An integrated energy collaborative optimization scheduling method considering multiple flexible resources

Abstract

The present application relates to a kind of integrated energy collaborative optimization scheduling method considering multiple flexibility resources, it is characterized in that, the scheduling method includes the following steps: the model of flexibility demand and flexibility resource of IES system is established, flexibility constraint is carried out;IES double-layer distributed coordination optimization scheduling model is established, and the optimization scheduling model includes upper energy supply system and lower IES park service system;Improved ATC algorithm is used to solve optimization scheduling model, and collaborative scheduling is carried out.Compared with prior art, the present application has the advantages of fully tapping the adjustment capacity of various flexibility resources, improving the economy and low carbon of system etc..

Description

Technical Field

[0001] This invention relates to the field of integrated energy collaborative optimization scheduling technology, and in particular to an integrated energy collaborative optimization scheduling method that takes into account multiple flexible resources. Background Technology

[0002] With the proposed goals of "peak carbon and carbon neutrality" and the large-scale integration of wind and solar power into integrated energy systems (IES), the demand for system flexibility is increasing. Regional integrated energy systems, with transmission and gas grids as their framework and energy hubs (EHs) as their nodes, are an effective means of connecting energy producers and consumers. They can utilize their diverse flexibility resources and multi-energy complementarity to meet the system's flexibility requirements. Secondly, in addition to the system's inherent flexibility resources, appropriate flexibility modifications are crucial for improving its flexibility and regulation capabilities. Carbon capture and storage (CCS), as an important emission reduction technology, is a key technological choice for my country in implementing its low-carbon development strategy and a crucial means of achieving carbon neutrality.

[0003] Chinese patent CN112417652A discloses an optimization scheduling method and system for an integrated energy system of electricity, gas, and heat. The method includes: constructing a low-carbon economic scheduling model for electricity, gas, and heat based on a tiered carbon trading mechanism; constructing the objective function and constraints of a low-carbon optimization model based on the low-carbon economic scheduling model of electricity, gas, and heat; solving the low-carbon optimization model to obtain low-carbon optimization parameters, so as to perform low-carbon scheduling of the integrated energy system according to the low-carbon optimization parameters.

[0004] Existing optimization scheduling methods do not provide reasonable constraints on the fluctuations generated by various energy sources in the integrated energy system (IES), nor do they effectively utilize the adjustment capabilities of various flexible resources. The scheduling models constructed by existing technologies only consider the operating costs of each energy hub (EH) and do not comprehensively consider factors such as energy supply costs, resulting in model biases. Therefore, the optimization results obtained by existing optimization scheduling schemes are unsatisfactory and deviate significantly from reality. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a comprehensive energy collaborative optimization scheduling method that takes into account multiple flexible resources.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] Compared with the prior art, the present invention has the following beneficial effects:

[0008] 1) This invention introduces the concept of flexibility margin constraints and establishes a model of flexibility requirements and flexibility resources. By considering flexibility margin constraints, the wind power absorption rate and energy storage utilization rate are improved, the adjustment capabilities of various flexibility resources are fully explored, and the system's flexibility requirements are met.

[0009] 2) This invention derives a flexible carbon capture operation model by analyzing the characteristics of carbon capture technology. Based on this, an IES distributed low-carbon economic dispatch model considering multiple flexible resources is established. This model fully utilizes the flexible operation mode of carbon capture power plants, reducing carbon capture output during peak load periods and storing it in memory; during off-peak load periods, increasing carbon capture equipment output and absorbing the stored data, thereby improving the unit's flexibility and enhancing the system's economy and low-carbon characteristics.

[0010] 3) This invention employs the ATC algorithm for collaborative scheduling. The goal cascade analysis method can quickly solve decentralized, hierarchical coordination problems. It allows each entity in the hierarchical structure to make autonomous decisions, and decentralized coordination optimization is achieved by each entity making decisions for its sub-entities to obtain the overall optimal solution of the system. The goal cascade method has advantages such as parallel optimization, unlimited number of stages, and rigorous convergence proof. Analysis using a numerical example of the ATC algorithm demonstrates the implementation of collaborative scheduling between energy suppliers and energy service providers at different levels. Attached Figure Description

[0011] Figure 1 This is a schematic diagram illustrating the optimized scheduling process of this invention.

[0012] Figure 2 This is a schematic diagram of the IES system framework of the present invention;

[0013] Figure 3 Schematic diagram of load flexibility analysis;

[0014] Figure 4 Schematic diagram of a flexible operation framework for a carbon capture power plant;

[0015] Figure 5 Schematic diagram of the energy hub EH;

[0016] Figure 6 This is a flowchart of the ATC algorithm used in this invention.

[0017] Figure 7 This is a load data diagram in an embodiment of the present invention;

[0018] Figure 8 This is a diagram showing the power data of the tie line in an embodiment of the present invention;

[0019] Figure 9This is a diagram showing the supply and demand relationship of flexibility in scenario 1 of this invention, where (a) represents the uplink flexibility demand and (b) represents the downlink flexibility demand.

[0020] Figure 10 This is a flexibility supply and demand diagram in scenario 2 of the present invention, where (c) represents uplink flexibility demand and (d) represents downlink flexibility demand.

[0021] Figure 11 This is a schematic diagram illustrating the relationship between flexibility safety margin and total cost in an embodiment of the present invention;

[0022] Figure 12 This is a schematic diagram of the energy consumption of the carbon capture device in an embodiment of the present invention;

[0023] Figure 13 This is a schematic diagram of the liquid volume within the storage device of the carbon capture device in an embodiment of the present invention;

[0024] Figure 14 This is a schematic diagram illustrating the sensitivity of carbon trading prices in an embodiment of the present invention. Detailed Implementation

[0025] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0026] To improve the flexibility and low-carbon nature of multi-park energy resource allocation systems (IES) and enhance the autonomy of various stakeholders, this invention proposes a distributed low-carbon economic scheduling model for IES that considers multiple flexible resources. The main work is as follows: 1) Modeling each flexible resource, proposing IES flexibility margin constraints, and establishing a two-level optimization model for IES that considers flexibility. 2) Introducing a flexible operation model for carbon capture units and a tiered carbon trading model to analyze the effectiveness of carbon capture technology in improving flexibility. 3) Employing the ATC algorithm to achieve balance and collaborative optimization between energy suppliers and energy operators. The specific steps of the technical solution of this invention are as follows: Figure 1 As shown:

[0027] S1 establishes a model of the flexibility requirements and flexibility resources of the IES system and imposes flexibility constraints;

[0028] S2 establishes an IES two-layer distributed coordinated optimization scheduling model, which includes an upper-layer energy supply system and a lower-layer IES campus service system;

[0029] S3 uses an improved ATC algorithm to solve the optimized scheduling model and perform cooperative scheduling.

[0030] 1. IES Flexibility Requirements and Flexibility Resource Modeling

[0031] Based on the operational structure of IES, the typical IES considered in this invention are as follows: Figure 2 As shown, the system consists of IES energy suppliers and IES energy operators. Energy suppliers comprise a multi-energy transmission network of the power grid and gas grid, focusing on meeting load demand, reducing energy supply costs, and ensuring flexibility. Energy operators consist of multi-energy hubs, focusing on serving users' multi-energy loads, improving energy efficiency, and meeting energy conversion needs. Since the system's flexibility requirements are mostly generated at the load end of the energy hubs, and flexibility resources are mostly provided by the energy supply system, flexibility requirements can be shifted to energy suppliers via tie lines.

[0032] 1.1 Flexibility Requirements

[0033] Fluctuations in renewable energy sources such as wind and solar power, errors in IES (Enhanced Energy Systems) load forecasting, and the efficiency of multi-energy conversion equipment all affect the system's flexibility and adaptability. This invention analyzes flexibility requirements, such as... Figure 3 As shown: L1 and L2 are the loads at times t1 and t2, respectively. and These are the upper and lower limits of load fluctuation at time t2, respectively. Therefore, the uplink flexibility requirement at time t1 is... The downlink flexibility requirement at time t1 is Therefore, the system's flexibility requirement model can be derived:

[0034]

[0035] In the formula: and P represents the uplink and downlink flexibility requirements at time t. load,t For load at time t, Y t up and Y t down These represent the uplink flexibility safety margin and the downlink flexibility safety margin at time t, respectively.

[0036] 1.2 Flexibility Resources

[0037] To ensure that the system's flexibility requirements are met and sufficient adjustment capabilities are provided, this invention analyzes the flexibility of carbon capture units, wind and solar power, energy storage, and gas grids.

[0038]

[0039] In the formula: F t up and F t down These represent the uplink and downlink flexibility provided by the system at time t, respectively. The uplink flexibility provided at time t for carbon capture units, wind power, photovoltaics, energy storage, and gas grids, respectively. and These represent the downlink flexibility capabilities provided at time t for carbon capture units, wind power, photovoltaics, energy storage, and gas grids, respectively.

[0040] Considering the system's economy, the flexibility constraint of this invention is that flexibility resources exceed flexibility requirements, in order to evaluate the system's flexibility, as shown in the equation:

[0041]

[0042] The following section will model each flexibility resource.

[0043] For carbon capture units, due to the uncertainty of wind and solar power, the units need to increase output when upstream flexibility is insufficient and decrease output when downstream flexibility is insufficient. The integrated flexible operation mode of carbon capture power plants is an effective means to improve system flexibility. A schematic diagram of the flexible operation framework of a carbon capture power plant is shown below. Figure 4 As shown, the total output of a carbon capture power plant consists of net output and carbon capture energy consumption. The absorbed carbon dioxide needs to be processed through an absorption tower, regeneration tower, and compressor before being stored. Additionally, the absorbed carbon dioxide can be stored in a storage tank for later use. On the one hand, during high-load periods, the carbon capture power plant can store the absorbed carbon dioxide in the storage tank, shifting high-energy-consuming processes such as regeneration and compression to low-load periods. On the other hand, during low-load periods, the carbon capture power plant can absorb wind power by increasing its carbon capture output. Through flexible operation and time-shifting of carbon dioxide in the storage tank, the system's flexibility and low-carbon characteristics are improved. The carbon capture power plant model is as follows:

[0044]

[0045] In the formula: P Gi,t P out,Gi,t and P ccs,i,t These represent the total output, net output, and carbon capture energy consumption of carbon capture unit i at time t, respectively. ccsy,i,t and P ccsg,i,t E represents the carbon capture operation energy consumption and stationary energy consumption of carbon capture unit i at time t. Gi,t E ccs,Gi,t and E sGi,t These represent the total CO2 production, CO2 capture amount, and amount of CO2 in the storage for capture of carbon capture unit i at time t, respectively. α i Let be the flue gas split ratio of carbon capture unit i, and α, λ, and η be the maximum operating condition coefficients of the regeneration tower and compressor, the unit capture energy consumption, and the carbon capture efficiency, respectively. Gi Let be the carbon emission intensity of carbon capture unit i. The memory model is as follows:

[0046]

[0047] In the formula: V s,i,t V is the volume of CO2 released from the storage of unit i at time t. m,i,t V is the volume of liquid in the storage tank of unit i at time t. m,max,i,t Let k be the maximum memory capacity of unit i at time t. s Here is the CO2 volume-to-mass conversion factor. Based on the carbon capture model, the output range of carbon capture power plants is derived as follows:

[0048]

[0049] In the formula: P Gi,max and P Gi,min These represent the upper and lower limits of the total output of carbon capture power plant unit i at time t, respectively. out,Gi,t,max and P out,Gi,t,min Let represent the upper and lower limits of the net output of carbon capture power plant unit i at time t, and I represent the number of units. It can be seen that the net output range of carbon capture power plants is significantly improved compared to conventional units. The flexibility model for carbon capture units is as follows:

[0050]

[0051] Where: ΔP ccs,i Let i be the ramp rate of unit i;

[0052] Wind and solar power are subject to certain uncertainties, and fluctuation coefficients are used to determine wind and solar power output. Increasing wind and solar power output provides uplink flexibility, while decreasing wind and solar power output provides downlink flexibility.

[0053]

[0054] In the formula: λ wind and λ pv The fluctuation coefficients P for wind power and solar power, respectively. w,t+1 and P w,t The output of wind power w at time t+1 and time t are respectively, P v,t+1 and P v,t Let V represent the output of photovoltaic power at time t+1 and time t, respectively, and let W and V represent the number of wind turbines and photovoltaic units, respectively.

[0055] It provides uplink flexibility when discharging energy storage and downlink flexibility when charging energy storage.

[0056]

[0057] In the formula: E soc,t Let be the amount of charge stored at time t. and P represents the minimum and maximum charge quantities of the stored energy s, respectively. soc,s,t Let be the charging and discharging power of energy storage s at time t. and Let be the maximum charging and discharging power of energy storage s at time t, respectively. and , where represents the charging and discharging efficiency of the energy storage, and S represents the amount of energy stored.

[0058] The flexibility of the gas grid is mainly reflected in the output of the gas turbine. The gas turbine needs to increase its output when the upward flexibility is insufficient and reduce its output when the downward flexibility is insufficient.

[0059]

[0060] In the formula: P GT,n,max and P GT,n,min P represents the maximum and minimum output of gas turbine n at time t. GT,n,t Let N be the output of gas turbine n at time t, and N be the number of gas turbines.

[0061] 2. IES Two-Layer Distributed Coordination and Optimization Scheduling Model

[0062] This invention considers the multi-entity operation characteristics of the IES system, as well as the information communication and security needs of the entities. It proposes a two-layer distributed coordination and optimization scheduling model for the IES. The upper-layer energy supply system takes the minimum energy supply cost as the objective function, while the lower-layer IES campus service system takes the minimum operating cost of each EH as the objective function. Considering decentralized coordination and optimization, the improved ATC algorithm is used for solving the problem.

[0063] 2.1 IES Energy Supply System Optimization Model

[0064] The upper-level energy supply system aims to minimize energy supply costs, including the total fuel cost of carbon capture power plants, natural gas extraction costs, wind curtailment costs, carbon trading costs, and revenue from the sale of captured CO2.

[0065]

[0066]

[0067] In the formula: f e f g f wind f ccs , and f ccs These are the total fuel cost of carbon capture power plants, natural gas extraction cost, wind curtailment cost, carbon trading cost, and revenue from the sale of captured CO2. i b i and c i Let be the power generation coefficient of unit i. Let ρ be the amount of natural gas produced by well j at time t. j Where J is the unit price of natural gas, and P is the number of gas wells. wp,w,t This is a wind power forecast. For the carbon trading cost of unit i, ρ ccs The unit price for selling high-concentration CO2, m i,ccs The mass of CO2 captured by unit i.

[0068] The carbon trading mechanism consists of three parts: carbon trading price, carbon emission allowances, and carbon emission volume. This invention employs a tiered carbon trading model, as shown in the equation:

[0069]

[0070] In the formula: Let φ1, φ2, and φ3 represent the carbon emissions of unit i, φ1, φ2, and φ3 represent the three-stage carbon trading allowances, and λ1, λ2, and λ3 represent the carbon trading unit prices.

[0071] This invention uses DC power flow constraints as power network constraints, and the constraints include node power balance constraints, phase angle constraints, generator output constraints, and transmission line constraints, as shown in the following equation.

[0072]

[0073] In the formula, f l,t For the power flow of line l at time t, P cl,c,t Let Δθ be the tie line power of tie line c at time t, and L and C be the number of lines and tie lines, respectively. l,t x is the voltage phase angle difference between the first and last segments of line l at time t. hj Let be the reactance of line l.

[0074] The natural gas network mainly consists of gas wells, pipelines, gas turbines, and interconnecting gas flow. Its constraints primarily consider node supply and demand balance constraints, node pressure constraints, gas network pipeline constraints, and gas source constraints, as shown below:

[0075]

[0076] In the formula, For natural gas from gas wells The output flow rate at time t, Q GT,n,t Let Q be the gas consumption of the gas turbine at time t. p,t The flow rate Q in pipe p at time t. cl,c,t P is the gas flow rate at time t of the connecting line. r,p,t Let P be the pressure of the natural gas pipeline p at time t. r，min,i and P r，max,i Upper and lower pressure limits of natural gas pipelines (p). and These represent the minimum and maximum output power of the air source, respectively.

[0077] This is a non-convex programming problem, which is difficult to solve using conventional methods. This invention uses an incremental piecewise linearization method to transform this problem into a linear programming problem.

[0078]

[0079] Q pi,t |Q pi,t Incremental piecewise linearization. n Here, N represents the segment position, and N is the number of segments. (x n ,y n ) and (x n+1 ,y n+1 The piecewise coordinates 'a' are used to describe the piecewise points of the function y = x|x|, therefore the piecewise position quantity 'a' can be used. n To describe (x, y), and to ensure that the segmented intervals are filled sequentially.

[0080] Energy storage constraints are as follows:

[0081]

[0082] The gas turbine constraints are as follows:

[0083] P GT,n,t =η GT Q GT,n,t (2.8)

[0084] P GT,min ≤P GT,n,t ≤P GT,max (2.9)

[0085] In the formula: η GT P represents the efficiency of the gas turbine. GT,min and P GT,max These represent the minimum and maximum output power of the gas turbine, respectively.

[0086] In addition, the constraints of the carbon capture unit are shown in Equation (1.4), and the flexibility margin constraints are shown in Equation (1.3).

[0087] 2.2 IES Campus Service System Optimization Model

[0088] The IES park service system takes minimizing the cost of purchasing electricity and gas at each energy hub as its objective function.

[0089] min F2=f EH,e +f EH,g (2.10)

[0090]

[0091]

[0092] In the formula: I EH f represents the number of EHs in the system. EH,e For EH's total electricity purchase cost, f EH,g EH's total gas purchase cost λ represents the total cost of carbon trading. e,t and λ g,t P represents the unit price of electricity and gas purchased by EH at time t. buy,c,t and Q buy,c,t These represent the electricity and gas purchases by EH at time t, respectively.

[0093] The energy hub EH model considered in this invention is as follows: Figure 5 As shown, the equipment includes photovoltaic, electricity-to-gas, heat pump, cogeneration and gas boiler, and the load considers electric load, heat load and gas load.

[0094] The power balance equations for the electric busbar, gas busbar, and thermal busbar are as follows:

[0095]

[0096]

[0097]

[0098] In the formula: P buy,c,t , and These represent the purchased power, EH photovoltaic output power, cogeneration power, electricity-to-gas power, and heat pump power at time t, respectively. buy,c,t , and These represent the gas purchase volume at time t, the gas consumption for electricity-to-gas conversion, the gas consumption for combined heat and power generation, and the gas consumption for gas-fired boilers. and P represents the thermal power of the combined heat and power plant, the thermal power of the heat pump, and the thermal power of the gas boiler at time t, respectively. load,c,t Q load,c,t and H load,c,t The electrical, gas, and heat loads at time t are respectively.

[0099] The equipment conversion constraints are as follows:

[0100]

[0101] In the formula: η CHP,P η CHP,H η P2G η HP and η GB These are respectively the power efficiency of cogeneration, the heat efficiency of cogeneration, the efficiency of electricity to gas conversion, the efficiency of heat pumps, and the efficiency of gas-fired boilers.

[0102] The upper and lower power limits for each device are as follows:

[0103] S i,min ≤S i ≤S i,max (2.17)

[0104] In the formula: S i For the power of each device, S i,min S represents the lower power limit for each device. i,max This represents the power limit for each device.

[0105] 2.3 Solving with ATC Algorithm

[0106] Objective cascade analysis is an effective method for rapidly solving decentralized, hierarchical coordination problems. It allows each entity in the hierarchy to make autonomous decisions, and through decentralized coordination optimization as each entity makes decisions about its sub-entities, the overall optimal solution of the system is obtained. Compared with other optimization methods, objective cascade analysis has advantages such as parallel optimization capability, unlimited number of levels, and rigorous convergence proof.

[0107] First, the coupling variables of the connection lines are decoupled by adding first- and second-order terms of the Lagrange penalty function to each principal objective function, as shown below:

[0108]

[0109]

[0110] In the formula: and The modified objective function, and Let be the first-order multipliers of the Lagrange penalty function for the power grid and the gas grid at time t, respectively. and Let the quadratic multipliers of the Lagrange penalty function of the power grid and the gas grid at time t be given respectively. The convergence conditions of the inner and outer loops are shown in equations (2.20) and (2.21) respectively.

[0111]

[0112]

[0113] In the formula: ε1, ε2, and ε3 represent the convergence accuracy of the grid coupling variables, gas grid coupling variables, and the difference in the objective function, respectively. Lagrange multiplier update formula:

[0114]

[0115] like Figure 6The flowchart shown includes the following steps:

[0116] IES service system initialization;

[0117] Enter the inner loop to solve the optimized scheduling model;

[0118] Determine if the inner loop has converged. If not, return to the inner loop; if so, enter the outer loop.

[0119] Determine if the outer loop has converged. If not, update the Lagrange multipliers and return to the inner loop. If yes, terminate the loop.

[0120] The inner loop solution for the optimized scheduling model includes the following steps:

[0121] Solve the economic dispatch of each EH in the energy supply system separately;

[0122] The coupling variables are passed to the IES power supply system;

[0123] IES functional system initialization;

[0124] Calculate the economic dispatch of the energy supply system.

[0125] 3. Case Analysis

[0126] 3.1 Basic Data

[0127] This invention utilizes the IEEE 30-node power grid and the Belgian 20-node gas grid to construct an IES energy supply system, with three EHs forming the IES campus service system, interconnected with the energy supply system. The IEEE 30-node power grid includes three carbon capture power plants, one wind farm, one photovoltaic system, and one gas turbine; the gas grid system includes two gas sources. Load data within the EHs is as follows: Figure 7 As shown.

[0128] This invention utilizes CPLEX for optimization, with the system simulated over a 24-hour period in 1-hour increments. To analyze the impact of flexibility constraints, carbon capture equipment, and carbon trading on the system, this invention sets up four scenarios:

[0129] Scenario 1: Considering flexibility constraints;

[0130] Scenario 2: Flexibility constraints are not considered;

[0131] Scenario 3: Considering flexibility constraints, without considering carbon capture units;

[0132] Scenario 4: Considering flexibility constraints, but not carbon trading.

[0133] 3.2 Scheduling Result Analysis

[0134] Table 1 Scheduling Results

[0135]

[0136] Table 1 shows the data for total cost, carbon trading cost, carbon capture revenue, and carbon emissions under four scenarios, with a flexibility safety margin of 250MW for scenarios 1, 3, and 4. The total cost of scenario 1 increased by 0.47% compared to scenario 2, the carbon trading cost increased by 2.98%, while the carbon capture revenue increased by 2.88%, and carbon emissions decreased by 4.35%. This indicates that considering flexible resources, the total cost increased slightly, but the system effectively utilized the adjustment capabilities of flexible resources, reduced wind curtailment, improved the effectiveness of carbon trading, and effectively controlled the cost increase through carbon capture technology. Compared to scenario 3, scenario 1, after considering carbon capture technology, saw a 7.57% reduction in the system's total cost and a significant reduction in carbon emissions. This demonstrates that carbon capture technology is beneficial for improving the system's economics and low-carbon performance, and significantly reduces the cost of carbon trading. After considering carbon trading, compared to scenario 4, scenario 1 saw a 1.88% increase in total cost and a 3.33% decrease in carbon emissions. This demonstrates that the tiered carbon trading model can significantly reduce carbon emissions and improve the system's economics with a slight increase in total cost, while also helping the system meet flexibility requirements.

[0137] Figure 8 As shown in the figure, the tie-line power increases significantly during the periods of 2:00-9:00 and 13:00-20:00, indicating a higher demand for uplink flexibility during these periods. Conversely, the power decreases significantly during the periods of 9:00-12:00 and 22:00-24:00, indicating a higher demand for downlink flexibility during these periods.

[0138] 3.3 Flexibility Analysis

[0139] Figure 9 and Figure 10 These are the supply and demand relationships for uplink and downlink flexibility in scenarios 1 and 2, respectively. Figure 9 It can be seen that the system's flexibility requirements change with the EH's load demand. Flexibility constraints allow the system to utilize various flexibility resources, including carbon capture units and energy storage, to meet these requirements. The graph of uplink flexibility demand shows that the system's flexibility resources are mainly provided by carbon capture units, due to their large output and wide operating range, while wind and solar power outputs are relatively low. Between 0:00-4:00 and 9:00-15:00, the system's load demand and fluctuations are relatively small; therefore, the uplink flexibility demand is low during these times, and the uplink flexibility resources far exceed the flexibility demand, indicating that the system has a large flexibility margin to meet flexibility requirements. Similarly, in the downlink flexibility demand graph, between 5:00-11:00 and 16:00-23:00, the downlink flexibility resources far exceed the flexibility demand.

[0140] If flexibility constraints are not considered Figure 10 It can be seen that the uplink flexibility requirements during 7:00-9:00 and 18:00-21:00 cannot be met, and similarly, the downlink flexibility requirements during 12:00-13:00 and 23:00-24:00 cannot be met, and compared to... Figure 9 The flexibility margin at other times is significantly reduced. This indicates that flexibility constraints can improve the flexibility margin at all times, effectively meeting the system's flexibility requirements.

[0141] Figure 11 To illustrate the relationship between flexibility safety margin and total cost, in Scenario 1, increasing the flexibility safety margin from 50MW to 200MW reduced the total cost by 5.89%. However, when the flexibility requirement increased to 250MW, the total system cost began to rise. This demonstrates that increasing the flexibility safety margin can effectively improve wind power utilization, increase the efficiency of carbon capture equipment, increase capture revenue, and reduce carbon trading costs. However, as the flexibility requirement continues to increase, the system's flexibility adjustment capability disappears, and the system's operating costs begin to rise. Increasing the flexibility safety margin to 300MW will no longer meet the requirements. Since Scenario 3 does not consider carbon capture units, the total cost increased significantly after the system met the flexibility requirement. This also shows that the flexible operation mode of carbon capture technology can effectively alleviate the economic cost pressure caused by the increased system flexibility requirement.

[0142] 3.4 Carbon Capture Analysis

[0143] Figure 11 This is a graph showing the power consumption of carbon capture in scenario 1. Figure 13 This represents the capacity of the solution in the carbon capture and storage device under scenario 1. Figure 12 It can be seen that the carbon capture equipment consumes more power during 1:00-5:00 and 11:00-17:00, indicating that the system load is lower and the demand for uplink flexibility is less during these periods. Therefore, the unit output is relatively sufficient, allowing for increased output from the carbon capture equipment. However, during 6:00-11:00 and 18:00-23:00, due to the higher system load and greater demand for uplink flexibility, the carbon capture unit does not have sufficient output available for carbon capture. Figure 13 It is known that the storage device stored carbon dioxide between 1:00-5:00 and 11:00-17:00. This indicates that during periods of high load, the carbon capture power plant stores the absorbed carbon dioxide in the storage device, shifting high-energy-consuming processes such as regeneration and compression to periods of low load. During periods of low load, the carbon capture power plant uses flexible operating methods to transfer the carbon dioxide in the storage device to the carbon capture equipment for compression. Therefore, the carbon capture equipment can improve the system's flexibility and low-carbon performance.

[0144] 3.5 Carbon Trading Analysis

[0145] Figure 14 The graph shows the impact of changes in carbon trading prices on system carbon emissions. When the carbon trading price increases by less than 40%, the system's carbon emissions decrease significantly. However, when the price increases by more than 50%, the change in carbon emissions becomes less noticeable. This indicates that a reasonable carbon trading price can effectively balance the system's economic and systemic requirements.

[0146] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1.A method for integrated energy collaborative optimization scheduling considering multiple flexible resources, characterized in that, The scheduling method comprises the following steps: A model of flexibility demand and flexibility resource of the IES system is established, and flexibility constraints are performed; The flexibility demand model of the system is: In the formula: and respectively are uplink and downlink flexibility requirements at is load at is load at and respectively are uplink flexibility safety margin and downlink flexibility safety margin at The flexibility resource considers carbon capture units, wind power, photovoltaic, energy storage and gas network, and the model is: In the formula: and respectively are uplink flexibility and downlink flexibility provided by the system at the moment, , , , , respectively are carbon capture unit, wind power, photovoltaic, energy storage and gas network uplink flexibility capacity provided at the moment, , , , and respectively are carbon capture unit, wind power, photovoltaic, energy storage and gas network downlink flexibility capacity provided at the moment; The flexibility constraint is that the flexibility resource is greater than the flexibility demand, as shown in the formula: ； An IES double-layer distributed coordinated optimization scheduling model is established, and the optimization scheduling model comprises an upper-layer energy supply system and a lower-layer IES park service system; An improved ATC algorithm is used to solve the optimization scheduling model and perform collaborative scheduling. 2.The method of claim 1, wherein, Carbon capture plant up flexible capability and down flexible is: A carbon capture power plant and storage model is established; According to the carbon capture model, the output range of the carbon capture power plant is derived; A flexibility model of the carbon capture unit is calculated The carbon capture power plant model is: wherein: , and are the total output, net output and carbon capture energy consumption of the carbon capture plant at , and are the carbon capture operation energy consumption and fixed energy consumption of the carbon capture plant at , , and are the total production, capture amount and amount in the storage for capture of the carbon capture plant at , , , , is the flue gas split ratio of the carbon capture plant , , and are the maximum working state coefficient of the regenerator and compressor, unit capture energy consumption and carbon capture efficiency, is the carbon emission intensity of the carbon capture plant ; The output range of the carbon capture power plant is: wherein: and are the upper and lower limits of the total output of the carbon capture power plant unit at , and are the upper and lower limits of the net output of the carbon capture power plant unit at , is the number of units; The flexibility model of the carbon capture unit is: In the formula: is the ramp rate of the machine set . 3.The method of claim 1, wherein, The wind power uplink flexibility and downlink flexibility with photovoltaic uplink flexibility and downlink flexibility is: wherein: and are the volatility coefficients of wind and photovoltaic power, respectively, and are the wind power at and , respectively, and are the photovoltaic power at and , respectively, and are the number of wind and photovoltaic generators, respectively. 4.The method of claim 1, wherein, The energy storage provides uplink flexibility when discharged and downlink flexibility when charged : In the formula: Qmin is the minimum charge amount of the energy storage Qmax is the maximum charge amount of the energy storage Q is the charge amount of the energy storage at the moment Qmin is the minimum charge amount of the energy storage Qmax is the maximum charge amount of the energy storage Q is the charge amount of the energy storage at the moment Q is the charge amount of the energy storage at the moment P is the charge-discharge power of the energy storage at the moment Pmax is the maximum charge-discharge power of the energy storage at the moment P is the charge-discharge power of the energy storage at the moment Pmax is the maximum charge-discharge power of the energy storage at the moment η is the efficiency of the charge-discharge of the energy storage η is the efficiency of the charge-discharge of the energy storage η is the efficiency of the charge-discharge of the energy storage N is the number of energy storages 5.The method of claim 1, wherein, The flexibility of the gas network is mainly reflected in the output of the gas turbine. The gas turbine needs to increase the output when the uplink flexibility is insufficient, and reduce the output when the downlink flexibility is insufficient. The uplink flexibility and downlink flexibility of the gas network are: and : where: and are the maximum and minimum power output of the gas turbine at the time instant, is the power output of the gas turbine at the time instant, is the number of gas turbines. 6.The method of claim 1, wherein, The upper energy supply system takes the minimum energy supply cost as the objective function, including the total fuel cost of the carbon capture power plant, the natural gas exploitation cost, the abandoned wind cost, the carbon trading cost and the capture The sale income is: In the formula: , , , , and are the total fuel cost of the carbon capture power plant, the natural gas extraction cost, the abandoned wind cost, the carbon trading cost and the capture sale revenue, , and are the power generation coefficients of the unit , is the extraction amount of the natural gas well j at the time t , is the unit price of natural gas, is the number of gas wells, is the wind power prediction value, is the carbon trading cost of the unit , is the unit price of high-concentration CO2, is the mass of CO2 captured by the unit . The carbon trading mechanism is composed of three parts: carbon trading price, carbon emission quota and carbon emission amount, and the carbon trading model adopts a ladder type: In the formula: is the carbon emission of the unit , , and are respectively carbon trading three-stage quota, , and are respectively carbon trading unit price, A direct current flow constraint is used as the power network constraint, and the constraint conditions comprise a node power balance constraint, a phase angle constraint, a generator output constraint and a power transmission line constraint, as shown in the following formula: In the formula, For the line exist The power flow at that time For connecting lines exist Power of the connecting line at that time and For the number of lines and connecting lines, For the line exist The voltage phase angle difference between the first and last segments at that time For the line The reactance; The natural gas network mainly comprises a gas well, a pipeline, a gas turbine and a tie-line gas flow, and the natural gas network constraint mainly considers a node supply-demand balance constraint, a node pressure constraint, a gas network pipeline constraint and a gas source constraint, as shown below: wherein Qg is the gas flow rate of the gas well Qg is the gas flow rate of the gas well t Qg is the gas flow rate of the gas well Qg is the gas flow rate of the gas well t Qg is the gas flow rate of the gas well Qg is the gas flow rate of the gas well t Qg is the gas flow rate of the gas well Qg is the gas flow rate of the gas well Qg is the gas flow rate of the gas well t Qg is the gas flow rate of the gas well Qg is the gas flow rate of the gas well Qg is the gas flow rate of the gas well t Qg is the gas flow rate of the gas well Qg is the gas flow rate of the gas well Qg is the gas flow rate of the gas well Qg is the gas flow rate of the gas well Qg is the gas flow rate of the gas well Qg is the gas flow rate of the gas well The energy storage constraint is as follows: The gas turbine constraint is as follows: where: is the efficiency of the gas turbine, and are the minimum and maximum power output of the gas turbine, respectively. 7.The method of claim 1, wherein, The IES park service system takes the minimum cost of electricity and gas purchase of each energy hub as an objective function: In the formula: is the number of systems EH, is the total purchase cost of electricity by EH, is the total purchase cost of gas by EH, is the total cost of carbon trading, and are respectively t the purchase price of electricity and gas by EH at the moment, and are respectively t the purchase amount of electricity and gas by EH at the moment; The equipment of the energy hub model comprises photovoltaic, electric-to-gas, heat pump, combined heat and power and gas boiler, and the load considers electric load, heat load and gas load; the power balance equations of the electric bus, gas bus and heat bus are as follows: wherein: , , , and are the electricity purchase power, the EH photovoltaic output power, the cogeneration power, the electricity-to-gas power and the heat pump power when t , , , and are the gas purchase volume, the electricity-to-gas gas consumption, the cogeneration gas consumption and the gas boiler gas consumption when t , , and are the cogeneration heat power, the heat pump heat power and the gas boiler heat power when t , , and are the electricity, gas and heat loads when t , The equipment conversion constraint is as follows: wherein: , , , and are the combined heat and power electrical efficiency, the combined heat and power thermal efficiency, the electricity-to-gas efficiency, the heat pump efficiency and the gas boiler efficiency, respectively; The upper and lower power limit constraints of each equipment are as follows: wherein: P is the power of the respective device, Pminis the lower power limit of the respective device, Pmaxis the upper power limit of the respective device. 8.The method of claim 1, wherein, The collaborative scheduling by using the ATC algorithm comprises the following steps: IES service system initialization; An inner loop is entered to solve the optimization scheduling model; It is judged whether the inner loop converges, if not, the inner loop is returned, and if yes, an outer loop is entered; It is judged whether the outer loop converges, if not, the Lagrange multiplier is updated and the inner loop is returned, and if yes, the process is ended; The inner loop solving the optimization scheduling model comprises the following steps: The economic dispatching of each EH in the energy supply system is solved respectively; The coupling variable is transmitted to the IES energy supply system; IES function system initialization; The economic dispatching of the energy supply system is calculated. 9.The method of claim 8, wherein, The collaborative scheduling by using the ATC algorithm further comprises: Firstly, the tie-line coupling variable is decoupled, and a Lagrange penalty function first and second term is added in each subject function, as shown below: In the formula: and The modified objective function, and For the power grid and gas grid respectively t The linear term multiplier of the Lagrange penalty function at time t, and The power grid and gas grid are respectively in t The quadratic multiplier of the Lagrange penalty function at time t, for t Power purchased at that time for t Gas purchase volume at that time For connecting lines exist Power of the connecting line at that time For connecting lines t Air flow rate at that time; The convergence conditions of the inner loop and the outer loop are shown in the formula and the formula respectively. In the formula: , and are the power grid coupling variable, the gas grid coupling variable and the convergence precision of the objective function difference value respectively; the Lagrange multiplier updating formula is: 。

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