Energy system low-carbon transformation path planning method and device and computer equipment

Abstract

The invention relates to an energy system low-carbon transformation path planning method and device and computer equipment. The method comprises the following steps: inputting operation characteristic data, economical efficiency data and demand predictive quantities of various energy sources into an annual path planning model to obtain an energy supply and transportation planning result, and inputting the energy supply and transportation planning result into an hour-level time sequence path planning model to obtain an energy production scheduling planning result; and returning the energy production scheduling planning result to the annual path planning model to correct the energy supply and transportation planning result through the annual path planning model, and inputting the corrected energy supply and transportation planning result to the hour-level time sequence path planning model to obtain a corrected energy production scheduling planning result, and convergence conditions are met. By adopting the method, the long-term macroscopic planning target and the short-term microcosmic production operation characteristics in the complex energy system can be considered, and the energy path planning accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of green and low-carbon technologies, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for planning a low-carbon transformation path for an energy system. Background Art

[0002] The current energy system low-carbon transformation path planning method is unable to achieve an organic combination of the energy system's medium- and long-term macro-planning goals and short-term micro-production and operation characteristics when faced with complex energy system scenarios with multiple energy-consuming departments, full energy processes, and multiple energy types. It is therefore unable to accurately provide effective energy path planning results, resulting in a low accuracy rate in energy path planning. Summary of the invention

[0003] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for low-carbon transformation path planning of energy systems, which can improve the accuracy of energy path planning, in response to the above-mentioned technical problems.

[0004] In the first aspect, the present application provides a method for planning a low-carbon transformation path for an energy system, including:

[0005] Obtaining operation characteristic data and economic data of various energy technology types; the energy technology types include energy supply technology types and energy transportation technology types;

[0006] Determine the forecasted demand of each department for each type of energy in the energy system based on the operating characteristic data and the economic data;

[0007] In the initial round of optimization calculation, the operating characteristic data, the economic data and the demand forecast of each type of energy are input into the annual path planning model to obtain the energy supply and transportation planning result, and then the energy supply and transportation planning result is input into the hourly time series path planning model to obtain the energy production scheduling planning result;

[0008] The energy production scheduling planning results are returned to the annual path planning model to carry out the next round of optimization calculation process, so as to accumulate part of the energy production scheduling planning results throughout the year through the annual path planning model, and the energy supply and transportation planning results are corrected according to part of the energy production scheduling planning results accumulated throughout the year, the operating characteristic data, the economic data and the demand forecast of each type of energy, and the corrected energy supply and transportation planning results are input into the hourly time series path planning model to obtain the corrected energy production scheduling planning results, and return to the step of returning the energy production scheduling planning results to the annual path planning model to carry out the next round of optimization calculation process until the convergence conditions are met.

[0009] In one embodiment, the operation characteristic data, the economic data and the demand forecast of each type of energy are input into an annual path planning model to obtain energy supply and transportation planning results, including:

[0010] Input the operating characteristic data, the economic data and the demand forecast of each type of energy into the annual path planning model, and calculate the energy supply and transportation planning result that minimizes the first objective function while satisfying various macro constraints;

[0011] Among them, the first objective function is determined based on the sum of the following data: the annual average cost data of equipment investment and construction for each of the energy supply technology types and each of the energy transportation technology types, equipment operation and maintenance cost data, equipment operation cost data, the net import and export cost data of each type of energy, and the carbon emission cost data of each of the energy supply technology types and each of the energy transportation technology types.

[0012] In one embodiment, the macro constraints include the following constraints: supply and demand balance constraints, equipment inventory constraints, technical energy consumption constraints, energy transportation volume limit constraints, node energy balance constraints, and carbon emission constraints;

[0013] Among them, the supply and demand balance constraint is used to constrain the balance relationship between the product demand forecast and product output of each department; the equipment inventory constraint is used to constrain the equipment inventory of each energy technology type after addition, elimination and depreciation; the technical energy consumption constraint is used to constrain the energy consumption of any department for any energy technology type; the energy transportation volume limit constraint is used to constrain the annual transportation volume between any two energy hub nodes in the energy system; the node energy balance constraint is used to constrain the balance relationship between the input and output of any type of energy in any energy hub node; the carbon emission constraint is used to constrain the sum of carbon emissions of each energy technology type in each department.

[0014] In one embodiment, the energy supply and transportation planning result is input into an hourly time sequence path planning model to obtain an energy production scheduling planning result, including:

[0015] Input the energy supply and transportation planning result into the hourly time sequence path planning model, and calculate the energy production scheduling planning result that minimizes the second objective function while satisfying various time sequence constraints;

[0016] The second objective function is determined based on the dispatching cost data of each power generation equipment and energy transmission equipment in the energy system and the investment, construction, operation and maintenance cost data of each energy storage unit in the energy system.

[0017] In one embodiment, the timing constraints include the following constraints: power balance constraints, power reserve constraints, unit operation constraints, new energy generator output constraints, energy transportation capacity constraints, and low-carbon policy constraints;

[0018] Among them, the power balance constraint is used to constrain the balance relationship between the power supply and power consumption in any energy hub node in the energy system; the power reserve constraint is used to constrain the power supply capacity of any of the energy hub nodes; the unit operation constraint is used to constrain the real-time operating power of the energy supply unit of any of the energy hub nodes; the new energy generator set output constraint is used to constrain the real-time operating power of the new energy generator set of any of the energy hub nodes according to environmental factors; the energy transportation capacity constraint is used to constrain the energy transmission power between each of the energy hub nodes; the low-carbon policy constraint is used to constrain the ratio of the sum of the real-time operating power of the new energy generator set throughout the year to the sum of the real-time operating power of all types of generator sets throughout the year.

[0019] In one embodiment, after obtaining the energy production scheduling planning result, the method further includes:

[0020] Determining total annualized energy cost data based on the energy supply and transportation planning results and the energy production scheduling planning results;

[0021] The step of returning to the step of inputting the energy supply and transportation planning result into the hourly time sequence path planning model to obtain the energy production scheduling planning result until the iteration condition is met includes:

[0022] When the total annualized energy cost data is less than the convergence threshold, return to the step of inputting the energy supply and transportation planning results into the hourly time series path planning model to obtain the energy production scheduling planning results until the total annualized energy cost data is greater than the convergence threshold.

[0023] In a second aspect, the present application also provides a low-carbon transformation path planning device for an energy system, comprising:

[0024] A data acquisition module, used to acquire operating characteristic data and economic data of various energy technology types; the energy technology types include energy supply technology types and energy transportation technology types;

[0025] A demand determination module, used to determine the forecast demand of each department for each type of energy in the energy system according to the operation characteristic data and the economic data;

[0026] A planning module, which is used to input the operating characteristic data, the economic data and the demand forecast of each type of energy into an annual path planning model during the initial round of optimization calculation to obtain an energy supply and transportation planning result, and then input the energy supply and transportation planning result into an hourly time series path planning model to obtain an energy production scheduling planning result;

[0027] A correction module is used to return the energy production scheduling planning results to the annual path planning model to carry out the next round of optimization calculation process, so as to accumulate part of the energy production scheduling planning results throughout the year through the annual path planning model, and correct the energy supply and transportation planning results according to part of the energy production scheduling planning results accumulated throughout the year, the operating characteristic data, the economic data and the demand forecast of each type of energy, and input the corrected energy supply and transportation planning results into the hourly time series path planning model to obtain the corrected energy production scheduling planning results, return to the step of returning the energy production scheduling planning results to the annual path planning model, and carry out the next round of optimization calculation process until the convergence conditions are met.

[0028] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0029] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0030] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the steps of the above method when executed by a processor.

[0031] The above-mentioned energy system low-carbon transformation path planning method, device, computer equipment, computer-readable storage medium and computer program product obtain the operating characteristic data and economic data of each energy technology type, wherein the energy technology type includes energy supply technology type and energy transportation technology type; according to the operating characteristic data and economic data, determine the demand forecast of each department for each type of energy in the energy system; in the initial round of optimization calculation process, input the operating characteristic data, economic data and the demand forecast of each type of energy into the annual path planning model to obtain the energy supply and transportation planning results, and then input the energy supply and transportation planning results into the hourly time series path planning model to obtain the energy production scheduling. The energy production scheduling planning results are returned to the annual path planning model to carry out the next round of optimization calculation process, so as to accumulate part of the energy production scheduling planning results throughout the year through the annual path planning model, and revise the energy supply and transportation planning results according to part of the energy production scheduling planning results accumulated throughout the year, operation characteristic data, economic data and demand forecast of various energy sources, and input the revised energy supply and transportation planning results into the hourly time series path planning model to obtain the revised energy production scheduling planning results, and return to the step of returning the energy production scheduling planning results to the annual path planning model to carry out the next round of optimization calculation process until the convergence conditions are met. By utilizing operating characteristic data and economic data, combined with the two-stage optimization mechanism of the annual path planning model and the hourly time series path planning model, the energy supply and transportation planning results are dynamically and iteratively corrected and the energy production scheduling planning results are updated to obtain accurate energy path planning results. Through the two-stage path planning models based on different time scales, the annual path planning model and the hourly time series path planning model, an organic combination of long-term macro planning and short-term micro scheduling is achieved, which can take into account the medium- and long-term macro planning goals and short-term micro production and operation characteristics of complex energy systems, thereby improving the accuracy of energy path planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 An application environment diagram of a low-carbon transformation path planning method for an energy system in an embodiment;

[0034] Figure 2 A schematic diagram of a process of a low-carbon transformation path planning method for an energy system in an embodiment;

[0035] Figure 3 A logic diagram of an annual path planning model in one embodiment;

[0036] Figure 4 A logic diagram of an hourly temporal path planning model in another embodiment;

[0037] Figure 5 A logic diagram of a cyclic iteration of an annual path planning model and an hourly temporal path planning model in another embodiment;

[0038] Figure 6 A structural block diagram of a low-carbon transformation path planning device for an energy system in an embodiment;

[0039] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0041] The energy system low-carbon transformation path planning method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The terminal 102 obtains the operating characteristic data and economic data of each energy technology type; the energy technology type includes the energy supply technology type and the energy transportation technology type; according to the operating characteristic data and the economic data, the demand forecast of each department for each type of energy in the energy system is determined; during the initial round of optimization calculations, the terminal 102 inputs the operating characteristic data, economic data and the demand forecast of each type of energy into the annual path planning model to obtain the energy supply and transportation planning results, and then inputs the energy supply and transportation planning results into the hourly time series path planning model to obtain the energy production scheduling planning results; the terminal 102 inputs the energy production scheduling planning The result is returned to the annual path planning model, and the next round of optimization calculation process is carried out to accumulate the partial energy production scheduling planning results throughout the year through the annual path planning model, and the energy supply and transportation planning results are corrected according to the partial energy production scheduling planning results accumulated throughout the year, the operating characteristics data, the economic data and the demand forecast of various types of energy, and the corrected energy supply and transportation planning results are input into the hourly time series path planning model to obtain the corrected energy production scheduling planning results, and return to the energy production scheduling planning results to the annual path planning model, and carry out the next round of optimization calculation process until the convergence condition is met. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices, and the Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server 104 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0042] In an exemplary embodiment, Figure 2 As shown in the figure, a low-carbon transformation path planning method for energy system is provided. Figure 1 The terminal 102 in FIG. 1 is used as an example for explanation, including:

[0043] Step S202, obtaining operation characteristic data and economic data of each energy technology type.

[0044] Among them, energy technology types include energy supply technology types and energy transportation technology types.

[0045] Among them, energy supply technology types may include energy supply technology types of multiple sectors such as electricity, industry, transportation, and construction. Specifically, energy supply technology types of the power sector may include thermal power, nuclear power, hydropower, wind power, photovoltaic power generation, biomass power generation, and new thermal power with carbon capture (CCUS) technology; energy supply technology types of the industrial sector may include long-process steelmaking technology and short-process steelmaking technology; energy supply technology types of the transportation sector may include traditional fuel vehicles, lithium battery electric vehicles, and hydrogen vehicles; energy supply technology types of the construction sector may include heating, cooling, lighting, hot water supply, cooking utensils and other equipment.

[0046] Among them, energy transportation technology types can include energy transportation technology types of various types of energy such as coal, oil, gas, electricity, etc. Energy transportation technology types for coal include road transportation, rail transportation, and water transportation; energy transportation technology types for oil include pipeline transportation, road transportation, rail transportation, and water transportation; energy transportation technology types for natural gas include pipeline transportation and liquefied natural gas (LNG) transportation; energy transportation technology types for electricity include AC transmission projects and DC transmission projects.

[0047] The operational characteristic data of energy technology types include operational characteristic data of energy supply technology types and operational characteristic data of energy transportation technology types. Specifically, the operational characteristic data of energy supply technology types may include equipment capacity, energy conversion efficiency, equipment depreciation rate, energy consumption, and carbon emission factor; the operational characteristic data of energy transportation technology types include energy transportation capacity limit, energy transmission loss, etc.

[0048] Among them, the economic data of each energy supply technology type and energy transportation technology type may include equipment investment cost, equipment operation cost, equipment maintenance cost and carbon emission cost.

[0049] Step S204, determining the forecast demand of each department for each type of energy in the energy system based on the operating characteristic data and the economic data.

[0050] In the specific implementation, the demand forecast of each department for various types of energy in the energy system is determined based on the operating characteristic data and economic data, which can include: based on population, gross domestic product (GDP), operating characteristic data and economic data, the statistical prediction method of polynomial fitting can be used to predict the demand for terminal products in multiple departments such as electricity, industry, transportation, and construction:

[0051] ;

[0052] in, represents the demand for terminal products of the kth sector, and are the predicted values ​​of population and GDP, respectively. and are the operating characteristics and economic performance data of the u-th energy technology type in the k-th sector, , , , , It is a constant parameter that describes the functional relationship between the demand for the above-mentioned terminal products and various types of input parameters.

[0053] Based on the above-calculated demand for end products in various sectors, the demand for various types of energy in various sectors such as electricity, industry, transportation, and construction can be predicted:

[0054] ;

[0055] in, represents the forecast demand of the kth sector for the cth energy, is the energy consumption factor corresponding to the terminal products of the kth department.

[0056] Step S206, during the initial round of optimization calculations, the operating characteristic data, economic data and demand forecasts for various types of energy are input into the annual path planning model to obtain energy supply and transportation planning results, and then the energy supply and transportation planning results are input into the hourly time series path planning model to obtain energy production scheduling planning results.

[0057] In order to facilitate the understanding of those skilled in the art, Figure 3 An exemplary logic diagram of an annual path planning model is provided. It can be seen that the annual path planning model may include an energy supply optimization module and an energy transportation optimization module. The energy supply optimization module may process the operating characteristic data and economic data of energy supply technology types of multiple sectors such as electricity, industry, transportation, and construction to obtain supply optimization processing results; the energy transportation optimization module may process the operating characteristic data and economic data of energy transportation technology types of multiple types of energy such as coal, oil, gas, and electricity to obtain transportation optimization processing results; the demand forecast of each type of energy is used as a constraint condition, and the energy supply and transportation planning results are determined according to the supply optimization processing results and the transportation optimization processing results.

[0058] The results of energy supply and transportation planning may include but are not limited to: the demand for coal, oil, gas and electricity, the combination of optimal energy supply technology types for each department, energy transportation planning and allocation results, energy import volume, etc.

[0059] This annual path planning model uses an annual time scale and, driven by low-carbon policies, derives the economically optimal energy supply and transportation plan for the entire energy system that meets the corresponding low-carbon constraints.

[0060] In order to facilitate the understanding of those skilled in the art, Figure 4 An exemplary logic diagram of an hourly temporal path planning model is provided. The hourly temporal path planning model may include an electric energy system scheduling optimization module. The energy supply and transportation planning results obtained by the annual path planning model are input into the hourly temporal path planning model. The hourly temporal path planning model may simulate the temporal energy production and transportation process with each power consumption area as the energy hub node and the energy system as the core based on the energy supply and transportation planning results to obtain the energy production scheduling planning results.

[0061] Among them, the results of energy production scheduling planning may include the operating characteristics of traditional units, the output characteristics of new energy units, the operating characteristics of power system flexibility resources, the power balance of the power system, safety constraints, etc.

[0062] This hourly time-series path planning model uses hours as the time scale and, driven by low-carbon policies, derives the economically optimal energy production scheduling plan for the entire energy system that meets the corresponding low-carbon constraints.

[0063] Step S208, returning the energy production scheduling planning results to the annual path planning model, and performing the next round of optimization calculation process, so as to accumulate part of the energy production scheduling planning results throughout the year through the annual path planning model, and correct the energy supply and transportation planning results according to the part of the energy production scheduling planning results accumulated throughout the year, the operating characteristic data, the economic data and the demand forecast of each type of energy, and input the corrected energy supply and transportation planning results into the hourly time series path planning model to obtain the corrected energy production scheduling planning results, and return to the step of returning the energy production scheduling planning results to the annual path planning model to perform the next round of optimization calculation process until the convergence conditions are met.

[0064] Optionally, the annual path planning model may be provided with an initial value of the energy production scheduling planning result, which may be a preset value determined based on technical experience. In the initial round of optimization calculation process, the energy supply and transportation planning result corresponding to the initial round of optimization calculation process may be obtained based on this initial value and combined with the operating characteristic data, economic data and the demand forecast of each type of energy. In the next round of optimization calculation process, the annual path planning model may replace the above initial value with the energy production scheduling planning result obtained by the hourly time series path planning model, and correct the energy supply and transportation planning result corresponding to the initial round of optimization calculation process in combination with the operating characteristic data, economic data and the demand forecast of each type of energy, to obtain the energy supply and transportation planning result corresponding to the next round of optimization calculation process.

[0065] In the specific implementation, since the hourly temporal path planning model processes data at the hourly granularity, the energy production scheduling planning result output by the hourly temporal path planning model is the hourly energy production scheduling planning result, that is, different update results correspond to different hours; and the annual path planning model processes data at the granularity of one year, therefore, the annual path planning model needs to accumulate the energy production scheduling planning results throughout the year, and use the accumulated energy production scheduling planning results throughout the year to correct the energy supply and transportation planning results. Since the annual path planning model does not necessarily use the energy production scheduling planning results of all hours, the annual path planning model can only accumulate some key energy production scheduling planning results throughout the year, thereby improving data processing efficiency.

[0066] Figure 5 An exemplary logic diagram of the cyclic iteration of the annual path planning model and the hourly time-series path planning model is provided. It can be seen that the energy supply and transportation planning results output by the annual path planning model can be input into the hourly time-series path planning model as constraints; the energy production scheduling planning results output by the hourly time-series path planning model can be fed back to the annual path planning model to correct the energy supply and transportation planning results.

[0067] In summary, as an example, the energy system low-carbon transformation path planning method may include:

[0068] (1) First, classify the operating characteristics and economic data of energy supply technology types in multiple sectors such as electricity, industry, transportation, and construction, as well as multiple types of energy transportation technology types such as coal, oil, gas, and electricity;

[0069] (2) Based on these data, the demand for end products in multiple sectors such as electricity, industry, transportation, and construction is predicted, and the demand forecast for each type of energy in the energy system is derived;

[0070] (3) Establish the first-stage model, that is, use the operating characteristics data, economic data and calculated demand forecast as model inputs, and establish an annual path planning model with an annual time scale in combination with low-carbon policies, and obtain the optimization calculation results of the first-stage model output, that is, the energy supply and transportation planning results output by the annual path planning model;

[0071] (4) Establish the model of the second stage, use the energy supply and transportation planning results output by the annual path planning model as the model input, and establish an hourly time-series path planning model with hours as the time scale in combination with the low-carbon policy, and obtain the optimization calculation results of the model output of the second stage, that is, the energy production scheduling planning results output by the hourly time-series path planning model;

[0072] (5) Entering the two-stage model iteration process, the optimization calculation results of the second-stage model are fed back to the first-stage model to perform a second round of calculations, and steps S3 to S5 are repeated for multiple rounds of iterative calculations until the overall two-stage model converges.

[0073] In the above energy system low-carbon transformation path planning method,

[0074] Obtain the operating characteristic data and economic data of each energy technology type, where energy technology types include energy supply technology types and energy transportation technology types; determine the demand forecasts of each department for each type of energy in the energy system based on the operating characteristic data and economic data; in the initial round of optimization calculations, input the operating characteristic data, economic data and demand forecasts of each type of energy into the annual path planning model to obtain the energy supply and transportation planning results, and then input the energy supply and transportation planning results into the hourly time series path planning model to obtain the energy production scheduling planning results; return the energy production scheduling planning results to the annual The path planning model is used to carry out the next round of optimization calculation process, so as to accumulate the partial energy production scheduling planning results throughout the year through the annual path planning model, and to correct the energy supply and transportation planning results according to the partial energy production scheduling planning results accumulated throughout the year, the operation characteristic data, the economic data and the demand forecast of various energy sources, and input the corrected energy supply and transportation planning results into the hourly time series path planning model to obtain the corrected energy production scheduling planning results, and return to the step of returning the energy production scheduling planning results to the annual path planning model to carry out the next round of optimization calculation process until the convergence condition is met. By using the operation characteristic data and the economic data, combined with the two-stage optimization mechanism of the annual path planning model and the hourly time series path planning model, the energy supply and transportation planning results are dynamically iterated and corrected and the energy production scheduling planning results are updated to obtain accurate energy path planning results. Through the two-stage path planning models based on different time scales, the annual path planning model and the hourly time series path planning model, the organic combination of long-term macro planning and short-term micro scheduling is realized, which can take into account the medium- and long-term macro planning goals and short-term micro production and operation characteristics of complex energy systems, and improve the accuracy of energy path planning.

[0075] In another embodiment, the operation characteristic data, economic data and demand forecasts of various energy sources are input into an annual path planning model to obtain energy supply and transportation planning results, including: inputting the operation characteristic data, economic data and demand forecasts of various energy sources into the annual path planning model, and calculating the energy supply and transportation planning results that minimize the first objective function while satisfying various macro constraints;

[0076] Among them, the first objective function is determined based on the sum of the following data: annual average cost data of equipment investment and construction for each energy supply technology type and each energy transportation technology type, equipment operation and maintenance cost data, equipment operation cost data, net import and export cost data of various energy types, and carbon emission cost data for each energy supply technology type and each energy transportation technology type.

[0077] Furthermore, in one embodiment, the macro constraints include at least one of the following constraints: supply and demand balance constraints, equipment inventory constraints, technical energy consumption constraints, energy transportation volume limitation constraints, node energy balance constraints, and carbon emission constraints; wherein, the supply and demand balance constraints are used to constrain the balance between the product demand forecast and product output of each department; the equipment inventory constraints are used to constrain the equipment inventory of each energy technology type after addition, elimination and depreciation; the technical energy consumption constraints are used to constrain the energy consumption of any department for any energy technology type; the energy transportation volume limitation constraints are used to constrain the annual transportation volume between any two energy hub nodes in the energy system; the node energy balance constraints are used to constrain the balance between the input and output of any type of energy in any energy hub node; the carbon emission constraints are used to constrain the sum of carbon emissions of each energy technology type in each department.

[0078] In the specific implementation, the supply and demand balance constraint can be expressed as:

[0079] ;

[0080] in, is the annual demand for terminal products of the kth sector; Represents the demand for terminal products of the kth department.

[0081] The demand for a department's products and its output should be balanced, so a supply-demand balance constraint for the energy system can be established.

[0082] In the specific implementation, the equipment inventory constraint can be expressed as:

[0083] ;

[0084] ;

[0085] ;

[0086] in, is the equipment stock in year t corresponding to the z-th energy technology type in the k-th sector, is the new equipment increment in year t corresponding to the z-th energy technology type in the k-th sector, is the amount of old equipment eliminated in year t corresponding to the z-th energy technology type of the k-th sector, is the annual equipment depreciation rate for the zth energy technology type in the kth sector, and The annual cumulative quantity limit for each type of equipment, is the operating volume of each type of equipment in year t.

[0087] Therefore, the equipment stock of a certain energy technology type in a certain department in the current year should meet the equipment stock constraint.

[0088] In specific implementation, the technical energy consumption constraint can be expressed as:

[0089] ;

[0090] ;

[0091] in, is the output of terminal products in year t corresponding to the z-th energy technology type of the k-th sector, is the corresponding energy consumption, is the corresponding annual energy consumption factor, and The annual energy consumption limit value is , therefore, the energy consumption of a certain energy technology type in a certain department should not exceed the limit value.

[0092] In the specific implementation, the energy transportation volume restriction constraint can be expressed as:

[0093] ;

[0094] in, is the annual energy transport volume of the cth energy between nodes n and m, The energy transmission construction scale between nodes n and m for the zth type of energy transmission technology type of the cth type of energy, is the time dimension of the corresponding energy transport, is the number of energy transport technology types corresponding to the cth energy.

[0095] Therefore, the annual transportation volume of a certain type of energy between any two energy hub nodes should not exceed the limit.

[0096] In the specific implementation, the node energy balance constraint can be expressed as:

[0097] ;

[0098] in, and is the annual import and export volume of the cth energy source at node n, and is the annual external node input and output of the cth energy source at node n, is the annual owned amount of the cth energy source at node n, and is the annual consumption and storage of the cth energy at node n.

[0099] Therefore, the input and output of each type of energy at any energy hub node should satisfy the energy balance constraints of the node.

[0100] In specific implementation, the carbon emission constraint can be expressed as:

[0101] ;

[0102] in, is the unit product carbon emission factor corresponding to the z-th energy technology type of the k-th sector, and are the number of energy technology types in the kth sector and the number of sectors in the overall energy system, respectively.

[0103] Therefore, the sum of carbon emissions from all types of energy technologies in all sectors should not exceed the limit.

[0104] In the specific implementation, the first objective function can be expressed as:

[0105] ;

[0106] in, , , They are the sum of the annual average cost data of equipment investment and construction, the sum of equipment operation and maintenance cost data, and the sum of equipment operating cost data of each department for each energy supply technology type and energy transportation technology type. is the sum of the net import and export cost data of various energy sources, It is the sum of the carbon emission cost data of each sector for each energy technology type.

[0107] Therefore, the first objective function can be established with the goal of minimizing the annual average technical construction cost and carbon emission cost of the energy system.

[0108] In another embodiment, the energy supply and transportation planning results are input into an hourly timing path planning model to obtain energy production scheduling planning results, including: inputting the energy supply and transportation planning results into an hourly timing path planning model, and calculating the energy production scheduling planning results that minimize the second objective function while satisfying various timing constraints; wherein the second objective function is determined based on the scheduling cost data of each power generation equipment and energy transmission equipment in the energy system, and the investment, construction, and operation and maintenance cost data of each energy storage unit in the energy system.

[0109] Furthermore, in one of the embodiments, the timing constraints include the following constraints: power balance constraint, power reserve constraint, unit operation constraint, new energy generator unit output constraint, energy transportation capacity constraint, and low-carbon policy constraint; wherein, the power balance constraint is used to constrain the balance between the power supply and power consumption in any energy hub node in the energy system; the power reserve constraint is used to constrain the power supply capacity of any energy hub node; the unit operation constraint is used to constrain the real-time operating power of the energy supply unit of any energy hub node; the new energy generator unit output constraint is used to constrain the real-time operating power of the new energy generator unit of any energy hub node according to environmental factors; the energy transportation capacity constraint is used to constrain the energy transmission power between each energy hub node; the low-carbon policy constraint is used to constrain the ratio of the sum of the real-time operating power of the new energy generator unit throughout the year to the sum of the real-time operating power of all types of generator units throughout the year.

[0110] In specific implementation, the power balance constraint can be expressed as:

[0111] ;

[0112] in, is the sum of the power generated by a certain type of generator set in node n at time t, is the total number of generator types, is the transmission power of the AC / DC transmission line connected to node n at time t (input is defined as positive and output is defined as negative), is the total number of AC and DC transmission channels connected to province n, and are the discharge power and charging power of the energy storage unit in node n, respectively. is the total number of energy storage units in node n, is the power load of node n at time t.

[0113] The supply and consumption of electricity within an energy hub should always be balanced, so an electricity balance constraint can be established.

[0114] In specific implementation, the power reserve constraint can be expressed as:

[0115] ;

[0116] in, is the sum of the installed capacity of a certain type of generating units in the energy hub node n, is the sum of the transmission capacities of the AC and DC transmission lines connected to the energy hub node n, is the sum of the installed capacities of energy storage units in energy hub node n, is the annual maximum power load value of energy hub node n.

[0117] Therefore, the power supply capacity within a certain energy hub node should be greater than the maximum load value and leave a certain margin to meet the power supply security, so power reserve constraints can be established.

[0118] In specific implementation, the unit operation constraints can be expressed as:

[0119] ;

[0120] ;

[0121] in, is the real-time power of a certain type of energy supply unit in the energy hub node n (generator, new energy station, energy storage unit, electric hydrogen production unit, heat pump and other equipment), is the capacity of the energy supply unit at energy hub node n, It is the hourly ramp rate limit value of the unit.

[0122] Therefore, the real-time operating power of the energy supply units in a certain energy hub node should not exceed its capacity, and the output change at adjacent moments should not exceed its climbing rate.

[0123] In specific implementation, the output constraint of the new energy generator set can be expressed as:

[0124] ;

[0125] ;

[0126] in, is the real-time power of the new energy generator set (wind power, photovoltaic) in the energy hub node n, It is the maximum output capacity of the new energy unit after considering environmental factors. is the hourly capacity factor sequence of new energy units, It is the installed capacity of new energy generating units.

[0127] Therefore, the real-time operating power of a new energy generator set within an energy hub node should not exceed its maximum output capacity at that moment, taking into account environmental factors such as wind speed, irradiance, and temperature.

[0128] In specific implementation, the energy transportation capacity constraint can be expressed as:

[0129] .

[0130] Therefore, the energy transmission power between energy hub nodes should not exceed the transmission capacity of the channel.

[0131] In specific implementation, the low-carbon policy constraint can be expressed as:

[0132] ;

[0133] in, is the proportion of electricity generated by new energy units, and are the total number of new energy units in the energy hub node n and the total number of nodes in the energy system, is the total number of epochs for optimizing the model, It is the annual electricity consumption of the whole system.

[0134] Therefore, in order to increase the proportion of renewable energy power generation, low-carbon policy constraints can be established.

[0135] In the specific implementation, the second objective function can be expressed as:

[0136] ;

[0137] in, It is the sum of the dispatching costs of power generation equipment and energy transmission equipment at each energy hub node in the power energy system. It is the sum of the investment, construction, operation and maintenance costs of energy storage units at each energy hub node.

[0138] Therefore, the second objective function can be established with the goal of minimizing the production scheduling cost of the energy system.

[0139] The energy system low-carbon transformation path planning method of the embodiment of the present application can take into account the annual medium- and long-term macro-construction planning of various departments in the complex energy system and the hour-level refined production and transportation scheduling timing simulation, so that the low-carbon transformation path planning scheme obtained by model optimization is more accurate, reasonable and economical; it is suitable for complex energy systems including multiple departments such as electricity, transportation, industry, and construction. The model can consider multiple energy types such as coal, oil, gas, and electricity, and can optimize and simulate the characteristics of all links of energy production, supply, transmission, storage, and consumption and provide a low-carbon transformation path planning scheme for the energy system.

[0140] In another embodiment, returning to the step of returning the energy production scheduling planning result to the annual path planning model and performing the next round of optimization calculation process until the convergence condition is met includes: when the difference between the total annualized energy cost data respectively calculated in the optimization calculation processes of the latest two rounds is greater than the convergence threshold, returning to the step of returning the energy production scheduling planning result to the annual path planning model and performing the next round of optimization calculation process until the difference is less than or equal to the convergence threshold;

[0141] The total annualized energy cost data is determined based on the energy supply and transportation planning results and the energy production scheduling planning results in the optimization calculation process of the corresponding round.

[0142] In specific implementation, the two-stage model iterative solution process may include:

[0143] (1) Starting from the first iteration, the demand for end products and the predicted demand for each type of energy obtained above are used as boundary conditions, and the annual path planning model is used to optimize and calculate the output of the energy supply and transportation planning results. The energy supply and transportation planning results may include the combined information of energy supply and energy transportation technology;

[0144] (2) The hourly time-series path planning model is based on the energy supply and transportation planning results. It optimizes the hourly time-series energy production and transmission process with the electric energy system as the core, and obtains the hourly time-series production scheduling simulation results of the energy system, that is, the energy production scheduling planning results. At the same time, based on the energy supply and transportation planning results and the energy production scheduling planning results, the total annualized energy cost data of the energy system (total annual average energy cost data) is calculated. Specifically, the total annualized energy cost data of each round can be equal to the sum of the first objective function in the annual path planning model of that round and the second objective function in the hourly time-series path planning model.

[0145] (3) At the beginning of the second iteration, the energy production scheduling planning results obtained by optimizing the hourly temporal path planning model in the first iteration are input into the annual path planning model to correct its energy supply and transportation planning results, that is, to correct the energy supply and transportation technology combination.

[0146] (4) The hourly time-series path planning model further optimizes the time-series energy production and transmission process based on the calculation results of the annual path planning model, obtains a new energy production scheduling planning result, and calculates the total annualized energy cost data of the new energy system.

[0147] (5) Repeat the above steps (3) to (4). If the difference between the total annualized energy cost data of the energy system in the calculation results of two adjacent rounds of iterations is less than the convergence threshold, the iteration is considered to have converged and the optimization calculation is terminated. Otherwise, the iteration continues.

[0148] As an example, a judgment criterion for the convergence of cyclic iterative calculations can be established. If the results of two adjacent rounds of iterative calculations meet the following judgment criterion, the annual path planning model and the hourly time series path planning model of the two stages are judged to have converged:

[0149] ;

[0150] in, The total annualized energy cost data for the latest round of optimization calculation process, The total annualized energy cost data of the optimization calculation process of the previous adjacent round; Convergence threshold calculated for two iterations.

[0151] The energy system low-carbon transformation path planning method of the embodiment of the present application is applicable to complex energy systems including multiple sectors such as electricity, transportation, industry, and construction. The model can take into account multiple energy types such as coal, oil, gas, and electricity, and can optimize and simulate the characteristics of all links of energy production, supply, transmission, storage, and consumption, and provide a low-carbon transformation path planning scheme for the energy system; it can take into account the annual medium- and long-term macro-construction plans of various departments within the complex energy system and the hourly refined production and transportation scheduling timing simulation, so that the low-carbon transformation path planning scheme obtained by model optimization is more accurate, reasonable, and economical.

[0152] This energy system low-carbon transformation path planning method is a two-stage optimization modeling method that considers different time scales. It can achieve an organic combination of the medium- and long-term macro-planning goals of complex energy systems and the micro-production and operation characteristics, improve the model solving efficiency without losing the model solving accuracy, and achieve a comprehensive analysis and transformation path optimization of multiple energy categories, energy production, supply, storage and consumption in complex energy systems, providing scientific decision-making support for government departments. At the same time, it has important theoretical value and practical significance for enterprises to explore suitable carbon emission reduction paths and models, and ultimately provide strong support for promoting the low-carbon transformation and sustainable development of the energy structure.

[0153] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0154] Based on the same inventive concept, the embodiment of the present application also provides an energy system low-carbon transformation path planning device for implementing the energy system low-carbon transformation path planning method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more energy system low-carbon transformation path planning device embodiments provided below can refer to the above limitations on the energy system low-carbon transformation path planning method, which will not be repeated here.

[0155] In an exemplary embodiment, Figure 6 As shown, a low-carbon transformation path planning device for an energy system is provided, comprising:

[0156] The data acquisition module 610 is used to acquire the operating characteristic data and economic data of each energy technology type; the energy technology type includes energy supply technology type and energy transportation technology type.

[0157] The demand determination module 620 is used to determine the forecast demand of each department for each type of energy in the energy system based on the operating characteristic data and the economic data.

[0158] The planning module 630 is used to input the operating characteristic data, the economic data and the demand forecast of each type of energy into the annual path planning model during the initial round of optimization calculations to obtain the energy supply and transportation planning results, and then input the energy supply and transportation planning results into the hourly time series path planning model to obtain the energy production scheduling planning results.

[0159] The correction module 640 is used to return the energy production scheduling planning results to the annual path planning model to carry out the next round of optimization calculation process, so as to accumulate part of the energy production scheduling planning results throughout the year through the annual path planning model, and correct the energy supply and transportation planning results according to part of the energy production scheduling planning results accumulated throughout the year, the operating characteristic data, the economic data and the demand forecast of each type of energy, and input the corrected energy supply and transportation planning results into the hourly time series path planning model to obtain the corrected energy production scheduling planning results, return to the step of returning the energy production scheduling planning results to the annual path planning model, and carry out the next round of optimization calculation process until the convergence conditions are met.

[0160] In one embodiment, the planning module 630 is specifically used to input the operating characteristic data, the economic data and the demand forecast of each type of energy into the annual path planning model, and calculate the energy supply and transportation planning result that minimizes the first objective function while satisfying various macro constraints; wherein the first objective function is determined based on the sum of the following data: the annual average cost data of equipment investment and construction for each type of energy supply technology and each type of energy transportation technology, equipment operation and maintenance cost data, equipment operation cost data, the net import and export cost data of each type of energy, and the carbon emission cost data of each type of energy supply technology and each type of energy transportation technology.

[0161] In one embodiment, the macro-constraints include at least one of the following constraints: supply and demand balance constraints, equipment inventory constraints, technical energy consumption constraints, energy transportation volume limitation constraints, node energy balance constraints and carbon emission constraints; wherein, the supply and demand balance constraints are used to constrain the balance relationship between the product demand forecast and product output of each department; the equipment inventory constraints are used to constrain the equipment inventory of each energy technology type after addition, elimination and depreciation; the technical energy consumption constraints are used to constrain the energy consumption of any department for any energy technology type; the energy transportation volume limitation constraints are used to constrain the annual transportation volume between any two energy hub nodes in the energy system; the node energy balance constraints are used to constrain the balance relationship between the input and output of any type of energy in any energy hub node; the carbon emission constraints are used to constrain the sum of carbon emissions of each energy technology type in each department.

[0162] In one embodiment, the planning module 630 is specifically used to input the energy supply and transportation planning results into the hourly timing path planning model to obtain the energy production scheduling planning results, including: inputting the energy supply and transportation planning results into the hourly timing path planning model, and calculating the energy production scheduling planning results that minimize the second objective function while satisfying various timing constraints; wherein the second objective function is determined based on the scheduling cost data of each power generation equipment and energy transmission equipment in the energy system, and the investment, construction, and operation and maintenance cost data of each energy storage unit in the energy system.

[0163] In one embodiment, the timing constraints include the following constraints: power balance constraint, power reserve constraint, unit operation constraint, new energy generator output constraint, energy transportation capacity constraint, and low-carbon policy constraint; wherein, the power balance constraint is used to constrain the balance relationship between the power supply and power consumption in any energy hub node in the energy system; the power reserve constraint is used to constrain the power supply capacity of any of the energy hub nodes; the unit operation constraint is used to constrain the real-time operating power of the energy supply unit of any of the energy hub nodes; the new energy generator output constraint is used to constrain the real-time operating power of the new energy generator set of any of the energy hub nodes according to environmental factors; the energy transportation capacity constraint is used to constrain the energy transmission power between each of the energy hub nodes; the low-carbon policy constraint is used to constrain the ratio of the sum of the real-time operating power of the new energy generator set throughout the year to the sum of the real-time operating power of all types of generator sets throughout the year.

[0164] In one embodiment, the correction module 640 is specifically used to return the energy production scheduling planning results to the annual path planning model, and perform the next round of optimization calculation process, so as to accumulate part of the energy production scheduling planning results throughout the year through the annual path planning model, and correct the energy supply and transportation planning results according to part of the energy production scheduling planning results accumulated throughout the year, the operating characteristic data, the economic data and the demand forecast of each type of energy, and input the corrected energy supply and transportation planning results into the hourly time series path planning model to obtain the corrected energy production scheduling planning results, and return to the step of returning the energy production scheduling planning results to the annual path planning model to perform the next round of optimization calculation process until the convergence conditions are met.

[0165] Each module in the above-mentioned energy system low-carbon transformation path planning device can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0166] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 7As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, near field communication (Near Field Communication, NFC) or other technologies. When the computer program is executed by the processor, a low-carbon transformation path planning method for an energy system is realized. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0167] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0168] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0169] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0170] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0171] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0172] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0173] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0174] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for low-carbon transformation path planning of energy system, characterized in that: The method comprises: Obtaining operation characteristic data and economic data of various energy technology types; the energy technology types include energy supply technology types and energy transportation technology types; Determine the forecasted demand of each department for each type of energy in the energy system based on the operating characteristic data and the economic data; In the initial round of optimization calculation, the operating characteristic data, the economic data and the demand forecast of each type of energy are input into the annual path planning model to obtain the energy supply and transportation planning result, and then the energy supply and transportation planning result is input into the hourly time series path planning model to obtain the energy production scheduling planning result; The energy production scheduling planning results are returned to the annual path planning model to carry out the next round of optimization calculation process, so as to accumulate part of the energy production scheduling planning results throughout the year through the annual path planning model, and the energy supply and transportation planning results are corrected according to part of the energy production scheduling planning results accumulated throughout the year, the operating characteristic data, the economic data and the demand forecast of each type of energy, and the corrected energy supply and transportation planning results are input into the hourly time series path planning model to obtain the corrected energy production scheduling planning results, and return to the step of returning the energy production scheduling planning results to the annual path planning model to carry out the next round of optimization calculation process until the convergence conditions are met.

2. The method according to claim 1, characterized in that: The operation characteristic data, the economic data and the demand forecast of each type of energy are input into the annual path planning model to obtain the energy supply and transportation planning results, including: Input the operating characteristic data, the economic data and the demand forecast of each type of energy into the annual path planning model, and calculate the energy supply and transportation planning result that minimizes the first objective function while satisfying various macro constraints; Among them, the first objective function is determined based on the sum of the following data: the annual average cost data of equipment investment and construction for each of the energy supply technology types and each of the energy transportation technology types, equipment operation and maintenance cost data, equipment operation cost data, the net import and export cost data of each type of energy, and the carbon emission cost data of each of the energy supply technology types and each of the energy transportation technology types.

3. The method according to claim 2, characterized in that The macro constraints include the following constraints: supply and demand balance constraints, equipment inventory constraints, technical energy consumption constraints, energy transportation volume limit constraints, node energy balance constraints and carbon emission constraints; Among them, the supply and demand balance constraint is used to constrain the balance relationship between the product demand forecast and product output of each department; the equipment inventory constraint is used to constrain the equipment inventory of each energy technology type after addition, elimination and depreciation; the technical energy consumption constraint is used to constrain the energy consumption of any department for any energy technology type; the energy transportation volume limit constraint is used to constrain the annual transportation volume between any two energy hub nodes in the energy system; the node energy balance constraint is used to constrain the balance relationship between the input and output of any type of energy in any energy hub node; the carbon emission constraint is used to constrain the sum of carbon emissions of each energy technology type in each department.

4. The method according to claim 1, characterized in that: The energy supply and transportation planning result is input into the hourly time sequence path planning model to obtain the energy production scheduling planning result, including: Input the energy supply and transportation planning result into the hourly time sequence path planning model, and calculate the energy production scheduling planning result that minimizes the second objective function while satisfying various time sequence constraints; The second objective function is determined based on the dispatching cost data of each power generation equipment and energy transmission equipment in the energy system and the investment, construction, operation and maintenance cost data of each energy storage unit in the energy system.

5. The method according to claim 4, characterized in that The timing constraints include the following constraints: power balance constraints, power reserve constraints, unit operation constraints, new energy generator output constraints, energy transportation capacity constraints, and low-carbon policy constraints; Among them, the power balance constraint is used to constrain the balance relationship between the power supply and power consumption in any energy hub node in the energy system; the power reserve constraint is used to constrain the power supply capacity of any of the energy hub nodes; the unit operation constraint is used to constrain the real-time operating power of the energy supply unit of any of the energy hub nodes; the new energy generator set output constraint is used to constrain the real-time operating power of the new energy generator set of any of the energy hub nodes according to environmental factors; the energy transportation capacity constraint is used to constrain the energy transmission power between each of the energy hub nodes; the low-carbon policy constraint is used to constrain the ratio of the sum of the real-time operating power of the new energy generator set throughout the year to the sum of the real-time operating power of all types of generator sets throughout the year.

6. The method according to claim 1, characterized in that The step of returning the energy production scheduling planning result to the annual path planning model and performing the next round of optimization calculation process until the convergence condition is met includes: When the difference between the total annualized energy cost data respectively calculated in the two latest rounds of optimization calculation processes is greater than the convergence threshold, return to the step of returning the energy production scheduling planning result to the annual path planning model to perform the next round of optimization calculation process until the difference is less than or equal to the convergence threshold; The total annualized energy cost data is determined based on the energy supply and transportation planning results and the energy production scheduling planning results in the optimization calculation process of the corresponding round.

7. A low-carbon transformation path planning device for an energy system, characterized in that: The device comprises: A data acquisition module, used to acquire operating characteristic data and economic data of various energy technology types; the energy technology types include energy supply technology types and energy transportation technology types; A demand determination module, used to determine the forecast demand of each department for each type of energy in the energy system according to the operation characteristic data and the economic data; A planning module, which is used to input the operating characteristic data, the economic data and the demand forecast of each type of energy into an annual path planning model during the initial round of optimization calculation to obtain an energy supply and transportation planning result, and then input the energy supply and transportation planning result into an hourly time series path planning model to obtain an energy production scheduling planning result; A correction module is used to return the energy production scheduling planning results to the annual path planning model to carry out the next round of optimization calculation process, so as to accumulate part of the energy production scheduling planning results throughout the year through the annual path planning model, and correct the energy supply and transportation planning results according to part of the energy production scheduling planning results accumulated throughout the year, the operating characteristic data, the economic data and the demand forecast of each type of energy, and input the corrected energy supply and transportation planning results into the hourly time series path planning model to obtain the corrected energy production scheduling planning results, return to the step of returning the energy production scheduling planning results to the annual path planning model, and carry out the next round of optimization calculation process until the convergence conditions are met.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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