A comprehensive energy system capacity configuration method based on CCER quota

Abstract

The present invention discloses a method for capacity configuration of an integrated energy system based on CCER quotas. First, source and load clustering is performed to obtain the corresponding typical daily frequency and clustering effect diagram; then, low-carbon coupling equipment is added to the thermal power units and modeled separately; based on the introduced ladder carbon trading model of carbon emission quotas, the method is solved through MATLAB to obtain the optimal capacity value of each device corresponding to the minimum objective function, as well as the output and electrical power balance of the equipment in each time period, the total system cost and carbon emissions, thereby completing the optimal capacity configuration of the energy system.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and more specifically, relates to a method for configuring the capacity of an integrated energy system based on CCER quotas. Background Art

[0002] With the rapid development of the world economy, energy demand and greenhouse gas emissions have increased significantly, and the contradiction between energy development and environmental issues has become increasingly prominent.

[0003] The Integrated Energy System (IES) internally couples multiple energy sources for joint supply, meeting the diverse energy demands of end-users. By integrating carbon capture and storage (CCS) from thermal power plants and power-to-gas (P2G) technology, the system's low-carbon impact is further optimized. The Emissions Trading Scheme (ETS) is an artificially created market created by establishing legal carbon emission rights, treating them as a commodity, and through government control of emissions from energy-consuming enterprises. Chinese Certified Emission Reductions (CCERs) are greenhouse gas emission reductions that are quantified and certified for specific projects within my country and registered in the National Greenhouse Gas Voluntary Emission Reduction Trading System. As an emerging carbon offsetting mechanism, CCERs could theoretically complement the ETS to mitigate global warming.

[0004] When the power system experiences a large amount of abandoned solar power and high carbon emissions, it is necessary to reasonably configure the capacity of the integrated energy production unit system, and introduce a carbon trading market under the CCER quota to further artificially control carbon emissions through a tiered carbon trading mechanism. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a capacity configuration method for an integrated energy system based on CCER quotas, which introduces carbon emission quotas under tiered carbon trading to achieve the optimal capacity configuration of the integrated energy system, thereby controlling carbon emissions.

[0006] To achieve the above-mentioned purpose, the present invention provides a method for configuring the capacity of an integrated energy system based on CCER quotas, which is characterized by comprising the following steps:

[0007] (1) Perform K-means clustering on the historical light intensity and electricity load data of the integrated energy system;

[0008] The light intensity and power load data of the integrated energy system in the past year are collected and then clustered using the K-means clustering algorithm to obtain S typical scenes and corresponding typical days. The light intensity and power load data corresponding to each typical scene are recorded as I s,t and T is the sampling period;

[0009] (2) Establish an output model for the integrated energy system;

[0010] (2.1) Establish the output model of photovoltaic power station in the integrated energy system:

[0011]

[0012] in, is the rated power of the photovoltaic power station, A PV is the area of ​​the photovoltaic panel A PV , k is the unit area of ​​photovoltaic panels under the light intensity of 1kW / m 2 The conversion coefficient between output power and area when s,t is the illumination intensity of scene s at time t, is the total electric energy generated by the photovoltaic power station under scenario s at time t, is the actual power generation and abandoned power of the photovoltaic power station under scenario s at time t, is the maximum electric energy value generated by the photovoltaic power station;

[0013] (2.2) Establish an output model for carbon capture power plants in an integrated energy system:

[0014]

[0015] in, and are the power generation and electricity production of the thermal power unit under scenario s at time t, ΔT is the sampling time interval, is the carbon dioxide production of the thermal power unit at time t under scenario s, e PGU is the carbon emission intensity of thermal power units; is the total amount of carbon dioxide actually captured by the carbon capture power plant at time t under scenario s, η CCPP is the capture efficiency of the carbon capture power plant, is the total amount of carbon dioxide captured by the carbon capture power plant at time t under scenario s, is the storage capacity of the carbon capture power plant at time t under scenario s, is the power consumption of the carbon capture power plant at time t under scenario s, is the power consumption coefficient corresponding to the unit carbon dioxide capture; and B is the upper and lower limits of thermal power unit output,PGU The operating status of the thermal power unit; and are the upper and lower limits of the thermal power unit climbing rate, is the power generation of the thermal power unit at time t under scenario s;

[0016] (2.3) Establish the output model of the electrolyzer in the integrated energy system:

[0017]

[0018] in, is the amount of hydrogen produced by the electrolyzer at time t under scenario s, is the power consumption of the electrolyzer at time t under scenario s, is the power consumption coefficient for producing unit hydrogen; is the power generation of the photovoltaic equipment at time t under scenario s; and are the upper and lower limits of the electrolytic cell power output, and is the upper and lower limits of the electrolytic cell capacity, B EL The operating status of the electrolytic cell equipment. and The upper and lower limits of the electrolytic cell climbing rate; is the configured electrolytic cell capacity.

[0019] (2.4) Establish the output model of the methane reactor in the integrated energy system:

[0020]

[0021] in, is the amount of methane produced by the methane reactor at time t under scenario s, is the power consumption of the methane reactor under scenario s at time t, λ g is the power consumption coefficient for producing unit methane; and are the amount of hydrogen and carbon dioxide required for the methane reactor to generate methane under scenario s at time t, and ω is the reaction equilibrium coefficient; and is the upper and lower limits of the capacity of the methane reactor, B MR The methane reactor is in operation state. and are the upper and lower limits of the methane reactor ramp rate; The configuration capacity of the methane reactor;

[0022] (2.5) Establish the output model of the carbon dioxide storage tank and hydrogen storage tank in the integrated energy system:

[0023]

[0024] Among them, V s,t is the gas storage capacity of the carbon dioxide storage tank or hydrogen storage tank under scenario s at time t, V is the amount of carbon dioxide stored in and taken out of the carbon dioxide storage tank or hydrogen storage tank under scenario s at time t; s,0 、V s,T is the gas volume of the carbon dioxide storage tank or hydrogen storage tank at the initial and final moments under scenario s; V max is the upper limit of the capacity of the carbon dioxide storage tank or hydrogen storage tank; β in-out is the storage and access ratio, B is the operating status of the carbon dioxide storage tank or hydrogen storage tank, V r The configuration capacity of the carbon dioxide storage tank or hydrogen storage tank;

[0025] (3) Construct the objective function and balance conditions for optimal configuration of integrated energy system capacity;

[0026] (3.1) Objective function of optimal capacity configuration

[0027]

[0028] Among them, f energy is the energy transaction cost, f inv is the equipment investment cost, f om For operation and maintenance costs, is the carbon trading cost, f cur The cost of abandoned light;

[0029] (3.2) Electric balance conditions:

[0030]

[0031] (3.3) Hydrogen balance conditions:

[0032]

[0033] in, is the amount of hydrogen stored in scenario s at time t, is the amount of hydrogen taken out under scenario s at time t;

[0034] (3.4) Carbon dioxide equilibrium conditions:

[0035]

[0036] in, is the amount of carbon dioxide stored under scenario s at time t, is the amount of carbon dioxide removed under scenario s at time t;

[0037] (4) Establish a tiered carbon trading model that introduces carbon emission quotas;

[0038] (4.1) Allocate carbon emission quotas to thermal power units and photovoltaic power stations:

[0039]

[0040] Among them, C IEPU is the total carbon emission quota of thermal power units and photovoltaic power stations, C PGU is the carbon emission quota of thermal power units, C PV is the carbon emission quota of the photovoltaic power station; e is the carbon emission quota consumed by thermal power units, χ v Carbon emission quotas consumed by photovoltaic power stations;

[0041] (4.2) Establish a net carbon emissions model for the integrated energy system;

[0042]

[0043] in, is the net carbon emissions, is the carbon emissions of the tiered carbon trading, The carbon emissions offset by voluntary emission reductions, N s is the corresponding frequency of a typical day, σ is the conversion coefficient, and r is the offset ratio;

[0044] (4.3) Establish a carbon trading model:

[0045]

[0046] in, is the tiered carbon trading price, λ is the carbon trading base price, d is the carbon emission range, and α is the price growth rate;

[0047] (5) Optimal capacity configuration of integrated energy systems;

[0048] The light intensity and electric load data under different scenarios after clustering are substituted into the output model of the integrated energy system. Then, the objective function of the optimal configuration of the integrated energy system capacity is used as the target and the corresponding balance condition is used as the constraint. Based on the introduced ladder carbon trading model of carbon emission quota, it is solved through MATLAB to obtain the optimal capacity value of each device corresponding to the minimum objective function, as well as the output and electrical power balance of the equipment in each period, the total system cost and carbon emissions, thereby completing the optimal capacity configuration of the energy system.

[0049] The object of the invention of the present invention is achieved like this:

[0050] The present invention adopts a comprehensive energy system capacity configuration method based on CCER quotas. First, source and load are clustered to obtain the corresponding typical daily frequency and clustering effect diagram; then low-carbon coupling equipment is added to the thermal power units and modeled separately; based on the introduced ladder carbon trading model of carbon emission quotas, it is solved through MATLAB to obtain the optimal capacity value of each equipment corresponding to the minimum objective function, as well as the output and electrical power balance of the equipment in each time period, the total system cost and carbon emissions, thereby completing the optimal capacity configuration of the energy system.

[0051] At the same time, the integrated energy system capacity configuration method based on CCER quotas of the present invention also has the following beneficial effects:

[0052] (1) This invention achieves dual constraints on system carbon emissions under the tiered carbon trading model of CCER quotas. Compared with the fixed carbon trading costs and traditional carbon quotas under the traditional carbon trading model, it achieves large-scale carbon emission reduction. Due to the combined effect of thermal power units and low-carbon coupling equipment, load fluctuations are smoothed on the basis of energy conservation, while taking into account emission reduction and economic benefits.

[0053] (2) By considering the coupling characteristics of low-carbon equipment, increasing the types of low-carbon coupling equipment in the system, and adding gas storage devices, the level of new energy absorption can be improved.

[0054] (3) Through sensitivity analysis of the ladder carbon trading parameters, the most sensitive interval is obtained, and reasonable base prices and price growth rates are set in this interval to achieve significant emission reduction effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a flow chart of the integrated energy system capacity configuration method based on CCER quotas of the present invention;

[0056] Figure 2 A diagram of sunlight intensity and electrical load throughout the year is shown;

[0057] Figure 3 A schematic diagram of the integrated energy system structure is shown;

[0058] Figure 4 Shows 3D images of abandoned light in different scenarios;

[0059] Figure 5 A diagram shows the impact of different carbon trading parameters on the integrated energy system. DETAILED DESCRIPTION

[0060] The following describes the specific embodiments of the present invention in conjunction with the accompanying drawings so that those skilled in the art can better understand the present invention. It should be noted that in the following description, when detailed descriptions of known functions and designs may dilute the main content of the present invention, such descriptions will be omitted here.

[0061] Example

[0062] Figure 1 This is a flow chart of the integrated energy system capacity configuration method based on CCER quotas of the present invention.

[0063] In this embodiment, if Figure 1 As shown, the present invention provides a method for configuring the capacity of an integrated energy system based on CCER quotas, comprising the following steps:

[0064] S1. Perform K-means clustering on the historical light intensity and electricity load data of the integrated energy system;

[0065] The light intensity and power load data of the integrated energy system in the past year were collected and then clustered using the K-means clustering algorithm to obtain 6 typical scenes and the corresponding 6 typical days. The light intensity and power load data corresponding to each typical scene are recorded as I s,t and T is the sampling period, which is 24;

[0066] S2. Establish an output model for the integrated energy system;

[0067] In this embodiment, the architecture diagram of the integrated energy system is as follows: Figure 3 As shown, we are building Figure 3 The output model of the integrated energy system shown in the figure models the photovoltaic power station, carbon capture power plant (CCPP), electrolysis bath (EL), methanation reactor (MR), carbon dioxide storage (CS), and hydrogen storage (HS) in the integrated energy system. The specific modeling process is as follows:

[0068] S2.1. Establishing the output model of photovoltaic power stations in the integrated energy system:

[0069]

[0070] in, is the rated power of the photovoltaic power station, A PV is the area of ​​the photovoltaic panel A PV, k is the unit area of ​​photovoltaic panels under the light intensity of 1kW / m 2 The conversion coefficient between output power and area when s,t is the illumination intensity of scene s at time t, is the total electric energy generated by the photovoltaic power station under scenario s at time t, is the actual power generation and abandoned power of the photovoltaic power station under scenario s at time t, is the maximum electric energy value generated by the photovoltaic power station;

[0071] S2.2. Develop an output model for a carbon capture power plant in an integrated energy system:

[0072]

[0073] in, and are the power generation and electricity production of the thermal power unit under scenario s at time t, ΔT is the sampling time interval, is the carbon dioxide production of the thermal power unit at time t under scenario s, e PGU is the carbon emission intensity of thermal power units; is the total amount of carbon dioxide actually captured by the carbon capture power plant at time t under scenario s, η CCPP is the capture efficiency of the carbon capture power plant, is the total amount of carbon dioxide captured by the carbon capture power plant at time t under scenario s, is the storage capacity of the carbon capture power plant at time t under scenario s, is the power consumption of the carbon capture power plant at time t under scenario s, is the power consumption coefficient corresponding to the unit carbon dioxide capture; and B is the upper and lower limits of thermal power unit output, PGU The operating status of the thermal power unit; and are the upper and lower limits of the thermal power unit climbing rate, is the power generation of the thermal power unit at time t under scenario s;

[0074] S2.3. Establish an output model of the electrolyzer in the integrated energy system:

[0075]

[0076] in, is the amount of hydrogen produced by the electrolyzer at time t under scenario s, is the power consumption of the electrolyzer at time t under scenario s, is the power consumption coefficient for producing unit hydrogen; is the power generation of the photovoltaic equipment at time t under scenario s; and are the upper and lower limits of the electrolytic cell power output, and is the upper and lower limits of the electrolytic cell capacity, B EL The operating status of the electrolytic cell equipment. and The upper and lower limits of the electrolytic cell climbing rate; is the configured electrolytic cell capacity.

[0077] S2.4. Establish an output model for the methane reactor in the integrated energy system:

[0078]

[0079] in, is the amount of methane produced by the methane reactor at time t under scenario s, is the power consumption of the methane reactor under scenario s at time t, λ g is the power consumption coefficient for producing unit methane; and are the amount of hydrogen and carbon dioxide required for the methane reactor to generate methane under scenario s at time t, and ω is the reaction equilibrium coefficient; and is the upper and lower limits of the capacity of the methane reactor, B MR The methane reactor is in operation state. and are the upper and lower limits of the methane reactor ramp rate; The configuration capacity of the methane reactor;

[0080] S2.5. Establish output models for CO2 storage tanks and hydrogen storage tanks in the integrated energy system:

[0081]

[0082] Among them, V s,t is the gas storage capacity of the carbon dioxide storage tank or hydrogen storage tank under scenario s at time t, V is the amount of carbon dioxide stored in and hydrogen taken out of the storage tank or hydrogen storage tank under scenario s at time t; s,0 、V s,T is the gas volume of the carbon dioxide storage tank or hydrogen storage tank at the initial and final moments under scenario s; V max is the upper limit of the capacity of the carbon dioxide storage tank or hydrogen storage tank; β in-out is the storage and access ratio, B is the operating status of the carbon dioxide storage tank or hydrogen storage tank, V r The configuration capacity of the carbon dioxide storage tank or hydrogen storage tank;

[0083] S3. Construct the objective function and balance conditions for optimal configuration of integrated energy system capacity;

[0084] S3.1. Objective function for optimal capacity allocation

[0085]

[0086] Among them, f energy is the energy transaction cost, f inv is the equipment investment cost, f om For operation and maintenance costs, is the carbon trading cost, f cur The cost of abandoned light;

[0087] S3.2. Electric balance conditions:

[0088]

[0089] S3.3. Hydrogen balance conditions:

[0090]

[0091] in, is the amount of hydrogen stored in scenario s at time t, is the amount of hydrogen taken out under scenario s at time t;

[0092] S3.4. Carbon dioxide equilibrium conditions:

[0093]

[0094] in, is the amount of carbon dioxide stored under scenario s at time t, is the amount of carbon dioxide removed under scenario s at time t;

[0095] S4. Establish a tiered carbon trading model that introduces carbon emission quotas;

[0096] S4.1. Allocation of carbon emission quotas for thermal power units and photovoltaic power plants:

[0097]

[0098] Among them, C IEPU is the total carbon emission quota of thermal power units and photovoltaic power stations, C PGU is the carbon emission quota of thermal power units, C PV is the carbon emission quota of the photovoltaic power station; e is the carbon emission quota consumed by thermal power units, χ v Carbon emission quotas consumed by photovoltaic power stations;

[0099] S4.2. Develop a model for the net carbon emissions of an integrated energy system;

[0100]

[0101] in, is the net carbon emissions, is the carbon emissions of the tiered carbon trading, Carbon emissions offset by voluntary emission reductions, N s is the corresponding frequency of a typical day, σ is the conversion coefficient, and r is the offset ratio;

[0102] S4.3. Establish a carbon trading model:

[0103]

[0104] in, is the tiered carbon trading price, λ is the carbon trading base price, d is the carbon emission range, and α is the price growth rate;

[0105] S5. Optimal capacity configuration of integrated energy systems;

[0106] The light intensity and electric load data under different scenarios after clustering are substituted into the output model of the integrated energy system. Then, the objective function of the optimal configuration of the integrated energy system capacity is used as the target and the corresponding balance condition is used as the constraint. Based on the introduced ladder carbon trading model of carbon emission quota, it is solved through MATLAB to obtain the optimal capacity value of each device corresponding to the minimum objective function, as well as the output and electrical power balance of the equipment in each period, the total system cost and carbon emissions, thereby completing the optimal capacity configuration of the energy system.

[0107] In this embodiment, the four scenarios constructed are shown in Table 1:

[0108] Photovoltaics Hydrogen production from electricity Methanation gas storage Scenario 1 × × × × Scenario 2 √ × × × Scenario 3 √ √ √ × Scene 4 √ √ √ √

[0109] Table 1

[0110] The new energy consumption capacity of the integrated energy system is as follows: Figure 4 As shown, the amount of abandoned light in different scenarios is shown. By introducing the amount of abandoned light to evaluate the new energy absorption capacity of the entire system, it can be seen from the figure that after the system is equipped with low-carbon coupling equipment, the amount of abandoned light is greatly reduced; and further, by configuring gas storage equipment, the absorption capacity of renewable energy can also be improved, and a certain amount of abandoned light can be reduced. In this embodiment, the model that does not consider CCER quotas in traditional carbon trading is called a traditional model; the model that considers CCER quotas in tiered carbon trading is called an optimized model. The specific configuration results, carbon emission costs and life cycle costs are shown in Table 2:

[0111] Traditional Model Optimization Model Photovoltaic / MW 102.11 199.02 Hydrogen production by electricity / MW 38.08 100 Methanation / MW 1.70 1.97 Carbon trading cost / yuan <![CDATA[3.22×10 8 ]]> <![CDATA[1.45×10 8 ]]> Full life cycle cost / yuan <![CDATA[-3.16×10 8 ]]> <![CDATA[-5.27×10 8 ]]>

[0112] Table 2

[0113] In this embodiment, if Figure 5 As shown, we also explored the impact of different carbon trading parameters on the integrated energy system. It can be intuitively seen that as the values ​​of the step-by-step carbon trading parameters increase, both carbon emissions and the total system revenue decrease. Furthermore, from the base price in Figure (a) and the price growth rate in Figure (b), we can conclude that the most sensitive intervals corresponding to the base price and price growth rate are shown in Table 3.

[0114] Carbon emissions reduction (tons) Reduce the ratio [0,0.3] 18423 26.77％ [0.3,0.5] 43954 63.86％ [0.5,0.9] 6452 9.37％ [100,140] 6798 12.90％ [140,160] 41502 78.79％ [160,200] 4378 8.31％

[0115] Table 3

[0116] Although the above describes the illustrative specific embodiments of the present invention to facilitate understanding of the present invention by those skilled in the art, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concepts of the present invention are protected.

Claims

1. A method for configuring the capacity of an integrated energy system based on CCER quotas, characterized in that: The following steps are involved: (1) Perform K-means clustering on the historical light intensity and electricity load data of the integrated energy system; The light intensity and power load data of the integrated energy system in the past year are collected and then clustered using the K-means clustering algorithm to obtain S typical scenes and corresponding typical days. The light intensity and power load data corresponding to each typical scene are recorded as I s,t and s=1,2,…,S,t=1,2,…,T,T is the sampling period; (2) Establish an output model for the integrated energy system; (2.1) Establish the output model of photovoltaic power station in the integrated energy system: in, is the rated power of the photovoltaic power station, A PV is the area of ​​the photovoltaic panel, η PV , k is the unit area of ​​photovoltaic panels under the light intensity of 1kW / m 2 The conversion coefficient of output power and unit area when I s,t is the illumination intensity of scene s at time t, is the total electric energy generated by the photovoltaic power station under scenario s at time t, is the actual power generation and abandoned power of the photovoltaic power station under scenario s at time t, is the maximum electric energy value generated by the photovoltaic power station; (2.2) Establish an output model for carbon capture power plants in an integrated energy system: in, and are the power generation and electricity production of the thermal power unit under scenario s at time t, ΔT is the sampling time interval, is the carbon dioxide production of the thermal power unit at time t under scenario s, e PGU is the carbon emission intensity of thermal power units; is the total amount of carbon dioxide actually captured by the carbon capture power plant at time t under scenario s, η CCPP is the capture efficiency of the carbon capture power plant, is the total amount of carbon dioxide captured by the carbon capture power plant at time t under scenario s, is the storage capacity of the carbon capture plant at time t under scenario s, is the power consumption of the carbon capture power plant at time t under scenario s, is the power consumption coefficient corresponding to the unit carbon dioxide capture; and B is the upper and lower limits of thermal power unit output, PGU The operating status of the thermal power unit; and are the upper and lower limits of the thermal power unit climbing rate, is the power generation of the thermal power unit at time t under scenario s; (2.3) Establish the output model of the electrolyzer in the integrated energy system: in, is the amount of hydrogen produced by the electrolyzer at time t under scenario s, is the power consumption of the electrolyzer at time t under scenario s, is the power consumption coefficient for producing unit hydrogen; and are the upper and lower limits of the electrolytic cell power output, and is the upper and lower limits of the electrolytic cell capacity, B EL The operating status of the electrolytic cell equipment. and The upper and lower limits of the electrolytic cell climbing rate; is the configured electrolytic cell capacity; (2.4) Establish the output model of the methane reactor in the integrated energy system: in, is the amount of methane produced by the methane reactor at time t under scenario s, is the power consumption of the methane reactor under scenario s at time t, λ g is the power consumption coefficient for producing unit methane; and are the amount of hydrogen and carbon dioxide required for the methane reactor to generate methane under scenario s at time t, and ω is the reaction equilibrium coefficient; and is the upper and lower limits of the capacity of the methane reactor, B MR The methane reactor is in operation state. and are the upper and lower limits of the methane reactor ramp rate; The configuration capacity of the methane reactor; (2.5) Establish the output model of the carbon dioxide storage tank and hydrogen storage tank in the integrated energy system: Among them, V s,t is the gas storage capacity of the carbon dioxide storage tank or hydrogen storage tank under scenario s at time t, V is the amount of carbon dioxide stored in and taken out of the carbon dioxide storage tank or hydrogen storage tank under scenario s at time t; s,0 、V s,T is the gas volume of the carbon dioxide storage tank or hydrogen storage tank at the initial and final moments under scenario s; V max is the upper limit of the capacity of the carbon dioxide storage tank or hydrogen storage tank; β in-out is the storage and access ratio, B is the operating status of the carbon dioxide storage tank or hydrogen storage tank, V r The configuration capacity of the carbon dioxide storage tank or hydrogen storage tank; (3) Construct the objective function and balance conditions for optimal configuration of integrated energy system capacity; (3.1) Objective function of optimal capacity configuration Among them, f energy is the energy transaction cost, f inv is the equipment investment cost, f om For operation and maintenance costs, is the carbon trading cost, f cur The cost of abandoned light; (3.2) Electric balance conditions: (3.3) Hydrogen balance conditions: in, is the amount of hydrogen stored in scenario s at time t, is the amount of hydrogen taken out under scenario s at time t; (3.4) Carbon dioxide equilibrium conditions: in, is the amount of carbon dioxide stored under scenario s at time t, is the amount of carbon dioxide removed under scenario s at time t; (4) Establish a tiered carbon trading model that introduces carbon emission quotas; (4.1) Allocate carbon emission quotas to thermal power units and photovoltaic power stations: Among them, C IEPU is the total carbon emission quota of thermal power units and photovoltaic power stations, C PGU is the carbon emission quota of thermal power units, C PV is the carbon emission quota of the photovoltaic power station; e is the carbon emission quota consumed by thermal power units, χ v Carbon emission quotas consumed by photovoltaic power stations; (4.2) Establish a net carbon emissions model for the integrated energy system; in, is the net carbon emissions, is the carbon emissions of the tiered carbon trading, Carbon emissions offset by voluntary emission reductions, N s is the corresponding frequency of a typical day, σ is the conversion coefficient, and r is the offset ratio; (4.3) Establish a carbon trading model: in, is the tiered carbon trading price, λ is the carbon trading base price, d is the carbon emission range, and α is the price growth rate; (5) Optimal capacity configuration of integrated energy systems; The clustered light intensity and electric load data under different scenarios are substituted into the output model of the integrated energy system. Then, the objective function of the optimal configuration of the integrated energy system capacity is used as the target and the corresponding balance condition is used as the constraint. The ladder carbon trading model based on the introduced carbon emission quota is solved through MATLAB to obtain the optimal capacity value of each device corresponding to the minimum objective function, as well as the output and electrical power balance of the equipment in each period, the total system cost and carbon emissions, thereby completing the optimal capacity configuration of the energy system.

Images