A method for optimizing configuration of carbon peak energy system considering climate change

Abstract

The application discloses a kind of optimization configuration methods of carbon peak energy system considering climate change, first study the influence of climate change on energy system load, then study the efficiency change of electric energy generation unit, thermal power generation, photovoltaic, solar energy collector, wind power generation, electric refrigerator and absorption refrigerator under climate change, finally propose a kind of carbon peak energy system considering climate change, weather data and load demand data are used as the input data of carbon peak energy system, the efficiency of CPES equipment is changed into the efficiency under climate change, and the carbon peak energy system is optimized and configured.The application can realize the "carbon peak" goal of energy system, can evaluate the cost required to improve for a energy system to complete carbon peak under climate change, and evaluate the relationship between system cost and carbon peak realization year.

Description

Technical Field

[0001] This invention belongs to the field of energy application, and specifically relates to an optimized configuration method for a carbon peaking energy system that takes into account climate change. Background Technology

[0002] Since energy supply is the primary source of carbon dioxide production, developing a carbon-peaking energy system and a carbon-neutral energy system is crucial for achieving carbon peaking and carbon neutrality. Integrated energy systems (IES) can provide reliable and cost-effective energy services to customers with minimal environmental impact; therefore, improving IES to obtain a carbon-peaking energy system (CPES) is feasible.

[0003] Energy system operation is a long-term process, influenced not only by operating methods and carbon policies but also by the environment. The environment affects the demand-side load and supply-side equipment output of the energy system. Studying the impact of climate change on the energy system is crucial for a more objective analysis and optimization of its performance under dual-carbon policies. On the demand side, under the RCP4.5 climate prediction model, with global warming, building cooling loads will increase while heating loads will decrease. On the supply side, when the temperature drops by 10°C, the efficiency of thermal power generation decreases by approximately 0.18%, and when the humidity increases from 35% to 75%, the efficiency decreases by approximately 0.86%. When the ambient temperature drops by 10°C, the efficiency of gas turbines increases by approximately 1%. With constant radiation, photovoltaic power generation efficiency is directly proportional to ambient temperature. The results above all indicate that both the supply and demand sides of the energy system will change under climate change. However, current research is not clear enough on whether the impact of demand-side changes on the energy system is positive or negative, and the research is not comprehensive enough on whether the impact of supply-side efficiency changes on the energy system is beneficial or detrimental. If the impact of these factors on the energy system is not considered, the performance of the energy system under the dual-carbon policy will also be affected. Summary of the Invention

[0004] To address the above problems, this invention discloses an optimized configuration method for a carbon peaking energy system that takes into account climate change. This method first studies the impact of climate change on the load of the energy system, then studies the efficiency changes of power generation units, thermal power generation, photovoltaic, solar collectors, wind power generation, electric chillers, and absorption chillers under climate change, and finally proposes a carbon peaking energy system that takes into account climate change.

[0005] This invention proposes an optimized configuration method for carbon peaking energy systems that take into account climate change, and the specific design scheme is as follows:

[0006] Step 1: Obtain weather data from the initial year to the target year, and then use Trnsys simulation software to obtain the cooling, heating and electrical load requirements of the simulated building from the initial year to the target year;

[0007] Meteonorm software was used to obtain weather data from the initial year to the target year, and the data was saved in TM2 output format. This software can obtain the weather conditions of a city for 8760 hours in a year, including parameters that reflect weather conditions such as temperature, radiation intensity, wind speed, and humidity.

[0008] The office building was simulated using Trnsys simulation software. By setting building parameters such as heating, cooling, ventilation, materials, and comfort, the model of the office building was generated. Then, TM2 format weather data obtained from Meteonorm was used as input for the office building to simulate the building's annual cooling and heating load requirements for 8760 hours.

[0009] Step 2: The efficiency changes of power generation units, thermal power generation, photovoltaic, solar collectors, wind power generation, electric chillers and absorption chillers under climate change are derived.

[0010] Carbon Peak Energy Systems can be divided into supply-side and demand-side based on energy supply and demand. The supply-side mainly consists of energy supply devices, conversion devices, and thermal storage devices. Energy supply devices primarily include photovoltaic (PV), solar thermal collectors (ST), wind turbines (WT), power generation units (PGU), auxiliary boilers (AB), and the power grid. Conversion devices mainly include heat recovery devices, electric chillers (EC), absorption chillers (AC), and heat exchangers (HE). Thermal storage devices primarily consist of thermal storage tanks (TST). The demand-side mainly comprises the building's cooling, heating, and electrical loads.

[0011] When the climate changes, environmental changes will affect both the supply and demand sides of the carbon peaking energy system. Changes in temperature, humidity, radiation and wind speed caused by climate change will affect the efficiency of supply-side equipment. In this invention, the equipment considered to be affected by climate change includes: thermal power generation, EC, AC, PGU, WT, PV and ST, while the impact of climate change on the demand side is mainly the change in cooling and heating loads.

[0012] To study the changes in supply-side equipment efficiency when the climate changes, it is necessary to obtain mathematical models relating various devices to climate-related parameters. The relationship between the efficiency of key IES devices and climate is expressed as follows:

[0013] (1)PGU

[0014] In a carbon peaking energy system, the PGU converts chemical energy into electrical and thermal energy. The PGU's power generation efficiency η e，pgu It can be calculated as follows:

[0015]

[0016] In the formula: E pgu For the final output electrical energy of the PGU, F pgu Energy is input to the PGU, E pgu and F pgu They can be calculated separately as follows:

[0017]

[0018] In the formula: W t and W c These represent the turbine power and the compressor power of the adiabatic compressor, respectively, in m. a Cp is the average air mass flow rate. a For the specific heat capacity of dry air, T c，out and T t，in These represent the compression chamber outlet temperature and the turbine inlet temperature, respectively. t W c and T c，out It can be calculated as follows:

[0019]

[0020] Where: m f γ represents the mass flow rate of fuel gas. a and γ g The specific heat ratios of air and natural gas are respectively (C). p / C v ), r c η represents the compression ratio. c η t T represents the isentropic efficiency of the compressor and the isentropic efficiency of the turbine, respectively. a Indicates ambient temperature.

[0021] (2) PV and ST

[0022] η power generation per unit area for PV and ST pv and η st Primarily influenced by temperature and radiation intensity, it can be represented as follows:

[0023]

[0024] In the formula: I represents solar radiation intensity, γ pv and β pv These represent solar radiation intensity and temperature coefficient, respectively, η ref At reference temperature T ref The efficiency of T. pv For the temperature of the photovoltaic cell, T radio T is the ratio of the average temperature of ST to the difference between the ambient air temperature and solar radiation. pv and T radio They can be calculated separately as follows:

[0025]

[0026] In the formula: T pv，noct The nominal operating temperature (NOCT) of a photovoltaic cell is represented by T. st，out and T st，in These represent the external and internal operating temperatures of ST, respectively.

[0027] (3) Thermal power generation

[0028] Studies have shown that the efficiency of thermal power generation is related to temperature, humidity, and wind speed. Therefore, the relationship between the environment and the power generation efficiency of the power grid can be expressed as follows:

[0029] η gird =f gird (T a ,RH,v a )

[0030] Among them, RH and v a Let η represent relative humidity and wind speed, respectively. Weather data is used as input to a two-layer neural network. gird As the output of the neural network, η can be obtained by training the neural network model with data. gird With T a RH and v a The black box model.

[0031] To evaluate the effectiveness of neural network models, root mean square error (RMSE), mean absolute error (MAE), and R-squared value (R²) are introduced. 2 As an evaluation index, its calculation can be expressed as follows:

[0032]

[0033] In the formula: m is the number of samples, y (i) and Let these represent the actual value and the predicted value of the i-th sample, respectively. This represents the average value of the sample.

[0034] (4) WT, EC and AC

[0035] To determine the impact of WT, EC, and AC on the environment, these three devices were modeled using TRNSYS simulation software. The relationship between WT, EC, and AC output and the environment can be expressed as follows:

[0036]

[0037] In the formula: r wt COP represents the ratio of WT's output power to its theoretical maximum possible output power. ec and COP ac These represent the coefficients of performance for EC and AC, respectively.

[0038] Step 3: Propose a mathematical model for a carbon peaking energy system;

[0039] System carbon peaking refers to the point at which carbon dioxide emissions cease to increase and reach their peak, subsequently gradually declining. To achieve the system's carbon peaking target, a Carbon Peak Energy System (CPES) has been proposed. The mathematical model for CPES carbon peaking primarily focuses on the carbon dioxide emissions (CDE) of the CPES, which can be expressed as follows:

[0040]

[0041] In the formula: Y goal This indicates the year in which the carbon peak target was achieved, where Y represents the year and ΔY represents the interval between years. Indicates in Y goal Under the conditions, the CDE of CPES in year Y, This represents the CDE of the split-production (SP) system in year Y. To prevent CPES from artificially inflating initial year carbon dioxide emissions to achieve a false carbon peak, it is required that... It is smaller than the CDE of the SP system back then. and Let CDE represent the power grid, PGU, and AB in year Y, respectively.

[0042] CPES takes into account the carbon dioxide coupling of neighboring years and optimizes the optimal configuration to meet the carbon peak year through cyclic optimization. The carbon dioxide emissions of different years are coupled with each other. The coupling information allows CPES to solve for a system configuration that can meet the requirements from the initial year to the target year, under the premise of achieving carbon peak.

[0043] Step 4: Input the results of Step 1 and Step 2 into the carbon peaking energy system of Step 3 to optimize the configuration of the carbon peaking energy system.

[0044] Using the load data and weather data obtained in step 1 as input data for CPES, and changing the equipment efficiency of CPES to the efficiency obtained in step 2, this invention aims to study the economics of energy systems under dual carbon targets. Therefore, the objective function of this invention, Total Operating Costs (TOC), can be expressed as follows:

[0045]

[0046] In the formula: Y final Y0 and Y0 represent the last and first years of the considered years, respectively. Considering the current internationally unified carbon policy, ΔY is 10 years. Let Y = Y0 + ΔY*i, and ATC be the total annual cost (ATC) in year (Y0 + ΔY*i). Y The following can be calculated:

[0047]

[0048] In the formula: and This represents the grid purchase cost, natural gas cost, equipment investment cost, maintenance cost, and carbon disposal cost in year Y. The carbon cost includes carbon penalty cost and carbon disposal cost (mainly carbon capture). The carbon disposal cost is calculated using a tiered carbon penalty model, as follows:

[0049]

[0050] In the formula: p and p(t) represent the carbon cost and the carbon market price at time t, respectively. This represents the share of carbon dioxide that needs to be paid as a carbon penalty at time t. When the value is less than 0, it indicates that the actual carbon emissions of the system are lower than the carbon quota, and carbon trading revenue can be obtained from the excess quota at the initial carbon trading price. 'a' and 'd' represent the step ranges of the carbon price growth rate. In this invention, 'a' = 50% and 'd' = 0.05 tons. The calculation is as follows:

[0051]

[0052] In the formula: and Let represent the system's carbon dioxide emissions and carbon quota at time t, respectively. During operation, the IES must also satisfy electrical balance, thermal balance, and equipment output constraints. At each time point, the IES's electrical and thermal balances satisfy the following equations:

[0053] E wt +E pv +E grid +E pgu =E ec +E ex +E

[0054] Q st +Q r +Q b +Q s，out =Q ac，in +Q he，in +Q s，in +Q ex

[0055] In the formula: E ex E represents wasted electrical energy and electricity demand. wt E pv E grid E pgu E ec These represent the power outputs of wind power generation, photovoltaic power generation, grid-purchased electricity, power generation unit, and electric chiller, respectively. Q ac，in and Q he，in Q represents the energy entering AC and HE. st Q r Q b Q s，out Q s，in and Q ex These represent the heat generated by the solar collector, the heat recovered by the heat recovery device, the heat supplemented by the boiler, the heat released by the heat storage tank, the heat absorbed by the heat storage tank, and the wasted heat, respectively. Simultaneously, the equipment must also meet certain constraints during operation, which can be represented as follows:

[0056] P eq，low ≤P eq ≤N eq

[0057] In the formula: P eq and P eq，low N represents the operating power and lower limit operating power of the device eq. eqThe rated power of the equipment is represented by eq, which includes the equipment in the CPES system. In addition, the mathematical model of CPES has some model assumptions as follows: (1) Carbon dioxide generated during the production and transportation of equipment is not considered. (2) Considering the power requirements of the grid for the carbon peaking energy system and the need for better power distribution, excess power can only be stored or wasted by energy storage devices. (3) Considering the characteristics of the configuration, it is assumed that only the electric cooling ratio can be changed each year, and the other configurations cannot be changed.

[0058] The present invention, by adopting the above technical solution, achieves the following beneficial effects:

[0059] (1) The invention can obtain the changes in energy system load and equipment efficiency under climate change, and the impact of these changes on the operation of the energy system.

[0060] (2) The invention can achieve the goal of “carbon peaking” of energy systems, assess the increased cost required for an energy system to achieve carbon peaking under climate change, and assess the relationship between system cost and the year of carbon peaking.

[0061] (3) The present invention found that in order to achieve China’s current carbon peaking target of 2030, carbon peaking energy systems need to increase costs by 1.97%. Attached Figure Description

[0062] Figure 1 This is a schematic diagram of the energy flow principle of a carbon peaking energy system;

[0063] Figure 2 This is a diagram illustrating the impact of climate change on carbon peaking energy systems.

[0064] Figure 3 This is a schematic diagram of a two-layer neural network;

[0065] Figure 4 This is a schematic diagram of the carbon peaking energy system operation principle;

[0066] Figure 5 This is a load forecast map for 2020-2060 from an embodiment of the present invention;

[0067] Figure 6 This refers to the efficiency or unit output performance of each device in the energy system in this embodiment of the invention;

[0068] Figure 7 This is an embodiment of the invention illustrating the impact of climate warming on integrated energy systems;

[0069] Figure 8 A comparison chart of annual performance without considering climate change and with consideration of climate change;

[0070] Figure 9Annual performance of the carbon peaking energy system. Detailed Implementation

[0071] Meteonorm software was used to obtain weather data from 2020 to 2060, which was saved in TM2 output format. This software can obtain the weather conditions of a city for 8760 hours in a year, including parameters that reflect weather conditions such as temperature, radiation intensity, wind speed, and humidity.

[0072] The office building was simulated using Trnsys simulation software. By setting building parameters such as heating, cooling, ventilation, materials, and comfort, the model of the office building was generated. Then, TM2 format weather data obtained from Meteonorm was used as input for the office building to simulate the building's annual cooling and heating load requirements for 8760 hours.

[0073] The operating principle of the carbon peaking energy system is as follows: Figure 1 As shown, energy can be divided into supply and demand sides according to supply and demand. The supply side mainly consists of energy supply devices, conversion devices, and thermal storage devices. Energy supply devices mainly include photovoltaic (PV), solar thermal collectors (ST), wind turbines (WT), power generation units (PGU), auxiliary boilers (AB), and the power grid. Conversion devices mainly include heat recovery devices, electric chillers (EC), absorption chillers (AC), and heat exchangers (HE). Thermal storage devices mainly consist of thermal storage tanks (TST). The demand side mainly consists of the building's cooling, heating, and electrical loads.

[0074] When the climate changes, environmental changes will impact both the supply and demand sides of integrated energy systems (IES), specifically as follows: Figure 2 As shown, changes in temperature, humidity, radiation, and wind speed caused by climate change can affect the efficiency of supply-side equipment. In this invention, the equipment considered to be affected by climate change includes: thermal power generation, EC, AC, PGU, WT, PV, and ST. The impact of climate change on the demand side is mainly the change in cooling and heating loads.

[0075] To study the changes in supply-side equipment efficiency when the climate changes, it is necessary to obtain mathematical models relating various devices to climate-related parameters. The relationship between the efficiency of key IES devices and climate is expressed as follows:

[0076] (1)PGU

[0077] In IES, PGU converts chemical energy into electrical and thermal energy. According to the literature, the power generation efficiency η of PGU is... e，pgu It can be calculated as follows:

[0078]

[0079] In the formula: E pgu For the final output electrical energy of the PGU, F pgu Energy is input to the PGU, E pgu and F pgu They can be calculated separately as follows:

[0080]

[0081] In the formula: W t and W c These represent the turbine power and the compressor power of the adiabatic compressor, respectively, in m. a Cp is the average air mass flow rate. a For the specific heat capacity of dry air, T c，out and T t，in These represent the compression chamber outlet temperature and the turbine inlet temperature, respectively. t W c and T c，out It can be calculated as follows:

[0082]

[0083] Where: m f γ represents the mass flow rate of fuel gas. a and γ g The specific heat ratios of air and natural gas are respectively (C). p / C v ), r c η represents the compression ratio. c η t T represents the isentropic efficiency of the compressor and the isentropic efficiency of the turbine, respectively. a Indicates ambient temperature.

[0084] (2) PV and ST

[0085] η power generation per unit area for PV and ST pv and η st Primarily influenced by temperature and radiation intensity, it can be represented as follows:

[0086]

[0087] In the formula: I represents solar radiation intensity, γ pv and β pv These represent solar radiation intensity and temperature coefficient, respectively, η ref At reference temperature T ref The efficiency of T. pv For the temperature of the photovoltaic cell, T radio T is the ratio of the average temperature of ST to the difference between the ambient air temperature and solar radiation. pv and T radio They can be calculated separately as follows:

[0088]

[0089] In the formula: T pv，noct The nominal operating temperature (NOCT) of a photovoltaic cell is represented by T. st，out and T st，in These represent the external and internal operating temperatures of ST, respectively.

[0090] (3) Thermal power generation

[0091] Studies have shown that the efficiency of thermal power generation is related to temperature, humidity, and wind speed. Therefore, the relationship between the environment and the power generation efficiency of the power grid can be expressed as follows:

[0092] η gird =f gird (T a ,RH,v a )

[0093] Among them, RH and v a These represent relative humidity and wind speed, respectively, as follows: Figure 3 As shown, the results data from the literature are used as the input data for a two-layer neural network, η gird As the output of the neural network, η can be obtained by training the neural network model with data. gird With T a RH and v a The black box model.

[0094] To evaluate the effectiveness of neural network models, root mean square error (RMSE), mean absolute error (MAE), and R-squared value (R²) are introduced. 2 As an evaluation index, its calculation can be expressed as follows:

[0095]

[0096] In the formula: m is the number of samples, y (i) and Let these represent the actual value and the predicted value of the i-th sample, respectively. This represents the average value of the sample.

[0097] (4) WT, EC and AC

[0098] To determine the impact of WT, EC, and AC on the environment, these three devices were modeled using TRNSYS simulation software. The relationship between WT, EC, and AC output and the environment can be expressed as follows:

[0099]

[0100] In the formula: r wt COP represents the ratio of WT's output power to its theoretical maximum possible output power. ec and COP ac These represent the coefficients of performance for EC and AC, respectively.

[0101] System carbon peaking refers to the point at which carbon dioxide emissions cease to increase and reach their peak, subsequently gradually declining. To achieve the system's carbon peaking target, a Carbon Peak Energy System (CPES) has been proposed. The mathematical model for CPES carbon peaking primarily focuses on the carbon dioxide emissions (CDE) of the CPES, which can be expressed as follows:

[0102]

[0103] In the formula: Y goal This indicates the year in which the carbon peak target was achieved, where Y represents the year and ΔY represents the interval between years. Indicates in Y goal Under the conditions, the CDE of CPES in year Y, This represents the CDE of the split-production (SP) system in year Y. To prevent CPES from artificially inflating initial year carbon dioxide emissions to achieve a false carbon peak, it is required that... It is smaller than the CDE of the SP system back then. and Let CDE represent the power grid, PGU, and AB in year Y, respectively.

[0104] CPES takes into account the coupling of carbon dioxide from neighboring years and optimizes the configuration to meet the carbon peak year through cyclic optimization. The specific principle is as follows: Figure 4 As shown, carbon dioxide emissions from different years are coupled together, and the coupled information allows CPES to solve for a system configuration that can meet the needs of 2020-2060, while achieving carbon peaking.

[0105] Using the load and weather data obtained in step 1 as input data for the CPES, and changing the CPES equipment efficiency to the efficiency obtained in step 2, this paper aims to study the economics of the energy system under dual carbon targets. Therefore, the objective function of this paper is the total operating costs (TOC), which can be expressed as follows:

[0106]

[0107] In the formula: Y final Y and Y0 represent the last year and the first year of the considered years, respectively. Considering the current internationally unified carbon policy, in this paper, Y... final Y0 equals 2060 and 2020, ΔY is 10 years. Let Y = Y0 + ΔY*i, and ATC be the total annual cost (ATC) in year (Y0 + ΔY*i). Y The following can be calculated:

[0108]

[0109] In the formula: and This represents the grid purchase cost, natural gas cost, equipment investment cost, maintenance cost, and carbon disposal cost in year Y. The carbon cost includes carbon penalty cost and carbon disposal cost (mainly carbon capture). The carbon disposal cost is calculated using a tiered carbon penalty model, as follows:

[0110]

[0111] In the formula: p and p(t) represent the carbon cost and the carbon market price at time t, respectively. This represents the share of carbon dioxide that needs to be paid as a carbon penalty at time t. When the value is less than 0, it indicates that the actual carbon emissions of the system are lower than the carbon quota, and carbon trading revenue can be obtained from the excess quota at the initial carbon trading price. 'a' and 'd' represent the step ranges of the carbon price growth rate. In this invention, 'a' = 50% and 'd' = 0.05 tons. The calculation is as follows:

[0112]

[0113] In the formula: and Let represent the system's carbon dioxide emissions and carbon quota at time t, respectively. During operation, the IES must also satisfy electrical balance, thermal balance, and equipment output constraints. At each time point, the IES's electrical and thermal balances satisfy the following equations:

[0114] Ewt +E pv +E grid +E pgu =E ec +E ex +E

[0115] Q st +Q r +Q b +Q s，out =Q ac，in +Q he，in +Q s，in +Q ex

[0116] In the formula: E ex E represents wasted electrical energy and electricity demand. wt E pv e grid E pgu E ec These represent the power outputs of wind power generation, photovoltaic power generation, grid-purchased electricity, power generation unit, and electric chiller, respectively. Q ac，in and Q he，in Q represents the energy entering AC and HE. st Q r Q b Q s，out Q s，in and Q ex These represent the heat generated by the solar collector, the heat recovered by the heat recovery device, the heat supplemented by the boiler, the heat released by the heat storage tank, the heat absorbed by the heat storage tank, and the wasted heat, respectively. Simultaneously, the equipment must also meet certain constraints during operation, which can be represented as follows:

[0117] P eq，low ≤P eq ≤N eq

[0118] In the formula: P eq and P eq，low N represents the operating power and lower limit operating power of the device eq. eq This indicates the rated power of the device (eq), where eq includes... Figure 1 The equipment.

[0119] In addition, the mathematical model of CPES has some model assumptions as follows: (1) It does not consider the carbon dioxide generated during the production and transportation of equipment. (2) Considering the power requirements of the grid for the carbon peaking energy system and the need for better power distribution, excess power can only be stored or wasted by energy storage devices. (3) Considering the characteristics of the configuration, it is assumed that only the electric cooling ratio can be changed each year, and the other configurations cannot be changed.

[0120] The variables and ranges for optimized configuration are shown in Table 1, and the system parameters are shown in Table 2.

[0121] Table 1

[0122]

[0123] Table 2

[0124]

[0125] Results Explanation:

[0126] To illustrate the impact of climate on IES and the carbon reduction performance of CPES, the following case is presented: Case 1-1: IES based on climate prediction using RCP4.5;

[0127] Case 1-2: IES for climate prediction based on RCP8.5;

[0128] Cases 1-3: SP for climate prediction based on RCP4.5;

[0129] Cases 1-4: SP for climate prediction based on RCP8.5;

[0130] Case 2-i: CPES based on RCP4.5 for climate prediction, completed in year (2020+10*i). Case 2-1 indicates carbon peaking in 2030, and Case 2-3 indicates carbon peaking in 2050.

[0131] Secondly, the optimized configurations obtained using the particle swarm optimization algorithm are shown in Table 3. Case 3-1 has the highest TOC, while Case 1-2 has the highest CO2 emissions. Since Case 1-4 and Case 1-3 are SP systems, no configuration is required; only their operating costs and CDE need to be calculated. The TOC and CO2 emissions for Case 1-3 are 3.182 × 10⁻⁶. 6 $ and 1.777×10 6 kg, the TOC and CO2 of Cases 1-4 are 3.211 × 10 kg, respectively. 6 $ and 1.798×10 6 kg.

[0132] Table 3 Results of Optimized Configuration

[0133]

[0134] Climate change will have a certain impact on the system's configuration and operation. Observing the solution results in Table 1 reveals the following:

[0135] For IES, the higher the degree of global warming (RCP8.5 vs. RCP4.5), the larger the capacity of TST (Total Heat Storage) will be to reduce heat waste, and the proportion of ST will be increased while the proportion of PV (Polygenous Power Storage) will be decreased. In the same year, the X value of RCP4.5 will be higher. c Both are better than the X of RCP8.5 c Larger, and during the period from 2020 to 2060, X c The cost of WT (Wastewater) energy has been increasing year by year. However, due to the lack of energy storage devices, the off-peak timing of WT and electricity load demand means that configuring WT does not reduce the operating cost of IES (Environmentally Insulated Systems). Therefore, IES does not configure WT. For CPES (Concentrated Capacity Energy Storage System), with the target year being brought forward, in order to achieve carbon peaking, CPES can only configure WT, which is relatively less economical but has good carbon reduction performance. The configuration value of WT increased from 8.1kW in Case 2-3 to 190.8kW in Case 2-1. At the same time, PGU (Power Generation Unit) and ST (Stalling Cost) also increased with the forward timing of the target year, with PGU increasing from 553.1kW to 605.3kW. PV (Power Generation Capacity) increased from 291.1m³. 2 Increase to 1000m 2 .

[0136] The impact of climate change on building energy storage systems (IES) is mainly reflected in changes in demand-side loads and on supply-side efficiency. With climate change, the main impact on IES is reflected in changes in building cooling and heating loads. Based on the Trnsys simulation model mentioned above, the monthly cooling and heating loads from 2020 to 2060 are as follows: Figure 5 As shown. Observation Figure 5 It can be observed that when climate prediction models are all based on RCP4.5 or RCP8.5, with global warming, the cooling demand of buildings gradually increases while the heating demand gradually decreases. However, when the year remains constant, comparing RCP4.5 under government intervention and RCP8.5 without government intervention reveals that the cooling demand under RCP4.5 is less than that under RCP8.5, while the heating demand under RCP4.5 is greater than that under RCP8.5. From 2020 to 2060, under the RCP4.5 projection, the cooling load increases by 6.869% per decade, while the heating demand decreases by 3.399% per decade. Under the RCP8.5 projection, the cooling load increases by 10.263% per decade, while the heating demand decreases by 4.602% per decade.

[0137] The impact of climate change on the supply side is reflected in changes in the output of various equipment. Based on the mathematical model in Part II, the efficiency (or unit output) of each piece of equipment on the supply side from 2020 to 2060 can be calculated as follows: Figure 6 As shown, where Figure 6 (a) represents the annual average power generation efficiency of PGU and the power grid, respectively. Figure 6 (b) indicates the unit output or power factor of PV, ST, and WT. Figure 6(c) represents the COP of EC and AC, from Figure 6 It can be observed that with global warming, firstly, the power generation efficiency of both the PGU (Power Generation Unit) and the power grid decreases year by year, by approximately 0.042% and 0.0513% respectively every 10 years. Secondly, the unit output of PV (Power Generation Unit) and ST (Power Grid) increases year by year, by approximately 0.24908 W / m² respectively every 10 years. 2 and 1.01098W / m 2 Because the average annual wind speed has not changed significantly despite climate change, the power coefficient of the European Wheatstone (WT) shows a fluctuating upward trend due to the combined effects of wind speed and temperature. From 2020 to 2060, the WT power coefficient increases from 0.1569 to 0.1576. Finally, the COP of both the European Central Bank (EC) and the European Central Bank (AC) gradually decreases with global warming. ac and COP ec They decreased by approximately 0.00136 and 0.00722 every 10 years, respectively.

[0138] The above discussion reveals that, on the demand side of IES, global warming leads to a decrease in overall IES heat demand and an increase in cooling load; on the supply side, global warming reduces the operating efficiency of thermal power generation, PGU, AC, and EC, while increasing the unit capacity of PV and ST. Under the dual influence of these demand and supply sides, the IES's ATC and annual CDE will also change. After optimization, the IES's CDE and annual operating cost (ATC) for 2020-2060 are obtained as follows: Figure 7 (a) and Figure 7 As shown in (b). Analysis Figure 7It can be observed that with global warming, both CO2 emissions and ATC increase year by year in both the IES and SP systems. The average ATC growth rates for Cases 1-1 to 1-4 are 1.18%, 1.54%, 0.82%, and 1.14%, respectively, while the average CO2 emission growth rates are 0.92%, 1.22%, 0.60%, and 1.10%, respectively. Comparing Cases 1-1 and 1-3, and Cases 1-2 and 1-4, it can be found that under the same climate prediction model, the CDE and ATC of the IES system are lower than those of the SP system. The IES system can achieve low carbon emissions while reducing ATC, and the average growth rates of both ATC and CO2 emissions in the IES system are greater than those in the SP system. Comparing Case 1-1 and Case 1-2, and Case 1-3 and Case 1-4, reveals that under the same system model, RCP8.5, without considering government climate intervention, has a greater negative impact on the IES and SP systems due to the greater degree of global warming. Both CO2 and ATC increase, and the average growth rates of ATC and CDE in RCP8.5 are greater than those in RCP4.5. In conclusion, global warming increases both ATC and CO2 emissions in the IES and SP systems, with the increase in RCP8.5 being greater than that in RCP4.5.

[0139] To analyze whether the rise in ATC and CO2 was due to supply-side or demand-side factors, a controlled variable method was used to compare TOC and CDE in Case 1-1 (considering changes in both supply and demand), Case 1-5 (considering only supply-side changes), Case 1-6 (considering only demand-side changes), and Case 1-7 (not considering changes in either supply or demand). Case 1-5 was based on RCP4.5, yielding comparative results. Figure 8As shown in the figure, TOCIR and CDEIR represent the increases in TOC and CDE for Cases 1-7, respectively. The TOC and CDE for Cases 1-7 are $2,911,600 and 11,523,000 kg, respectively. Except for Cases 1-6, both TOC and CDE increased in Cases 1-1 and 1-5, which considered supply-side changes. However, both TOC and CDE decreased in Cases 1-6, which only considered demand-side changes and not supply-side changes. Comparing the increase rates of Cases 1-1 and 1-5, it can be seen that the growth rate of Case 1-5, which did not consider demand-side changes, was greater than that of Case 1-1, which did consider demand-side changes. In conclusion, with global warming, the increase in cooling load and the decrease in heating load on the demand side will cause the TOC and CDE of IES to decrease, while changes in equipment efficiency on the supply side will cause TOC and CDE to increase. The reason for this phenomenon may be that the increased cooling load due to global warming is less than the increased heating load (because according to the law of conservation of energy, global warming increases the radiant energy provided by the sun to the entire IES), the total energy required by the IES decreases, and since the COP of the EC is greater than 1, for every 1 kW increase in cooling demand, the electrical energy required by the IES is less than 1 kW.

[0140] To demonstrate the performance of CPES, Case 1-1 (excluding carbon policy) and Cases 2-1 to 2-3 of CPES were compared. Specific annual performance is as follows: Figure 9 As shown, for Figure 9 Analysis of the economic indicators in (a) reveals that, compared to Case 1-1, the ATC of the other three CPES cases has improved. Case 2-1 achieved its target year earliest, hence its ATC is higher than the other cases. Comparing Cases 2-1 to 2-3 shows that the earlier the carbon peak target year, the higher the ATC. This phenomenon may be due to the system choosing a lower-carbon but also more costly operating mode to reduce carbon emissions. Figure 9 (b) Analysis of the CDE reveals that, under the corresponding mathematical model for carbon peaking, all cases achieved their respective carbon peaking targets. Furthermore, the CDE for subsequent years of Case 2-1 shows a continuous decline, and the CDE curve considering carbon peaking is lower than that without considering it. For every 10 years the carbon peaking target is achieved earlier (2030 being the earliest and 2050 the latest), the TOC increases by an average of 0.93%, and the CDE decreases by an average of 0.24%. To achieve China's current carbon peaking target, the CPES (Concentration, Cost, and Efficiency) needs to be increased by 1.97% (to achieve carbon peaking by 2030).

[0141] The above specific implementation examples are only for the purpose of helping those skilled in the art to understand the present invention. However, the present invention is not limited to the situations in the examples. For those skilled in the art, as long as the various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious. All inventions utilizing the concept of the present invention are protected.

Claims

1. An optimal allocation method for a carbon peaking energy system considering climate change, comprising the following steps: Step 1: Obtain weather data from the initial year to the target year, and then use Trnsys simulation software to obtain the cooling, heating and electrical load requirements of the simulated building from the initial year to the target year. Step 2: The efficiency changes of power generation units, thermal power generation, photovoltaic, solar collectors, wind power generation, electric chillers and absorption chillers under climate change are derived. Step 3: Propose a mathematical model for the Carbon Peak Energy System (CPES). The CPES is derived from an improved Integrated Energy System (IES). The mathematical model of the CPES mainly focuses on the carbon dioxide emissions (CDE) of the CPES. The mathematical model of the CPES is expressed as follows: ； ； In the formula: Indicates the year in which the carbon peak target is achieved. Indicates the year. Indicates the interval years, Indicates in Under the condition of the first CDE of CPES in 2018 The CDE of the SP system is in the first stage. CDE of the year; , and They represent the first time. Y CDE of the annual power grid, PGU and AB; In the mathematical model of the CPES The CDE is lower than that of the SP system in that year, thus preventing CPES from raising the carbon dioxide emissions of the initial year to achieve a false carbon peak. In the mathematical model of CPES, CPES considers the carbon dioxide coupling of neighboring years and optimizes the optimal configuration to meet the carbon peak year through cyclic optimization. The carbon dioxide emissions of different years are coupled with each other. The coupling information allows CPES to solve a system configuration that can meet the requirements from the initial year to the target year under the premise of achieving carbon peak. Step 4: Use the weather data and load demand data obtained in Step 1 as input data for the Carbon Peak Energy System (CPES), and change the equipment efficiency of the CPES to the efficiency obtained in Step 2 to optimize the configuration of the Carbon Peak Energy System.

2. The method for optimizing the configuration of a carbon peaking energy system considering climate change according to claim 1, characterized in that, The specific steps of step 1 are as follows: Meteonorm software was used to obtain weather data from the initial year to the target year, and the data was saved in TM2 output format. The software was used to obtain the parameters reflecting the weather conditions of a city for each hour in a calendar year, including temperature, radiation intensity, wind speed and humidity. The office building was simulated using Trnsys simulation software. By setting building parameters for heating, cooling, ventilation, materials, and comfort, the model of the office building was generated. Then, TM2 format weather data obtained from Meteonorm was used as input for the office building to simulate the building's cooling and heating load requirements for each hour of each year.

3. The method for optimizing the configuration of a carbon peaking energy system considering climate change according to claim 1, characterized in that, The specific steps of step 2 are as follows: Carbon Peak Energy System is divided into supply side and demand side according to energy supply and demand. The supply side mainly consists of energy supply devices, conversion devices, and thermal storage devices. The energy supply devices mainly include photovoltaic (PV), solar collectors (ST), wind power generation (WT), power generation units (PGU), auxiliary boilers (AB), and the power grid. The conversion devices mainly include heat recovery devices, electric chillers (EC), absorption chillers (AC), and heat exchangers (HE). The thermal storage devices mainly consist of thermal storage tanks (TST). The demand side mainly includes the building's cooling, heating, and electrical loads. Climate change-induced changes in temperature, humidity, radiation, and wind speed affect the efficiency of supply-side equipment in carbon peaking energy systems. Equipment affected by climate change includes thermal power generation, EC, AC, PGU, WT, PV, and ST. On the demand side, climate change mainly affects the changes in cooling and heating loads. The relationship between the efficiency of key supply-side equipment in a carbon peaking energy system and climate is modeled as follows: (1) PGU PGU power generation efficiency The calculation formula is as follows: ； In the formula: The final output electrical energy of the PGU, Input energy into the PGU. and The calculation formula is as follows: ； In the formula: and These represent the turbine power and the compressor power of the adiabatic compressor, respectively. The average air mass flow rate, The specific heat capacity of dry air. and These represent the compression chamber outlet temperature and the turbine inlet temperature, respectively. , and The calculation is as follows: ； ； ； In the formula: Indicates the mass flow rate of fuel gas. and The specific heat ratios of air and natural gas are respectively (C). p / C v ), Indicates the compression ratio. , These represent the isentropic efficiency of the compressor and the isentropic efficiency of the turbine, respectively. Indicates ambient temperature; (2) PV and ST Power generation per unit area of ​​PV and ST and It is mainly affected by temperature and radiation intensity, and its calculation formula is as follows: ； In the formula: Indicates the intensity of solar radiation. and These are solar radiation intensity and temperature coefficient, respectively. At reference temperature The efficiency of the lower; For the temperature of photovoltaic cells, This is the ratio of the average temperature of ST to the difference between the ambient air temperature and solar radiation. and The calculations are as follows: ； In the formula: Indicates the nominal operating temperature (NOC) of the photovoltaic cell. and These represent the external and internal operating temperatures of ST, respectively. (3) Thermal power generation Studies have shown that the efficiency of thermal power generation is related to temperature, humidity, and wind speed. Therefore, the relationship between the environment and the power generation efficiency of the power grid can be expressed as follows: ； in, and Relative humidity and wind speed are represented respectively. Weather data is used as input data for a two-layer neural network. As the output of the neural network, the neural network model is trained using data, thereby obtaining... and and The black box model; To evaluate the effectiveness of the neural network model, the root mean square error (RMSE), mean absolute error (MAE), and R-squared value (Rsquared) are introduced. 2 As an evaluation indicator, its calculation is expressed as follows: ； In the formula: m For the number of samples, and Let these represent the actual value and the predicted value of the i-th sample, respectively. Represents the average value of the sample; (4) WT, EC and AC The three devices, WT, EC, and AC, were modeled using TRNSYS simulation software. The relationship between the output of WT, EC, and AC and the environment is shown below: ； In the formula: This represents the ratio of WT's output power to its theoretical maximum possible output power. and These represent the coefficients of performance for EC and AC, respectively.

4. The method for optimizing the configuration of a carbon peaking energy system considering climate change according to claim 1, characterized in that, The specific steps of step 4 are as follows: The load data and weather data obtained in step 1 are used as input data for CPES, and the equipment efficiency of CPES is changed to the efficiency obtained in step 2. The objective function of CPES is the total operating cost (TOC), expressed as follows: ； In the formula: and These represent the last year and the first year of the years under consideration, respectively. For 10 years, Indicates the ( ) Mid-year total cost ATC, making Y = , The calculation formula is as follows: ； In the formula: , , , and Indicates the first Y Annual grid power purchase cost, natural gas cost, equipment investment cost, maintenance cost, and carbon disposal cost; Carbon cost includes carbon penalty cost and carbon treatment cost. The carbon treatment cost is calculated using a tiered carbon penalty model, as follows: ； ； In the formula: and They represent in t Real-time carbon costs and carbon prices in the carbon market This represents the share of carbon dioxide that needs to be paid as a carbon penalty at time t. When the value is less than 0, it means that the actual carbon emissions of the system are lower than the carbon quota, and the excess quota is obtained from carbon trading revenue at the initial carbon trading price. and These represent the tiered ranges for carbon price growth rates. =50%, =0.05 tons, The calculation is as follows: ； In the formula: and Let represent the system's carbon dioxide emissions and carbon quota at time t, respectively.

5. The method for optimizing the configuration of a carbon peaking energy system considering climate change according to claim 4, characterized in that, The CPES system also needs to satisfy electrical balance and thermal balance during operation. At each moment, the electrical balance and thermal balance of the CPES satisfy the following equations: ； ； In the formula: and This indicates a waste of electrical energy and a shortage of electricity. , , , , These represent the power outputs of wind power generation, photovoltaic power generation, grid-purchased electricity, power generation unit, and electric chiller, respectively. and This represents the energy entering AC and HE. , , , , and These respectively represent the heat generated by the solar collector, the heat recovered by the heat recovery device, the heat supplemented by the boiler, the heat released by the heat storage tank, the heat absorbed by the heat storage tank, and the heat wasted.

6. The method for optimizing the configuration of a carbon peaking energy system considering climate change according to claim 4, characterized in that, The CPES system equipment satisfies equipment output constraints during operation, as shown below: ； In the formula: and The operating power and lower limit operating power of the equipment are eq. The rated power of the device eq is indicated, which includes the devices on both the supply and demand sides of the CPES.

7. The method for optimizing the configuration of a carbon peaking energy system considering climate change according to claim 4, characterized in that, The mathematical model of the CPES system is set under the following conditions: (1) Carbon dioxide generated during equipment production and transportation is not considered; (2) Considering the power requirements of the grid for the carbon peak energy system and the fact that excess power can only be stored or wasted by energy storage devices in order to better allocate power; (3) Considering the characteristics of the configuration, it is assumed that only the electric cooling ratio can be changed each year, and the other configurations cannot be changed.

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