Capacity configuration method and system based on carbon transaction and evolution path analysis

Abstract

The invention provides a capacity configuration method and system based on carbon transaction and evolution path analysis. The method comprises the following steps: acquiring basic planning data of an integrated energy system; 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, the simulation evolution path comprises capacity configuration of each equipment type of the integrated energy system every year, and the plurality of equipment cost data sets are generated through Monte Carlo random sampling; performing evolution analysis on each simulation evolution path according to a preset analysis index, and determining an integrated energy system evolution path; and in the planning period, adjusting the capacity configuration of each equipment type of the integrated energy system according to the evolution path of the integrated energy system, thereby improving the rationality and scientificity of the capacity configuration of each equipment of the integrated energy system.

Description

Technical Field

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

[0002] Carbon trading is a general term for greenhouse gas emission rights trading. The basic principle of carbon trading is that one party to the contract obtains greenhouse gas emission reduction quotas by paying the other party, and the buyer can use the purchased emission reduction quotas to mitigate the greenhouse effect and achieve its emission reduction goals. Among the six greenhouse gases that are required to reduce emissions, carbon dioxide (CO2) is the largest, so this transaction is calculated in units of tonnes of carbon dioxide equivalent (tCO2e), so it is generally called "carbon trading". Its trading market is called the carbon market (Carbon Market). After the Kyoto Protocol officially came into effect in 2005, the global carbon trading market has experienced explosive growth. In 2007, the carbon trading volume jumped from 1.6 billion tons in 2006 to 2.7 billion tons, an increase of 68.75%. The growth of transaction volume is even faster. In 2007, the global carbon trading market was worth 40 billion euros, an increase of 81.8% from 22 billion euros in 2006. The total value of the global carbon trading market in the first half of 2008 was even the same as the whole of 2007. As of July 15, 2024, the cumulative trading volume of carbon quotas in China's carbon market reached 465 million tons, with a cumulative trading volume of nearly 27 billion yuan.

[0003] An integrated energy system refers to a new integrated energy system that uses advanced physical information technology and innovative management models to integrate multiple energy sources such as coal, oil, natural gas, electricity, and heat energy in a certain area, and realizes coordinated planning, optimized operation, collaborative management, interactive response, and mutual complementation among multiple heterogeneous energy subsystems. While meeting the diversified energy demand within the system, it is necessary to effectively improve energy utilization efficiency and promote sustainable energy development. An integrated energy system specifically refers to an integrated energy production, supply, and marketing system formed by organically coordinating and optimizing the generation, transmission, and distribution (energy network), conversion, storage, and consumption of energy during the planning, construction, and operation process. It is mainly composed of energy supply networks (such as power supply, gas supply, cooling / heat supply, etc.), energy exchange links (such as CCHP units, generator sets, boilers, air conditioners, heat pumps, etc.), energy storage links (electricity storage, gas storage, heat storage, cooling storage, etc.), terminal integrated energy supply and use units (such as microgrids), and a large number of terminal users. It is precisely because of the complexity of the structure of the integrated energy system that in the planning and development of the park's integrated energy system, we need to pay attention not only to the investment costs of wind power, photovoltaic and other equipment, but also to consider how to reasonably allocate carbon trading resources in the market and reduce carbon trading issues. In addition, in the actual production process, the cost estimation of each device has a large uncertainty. It is usually difficult for us to accurately estimate the unit cost of each device in various time periods in the future, which in turn affects the rationality and accuracy of the capacity configuration planning of the integrated energy system. Summary of the invention

[0004] In response to the above technical problems, the present application provides a capacity configuration method and system based on carbon trading and evolution path analysis to improve the rationality and scientificity of capacity configuration of various equipment in the integrated energy system.

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

[0006] Obtaining basic planning data of the integrated energy system, the basic planning data including planning period, equipment parameters, carbon market transaction information and various load data;

[0007] Inputting the basic planning data and the 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 constructed according to the system structure and operation mode of the integrated energy system, the simulation evolution path includes the capacity configuration of each equipment type of the integrated energy system each year, and the plurality of equipment cost data sets are generated by Monte Carlo random sampling;

[0008] Performing evolution analysis on each of the simulated evolution paths according to preset analysis indicators to determine the evolution path of the integrated energy system;

[0009] During the planning period, the capacity configuration of each equipment type of the integrated energy system is adjusted according to the evolution path of the integrated energy system.

[0010] 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 several equipment cost data sets generated by Monte Carlo random sampling, a preset planning model is used to simulate the basic planning data and the equipment cost data set, and multiple simulated evolution paths of the integrated energy system can be generated. Each simulated evolution path records the change process of the capacity configuration of each equipment type in the future for a long time. Then, each path is subjected to evolution analysis according to the preset analysis index, and the optimal simulated evolution path is selected as the evolution path of the integrated energy system. Finally, in the development process of the integrated energy system, the capacity configuration of each equipment type of the integrated energy system is continuously adjusted according to the evolution path of the integrated energy system. The embodiment of the present application is based on the overall structure and carbon trading process of the integrated energy system, and generates corresponding simulated evolution paths through the planning model. Each evolution path is the optimal capacity configuration scheme obtained under the condition of the known corresponding equipment cost data set, and 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 various equipment in the integrated energy system. In addition, the embodiment of the present application also introduces a Monte Carlo random sampling method to generate several equipment cost data sets. Through a large number of random sampling, several equipment investment cost combinations that are most likely to appear in the future are obtained and several equipment cost data sets are constructed, which solves the problem of large uncertainty in the cost estimation of each equipment in conventional planning methods. Subsequently, a large number of simulated evolution paths are generated through several equipment cost data sets, and the optimal path is selected from them, further improving the rationality and scientificity of the capacity configuration of various equipment in the integrated energy system.

[0011] Furthermore, the generating of the plurality of equipment cost data sets by Monte Carlo random sampling includes:

[0012] Obtain historical cost data for each equipment type;

[0013] Predicting the cost range of each equipment type based on each of the historical cost data;

[0014] Determining a probability distribution of a range of cost values ​​for each of the equipment types;

[0015] According to the cost value range of each equipment type and the corresponding probability distribution, several Monte Carlo random samplings are performed to generate several equipment cost data sets, wherein in each Monte Carlo random sampling, the random cost value of each equipment type is randomly extracted from the cost value range of each equipment type based on the corresponding probability distribution, and then the random cost values ​​of each equipment type constitute the equipment cost data set.

[0016] The embodiment of the present application provides a method for generating several equipment cost data sets through Monte Carlo random sampling. First, the cost value range of each equipment type is predicted based on the historical cost data of each equipment type, and then random sampling is performed from each cost value range. Before performing random sampling, it is necessary to determine the probability distribution of the cost value range of each equipment type. It is generally believed that the cost value range of each equipment type conforms to the normal distribution. Technicians in this field can also select other suitable probability distributions based on historical cost data. Finally, according to the cost value range of each equipment type and the corresponding probability distribution, several Monte Carlo random samplings are performed, which can not only eliminate the problem of uncertainty in the cost estimation of each equipment, but also provide multiple equipment 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 types of equipment in the integrated energy system.

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

[0018] Simulating the operation of the integrated energy system according to the basic planning data and the constraints of the planning model;

[0019] In the process of simulating the operation of the integrated energy system, calculations are performed respectively according to the objective function and each of the equipment cost data sets to generate each simulation evolution path corresponding to each of the equipment cost data sets.

[0020] The embodiment of the present application provides a method for generating various simulated evolution paths using a planning model, and simulates the operation of an integrated energy system through the constraints of the planning model and the input basic planning data. In this process, the planning model calculates according to the objective function and the input equipment cost data sets, and can obtain various simulated evolution paths corresponding to the equipment cost data sets, thereby realizing the prediction of the future equipment cost and evolution path of the integrated energy system, and providing data support for the equipment capacity configuration of the integrated energy system.

[0021] In a possible implementation, performing evolution analysis on each simulated evolution path according to a preset analysis index to determine the evolution path of the integrated energy system includes:

[0022] Calculating the life cycle cost, carbon emissions and carbon trading costs of each of the simulated evolution paths;

[0023] According to the preset weight coefficient, the life cycle cost, carbon emissions and carbon trading cost of each simulated evolution path are weighted and calculated to obtain the optimization benefit of each simulated evolution path;

[0024] According to the optimization benefits of each of the simulated evolution paths, the evolution path of the integrated energy system is determined from each of the simulated evolution paths.

[0025] The embodiment of the present application provides a method for performing evolutionary analysis on each simulated evolutionary path, calculates three analysis indicators of the full life cycle cost, carbon emissions and carbon trading costs under each simulated evolutionary path, and performs weighted calculation on the three analysis indicators to obtain the optimization benefits of each simulated evolutionary path, and finally determines the evolutionary path of the integrated energy system from each simulated evolutionary path according to the optimization benefits. Among them, the full life cycle cost reflects the economy of each simulated evolutionary path, and the carbon emissions and carbon trading costs reflect the environmental protection of each simulated evolutionary path. The embodiment of the present application achieves a balance between the economy and environmental protection of the integrated energy system in the development process through a preset weight coefficient. Technicians in this field can set the weight coefficient according to the actual situation, which reflects the flexibility and scientificity of the embodiment of the present application in the process of selecting the evolutionary path.

[0026] Furthermore, constructing and obtaining the planning model according to the system structure and operation mode of the integrated energy system includes:

[0027] Construct corresponding operation models of various equipment according to the system structure and operation mode of the comprehensive energy system, including a gas boiler equipment model, a cogeneration equipment model, an electric refrigerator equipment model, an absorption refrigerator equipment model and an energy storage model;

[0028] Constructing various carbon emission models of the integrated energy system according to the operation models of the various devices;

[0029] Constructing various constraint conditions of the planning model according to each of the equipment operation models and each of the carbon emission models;

[0030] According to the operation models of each device and the carbon emission models, construct an investment cost model, an operation cost model, a carbon trading cost model and a maintenance cost model of the comprehensive energy system;

[0031] Constructing the objective function of the planning model according to the investment cost model, operation cost model, carbon trading cost model and maintenance cost model of the integrated energy system;

[0032] The planning model is constructed by combining the various constraints and the objective function.

[0033] The embodiment of the present application provides a method for constructing a planning model. First, each equipment operation model is constructed through the system structure. At the same time, considering the existence of carbon emissions and carbon trading processes during the operation of the integrated energy system, each carbon emission model is constructed so that the planning model can simulate the normal operation of the integrated energy system. When the integrated energy system is running, it will be affected by many factors. Therefore, corresponding constraints are constructed based on each equipment operation model and each carbon emission model. These constraints ensure that the integrated energy system operates stably, safely and efficiently during the simulated operation process, so that the generated planning scheme is more in line with the actual situation. Finally, 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 integrated energy system, and the optimization direction of the planning model is determined, so that the final planning scheme can effectively reduce various costs and improve the rationality and scientificity of the capacity configuration of various equipment in the integrated energy system.

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

[0035] The acquisition module is used to acquire basic planning data of the integrated energy system, and the basic planning data includes planning cycle, equipment parameters, carbon market transaction information and various load data;

[0036] The path evolution module is used to input the basic planning data and the 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 constructed according to the system structure and operation mode of the integrated energy system, the simulation evolution path includes the capacity configuration of each equipment type of the integrated energy system each year, and the plurality of equipment cost data sets are generated by Monte Carlo random sampling;

[0037] The path determination module is used to perform evolution analysis on each simulated evolution path according to preset analysis indicators to determine the evolution path of the integrated energy system;

[0038] The capacity configuration module is used to adjust the capacity configuration of each equipment type of the integrated energy system according to the evolution path of the integrated energy system within the planning period.

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

[0040] Obtain historical cost data for each equipment type;

[0041] Predicting the cost range of each equipment type based on each of the historical cost data;

[0042] Determining a probability distribution of a range of cost values ​​for each of the equipment types;

[0043] According to the cost value range of each equipment type and the corresponding probability distribution, several Monte Carlo random samplings are performed to generate several equipment cost data sets, wherein in each Monte Carlo random sampling, the random cost value of each equipment type is randomly extracted from the cost value range of each equipment type based on the corresponding probability distribution, and then the random cost values ​​of each equipment type constitute the equipment cost data set.

[0044] In a possible implementation, the planning model generates corresponding simulation evolution paths according to the basic planning data and the plurality of equipment cost data sets, including:

[0045] Simulating the operation of the integrated energy system according to the basic planning data and the constraints of the planning model;

[0046] In the process of simulating the operation of the integrated energy system, calculations are performed respectively according to the objective function and each of the equipment cost data sets to generate each simulation evolution path corresponding to each of the equipment cost data sets.

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

[0048] Calculating the life cycle cost, carbon emissions and carbon trading costs of each of the simulated evolution paths;

[0049] According to the preset weight coefficient, the life cycle cost, carbon emissions and carbon trading cost of each simulated evolution path are weighted and calculated to obtain the optimization benefit of each simulated evolution path;

[0050] According to the optimization benefits of each of the simulated evolution paths, the evolution path of the integrated energy system is determined from each of the simulated evolution paths.

[0051] Furthermore, constructing and obtaining the planning model according to the system structure and operation mode of the integrated energy system includes:

[0052] Construct corresponding operation models of various equipment according to the system structure and operation mode of the comprehensive energy system, including a gas boiler equipment model, a cogeneration equipment model, an electric refrigerator equipment model, an absorption refrigerator equipment model and an energy storage model;

[0053] Constructing various carbon emission models of the integrated energy system according to the operation models of the various devices;

[0054] Constructing various constraint conditions of the planning model according to each of the equipment operation models and each of the carbon emission models;

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

[0056] Constructing the objective function of the planning model according to the investment cost model, operation cost model, carbon trading cost model and maintenance cost model of the integrated energy system;

[0057] The planning model is constructed by combining the various constraints and the objective function. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

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

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

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

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

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

[0066] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this 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 to limit the order in which the steps are executed. In the description of this application, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features.

[0068] Embodiment 1:

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

[0070] Step S1, obtaining basic planning data of the integrated energy system, wherein the basic planning data includes planning cycle, equipment parameters, carbon market transaction information and various load data;

[0071] Step S2, inputting the basic planning data and the 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 constructed according to the system structure and operation mode of the integrated energy system, the simulation evolution path includes the capacity configuration of each equipment type of the integrated 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 each simulated evolution path according to preset analysis indicators to determine the evolution path of the integrated energy system;

[0073] Step S4: During the planning period, the capacity configuration of each equipment type of the integrated energy system is adjusted according to the evolution path of the integrated energy system.

[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 several equipment cost data sets generated by Monte Carlo random sampling, a preset planning model is used to simulate the basic planning data and the equipment cost data set, and multiple simulated evolution paths of the integrated energy system can be generated. Each simulated evolution path records the change process of the capacity configuration of each equipment type in the future for a long time. Then, each path is subjected to evolution analysis according to the preset analysis index, and the optimal simulated evolution path is selected as the evolution path of the integrated energy system. Finally, in the development process of the integrated energy system, the capacity configuration of each equipment type of the integrated energy system is continuously adjusted according to the evolution path of the integrated energy system. The embodiment of the present application is based on the overall structure and carbon trading process of the integrated energy system, and generates corresponding simulated evolution paths through the planning model. Each evolution path is the optimal capacity configuration scheme obtained under the condition of the known corresponding equipment cost data set, and 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 various equipment in the integrated energy system. In addition, the embodiment of the present application also introduces a Monte Carlo random sampling method to generate several equipment cost data sets. Through a large number of random sampling, several equipment investment cost combinations that are most likely to appear in the future are obtained and several equipment cost data sets are constructed, which solves the problem of large uncertainty in the cost estimation of each equipment in conventional planning methods. Subsequently, a large number of simulated evolution paths are generated through several equipment cost data sets, and the optimal path is selected from them, further improving the rationality and scientificity of the capacity configuration of various equipment in 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 in the figure, at the input end, the wind turbines in the upper power grid and the upper gas grid jointly build a stable energy supply system. In the process of energy conversion, equipment such as combined cooling, heating and power (CCHP), electric chillers (ER, electrical chillers) and gas-fired boilers (GB, gas-fired boilers) improve the overall energy utilization efficiency through efficient energy conversion and utilization. In particular, the CCHP system further optimizes the energy utilization effect through the coordinated work of gas turbines, waste heat boilers and absorption chillers (AC, absorption chillers). At the load end, the integrated energy system can respond to various energy demands, demonstrating its strong energy supply flexibility and reliability.

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

[0077] Obtain historical cost data for each equipment type;

[0078] Predicting the cost range of each equipment type based on each of the historical cost data;

[0079] Determining a probability distribution of a range of cost values ​​for each of the equipment types;

[0080] According to the cost value range of each equipment type and the corresponding probability distribution, several Monte Carlo random samplings are performed to generate several equipment cost data sets, wherein in each Monte Carlo random sampling, the random cost value of each equipment type is randomly extracted from the cost value range of each equipment type based on the corresponding probability distribution, and then the random cost values ​​of each equipment type constitute the equipment 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 dealing with cost estimation problems with uncertainty. The specific steps for its implementation are as follows:

[0082] 1) Define variables:

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

[0084] 2) Select distribution:

[0085] Select a suitable probability distribution. This patent assumes that the investment cost is normally distributed between the upper and lower limits, and defines the investment cost for each year as follows:

[0086]

[0087] Where C i,n (t) is the unit investment cost of the i-th equipment in the n-th year, U is the investment cost distribution function that follows the normal distribution, is the lower limit of the i-th investment cost in the n-th year, is the upper limit of the i-th investment cost in the n-th year.

[0088] 3) Perform random sampling:

[0089] A number of investment cost scenarios for each equipment and year are generated using a random number generator to draw samples from a selected probability distribution.

[0090]

[0091] U i，n ~N(0,1)

[0092] Where U i,n is a random number that follows a standard normal distribution.

[0093] The embodiment of the present application provides a method for generating several equipment cost data sets through Monte Carlo random sampling. First, the cost value range of each equipment type is predicted based on the historical cost data of each equipment type, and then random sampling is performed from each cost value range. Before performing random sampling, it is necessary to determine the probability distribution of the cost value range of each equipment type. It is generally believed that the cost value range of each equipment type conforms to the normal distribution. Technicians in this field can also select other suitable probability distributions based on historical cost data. Finally, according to the cost value range of each equipment type and the corresponding probability distribution, several Monte Carlo random samplings are performed, which can not only eliminate the problem of uncertainty in the cost estimation of each equipment, but also provide multiple equipment 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 types of equipment in the integrated energy system.

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

[0095] Simulating the operation of the integrated energy system according to the basic planning data and the constraints of the planning model;

[0096] In the process of simulating the operation of the integrated energy system, calculations are performed respectively according to the objective function and each of the equipment cost data sets to generate each simulation evolution path corresponding to each of the equipment cost data sets.

[0097] The embodiment of the present application provides a method for generating various simulated evolution paths using a planning model, and simulates the operation of an integrated energy system through the constraints of the planning model and the input basic planning data. In this process, the planning model calculates according to the objective function and the input equipment cost data sets, and can obtain various simulated evolution paths corresponding to the equipment cost data sets, thereby realizing the prediction of the future equipment cost and evolution path of the integrated energy system, and providing data support for the equipment capacity configuration of the integrated energy system.

[0098] In a possible implementation, in step S3, performing evolution analysis on each simulated evolution path according to a preset analysis index to determine the evolution path of the integrated energy system includes:

[0099] Calculating the life cycle cost, carbon emissions and carbon trading costs of each of the simulated evolution paths;

[0100] According to the preset weight coefficient, the life cycle cost, carbon emissions and carbon trading cost of each simulated evolution path are weighted and calculated to obtain the optimization benefit of each simulated evolution path;

[0101] According to the optimization benefits of each of the simulated evolution paths, the evolution path of the integrated energy system is determined from each of the simulated evolution paths.

[0102] Specifically, the calculation model of the optimization benefit of each simulated evolution path is shown in the following formula:

[0103]

[0104] In the formula, δ represents the optimization benefit of the simulated evolution path, ω1, ω2, and ω3 represent different weight coefficients that reflect the importance of different factors in the optimization of low-carbon costs, and C ALL represents the full life cycle cost of the simulated evolution path, represents the carbon trading cost of the simulated evolution path, E PIES Represents the carbon emissions of the simulated evolutionary pathway.

[0105] The embodiment of the present application provides a method for performing evolutionary analysis on each simulated evolutionary path, calculates three analysis indicators of the full life cycle cost, carbon emissions and carbon trading costs under each simulated evolutionary path, and performs weighted calculation on the three analysis indicators to obtain the optimization benefits of each simulated evolutionary path, and finally determines the evolutionary path of the integrated energy system from each simulated evolutionary path according to the optimization benefits. Among them, the full life cycle cost reflects the economy of each simulated evolutionary path, and the carbon emissions and carbon trading costs reflect the environmental protection of each simulated evolutionary path. The embodiment of the present application achieves a balance between the economy and environmental protection of the integrated energy system in the development process through a preset weight coefficient. Technicians in this field can set the weight coefficient according to the actual situation, which reflects the flexibility and scientificity of the embodiment of the present application in the process of selecting the evolutionary path.

[0106] Furthermore, constructing and obtaining the planning model according to the system structure and operation mode of the integrated energy system includes:

[0107] Construct corresponding operation models of various equipment according to the system structure and operation mode of the comprehensive energy system, including a gas boiler equipment model, a cogeneration equipment model, an electric refrigerator equipment model, an absorption refrigerator equipment model and an energy storage model;

[0108] Constructing various carbon emission models of the integrated energy system according to the operation models of the various devices;

[0109] Constructing various constraint conditions of the planning model according to each of the equipment operation models and each of the carbon emission models;

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

[0111] Constructing the objective function of the planning model according to the investment cost model, operation cost model, carbon trading cost model and maintenance cost model of the integrated energy system;

[0112] The planning model is constructed by combining the various constraints and the objective function.

[0113] The embodiment of the present application provides a method for constructing a planning model. First, each equipment operation model is constructed through the system structure. At the same time, considering the existence of carbon emissions and carbon trading processes during the operation of the integrated energy system, each carbon emission model is constructed so that the planning model can simulate the normal operation of the integrated energy system. When the integrated energy system is running, it will be affected by many factors. Therefore, corresponding constraints are constructed based on each equipment operation model and each carbon emission model. These constraints ensure that the integrated energy system operates stably, safely and efficiently during the simulated operation process, so that the generated planning scheme is more in line with the actual situation. Finally, 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 integrated energy system, and the optimization direction of the planning model is determined, so that the final planning scheme can effectively reduce various costs and improve the rationality and scientificity of the capacity configuration of various equipment in the integrated energy system.

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

[0115] 1) Gas boiler (GB) equipment model, its operating principle can be described as follows:

[0116]

[0117] Where P h,GB (t) is the thermal power output of the gas boiler, P g,GB (t) is the gas power input to GB during period t; η GB is the energy conversion efficiency of GB; is the upper limit of the power input to GB.

[0118] 2) Combined Heat and Power (CHP) equipment model, its operating principle can be described as follows:

[0119]

[0120] Where P e,CHP (t) is the electric power output of CHP during period t, P g,CHP (t) is the natural gas power input to CHP during period t; η e,CHP , η h,CHP are the energy conversion efficiencies of CHP into electrical energy and thermal energy, respectively; is the upper limit of the power input to CHP; P h,CHP (t) is the thermal power output of CHP during period t; and They are the upper and lower limits of the thermal-to-electricity ratio of CHP respectively.

[0121] 3) Electric Refrigerator (ER) equipment model, its operating principle can be described as follows:

[0122]

[0123] Where P c,ER (t) is the cooling power output by ER at time t; η ER P is the conversion efficiency of the electric refrigerator; e,ER (t) is the electrical power input to ER at time t; is the upper limit of the electric power input to ER.

[0124] 4) Absorption refrigeration machine (AC) equipment model, its operating principle can be described as follows:

[0125]

[0126] Where P c,AC (t) is the cooling power output by AC at time t; η AC P is the conversion efficiency of absorption refrigeration; h,AC (t) is the thermal power input to AC at time t; It is the upper limit of the thermal power of the input AC.

[0127] 5) Since the models of various energy storage are similar, the energy storage models of various energy sources can be uniformly modeled:

[0128]

[0129] Where i∈Ω,Ω={ES,HS,GS} is the set of electricity storage, heat storage and gas storage; P i cha (t), P i dis (t) are the charging and discharging powers of the i-th energy storage device in time period t; and are the maximum power of a single charge and discharge of the i-th energy storage device; are the state parameters of the charging and discharging of the i-th energy storage device in time period t, which are binary variables. That is, when When When , the energy storage device is in the energy 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 the three carbon emission sources of purchased electricity, gas turbines and gas boilers, and considering that all the purchased electricity of the upper power grid comes from thermal power units, the carbon emission models of the integrated energy system are constructed according to the operation models of each device, and the carbon emission models are specifically:

[0131] E PIES =E e,buy +E GB +E CHP

[0132]

[0133] In the formula, E PIES 、E e,buy 、E GB 、E CHP are the actual carbon emissions of the park's integrated energy system, purchased electricity, gas boilers, and cogeneration units; P e,buy P is the power purchased by the park comprehensive energy system from the upper power grid during period t; h,GB (t) is the heating power of the gas boiler during period t; P h,CHP (t), P e,CHP (t) are the heating and power supply power of the CHP unit during period t; μ e and μ h are the carbon emission intensity per unit of electricity and per unit of heat respectively; is the conversion coefficient of the CHP unit's power generation to heat; the carbon trading cost in this paper is calculated on a yearly basis, so T is 8760.

[0134] The carbon quota calculation model for the park's comprehensive energy system is as follows:

[0135] E' PIES =E e ' ,buy +E' GB +E' CHP

[0136] Where, E' PIES 、E e ' ,buy , E' GB , E' CHP They are the carbon emission quotas for the park's integrated energy system, purchased electricity, gas boilers, and cogeneration units.

[0137] In summary, the trading carbon emissions of the park's comprehensive energy system after considering carbon quotas are:

[0138] E0=E PIES -E' PIES

[0139] According to each of the equipment operation models and each of the carbon emission models, various constraints of the planning model are constructed, including electric power balance constraints, thermal power balance constraints, gas power balance constraints, cooling power balance constraints, new energy output constraints, energy storage constraints and carbon emission constraints.

[0140] The electric power balance constraint conditions are specifically:

[0141]

[0142] Since wind power and photovoltaic power have great randomness and volatility, in order to reduce the pressure on the main grid, the present embodiment does not consider the sale of electricity from the park integrated energy system to the upper grid. e,load (t) represents the electric load power during period t; and They represent the discharge and charge power of the energy storage at time t respectively.

[0143] The thermal power balance constraint condition is specifically:

[0144]

[0145] Where P h,load (t) represents the heat load power during period t; and They represent the heat release and charging power of the heat storage at time t respectively.

[0146] The gas power balance constraint condition is specifically:

[0147]

[0148] Where P g,load (t) represents the gas load power during period t; and They represent the deflation and inflation power of the gas storage at time t respectively.

[0149] The cooling power balance constraint condition is specifically:

[0150] P c,ER (t)+P c,AC (t) = P c,load (t)

[0151] Where P c,load (t) represents the cooling load power during period t.

[0152] The new energy output constraint conditions are specifically:

[0153]

[0154] Where P w,t , P pv,t They represent the power output of wind power and photovoltaic power in period t respectively; Respectively represent the upper limits of wind power and photovoltaic output power, Indicates the upper limit of wind power and photovoltaic capacity.

[0155] Considering that the energy storage equipment needs to return to the initial state of the dispatch after running one dispatch cycle, the following constraint conditions are further established on the basis of the original constraints:

[0156] S i (t0) = S i (t T )

[0157] In the formula, S i (t) is the capacity of the i-th energy storage device in period t.

[0158] The carbon peak constraint can be expressed as:

[0159] E 0,x ≥E0

[0160] In the formula, x means that the carbon peak will be achieved in the xth year of the plan.

[0161] In combination with the "dual carbon" goal, after carbon emissions reach their peak, in order to ensure that carbon emissions fall back in an orderly and steady manner, it is set that in each year after the carbon peak, the traded carbon emissions must decrease year by year at a fixed rate of v%. Therefore, the carbon emission constraints are specifically as follows:

[0162] E 0,y+1 ≤(1-v%)·E 0,y ,y≥x

[0163] According to the operation models of each device and the carbon emission models, the investment cost model, operation cost model, carbon trading cost model and maintenance cost model of the comprehensive energy system are constructed, wherein the investment cost model of the comprehensive energy system is:

[0164] C inv＝ ∑ j∈φ f j e j W j

[0165] Among them, e j is the unit price of equipment j, RMB / kW; W j is the configuration capacity of device j, kW; φ is the equipment set including wind power, photovoltaic equipment, CHP, AC, ER, GB, EC and various energy storage equipment; fj A Boolean variable with a value of 1 or 0.

[0166] The operating cost model of the integrated energy system is:

[0167]

[0168] Among them, C buy (t) is the energy purchase cost of the park; a t 、b t Represent the electricity price and gas price at time t in year n respectively; C w,cut (t) is the wind curtailment cost of the park’s integrated energy system; c t is the penalty cost for unit wind curtailment; C ope P is the operating cost of the park's comprehensive energy system, which consists of the park's energy purchase cost and wind abandonment cost. The energy purchase cost includes the cost of purchasing electricity and gas. g,buy,n (t) is the gas purchase power in period t of year n; P w,cut,n (t) is the wind power abandoned in period t in year n.

[0169] Considering that the maintenance cost of the park's integrated energy system consists of the maintenance costs of wind power, photovoltaics, energy conversion, and various energy storage devices, it is determined by the actual output power of various devices during operation. The vector composed of the output power of various devices during time period t is Then the maintenance cost model of the integrated energy system is:

[0170]

[0171] in, is the maintenance cost vector per unit power of each type of equipment to be planned; is the output power of the j-type device in period t; if the j-type device represents an energy storage device, It represents the sum of the charging and discharging power of the energy storage device.

[0172] The specific formula of the carbon trading cost model is:

[0173]

[0174] in, is the carbon trading cost, λ is the carbon trading benchmark price, l is the length of the carbon emission interval, α is the increase in the carbon trading price, and E0 is the traded carbon emissions.

[0175] According to the investment cost model, operation cost model, carbon trading cost model and maintenance cost model of the comprehensive energy system, the objective function of the planning model is constructed, and the specific formula is:

[0176]

[0177] Where C represents the present value of the cost of the integrated energy system, C inv is the construction investment cost, C ope , C main are the operation and maintenance costs of the integrated energy system, The cost of carbon trading.

[0178] In a preferred embodiment, a detailed flow chart of a capacity configuration method based on carbon trading and evolution path analysis provided by the present application is as follows: Figure 3 As shown in the figure, firstly, the overall framework of the integrated energy system is clarified, the equipment unit model is constructed, and the integrated energy system structure is constructed through a unified busbar structure. Then, based on the system structure and operation mode of the integrated energy system, the PIES multi-stage low-carbon planning model architecture, constraints and objective functions are constructed. Finally, a large number of cost scenarios of wind power unit investment cost, photovoltaic unit investment cost and investment cost of each equipment are obtained through Monte Carlo simulation. The planning model is calculated according to different investment cost values ​​to obtain the planned capacity value range of each equipment in each year, thereby obtaining a variety of simulated evolution paths, and comparing the economy and environmental protection of each simulated evolution path to obtain the optimal evolution path solution.

[0179] In a preferred embodiment, the capacity configuration method provided by the present application is used to generate a capacity configuration plan 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: Monte Carlo random sampling of uncertainty parameters, integrated planning of the optimal configuration of source, load, and storage resources in multiple stages based on the constructed planning model, comparative analysis of different plans, considering the optimal goals of economy and environmental protection, and providing the optimal plan for the future park's comprehensive energy system. In order to fully reveal the actual operating characteristics of the park, data from three typical days in summer, winter and transition seasons were selected for analysis. At the same time, taking into account the characteristics of the park's load growth, according to historical data and planning requirements, the load growth rates in the early, mid-term and late stages of the planning are given, as shown in Table 1.

[0180] Table 1 Load growth rate of various loads during the planning period

[0181]

[0182] In order to cope with the uncertainty faced by the evolution of the park's integrated energy system, the embodiment of the present application considers the use of Monte Carlo random sampling for the uncertainty factors of the unit investment cost of each device, and obtains a large number of evolution paths based on a multi-stage low-carbon planning model, wherein 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:

[0183] Table 1 Carbon emission related parameters

[0184]

[0185]

[0186] Table 2 Time-of-use electricity prices, gas prices and wind and solar power curtailment prices

[0187]

[0188] Table 3 Parameters of various types of equipment to be planned

[0189]

[0190] Table 4 Uncertainty factor value range

[0191]

[0192]

[0193] The different unit investment cost combinations obtained by sampling are input into the planning model for optimization calculation. Each unit investment cost combination will obtain a simulated evolution path. In the simulated evolution path, each type of equipment may be newly added every year, and the total capacity of a single equipment each year is the sum of the newly added equipment capacity in previous years. By analyzing the data of all evolution paths, we can obtain the capacity evolution path diagram of each equipment type, the evolution path diagram of various types of costs, and the evolution path diagram of carbon emissions, as shown in the figure below. Figure 4 , Figure 5 as well as Figure 6 As shown. Figure 4 It can be found that at the equipment capacity configuration level, as the load of the park increases, the capacity configuration of each device also increases accordingly, wind power and photovoltaic power continue to grow, and energy storage and hydrogen-related clean equipment gradually become the dominant resources. The system's full life cycle cost and cost structure are as follows: Figure 5 As shown in the figure, the life cycle cost decreased in the first three years and then remained stable. The operating cost decreased in the middle and late stages of the plan, which may be because the equipment capacity in the later stages can well meet the load requirements of the park, so the demand for purchased energy is reduced. As for the maintenance cost, it can be seen that it shows a slow upward trend. This is because the overall equipment capacity configuration in the middle and late stages of the plan is increasing, and the maintenance cost of the equipment is also increasing accordingly. The actual carbon emission evolution path of the system is as follows: Figure 6 As shown in the figure, the actual annual carbon emissions show a trend of first peaking and then decreasing.

[0194] Because my country pays more attention to environmental impact under the green and low-carbon transformation development path, therefore, on the basis of standardizing the whole life cycle, carbon trading costs and carbon emissions, a weight coefficient of 1:1:3 is selected to calculate the optimization benefits of each simulated evolution path. The calculation results are as follows: Figure 7 Based on the above evolutionary path, different low-carbon cost optimization benefit values ​​are obtained, as shown in Figure 7 It can be seen that the optimization benefit value of the simulated evolution path 8 is significantly higher than that of other paths, that is, the life cycle cost and carbon emissions of path 8 are the lowest among all paths, and this path is the optimal evolution path for the integrated energy system of the park. 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 reflects the advantages of path 8 in cost control and carbon emission reduction, but also proves the effectiveness of this path in promoting the low-carbon transformation of the park's energy system. The annual new capacity of various types of equipment in the simulated evolution path 8 in the next 1-15 years is shown in Table 6.

[0195] Table 5 Annual additional capacity of each device under the optimal path

[0196]

[0197]

[0198] In the development process of the integrated energy system, the annual capacity configuration of each equipment type of the integrated energy system can be adjusted according to Table 6 to improve the rationality and scientificity of the capacity configuration of various equipment in the integrated energy system.

[0199] Embodiment 2:

[0200] like Figure 8 As shown, accordingly, embodiment 2 provides a capacity configuration system based on carbon trading and evolution path analysis, including an acquisition module 10, a path evolution module 20, a path determination module 30 and a capacity configuration module 40;

[0201] The acquisition module 10 is used to acquire basic planning data of the integrated energy system, and the basic planning data includes planning cycle, equipment parameters, carbon market transaction information and various load data;

[0202] The path evolution module 20 is used to input the basic planning data and the 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 constructed according to the system structure and operation mode of the integrated energy system, the simulation evolution path includes the capacity configuration of each equipment type of the integrated energy system each year, and the plurality of equipment cost data sets are generated by Monte Carlo random sampling;

[0203] The path determination module 30 is used to perform evolution analysis on each simulated evolution path according to preset analysis indicators to determine the evolution path of the integrated energy system;

[0204] The capacity configuration module 40 is used to adjust the capacity configuration of each device type of the integrated energy system according to the evolution path of the integrated energy system within the planning period.

[0205] Furthermore, the generating of the plurality of equipment cost data sets by Monte Carlo random sampling includes:

[0206] Obtain historical cost data for each equipment type;

[0207] Predicting the cost range of each equipment type based on each of the historical cost data;

[0208] Determining a probability distribution of a range of cost values ​​for each of the equipment types;

[0209] According to the cost value range of each equipment type and the corresponding probability distribution, several Monte Carlo random samplings are performed to generate several equipment cost data sets, wherein in each Monte Carlo random sampling, the random cost value of each equipment type is randomly extracted from the cost value range of each equipment type based on the corresponding probability distribution, and then the random cost values ​​of each equipment type constitute the equipment cost data set.

[0210] In a possible implementation, the planning model generates corresponding simulation evolution paths according to the basic planning data and the plurality of equipment cost data sets, including:

[0211] Simulating the operation of the integrated energy system according to the basic planning data and the constraints of the planning model;

[0212] In the process of simulating the operation of the integrated energy system, calculations are performed respectively according to the objective function and each of the equipment cost data sets to generate each simulation evolution path corresponding to each of the equipment cost data sets.

[0213] In a possible implementation, the path determination module 30 performs evolution analysis on each simulated evolution path according to a preset analysis index to determine the evolution path of the integrated energy system, including:

[0214] Calculating the life cycle cost, carbon emissions and carbon trading costs of each of the simulated evolution paths;

[0215] According to the preset weight coefficient, the life cycle cost, carbon emissions and carbon trading cost of each simulated evolution path are weighted and calculated to obtain the optimization benefit of each simulated evolution path;

[0216] According to the optimization benefits of each of the simulated evolution paths, the evolution path of the integrated energy system is determined from each of the simulated evolution paths.

[0217] Furthermore, constructing and obtaining the planning model according to the system structure and operation mode of the integrated energy system includes:

[0218] Construct corresponding operation models of various equipment according to the system structure and operation mode of the comprehensive energy system, including a gas boiler equipment model, a cogeneration equipment model, an electric refrigerator equipment model, an absorption refrigerator equipment model and an energy storage model;

[0219] Constructing various carbon emission models of the integrated energy system according to the operation models of the various devices;

[0220] Constructing various constraint conditions of the planning model according to each of the equipment operation models and each of the carbon emission models;

[0221] According to the operation models of each device and the carbon emission models, construct an investment cost model, an operation cost model, a carbon trading cost model and a maintenance cost model of the comprehensive energy system;

[0222] Constructing the objective function of the planning model according to the investment cost model, operation cost model, carbon trading cost model and maintenance cost model of the integrated energy system;

[0223] The planning model is constructed by combining the various constraints and the objective function.

[0224] The embodiment of the present application provides a capacity configuration system based on carbon trading and evolution path analysis. By obtaining the basic planning data of the integrated energy system and several equipment cost data sets generated by Monte Carlo random sampling, a preset planning model is used to simulate the basic planning data and the equipment cost data set, and multiple simulated evolution paths of the integrated energy system can be generated. Each simulated evolution path records the change process of the capacity configuration of each equipment type in the future for a long time. Then, each path is subjected to evolution analysis according to the preset analysis index, and the optimal simulated evolution path is selected as the evolution path of the integrated energy system. Finally, in the development process of the integrated energy system, the capacity configuration of each equipment type of the integrated energy system is continuously adjusted according to the evolution path of the integrated energy system. The embodiment of the present application is based on the overall structure and carbon trading process of the integrated energy system, and generates corresponding simulated evolution paths through the planning model. Each evolution path is the optimal capacity configuration scheme obtained under the condition of the known corresponding equipment cost data set, and 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 various equipment in the integrated energy system. In addition, the embodiment of the present application also introduces a Monte Carlo random sampling method to generate several equipment cost data sets. Through a large number of random sampling, several equipment investment cost combinations that are most likely to appear in the future are obtained and several equipment cost data sets are constructed, which solves the problem of large uncertainty in the cost estimation of each equipment in conventional planning methods. Subsequently, a large number of simulated evolution paths are generated through several equipment cost data sets, and the optimal path is selected from them, further improving the rationality and scientificity of the capacity configuration of various equipment in the integrated energy system.

[0225] The more detailed working principle and step flow of this embodiment can refer to, but are not limited to, the relevant records of Embodiment 1.

[0226] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection 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 should be included in the scope of protection of the present application.

Claims

1. A capacity configuration method based on carbon trading and evolution path analysis, characterized in that: include: Obtaining basic planning data of the integrated energy system, the basic planning data including planning period, equipment parameters, carbon market transaction information and various load data; Inputting the basic planning data and the 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 constructed according to the system structure and operation mode of the integrated energy system, the simulation evolution path includes the capacity configuration of each equipment type of the integrated energy system each year, and the plurality of equipment cost data sets are generated by Monte Carlo random sampling; Performing evolution analysis on each of the simulated evolution paths according to preset analysis indicators to determine the evolution path of the integrated energy system; During the planning period, the capacity configuration of each equipment type of the integrated energy system is adjusted according to the evolution path of the integrated energy system.

2. A capacity configuration method based on carbon trading and evolution path analysis as claimed in claim 1, characterized in that: The generating of the plurality of equipment cost data sets by Monte Carlo random sampling comprises: Obtain historical cost data for each equipment type; Predicting the cost range of each equipment type based on each of the historical cost data; Determining a probability distribution of a range of cost values ​​for each of the equipment types; According to the cost value range of each equipment type and the corresponding probability distribution, several Monte Carlo random samplings are performed to generate several equipment cost data sets, wherein in each Monte Carlo random sampling, the random cost value of each equipment type is randomly extracted from the cost value range of each equipment type based on the corresponding probability distribution, and then the random cost values ​​of each equipment type constitute the equipment cost data set.

3. The capacity configuration method based on carbon trading and evolution path analysis according to claim 1, characterized in that: The planning model generates corresponding simulation evolution paths according to the basic planning data and the plurality of equipment cost data sets, including: Simulating the operation of the integrated energy system according to the basic planning data and the constraints of the planning model; In the process of simulating the operation of the integrated energy system, calculations are performed respectively according to the objective function and each of the equipment cost data sets to generate each simulation evolution path corresponding to each of the equipment cost data sets.

4. The capacity configuration method based on carbon trading and evolution path analysis according to claim 1, characterized in that: The step of performing evolution analysis on each simulated evolution path according to preset analysis indicators to determine the evolution path of the integrated energy system includes: Calculating the life cycle cost, carbon emissions and carbon trading costs of each of the simulated evolution paths; According to the preset weight coefficient, the life cycle cost, carbon emissions and carbon trading cost of each simulated evolution path are weighted and calculated to obtain the optimization benefit of each simulated evolution path; According to the optimization benefits of each of the simulated evolution paths, the evolution path of the integrated energy system is determined from each of the simulated evolution paths.

5. A capacity configuration method based on carbon trading and evolution path analysis as claimed in any one of claims 1 to 4, characterized in that: The planning model is constructed according to the system structure and operation mode of the integrated energy system, including: Construct corresponding operation models of various equipment according to the system structure and operation mode of the comprehensive energy system, including a gas boiler equipment model, a cogeneration equipment model, an electric refrigerator equipment model, an absorption refrigerator equipment model and an energy storage model; Constructing various carbon emission models of the integrated energy system according to the operation models of the various devices; Constructing various constraint conditions of the planning model according to each of the equipment operation models and each of the carbon emission models; According to the operation models of each device and the carbon emission models, construct an investment cost model, an operation cost model, a carbon trading cost model and a maintenance cost model of the comprehensive energy system; Constructing the objective function of the planning model according to the investment cost model, operation cost model, carbon trading cost model and maintenance cost model of the integrated energy system; The planning model is constructed by combining the various constraints and the objective function.

6. A capacity configuration system based on carbon trading and evolution path analysis, characterized in that: It includes an acquisition module, a path evolution module, a path determination module and a capacity configuration module; The acquisition module is used to acquire basic planning data of the integrated energy system, and the basic planning data includes planning cycle, equipment parameters, carbon market transaction information and various load data; The path evolution module is used to input the basic planning data and the 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 constructed according to the system structure and operation mode of the integrated energy system, the simulation evolution path includes the capacity configuration of each equipment type of the integrated energy system each year, and the plurality of equipment cost data sets are generated by Monte Carlo random sampling; The path determination module is used to perform evolution analysis on each simulated evolution path according to preset analysis indicators to determine the evolution path of the integrated energy system; The capacity configuration module is used to adjust the capacity configuration of each equipment type of the integrated energy system according to the evolution path of the integrated energy system within the planning period.

7. A capacity configuration system based on carbon trading and evolution path analysis as claimed in claim 6, characterized in that: The generating of the plurality of equipment cost data sets by Monte Carlo random sampling comprises: Obtain historical cost data for each equipment type; Predicting the cost range of each equipment type based on each of the historical cost data; Determining a probability distribution of a range of cost values ​​for each of the equipment types; According to the cost value range of each equipment type and the corresponding probability distribution, several Monte Carlo random samplings are performed to generate several equipment cost data sets, wherein in each Monte Carlo random sampling, the random cost value of each equipment type is randomly extracted from the cost value range of each equipment type based on the corresponding probability distribution, and then the random cost values ​​of each equipment type constitute the equipment cost data set.

8. A capacity configuration system based on carbon trading and evolution path analysis as claimed in claim 6, characterized in that: The planning model generates corresponding simulation evolution paths according to the basic planning data and the plurality of equipment cost data sets, including: Simulating the operation of the integrated energy system according to the basic planning data and the constraints of the planning model; In the process of simulating the operation of the integrated energy system, calculations are performed respectively according to the objective function and each of the equipment cost data sets to generate each simulation evolution path corresponding to each of the equipment cost data sets.

9. A capacity configuration system based on carbon trading and evolution path analysis as claimed in claim 6, characterized in that: The path determination module performs evolution analysis on each simulated evolution path according to preset analysis indicators to determine the evolution path of the integrated energy system, including: Calculating the life cycle cost, carbon emissions and carbon trading costs of each of the simulated evolution paths; According to the preset weight coefficient, the life cycle cost, carbon emissions and carbon trading cost of each simulated evolution path are weighted and calculated to obtain the optimization benefit of each simulated evolution path; According to the optimization benefits of each of the simulated evolution paths, the evolution path of the integrated energy system is determined from each of the simulated evolution paths.

10. A capacity configuration system based on carbon trading and evolution path analysis as claimed in any one of claims 6 to 9, characterized in that: The planning model is constructed according to the system structure and operation mode of the integrated energy system, including: Construct corresponding operation models of various equipment according to the system structure and operation mode of the comprehensive energy system, including a gas boiler equipment model, a cogeneration equipment model, an electric refrigerator equipment model, an absorption refrigerator equipment model and an energy storage model; Constructing various carbon emission models of the integrated energy system according to the operation models of the various devices; Constructing various constraint conditions of the planning model according to each of the equipment operation models and each of the carbon emission models; According to the operation models of each device and the carbon emission models, construct an investment cost model, an operation cost model, a carbon trading cost model and a maintenance cost model of the comprehensive energy system; Constructing the objective function of the planning model according to the investment cost model, operation cost model, carbon trading cost model and maintenance cost model of the integrated energy system; The planning model is constructed by combining the various constraints and the objective function.

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