A capacity configuration method and system based on carbon trading and evolutionary path analysis

Abstract

The application provides a capacity configuration method and system based on carbon trading and evolutionary path analysis, the method comprising: acquiring basic planning data of a comprehensive energy system; inputting the basic planning data and a plurality of device cost data sets into a preset planning model, so that the planning model generates corresponding respective simulation evolutionary paths according to the basic planning data and the plurality of device cost data sets, the simulation evolutionary paths comprising capacity configurations of respective device types of the comprehensive energy system in each year, and the plurality of device cost data sets are generated by Monte Carlo random sampling; performing evolutionary analysis on the respective simulation evolutionary paths according to preset analysis indexes to determine an evolutionary path of the comprehensive energy system; and adjusting the capacity configurations of respective device types of the comprehensive energy system according to the evolutionary path of the comprehensive energy system in the planning period, so as to improve the rationality and scientificity of the capacity configurations of respective devices of the comprehensive energy system.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power system planning, in particular to a capacity configuration method and system based on carbon trading and evolution path analysis. BACKGROUND

[0002] Carbon trading is the general term for greenhouse gas emission right trading. The basic principle of carbon trading is that one party of a contract obtains greenhouse gas emission reduction amount by paying another party. The buyer can use the purchased emission reduction amount to mitigate the greenhouse effect to achieve the emission reduction target. Among the six types of greenhouse gases required to be reduced, carbon dioxide (CO2) is the largest, so the transaction is calculated in units of tons of carbon dioxide equivalent (tCO2e), so it is commonly known as "carbon trading". Its trading market is called the carbon market (Carbon Market). After the Kyoto Protocol came into effect in 2005, the global carbon trading market has experienced explosive growth. In 2007, carbon trading volume jumped from 1.6 billion tons in 2006 to 2.7 billion tons, an increase of 68.75%. The growth of the transaction amount is even more rapid. In 2007, the global carbon trading market was worth 40 billion euros, an increase of 81.8% over 2006's 22 billion euros. In the first half of 2008, the total value of the global carbon trading market was even equal to that of the whole year of 2007. As of July 15, 2024, the cumulative transaction volume of carbon quotas in the Chinese carbon market reached 465 million tons, and the cumulative transaction amount was nearly 27 billion yuan.

[0003] The integrated energy system refers to a region using advanced physical information technology and innovative management mode, integrating coal, oil, natural gas, electric energy, heat energy and other multiple energies in the region, realizing coordinated planning, optimized operation, collaborative management, interactive response and complementary mutual aid among multiple heterogeneous energy subsystems. While meeting the diversified energy demand in the system, the integrated energy system effectively improves energy utilization efficiency and promotes sustainable development of a new integrated energy system. The integrated energy system specifically refers to an integrated energy production and sales system formed by organic coordination and optimization of energy generation, transmission and distribution (energy network), conversion, storage and consumption in the process of planning, construction and operation. It is mainly composed of energy supply network (such as power supply, gas supply, cooling / heat supply network), energy exchange link (such as CCHP unit, generator set, boiler, air conditioner, heat pump), energy storage link (electricity storage, gas storage, heat storage, cold storage), terminal integrated energy supply and use unit (such as microgrid) and a large number of terminal users. Due to the complexity of the structure and composition of the integrated energy system, in the planning and development process of the park integrated energy system, not only the investment cost of wind power, photovoltaic and other equipment needs to be concerned, but also how to reasonably allocate carbon trading resources in the market and reduce carbon trading needs to be considered. In addition, in the actual production process, the cost estimation of each device has great uncertainty, and we usually cannot accurately estimate the unit cost of each device in each time period in the future, which further affects the rationality and accuracy of the capacity configuration planning of the integrated energy system. SUMMARY

[0004] In view of the above technical problems, the application provides a capacity configuration method and system based on carbon trading and evolutionary path analysis, which improves the rationality and scientificity of capacity configuration of various devices of the integrated energy system.

[0005] In a first aspect, the application provides a capacity configuration method based on carbon trading and evolutionary path analysis, comprising:

[0006] obtaining basic planning data of the integrated energy system, wherein the basic planning data includes planning period, device parameters, carbon market trading information and various load data;

[0007] inputting the basic planning data and a plurality of device cost data sets into a preset planning model, so that the planning model generates corresponding simulation evolutionary paths according to the basic planning data and the plurality of device cost data sets, wherein the planning model is obtained according to the system structure and operation mode of the integrated energy system, the simulation evolutionary path includes the capacity configuration of each device type of the integrated energy system in each year, and the plurality of device cost data sets are generated by Monte Carlo random sampling;

[0008] evolution analysis is performed on the simulation evolution paths according to preset analysis indexes, and an evolution path of the comprehensive energy system is determined;

[0009] In the planning period, the capacity configuration of each device type of the comprehensive energy system is adjusted according to the evolution path of the comprehensive energy system.

[0010] The embodiment of the application provides a capacity configuration method based on carbon trading and evolution path analysis. By obtaining basic planning data of a comprehensive energy system and a plurality of device cost data sets generated by Monte Carlo random sampling, a preset planning model is used to perform simulation operation according to the basic planning data and the device cost data sets, a plurality of simulation evolution paths of the comprehensive energy system can be generated, and the capacity configuration of each device type in each simulation evolution path records the change process in a long time in the future. Then, evolution analysis is performed on each path according to preset analysis indexes, and the optimal simulation evolution path is selected as the evolution path of the comprehensive energy system. Finally, in the development process of the comprehensive energy system, the capacity configuration of each device type of the comprehensive energy system is continuously adjusted according to the evolution path of the comprehensive energy system. The embodiment of the application generates corresponding simulation evolution paths of each device type based on the overall structure of the comprehensive energy system and the carbon trading process through a planning model, each evolution path is an optimal capacity configuration scheme obtained under the condition of a corresponding device cost data set, and then each evolution path is analyzed and evaluated, and the optimal evolution path is selected to perform capacity configuration on the comprehensive energy system, thereby improving the rationality and scientificity of the capacity configuration of each device of the comprehensive energy system. In addition, the Monte Carlo random sampling method is introduced to generate a plurality of device cost data sets, a plurality of device investment cost combinations most likely to occur in the future are obtained through a large amount of random sampling, and a plurality of device cost data sets are constructed, thereby solving the problem that the cost estimation of each device has great uncertainty in the conventional planning method. A large number of simulation evolution paths are generated through the plurality of device cost data sets, and the optimal path is selected, thereby further improving the rationality and scientificity of the capacity configuration of each device of the comprehensive energy system.

[0011] Further, the plurality of device cost data sets are generated by Monte Carlo random sampling, including:

[0012] Obtaining historical cost data of each device type;

[0013] According to each of the historical cost data, the cost value range of each device type is predicted;

[0014] The probability distribution of the cost value range of each device type is determined;

[0015] According to the cost value range of each device type and the corresponding probability distribution, a plurality of Monte Carlo random sampling is performed to generate a plurality of device cost data sets, wherein in each Monte Carlo random sampling, a random cost value of each device type is randomly selected from the cost value range of each device type based on the corresponding probability distribution, and then the random cost value of each device type constitutes the device cost data set.

[0016] The embodiment of the application provides a method for generating a plurality of device cost data sets through Monte Carlo random sampling, which firstly predicts the cost value range of each device type according to the historical cost data of each device type, and then randomly samples from each cost value range. Before random sampling, the probability distribution of the cost value range of each device type needs to be determined. It is generally believed that the cost value range of each device type conforms to the normal distribution, and the person skilled in the art can also select other appropriate probability distributions according to the historical cost data. Finally, according to the cost value range of each device type and the corresponding probability distribution, a plurality of Monte Carlo random sampling is performed, which can not only eliminate the uncertainty of the cost estimation of each device, but also provide a plurality of device cost data sets for subsequent simulation evolution path generation, thereby generating a large number of simulation evolution paths and improving the rationality and scientificity of the capacity configuration of various devices of the comprehensive energy system.

[0017] In a possible implementation manner, the planning model generates corresponding simulation evolution paths according to the basic planning data and the plurality of device cost data sets, comprising:

[0018] According to the basic planning data and the constraint conditions of the planning model, the operation of the comprehensive energy system is simulated;

[0019] In the process of simulating the operation of the comprehensive energy system, the target function and each device cost data set are calculated respectively to generate each simulation evolution path corresponding to each device cost data set.

[0020] The embodiment of the application provides a method for generating each simulation evolution path using a planning model, which simulates the operation of the comprehensive energy system through the constraint conditions of the planning model and the input basic planning data. In this process, the planning model performs calculation according to the target function and the input device cost data set, and each simulation evolution path corresponding to each device cost data set can be obtained, which realizes the prediction of the future device cost and evolution path of the comprehensive energy system and provides data support for the capacity configuration of the devices of the comprehensive energy system.

[0021] In a possible implementation manner, the evolution analysis on the simulation evolution paths according to preset analysis indexes includes:

[0022] The life cycle cost, carbon emission and carbon trading cost of each simulation evolution path are calculated.

[0023] The life cycle cost, carbon emission and carbon trading cost of each simulation evolution path are weighted according to preset weight coefficients to obtain optimization benefits of each simulation evolution path.

[0024] The comprehensive energy system evolution path is determined from the simulation evolution paths according to the optimization benefits of the simulation evolution paths.

[0025] The method for evolution analysis on the simulation evolution paths is provided in the embodiments of the present application, the life cycle cost, carbon emission and carbon trading cost under each simulation evolution path are calculated, the three analysis indexes are weighted to obtain the optimization benefits of each simulation evolution path, and finally the comprehensive energy system evolution path is determined from the simulation evolution paths according to the optimization benefits. The life cycle cost reflects the economy of each simulation evolution path, and the carbon emission and carbon trading cost reflect the environmental protection of each simulation evolution path. The preset weight coefficients are used to balance the economy and environmental protection of the comprehensive energy system in the development process, the weight coefficients can be set by the person skilled in the art according to the actual situation, and the flexibility and scientificity of the evolution path selection process are embodied.

[0026] Further, the planning model is constructed according to the system structure and operation mode of the comprehensive energy system, including:

[0027] Each device operation model corresponding to the system structure and operation mode of the comprehensive energy system is constructed, including a gas boiler device model, a combined heat and power device model, an electric refrigerator device model, an absorption refrigerator device model and an energy storage model.

[0028] Each carbon emission model of the comprehensive energy system is constructed according to the device operation models.

[0029] Each constraint condition of the planning model is constructed according to each device operation model and each carbon emission model.

[0030] An investment cost model, an operation cost model, a carbon trading cost model and a maintenance cost model of the comprehensive energy system are constructed according to each device operation model and each carbon emission model.

[0031] construct a target function of the planning model according to an investment cost model, an operation cost model, a carbon trading cost model and a maintenance cost model of the integrated energy system;

[0032] construct the planning model in combination with each constraint condition and the target function.

[0033] The embodiment of the present application provides a method for constructing a planning model. Firstly, each device operation model is constructed through system structure. Meanwhile, each carbon emission model is constructed considering that there are carbon emission and carbon trading processes in the operation of the integrated energy system, so that the planning model can simulate the normal operation of the integrated energy system. The integrated energy system is influenced by many factors during operation, so each constraint condition is constructed based on each device operation model and each carbon emission model. These constraints ensure that the integrated energy system is stably, safely and efficiently operated during simulation, so that the generated planning scheme is more in line with the actual situation. Finally, the target function of the planning model is constructed according to the investment cost model, the operation cost model, the carbon trading cost model and the maintenance cost model of the integrated energy system, so as to determine the optimization direction of the planning model, so that the final planning scheme can effectively reduce various costs and improve the rationality and scientificity of capacity configuration of various devices of the integrated energy system.

[0034] In a second aspect, the present application provides a capacity configuration system based on carbon trading and evolution path analysis, comprising an acquisition module, a path evolution module, a path determination module and a capacity configuration module.

[0035] The acquisition module is configured to acquire basic planning data of the integrated energy system, wherein the basic planning data comprises a planning period, device parameters, carbon market trading information and various load data.

[0036] The path evolution module is configured to input the basic planning data and a plurality of device cost data sets into a preset planning model, so that the planning model generates corresponding each simulation evolution path according to the basic planning data and the plurality of device cost data sets, wherein the planning model is constructed according to the system structure and operation mode of the integrated energy system, the simulation evolution path comprises the capacity configuration of each device type of the integrated energy system in each year, and the plurality of device cost data sets are generated by Monte Carlo random sampling.

[0037] The path determination module is configured to perform evolution analysis on each simulation evolution path according to a preset analysis index, and determine an integrated energy system evolution path.

[0038] The capacity configuration module is configured to adjust the capacity configuration of each device type of the integrated energy system according to the integrated energy system evolution path in the planning period.

[0039] Further, the generating the plurality of equipment cost data sets by Monte Carlo random sampling comprises:

[0040] obtaining historical cost data of each equipment type respectively;

[0041] predicting a cost value range of each equipment type according to each historical cost data;

[0042] determining a probability distribution of the cost value range of each equipment type;

[0043] generating a plurality of equipment cost data sets by a plurality of Monte Carlo random samplings according to the cost value range of each equipment type and the corresponding probability distribution, wherein in each Monte Carlo random sampling, a random cost value of each equipment type is randomly selected from the cost value range of each equipment type based on the corresponding probability distribution, and then the random cost value of each equipment type constitutes the equipment cost data set.

[0044] In a possible implementation manner, the planning model generates a corresponding simulation evolution path of each equipment cost data set according to the basic planning data and the plurality of equipment cost data sets, comprising:

[0045] simulating the operation of the integrated energy system according to the basic planning data and the constraint conditions of the planning model;

[0046] In the process of simulating the operation of the integrated energy system, a target function and each equipment cost data set are calculated respectively to generate each simulation evolution path corresponding to each equipment cost data set respectively.

[0047] In a possible implementation manner, the path determination module performs evolution analysis on each simulation evolution path according to a preset analysis index to determine the integrated energy system evolution path, comprising:

[0048] calculating the life cycle cost, carbon emission and carbon trading cost of each simulation evolution path;

[0049] performing weighted calculation on the life cycle cost, carbon emission and carbon trading cost of each simulation evolution path according to a preset weight coefficient to obtain the optimization benefit of each simulation evolution path;

[0050] determining the integrated energy system evolution path from each simulation evolution path according to the optimization benefit of each simulation evolution path.

[0051] Further, the planning model is constructed according to the system structure and operation mode of the integrated energy system, comprising:

[0052] According to the system structure and operation mode of the integrated energy system, a corresponding device operation model of each device is constructed, including a gas boiler device model, a combined heat and power device model, an electric refrigerator device model, an absorption refrigerator device model, and an energy storage model;

[0053] According to the device operation model of each device, a carbon emission model of the integrated energy system is constructed;

[0054] According to the device operation model of each device and the carbon emission model of each device, a constraint condition of the planning model is constructed;

[0055] According to the device operation model of each device and the carbon emission model of each device, an investment cost model, an operation cost model, a carbon trading cost model, and a maintenance cost model of the integrated energy system are constructed;

[0056] According to the investment cost model, the operation cost model, the carbon trading cost model, and the maintenance cost model of the integrated energy system, an objective function of the planning model is constructed;

[0057] In combination with the constraint condition and the objective function, the planning model is constructed. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 : a flowchart of a capacity configuration method based on carbon trading and evolution path analysis provided by an embodiment of the present application.

[0059] Figure 2 : a schematic diagram of a system structure and operation mode of an integrated energy system provided by an embodiment of the present application.

[0060] Figure 3 : a detailed flowchart of a capacity configuration method based on carbon trading and evolution path analysis provided by an embodiment of the present application.

[0061] Figure 4 : a schematic diagram of a capacity evolution path of each device type in a capacity configuration method based on carbon trading and evolution path analysis provided by an embodiment of the present application.

[0062] Figure 5 : a schematic diagram of a cost evolution path of each type in a capacity configuration method based on carbon trading and evolution path analysis provided by an embodiment of the present application.

[0063] Figure 6 : a schematic diagram of a carbon emission evolution path in a capacity configuration method based on carbon trading and evolution path analysis provided by an embodiment of the present application.

[0064] Figure 7A comparison diagram of optimization benefits of various simulation evolution paths in a capacity configuration method based on carbon trading and evolution path analysis provided by an embodiment of the present application.

[0065] Figure 8 A structure diagram of a capacity configuration system based on carbon trading and evolution path analysis provided by an embodiment of the present application. DETAILED DESCRIPTION

[0066] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0067] It should be noted that the step numbers in the text are only for the convenience of explanation of the specific embodiments, and do not serve as the function of limiting the execution sequence of the steps. In the description of the present application, the terms "first", "second" are only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features limited by "first", "second" can explicitly or implicitly include one or more of the features.

[0068] Embodiment one:

[0069] As shown in Figure 1 Embodiment one provides a capacity configuration method based on carbon trading and evolution path analysis, comprising steps S1-S4:

[0070] Step S1, obtaining basic planning data of a comprehensive energy system, wherein the basic planning data comprises a planning period, equipment parameters, carbon market transaction information and various load data;

[0071] Step S2, inputting the basic planning data and a plurality of equipment cost data sets into a preset planning model, so that the planning model generates corresponding various simulation evolution paths according to the basic planning data and the plurality of equipment cost data sets, wherein the planning model is obtained according to the system structure and operation mode of the comprehensive energy system, the simulation evolution path comprises the capacity configuration of each equipment type of the comprehensive energy system each year, and the plurality of equipment cost data sets are generated by Monte Carlo random sampling;

[0072] Step S3, performing evolution analysis on the various simulation evolution paths according to a preset analysis index to determine the evolution path of the comprehensive energy system;

[0073] Step S4, in the planning period, adjusting the capacity configuration of each device type of the integrated energy system according to the integrated energy system evolution path.

[0074] The embodiment of the present application provides a capacity configuration method based on carbon trading and evolution path analysis. By obtaining the basic planning data of the integrated energy system and a plurality of device cost data sets generated by Monte Carlo random sampling, a preset planning model is used to perform simulation operation according to the basic planning data and the device cost data sets, a plurality of simulation evolution paths of the integrated energy system can be generated, and the capacity configuration of each device type in each simulation evolution path is recorded in the change process in the future long time. Then, according to the preset analysis index, evolution analysis is performed on each path, and the optimal simulation evolution path is selected as the integrated energy system evolution path. Finally, in the development process of the integrated energy system, the capacity configuration of each device type of the integrated energy system is continuously adjusted according to the integrated energy system evolution path. The embodiment of the present application is based on the overall structure of the integrated energy system and the carbon trading process, and each simulation evolution path is generated by a planning model. Each evolution path is an optimal capacity configuration scheme obtained under the condition of a corresponding device cost data set. Then, each evolution path is analyzed and evaluated, and the optimal evolution path is selected to configure the capacity of the integrated energy system, which improves the rationality and scientificity of the capacity configuration of each device of the integrated energy system. In addition, the Monte Carlo random sampling method is introduced to generate a plurality of device cost data sets. A plurality of device investment cost combinations most likely to occur in the future are obtained by a large amount of random sampling, and a plurality of device cost data sets are constructed, which solves the problem that the cost estimation of each device has great uncertainty in the conventional planning method. A large number of simulation evolution paths are generated by the plurality of device cost data sets, and the optimal path is selected, which further improves the rationality and scientificity of the capacity configuration of each device of the integrated energy system.

[0075] In a preferred embodiment, in step S2, the system structure and operation mode of the integrated energy system are as follows Figure 2As shown, at the input end, the wind turbines in the superior power grid and the superior gas grid jointly build a stable energy supply system. In the energy conversion process, devices such as combined cooling, heating and power (CCHP), electrical chillers (ER) and gas-fired boilers (GB) improve the overall energy utilization efficiency through efficient energy conversion and utilization. In particular, the CCHP system further optimizes energy utilization through the coordinated work of the gas turbine, the waste heat boiler and the absorption chiller (AC). At the load end, the integrated energy system can respond to various energy demands and exhibit strong energy supply flexibility and reliability.

[0076] Further, in step S2, the generating the plurality of device cost data sets by Monte Carlo random sampling includes:

[0077] Obtaining historical cost data of each device type;

[0078] Predicting the cost value range of each device type according to each historical cost data;

[0079] Determining the probability distribution of the cost value range of each device type;

[0080] According to the cost value range of each device type and the corresponding probability distribution, a plurality of Monte Carlo random samplings are performed to generate a plurality of device cost data sets, wherein in each Monte Carlo random sampling, a random cost value of each device type is randomly selected from the cost value range of each device type based on the corresponding probability distribution, and then the random cost values of each device type are used to form the device cost data set.

[0081] The core idea of Monte Carlo simulation is to estimate the possibility and distribution of system output variables by randomly sampling a large number of possible input variables and performing multiple simulations. This method is particularly suitable for handling cost estimation problems with uncertainties, and the specific steps of its implementation are as follows:

[0082] 1) Define variables:

[0083] Determine the unit investment cost of the device to be simulated, such as the investment cost of wind power, photovoltaic and other devices. For each device, define the upper and lower limits of its annual unit investment cost within 15 years.

[0084] 2) Select distribution:

[0085] Selecting a suitable probability distribution, the patent assumes that the investment cost is normally distributed within the upper and lower limits, and the investment cost of each year is defined as follows:

[0086]

[0087] In the formula is the unit investment cost of the ith device in the nth year, is normally distributed, is the lower limit of the investment cost of the ith device in the nth year, is the upper limit of the investment cost of the ith device in the nth year.

[0088] 3) Random sampling:

[0089] Use a random number generator to extract samples from the selected probability distribution to generate a large number of investment cost scenarios for each device each year.

[0090]

[0091]

[0092] In the formula is a random number subject to standard normal distribution.

[0093] The embodiment of the application provides a method for generating a plurality of device cost data sets by Monte Carlo random sampling, which first predicts the cost value range of each device type according to the historical cost data of each device type, and then randomly samples from each cost value range. Before random sampling, the probability distribution of the cost value range of each device type needs to be determined, and it is generally considered that the cost value range of each device type conforms to the normal distribution, and the person skilled in the art can also select other suitable probability distribution according to the historical cost data. Finally, according to the cost value range of each device type and the corresponding probability distribution, a plurality of Monte Carlo random samplings are performed, which can not only eliminate the uncertainty of the cost estimation of each device, but also provide a plurality of device cost data sets for subsequent simulation evolution path generation, thereby generating a large number of simulation evolution paths and improving the rationality and scientificity of the capacity configuration of various devices of the comprehensive energy system.

[0094] In a possible implementation manner, in step S2, the planning model generates corresponding simulation evolution paths of each device according to the basic planning data and the plurality of device cost data sets, including:

[0095] According to the basic planning data and the constraint conditions of the planning model, the operation of the comprehensive energy system is simulated;

[0096] In the process of simulating the operation of the comprehensive energy system, each simulation evolution path corresponding to each device cost dataset is generated by calculating according to the objective function and each device cost dataset.

[0097] The embodiment of the application provides a method for generating each simulation evolution path using a planning model, simulating the operation of the comprehensive energy system through the constraint condition of the planning model and the input basic planning data, in the process, the planning model calculates according to the objective function and each input device cost dataset, and each simulation evolution path corresponding to each device cost dataset is obtained, the future device cost and evolution path of the comprehensive energy system are predicted, and data support is provided for the device capacity configuration of the comprehensive energy system.

[0098] In a possible implementation manner, in step S3, the evolution analysis of each simulation evolution path according to the preset analysis index is performed to determine the comprehensive energy system evolution path, including:

[0099] The life cycle cost, carbon emission and carbon trading cost of each simulation evolution path are calculated.

[0100] The life cycle cost, carbon emission and carbon trading cost of each simulation evolution path are weighted and calculated according to a preset weight coefficient to obtain the optimization benefit of each simulation evolution path.

[0101] The comprehensive energy system evolution path is determined from each simulation evolution path according to the optimization benefit of each simulation evolution path.

[0102] Specifically, the calculation model of the optimization benefit of each simulation evolution path is as follows:

[0103]

[0104] In the formula, the optimization benefit of the simulation evolution path is represented by Y, the life cycle cost of the simulation evolution path is represented by C, the carbon trading cost of the simulation evolution path is represented by T, and the carbon emission of the simulation evolution path is represented by E. Different weight coefficients reflect the importance of different factors in low-carbon cost optimization.

[0105] ​​​​​​The embodiment of the present application provides a method for evolution analysis of each simulation evolution path, calculates three analysis indexes of the full life cycle cost, the carbon emission and the carbon trading cost under each simulation evolution path, and obtains the optimization benefit of each simulation evolution path through weighted calculation of the three analysis indexes, and finally determines the comprehensive energy system evolution path from each simulation evolution path according to the optimization benefit. Wherein, the full life cycle cost reflects the economy of each simulation evolution path, and the carbon emission and the carbon trading cost reflect the environmental protection of each simulation evolution path, the embodiment of the present application realizes the balance between the economy and the environmental protection in the development process of the comprehensive energy system through the preset weight coefficient, the person skilled in the art can set the weight coefficient according to the actual situation, which reflects the flexibility and scientific nature of the embodiment of the present application in the evolution path selection process.

[0106] Further, the planning model is constructed according to the system structure and the operation mode of the comprehensive energy system, and the planning model comprises:

[0107] According to the system structure and the operation mode of the comprehensive energy system, each device operation model corresponding to the comprehensive energy system is constructed, including a gas boiler device model, a combined heat and power device model, an electric refrigerator device model, an absorption refrigerator device model and an energy storage model;

[0108] According to the each device operation model, each carbon emission model of the comprehensive energy system is constructed;

[0109] According to each device operation model and each carbon emission model, each constraint condition of the planning model is constructed;

[0110] According to each device operation model and each carbon emission model, an investment cost model, an operation cost model, a carbon trading cost model and a maintenance cost model of the comprehensive energy system are constructed;

[0111] According to the investment cost model, the operation cost model, the carbon trading cost model and the maintenance cost model of the comprehensive energy system, a target function of the planning model is constructed;

[0112] The planning model is constructed by combining each constraint condition and the target function.

[0113] The embodiment of the application provides a method for constructing a planning model, first, constructing various device operation models through a system structure, meanwhile, considering that there are carbon emission and carbon trading processes in the operation process of the integrated energy system, various carbon emission models are constructed, so that the planning model can simulate normal operation of the integrated energy system. The integrated energy system is influenced by various factors during operation, therefore, various constraint conditions are constructed based on the various device operation models and the various carbon emission models, the constraints ensure that the integrated energy system is stably, safely and efficiently operated during simulation operation, so that the generated planning scheme is more in line with actual conditions. Finally, an objective function of the planning model is constructed according to an investment cost model, an operation cost model, a carbon trading cost model and a maintenance cost model of the integrated energy system, an optimization direction of the planning model is determined, so that the finally obtained planning scheme can effectively reduce various costs and improve rationality and scientificity of capacity configuration of various devices of the integrated energy system.

[0114] In a preferred embodiment, the various device operation models are constructed according to the system structure and operation mode of the integrated energy system, specifically:

[0115] 1) A gas boiler (GB) device model, the operation principle of which can be described as follows:

[0116]

[0117] In the formula, is a heat output power of the gas boiler, is a gas power input into the GB at t period; is an energy conversion efficiency of the GB; is an upper limit of the power input into the GB.

[0118] 2) A combined heat and power (CHP) device model, the operation principle of which can be described as follows:

[0119]

[0120] In the formula, is an electric power output by the CHP at t period, is a natural gas power input into the CHP at t period; are energy conversion efficiencies of the CHP for converting into electric energy and heat energy respectively; is an upper limit of the power input into the CHP; is a heat power output by the CHP at t period; and are an upper limit and a lower limit of a heat-to-power ratio of the CHP respectively.

[0121] 3) the electric refrigerator (ER) equipment model, whose operation principle can be described as follows:

[0122]

[0123] wherein, is the cold power output by the ER at time t; is the conversion efficiency of the electric refrigerator; is the electric power input to the ER at time t; is the upper limit value of the electric power input to the ER.

[0124] 4) the absorption refrigerator (AC) equipment model, whose operation principle can be described as follows:

[0125] (4)

[0126] wherein, is the cold power output by the AC at time t; is the conversion efficiency of the absorption refrigerator; is the thermal power input to the AC at time t; is the upper limit value of the thermal power input to the AC.

[0127] 5) since the models of various energy storages are similar, the energy storage models of various types of energy can be unified as follows:

[0128]

[0129] wherein, is the set of electric storage, thermal storage and gas storage; are the charging and discharging power of the i-th energy storage device at time t, respectively; and are the maximum charging and discharging power of the i-th energy storage device in a single cycle, respectively; are the state parameters of the charging and discharging of the i-th energy storage device at time t, which are binary variables. That is, when the energy storage device is in the charging state, and when the energy storage device is in the discharging state; are the charging and discharging efficiencies of the energy storage device, respectively; and are the upper and lower limits of the capacity of the i-th energy storage device, respectively.

[0130] Since the carbon emissions in the integrated energy system mainly come from three carbon emission sources, namely, purchased electricity, gas turbine and gas boiler, and considering that the purchased electricity from the upper-level power grid is all from the thermal power unit, the carbon emission models of the integrated energy system are constructed according to the operation models of the various devices, and each carbon emission model is as follows:

[0131]

[0132]

[0133]

[0134]

[0135] wherein, , , , are actual carbon emissions of the park comprehensive energy system, purchased electricity, gas boiler and combined heat and power unit respectively; is the purchased power of the park comprehensive energy system to the upper grid at time t; is the heat supply power of the gas boiler at time t; , are heat supply and power supply of the CHP unit at time t respectively; and are carbon emission intensities per unit of electricity and per unit of heat respectively; is the conversion coefficient of the power generation of the CHP unit to heat supply, and T is 8760.

[0136] The carbon quota calculation model of the park comprehensive energy system is as follows:

[0137]

[0138] wherein, are carbon emission quotas of the park comprehensive energy system, purchased electricity, gas boiler and combined heat and power unit respectively.

[0139] Therefore, the traded carbon emissions of the park comprehensive energy system considering carbon quota are as follows:

[0140]

[0141] The various constraint conditions of the planning model are constructed according to the various device operation models and the various carbon emission models, and include an electric power balance constraint condition, a heat power balance constraint condition, a gas power balance constraint condition, a cold power balance constraint condition, a new energy output constraint condition, an energy storage constraint condition and a carbon emission constraint condition.

[0142] The electric power balance constraint condition is specifically:

[0143]

[0144] Since wind power and photovoltaic power have great randomness and volatility, in order to reduce the pressure of the main grid, the embodiments of the present application do not consider selling electricity to the upper-level grid by the park comprehensive energy system. In the formula, represents the electric load power at t period; and respectively represent the discharging and charging power of the electric storage at t moment.

[0145] The thermal power balance constraint condition is specifically:

[0146]

[0147] In the formula, represents the thermal load power at t period; and respectively represent the heat release and heat charging power of the thermal storage at t moment.

[0148] The gas power balance constraint condition is specifically:

[0149]

[0150] In the formula, represents the gas load power at t period; and respectively represent the gas release and gas charging power of the gas storage at t moment.

[0151] The cold power balance constraint condition is specifically:

[0152]

[0153] In the formula, represents the cold load power at t period.

[0154] The new energy output constraint condition is specifically:

[0155]

[0156] In the formula, respectively represent the wind power and photovoltaic output power at t period; respectively represent the upper limit values of the wind power and photovoltaic output power, represents the upper limit value of the wind power and photovoltaic capacity.

[0157] Considering that the state of charge and discharge of the energy storage device needs to return to the state at the beginning of the scheduling after running for 1 scheduling period, on the basis of the original constraint, the constraint condition shown in the following formula is further established:

[0158]

[0159] In the formula, The capacity of the i-th energy storage device at the t-th time period.

[0160] The carbon peak constraint condition can be expressed as:

[0161]

[0162] In the formula, x represents that the carbon peak is achieved in the x-th year of the planning.

[0163] In combination with the “double carbon” target, in order to ensure that the carbon emissions fall in an orderly and stable manner after reaching the peak, it is stipulated that the traded carbon emissions need to be reduced by a fixed proportion of v% every year after the carbon peak. Therefore, the carbon emission constraint condition is specifically:

[0164]

[0165] According to the respective device operation model and the respective carbon emission model, an investment cost model, an operation cost model, a carbon trading cost model and a maintenance cost model of the integrated energy system are constructed, wherein the investment cost model of the integrated energy system is:

[0166]

[0167] wherein, is the unit price of the device j, yuan / kW; is the configuration capacity of the device j, kW; is the device set including wind power, photovoltaic device, CHP, AC, ER, GB, EC and various energy storage devices; is a Boolean variable, taking the value of 1 or 0.

[0168] The operation cost model of the integrated energy system is:

[0169]

[0170]

[0171]

[0172] wherein, is the energy purchasing cost of the park; , respectively represent the electricity price and the gas price at the t-th time of the n-th year; is the wind curtailment cost of the integrated energy system of the park; is the unit wind curtailment penalty cost; is the operation cost of the integrated energy system of the park, which is composed of the energy purchasing cost and the wind curtailment cost, and the energy purchasing cost further includes the electricity purchasing cost and the gas purchasing cost; is the gas purchasing power at the t-th time period of the n-th year; The wind power abandoned in the nth year and t period.

[0173] The maintenance cost of the park comprehensive energy system is composed of the maintenance costs of wind power, photovoltaic, energy conversion and various energy storage devices, and is determined by the actual output power of various devices during operation. The vector composed of the output power of various devices in period t is The maintenance cost model of the comprehensive energy system is:

[0174]

[0175] Wherein, is the maintenance cost vector of each type of device to be planned per unit power; is the output power of the j-type device in period t; if the j-type device represents an energy storage device, represents the sum of the charging and discharging power of the energy storage device.

[0176] The specific formula of the carbon trading cost model is:

[0177]

[0178] Wherein, is the carbon trading cost, is the carbon trading benchmark price, is the carbon emission interval length, is the carbon trading price growth rate, and E0 is the traded carbon emission.

[0179] The objective function of the planning model is constructed according to the investment cost model, operation cost model, carbon trading cost model and maintenance cost model of the comprehensive energy system, and the specific formula is:

[0180]

[0181] In the formula, C represents the cost present value of the comprehensive energy system, is the construction investment cost, are the operation and maintenance costs of the comprehensive energy system, respectively, is the carbon trading cost.

[0182] In a preferred embodiment, the detailed flowchart of a capacity configuration method based on carbon trading and evolutionary path analysis provided by the present application is as shown in Figure 3As shown, first, the overall framework of the integrated energy system is determined, the equipment unit model is built, and the integrated energy system structure is constructed through a unified bus structure. Then, based on the system structure and operation mode of the integrated energy system, the PIES multi-stage low-carbon planning model architecture, constraint conditions and objective function are constructed. Finally, a large number of value cost scenarios of the unit investment cost of wind power, the unit investment cost of photovoltaic and the investment cost of each device are obtained through Monte Carlo simulation, the planning model is calculated according to different investment cost values, the planning capacity value range of each device in each year is obtained, and thus a plurality of simulation evolution paths are obtained. The optimal evolution path scheme is obtained by comparing the economy and environmental protection of each simulation evolution path.

[0183] In a preferred embodiment, the capacity configuration method provided in the present application is used to generate a capacity configuration scheme for a park in a certain region of China, and the evolution time range is set to 1 to 15 years. The overall process is as follows: Monte Carlo random sampling is performed on the uncertainty parameters, the optimal configuration of sources, loads and storage resources in multiple stages is planned based on the constructed planning model, different schemes are compared and analyzed, the optimal economic and environmental protection targets are considered, and the optimal scheme of the future park integrated energy system is given. In order to fully reveal the actual operation characteristics of the park, the data of three typical days of summer, winter and transition season are selected for analysis. At the same time, considering the characteristics of load growth in the park, according to historical data and planning requirements, the load growth rates in the early, middle and late stages of planning are given, as shown in Table 1.

[0184] Table 1 Load growth rate of various loads in the planning period

[0185]

[0186] In order to cope with the uncertainty faced by the evolution of the park integrated energy system, therefore, the embodiments of the present application consider that the uncertainty factors of the unit investment cost of each device are randomly sampled by Monte Carlo, and based on the multi-stage low-carbon planning model, a large number of evolution paths are obtained. The preset parameters in the planning model are shown in Tables 2 to 4, and the value range of the uncertainty factors of the unit investment cost of each device is shown in Table 5:

[0187] Table 2 Carbon emission related parameters

[0188]

[0189] Table 3 Time-of-use electricity price, gas price and wind and light curtailment price

[0190]

[0191] Table 4 Parameters of various devices to be planned

[0192]

[0193] Table 5. Range of values ​​for uncertainty factors

[0194]

[0195] Different combinations of unit investment costs obtained from sampling are input into the planning model for optimization calculations. Each combination of unit investment costs yields a simulated evolution path. In this path, each type of equipment may have new equipment added each year, and the total capacity of a single piece of equipment each year is the sum of the capacities of new equipment added in the previous years. Data analysis of all evolution paths yields capacity evolution path diagrams for each equipment type, cost evolution path diagrams, and carbon emission evolution path diagrams, as shown below. Figure 4 , Figure 5 as well as Figure 6 As shown. From Figure 4 It can be observed that, in terms of equipment capacity configuration, as the park's load increases, the capacity configuration of each piece of equipment also increases accordingly. Wind power and photovoltaic power continue to grow, while energy storage and hydrogen-related clean equipment are gradually becoming the dominant resources. The system's total life cycle cost and cost composition are as follows: Figure 5 As shown, the total lifecycle cost decreased in the first three years and then remained relatively stable. Operating costs decreased in the later stages of planning, likely because the equipment capacity could adequately meet the park's load demands, thus reducing the need for externally purchased energy. Maintenance costs, however, showed a slow upward trend, due to the overall increase in equipment capacity during the later stages of planning, leading to a corresponding increase in maintenance costs. The actual carbon emission evolution path of the system is as follows: Figure 6 As shown, the actual annual carbon emissions show a trend of peaking first and then declining.

[0196] Because my country's green and low-carbon transformation development path places greater emphasis on environmental impact, this study calculates the optimization benefits of each simulated evolution path using a weighting coefficient of 1:1:3, based on the standardized life cycle, carbon trading costs, and carbon emissions. The calculation results are as follows: Figure 7 As shown. Based on the above evolutionary paths, different low-carbon cost-benefit values ​​are obtained, from... Figure 7 It can be seen that the optimization benefit value of simulated evolution path 8 is significantly higher than that of other paths. That is, path 8 has the lowest life-cycle cost and carbon emissions among all paths, making it the optimal evolution path for the park's integrated energy system. Under this path, the present value of the life-cycle cost is 54.938 million yuan, the actual carbon emissions are controlled at 12,965.06 tons, and the carbon trading cost is 191,800 yuan. This result not only demonstrates the advantages of path 8 in cost control and carbon emission reduction but also proves its effectiveness in promoting the low-carbon transformation of the park's energy system. Table 6 shows the annual new capacity of various equipment under simulated evolution path 8 for the next 1-15 years.

[0197] Table 6: New capacity of each device per year under the optimal path

[0198]

[0199] In the development process of the integrated energy system, the capacity configuration of each device type of the integrated energy system per year can be adjusted according to Table 6 to improve the rationality and scientificity of the capacity configuration of each type of device of the integrated energy system.

[0200] Embodiment Two:

[0201] As shown in Figure 8 , accordingly, embodiment two provides a capacity configuration system based on carbon trading and evolutionary path analysis, including an acquisition module 10, a path evolution module 20, a path determination module 30, and a capacity configuration module 40.

[0202] The acquisition module 10 is configured to acquire basic planning data of the integrated energy system, the basic planning data including a planning period, device parameters, carbon market transaction information, and various load data.

[0203] The path evolution module 20 is configured to input the basic planning data and a plurality of device cost data sets into a preset planning model, so that the planning model generates corresponding simulation evolutionary paths according to the basic planning data and the plurality of device cost data sets, wherein the planning model is constructed according to the system structure and operation mode of the integrated energy system, the simulation evolutionary paths include the capacity configuration of each device type of the integrated energy system per year, and the plurality of device cost data sets are generated by Monte Carlo random sampling.

[0204] The path determination module 30 is configured to perform evolutionary analysis on the simulation evolutionary paths according to a preset analysis index to determine an integrated energy system evolutionary path.

[0205] The capacity configuration module 40 is configured to adjust the capacity configuration of each device type of the integrated energy system according to the integrated energy system evolutionary path within the planning period.

[0206] Further, the plurality of device cost data sets are generated by Monte Carlo random sampling, including:

[0207] acquiring historical cost data of each device type;

[0208] predicting the cost value range of each device type according to each historical cost data;

[0209] determining the probability distribution of the cost value range of each device type;

[0210] According to the cost value range of each device type and the corresponding probability distribution, a plurality of Monte Carlo random samplings are performed to generate a plurality of device cost data sets, wherein in each Monte Carlo random sampling, a random cost value of each device type is randomly selected from the cost value range of each device type based on the corresponding probability distribution, and then the random cost value of each device type constitutes the device cost data set.

[0211] In a possible implementation manner, the planning model generates corresponding simulation evolution paths of each device cost data set according to the basic planning data and the plurality of device cost data sets, including:

[0212] According to the basic planning data and the constraint conditions of the planning model, the operation of the integrated energy system is simulated;

[0213] In the process of simulating the operation of the integrated energy system, the objective function and each device cost data set are calculated respectively to generate each simulation evolution path corresponding to each device cost data set.

[0214] In a possible implementation manner, the path determination module 30 performs evolution analysis on the simulation evolution paths according to a preset analysis index to determine the integrated energy system evolution path, including:

[0215] The life cycle cost, carbon emission and carbon trading cost of each simulation evolution path are calculated;

[0216] According to a preset weight coefficient, the life cycle cost, carbon emission and carbon trading cost of each simulation evolution path are weighted to obtain the optimization benefit of each simulation evolution path;

[0217] According to the optimization benefit of each simulation evolution path, the integrated energy system evolution path is determined from each simulation evolution path.

[0218] Further, the planning model is constructed according to the system structure and operation mode of the integrated energy system, including:

[0219] According to the system structure and operation mode of the integrated energy system, corresponding device operation models are constructed, including a gas boiler device model, a combined heat and power device model, an electric refrigerator device model, an absorption refrigerator device model and an energy storage model;

[0220] According to the device operation models, carbon emission models of the integrated energy system are constructed;

[0221] constructing each constraint condition of the planning model according to each device operation model and each carbon emission model;

[0222] constructing an investment cost model, an operation cost model, a carbon trading cost model and a maintenance cost model of the integrated energy system according to each device operation model and each carbon emission model;

[0223] constructing an objective function of the planning model according to the investment cost model, the operation cost model, the carbon trading cost model and the maintenance cost model of the integrated energy system;

[0224] constructing the planning model by combining each constraint condition and the objective function.

[0225] The embodiment of the present application provides a capacity configuration system based on carbon trading and evolution path analysis. By obtaining basic planning data of an integrated energy system and a plurality of device cost data sets generated by Monte Carlo random sampling, a preset planning model is used to simulate operation according to the basic planning data and the device cost data sets, a plurality of simulation evolution paths of the integrated energy system can be generated, and each simulation evolution path records a change process of capacity configuration of each device type in a long future time. Then, evolution analysis is performed on each path according to a preset analysis index, and an optimal simulation evolution path is selected as an integrated energy system evolution path. Finally, in the development process of the integrated energy system, capacity configuration of each device type of the integrated energy system is continuously adjusted according to the integrated energy system evolution path. The embodiment of the present application generates corresponding simulation evolution paths through a planning model based on the overall structure of the integrated energy system and the carbon trading process, each evolution path is an optimal capacity configuration scheme obtained under the condition of a corresponding device cost data set, and then each evolution path is analyzed and evaluated, and an optimal evolution path is selected to perform capacity configuration on the integrated energy system, thereby improving rationality and scientificity of capacity configuration of each device of the integrated energy system. In addition, the embodiment of the present application introduces a Monte Carlo random sampling method to generate a plurality of device cost data sets, a plurality of device investment cost combinations most likely to occur in the future are obtained through a large amount of random sampling, and a plurality of device cost data sets are constructed, thereby solving the problem that cost estimation of each device has great uncertainty in a conventional planning method. A large number of simulation evolution paths are generated through the plurality of device cost data sets, and an optimal path is selected, thereby further improving rationality and scientificity of capacity configuration of each device of the integrated energy system.

[0226] The more detailed working principle and step flow of the embodiment can be but not limited to the related description of the first embodiment.

[0227] The above-described specific embodiments, purposes, technical solutions and beneficial effects of the present application are further described in detail, and it should be understood that the above-described is only a specific embodiment of the present application and is not used to limit the protection scope of the present application. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A capacity configuration method based on carbon trading and evolutionary path analysis, characterized in that, The application relates to a method for generating an evolution path of a comprehensive energy system. The method comprises the following steps: acquiring basic planning data of the comprehensive energy system, wherein the basic planning data comprises a planning period, equipment parameters, carbon market transaction information and various load data; inputting the basic planning data and a plurality of equipment cost data sets into a preset planning model, so that the planning model generates corresponding simulation evolution paths according to the basic planning data and the plurality of equipment cost data sets, wherein the planning model is obtained according to a system structure and an operation mode of the comprehensive energy system, the simulation evolution paths comprise capacity configurations of various equipment types of the comprehensive energy system in each year, and the plurality of equipment cost data sets are generated through Monte Carlo random sampling; the planning model generates corresponding simulation evolution paths according to the basic planning data and the plurality of equipment cost data sets, which comprises the following steps: simulating the operation of the comprehensive energy system according to the basic planning data and constraint conditions of the planning model; and generating simulation evolution paths corresponding to each of the plurality of equipment cost data sets respectively by calculating the target function and each of the plurality of equipment cost data sets in the process of simulating the operation of the comprehensive energy system; evolution analysis is performed on the simulation evolution paths according to preset analysis indexes, and an evolution path of the comprehensive energy system is determined, which comprises the following steps: calculating the life cycle cost, carbon emission and carbon transaction cost of each of the simulation evolution paths; performing weighted calculation on the life cycle cost, carbon emission and carbon transaction cost of each of the simulation evolution paths according to preset weight coefficients to obtain the optimization benefit of each of the simulation evolution paths; and determining the evolution path of the comprehensive energy system from each of the simulation evolution paths according to the optimization benefit of each of the simulation evolution paths; 2. The capacity configuration method based on carbon trading and evolutionary path analysis according to claim 1, characterized in that, in the planning period, the capacity configurations of various equipment types of the comprehensive energy system are adjusted according to the evolution path of the comprehensive energy system. the plurality of equipment cost data sets are generated through Monte Carlo random sampling, which comprises the following steps: acquiring historical cost data of each of various equipment types; predicting the cost value range of each of the various equipment types according to each of the historical cost data; determining the probability distribution of the cost value range of each of the various equipment types; 3. The capacity configuration method based on carbon trading and evolutionary path analysis according to any one of claims 1 or 2, characterized in that, generating a plurality of equipment cost data sets through a plurality of times of Monte Carlo random sampling according to the cost value range and the corresponding probability distribution of each of the various equipment types, wherein in each time of the Monte Carlo random sampling, a random cost value of each of the various equipment types is randomly extracted from the cost value range of each of the various equipment types based on the corresponding probability distribution, and then the random cost value of each of the various equipment types constitutes the equipment cost data set. the planning model is obtained according to the system structure and the operation mode of the comprehensive energy system, which comprises the following steps: constructing corresponding equipment operation models of various equipment types according to the system structure and the operation mode of the comprehensive energy system, wherein the equipment operation models comprise a gas boiler equipment model, a combined heat and power equipment model, an electric refrigerator equipment model, an absorption refrigerator equipment model and an energy storage model; constructing each carbon emission model of the integrated energy system according to the respective device operation model; constructing each constraint condition of the planning model according to each device operation model and each carbon emission model; constructing an investment cost model, an operation cost model, a carbon trading cost model and a maintenance cost model of the integrated energy system according to each device operation model and each carbon emission model; constructing an objective function of the planning model according to the investment cost model, the operation cost model, the carbon trading cost model and the maintenance cost model of the integrated energy system; constructing the planning model by combining each constraint condition and the objective function.

4. A capacity configuration system based on carbon trading and evolutionary path analysis, characterized by, The method comprises an acquisition module, a path evolution module, a path determination module and a capacity configuration module. The acquisition module is configured to acquire basic planning data of an integrated energy system, the basic planning data comprising a planning period, device parameters, carbon market transaction information and various types of load data. The path evolution module is configured to input the basic planning data and a plurality of device cost data sets into a preset planning model, so that the planning model generates corresponding respective simulation evolution paths according to the basic planning data and the plurality of device cost data sets, wherein the planning model is constructed according to the system structure and operation mode of the integrated energy system, the simulation evolution paths comprise the capacity configuration of each device type of the integrated energy system in each year, and the plurality of device cost data sets are generated by Monte Carlo random sampling. The planning model generates corresponding respective simulation evolution paths according to the basic planning data and the plurality of device cost data sets, comprising: simulating the operation of the integrated energy system according to the basic planning data and the constraint conditions of the planning model; and generating respective simulation evolution paths corresponding to each of the plurality of device cost data sets by calculating each device cost data set according to a target function during the simulation of the operation of the integrated energy system. The path determination module is configured to perform evolution analysis on the respective simulation evolution paths according to a preset analysis index to determine an integrated energy system evolution path, comprising: calculating the life cycle cost, carbon emission and carbon trading cost of each simulation evolution path; performing weighted calculation on the life cycle cost, carbon emission and carbon trading cost of each simulation evolution path according to a preset weight coefficient to obtain the optimization benefit of each simulation evolution path; and determining the integrated energy system evolution path from the respective simulation evolution paths according to the optimization benefit of each simulation evolution path. The capacity configuration module is configured to adjust the capacity configuration of each device type of the integrated energy system according to the integrated energy system evolution path within the planning period.

5. The capacity configuration system based on carbon trading and evolutionary path analysis of claim 4, wherein, The plurality of device cost data sets are generated by Monte Carlo random sampling, comprising: acquiring historical cost data of each device type; predicting the cost value range of each device type according to each historical cost data; determining the probability distribution of the cost value range of each device type; and According to the cost value range of each device type and the corresponding probability distribution, a plurality of Monte Carlo random samplings are performed to generate a plurality of device cost data sets, wherein in each Monte Carlo random sampling, a random cost value of each device type is randomly selected from the cost value range of each device type based on the corresponding probability distribution, and then the random cost value of each device type is used to form the device cost data set.

6. A capacity configuration system based on carbon trading and evolutionary path analysis according to any one of claims 4 or 5, characterized in that, The planning model is constructed according to the system structure and operation mode of the integrated energy system, including: According to the system structure and operation mode of the integrated energy system, a corresponding device operation model is constructed, including a gas boiler device model, a combined heat and power device model, an electric refrigerator device model, an absorption refrigerator device model, and an energy storage model; According to the device operation model, a carbon emission model of the integrated energy system is constructed; According to each device operation model and each carbon emission model, each constraint condition of the planning model is constructed; According to each device operation model and each carbon emission model, an investment cost model, an operation cost model, a carbon trading cost model, and a maintenance cost model of the integrated energy system are constructed; According to the investment cost model, the operation cost model, the carbon trading cost model, and the maintenance cost model of the integrated energy system, an objective function of the planning model is constructed; The planning model is constructed in combination with each constraint condition and the objective function.

Images