A scheduling method for integrated energy systems

Abstract

The present application provides a scheduling method for an integrated energy system, comprising: constructing an uncertainty model: constructing a wind power uncertainty model based on the uncertainty of wind turbine power generation; constructing a load uncertainty model based on the uncertainty of load variation; constructing a carbon capture uncertainty model based on the operation of a carbon capture system; formulating a demand response model based on carbon trading prices in combination with user demand response and load characteristics; formulating a start and stop plan for each generator set; optimizing the integrated energy system using mixed integer linear programming technology to obtain a scheduling plan; executing the scheduling plan on a time scale of 1 hour, and adjusting the output power of each generator set in real time; through the scheduling method, while ensuring the stable operation of the integrated energy system, it is possible to improve the utilization rate of renewable energy, effectively control carbon emissions, and reduce carbon emissions in the power industry, thereby having a positive impact on environmental protection and climate change response.

Description

Technical Field

[0001] The present application belongs to the technical field of prediction and scheduling of integrated energy systems, and in particular relates to a scheduling method for integrated energy systems. Background Art

[0002] In industrial production, coal combustion, as a primary energy source, releases large amounts of CO2, a major contributor to increased greenhouse gas emissions. In the power industry, coal-fired power plants are a major source of CO2 emissions. Therefore, reducing CO2 emissions from the power sector is of great importance, and carbon capture and storage (CCS) technology has attracted widespread attention as an effective method for reducing CO2 emissions. In power systems, carbon capture power plants (CCPPs) can effectively reduce CO2 emissions by capturing and storing CO2. However, with the rapid development of renewable energy sources such as wind and solar power, the operating environment of power systems has become increasingly complex and uncertain. Furthermore, the operation of carbon capture equipment can also create uncertainties in power system operations. Factors such as maintenance and operational failures can affect output. Therefore, optimizing energy system scheduling while considering both carbon capture and source-load uncertainties has become an important and pressing research topic.

[0003] Traditional energy system scheduling methods often ignore the output uncertainty of carbon capture equipment, leading to inaccurate and unstable scheduling plans. Therefore, the main goal of this invention is to propose an integrated energy system optimization scheduling method that effectively accounts for carbon trading prices and the output uncertainty of carbon capture equipment, thereby improving the accuracy and stability of scheduling plans. Such a method will help reduce CO2 emissions in the power industry, improve the reliability and stability of the power grid, and promote the development and utilization of clean energy. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method that generates a scheduling plan for an integrated energy system by constructing a wind power uncertainty model, a load uncertainty model, and a carbon capture uncertainty model, and combining it with a demand response model based on carbon trading prices, and adjusts the output power of each generator set in real time to ensure the stable operation of the integrated energy system while improving the utilization rate of renewable energy, effectively controlling carbon emissions, and reducing carbon emissions in the power industry, thereby having a positive impact on environmental protection and climate change response.

[0005] The present application provides a scheduling method for an integrated energy system, the scheduling method comprising:

[0006] 1) Construct uncertainty model:

[0007] 11) Based on the uncertainty of wind turbine power generation, a wind power uncertainty model is constructed to obtain wind power generation capacity;

[0008] The production of wind turbines follows a Weibull distribution, and the density function of the wind energy probability is expressed as:

[0009]

[0010] Where, is the wind power value with uncertainty, K is the shape parameter, and C is the scale parameter; the cumulative distribution function is calculated using the following formula

[0011]

[0012] Calculate random variables using the inverse of the cumulative distribution function

[0013]

[0014] In the formula, using The mean P WTm Calculate C, use The standard deviation S is calculated to get K,

[0015] 12) Based on the uncertainty of load variation, a load uncertainty model is constructed to obtain the load with fluctuation;

[0016] The load variation obeys the normal distribution, and the probability density of the load variation is expressed as a function of time:

[0017]

[0018] Where δ is the expectation of the normal distribution, i.e. the load forecast value, μ is the variance, i.e. 1 percentage point of the load forecast value, ΔP L is the load fluctuation;

[0019] The cumulative distribution function is calculated using the following formula

[0020]

[0021] Generate a random variable ΔP using the inverse of the cumulative distribution function L :

[0022]

[0023] The error function z is solved using the following formula:

[0024]

[0025] Where r1 and r2 are random variables uniformly distributed on [0,1];

[0026] The error function z is used to obtain the load fluctuation ΔP after eliminating the error. L :

[0027] ΔP L =μ+δ·z

[0028] The fluctuating load P is derived using the following formula: L :

[0029] P L =P LP +ΔP L

[0030] Where, P LP is the wind power output forecast value;

[0031] 13) Construct a carbon capture uncertainty model based on the operation of the carbon capture system;

[0032] Add Gaussian noise to the average power value to simulate the disturbances in the operation of the carbon capture equipment:

[0033]

[0034] Where, P Bi,t is the unit operating energy consumption, P Bim,t P is the energy consumption of the unit without considering noise interference. Ji,t is the net output power, P Jim,t To ignore the noise interference of the unit net output power, P Di,t is the expected energy consumption of the unit, P Dim,t The fixed energy consumption of the unit without considering noise interference, noi i is the noise level, noi i =ε i *P ccsmean , i=1,2,3, where ε i is the noise standard deviation, P ccsmean is the mean carbon capture power;

[0035] According to the operation mode of the carbon capture system, a carbon capture uncertainty model is constructed as shown in the following formula:

[0036]

[0037] Where, e gi E is the unit emission intensity of carbon emissions; Gi,t is the total carbon dioxide production from carbon capture and storage; PGi,t is the total output power of carbon capture and storage; δ ci is the flue gas split ratio; is the total amount of captured carbon dioxide; E CGi,t CO2 capture provided for solution storage; β is the carbon capture efficiency; η is the maximum operating coefficient of the compressor and regeneration tower; P Gi,max is the maximum technical output of the thermal power unit during operation; λ is the energy required to capture unit carbon dioxide; E PCTDR,t CO2 used for carbon trading;

[0038] 2) Developing a demand response model based on carbon trading prices, combining user demand response and load characteristics, to regulate user electricity consumption behavior and optimize load distribution of the integrated energy system;

[0039] When electricity demand is high and the carbon trading price is high, the CO2 produced by the carbon capture system is traded to increase profits;

[0040] When the carbon trading price is low, the CO2 generated by the carbon capture system is stored to reduce the energy consumption of the carbon capture system;

[0041] The constraints of the demand response model are as follows:

[0042]

[0043] Where, ξ t is the carbon trading price elasticity coefficient at time t, ΔP t is the change in electricity demand before and after the implementation of demand response at time t, Δρ t is the change in electricity consumption before and after the implementation of demand response at time t, ρ Peak is the carbon trading price during peak hours, T Peak is the peak period of carbon trading price, ρ Valley is the carbon trading price during the low period, T Valley is the period of low carbon trading price, ρ t and are the carbon trading prices before and after the implementation of demand response at time t, is the load after demand response is implemented at time t, and are the upper and lower limits of the carbon trading price after the implementation of demand response at time t;

[0044] 3) Real-time monitoring of source-side and load-side data, and formulating start-up and shutdown plans for each generator set based on the source-side and load-side data, a wind power uncertainty model, a load uncertainty model, a carbon capture uncertainty model, and a demand response model to ensure supply and demand balance and optimal carbon emissions within different time periods; wherein the generator sets include: wind turbines, low-carbon thermal power units, and conventional thermal power units;

[0045] 4) Using mixed integer linear programming techniques to optimize the integrated energy system to obtain a scheduling plan;

[0046] The dispatching cost of the comprehensive energy system includes: thermal power cost C H , electricity and heat demand response cost C ADR and C HDR , the start-up and shutdown cost of thermal power units C k , the solvent loss cost of carbon capture equipment C R , carbon trading cost C T , gas cost C g , electric boiler cost C EB and the wind curtailment cost C q ;

[0047] The thermal power cost is obtained through piecewise linearization:

[0048]

[0049] Where, is the unit output after linearization; a i ,b i ,c i are thermal power cost coefficients;

[0050] The other scheduling costs are obtained by the following formula:

[0051]

[0052] Where K Htran is the heat load transfer cost coefficient, P Htran,t is the heat load transfer, K Hcut is the heat load reduction coefficient, P Hcut,t is the heat load reduction, K R is the cost coefficient of ethanolamine solvent, is the solvent operation loss coefficient, E CO2i,t is the mass of CO2 captured by unit i in period t, E PCTDR,t CO2 used for carbon trading, c gas is the natural gas cost coefficient, P gas,t is the natural gas power, c ele is the electricity price, P EB,tis the output power of the electric boiler, K q K is the penalty coefficient for curtailing wind and solar power, S is the penalty cost per unit load loss, P S,t is the load loss power;

[0053] The objective function for scheduling the integrated energy system is as follows:

[0054] f=min(C H +C ADR +C k +C HDR +C R +C T +C g +C EB +C q )

[0055] The constraints of the scheduling method are as follows:

[0056] ① The balance limit of electric power and thermal power is as follows:

[0057]

[0058] Where, 0≤P w.t ≤P WT , P GT,t is the power of the gas generator set, H DE is the heat load after heat load demand response, H EB,t is the heat output of the electric boiler, H GT,t is the heat output of the gas unit, P S,t is the load loss power;

[0059] ② The relationship between the gas turbine ramp constraint and the electric power and thermal power is as follows:

[0060]

[0061] Where, α H is the thermal power coefficient of the gas turbine, η H is the power output per unit of natural gas of the gas turbine, P gas is the natural gas consumption of the gas turbine, is the maximum power of the gas turbine;

[0062] ③ The output, ramping, and start-stop constraints of the thermal power unit are as follows:

[0063]

[0064] Where, P Gi,min is the minimum technical output of thermal power unit i, P Gi,max is the maximum technical output of thermal power unit i, Ui,t The start and stop status of the thermal power unit;

[0065]

[0066] Where, P Gi,t-1 is the output power of the unit at time t-1, is the ramp-up rate of the unit, is the unit's ramp-down rate, U i,t-1 The start and stop status of the unit at time t-1;

[0067]

[0068] Where, is the minimum startup time of unit i at time t-1, is the minimum startup time of unit i at time t, is the minimum shutdown time of unit i at time t-1, is the minimum shutdown time of unit i at time t;

[0069] ④ The balance and limitation relationship between the electrical load and the thermal load is:

[0070]

[0071] Where, P Ltran,t is the electric load transfer amount, P Htran,t is the heat load transfer, P HL is the heat load power;

[0072] ⑤ The CO2 in the solution storage exists in the ethanolamine solution. The extracted CO2 mass is expressed by volume. The stability constraint and storage volume constraint are as follows:

[0073]

[0074] Where V CAi,t The volume of solution required to release CO2 from the solution storage installed at power plant i at time t, E CGi,t M is the amount of CO2 to be captured supplied by the solution storage of unit i during period t, MEL is the molar mass of ethanolamine, M CO2 is the molar mass of CO2, C R is the concentration of ethanolamine solution, ρ R is the density of ethanolamine solution;

[0075]

[0076] Where V FYi,t With V PYi,tis the volume of the solution in the rich liquid storage and lean liquid storage of unit i during period t, V FYi,t-1 With V PYi,t-1 is the volume of solution in the rich liquid storage and lean liquid storage of unit i at time t-1, V CRi is the capacity of the solution storage of unit i, V PYi,0 With V PYi,0 is the initial solution volume of the rich solution storage and lean solution storage of unit i, V FYi,24 With V PYi,24 is the volume of solution in the rich liquid storage and lean liquid storage at the end of the dispatch period of unit i;

[0077] ⑥ The thermal device is combined with the carbon capture device to form the integrated energy system rotating reserve device. The rotating reserve device is subject to the following constraints:

[0078]

[0079] Where, is the ramp rate of unit i, P Gji,max With P Gji,min are the upper and lower limits of the net output of thermal power unit i, and are the upper and lower spinning reserves required by the system during period t, is the ramp-down rate of unit i;

[0080] ⑦ In order to ensure that carbon capture power generation has a certain operating margin in the real-time stage to cope with the imbalance caused by wind power generation or load forecast errors, the split-type carbon capture power plant limits the flue gas fractionation ratio to minimize carbon capture energy consumption. The limit of flue gas fractionation is as follows:

[0081]

[0082] Where, δ xz is the limit of flue gas split ratio, e gi is the carbon emission intensity of unit i;

[0083] ⑧The ramping constraint and spinning reserve constraint of the thermal unit are as follows:

[0084]

[0085] Where U i,i The start and stop status of the unit, P GJi,t is the net output of thermal power unit i, and They are the upper and lower spinning reserves required by the system during period t respectively;

[0086] 5) executing the scheduling plan on a 1-hour time scale, adjusting the output power of each generator set in real time, and achieving low carbon emissions while ensuring stable operation of the integrated energy system; wherein the scheduling plan has a period of 24 hours.

[0087] The scheduling method for the integrated energy system provided in this application has the following beneficial effects: 1) It comprehensively considers the uncertainty of wind power, load uncertainty and carbon capture uncertainty, ensuring the supply and demand balance and optimal carbon emissions of the integrated energy system; 2) The demand response model based on carbon trading prices plays an important role in peak shaving and valley filling, and further reduces overall carbon emissions by adjusting user electricity consumption behavior and optimizing power distribution; 3) The flexible operation technology of the carbon capture power plant significantly improves the overall efficiency of the integrated energy system and the utilization rate of renewable energy through energy time shifting and effective utilization of wind energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 A schematic diagram of energy flow in a carbon capture power plant provided by an embodiment of the present application is shown;

[0089] Figure 2 A schematic diagram showing original wind power output data and electrical and thermal load data provided by an embodiment of the present application is shown;

[0090] Figure 3 A comparison chart of wind power data for scenarios 1 and 2 provided in an embodiment of the present application is shown;

[0091] Figure 4 A comparison chart of load data for scenarios 1 and 2 provided in an embodiment of the present application is shown;

[0092] Figure 5 A comparison diagram of energy consumption of carbon capture systems in scenarios 1 and 2 provided in an embodiment of the present application is shown;

[0093] Figure 6 A schematic diagram of the power dispatching results of scenario 1 provided in an embodiment of the present application is shown;

[0094] Figure 7 A schematic diagram of the thermal scheduling results of scenario 1 provided in an embodiment of the present application is shown;

[0095] Figure 8 The figure shows the CO2 capture capacity of different units of the carbon capture power plant provided in the embodiment of the present application. DETAILED DESCRIPTION

[0096] In order to make the purpose, technical solution and advantages of this technical solution more clear, the following technical solution is further described in detail in conjunction with specific implementation methods. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of this technical solution.

[0097] First, the implementation scenario of this application is introduced. This application is applicable to an integrated energy system including at least one carbon capture power plant.

[0098] The scheduling method of the integrated energy system provided in the embodiment of the present application includes the following steps:

[0099] 1) Construct uncertainty model:

[0100] 11) Based on the uncertainty of wind turbine power generation, a wind power uncertainty model is constructed to obtain wind power generation capacity;

[0101] The production of wind turbines follows a Weibull distribution, and the density function of the wind energy probability is expressed as: (1)

[0102]

[0103] Where, is the wind power value with uncertainty, K is the shape parameter, and C is the scale parameter; the cumulative distribution function is calculated using the following formula

[0104]

[0105] Calculate random variables using the inverse of the cumulative distribution function

[0106]

[0107] In the formula, using The mean P WTm Calculate C, use The standard deviation S is calculated to get K,

[0108] 12) Based on the uncertainty of load variation, a load uncertainty model is constructed to obtain the load with fluctuation;

[0109] The load variation obeys the normal distribution, and the probability density of the load variation is expressed as a function of time:

[0110]

[0111] Where δ is the expectation of the normal distribution, i.e. the load forecast value, μ is the variance, i.e. 1 percentage point of the load forecast value, ΔP L is the load fluctuation;

[0112] The cumulative distribution function is calculated using the following formula

[0113]

[0114] Generate a random variable ΔP using the inverse of the cumulative distribution function L :

[0115]

[0116] The error function z is solved using the following formula:

[0117]

[0118] Where r1 and r2 are random variables uniformly distributed on [0,1];

[0119] The error function z is used to obtain the load fluctuation ΔP after eliminating the error. L :

[0120] ΔP L =μ+δ·z (8)

[0121] The fluctuating load P is derived using the following formula: L :

[0122] P L =P LP +ΔP L (9)

[0123] Where, P LP is the predicted value of wind power output.

[0124] 13) Construct a carbon capture uncertainty model based on the operation of the carbon capture system;

[0125] In this step, first, the energy flow of the carbon capture power plant is introduced. Figure 1 , Figure 1 The energy flow diagram of the carbon capture power plant provided by the embodiment of the present application is shown. Figure 1 As shown, the carbon capture power plant includes: a thermal power plant, which is used to use non-renewable energy to generate electricity to supply the load and the carbon capture system, and at the same time will produce CO2; a carbon capture system (including: an absorption tower, a solution storage tank, a regeneration tower and a compressor), which is used to absorb, decompose, compress and process CO2, and finally produce CO2 that can be traded and seal it; a wind farm, which is used to use renewable energy (wind energy) to generate electricity to supply the load.

[0126] Among them, the carbon capture system will cause power loss while capturing CO2 to reduce carbon emissions. The loss mainly comes from two types of energy loss. The first type of energy loss has nothing to do with the operating state of the carbon capture system. It is mainly due to the energy consumption caused by the change of power plant structure and operating conditions caused by the introduction of the carbon capture system. In the embodiment of this application, it is represented by PDi,t , the unit is expected to consume energy; the second type of energy loss is the energy loss generated by the carbon capture system in the process of absorbing, decomposing, and compressing CO2, which is related to the operating level of the carbon capture system and is represented by P in the embodiment of this application. Bi,t , unit operating energy consumption.

[0127] The following combination Figure 1 This section describes the CO2 treatment process in a carbon capture system. Part of the flue gas generated by a thermal power plant is discharged into the atmosphere, while the remaining portion passes through an absorption tower from bottom to top, where it comes into contact with a rich liquid flowing into the tower. The rich liquid absorbs the CO2, and the decarbonized flue gas is discharged into the atmosphere. The rich liquid, which has absorbed the CO2, can be temporarily stored in a solution storage tank. The regeneration tower is used to decompose the CO2 in the rich liquid. The decomposed CO2 is then processed to produce high-concentration CO2, which is then compressed and used for carbon trading.

[0128] Based on the operation of the above carbon capture system, a carbon capture uncertainty model is constructed:

[0129] Add Gaussian noise to the average power value to simulate the disturbances in the operation of the carbon capture equipment:

[0130]

[0131] Where, P Bi,t is the unit operating energy consumption, P Bim,t P is the energy consumption of the unit without considering noise interference. Ji,t is the net output power, P Jim,t is the net output power of the unit without considering noise interference, P Di,t is the expected energy consumption of the unit, P Dim,t The fixed energy consumption of the unit without considering noise interference, noi i is the noise level, noi i =ε i *P ccsmean , i=1,2,3, where ε i is the noise standard deviation, P ccsmean is the mean carbon capture power;

[0132] According to the operation mode of the carbon capture system, a carbon capture uncertainty model is constructed as shown in the following formula:

[0133]

[0134] Where, e gi E is the unit emission intensity of carbon emissions; Gi,t is the total carbon dioxide production from carbon capture and storage; P Gi,t is the total output power of carbon capture and storage; δ ci is the flue gas split ratio; is the total amount of captured carbon dioxide; E CGi,t CO2 capture provided for solution storage; β is the carbon capture efficiency; η is the maximum operating coefficient of the compressor and regeneration tower; P Gi,max is the maximum technical output of the thermal power unit during operation; λ is the energy required to capture unit carbon dioxide; E PCTDR,t CO2 used for carbon trading.

[0135] 2) Combining the user's demand response and load characteristics, a demand response model based on carbon trading prices is formulated to regulate the user's electricity consumption behavior and optimize the load distribution of the integrated energy system.

[0136] In this step, the carbon trading price is the key factor affecting the carbon trading decision. Based on this, a demand response model based on the carbon trading price is proposed. When the electricity demand is high, the system will implement demand response. The demand response here includes PDR and IDR. On this basis, for the CO2 generated by the carbon capture system, when the carbon trading price is high, the CO2 generated by the carbon capture system is traded or stored to increase profits; when the carbon trading price is low, the CO2 generated by the carbon capture system is stored to reduce the energy consumption of the carbon capture system. This process is achieved through Figure 1 This is achieved by the solution storage tank shown in , which serves as a CO2 storage device and can temporarily store untraded CO2.

[0137] The constraints of the demand response model are as follows:

[0138]

[0139] Where, ξ t is the carbon trading price elasticity coefficient at time t, ΔP t is the change in electricity demand before and after the implementation of demand response at time t, Δρ t is the change in electricity consumption before and after the implementation of demand response at time t, ρ Peak is the carbon trading price during peak hours, T Peak is the peak period of carbon trading price, ρ Valley is the carbon trading price during the low period, T Valley is the period of low carbon trading price, ρ t and are the carbon trading prices before and after the implementation of demand response at time t, is the load after demand response is implemented at time t, and are the upper and lower limits of the carbon trading price after the implementation of demand response at time t;

[0140] 3) Real-time monitoring of source-side and load-side data, and formulating start-up and shutdown plans for each generator set based on the source-side and load-side data, a wind power uncertainty model, a load uncertainty model, a carbon capture uncertainty model, and a demand response model to ensure supply and demand balance and optimal carbon emissions in different time periods; wherein the generator sets include: wind turbines, low-carbon thermal power units, and conventional thermal power units.

[0141] 4) Using mixed integer linear programming techniques to optimize the integrated energy system to obtain a scheduling plan;

[0142] The dispatching cost of the comprehensive energy system includes: thermal power cost C H , electricity and heat demand response cost C ADR and C HDR , the start-up and shutdown cost of thermal power units C k , the solvent loss cost of carbon capture equipment C R , carbon trading cost C T , gas cost C g , electric boiler cost C EB and the wind curtailment cost C q ;

[0143] The thermal power cost is obtained through piecewise linearization:

[0144]

[0145] Where, is the unit output after linearization; a i ,b i ,c i are thermal power cost coefficients;

[0146] The other scheduling costs are obtained by the following formula:

[0147]

[0148] Where K Htran is the heat load transfer cost coefficient, P Htran,t is the heat load transfer, K Hcut is the heat load reduction coefficient, P Hcut,t is the heat load reduction, K R is the cost coefficient of ethanolamine solvent, is the solvent operation loss coefficient, E CO2o,t is the mass of CO2 captured by unit i in period t, E PCTDR,t CO2 used for carbon trading, c gas is the natural gas cost coefficient, P gas,t is the natural gas power, c ele is the electricity price, P EB,tis the output power of the electric boiler, K q K is the penalty coefficient for curtailing wind and solar power, S is the penalty cost per unit load loss, P S,t is the load loss power;

[0149] The objective function for scheduling the integrated energy system is as follows:

[0150] f=min(C H +C ADR +C k +C HDR +C R +C T +C g +C EB +C q ) (15)

[0152] The constraints of the scheduling method are as follows:

[0153] ① The balance limit of electric power and thermal power is as follows:

[0154]

[0155] Where, 0≤P w.t ≤P WT , P GT,t is the power of the gas generator set, H DE is the heat load after heat load demand response, H EB,t is the heat output of the electric boiler, H GT,t is the heat output of the gas unit, P S,t is the load loss power;

[0156] ② The relationship between the gas turbine ramp constraint and the electric power and thermal power is as follows:

[0157]

[0158] Where, α H is the thermal power coefficient of the gas turbine, η H is the power output per unit of natural gas of the gas turbine, P gas is the natural gas consumption of the gas turbine, is the maximum power of the gas turbine;

[0159] ③ The output, ramping, and start-stop constraints of the thermal power unit are as follows:

[0160]

[0161] Where, P Gi,min is the minimum technical output of thermal power unit i, P Gi,maxis the maximum technical output of thermal power unit i, U i,t The start and stop status of the thermal power unit;

[0162]

[0163] Where, P Gi,t-1 is the output power of the unit at time t-1, is the ramp-up rate of the unit, is the unit's ramp-down rate, U i,t-1 The start and stop status of the unit at time t-1;

[0164]

[0165] Where, is the minimum startup time of unit i at time t-1, is the minimum startup time of unit i at time t, is the minimum shutdown time of unit i at time t-1, is the minimum shutdown time of unit i at time t;

[0166] ④ The balance and limitation relationship between the electrical load and the thermal load is:

[0167]

[0168] Where, P Ltran,t is the electric load transfer amount, P Htran,t is the heat load transfer, P HL is the heat load power;

[0169] ⑤ The CO2 in the solution storage exists in the ethanolamine solution. The extracted CO2 mass is expressed by volume. The stability constraint and storage volume constraint are as follows:

[0170]

[0171] Where V CAi,t The volume of solution required to release CO2 from the solution storage installed at power plant i at time t, E CGi,t M is the amount of CO2 to be captured supplied by the solution storage of unit i during period t, MEL is the molar mass of ethanolamine, M CO2 is the molar mass of CO2, C R is the concentration of ethanolamine solution, ρ R is the density of ethanolamine solution;

[0172]

[0173] Where V FYi,t With V PYi,tis the volume of the solution in the rich liquid storage and lean liquid storage of unit i during period t, V FYi,t-1 With V PYi,t-1 is the volume of solution in the rich liquid storage and lean liquid storage of unit i at time t-1, V CRi is the capacity of the solution storage of unit i, V FYi,0 With V PYi,0 is the initial solution volume of the rich solution storage and lean solution storage of unit i, V FYi,24 With V PYi,24 is the volume of solution in the rich liquid storage and lean liquid storage at the end of the dispatch period of unit i;

[0174] ⑥ The thermal device is combined with the carbon capture device to form the integrated energy system rotating reserve device. The rotating reserve device is subject to the following constraints:

[0175]

[0176] Where, is the ramp rate of unit i, P Gji,max With P Gji,min are the upper and lower limits of the net output of thermal power unit i, and are the upper and lower spinning reserves required by the system during period t, is the ramp-down rate of unit i;

[0177] ⑦ In order to ensure that carbon capture power generation has a certain operating margin in the real-time stage to cope with the imbalance caused by wind power generation or load forecast errors, the split-type carbon capture power plant limits the flue gas fractionation ratio to minimize carbon capture energy consumption. The limit of flue gas fractionation is as follows:

[0178]

[0179] Where, δ xz is the limit of flue gas split ratio, e gi is the carbon emission intensity of unit i;

[0180] ⑧The ramping constraint and spinning reserve constraint of the thermal unit are as follows:

[0181]

[0182] Where U i,i The start and stop status of the unit, P GJi,t is the net output of thermal power unit i, and They are the upper and lower spinning reserves required by the system during period t respectively.

[0183] 5) executing the scheduling plan on a 1-hour time scale, adjusting the output power of each generator set in real time, and achieving low carbon emissions while ensuring stable operation of the integrated energy system; wherein the scheduling plan period is 24 hours.

[0184] In this step, while ensuring the stable operation of the integrated energy system, the output power of each generator set can be adjusted to improve the utilization rate of renewable energy (wind energy) and reduce carbon emissions.

[0185] To verify the feasibility of the scheduling method of the embodiment of the present application, the embodiment of the present application constructed an integrated energy system including two carbon capture power plants and one conventional thermal power plant, and created two scenarios. In scenario 1, the scheduling method proposed in the present application was applied to schedule the integrated energy system to obtain a scheduling result; in scenario 2, the integrated energy system was scheduled without considering the uncertainty model and demand response model to obtain the original scheduling result. The scheduling results obtained for scenarios 1 and 2 were compared, and the following results were obtained:

[0186] first, Figure 2 Schematic diagram of original wind power output data and electricity and heat load data obtained by scheduling without considering the uncertainty model and demand response model provided in the embodiment of the present application; Figure 3 A comparison chart of wind power data for scenarios 1 and 2 provided in the embodiments of this application; Figure 4 A comparison chart of load data for scenarios 1 and 2 provided in an embodiment of this application; Figure 5 This is a comparison chart of the energy consumption of the carbon capture system in scenarios 1 and 2 provided in the embodiment of this application. In addition, this embodiment of the application also provides a carbon trading price table for peak, flat and low periods as shown in Table 1:

[0187] Table 1 Carbon trading price list during peak and valley periods

[0188]

[0189] Furthermore, the scheduling results for scenarios 1 and 2 are shown in Table 2:

[0190] Table 2 Scheduling results for scenarios 1 and 2 (104$)

[0191]

[0192] As shown in Table 2, the wind curtailment penalty and thermal demand response are the same in the two scenarios. The total cost of Scenario 2 is about 2.9% higher than that of Scenario 1, while the changes in natural gas cost and fuel cost are negligible. The increase in revenue from carbon trading shows the positive impact of considering the uncertainty of carbon capture equipment and the demand response model based on carbon trading prices.

[0193] In addition, the present application also provides the following examples: Figure 6 The schematic diagram of the power dispatch results of scenario 1 is shown in FIG. Figure 7 The schematic diagram of thermal scheduling results for scenario 1 is shown in FIG. Figure 6 and 7 As shown in the figure, a demand response model based on carbon trading prices plays a crucial role in peak load shifting and valley filling, effectively balancing loads during peak and valley periods, thereby achieving more optimized power distribution. For specific examples, see the 1:00-5:00 AM, 10:00-14:00 PM, and 5:00-8:00 PM periods. This demand response model not only regulates user electricity usage and reduces peak demand, but also, through the incentive mechanism of carbon trading prices, encourages users to use electricity during low-carbon emission periods, further reducing overall carbon emissions.

[0194] At the same time, the output of thermal power units with different carbon emissions has a significant correlation with electricity demand. When the electricity demand is large, the system will enable three thermal power units to output simultaneously to meet the electricity demand; when the electricity demand is small, low-carbon thermal power units will be used for power generation to reduce carbon emissions. In terms of heating equipment, equipment including gas turbines and electric boilers can also optimize thermal resource allocation and improve the stability of system operation through heat load demand response. Figure 7 As shown in Figure 3, the introduction of heat load demand response enables the system to allocate heat resources more flexibly while meeting heat demand, thereby improving operational efficiency and stability.

[0195] By adopting the integrated flexible operation technology of carbon capture power plants and the demand response model based on carbon trading prices, the energy time shift of carbon capture energy consumption is realized, so that the integrated energy system can compensate for carbon capture energy consumption by effectively utilizing wind energy during high load, thereby improving the overall efficiency of the system. Figure 8 The figure shows the CO2 capture capacity of different units of the carbon capture power plant. During the two high-load time periods of 10:00-14:00 and 17:00-19:00, the CO2 production of the carbon capture power plant is significantly reduced, allowing more wind energy to be effectively utilized during these periods, further increasing the proportion of renewable energy use and reducing the system's carbon emissions.

[0196] The above content is only a preferred embodiment of the present invention. For ordinary technicians in this field, many changes can be made in the specific implementation methods and application scope based on the ideas of the present technical content. As long as these changes do not deviate from the concept of the present invention, they all fall within the scope of protection of this patent.

Claims

1. A scheduling method for an integrated energy system, characterized in that: The scheduling method includes: 1) Construct uncertainty model: 11) Based on the uncertainty of wind turbine power generation, a wind power uncertainty model is constructed to obtain wind power generation capacity; The production of wind turbines follows a Weibull distribution, and the density function of the wind energy probability is expressed as: Where, is the wind power value with uncertainty, K is the shape parameter, and C is the scale parameter; Calculated using the following formula Cumulative distribution function of use Calculate the inverse function of the cumulative distribution function In the formula, using The mean Calculate C, use The standard deviation S is calculated to get K, 12) Based on the uncertainty of load variation, a load uncertainty model is constructed to obtain the load with fluctuation; The load variation obeys the normal distribution, and the probability density of the load variation is expressed as a function of time: Where δ is the expectation of the normal distribution, i.e. the load forecast value, μ is the variance, i.e. 1 percentage point of the load forecast value, ΔP L is the load fluctuation; Calculate ΔP using the following formula L Cumulative distribution function of Using ΔP L The inverse function of the cumulative distribution function generates ΔP L : The error function z is solved using the following formula: Where r1 and r2 are random variables uniformly distributed on [0,1]; The load fluctuation ΔP after error elimination is obtained using the error function z L ': ΔP L '=μ+δ·z The fluctuating load P is derived using the following formula: L : P L =P LP +ΔP L ' Where, P LP is the wind power output forecast value; 13) Construct a carbon capture uncertainty model based on the operation of the carbon capture system; Add Gaussian noise to the average power value to simulate the disturbances in the operation of the carbon capture equipment: Where, P Bi,t is the unit operating energy consumption, P Bim,t P is the energy consumption of the unit without considering noise interference. Ji,t is the net output power, P Jim,t is the net output power of the unit without considering noise interference, P Di,t is the expected energy consumption of the unit, P Dim,t The fixed energy consumption of the unit without considering noise interference, noi l is the noise level, noi l =ε i *P ccsmean , l=1,2,3, where ε l is the noise standard deviation, P ccsmean is the mean carbon capture power; According to the operation mode of the carbon capture system, a carbon capture uncertainty model is constructed as shown in the following formula: Where, e gi E is the unit emission intensity of carbon emissions; Gi,t is the total carbon dioxide production from carbon capture and storage; P Gi,t is the total output power of carbon capture and storage; δ ci is the flue gas split ratio; is the total amount of captured carbon dioxide; E CGi,t CO2 capture provided for solution storage; β is the carbon capture efficiency; η is the maximum operating coefficient of the compressor and regeneration tower; P Gi,max is the maximum technical output of the thermal power unit during operation; λ is the energy required to capture unit carbon dioxide; E PCTDR,t CO2 used for carbon trading; 2) Developing a demand response model based on carbon trading prices, combining user demand response and load characteristics, to regulate user electricity consumption behavior and optimize load distribution of the integrated energy system; When electricity demand is high and the carbon trading price is high, the CO2 produced by the carbon capture system is traded to increase profits; When the carbon trading price is low, the CO2 generated by the carbon capture system is stored to reduce the energy consumption of the carbon capture system; The constraints of the demand response model are as follows: Where, ξ t is the carbon trading price elasticity coefficient in period t, ΔP t is the change in electricity demand before and after the implementation of demand response in period t, Δρ t is the change in carbon trading price before and after the implementation of demand response in period t, ρ Peak is the carbon trading price during peak hours, T Peak is the peak period of carbon trading price, ρ Valley is the carbon trading price during the low period, T Valley is the period of low carbon trading price, ρ t and are the carbon trading prices before and after the implementation of demand response in period t, is the load after implementing demand response in period t, and are the upper and lower limits of the carbon trading price after the implementation of demand response in period t; 3) Real-time monitoring of source-side and load-side data, and formulating start-up and shutdown plans for each generator set based on the source-side and load-side data, a wind power uncertainty model, a load uncertainty model, a carbon capture uncertainty model, and a demand response model to ensure supply and demand balance and optimal carbon emissions within different time periods; wherein the generator sets include: wind turbines, low-carbon thermal power units, and conventional thermal power units; 4) Using mixed integer linear programming techniques to optimize the integrated energy system to obtain a scheduling plan; The dispatching cost of the comprehensive energy system includes: thermal power cost C H , electricity and heat demand response cost C ADR and C HDR , the start-up and shutdown cost of thermal power units C k , the solvent loss cost of carbon capture equipment C R , carbon trading cost C T , gas cost C g , electric boiler cost C EB and the wind curtailment cost C q ; The thermal power cost is obtained through piecewise linearization: Where, is the unit output after linearization; a i ,b i ,c i are thermal power cost coefficients; The other scheduling costs are obtained by the following formula: Where K Htran is the heat load transfer cost coefficient, P Htran,t is the heat load transfer amount during period t, K Hcut is the heat load reduction factor, P Hcut,t is the heat load reduction during period t, K R is the cost coefficient of ethanolamine solvent, is the solvent operation loss coefficient, E CO2i,t is the mass of CO2 captured by unit i in period t, E PCTDR,t CO2 used for carbon trading, c gas is the natural gas cost coefficient, P gas,t is the natural gas power, c ele is the electricity price, P EB,t is the output power of the electric boiler, K q K is the penalty coefficient for curtailing wind and solar power, S is the penalty cost per unit load loss, P S,t is the load loss power; The objective function for scheduling the integrated energy system is as follows: f=min(C H +C ADR +C k +C HDR +C R +C T +C g +C EB +C q ) The constraints of the scheduling method are as follows: ① The balance limit of electric power and thermal power is as follows: Where, P GT,t is the power of the gas generator set, H DE is the heat load after heat load demand response, H EB,t is the heat output of the electric boiler, H GT,t is the heat output of the gas unit; ② The relationship between the gas turbine ramp constraint and the electric power and thermal power is as follows: Where, α H is the thermal power coefficient of the gas turbine, η H is the power output per unit of natural gas of the gas turbine, P gas is the natural gas consumption of the gas turbine, is the maximum power of the gas turbine; ③ The output, ramping, and start-stop constraints of the thermal power unit are as follows: Where, P Gi,min is the minimum technical output of thermal power unit i, P Gi,max is the maximum technical output of thermal power unit i, U i,t The start and stop status of the thermal power unit; Where, P Gi,t-1 is the output power of the unit during period t-1, is the ramp-up rate of the unit, is the unit's ramp-down rate, U i,t-1 The start and stop status of the unit during period t-1; Where, is the minimum startup time of unit i in period t-1, is the minimum startup time of unit i in period t, is the minimum shutdown time of unit i in period t-1, is the minimum shutdown time of unit i during period t; ④ The balance and limitation relationship between electrical load and thermal load is: Where, P Ltran,t is the electric load transfer amount in period t, P HL is the heat load power, P Htran,t is the heat load transfer amount during period t; ⑤ The CO2 in the solution storage exists in the ethanolamine solution. The extracted CO2 mass is expressed by volume. The stability constraint and storage volume constraint are as follows: Where V CAj,t The volume of solution required for the solution storage installed in power plant j to release CO2 during period t, E CGi,t M is the amount of CO2 to be captured supplied by the solution storage of unit i during period t, MEL is the molar mass of ethanolamine, M CO2 is the molar mass of CO2, C R is the concentration of ethanolamine solution, ρ R is the density of ethanolamine solution; Where V FYi,t With V PYi,t is the volume of the solution in the rich liquid storage and lean liquid storage of unit i during period t, V FYi,t-1 With V PYi,t-1 is the volume of solution in the rich liquid storage and lean liquid storage of unit i at time t-1, V CRi is the capacity of the solution storage of unit i, V FYi,0 With V PYi,0 is the initial solution volume of the rich solution storage and lean solution storage of unit i, V FYi,24 With V PYi,24 is the volume of solution in the rich liquid storage and lean liquid storage at the end of the dispatch period of unit i; ⑥ The thermal device is combined with the carbon capture device to form the integrated energy system rotating reserve device. The rotating reserve device is subject to the following constraints: Where, is the ramp-up rate of unit i, P GJi,t is the net output of thermal power unit i, P Gji,max With P Gji,min are the upper and lower limits of the net output of thermal power unit i, and are the upper and lower spinning reserves required by the system during period t, is the ramp-down rate of unit i; ⑦ In order to ensure that carbon capture power generation has a certain operating margin in the real-time stage to cope with the imbalance caused by wind power generation or load forecast errors, the split-type carbon capture power plant limits the flue gas diversion ratio to minimize carbon capture energy consumption. The limit of flue gas diversion is as follows: Where, δ xz is the limit of flue gas split ratio, e gi is the carbon emission intensity of unit i; ⑧The ramp constraint of the thermal unit is as follows: 5) executing the scheduling plan on a 1-hour time scale, adjusting the output power of each generator set in real time, and achieving low carbon emissions while ensuring stable operation of the integrated energy system; wherein the scheduling plan has a period of 24 hours.

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