt is the heat load in the tth time period, unit is MW; m kt is the carbon emission and carbon quota conversion cost coefficient of the unit in the tth time period; C outkt is the CO2 emission of the unit in the tth time period, unit is t;

[0064]

[0065] Wherein, s gt is the heat source heat load conversion cost coefficient in the tth time period; H gt is the heat load in the tth time period, unit is MW; q gt is the carbon emission and carbon quota conversion cost coefficient in the tth time period; C outgt is the heat supply conversion CO2 emission in the tth time period, unit is t.

[0066] The constraint conditions include mainly equal constraint and inequality constraint:

[0067]

[0068] minS≤S k ≤maxS;

[0069] Wherein, Z is the parameter meeting the equal constraint; S is the parameter meeting the inequality constraint.

[0070] Further, the method for constructing the low-carbon optimization operation scheduling decision model through the process flow model, the constraint condition and the objective function comprises:

[0071] Establishing the process flow model, determining the constraint condition and the objective function according to the demand;

[0072] Determining the optimization algorithm according to the type of the objective function and the constraint condition;

[0073] Writing the optimization calculation code;

[0074] The optimization result is: the heat supply system optimization scheduling scheme, the carbon emission, the heat and power output.

[0075] Further, the optimization scheduling decision model is based on real-time scheduling commands to optimize the heating mode of each heat source unit in real time and to schedule the thermal power output.

[0076] According to the real-time scheduling command, the constraint condition is modified, the optimization scheduling decision model is debugged, and the optimization scheme is output.

[0077] In another aspect, the present application also provides a heating system carbon conversion process modeling and low-carbon optimization operation scheduling decision system, comprising:

[0078] A boundary determination module determines the nature and carbon emission boundary of the heating system.

[0079] A heating model construction module constructs a carbon conversion process model of the equipment and process flow of single heat source, multiple heat sources and heat network.

[0080] An identification correction module corrects the model of the heating system.

[0081] A parameter optimization module optimizes the parameters of the heating system.

[0082] A simulation calculation module simulates the carbon flow footprint between the equipment and the process flow layer by layer.

[0083] A function construction module establishes different levels of total expected income objective functions according to optimization requirements.

[0084] A constraint condition module establishes the constraint conditions of carbon emission quota and thermal power output of each heat source heating system.

[0085] A decision model construction module constructs a low-carbon optimization operation scheduling decision model through the process flow model, constraint condition and objective function.

[0086] A scheduling module based on the optimization scheduling decision model, according to the real-time scheduling command, optimizes the heating mode of each heat source unit in real time and schedules the thermal power output.

[0087] The beneficial effects of the present application are that the present application determines the heat supply system property and the carbon emission boundary; constructs the equipment and process flow carbon conversion process model of single heat source, multiple heat sources and heat network based on process flow modeling software; corrects the heat supply system model through identification; optimizes the parameters of the heat supply system; simulates and calculates the carbon flow footprint between equipment and in the process flow layer by layer; establishes the total expected income target function and constraint condition of different levels according to the optimization demand; constructs the low-carbon optimization operation scheduling decision model through the process flow model, constraint condition and target function; based on the optimization scheduling decision model, according to the real-time scheduling command, the heat supply mode of each heat source unit is optimized in real time, and the thermal power output is scheduled, which fully considers the heat supply mode of different heat sources and the carbon emission quota, realizes the parameter optimization of the heat source unit and the real-time optimization of the thermal power load, so that the optimization scheduling result considers the economy and environmental protection.

[0088] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application.

[0089] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the following preferred embodiments are specifically described, and the accompanying drawings are described in detail as follows. BRIEF DESCRIPTION OF DRAWINGS

[0090] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0091] Figure 1 is the flow chart of the method of heat supply system carbon conversion process modeling and low-carbon optimization operation scheduling decision in the present application;

[0092] Figure 2 is the first specific flow chart of the method of heat supply system carbon conversion process modeling and low-carbon optimization operation scheduling decision in the present application;

[0093] Figure 3 is the second specific flow chart of the method of heat supply system carbon conversion process modeling and low-carbon optimization operation scheduling decision in the present application;

[0094] Figure 4 is the carbon emission schematic diagram of the heat supply system in the present application. DETAILED DESCRIPTION

[0095] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0096] Example 1

[0097] like Figures 1-4 As shown in Embodiment 1, this method provides a modeling method for carbon conversion processes in heating systems and a low-carbon optimized operation scheduling decision, including: determining the properties of the heating system and carbon emission boundaries; constructing equipment and process carbon conversion process models for single heat sources, multiple heat sources, and heating networks based on process flow modeling software; identifying and correcting the heating system model; optimizing the parameters of the heating system; simulating and calculating the carbon flow footprint between equipment and in the process flow level by level; establishing objective functions and constraints for total expected revenue at different levels according to optimization requirements; constructing a low-carbon optimized operation scheduling decision model through the process flow model, constraints, and objective function; and, based on the optimized scheduling decision model, optimizing the heating mode of each heat source unit in real time according to real-time scheduling commands, and scheduling the thermal power output, fully considering the heating modes of different heat sources and carbon emission quotas, showing the carbon flow footprint, realizing parameter optimization of heat source units and real-time optimization of thermal power load, so that the optimized scheduling results take into account both economic and environmental benefits.

[0098] In this embodiment, the method for determining the nature of the heating system and its carbon emission boundary includes: determining the nature of the heating system, such as cogeneration power plants, gas-fired combined cycle power plants, biomass power plants, waste-to-energy plants, heat pumps, thermal storage tanks, etc., using heating modes such as high back pressure, extraction steam, axial winding, and cylinder cutting. Different heating systems have different modeling focuses, and clarifying the nature of the heating system helps to indicate the technical direction for modeling; analyzing the heating mode type based on the heating system's heat balance diagram or DCS operation interface to determine the nature of the heating system, clarify the sources of carbon emissions, and determine the carbon emission boundary of the heating system, i.e., carbon inflow factors and carbon outflow factors, which helps to clarify the key modeling equipment or process flow for carbon emissions; the carbon inflow factors include: combustion of fossil fuels, purchased electricity, purchased heat, and purchased steam, etc.; the carbon outflow factors include: external power supply, external heat supply, external steam supply, and carbon capture, etc. For specific equipment within the heating system, in addition to considering carbon inflow and carbon outflow factors, carbon emissions also need to consider equipment performance factors, such as boiler combustion efficiency, etc.

[0099] In the embodiment, the method for modeling the carbon conversion process of the device and process flow of the single heat source, multiple heat sources and heat network comprises: determining the process flow simulation software according to the property of the heating system, including but not limited to ViExergy, ViHeating, Ebsilon, AspenPlus and the like; determining the modeling object according to the actual demand, the modeling object being divided into single heat source, multiple heat sources and heat network; the carbon conversion process modeling of the single heat source, multiple heat sources and heat network comprises: device level modeling and process flow modeling, namely, device level modeling: modeling the devices inside the heating system, considering the carbon emission generated after the material flows through the devices, splitting the devices placed in the black box of the process flow, comprehensively considering the property factors of the devices, simulating and calculating the carbon emission, compared with the current carbon accounting method of the power industry, the method can study the carbon emission of a single device through modeling, even simulate the internal parameters of the device, and is more conducive to proposing the technical improvement scheme for emission reduction; process flow modeling: modeling the process flow of the heating system, regarding the internal process flow as a black box, and simulating and calculating the carbon emission of the entire system under different heating modes, on the basis of the device modeling, the method can provide an optimal technical scheme for the heat sources adopting multiple heating modes; the single heat source modeling process based on the process flow modeling software comprises: modeling the devices according to the property factors of the heating devices, determining the input and output parameters of different devices of the heating system and the initial values of the input parameters under the rated working condition, and constructing the device models; based on the device models, connecting the steam-water flow process, and constructing the carbon conversion model of the entire process flow, combining the macro modeling and the detailed modeling; the input and output parameters are as follows: Wherein, X is the input parameter, including the coal type characteristics, the main steam temperature, the main steam pressure, the main steam flow, the extraction steam amount and the like; Y is the output parameter, including the electric power, the steam consumption rate, the coal consumption rate and the like; i is the i th input parameter of the heat source unit; j is the j th output parameter of the heat source unit; and m is the m th heat source. The above modeling object is a single heat source system, the internal single heat source system includes different unit types, the unit is composed of multiple devices; multiple single heat source systems constitute a multiple heat source system, all the heat sources are included in the heating pipe network to constitute the entire heat network, and the carbon emission of the multiple heat sources and the heat network is simulated and analyzed through the step-by-step modeling, namely, multiple heat source modeling: determining the types and properties of the heat sources in the multiple heat sources of the central heating system, and modeling one by one according to the single heat source modeling mode; heat network modeling: after the multiple heat source modeling is completed, connecting the pipes and pumps to construct the entire heat network model. Through the multi-level modeling of the heating system, the operation conditions of the devices and the system can be simulated in real time according to different demands, thereby laying a foundation for subsequent scheduling decision.

[0100] In the embodiment, the method for identifying and correcting the model of the heating system unit comprises: based on the operation data of the distributed control (DCS) system, establishing a parameter data sample set of different working condition relationship models, and identifying and correcting the equipment model of the heating system unit; for example, the operation data of the DCS system is cleaned through a data cleaning algorithm, the correct parameter data sample set of the main steam pressure, the reheat steam pressure, the electric power, the exhaust pressure and the like is determined, and the parameter curve of the equipment model is corrected; for example, the boiler equipment model which has a greater impact on the carbon emission needs to correct the sliding pressure curve, the main steam pressure loss curve and the reheat steam pressure loss curve according to the parameter data sample set data.

[0101] P1 / P1D=f1(M1 / M1D);

[0102] DP 12 / DP 12 D=f2(M1 / M1D);

[0103] CDP 24 / CDP 24 D=f3(M1 / M1D);

[0104] Wherein, P1 is the main steam pressure, the unit is MPa; P1D is the nominal value of the main steam pressure, the unit is MPa; M1 is the main steam flow, the unit is t / h; M1D is the nominal value of the main steam flow, the unit is t / h; DP 12 is the main steam pressure loss, the unit is MPa; DP 12 D is the nominal value of the main steam pressure loss, the unit is MPa; CDP 34 is the reheat steam pressure loss, the unit is MPa; CDP 34 D is the nominal value of the reheat steam pressure loss, the unit is MPa; through the above method, the model of each equipment model is identified and corrected based on the parameter data sample set, and the unit process flow model strictly consistent with the actual operation condition of the heating system is constructed.

[0105] In the embodiment, the method for optimizing the parameters of the heating system comprises: according to the input parameter X i , adopting the control variable mode, adjusting the parameter range [MinX i ,MaxX i ], and performing sensitivity analysis on the output parameter Y j .

[0106] determining the relationship between a single input parameter and an output parameter, and obtaining the best optimized adjustment parameter range according to the sensitivity analysis curve;

[0107] Y i =f(X i ,(X1,X2,......,X i-1 ,Xi+1 ,......));

[0108] wherein the input variables (X1, X2,..., X i-1 , i+1 ,......) are constant values;

[0109] According to the optimization result, the single device and single unit of the single heat source of the heat supply system are overall debugged to ensure the lowest carbon emission on the basis of ensuring the heat and power output.

[0110] In the embodiment, the method for simulating carbon flow footprint between devices and in the process flow layer by layer includes: model identification ensures to establish a device and process flow model strictly in accordance with the actual operation, and simulation calculation on the device model can obtain the carbon emission of the specific device; simulation calculation on the entire process flow model can obtain the carbon flow footprint of all devices in the process flow and the overall carbon emission of the entire heat supply system; the macro simulation calculation and the detailed simulation calculation are combined to realize all-round coverage from local parameter optimization to system parameter optimization.

[0111] In the embodiment, the method for establishing different levels of total expected revenue target functions and constraint conditions according to optimization requirements includes: determining the optimization level, and the different levels include expected revenue target functions of different heat supply modes in each heat source system and total expected revenue target functions of heat supply modes of each heat source system; the expected revenue target functions of different heat supply modes in each heat source system include:

[0112]

[0113] wherein Pk is the electric load of the Kth unit, unit: MW; Hk is the heat load of the Kth unit, unit: MW; k is the Kth unit of the heat source; n is the number of units in the heat source; bk is the unit electric load conversion cost coefficient; ck is the unit heat load conversion cost coefficient; mk is the unit carbon emission and carbon quota conversion cost coefficient; Ck is the CO2 emission of the unit, unit: t; k k k k k outk

[0114] The total expected revenue of the heat supply modes of each heat source system includes:

[0115]

[0116] wherein g is the Gth heat source; m is the number of heat sources; s g g ​​​​​​​Heat load of the gth heat source, unit: MW; q g Carbon emission and carbon quota conversion cost coefficient; C outg Heat supply conversion CO2 emission, unit: t;

[0117] When the operation scheduling period is E, the objective functions of the above two levels are:

[0118]

[0119] Where t is the tth time period of the Eth operation scheduling period, unit: h; b kt Unit load conversion cost coefficient of the unit in the tth time period; P Kt Power generation in the tth time period, unit: MW; c kt Heat load conversion cost coefficient of the unit in the tth time period; H Kt Heat load in the tth time period, unit: MW; m kt Carbon emission and carbon quota conversion cost coefficient of the unit in the tth time period; C outkt CO2 emission of the unit in the tth time period, unit: t;

[0120]

[0121] Where s gt Heat load conversion cost coefficient of the heat source in the tth time period; H gt Heat load in the tth time period, unit: MW; q gt Carbon emission and carbon quota conversion cost coefficient in the tth time period; C outgt Heat supply conversion CO2 emission in the tth time period, unit: t.

[0122] In the embodiment, the method for establishing the carbon emission quota and the constraint condition of the heat and power output of each heat source heat supply system comprises: determining the constraint condition, which is mainly divided into an equality constraint and an inequality constraint:

[0123]

[0124] minS≤S k ≤maxS;

[0125] Where Z is a parameter meeting the equality constraint; and S is a parameter meeting the inequality constraint.

[0126] For example, a combined heat and power plant mainly comprises the following constraints: total unit load constraint, total heat load constraint, total carbon quota constraint, unit load constraint of each unit, heat load constraint of each unit, and other part constraints which need to be determined according to the specific unit operation condition, such as the steam supply constraint of the heat source; the constraint condition is as follows:

[0127] total electrical load constraint,

[0128] total heat load constraint,

[0129] total carbon quota constraint,

[0130] unit electrical load constraint, minP≤P k ≤maxP;

[0131] unit heat load constraint, minH≤H k ≤maxH;

[0132] wherein C is the carbon quota, in t.

[0133] In the embodiment, the method for constructing the low-carbon optimal operation optimization scheduling decision model through the process flow model, the constraint condition and the objective function comprises: calculating the optimal carbon emission and the heat and power output based on the process flow model, i.e. establishing a carbon conversion process model, determining the constraint condition and the objective function according to the demand; determining the optimization algorithm according to the type of the objective function and the constraint condition, which can select a heuristic algorithm or a deterministic algorithm, such as cvxpy (solving convex optimization problems), scipy.optimize.Minimize (nonlinear programming), RWCE (forced evolutionary random walk algorithm), GA (genetic algorithm), ACO (ant colony algorithm) and the like; writing the optimization calculation code; and the optimization result: the heat supply system optimization scheduling scheme, the carbon emission, the heat and power output.

[0134] In the embodiment, the method for performing real-time optimization on the heat supply mode of each heat source unit and scheduling the heat and power output based on the optimization scheduling decision model according to the real-time scheduling command comprises: modifying the constraint condition according to the real-time scheduling command, debugging the optimization scheduling decision model and outputting the optimization scheme.

[0135] Embodiment 2

[0136] On the basis of embodiment 1, the embodiment 2 also provides a heat supply system carbon conversion process modeling and low-carbon optimal operation scheduling decision system, comprising: a boundary determination module, which determines the heat supply system property and carbon emission boundary; a heat supply model construction module, which constructs the carbon conversion process model of the equipment and process flow of single heat source, multiple heat sources and heat network; an identification correction module, which identifies and corrects the heat supply system model; a parameter optimization module, which optimizes the parameters of the heat supply system; a simulation calculation module, which simulates and calculates the carbon flow footprint in the equipment and process flow layer by layer; a function construction module, which establishes different levels of total expected income objective functions according to optimization requirements; a constraint condition module, which establishes the constraint conditions of carbon emission quota and heat and power output of each heat source heat supply system; a decision model construction module, which constructs a low-carbon optimal operation scheduling decision model through the process flow model, constraint conditions and objective functions; and a scheduling module, which optimizes the heat supply mode of each heat source unit and schedules the heat and power output according to real-time scheduling commands based on the optimal scheduling decision model.

[0137] In the embodiment, the specific functions of each module have been described in detail in embodiment 1, and will not be repeated here.

[0138] In summary, the application determines the heat supply system property and carbon emission boundary; constructs the carbon conversion process model of the equipment and process flow of single heat source, multiple heat sources and heat network based on process flow modeling software; identifies and corrects the heat supply system model; optimizes the parameters of the heat supply system; simulates and calculates the carbon flow footprint in the equipment and process flow layer by layer; establishes different levels of total expected income objective functions and constraint conditions according to optimization requirements; constructs a low-carbon optimal operation scheduling decision model through the process flow model, constraint conditions and objective functions; optimizes the heat supply mode of each heat source unit and schedules the heat and power output according to real-time scheduling commands based on the optimal scheduling decision model, fully considers the heat supply mode of different heat sources and carbon emission quota, realizes the parameter optimization of the heat source unit and the real-time optimization of the heat and power load, and makes the optimal scheduling result take into account the economy and environmental protection.

[0139] In the several embodiments provided in the present application, it should be understood that the disclosed system, device, and method can be implemented in other manners. The above-described device embodiments are only schematic. For example, the division of the units is only a logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between the units can be indirect couplings or communication connections through some interfaces, devices, or unit intermediaries, and can be electrical, mechanical, or in other forms.

[0140] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0141] In addition, each functional unit in each embodiment of the application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit.

[0142] Based on the above ideal embodiments according to the application, through the above description, relevant personnel can make various changes and modifications without deviating from the technical idea of the application. The technical scope of the application is not limited to the contents of the specification, and the technical scope must be determined according to the scope of claims.

Claims

1. A method for modeling carbon conversion process of a heat supply system and low-carbon optimal operation scheduling decision, characterized in that, The method comprises the following steps: determining the property of the heating system and the carbon emission boundary; constructing the carbon conversion process model of the equipment and the process flow of single heat source, multiple heat sources and heat network based on process flow modeling software; identifying and correcting the heating system model; optimizing the parameters of the heating system; simulating and calculating the carbon flow footprint between the equipment and the process flow layer by layer; establishing the total expected income objective function and constraint condition of different levels according to the optimization requirement; constructing the low-carbon optimization operation scheduling decision model through the process flow model, constraint condition and objective function; optimizing the heating mode of each heat source unit and scheduling the thermal power output in real time based on the optimization scheduling decision model and real-time scheduling command; The method for constructing the carbon conversion process model of single heat source, multiple heat sources and heat network based on process flow modeling software comprises the following steps: determining the process flow modeling software according to the property of the heating system; determining the modeling object according to the actual requirement, which is divided into single heat source, multiple heat sources and heat network; The carbon conversion process modeling of single heat source, multiple heat sources and heat network comprises equipment level modeling and process flow modeling, i.e. equipment level modeling: modeling the equipment inside the heating system, considering the carbon emission generated after the material flow passes through the equipment, splitting the equipment in the process flow black box, comprehensively considering the property factors of the equipment, and simulating and calculating the carbon emission; process flow modeling: modeling the process flow of the heating system, regarding the process flow inside as a black box, and simulating and calculating the carbon emission of the entire system under different heating modes from a macro perspective; The single heat source modeling process based on process flow modeling software comprises the following steps: modeling the equipment according to the property of the heating equipment, determining the input and output parameters of different equipment of the heating system and the initial values of the input parameters under the rated working condition, constructing the equipment model, connecting the steam-water flow process based on the equipment model, and constructing the carbon conversion model of the entire process flow, combining macro modeling and detailed modeling; The input-output parameter is: wherein X is the input parameter, Y is the output parameter, i is the i-th input parameter of the heat source unit, j is the j-th output parameter of the heat source unit, and m is the m-th heat source; The modeling object is a single heat source system, which includes different unit types, and the unit is composed of multiple equipment; Multiple single heat source systems constitute a multiple heat source system, all heat sources are included in the heating pipe network to form the entire heat network, and the carbon emission of the multiple heat source and the heat network is simulated and analyzed through step-by-step modeling, i.e. multiple heat source modeling: determining the type and property of each heat source in the multiple heat source of the central heating system, and modeling them one by one in the manner of single heat source modeling; heat network modeling: after the multiple heat source modeling is completed, connecting the pipeline and the pump to construct the entire heat network model.

2. The method for carbon conversion process modeling and low-carbon optimization operation scheduling of the heating system according to claim 1, wherein The method for determining the property of the heating system comprises the following steps: analyzing the heating mode type according to the heat balance diagram or DCS operation interface of the heating system, and determining the property of the heating system; The method for determining the carbon emission boundary of the heating system comprises the following steps: determining the carbon emission boundary of the heating system, i.e. carbon inflow factors and carbon outflow factors, according to the property of the heating system and the source of carbon emission; The carbon inflow factors include: combustion of fossil fuels, purchased electricity, purchased heat, purchased steam and other carbon-containing substances; The carbon outflow factors include: power supply, heat supply, steam supply, carbon capture and other carbon-containing products; For specific equipment inside the heating system, the carbon emission factors include: carbon inflow, carbon outflow factors and equipment performance factors.

3. The method of claim 2, wherein the method of modeling and low-carbon optimal operation scheduling decision-making of the heating system carbon conversion process comprises: The method of identifying and correcting the heating system model comprises: Based on the operation data of the distributed control system, the parameter data sample set of each working condition relationship model is established to identify and correct the equipment model of the heating system: The operation data of the DCS system is cleaned through a data cleaning algorithm to determine the correct parameter data sample set and correct the parameter curve of the equipment model: N / ND=f(M / MD) Where N is the parameter to be corrected; ND is the nominal value of the corrected parameter; M is the flow rate through the equipment; and MD is the nominal value of the flow rate through the equipment; Based on the parameter data sample set, the model of each equipment is identified and corrected to build a process flow model that strictly conforms to the actual operation conditions of the heating system.

4. The method of claim 3, wherein the method of identifying and correcting the heating system model comprises: The method of identifying and correcting the heating system model comprises: According to the input parameter X i , the parameter range [MinX i , MaxX i ] is adjusted in a controlled variable manner, and sensitivity analysis is performed on the output parameter Y j . Determine the relationship between a single input parameter and an output parameter, and obtain the optimal optimization adjustment parameter range according to the sensitivity analysis curve; Y i = f(X i ,(X1,X2,......,X i-1 ,X i+1 ,......)) Wherein, (X1, X2, ..., X i-1 ,X i+1 (,......) The input variables are fixed values; According to the optimization result, the single equipment and single unit of a single heat source of the heating system are debugged to ensure the lowest carbon emission while taking into account the heat and power output.

5. The method of claim 4, wherein the method of simulating and calculating the carbon flow footprint between devices and in the process flow at each level comprises: The model of each equipment is identified and corrected to build a device and process flow model that strictly conforms to the actual operation conditions, the equipment model is simulated and calculated to obtain the carbon emission of the specific equipment; The entire process flow model is simulated and calculated to obtain the carbon flow footprint of all equipment in the process flow and the overall carbon emission of the entire heating system; Combine macro simulation calculation and detailed simulation calculation.

6. The method of claim 5, wherein the method of establishing different levels of total expected revenue target functions and constraint conditions according to optimization requirements comprises: Determine the optimization level, which includes the expected revenue target function of each heat source system internal different heating mode and the total expected revenue target function of each heat source system heating mode; The expected revenue target function of each heat source system internal different heating mode comprises: The total expected revenue of each heat source system heating mode comprises: When the operation scheduling period is E, the target functions of the two levels are: Among them, P k H represents the electrical load of the Kth generating unit, in MW. k Let n be the heat load of the Kth unit, in MW; k is the kth unit of the heat source; n is the number of units in the heat source; b k c is the cost factor for calculating the unit's electrical load; k The cost factor for calculating the unit's heat load; m k Cost factor for calculating unit carbon emissions and carbon quotas; C outk This refers to the unit's CO2 emissions, expressed in tons (t). The constraint conditions of each heat source system internal different heating mode include: wherein g is the gth heat source; m is the number of heat sources; s g is the heat load conversion cost coefficient of the heat source; H g is the heat load of the gth heat source, with the unit of MW; q g is the carbon emission and carbon quota conversion cost coefficient; C outg is the conversion CO2 emission of heating, with the unit of t; Determine the constraint conditions including equality constraints and inequality constraints: Wherein, t is the tth time period of the Eth operation scheduling cycle, unit is h; b kt is the electricity load conversion cost coefficient of the tth time period of the unit; P Kt is the power generation of the tth time period, unit is MW; c kt is the heat load conversion cost coefficient of the tth time period of the unit; H Kt is the heat load of the tth time period, unit is MW; m kt is the carbon emission and carbon quota conversion cost coefficient of the tth time period of the unit; C outkt is the CO2 emission of the tth time period of the unit, unit is t; wherein s gt is the heat source heat load conversion cost coefficient of the tth time period; H gt is the heat load of the tth time period, with units of MW; q gt is the carbon emission and carbon quota conversion cost coefficient of the tth time period; C outgt is the heating conversion CO2 emission of the tth time period, with units of t; ​ ​ minS < S k ≤ maxS; Wherein, Z is a parameter meeting the equality constraint; S is a parameter meeting the inequality constraint.

7. The method of claim 6, wherein the low-carbon optimization operation scheduling decision model is constructed based on a process flow model, constraint conditions, and an objective function. The method of constructing the low-carbon optimization operation scheduling decision model based on the process flow model, the constraint conditions, and the objective function comprises: calculating the optimal carbon emission and the thermal power output based on the carbon conversion process model, i.e. The process flow model is established, and the constraint conditions and the objective function are determined according to the demand. The optimization algorithm is determined according to the type of the objective function and the constraint conditions. The optimization calculation code is written. The optimization result is the optimization scheduling scheme of the heating system, the carbon emission, and the thermal power output.

8. The method of claim 7, wherein the method of scheduling each heat source unit based on the optimization scheduling decision model and the real-time scheduling command comprises: The constraint conditions are modified according to the real-time scheduling command, the optimization scheduling decision model is debugged, and the optimization scheme is output. The method comprises:

9. A heat supply system carbon conversion process modeling and low-carbon optimal operation scheduling decision system using the method of claim 1, characterized in that, A boundary determination module for determining the nature of the heating system and the carbon emission boundary; A heating model construction module for constructing the carbon conversion process model of the equipment and the process flow of the single heat source, the multiple heat sources, and the heat network; An identification correction module for identifying and correcting the heating system model; A parameter optimization module for optimizing the parameters of the heating system; A simulation calculation module for simulating the carbon flow footprint between the equipment and the process flow layer by layer; A function construction module for establishing the total expected income objective function of different levels according to the optimization demand; A constraint condition module for establishing the constraint conditions of the carbon emission quota and the thermal power output of each heat source of the heating system; A decision model construction module for constructing the low-carbon optimization operation scheduling decision model based on the process flow model, the constraint conditions, and the objective function; A scheduling module for scheduling each heat source unit based on the optimization scheduling decision model and the real-time scheduling command. ​

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