Multi-energy production simulation method, device and equipment and storage medium

Abstract

The invention provides a multi-energy production simulation method, device and equipment and a storage medium, and the method comprises the steps: predicting a hourly demand curve of joint links, the joint links comprising an electric energy link, a hydrogen energy link, a carbon dioxide energy link and a liquid fuel energy link; a pre-established cross-regional production simulation model is obtained, and the cross-regional production simulation model is a planning model with the lowest total cost as an objective function; and on the basis of a cross-regional production simulation model, the value of each time point of the demand curve is substituted into a constraint condition, and under the condition that the constraint condition is met, the production capacity of related simulation of each link in each year is solved. According to the method, the production indexes of key operation of the target system are solved by using the cross-regional production simulation model, the operation characteristics of different links are fully considered, the close coupling relationship among the power system, the hydrogen energy system and the carbon system is integrated, and the joint simulation effect of multi-energy production simulation is improved.

Description

Technical Field

[0001] The present invention relates to the field of production simulation technology, and in particular to a multi-energy production simulation method, device, equipment and storage medium. Background Art

[0002] Production simulation can further analyze and evaluate the rationality and feasibility of the scheme by simulating the hourly operating characteristics of the target and calculating the key operating indicators of the target system.

[0003] In the related technologies, the production simulations for power systems, hydrogen energy systems, carbon systems, etc. are mostly independent of each other. In recent years, with the increasing maturity of hydrogen production technology, hydrogen fuel cell technology, coal-fired power with CCUS technology, and P2X power-to-other technologies, the coupling relationship between power flow, hydrogen energy flow and carbon flow has become increasingly close. However, in the existing technologies, the current production simulation methods of energy systems do not consider the coupling interaction between power flow, hydrogen energy flow and carbon flow.

[0004] Based on the above analysis of the development status of this technology field, the existing technology lacks a cross-regional multi-energy production simulation solution coupled with electricity, hydrogen and carbon. Summary of the invention

[0005] The purpose of the present invention is to provide a multi-energy production simulation method, device, equipment and storage medium, aiming to solve the above-mentioned problems in the prior art.

[0006] According to a first aspect of an embodiment of the present invention, a multi-energy production simulation method is provided, comprising:

[0007] Predict the hourly demand curve of the combined links, where the combined links include the electric energy link, the hydrogen energy link, the carbon dioxide energy link and the liquid fuel energy link;

[0008] Obtaining a pre-established cross-regional production simulation model, wherein the cross-regional production simulation model is a planning model with the minimum total cost as the objective function;

[0009] Based on the cross-regional production simulation model, the values ​​of the demand curve at each time point are substituted into the constraints. Under the constraints, the production indicators of the relevant simulations in each year and each link are solved.

[0010] According to a second aspect of an embodiment of the present invention, there is provided a multi-energy production simulation device, comprising:

[0011] A forecasting module is used to forecast the hourly demand curve of the combined links, wherein the combined links include the electric energy link, the hydrogen energy link, the carbon dioxide energy link and the liquid fuel energy link;

[0012] A model acquisition module is used to acquire a pre-established cross-regional production simulation model, wherein the cross-regional production simulation model is a planning model with the lowest total cost as the objective function;

[0013] The solution module is used to bring the values ​​of each time point of the demand curve into the constraint conditions based on the cross-regional production simulation model, and solve the production indicators of the relevant simulations in each link of each year while satisfying the constraint conditions.

[0014] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the multi-energy production simulation method provided in the first aspect of the present disclosure are implemented.

[0015] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which a program for implementing information transmission is stored. When the program is executed by a processor, the steps of the multi-energy production simulation method provided in the first aspect of the present disclosure are implemented.

[0016] The technical solution provided by the embodiment of the present invention includes the following beneficial effects: using a cross-regional production simulation model to solve the production indicators of the key operation of the target system, fully considering the operating characteristics of different links, integrating the close coupling relationship between the power system, hydrogen energy system and carbon system, and improving the joint simulation effect of multi-energy production simulation.

[0017] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0019] Figure 1 is a flow chart of a multi-energy production simulation method according to an embodiment of the present invention;

[0020] Figure 2 is a schematic diagram of the basic framework of an embodiment of the present invention;

[0021] Figure 3 is a schematic diagram of a detailed design of an embodiment of the present invention;

[0022] Figure 4 is a schematic diagram of a multi-energy production simulation device according to an embodiment of the present invention;

[0023] Figure 5 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0025] Method Embodiment

[0026] According to an embodiment of the present invention, a multi-energy production simulation method is provided. Figure 1 is a flow chart of a multi-energy production simulation method according to an embodiment of the present invention. Figure 1 As shown, the multi-energy production simulation method according to an embodiment of the present invention specifically includes:

[0027] In step S110, the hourly demand curve of the combined link is predicted, wherein the combined link includes the electric energy link, the hydrogen energy link, the carbon dioxide energy link and the liquid fuel energy link, specifically including:

[0028] Preferably, the operating characteristics are obtained in advance to serve as parameter values ​​in the cross-regional production simulation model;

[0029] Specifically analyze the operating characteristics of hydrogen-fired units and thermal power, gas-fired power and other power sources after CCUS transformation, including: collecting and collating the operation and maintenance costs, emission coefficients, adjustable output ranges, minimum technical outputs, ramp-up / slope rates of various power sources including hydrogen-fired units, as well as the changes in output characteristics of thermal power and gas-fired units after CCUS transformation, CCUS operating costs and other related technical and economic parameters;

[0030] As well as the operating characteristics of power transmission channels, hydrogen pipelines, carbon dioxide pipelines, and liquid fuel pipelines, including: collecting and collating the operation and maintenance costs, maximum transmission power, minimum transmission power, maximum climbing / slope rate, line loss rate, minimum utilization hours and other related technical and economic parameters of power transmission lines in each region, as well as the operation and maintenance costs, typical flow curves, loss rates and other related technical and economic parameters of hydrogen pipelines, carbon dioxide pipelines, and liquid fuel pipelines;

[0031] Collect the technical and economic characteristics of processes such as water electrolysis to produce hydrogen and carbon dioxide hydrogenation to produce methanol to express the cost, that is, collect and organize the key technical and economic parameters such as the adjustable range, adjustment rate, and electricity-hydrogen conversion efficiency of water electrolysis hydrogen production equipment. In addition, analyze the relevant technical and economic parameters such as the reactants, outputs, input and output energy, and equipment operation and maintenance costs required for the carbon dioxide hydrogenation process to produce methanol;

[0032] Based on the demand of the joint links in previous years, a long short-term memory network is used to predict the demand curve. In the embodiment of the present invention, the neural network model used is RNN;

[0033] In step S120, a pre-established cross-regional production simulation model is obtained, wherein the cross-regional production simulation model is a planning model with the lowest total cost as the objective function, specifically including:

[0034] Get the cross-regional production simulation model based on Formula 1:

[0035]

[0036] Where F represents the total cost; T represents the number of simulated time periods, such as 168 hours in a typical week or 24 hours in a typical day; R represents the number of regions; X represents the number of different power sources including wind power, photovoltaic power, coal power, gas power, hydropower, and hydrogen-fired units; Px r,i,t represents the output of the i-th power source in the r-th region at time t; Ox i represents the operating cost of the i-th power source; S represents the number of energy storage types; Ps r,i,t represents the output of the i-th type of energy storage in the r-th region at time t; Os i represents the operating cost of the i-th energy storage; Pe ij,t represents the power transmitted from the i-th region to the j-th region at time t; Oe ij represents the operating cost of transmitting electricity from the i-th region to the j-th region; Y represents the number of different water electrolysis hydrogen production technology routes; Py rite represents the operating power of the i-th hydrogen production method in the r-th region at time t; Oy i represents the operating cost of the i-th hydrogen production method; Q represents the number of different hydrogen storage technology routes; Pq r,i,t is the operating power of the i-th hydrogen storage method in the r-th region at time t; Oq i represents the operating cost of the i-th hydrogen storage method; Ph ij,t represents the hydrogen transmission power from the i-th area to the j-th area at time t; Oh ij represents the operating cost of transporting hydrogen from the i-th area to the j-th area; Pc ij,trepresents the carbon dioxide transmission power from the i-th region to the j-th region at time t; Oc ij represents the operating cost of transporting carbon dioxide from the i-th region to the j-th region; Pm r,t represents the operating power of CO2 hydrogenation to methanol in the rth region at time t; Om represents the operating cost of CO2 hydrogenation to methanol; Pf ij,t represents the liquid fuel (methanol) transmission power from the i-th area to the j-th area at time t; Of ij represents the operating cost of transporting liquid fuel (methanol) from the i-th area to the j-th area; C c represents the carbon emission cost; e i is the carbon emission coefficient of the i-th power source;

[0037] The quantities to be solved in formula 1 are production indicators, that is, all quantities involving power, including: Pe ij,t , Py rite , Pq r,i,t , Ph ij,t 、Pc ij,t 、Pm r,t and Pf ij,t .

[0038] Figure 2 It is a schematic diagram of the basic framework of an embodiment of the present invention, such as Figure 2 As shown, a conceptual diagram of an embodiment of the present invention is shown, that is, the idea of ​​converting a problem into a planning model;

[0039] Obtain actual application constraints as constraints for cross-regional production simulation models, Figure 3 Detailed design diagram of an embodiment of the present invention is shown in FIG. Figure 3 As shown, the detailed definition of the constraints is shown, including: obtaining the actual constraints including balance constraints, conversion constraints, process characterization constraints, output constraints and energy storage constraints. The demands involved in the constraints are predicted by the long short-term memory network;

[0040] Among them, the balance constraints include the power balance constraints of each region based on formula 2, the hydrogen production and sales balance constraints of each region based on formula 3, the carbon dioxide production and sales balance constraints of each region based on formula 4, and the

[0041] The liquid fuel production and marketing balance constraints in each region in Equation 5 are:

[0042]

[0043] In the formula, Le rt It represents the power load demand of region r at time t, excluding the water electrolysis hydrogen production facilities;

[0044]

[0045] In the formula, Hy r,i,t Hq represents the hydrogen production power of the i-th electrolyzer in the r-th region at time t; r,i,t Hx represents the hydrogen release power of the i-th type of hydrogen storage in the r-th region at time t; r,t Hm represents the hydrogen consumption of the hydrogen unit ignited in the rth region at time t; r,t Lh represents the hydrogen consumption of CO2 hydrogenation to methanol in the rth region at time t; r,t It represents the hydrogen load demand of region r at time t, excluding hydrogen-fired units and CO2 hydrogenation to methanol;

[0046]

[0047] Where Dx r,i,t Dm represents the carbon dioxide capture power of the rth region and the i-th power source at time t; r,t Rd represents the CO2 consumption power of CO2 hydrogenation to methanol in the rth region at time t; r,t Ld represents the carbon dioxide geological storage capacity of region r at time t; r,t represents the demand for CO2 utilization in region r at time t, except for CO2 hydrogenation to methanol and geological storage;

[0048]

[0049] In the formula, F r,t Lf represents the power of producing methanol from carbon dioxide hydrogenation in the rth region at time t; r,t represents the liquid fuel (methanol) demand of region r at time t;

[0050] The conversion constraints include the electrolyzer electricity-to-hydrogen conversion constraints based on Formula 6 and the hydrogen-to-electricity conversion constraints of the hydrogen-fired unit based on Formula 7:

[0051]

[0052] In the formula, El i represents the efficiency of the i-th electrolytic cell; CT e Indicates the calorific value conversion coefficient between electrical energy and hydrogen energy;

[0053]

[0054] Where Ht r.t Indicates the hydrogen consumption power of the hydrogen-fired unit at time t; Eh i Indicates the efficiency of the hydrogen-fired unit; Pt r,t Indicates the power generation of the hydrogen-fired unit at time t; CT e Indicates the calorific value conversion coefficient between electrical energy and hydrogen energy;

[0055] The process characterization constraints include the CCUS CO2 capture constraint based on Formula 8 and the CO2 hydrogenation to methanol constraint based on Formula 9:

[0056] Px r,i,t ·e i · χ i =Dx r,i,t Formula 8;

[0057] In the formula, χ i represents the CO2 capture rate after the i-th power source is equipped with CCU S;

[0058]

[0059] In the formula, α represents the mass ratio of methanol produced to carbon dioxide consumed in the process of hydrogenation of carbon dioxide to methanol, and β represents the mass ratio of methanol produced to hydrogen consumed;

[0060] Among them, the output constraints include the power output range constraints based on formula 10 and the wind power and photovoltaic output constraints based on formula 11:

[0061] Po i,min I i,t ≤Ps r,i,t I r,i,t ≤Po i,max I i,t (r=1,2,...,R; i=1,2,3,...,S; t=1,2,...,T) Formula 10;

[0062]

[0063] In the formula, Po i,min represents the minimum technical output of the ith power source, Po i,max Indicates the maximum technical output of the i-th power source, Pw r,t Indicates Ps r,i,t Medium wind power output, Pp r,t Indicates Ps r,i,t Medium photovoltaic output, Rw r,t and Rp r,t They represent the per-unit output of wind power and photovoltaic power in region r at time t; Cw r and Cp r denote the total installed capacity of wind power and photovoltaic power in region r, respectively;

[0064] Among them, the energy storage constraints include the energy storage charge and discharge equality constraints based on formula 12 and the energy storage surplus constraints based on formula 13:

[0065]

[0066]

[0067] In the formula, Rs ,i,min and Rs. r,i,max are the upper and lower limits of the remaining power in the i-th energy storage device in the r region; Rsi r,i is the amount of electricity in the i-th energy storage device in the r region at the start time; Es r,i is the total capacity of the i-th energy storage device in region r; ηs i is the charging efficiency of the i-th energy storage device; Rq r,i,min and Rq r,i,max are the upper and lower limits of the amount of remaining hydrogen in the i-th hydrogen storage device in the r region; Rqi r,i is the amount of hydrogen in the i-th hydrogen storage device in the r region at the start time; Eq r,i is the total capacity of the i-th hydrogen storage device in region r; ηq i is the charging and discharging efficiency of the ith hydrogen storage device.

[0068] In step S130, based on the cross-regional production simulation model, the values ​​of the demand curve at each time point are brought into the constraint conditions, and the production indicators of the relevant simulations of each link in each year are solved under the constraint conditions, specifically including:

[0069] The model solving tool is used to solve the production indicators and obtain the operating power of each region, each type of equipment, cross-regional transmission lines, hydrogen transmission network, carbon dioxide transmission pipeline, and liquid fuel (methanol) pipeline at each point in time during the simulation period.

[0070] The method further comprises:

[0071] In step S140, a simulation curve chart and a simulation report are generated based on the production indicators. Preferably, further analysis can be carried out in combination with the predicted operating power, such as application efficiency, key equipment utilization, etc. The operating efficiency usually refers to the power actually consumed or transmitted by the equipment or system under normal working conditions, and the operating power often refers to the output power. The efficiency, energy consumption, etc. can be calculated or deduced, and MATLAB or Origin can be used to generate charts for visual display.

[0072] To sum up, in response to the existing problems, this invention has invented a multi-energy production simulation method, which uses a cross-regional production simulation model to solve the production indicators of the key operation of the target system, fully considers the operating characteristics of different links, integrates the close coupling relationship between the power system, the hydrogen energy system and the carbon system, and improves the joint simulation effect of the multi-energy production simulation; the cross-regional production simulation model takes the lowest total cost as the objective function and the actual application constraints as the constraint conditions of the model, fully utilizes the coupling interaction relationship of the key equipment in each link, considers the processes of production, storage, transmission and use, and can simultaneously solve the output of each time point, each region, and each type of equipment in a typical week, as well as the optimal solution of the transmission power of cross-regional power transmission channels, hydrogen pipelines, carbon dioxide, and liquid fuel pipelines; in the setting of constraints, the flexible adjustment characteristics of hydrogen production by electricity are fully utilized by adjusting the operating power of the water electrolysis hydrogen production equipment, and adapt to the fluctuating output of new energy sources such as wind power and photovoltaics.

[0073] Device Embodiment

[0074] According to an embodiment of the present invention, a multi-energy production simulation device is provided. Figure 4 is a schematic diagram of a multi-energy production simulation device according to an embodiment of the present invention. Figure 4 As shown, the multi-energy production simulation device according to an embodiment of the present invention specifically includes:

[0075] A prediction module 40 is used to predict the hourly demand curve of the combined link, wherein the combined link includes the electric energy link, the hydrogen energy link, the carbon dioxide energy link and the liquid fuel energy link;

[0076] A model acquisition module 42 is used to acquire a pre-established cross-regional production simulation model, wherein the cross-regional production simulation model is a planning model with the lowest total cost as the objective function;

[0077] The solution module 44 is used to bring the value of each time point of the demand curve into the constraint conditions based on the cross-regional production simulation model, and solve the production volume of the relevant simulation of each link in each year when the constraint conditions are met.

[0078] The device further comprises:

[0079] The network update module 46 is used to generate simulation curve graphs and simulation reports based on production indicators.

[0080] To sum up, in response to the existing problems, this invention has invented a multi-energy production simulation device, which uses a cross-regional production simulation model to solve the production indicators of the key operation of the target system, fully considers the operating characteristics of different links, integrates the close coupling relationship between the power system, the hydrogen energy system and the carbon system, and improves the joint simulation effect of the multi-energy production simulation; the cross-regional production simulation model takes the lowest total cost as the objective function and the actual application constraints as the constraint conditions of the model, fully utilizes the coupling interaction relationship of the key equipment in each link, considers the processes of production, storage, transmission and use, and can simultaneously solve the output of each time point, each region, and each type of equipment in a typical week, as well as the optimal solution of the transmission power of cross-regional power transmission channels, hydrogen pipelines, carbon dioxide, and liquid fuel pipelines; in the setting of constraints, the flexible adjustment characteristics of hydrogen production by electricity are fully utilized by adjusting the operating power of the water electrolysis hydrogen production equipment, and adapt to the fluctuating output of new energy sources such as wind power and photovoltaics.

[0081] Electronic device embodiment

[0082] Figure 5 Schematic diagram of an electronic device according to an embodiment of the present invention. The electronic device 500 may include at least one processor 510 and a memory 520. The processor 510 may execute instructions stored in the memory 520. The processor 510 is connected to the memory 520 through a data bus. In addition to the memory 520, the processor 510 may also be connected to an input device 530, an output device 540, and a communication device 550 through a data bus.

[0083] The processor 510 may be any conventional processor, such as a commercially available CPU. The processor may also include a graphics processor (Graphic Process Unit, GPU), a field programmable gate array (Field Programmable Gate Array, FPGA), a system on chip (System on Chip, SOC), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC) or a combination thereof.

[0084] The memory 520 may be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0085] In the embodiment of the present disclosure, executable instructions are stored in the memory 520, and the processor 510 can read the executable instructions from the memory 520 and execute the instructions to implement all or part of the steps of any multi-energy production simulation method in the above exemplary embodiments.

[0086] Computer Readable Storage Medium Embodiments

[0087] In addition to the above-mentioned methods and devices, the exemplary embodiments of the present disclosure may also be a computer program product or a computer-readable storage medium storing the computer program product, wherein the computer product includes computer program instructions that can be executed by a processor to implement all or part of the steps described in any of the multi-energy production simulation methods in the above-mentioned exemplary embodiments.

[0088] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as "C" language or similar programming languages ​​and scripting languages ​​(e.g., Python). The program code may be executed entirely on the user computing device, partially on the user computing device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0089] Computer readable storage media can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples of readable storage media include: a static random access memory (SRAM) with one or more wires electrically connected, an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk, or any suitable combination of the above.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-energy production simulation method, characterized in that: include: Predicting hourly demand curves of combined links, wherein the combined links include electric energy links, hydrogen energy links, carbon dioxide energy links, and liquid fuel energy links; Acquire a pre-established cross-regional production simulation model, wherein the cross-regional production simulation model is a planning model with the lowest total cost as the objective function; Based on the cross-regional production simulation model, the value of each time point of the demand curve is brought into the constraint conditions, and the production index of the relevant simulation of each link in each year is solved while satisfying the constraint conditions.

2. The method according to claim 1, characterized in that The method further comprises: Based on the production indicators, a simulation curve chart and a simulation report are generated.

3. The method according to claim 1, characterized in that The predicting of the hourly demand curve of the joint link specifically includes: based on the demand of the joint link in previous years, using a long short-term memory network to predict the demand curve.

4. The method according to claim 1, characterized in that: The obtaining of the pre-established cross-regional production simulation model specifically includes: Get the cross-regional production simulation model based on Formula 1: Where F represents the total cost; T represents the number of simulation time periods; R represents the number of regions; X represents the number of different power sources including wind power, photovoltaic power, coal power, gas power, hydropower, and hydrogen-fired units; Px r,i,t represents the output of the i-th power source in the r-th region at time t; Ox i represents the operating cost of the i-th power source; S represents the number of energy storage types; Ps r,i,t represents the output of the i-th type of energy storage in the r-th region at time t; Os i represents the operating cost of the i-th energy storage; Pe ij,t represents the power transmitted from the i-th region to the j-th region at time t; Oe ij represents the operating cost of transmitting electricity from the i-th region to the j-th region; Y represents the number of different water electrolysis hydrogen production technology routes; Py r,i,t represents the operating power of the i-th hydrogen production method in the r-th region at time t; Oy i represents the operating cost of the i-th hydrogen production method; Q represents the number of different hydrogen storage technology routes; Pq r,i,t is the operating power of the i-th hydrogen storage method in the r-th region at time t; Oq i represents the operating cost of the i-th hydrogen storage method; Ph ij,t represents the hydrogen transmission power from the i-th area to the j-th area at time t; Oh ij represents the operating cost of transporting hydrogen from the i-th area to the j-th area; Pc ij,t represents the carbon dioxide transmission power from the i-th region to the j-th region at time t; Oc ij represents the operating cost of transporting carbon dioxide from the i-th region to the j-th region; Pm r,t represents the operating power of CO2 hydrogenation to methanol in the rth region at time t; Om represents the operating cost of CO2 hydrogenation to methanol; Pf ij,t represents the liquid fuel delivery power from the i-th region to the j-th region at time t; ij represents the operating cost of transporting liquid fuel from the i-th area to the j-th area; C c represents the carbon emission cost; e i is the carbon emission coefficient of the i-th power source; The actual application constraints are obtained as the constraints of the cross-regional production simulation model.

5. The method according to claim 4, characterized in that The obtaining of actual application constraints as constraint conditions of the cross-regional production simulation model specifically includes: obtaining actual constraint conditions including balance constraints, conversion constraints, process characterization constraints, output constraints and energy storage constraints; The balance constraints include the power balance constraints of each region based on Formula 2, the hydrogen production and sales balance constraints of each region based on Formula 3, the carbon dioxide production and sales balance constraints of each region based on Formula 4, and the liquid fuel production and sales balance constraints of each region based on Formula 5: In the formula, Le r,t It represents the power load demand of region r at time t, excluding the water electrolysis hydrogen production facilities; In the formula, Hy r,i,t Hq represents the hydrogen production power of the i-th electrolyzer in the r-th region at time t; r,i,t Hx represents the hydrogen release power of the i-th type of hydrogen storage in the r-th region at time t; r,t Hm represents the hydrogen power consumption of the hydrogen unit ignited in the rth zone at time t; r,t Lh represents the hydrogen consumption of CO2 hydrogenation to methanol in the rth region at time t; r,t It represents the hydrogen load demand of region r at time t, excluding hydrogen-fired units and CO2 hydrogenation to methanol; Where Dx r,i,t Dm represents the carbon dioxide capture power of the rth region and the i-th power source at time t; r,t Rd represents the CO2 consumption power of CO2 hydrogenation to methanol in the rth region at time t; r,t Ld represents the carbon dioxide geological storage capacity of region r at time t; r,t represents the demand for CO2 utilization in region r at time t, except for CO2 hydrogenation to methanol and geological storage; In the formula, F r,t Lf represents the power of producing methanol from carbon dioxide hydrogenation in the rth region at time t; r,t represents the liquid fuel demand of region r at time t; The conversion constraints include the electrolyzer electricity-to-hydrogen conversion constraints based on Formula 6 and the hydrogen-to-electricity conversion constraints of the hydrogen-fired unit based on Formula 7: In the formula, El i represents the efficiency of the i-th electrolytic cell; CT e Indicates the calorific value conversion coefficient between electrical energy and hydrogen energy; Where Ht r.t Indicates the hydrogen consumption power of the hydrogen-fired unit at time t; Eh i Indicates the efficiency of the hydrogen-fired unit; Pt r,t Indicates the power generation of the hydrogen-fired unit at time t; CT e Indicates the calorific value conversion coefficient between electrical energy and hydrogen energy; The process characterization constraints include the CCUS CO2 capture constraint based on Formula 8 and the CO2 hydrogenation to methanol constraint based on Formula 9: Px r,i,t ·e i · χ i =Dx r,i,t Formula 8; In the formula, χ i represents the CO2 capture rate after CCUS is installed on the i-th power source; In the formula, α represents the mass ratio of methanol produced to carbon dioxide consumed in the process of hydrogenation of carbon dioxide to methanol, and β represents the mass ratio of methanol produced to hydrogen consumed; The output constraints include the power output range constraints based on formula 10 and the wind power and photovoltaic output constraints based on formula 11: Po i,min ·I i,t ≤Ps r,i,t ·I r,i,t ≤Po i,max ·I i,t (r = 1, 2, ..., R; i = 1, 2, 3, ..., S; t = 1, 2, ..., T) Formula 10; In the formula, Po i,min represents the minimum technical output of the ith power source, Po i,max Indicates the maximum technical output of the i-th power source, Pw r,t Indicates Ps r,i,t Medium wind power output, Pp r,t Indicates Ps r,i,t Medium photovoltaic output, Rw r,t and Rp r,t They represent the per-unit output of wind power and photovoltaic power in region r at time t; Cw r and Cp r denote the total installed capacity of wind power and photovoltaic power in region r, respectively; The energy storage constraint includes an energy storage charge and discharge equality constraint based on formula 12 and an energy storage surplus constraint based on formula 13: In the formula, Rs r,i,min and Rs. r,i,max are the upper and lower limits of the remaining power in the i-th energy storage device in the r region; Rsi r,i is the amount of electricity in the i-th energy storage device in the r region at the start time; Es r,i is the total capacity of the i-th energy storage device in region r; ηs i is the charging efficiency of the i-th energy storage device; Rq r,i,min and Rq r,i,max are the upper and lower limits of the amount of remaining hydrogen in the i-th hydrogen storage device in the r region; Rqi r,i is the amount of hydrogen in the i-th hydrogen storage device in the r region at the start time; Eq r,i is the total capacity of the i-th hydrogen storage device in region r; ηq i is the charging and discharging efficiency of the ith hydrogen storage device.

6. The method according to claim 1, characterized in that The solving of the production indexes of the relevant simulations of each link in each year specifically includes: solving the production indexes using a model solving tool.

7. A multi-energy production simulation device, characterized in that: include: A prediction module, used to predict the hourly demand curve of the combined link, wherein the combined link includes the electric energy link, the hydrogen energy link, the carbon dioxide energy link and the liquid fuel energy link; A model acquisition module, used to acquire a pre-established cross-regional production simulation model, wherein the cross-regional production simulation model is a planning model with the lowest total cost as the objective function; A solution module is used to bring the value of each time point of the demand curve into the constraint conditions based on the cross-regional production simulation model, and solve the production indicators of the relevant simulations in each link of each year while satisfying the constraint conditions.

8. The device according to claim 7, characterized in that The device further comprises: The network update module is used to generate a simulation curve chart and a simulation report based on the production index.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the multi-energy production simulation method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an implementation program for information transmission, and when the program is executed by a processor, the steps of the multi-energy production simulation method as described in any one of claims 1 to 6 are implemented.

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