Infrastructure planning method and device, equipment and storage medium

Abstract

The invention provides an infrastructure planning method and device, equipment and a storage medium, and the method comprises the steps: obtaining the facility scale of a historical year, determining the length of a planning period, predicting the annual facility planning amount of each cooperation field in each region in the planning period through a time series method, and enabling the annual facility planning amount to serve as a revision standard, and enabling the cooperation fields to comprise electric hydrogen and carbon; obtaining a pre-established cross-regional domain collaboration model which is a planning model taking the lowest total feasible cost as an objective function; using a decomposition method to solve the facility planning quantity of each collaborative field in each year and each region of a planning period year by year under the condition that the objective function is met, taking a reference year as an initial comparison year, enabling the single-year cost of facility planning growth corresponding to a to-be-solved year and a previous comparison year to be lowest under the condition that the model constraint condition is met, and obtaining an initial result; correcting the initial result by using a revision standard to obtain the facility planning quantity of the to-be-solved year; and taking the facility planning quantity of the last year of the planning period as output.

Description

Technical Field

[0001] The present invention relates to the technical field of facility planning, and in particular to an infrastructure planning method, device, equipment and storage medium. Background Art

[0002] Infrastructure planning refers to the construction plan of infrastructure scale in the future. It is an important foundation to support social development and ensure public safety and normal social operation.

[0003] In the relevant technologies, the current basic solutions have the following defects: the planning of infrastructure such as power transmission channels, hydrogen pipelines, carbon dioxide and liquid fuel pipelines is mostly independent of each other, while in actual applications, there are various types of infrastructure connected between different regions, and the comprehensive planning capabilities of infrastructure are poor; the field synergy between different fields is not considered, and the synergistic interaction between electricity, carbon and hydrogen in infrastructure planning is more significant. For example, power supply planning will greatly affect the layout of electrolyte water hydrogen production projects, and the CCUS transformation of thermal power and gas power will affect the carbon flow and downstream consumption of liquid fuels such as methane and methanol. Failure to consider field synergy will not be able to capture the close interaction between electricity flow, hydrogen energy flow and carbon flow, resulting in inaccurate infrastructure planning; in addition, planning models often set up complex programs in computers to continuously iterate and recursively solve, making the model solution inefficient.

[0004] Based on the above analysis of the development status of this technical field, the existing technology lacks a planning solution model that comprehensively considers cross-regional and multi-field collaboration, and a solution that uses a layer-by-layer decomposition solution method to solve the model. Summary of the invention

[0005] The purpose of the present invention is to provide an infrastructure planning 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, there is provided an infrastructure planning method, comprising: Obtain the facility scale of the historical years and determine the length of the planning period. Based on the facility scale of the historical years, use the time series method to predict the annual facility planning quantity of each coordinated field in each region during the planning period. Use the predicted annual facility planning quantity as the revision standard. The last year of the historical years is the base year. The coordinated fields include the power system field, hydrogen energy system field, carbon dioxide system field and liquid fuel field. Obtaining a pre-established cross-regional domain collaboration model, wherein the cross-regional domain collaboration model is a planning model with the lowest overall feasible cost as an objective function; Based on the cross-regional field coordination model, the decomposition method is used to solve the facility planning quantity of each coordinated field in each year of the planning period under the objective function. The base year is used as the initial comparison year. Under the constraints of the model, the single-year cost of the facility planning growth corresponding to the year to be solved and the previous comparison year is minimized to obtain the initial result. The initial result is corrected using the revised standard to obtain the facility planning quantity of the year to be solved. The facility planning quantity of each collaborative field in each region in the last year of the planning period is taken as the output of the cross-regional collaborative model to obtain the planning results of the infrastructure after the end of the planning period.

[0007] According to a second aspect of an embodiment of the present invention, there is provided an infrastructure planning device, including: The preprocessing module is used to obtain the facility scale of the historical years and determine the length of the planning period. Based on the facility scale of the historical years, the time series method is used to predict the annual facility planning quantity of each coordinated field in each region during the planning period. The predicted annual facility planning quantity is used as the revision standard. Among them, the last year of the historical years is the base year. The coordinated fields include the power system field, the hydrogen energy system field, the carbon dioxide system field and the liquid fuel field. A model acquisition module is used to acquire a pre-established cross-regional domain collaboration model, wherein the cross-regional domain collaboration model is a planning model with the lowest overall feasible cost as the objective function; The decomposition and year-by-year solution module is used to solve the facility planning quantity of each coordinated field in each region in each year of the planning period under the objective function based on the cross-regional field coordination model by using the decomposition method year by year. The base year is used as the initial comparison year. Under the condition of satisfying the model constraints, the single-year cost of the facility planning growth corresponding to the year to be solved and the previous comparison year is minimized to obtain the initial result, and the initial result is corrected by using the revision standard to obtain the facility planning quantity of the year to be solved; The result acquisition module is used to take the facility planning quantity of each collaborative field in each region in the last year of the planning period as the output of the cross-regional collaborative model to obtain the planning results of the infrastructure after the end of the planning period.

[0008] 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 infrastructure planning method provided in the first aspect of the present disclosure are implemented.

[0009] 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, and when the program is executed by a processor, the steps of the infrastructure planning method provided in the first aspect of the present disclosure are implemented.

[0010] The technical solution provided by the embodiment of the present invention includes the following beneficial effects: using a cross-regional collaborative model to solve infrastructure planning problems, comprehensively considering the coordinated planning between different regions and the synergy of data interaction between various fields, so that the model has a certain complexity and contains rich attributes to comprehensively represent the coordinated goals of facility planning problems; in the process of solving the model, the decomposition method is used to solve the facility planning volume in each collaborative field in each year of the planning period year by year, avoiding excessive repeated cycles and iterations, and improving the solution efficiency of the model to a certain extent.

[0011] 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

[0012] 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.

[0013] Figure 1 is a flow chart of an infrastructure planning method according to an embodiment of the present invention; Figure 2 is a schematic diagram of an infrastructure planning framework 1 according to an embodiment of the present invention; Figure 3 is a schematic diagram of infrastructure planning framework 2 of an embodiment of the present invention; Figure 4 is a schematic diagram of an infrastructure planning device according to an embodiment of the present invention; Figure 5 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0014] 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.

[0015] Method Embodiment According to an embodiment of the present invention, a method for infrastructure planning is provided. Figure 1is a flow chart of an infrastructure planning method according to an embodiment of the present invention. The method is applied in a long-term energy substitution planning system in the field of facility planning, such as Figure 1 As shown, the infrastructure planning method according to an embodiment of the present invention specifically includes: In step S110, the facility scale of the historical year is obtained, and the length of the planning period is determined. Based on the facility scale of the historical year, the annual facility planning quantity of each collaborative field in each region during the planning period is predicted using the time series method, and the predicted annual facility planning quantity is used as the revision standard, wherein the last year of the historical year is the base year, and the collaborative fields include the power system field, the hydrogen energy system field, the carbon dioxide system field and the liquid fuel field, specifically including: The base year is the starting state of the planning. Since the embodiment of the present invention solves the problem by the annual growth of the scale, it is necessary to determine the starting state. The base year is the last year of the known facility scale quantity. For example, if the base year is AD x and the planning period is 20 years, the embodiment of the present invention predicts the planning quantity of the infrastructure in AD x+20. The known facility scale and unknown facility planning of the embodiments of the present invention need to comprehensively consider different regions and the fields of electricity, hydrogen and carbon synergy, that is, the scale of the power system, hydrogen energy system, carbon dioxide system and liquid fuel field in each region, that is, electricity, hydrogen and carbon synergy, and the combination of liquid fuel and hydrogen and carbon; There are various types of facilities in the infrastructure under each category. In the embodiment of the present invention, the scale of infrastructure planning is expressed by capacity. The power system mainly includes the capacity of power supply, power grid, load, and energy storage. The hydrogen energy system mainly includes the scale of hydrogen production, hydrogen storage, hydrogen transmission, and hydrogen use. The carbon dioxide system mainly includes carbon emission sources, carbon dioxide transmission pipelines, and carbon dioxide geological storage potential. Based on the facility scale of historical years, the time series analysis method ARIMA is used to predict the annual facility planning volume of each collaborative field during the planning period. As a revision standard, the ARIMA model combines three statistical models to predict future values, including: autoregressive AR, difference I and moving average MA. It can directly obtain the predicted value based on the historical change trend. The facility planning volume predicted each year can be used as data in the historical change trend. The purpose of predicting revision standards is to obtain alternative planning schemes that conform to actual changing trends in order to optimize the results obtained by cross-regional collaborative models.

[0016] In step S120, a pre-established cross-regional domain collaboration model is obtained, wherein the cross-regional domain collaboration model is a planning model with the lowest overall feasible cost as the objective function, specifically including: The lowest overall feasible cost is to minimize the net value of the total infrastructure cost of electricity, hydrogen and carbon synergy, and consider the overall optimization between different regions to obtain the objective function of the cross-regional field synergy model established based on Formula 1: Formula 1; Where F represents the total feasible cost, T represents the planning period, and t represents the order of the years in the planning period. represents a collection of regions, Represents a collection of power types. Q g,q,t Indicates t Year q In the region g The capacity of the power supply, C g,q,t Indicates t Year q In the region g The unit capacity cost of the power source, B g,q,t Indicates t Year q Region No. g The power capacity for CCUS transformation, A g,q,t Indicates t Year q Region No. g The unit capacity cost of CCUS transformation is represents the collection of energy storage, Q s,q,t Indicates t Year q Region No. s The capacity of energy storage, H s,q,t Indicates t Year q Region No. s The charging time at the maximum charging power of the energy storage, C s,q,t Indicates t Year q Region No. s The cost per unit capacity time of energy storage, O g,q,t Indicates t Year q Region No. g The operation and maintenance costs of the power supply are g,q,t Indicates t Year q Region No. g The carbon emission coefficient of the power source, Ug,q,t Indicates t Year q Region No. g The annual utilization hours of the power source, Indicates t Year q Region No. g CO2 capture rate of CCUS-converted power sources, C c,t Indicates t The annual carbon emission cost E ij,t Indicates t Year Connection i Regions and j The capacity of regional transmission lines, X ij Indicates the connection i Regions and j The distance between regional transmission lines, V ij,t Indicates t Year Connection i Regions and j The cost per unit capacity distance of a regional transmission line, QUR q,t Indicates t Year q The capacity of the electrolyzer in each area, Ce q,t Indicates t Year q The cost per unit capacity of the regional electrolyzer, Q q,t Indicates t Year q The capacity of the regional hydrogen storage equipment, Ch q,t Indicates t Year q The unit capacity cost of regional hydrogen storage equipment, H ij,t Indicates t Year Connection i Regions and j The capacity of the regional hydrogen pipeline, Y ij,t Indicates t Year Connection i Regions and j The cost per unit capacity distance of a regional hydrogen pipeline, C ij,t Indicates the connection of the first i Regions and j The capacity of the regional CO2 pipeline,Z ij,t Indicates t Year Connection i Regions and j The cost per unit capacity distance of the regional carbon dioxide transmission pipeline, Q q,t Indicates t Year q The capacity of the regional CO2 storage facility, Cc q,t Indicates t Year q The cost per unit capacity of a regional CO2 storage facility, Q q,t Indicates t Year q The capacity of the CO2 hydrogenation to methanol plant in each region, Cm q,t Indicates t Year q The unit capacity cost of CO2 hydrogenation to methanol plant in each region, Q q,t Indicates t Year q The capacity of methanol liquid fuel storage facilities in each region, Cf q,t Indicates t Year q Unit capacity cost of methanol liquid fuel storage equipment in each region, F ij,t Indicates t Year Connection i Regions and j The capacity of methanol liquid fuel pipelines in each region, W ij,t Indicates t Year Connection i Regions and j The cost per unit capacity distance of methanol liquid fuel pipeline in each region, R represents the discount rate, where the parameter representing the capacity is the facility planning quantity of each facility to be solved; The quantities to be solved include: Q g,q,t , B g,q,t , Q s,q,t , E ij,t , QUR q,t , Q q,t , Q q,t , Q q,t and Q q,t The above facilities are all basic settings; the discount rate is used to measure the time value of money. In addition to the above, the other parameters in Formula 1 are all basic setting parameters, that is, they do not need to be obtained by technical means, and are the values ​​describing the objective status quo; Figure 2 Schematic diagram of infrastructure planning framework 1 of an embodiment of the present invention. Figure 2 As shown, the conceptual diagram of cross-regional infrastructure joint planning based on the synergistic interaction of electricity, hydrogen and carbon is presented, which transforms the problem into a cross-regional field collaborative model for target planning; Obtain actual application constraints as constraints of the cross-regional collaborative model; the actual constraints obtained specifically include: power system constraints, hydrogen energy system constraints, carbon dioxide system constraints, liquid fuel constraints and collaborative balance constraints; the constraints are actual application constraints, for example, the capacity generated under the planned scale can be greater than or equal to the predicted demand; in the constraints, there are multiple parameters for representing the demand, preferably, the demand can be obtained based on previous demand combined with artificial intelligence prediction algorithms, or determined according to policy development regulations; except for the capacity and demand values ​​waiting to be solved, other parameters in the constraints are basic setting parameters that can be directly obtained; Figure 3 Schematic diagram of infrastructure planning framework 2 of an embodiment of the present invention. Figure 3 As shown, the detailed design of the cross-regional collaborative model is presented, including the facility scale data of the base year, the meaning of the objective function, and the detailed design of the constraints.

[0017] Among them, the power system constraints include the power balance constraints of each region based on formula 2, the power balance constraints of each region based on formula 3, the transmission capacity constraints based on formula 4, and the transmission power constraints based on formula 5: Formula 2; in, q ∈ , t =1,2,3…… T , Indicates g Power sources in the area q The peak load output confidence factor is pe qj,t Indicates t Year Region q To the j The value of electricity delivered to each region, pe iq,t Indicates t Year i Region to Region qThe value of the power delivered; le iq To connect i Regions and q Line loss rate of regional transmission channels, le q Indicates area q Average transmission and distribution line loss rate, Indicates the reserve factor of the electrical load, Le q,t Indicates t Year Region q The maximum power load forecast value; Formula 3; in, q ∈ , t =1,2,3…… T , P qj,t Indicates t Year Region q To the j The amount of electricity delivered to a region, P iq,t Indicates t Year i Region to Region q The amount of electricity delivered, LE q,t Indicates t Year Region q The predicted value of electricity demand is Indicates s The charging and discharging efficiency of energy storage; Formula 4; Formula 5; in, pe ij,t and P qj,t The meaning is the same as that of other constraints, except that the subscripts used to distinguish relative regions are different. Ue ij represents the maximum utilization hours of the transmission line connecting the i-th region and the j-th region; The hydrogen energy system constraints include the hydrogen production and sales balance constraints of each region based on formula 6, the hydrogen mass balance constraints of each region based on formula 7, the hydrogen transmission power constraints based on formula 8, and the hydrogen transmission quality constraints based on formula 9: Formula 6; in, q ∈ , t =1,2,3……T , represents the efficiency of the electrolyzer, Indicates the calorific value conversion coefficient between electrical energy and hydrogen energy, ph qj,t Indicates t Year Region q To the j The hydrogen flow value transported to each region, ph iq,t Indicates t Year i Region to Region q The hydrogen flow rate value delivered, lh iq Indicates the connection i Regions and q The loss rate of hydrogen pipelines in each region, represents the reserve factor of hydrogen load, Lh q,t Indicates t Year Region q The maximum green hydrogen demand forecast value; Formula 7; in, q ∈ , t =1,2,3…… T , Ue q,t Indicates t Year q Utilization hours of electrolyzers in each region, Ph qj,t Indicates t Year Region q To the j The quality of hydrogen delivered to each region, Ph iq,t Indicates t Year i Region to Region q The quality of hydrogen delivered, LH q,t Indicates t Year Region q Forecast value of green hydrogen demand, Indicates the charging and discharging efficiency of hydrogen storage; Formula 8; Formula 9; in, ph ij,t and Ph ij,t The meaning is the same as that of other constraints, except that the subscripts used to distinguish relative regions are different. Uh ij Indicates the connection i Regions and j The maximum utilization hours of the hydrogen pipeline in each region; The carbon dioxide system constraints include the carbon dioxide production and sales balance constraints of each region based on formula 10, the carbon dioxide mass balance constraints of each region based on formula 11, the carbon dioxide transmission power constraints based on formula 12, the carbon dioxide transmission quality constraints based on formula 13, and the carbon dioxide emission constraints based on formula 14: Formula 10; in, q ∈ , t =1,2,3…… T , pc qj,t Indicates t Year Region q To the j The carbon dioxide flow value transported to each region, pc iq,t Indicates t Year i Region to Region q The delivered carbon dioxide flow rate value, lc iq Indicates the connection i Regions and q The loss rate of CO2 transmission pipeline in each region, represents the reserve factor for CO2 demand, f q,t Indicates t Year Region q The maximum predicted CO2 demand for the production of liquid fuels, Lc q,t Indicates t Year Region q The predicted maximum CO2 utilization demand (except for the production of liquid fuels) s q,t Indicates t Year Region q The maximum CO2 storage demand forecast for Formula 11; in, q ∈ , t =1,2,3…… T , Pc qj,t Indicates t Year Region q To the jThe mass of carbon dioxide delivered to each region, Pc iq,t Indicates t Year i Region to Region q The mass of carbon dioxide delivered, F q,t Indicates t Year Region q Forecasted CO2 demand for green methanol production, LC q,t Indicates t Year Region q Forecast of CO2 utilization demand (except for the production of green methanol), S q,t Indicates t Year Region q Forecast of CO2 storage demand, Indicates the charging and discharging efficiency of the CO2 storage facility; Formula 12; Formula 13; in, pc qj,t and Pc ij,t The meaning is the same as that of other constraints, except that the subscripts used to distinguish relative regions are different. Uc ij Indicates the connection i Regions and j The maximum utilization hours of the CO2 transmission pipeline in each region; Formula 14; in, Ce t Indicates t an annual cap on carbon emissions from the electricity system; Liquid fuel constraints include the liquid fuel production and sales balance constraints of each region based on formula 15, the liquid fuel mass balance constraints of each region based on formula 16, the liquid fuel delivery power constraints based on formula 17, and the liquid fuel delivery mass constraints based on formula 18: Formula 15; in, q ∈ , t =1,2,3…… T , pf qj,t Indicates t Year Region q To the jThe methanol liquid fuel flow value transported to each area, pf iq,t Indicates t Year i Region to Region q The transported methanol liquid fuel flow value, lf iq Indicates the connection i Regions and q The loss rate of methanol liquid fuel pipelines in each region, represents the reserve factor of methanol liquid fuel load, Lf q,t Indicates t Year Region q The maximum predicted demand for methanol liquid fuel; Formula 16; in, Um q,t Indicates t Year q The utilization hours of CO2 hydrogenation to methanol units in each region, expressed as: Pf qj,t Indicates t Year Region q To the j The mass of liquid fuel delivered to each area, Pf iq,t Indicates t Year q Region to Region r The mass of liquid fuel delivered, LF q,t Indicates t Year Region q Liquid fuel demand forecasts, Indicates the charging and discharging efficiency of liquid fuel storage facilities; Formula 17; Formula 18; in, q ∈ , t =1,2,3…… T , pf ij,t and Pf ij,t The meaning is the same as that of other constraints, except that the subscripts used to distinguish relative regions are different. Uf ij Indicates the connection i Regions and j The maximum utilization hours of liquid fuel pipelines in each region; The coordinated balance constraints include the regulation capacity constraints of each regional system based on formula 19 and the regulation rate constraints of each regional system based on formula 20: Formula 19; in, Represents a collection of power supplies with regulation capabilities, g,q Indicates q Region No. g The average peak-shaving depth of the power supply, e,q Indicates q The adjustable range of the electrolyzer in each zone, q,t Indicates t Year q The predicted value of the peak-to-valley difference rate of load in each region, represents the set of uncertain power sources, g,q Indicates q Region No. g Peak load demand factor of the power source; Formula 20; in, g,q Indicates area q No. g The output regulation rate of the power supply, e,q Indicates area q No. g The output regulation rate of the electrolyzer is q,t Indicates q The rate of change of load demand per unit time in an area.

[0018] In step S130, based on the cross-regional field collaboration model, the decomposition method is used to solve the facility planning quantity of each collaborative field in each year of the planning period under the objective function. The base year is used as the initial comparison year. Under the constraints of the model, the single-year cost of the facility planning growth corresponding to the year to be solved and the previous comparison year is minimized to obtain the initial result. The initial result is corrected using the revision standard to obtain the facility planning quantity of the year to be solved, which specifically includes: Other objective function planning models in the field only focus on the value of the objective function F, and need to repeat multiple cycles to try to obtain the various quantities to be solved that make the objective function F approach the minimum value. Poor final results may lead to re-iterative calculation. Due to the particularity of the objective function form of the embodiment of the present invention, the cost caused by the annual increase in the internal calculation planning quantity, layer-by-layer solution can focus on the internal solution process, avoid too many repeated cycles, and improve the solution efficiency; the annual cost of the solution is the lowest, so that the final overall feasible cost is the lowest.

[0019] Using revised criteria to optimize initial results specifically involves: If the initial result corresponding to a facility plan is less than the revised standard of the corresponding year, and the absolute value of the difference is greater than the preset threshold, the result of the corresponding annual revised standard minus the preset threshold is used as the facility planning volume of the facility in the year to be solved; the revised standard is obtained based on historical trend prediction, and if it is too much lower than the revised standard, it means that the facility capacity result solved by the model is too different from the development trend, and the planning volume needs to be appropriately increased. In this case, since the planning volume has been increased, the constraint conditions must be met, and there is no need to repeat the judgment; Otherwise, the initial result is directly used as the facility planning quantity of the facility in the year to be solved; The year to be solved is used as the comparison year for the next round to calculate the growth difference; If the initial results are not optimized, the growth of planning results may not conform to normal development laws.

[0020] In step S140, the facility planning quantity of each collaborative field in each region in the last year of the planning period is used as the output of the cross-regional collaborative model to obtain the planning results of the infrastructure after the end of the planning period, specifically including: By solving the cross-regional collaborative model based on electricity, hydrogen and carbon, in addition to obtaining the annual development scale of infrastructure such as power transmission channels, hydrogen pipelines, carbon dioxide, and liquid fuel pipelines in the target year, which is the last year of the planning period, we can also obtain the gradual development scale of each year of the planning period.

[0021] The method further comprises: In step S150, after the output of the cross-regional domain collaboration model is obtained, a planning report is generated based on the facility planning quantity in each domain in each region in the last year of the planning period output by the cross-regional domain collaboration model, specifically including: The planning report includes the planned quantity of facilities in the target year. Preferably, the planning report also includes the results of the annual changes in the planned quantity of facilities during the planning period. The planning report is generated using the data filling method under the template.

[0022] To sum up, in response to the existing problems, the infrastructure planning method proposed in the embodiment of the present invention uses a cross-regional collaborative model to solve the infrastructure planning problem, comprehensively considers the overall planning between different regions, and the synergy of data interaction between the fields of electricity, hydrogen and carbon, and can capture the close interaction between power flow, hydrogen energy flow and carbon flow, so that the model has a certain complexity. The model takes into account the technical and economic characteristics of equipment such as fuel cells, hydrogen-fired units, transmission lines, hydrogen transmission pipelines, CCUS, etc., and contains rich attributes to comprehensively represent the overall coordination goals of facility planning problems; in the process of solving the model, the decomposition method is used to solve the facility planning quantity in each collaborative field in each year of the planning period year by year, avoiding excessive repeated cycles and iterations, and improving the solution efficiency of the model to a certain extent. The single-year cost of each year is the lowest, so as to achieve the lowest overall feasible cost; the revised standard of sequence prediction is used to optimize the initial results, so that the results predicted by the model year by year are in line with the actual growth law; in general, the embodiment of the present invention comprehensively coordinates and optimizes the solution of infrastructure planning problems.

[0023] Device Embodiment According to an embodiment of the present invention, an infrastructure planning device is provided. Figure 4 Schematic diagram of an infrastructure planning device according to an embodiment of the present invention. Figure 4 As shown, the infrastructure planning device according to an embodiment of the present invention specifically includes: The preprocessing module 40 is used to obtain the facility scale of the historical years and determine the length of the planning period. Based on the facility scale of the historical years, the time series method is used to predict the annual facility planning quantity of each coordinated field in each region during the planning period, and the predicted annual facility planning quantity is used as the revision standard, wherein the last year of the historical years is the base year, and the coordinated fields include the power system field, the hydrogen energy system field, the carbon dioxide system field and the liquid fuel field; A model acquisition module 42 is used to acquire a pre-established cross-regional domain collaboration model, wherein the cross-regional domain collaboration model is a planning model with the lowest overall feasible cost as an objective function; The decomposition and year-by-year solution module 44 is used to use the decomposition method to solve the facility planning quantity of each coordinated field in each region in each year of the planning period under the objective function year by year, take the base year as the initial comparison year, and make the single-year cost of the facility planning growth corresponding to the year to be solved and the previous comparison year the lowest under the model constraint condition, obtain the initial result, and use the revision standard to correct the initial result to obtain the facility planning quantity of the year to be solved; The result acquisition module 46 is used to use the facility planning quantity of each field in each region in the last year of the planning period as the output of the cross-regional field collaboration model to obtain the planning results of the infrastructure after the end of the planning period.

[0024] The device further comprises: The report generation module 48 is used to generate a planning report based on the facility planning quantity in each field in each region in the last year of the planning period output by the cross-regional field collaboration model after obtaining the output of the cross-regional field collaboration model.

[0025] To sum up, in response to the existing problems, the infrastructure planning device proposed in the embodiment of the present invention uses a cross-regional collaborative model to solve the infrastructure planning problem, comprehensively considers the overall planning between different regions, and the synergy of data interaction between the fields of electricity, hydrogen and carbon, and can capture the close interaction between power flow, hydrogen energy flow and carbon flow, so that the model has a certain complexity, and the model takes into account the technical and economic characteristics of equipment such as fuel cells, hydrogen-fired units, transmission lines, hydrogen transmission pipelines, CCUS, etc., and contains rich attributes to comprehensively represent the overall coordination goals of facility planning problems; in the process of solving the model, the decomposition method is used to solve the facility planning quantity in each collaborative field in each year of the planning period year by year, avoiding too many repeated cycles and iterations, and improving the solution efficiency of the model to a certain extent, so that the single-year cost of each year is the lowest, so as to achieve the lowest overall feasible cost; the revised standard of sequence prediction is used to optimize the initial results, so that the results predicted by the model year by year are in line with the actual growth law; in general, the embodiment of the present invention comprehensively coordinates and optimizes the solution of infrastructure planning problems.

[0026] Electronic device embodiment 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.

[0027] 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.

[0028] The memory 520 may be implemented by any type of volatile or nonvolatile memory 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.

[0029] 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 of the infrastructure planning methods in the above exemplary embodiments.

[0030] Computer Readable Storage Medium Embodiments 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 infrastructure planning methods in the above-mentioned exemplary embodiments.

[0031] 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.

[0032] 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, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, 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.

[0033] 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 method for infrastructure planning, characterized in that include: Obtain the facility scale of the historical years and determine the length of the planning period. Based on the facility scale of the historical years, use the time series method to predict the annual facility planning quantity of each coordinated field in each region during the planning period, and use the predicted annual facility planning quantity as the revision standard, wherein the last year of the historical years is the base year, and the coordinated fields include the power system field, the hydrogen energy system field, the carbon dioxide system field and the liquid fuel field; Acquire a pre-established cross-regional domain collaboration model, wherein the cross-regional domain collaboration model is a planning model with the lowest overall feasible cost as an objective function; Based on the cross-regional collaborative model, the decomposition method is used to solve the facility planning quantity of each collaborative field in each region in each year of the planning period under the objective function year by year, and the base year is used as the initial comparison year. Under the condition of satisfying the model constraints, the single-year cost of the facility planning growth corresponding to the year to be solved and the previous comparison year is minimized to obtain the initial result, and the initial result is corrected using the revision standard to obtain the facility planning quantity of the year to be solved; The facility planning quantity of each collaborative field in each region in the last year of the planning period is used as the output of the cross-regional collaborative model to obtain the planning result of the infrastructure after the end of the planning period.

2. The method according to claim 1, characterized in that The method further comprises: After obtaining the output of the cross-regional domain collaboration model, a planning report is generated based on the facility planning quantity in each domain in each region in the last year of the planning period output by the cross-regional domain collaboration model.

3. The method according to claim 1, characterized in that The use of the time series method to predict the annual facility planning quantity of each collaborative field in each region during the planning period specifically includes: using the time series analysis method ARIMA to predict the annual facility planning quantity of each collaborative field in each region during the planning period.

4. The method according to claim 1, characterized in that: The obtaining of the pre-established cross-regional domain collaboration model specifically includes: Obtain the objective function of the cross-regional domain collaboration model established based on Formula 1: Formula 1; Where F represents the total feasible cost, T represents the planning period, and t represents the order of the years in the planning period. represents a collection of regions, Represents a collection of power types. Q g,q,t Indicates t Year q In the region g The capacity of the power supply, C g,q,t Indicates t Year q In the region g The unit capacity cost of the power source, B g,q,t Indicates t Year q Region No. g The power capacity of CCUS transformation is A g,q,t Indicates t Year q Region No. g The unit capacity cost of CCUS transformation is represents the collection of energy storage, Q s,q,t Indicates t Year q Region No. s The capacity of energy storage, H s,q,t Indicates t Year q Region No. s The charging time at the maximum charging power of the energy storage, C s,q,t Indicates t Year q Region No. s The cost per unit capacity time of energy storage, O g,q,t Indicates t Year q Region No. g The operation and maintenance costs of the power supply are g,q,t Indicates t Year q Region No. g The carbon emission coefficient of the power source, U g,q,t Indicates t Year q Region No. g The annual utilization hours of the power source, Indicates t Year q Region No. g CO2 capture rate of CCUS-converted power sources, C c,t Indicates t The annual carbon emission cost E ij,t Indicates t Year Connection i Regions and j The capacity of regional transmission lines, X ij Indicates the connection i Regions and j The distance between regional transmission lines, V ij,t Indicates t Year Connection i Regions and j The cost per unit capacity distance of a regional transmission line, QUR q,t Indicates t Year q The capacity of the electrolyzer in each area, Ce q,t Indicates t Year q The cost per unit capacity of the regional electrolyzer, Q q,t Indicates t Year q The capacity of the regional hydrogen storage equipment, Ch q,t Indicates t Year q The unit capacity cost of regional hydrogen storage equipment, H ij,t Indicates t Year Connection i Regions and j The capacity of the regional hydrogen pipeline, Y ij,t Indicates t Year Connection i Regions and j The cost per unit capacity distance of a regional hydrogen pipeline, C ij,t Indicates t Year Connection i Regions and j The capacity of the regional CO2 pipeline, Z ij,t Indicates t Year Connection i Regions and j The cost per unit capacity distance of the regional carbon dioxide transmission pipeline, Q q,t Indicates t Year q The capacity of the regional CO2 storage facility, Cc q,t Indicates t Year q The cost per unit capacity of a regional CO2 storage facility, Q q,t Indicates t Year q The capacity of the CO2 hydrogenation to methanol plant in each region, Cm q,t Indicates t Year q The unit capacity cost of CO2 hydrogenation to methanol plant in each region, Q q,t Indicates t Year q The capacity of methanol liquid fuel storage facilities in each region, Cf q,t Indicates t Year q Unit capacity cost of methanol liquid fuel storage equipment in each region, F ij,t Indicates t Year Connection i Regions and j The capacity of the methanol liquid fuel pipeline in each region, W ij,t Indicates t Year Connection i Regions and j The cost per unit capacity distance of methanol liquid fuel pipeline in each region, R represents the discount rate, where the parameter representing the capacity is the facility planning quantity of each facility to be solved; Obtain practical application constraints as constraints for cross-regional domain collaboration models.

5. The method according to claim 4, characterized in that The obtaining of actual application constraints as constraint conditions of the cross-regional collaborative model specifically includes: obtaining actual constraint conditions including power system constraints, hydrogen energy system constraints, carbon dioxide system constraints, liquid fuel constraints and collaborative balance constraints; The power system constraints include the power balance constraints of each region based on formula 2, the power balance constraints of each region based on formula 3, the transmission capacity constraints based on formula 4, and the transmission power constraints based on formula 5: Formula 2: in, q ∈ , t =1,2,3…… T , Indicates g Power sources in the area q The peak load output confidence factor is pe qj,t Indicates t Year Region q To the j The value of electricity delivered to each region, pe iq,t Indicates t Year i Region to Region q The value of the power delivered; le iq To connect i Regions and q The line loss rate of the regional transmission channel, le q Indicates area q Average transmission and distribution line loss rate, Indicates the reserve factor of the electrical load, Le q,t Indicates t Year Region q The maximum power load forecast value; Formula 3: in, q ∈ , t =1,2,3…… T , P qj,t Indicates t Year Region q To the j The amount of electricity delivered to a region, P iq,t Indicates t Year i Region to Region q The amount of electricity delivered, LE q,t Indicates t Year Region q The predicted value of electricity demand is Indicates s The charging and discharging efficiency of energy storage; Formula 4: Formula 5: in, Ue ij Indicates the connection i Regions and j The maximum utilization hours of regional transmission lines; The hydrogen energy system constraints include the hydrogen production and sales balance constraints of each region based on formula 6, the hydrogen mass balance constraints of each region based on formula 7, the hydrogen transmission power constraints based on formula 8, and the hydrogen transmission quality constraints based on formula 9: Formula 6: in, q ∈ , t =1,2,3…… T , represents the efficiency of the electrolyzer, Indicates the calorific value conversion coefficient between electrical energy and hydrogen energy, ph qj,t Indicates t Year Region q To the j The hydrogen flow value transported to each region, ph iq,t Indicates t Year i Region to Region q The hydrogen flow rate value delivered, lh iq Indicates the connection i Regions and q The loss rate of hydrogen pipelines in each region, represents the reserve factor of hydrogen load, Lh q,t Indicates t Year Region q The maximum green hydrogen demand forecast value; Formula 7: in, q ∈ , t =1,2,3…… T , Ue q,t Indicates t Year q Utilization hours of electrolyzers in each region, Ph qj,t Indicates t Year Region q To the j The quality of hydrogen delivered to each region, Ph iq,t Indicates t Year i Region to Region q The quality of hydrogen delivered, LH q,t Indicates t Year Region q Forecast value of green hydrogen demand, Indicates the charging and discharging efficiency of hydrogen storage; Formula 8: Formula 9: in, Uh ij Indicates the connection i Regions and j The maximum utilization hours of the hydrogen pipeline in each region; The carbon dioxide system constraints include the carbon dioxide production and sales balance constraints of each region based on formula 10, the carbon dioxide mass balance constraints of each region based on formula 11, the carbon dioxide transmission power constraints based on formula 12, the carbon dioxide transmission quality constraints based on formula 13, and the carbon dioxide emission constraints based on formula 14: Formula 10: in, q ∈ , t =1,2,3…… T , pc qj,t Indicates t Year Region q To the j The carbon dioxide flow value transported to each region, pc iq,t Indicates t Year i Region to Region q The delivered carbon dioxide flow rate value, lc iq Indicates the connection i Regions and q The loss rate of CO2 transmission pipeline in each region, represents the reserve factor for CO2 demand, f q,t Indicates t Year Region q The maximum predicted CO2 demand for the production of liquid fuels, Lc q,t Indicates t Year Region q The maximum carbon dioxide utilization demand forecast value, s q,t Indicates t Year Region q The maximum CO2 storage demand forecast for Formula 11: in, q ∈ , t =1,2,3…… T , Pc qj,t Indicates t Year Region q To the j The mass of carbon dioxide delivered to each region, Pc iq,t Indicates t Year i Region to Region q The mass of carbon dioxide delivered, F q,t Indicates t Year Region q Forecasted CO2 demand for green methanol production, LC q,t Indicates t Year Region q The predicted value of carbon dioxide utilization demand, S q,t Indicates t Year Region q Forecast of CO2 storage demand, Indicates the charging and discharging efficiency of the CO2 storage facility; Formula 12: Formula 13: in, Uc ij Indicates the connection i Regions and j The maximum utilization hours of the CO2 transmission pipeline in each region; Formula 14: in, Ce t Indicates t an annual cap on carbon emissions from the electricity system; The liquid fuel constraints include the liquid fuel production and sales balance constraints of each region based on formula 15, the liquid fuel mass balance constraints of each region based on formula 16, the liquid fuel delivery power constraints based on formula 17, and the liquid fuel delivery mass constraints based on formula 18: Formula 15: in, q ∈ , t =1,2,3…… T , pf qj,t Indicates t Year Region q To the j The methanol liquid fuel flow value transported to each area, pf iq,t Indicates t Year i Region to Region q The transported methanol liquid fuel flow value, lf iq Indicates the connection i Regions and q The loss rate of methanol liquid fuel pipelines in each region, represents the reserve factor of methanol liquid fuel load, Lf q,t Indicates t Year Region q The maximum predicted demand for methanol liquid fuel; Formula 16: in, Um q,t Indicates t Year q The utilization hours of CO2 hydrogenation to methanol units in each region, expressed as: Pf qj,t Indicates t Year Region q To the j The mass of liquid fuel delivered to each area, Pf iq,t Indicates t Year q Region to Region r The mass of liquid fuel delivered, LF q,t Indicates t Year Region q Liquid fuel demand forecasts, Indicates the charging and discharging efficiency of liquid fuel storage facilities; Formula 17: Formula 18: in, q ∈ , t =1,2,3…… T , Uf ij Indicates the connection i Regions and j The maximum utilization hours of liquid fuel pipelines in each region; The collaborative balance constraints include the regulation capacity constraints of each regional system based on formula 19 and the regulation rate constraints of each regional system based on formula 20: Formula 19: in, Represents a collection of power supplies with regulation capabilities, g,q Indicates q Region No. g The average peak-shaving depth of the power supply type, e,q Indicates q The adjustable range of the electrolyzer in each zone, q,t Indicates t Year q The predicted value of the peak-to-valley difference rate of load in each region, represents the set of uncertain power sources, g,q Indicates q Region No. g Peak load demand factor of the power source; Formula 20: in, g,q Indicates area q No. g The output regulation rate of the power supply, e,q Indicates area q The output adjustment rate of the electrolyzer, q,t Indicates q The rate of change of load demand per unit time in an area.

6. The method according to claim 1, characterized in that The use of the revised standard to correct the initial result specifically includes: If the initial result corresponding to a facility plan is less than the revised standard of the corresponding year, and the absolute value of the difference is greater than the preset threshold, the result of subtracting the preset threshold from the revised standard of the corresponding year is used as the facility planning quantity of the facility in the year to be solved; Otherwise, the initial result is directly used as the facility planning quantity of the facility in the year to be solved.

7. An infrastructure planning device, characterized in that: include: A preprocessing module is used to obtain the facility scale of the historical years and determine the length of the planning period. Based on the facility scale of the historical years, the time series method is used to predict the annual facility planning quantity of each coordinated field in each region during the planning period, and the predicted annual facility planning quantity is used as the revision standard, wherein the last year of the historical years is the base year, and the coordinated fields include the power system field, the hydrogen energy system field, the carbon dioxide system field and the liquid fuel field; A model acquisition module, used to acquire a pre-established cross-regional domain collaboration model, wherein the cross-regional domain collaboration model is a planning model with the lowest overall feasible cost as an objective function; A decomposition and year-by-year solution module is used to solve the facility planning quantity of each collaborative field in each region in each year of the planning period under the objective function based on the cross-regional field collaboration model by using a decomposition method year by year, taking the base year as the initial comparison year, and making the single-year cost of the facility planning growth corresponding to the year to be solved and the previous comparison year the lowest under the model constraint condition, to obtain an initial result, and to use the revision standard to amend the initial result to obtain the facility planning quantity of the year to be solved; The result acquisition module is used to use the facility planning quantity of each collaborative field in each region in the last year of the planning period as the output of the cross-regional collaborative model to obtain the planning results of the infrastructure after the end of the planning period.

8. The device according to claim 7, characterized in that The device further comprises: The report generation module is used to generate a planning report based on the facility planning quantity in each field in each region in the last year of the planning period output by the cross-regional field collaboration model after obtaining the output of the cross-regional field collaboration model.

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 when the computer program is executed by the processor, the steps of the infrastructure planning method according to any one of claims 1 to 6 are implemented.

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 infrastructure planning method according to any one of claims 1 to 6 are implemented.

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