An IES scheduling method considering multiple uncertainties and electric-hydrogen-carbon coupling

Abstract

This invention discloses an IES scheduling method considering multiple uncertainties and the coupling of electricity, hydrogen, and carbon, belonging to the field of integrated energy system scheduling. The method mainly includes: establishing an electricity-hydrogen-carbon coupling model to realize the conversion and storage between different energy sources; integrating an integrated demand response model to generate a collaborative scheduling strategy for the interaction between the integrated energy system and the load side, resulting in an improved electricity-hydrogen-carbon coupling model; establishing and solving a two-layer scheduling model with minimizing the total system operating cost as the inner objective and minimizing the system carbon emissions as the outer objective, obtaining the optimal scheduling model; and deploying the optimal scheduling model to a real integrated energy system. Experiments show that this invention can solve the problems of insufficient analysis of hydrogen energy operation under uncertain environments, rigid tiered carbon trading parameters, single demand response models, and conservative or inaccurate uncertainty optimization methods in current research, thereby effectively improving the system's economy, reliability, and low-carbon performance.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system scheduling, specifically involving an IES scheduling method that considers multiple uncertainties and the coupling of electricity, hydrogen and carbon. Background Technology

[0002] Under the dual pressure of "dual carbon" targets and continuously growing energy demand, the global energy structure is transforming towards a more efficient and low-carbon direction. As a system in which multiple energy forms are coupled together, the integrated energy system (IES) has significant advantages such as multi-energy complementarity and cascaded energy utilization.

[0003] Currently, research on IES scheduling mainly focuses on the following four aspects: research on hydrogen-containing IES, research on the improvement of carbon trading mechanisms, research on the application of integrated demand response (IDR), and research on the uncertainty optimization scheduling of renewable energy utilization such as wind power (WT) and photovoltaic (PV).

[0004] Among them, research on hydrogen-containing IES mainly focuses on the steady-state energy flow relationship of hydrogen, modeling of coupled devices, and energy utilization. For example, the diversified utilization of hydrogen energy has improved the economic and environmental benefits of the system; by introducing electrolyzers, hydrogen storage tanks, and hydrogen fuel cells, the improvement of energy cascade utilization and system energy efficiency of hydrogen systems has been demonstrated; by integrating power-to-gas (P2G) and carbon capture and storage (CCS), an optimal operating model considering carbon trading constraints has been proposed, which has significantly improved the reliability and economy of the system; and by combining P2G and hydrogen fuel cell electric vehicles, an electric-hydrogen-gas coordinated scheduling model has been proposed, which has improved energy utilization efficiency and economic benefits.

[0005] Research on improving carbon trading mechanisms mainly involves methods such as tiered pricing, dynamic carbon price adjustment, and multi-factor coupling modeling. For example, establishing a full life-cycle tiered carbon trading model can achieve continuous monitoring and precise control of the system's carbon footprint; combining the IDR mechanism with tiered carbon trading can achieve synergistic optimization of system operating costs and carbon emissions; and constructing a seasonally differentiated tiered carbon trading model can achieve a balance between emission reduction benefits and economic efficiency by optimizing the price growth rate.

[0006] IDR research primarily achieves optimization through multi-energy coupling modeling, intelligent algorithm-driven approaches, and user behavior characterization. For example: constructing a response model integrating price and incentive mechanisms enhances system interaction benefits based on user economic assessment; introducing reinforcement learning to optimize price response strategies synergistically enhances consumer benefits and system operational reliability; constructing a response model considering user comfort and spatiotemporal coupling characteristics enables refined load management under dynamic energy pricing mechanisms; applying machine learning to identify user response intentions allows for accurate prediction of potential demand response participation; and designing a response model that balances price signals and load satisfaction achieves a balanced optimization of user energy experience and system scheduling economics.

[0007] Research on the optimization scheduling of uncertainties in renewable energy utilization mainly improves system performance by improving uncertainty modeling methods and optimization solution frameworks. For example, scenario generation technology is used to describe photovoltaic and load fluctuations, realizing safe and economical scheduling of microgrids; data-driven two-stage learning is used to construct uncertainty sets, reducing the scheduling cost and conservatism of multi-energy building IES; and by characterizing source-load uncertainties through box-shaped uncertainty sets and establishing a two-stage robust optimization model, the economic efficiency and carbon benefits of seaport IES are synergistically improved.

[0008] However, existing research still has certain limitations. First, hydrogen energy utilization analysis is mostly limited to deterministic environments, lacking research on two-stage P2G and hydrogen-blended equipment operation under uncertainty. Second, key parameters of the tiered carbon trading mechanism are mostly fixed, lacking dynamic adaptive adjustment methods, which limits the effectiveness of the tiered carbon trading mechanism. Third, the analysis of IDR type is relatively simple, failing to fully consider the differences in response characteristics of various load types and the uncertainty of user group behavior. Fourth, the two mainstream uncertainty handling methods each have their own limitations. Stochastic optimization relies on probability distribution assumptions, making it difficult to fully reflect the actual state of the system, while robust optimization often uses symmetric box-shaped intervals, which, although reducing the computational scale, fails to accurately describe uncertainty, leading to overly conservative and extreme scheduling schemes. Summary of the Invention

[0009] The purpose of this invention is to provide an IES scheduling method that considers multiple uncertainties and the coupling of electricity, hydrogen and carbon. This method can solve the problems in current research, such as insufficient analysis of hydrogen energy operation under uncertain environments, rigid tiered carbon trading parameters, single demand response model, and conservative or inaccurate uncertainty optimization methods. This will effectively improve the economy, reliability and low-carbon performance of the system.

[0010] To achieve the above objectives, the technical solution adopted by this invention is: an IES scheduling method considering multiple uncertainties and electro-hydrogen-carbon coupling, comprising the following steps: S1. Establish an electricity-hydrogen-carbon coupling model to realize the conversion and storage of different energies; S2. Based on the electricity-hydrogen-carbon coupling model, an integrated demand response model is integrated to generate a coordinated scheduling strategy for the interaction between the integrated energy system and the load side, resulting in an improved electricity-hydrogen-carbon coupling model. S3. Based on the improved electricity-hydrogen-carbon coupling model in step S2, establish a two-layer scheduling model with minimizing the total system operating cost as the inner objective and minimizing the system carbon emissions as the outer objective, and solve the two-layer scheduling model to obtain the optimal scheduling model; the specific steps are as follows: S31. Establish an inner-layer model to obtain the minimum overall operating cost of IES; S32. Based on the inner model in step S31, introduce a multi-interval uncertainty set and establish a two-stage robust optimization model. S33. Establish an outer-layer optimization model to obtain the minimum total carbon emissions of the integrated energy system and define the parameters of the carbon trading mechanism; S34. Set up an iterative optimization mechanism for a two-layer model. The outer layer model passes carbon trading parameters to the inner layer model. The inner layer model solves the problem based on these parameters and feeds back the resulting system carbon emissions to the outer layer model. The outer layer model adjusts and optimizes the carbon trading parameters based on this feedback. S35. Repeat the iterative process of step S34 until the convergence condition is met, thereby obtaining the optimal carbon trading parameters and the final scheduling scheme. S4. Deploy the optimal scheduling model obtained in step S3 to the actual integrated energy system.

[0011] Furthermore, the electricity-hydrogen-carbon coupling model in step S1 includes three stages: hydrogen production, hydrogen utilization, and hydrogen storage. In the hydrogen production stage, the electrolyzer utilizes surplus electricity from wind and solar power to produce hydrogen. In the hydrogen utilization stage, carbon capture and storage equipment captures CO2 emitted from combined heat and power (CHP) and gas-fired boiler equipment, and uses a methane reactor to react the captured CO2 with hydrogen to produce methane. The hydrogen is then directly used in hydrogen fuel cells for CHP production, or mixed with natural gas and supplied to a gas-fired hydrogen blending system. In the hydrogen storage stage, the remaining hydrogen is stored in hydrogen storage tanks.

[0012] Furthermore, hydrogen is incorporated into the combined heat and power (CHP) and gas-fired boiler equipment used in the hydrogen production process, with the incorporation ratios being 10%~20% and 0%~20%, respectively.

[0013] Furthermore, the integrated demand response model in step S2 first models the flexible loads in the integrated energy system, then introduces a dynamic compensation pricing mechanism, and finally obtains the optimal flexible load scheduling strategy. The flexible loads include reduceable electrical loads, transferable electrical loads, and flexible heat loads. The expression for reducing electrical load is: In the formula: The electrical load power can be reduced during time period t. This is a Boolean variable indicating whether the electrical load is reduced during time period t. The maximum electrical load that can be reduced during time period t. This is the maximum number of times the electrical load response can be reduced; The expression for transferable electrical load is: In the formula: and These represent the incoming and outgoing power of the transferable electrical load during time period t, respectively. and This is a Boolean variable, indicating whether a load transfer response is performed during time period t. and These represent the upper limits of the incoming and outgoing electrical loads during time period t, respectively. Indicates the maximum number of times a transferable electrical load can respond; The expression for flexible heat load is: In the formula: and This represents the power of the flexible heat load adjusted upwards and downwards during time period t. This represents the maximum adjustable power of the flexible heat load during time period t. and This is a Boolean variable representing whether a heat load adjustment response is initiated. This represents the maximum number of responses to the flexible thermal load.

[0014] Furthermore, the expression for the integrated demand response model is: In the formula: and These are the electrical load and thermal load after comprehensive demand response; and These are the initial electrical load and initial thermal load before the integrated demand response.

[0015] Furthermore, the expression for the objective function of the inner model in step S31 is: In the formula: The cost of energy interaction between IES and the power grid. For carbon trading costs, For the operation and maintenance costs of IES, For gas purchase costs, To account for the cost of wind and solar power curtailment Cost of responding to demand.

[0016] Furthermore, the expression for the multi-interval uncertain set mentioned in step S32 is: In the formula: For the set of uncertain variables of source load, j is an uncertain variable of the source load. (wt, pv, e, h), where each source charge uncertainty interval is divided into... Each interval Let t be the predicted source load value. , These represent the positive and negative deviations of the source load at the endpoint of the k-th interval at time t, respectively. The uncertain budget parameters for each source load's uncertain interval are set according to the distribution of uncertain variables; These represent the state variables at time t where the uncertainty variable of the source load in the k-th interval is located in the positive and negative intervals, respectively. The expression for the two-stage robust optimization model is: In the formula: c represents the coefficient column variable corresponding to the objective function of the inner model; B, C, D, G, H, and J are the coefficient matrices of the variables under the corresponding constraints; b, d, f, and g are constant column vectors; and x and y are optimization variables. This is a set of uncertain variables in the overall demand response.

[0017] Furthermore, the carbon trading mechanism parameters involved in the outer model described in step S33 include the basic carbon trading price, the growth rate of the carbon trading price, and the length of the carbon emission range, and their expressions are as follows: In the formula: For tiered carbon trading costs, This is the basic price for carbon trading; The length of the carbon emission range; This represents the growth rate of carbon trading prices.

[0018] Furthermore, the solution steps for step S34 are as follows: Step 1: Input the unit operating parameters in IES, as well as the power of wind power, photovoltaic, electrical load, thermal load, and gas load; Step 2: Initialize the particle population and set the basic carbon trading price, the growth rate of the carbon trading price, and the length of the carbon emission range for the tiered carbon trading mechanism; Step 3: Given a set of uncertain variables as the initial worst-case scenario, set the lower bound LB, upper bound UB, maximum number of iterations, and maximum allowable error ε for the running cost of the final scheduling scheme; Step 4: Substitute the source-load power data under the worst-case operating condition into the main problem formula, solve and update the lower bound LB of the function, and output the optimized start-stop variable value x. k ; Step 5: Set x k Substitute the formulas of the subproblems to solve the problem, and update the source load power data and upper bound UB under the worst operating conditions; Step 6: Calculate the error UB-LB between the two-stage results. If the error is less than ε, exit the run; otherwise, add a new variable y. k+1 Update the main problem constraints and continue iterating until the conditions are met or the maximum number of iterations is reached; Step 7: Calculate the system's carbon emission fitness value and update the global optimal solution and particle parameters; Step 8: Determine if the algorithm termination condition is met. If not, return to step 2. If it is met, the iteration process ends. Step 9: Output the optimized parameters of the tiered carbon trading mechanism and the optimal operation scheduling strategy.

[0019] Furthermore, the expression for the main problem is: In the formula: δ is the running cost of IES; k is the current iteration number. Let this be the return optimization variable for the subproblem after k-1 iterations. and These are the values ​​of the source load uncertainty variable and the comprehensive demand response uncertainty variable after k-1 iterations, respectively. This represents the maximum allowed number of iterations. The expression for the subproblem is: In the formula: These are the dual variables of the constraints related to the subproblem optimization variable y, corresponding to the constraints in lines 4 to 8 of the main problem expression.

[0020] The beneficial effects of the above technical solution are as follows: This invention constructs an integrated electricity-hydrogen-carbon coupling model with comprehensive demand response and introduces a two-layer scheduling model to achieve synergistic optimization of economic costs and carbon emission targets. The inner layer model aims to minimize the total system operating cost, while the outer layer model aims to minimize the system's carbon emissions. The outer layer model actively guides the system towards a low-carbon operating mode by adapting carbon trading mechanism parameters. This synergistic mechanism avoids the one-sidedness of single-objective optimization and can find the best balance between economy and environmental protection while ensuring system reliability, significantly reducing the system's unit energy consumption carbon emission intensity and overall operating costs.

[0021] This invention addresses the dual uncertainties of wind and solar power output and load fluctuations by introducing a multi-interval uncertainty set into the inner-layer model. This effectively overcomes the shortcomings of traditional deterministic optimization or single-interval robust optimization methods, which are either too conservative or overly aggressive, enabling the scheduling strategy to withstand extreme scenarios while flexibly adapting to normal fluctuations.

[0022] The integrated demand response model in this invention performs a detailed classification of the energy satisfaction of various flexible loads and formulates the compensation price to be paid to users in real time based on the energy satisfaction, so as to maximize the enthusiasm of the demand side to participate in the flexible interaction of IES. Attached Figure Description

[0023] Figure 1 This is a diagram of the IES runtime architecture based on EHCC; Figure 2 Diagram of the EHCC collaborative operation model; Figure 3 Flowchart for solving the two-layer model; Figure 4 A schematic diagram of predicted loads for wind, solar, and thermal power. Figure 5 This is a schematic diagram of the power flow scheduling results; Figure 6 This is a schematic diagram of the optimal scheduling result of gas power flow; Figure 7 This is a schematic diagram of the optimal scheduling result for heat power flow; Figure 8 This is a schematic diagram of the optimal scheduling result for hydrogen power flow; Figure 9 This is a schematic diagram of the source load power distribution in scenario 6; Figure 10 A graph showing the relationship between the effects of flexible electrical and thermal loads on the load curve; Figure 11 A diagram illustrating the relationship between the carbon trading base price and costs and carbon emissions; Figure 12 A graph showing the relationship between price growth rate and cost and carbon emissions; Figure 13This diagram illustrates the relationship between carbon trading ranges and costs and carbon emissions. Detailed Implementation

[0024] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0025] It should be noted that, unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0026] To address the uncertainties of IDR and the problem of carbon trading mechanism parameter settings relying on experience and lacking reasonable setting basis, this invention designs an IES scheduling method that considers multiple uncertainties and the coupling of electricity, hydrogen, and carbon, including the following steps: S1. Establish an electricity-hydrogen-carbon coupling model to realize the conversion and storage of different energies; S2. Based on the electricity-hydrogen-carbon coupling model, an integrated demand response model is integrated to generate a coordinated scheduling strategy for the interaction between the integrated energy system and the load side, resulting in an improved electricity-hydrogen-carbon coupling model. S3. Based on the improved electricity-hydrogen-carbon coupling model in step S2, establish a two-layer scheduling model with minimizing the total operating cost of the system as the inner objective and minimizing the carbon emissions of the system as the outer objective, and solve the two-layer scheduling model to obtain the optimal scheduling model. S4. Deploy the optimal scheduling model obtained in step S3 to the actual integrated energy system.

[0027] The steps of this invention will be described in detail below: like Figure 1 As shown, the integrated energy system includes a gas network (GN), P2G units, combined heat and power (CHP) units, gas boilers (GB), a grid, wind power (WT) units, photovoltaic (PV) units, hydrogen fuel cell (HFC) units, hydrogen storage (HS) units, thermal storage (TS) units, gas storage (GS) units, electrical storage (ESS) units, electrolyzers (EL), methane reactors (MR), carbon capture and storage (CCS) equipment, and user electrical loads (ElectricLoad), thermal loads (ThermalLoad), and gas loads (GasLoad). All devices are coupled through the power network, thermal network, and gas network to achieve the coordinated conversion, storage, and distribution of electrical, thermal, and gas energy.

[0028] The gas energy is mainly provided by the gas pipeline network and P2G units, with a portion directly meeting the gas load demand and the remainder consumed by CHP units and GB units. The electricity demand is jointly supplied by the power grid, WT units, PV units, CHP units, and HFC units. The energy storage equipment can regulate the power generation when there are fluctuations. P2G converts excess electricity into hydrogen and natural gas. CHP uses natural gas as fuel to produce electricity and heat, while HFC uses hydrogen as fuel to produce electricity and heat. The thermal energy storage equipment regulates the supply and demand of heat. The carbon source required for the methane reactor is provided by CCS equipment.

[0029] S1. Establish an electro-hydrogen-carbon coupling model.

[0030] The Electro-Hydrogen-Carbon Coupling (EHCC) model comprises three stages: hydrogen production, hydrogen utilization, and hydrogen storage. In hydrogen production, an electrolyzer utilizes surplus electricity from renewable energy sources such as wind and solar power to produce hydrogen. In hydrogen utilization, a CCS device captures CO2 emitted by CHP, GB, and other equipment, and an MR device reacts the captured CO2 with hydrogen to produce methane. The hydrogen can also be directly used in hydrogen fuel cells for thermoelectric power generation, or mixed with natural gas to supply a gas-fuel blending system. In hydrogen storage, the remaining hydrogen is stored in a hydrogen storage tank. In this way, the EHCC model can reduce energy cascade losses and improve the overall energy efficiency of the energy conversion process (EHC). The energy conversion process and the relationships between the various devices involved in the EHCC model are as follows: Figure 2 As shown.

[0031] The EHCC model includes an electrolyzer model, a methane reactor model, an HFC model, and a CCS model, specifically, The electrolyzer model utilizes surplus wind and solar energy to produce hydrogen through water electrolysis. Its expression is: (1) In the formula: Let be the hydrogen energy produced by EL during time period t. For the energy conversion efficiency of EL, Let be the electrical power consumed by EL during time period t. , These represent the minimum and maximum power consumption of EL, respectively.

[0032] The methane reactor model involves the MR reactor reacting a portion of the hydrogen produced by the EL reactor with carbon dioxide collected by the CCS unit to produce methane, which is then fed into the natural gas network. Its expression is as follows: (2) In the formula: Let t be the gas production power of MR during time period t. The hydrogen consumption power of MR The energy conversion efficiency of MR, This represents the amount of CO2 absorbed by the MR. The absorption intensity of CO2, , These represent the upper and lower limits of MR hydrogen consumption power, respectively.

[0033] The HFC model is based on the principle of electro-thermal-hydrogen coupling, which generates electrical and thermal energy through the combustion of hydrogen. Its expression is: (3) In the formula: , These represent the electrical power and thermal power output by the HFC during time period t, respectively. , These are the power generation and heat generation efficiencies of HFC, respectively. The hydrogen consumption power of HFC during time period t. , These represent the upper and lower limits of hydrogen consumption power for HFCs, respectively.

[0034] The CCS model is a method where the CCS consumes electrical energy to capture and store CO2, and its expression is: (4) In the formula: Let be the total energy consumption of the CCS system during time period t. and The fixed power consumption and operating power consumption of the CCS system during time period t. Let t be the amount of CO2 captured by CCS during time period t. The energy consumption coefficient for CCS to capture one unit of CO2. and These represent the amounts of CO2 produced by the GB and CHP devices, respectively. This represents the amount of CO2 consumed by MR during time period t. This represents the amount of carbon dioxide sealed during time period t. The carbon capture rate of CCS.

[0035] Meanwhile, to further enrich hydrogen energy application scenarios and reduce IES carbon emissions, hydrogen was incorporated into both the CHP unit and GB in this invention during operation, resulting in a hydrogen-infused CHP unit model and a hydrogen-infused GB model. The expression for the hydrogen-blended CHP unit model is: (5) In the formula: Let be the electrical energy output by CHP during time period t. The natural gas power input to CHP during time period t. The hydrogen power input to CHP during time period t. The heat energy output by CHP during time period t. The electrical energy conversion efficiency of CHP. The thermal energy conversion efficiency of CHP. and These are the upper and lower limits of the natural gas input power to CHP, respectively. and These represent the upper and lower limits of the hydrogen input power for CHP. and These represent the upper and lower limits of CHP ramp rate. and These are the upper and lower limits of the CHP thermoelectric ratio, respectively.

[0036] The expression for the hydrogen-doped GB model is: (6) In the formula: Let GB be the heat energy output during time period t. Input GB of natural gas power for time period t. The input hydrogen power in GB during time period t. The efficiency of GB conversion into heat energy. and These represent the upper and lower limits of the GB ramp rate, respectively.

[0037] The hydrogen blending ratio in the CHP unit model is 10%~20%, and the hydrogen blending ratio in the GB model is 0%~20%, as expressed in the following formula: (7) In the formula: and These represent the hydrogen blending ratios for the GB and CHP units during time period t, respectively. , The values ​​are calorific values ​​of hydrogen and natural gas, respectively, which are 33.3 and 9.7 (kWh / m3).

[0038] S2. Based on the electricity-hydrogen-carbon coupling model, an integrated demand response model is integrated to obtain an improved electricity-hydrogen-carbon coupling model.

[0039] Based on whether they can participate in IDR, loads are divided into basic loads and flexible loads. The loads considered in the integrated demand response model are flexible loads, including reduceable electrical loads, transferable electrical loads, and flexible thermal loads.

[0040] Reduceable load refers to the portion of the load that IES (Environmental Engineering System) incentivizes users to reduce unnecessary electricity demand during peak hours through economic compensation, thereby alleviating grid pressure and ensuring the safe and stable operation of the system. Its expression is: (8) In the formula: The electrical load power can be reduced during time period t. This is a Boolean variable indicating whether the electrical load is reduced during time period t. The maximum electrical load that can be reduced during time period t. This is the maximum number of times the electrical load response can be reduced.

[0041] Transferable load refers to the portion of the load that IES (Environmental Engineering Society) incentivizes users to shift from peak to off-peak hours through economic compensation, thereby achieving peak shaving and valley filling and balancing electricity supply and demand. Its expression is: (9) In the formula: and These represent the power transferred into and the power transferred out of the transferable electrical load during time period t. and This is a Boolean variable, indicating whether a load transfer response is performed during time period t. and These represent the upper limits of the incoming and outgoing electrical loads during time period t, respectively. This indicates the maximum number of times a transferable electrical load can respond.

[0042] Flexible heat load refers to the portion of the heat load that users can tolerate within a certain range of temperature adjustments during their thermal comfort period without significantly affecting their comfort. Its expression is: (10) In the formula: and This represents the power of the flexible heat load adjusted upwards and downwards during time period t. This represents the maximum adjustable power of the flexible heat load during time period t. and This is a Boolean variable representing whether a heat load adjustment response is initiated. This represents the maximum number of responses to the flexible thermal load.

[0043] The expression for the integrated demand response model is: (11) In the formula: and These are the electrical and thermal loads after IDR; and These are the initial electrical load and initial thermal load before IDR, respectively.

[0044] Meanwhile, considering that in actual operation of IES, in addition to the uncertainty of source load, the uncertainty of IDR itself will also exist, so when integrating the IDR model, considering the uncertainty of IDR helps to improve the robustness of the scheduling method.

[0045] Among them, the uncertainty of the reducible electricity load is reflected in the deviation between the actual user response behavior and the economic incentive, and its expression is: (12) In the formula: To achieve a practical reduction in electrical load response, For deviation parameters, This is a Boolean variable indicating whether a deviation has occurred. For uncertain budget parameters that can reduce electrical load.

[0046] Since the transferable electrical load has the same transfer-in and transfer-out power, the uncertainty of the transferable electrical load only considers the uncertainty of the transfer-in load, and its expression is: (13) In the formula: and These represent the actual transfer-in and transfer-out responses of transferable electrical load, respectively. This is a Boolean variable indicating whether a deviation has occurred. This refers to the uncertain budget parameters for the transferable electrical load.

[0047] The uncertainty of flexible heat load manifests as the deviation between the actual adjustment range of the user's heat load and the planned value of the system, and its expression is: (14) In the formula: , These are the actual upward and downward adjustment values ​​for the flexible heat load. This is a Boolean variable indicating whether a deviation has occurred. For the uncertain budget parameters of flexible heat load.

[0048] S3. Based on the improved electricity-hydrogen-carbon coupling model in step S2, a two-layer scheduling model is established, and the optimal scheduling model is obtained by solving the two-layer scheduling model. The specific steps are as follows: S31. Establish an inner-layer model to obtain the minimum value of the overall operating cost F of the IES.

[0049] For an integrated energy system, its total cost This mainly includes the energy interaction cost between the IES and the power grid. Carbon trading costs IES operation and maintenance costs Gas purchase cost Costs of wind and solar power curtailment and demand response costs Its expression is: (15) The expression for the energy interaction cost between the IES and the power grid is as follows: (16) In the formula: , These are time-of-use electricity purchase prices and electricity sales prices, respectively. , These represent the electricity purchased and sold by IES during time period t, respectively.

[0050] The expression for the operation and maintenance cost of IES: (17) In the formula: This refers to various energy coupling devices; Indicates the type of energy storage (ES). and They represent the first The operation and maintenance cost coefficient and power of energy-coupled devices This represents the operational cost coefficient of Elasticsearch (ES). and They represent the first The charging and discharging power of ES-like devices and For the first The charge and discharge efficiency of ES-like devices.

[0051] The energy coupling equipment includes P2G, CHP and HFC units, and the energy storage types include electrical energy storage (ESS), hydrogen energy storage (HS), thermal energy storage (TS) and gas energy storage (GS).

[0052] The expression for gas purchase cost: (18) In the formula: For the gas purchase price, The gas purchase volume during time period t.

[0053] The expression for the cost of wind and solar power curtailment: (19) In the formula: This refers to the cost coefficient for wind and solar power curtailment. Let be the amount of light wasted during time period t. Let t be the amount of wind curtailment during time period t.

[0054] The expression for demand response cost: (20) In the formula: , and These are the unit incentive costs for reducible, transferable, and flexible heat loads, respectively.

[0055] Meanwhile, the inner model also needs to meet power balance constraints, hydrogen balance constraints, and energy storage operation constraints to ensure the safe and stable operation of the system.

[0056] The expression for the power balance constraint is as follows: (twenty one) (twenty two) In the formula: , The PV and WT power generation at time t are respectively. This represents the upper limit of the electricity that IES can purchase during time period t. Let REG be the gas quantity during time period t. This represents the final output power of the energy storage device.

[0057] The expression for the thermal power balance constraint is: (twenty three) In the formula, This represents the final output power of the thermal energy storage device.

[0058] The expression for the gas power balance constraint is: (twenty four) (25) In the formula: Gas load at time t This represents the final output power of the gas energy storage device. This represents the amount of gas purchased by IES during time period t.

[0059] The expression for the hydrogen balance constraint is: (26) In the formula, This represents the final output power of the hydrogen energy storage device.

[0060] Since the various ES models in IES are similar, this invention provides a unified model for the four types of ES, and the expression for its operational constraints is as follows: (27) In the formula: and The first The charge / discharge power of the ES during time period t. and They represent the first The maximum power of ES charging and discharging and A binary variable used to represent the first... The charge and discharge states of ES during time period t. For the first The final output power of this type of energy storage device, For the first The capacity of the energy storage device during time period t. For the first The capacity of the energy storage device during the time period t-1. and They are the first The upper and lower limits of the capacity of various energy storage devices. For the first The rated capacity of the energy storage device For the first The capacity of this type of energy storage device in the initial period. For the first The capacity of a type of energy storage device at the end of its lifecycle.

[0061] S32. Based on the inner model in step S31, introduce a multi-interval uncertainty set to establish a two-stage robust optimization model.

[0062] The two-stage robust optimization model divides the traditional single-interval uncertain set into multiple sub-interval uncertain sets and assigns different uncertain budget parameters to each sub-interval uncertain set according to the probability distribution characteristics of the uncertain parameters, so as to obtain a multi-interval uncertain set that is closer to the actual situation. This effectively reduces the conservatism of the optimization scheduling method and improves the economy and reliability of the system in uncertain environments.

[0063] According to the definition of the source-load multi-interval uncertainty set, the expression for the uncertainty of wind, solar and load in IES is: (28) In the formula: For the set of uncertain variables of source load, j is an uncertain variable of the source load. (wt, pv, e, h), where each source charge uncertainty interval is divided into... Each interval Let t be the predicted source load value. , These represent the positive and negative deviations of the source load at the endpoint of the k-th interval at time t, respectively. The uncertain budget parameters for each source load's uncertain interval are set according to the distribution of uncertain variables; Let represent the state variables at time t where the uncertainty variable of the source load is located in the positive and negative intervals of the k-th interval, respectively.

[0064] The expression for the two-stage robust optimization model is: (29) In the formula: c is the coefficient column variable corresponding to the objective function (15), B, C, D, G, H and J are the coefficient matrices of the variables under the corresponding constraints, b, d, f and g are constant column vectors, and x and y are optimization variables. This is the set of uncertain variables for IDR.

[0065] S33. Establish an outer-layer optimization model and define the parameters of the carbon trading mechanism.

[0066] The outer model involves carbon trading mechanism parameters including the basic carbon trading price, the growth rate of the carbon trading price, and the length of the carbon emission range. This invention uses the tiered carbon emission cost as the fitness function, with the above three carbon trading mechanism parameter values ​​as inputs and the operating cost and carbon trading cost of the IES as outputs, thereby determining the optimal tiered carbon trading parameters.

[0067] (1) Allocation of carbon trading quotas In the IES (Environmental Engineering System), carbon emissions primarily originate from electricity purchased from the upstream power grid and CO2 emissions from GB (Government Power Generation) and CHP (Content Management System) units. In this invention, the primary carbon allowance allocation method is a free allowance system, and the electricity purchased from the upstream power grid is entirely sourced from thermal power units. The expression for this is: (30) In the formula, , , and These are carbon emission allowances for IES, Energy Purchase, CHP, and GB, respectively. , These are the carbon emission allowances obtained during the generation of electricity and heat, respectively. t represents the purchased electricity during the time period; T is the operating cycle.

[0068] Furthermore, because CCS captures a portion of CO2, this impact on carbon emissions needs to be considered during modeling. The actual carbon emission model expression for IES is: (31) in, , , and These are the actual carbon emissions from IES, Energy Purchasing, CHP, and GB, respectively. This refers to the actual carbon emissions absorbed by the CCS equipment. , Calculate the coefficient for the actual carbon emissions of each piece of equipment; The coefficient for CO2 absorption by CCS equipment.

[0069] After obtaining IES carbon emission allowances and actual carbon emissions, the expression for the actual carbon emissions trading amount is as follows: (32) In the formula, This represents the actual carbon emissions trading amount of IES.

[0070] To further reduce IES carbon emissions, this invention employs a tiered carbon trading pricing mechanism (LCTM). The tiered carbon pricing mechanism divides carbon trading prices into multiple tiers. Each tier corresponds to a different carbon emission level; the higher the emission level, the higher the price per unit of carbon emission. The expression for the tiered carbon trading cost model is as follows: (33) In the formula: For tiered carbon trading costs, This is the basic price for carbon trading; The length of the carbon emission range; This represents the growth rate of carbon trading prices.

[0071] Meanwhile, to enhance the flexibility and adaptability of LCTM in practical applications, this invention introduces an adaptive carbon trading mechanism (ACTM). This mechanism constructs a carbon trading model with dynamically optimizable parameters, enabling the basic carbon trading price, the length of the carbon emission range, and the growth rate of the carbon trading price to be adaptively adjusted according to the real-time operating status of the system and the carbon emission intensity. This allows for more precise guidance of low-carbon scheduling and enhances the dual economic and environmental regulatory effectiveness of carbon market signals on system operation.

[0072] S34. Set up an iterative optimization mechanism for the two-layer model. Specifically, Step 1: Input the unit operating parameters in IES, as well as the power of wind power, photovoltaic, electrical load, thermal load, and gas load; Step 2: Initialize the particle population and set the basic carbon trading price, the growth rate of the carbon trading price, and the length of the carbon emission range for the tiered carbon trading mechanism; Step 3: Given a set of uncertain variables as the initial worst-case scenario, set the lower bound LB, upper bound UB, maximum number of iterations, and maximum allowable error ε for the running cost of the final scheduling scheme; Step 4: Substitute the source load power data under the worst operating conditions into the main problem formula, solve and update the lower bound LB of the function, and output the optimized start-stop variable value xk; Step 5: Substitute xk into the subproblem formula to solve, and update the source load power data and upper bound UB under the worst-case operating condition; Step 6: Calculate the error UB-LB between the two-stage results. If the error is less than ε, exit the run; otherwise, add a new variable yk+1, update the main problem constraints, and continue iterating until the conditions are met or the maximum number of iterations is reached. Step 7: Calculate the system's carbon emission fitness value and update the global optimal solution and particle parameters; Step 8: Determine if the algorithm termination condition is met. If not, return to step 2. If it is met, the iteration process ends. Step 9: Output the optimized parameters of the tiered carbon trading mechanism and the optimal operation scheduling strategy.

[0073] The expression for the main problem is: (34) In the formula: δ is the running cost of IES; k is the current iteration number. Let this be the return optimization variable for the subproblem after k-1 iterations. and These are the values ​​of the source load uncertainty variable and the comprehensive demand response uncertainty variable after k-1 iterations, respectively. This represents the maximum allowed number of iterations.

[0074] Since the subproblem is a double-layered, minimization problem, to facilitate solving it, the inner min form is transformed into a max form using the strong duality theorem. Then, the inner and outer max problems are merged and transformed into a single-layered optimization problem, the expression of which is: (35) In the formula: y is the dual variable of the constraint related to the subproblem optimization variable y, and corresponds to the constraints in lines 4 to 8 of formula (34).

[0075] Due to the existence of bilinear terms and Since the product of a continuous variable and a 0-1 variable is in the form of a product, the subproblem is an NP-hard problem that is difficult to solve directly.

[0076] By linearizing equation (35) using the Big M method, we obtain a subproblem in the form of a mixed-integer linear programming problem, whose expression is: (36) In the formula: , These represent the positive and negative deviations of the power prediction error, respectively. , and For the introduction of continuous auxiliary variables, To account for the overall demand response forecast bias, , Let M be a binary variable, and M be the upper bound of the dual variable, taking a sufficiently large positive integer. The state variables are used to determine whether there is a deviation in the overall demand response. Budget parameters for comprehensive demand response.

[0077] S35. Repeat the iterative process of step S34 until the convergence condition is met, thereby obtaining the optimal carbon trading parameters and the final scheduling scheme.

[0078] S4. Deploy the optimal scheduling model obtained in step S3 to the actual integrated energy system.

[0079] To verify the feasibility of the model and method of this invention, multiple scenarios were set up for comparative analysis. All scenarios were modeled and solved using the CPLEX solver within the Matlab R2021b simulation software and the Yalmip platform. All programs were run on a PC configured with an Intel Core i7 CPU and 32GB of RAM.

[0080] The scheduling cycle for the scenario is 24 hours, with a step size of 1 hour. The main equipment parameters of the IES system are shown in Tables 1-3, the time-of-use electricity price with the upper-level energy grid is shown in Table 4, the purchased natural gas price is 0.35 yuan / kWh, and the predicted power of renewable energy and multi-source loads is as follows: Figure 4 As shown, the unit incentive costs for reduceable electrical load, transferable electrical load, and flexible thermal load are 0.5 yuan / kWh, 0.3 yuan / kWh, and 0.3 yuan / kWh, respectively. The maximum proportion of each type of flexible load is 20%, the population size of the particle swarm optimization algorithm is set to 50, and the maximum number of iterations is 100.

[0081] Table 1 Parameter Settings Table 2 Energy Storage Parameter Settings Table 3 Equipment Operation and Maintenance Cost Coefficient Table 4 Energy Time-of-Use Prices Scenario 1 serves as the baseline scenario, acting as the starting point for comparative analysis. It excludes any of the EHCC, IDR, or ACTM models and does not consider source load uncertainties. IES operation is based on traditional scheduling methods, providing a benchmark for performance improvement in all subsequent scenarios.

[0082] Scenario 2 adds EHCC to the baseline scenario, enabling the coordinated conversion and storage of electrical energy, hydrogen energy and carbon flow, but does not introduce IDR and ACTM, nor consider source-load uncertainty, and is used to analyze the role of EHCC in IES.

[0083] Scenario 3 adds IDR to Scenario 2 to analyze the combined gains of EHCC and IDR on system economy and regulation capability.

[0084] Scenario 4 adds ACTM to Scenario 2 to analyze the impact of the combination of EHCC and ACTM on the system's carbon emissions.

[0085] Scenario 5 builds upon Scenario 2 by combining IDR and EHCC to form a complete synergistic framework of ACTM-EHCC-IDR, but still operates under a deterministic environment. This scenario is used to verify whether, under ideal conditions, the combined action of multiple mechanisms can achieve a synergistic optimal balance between economic costs and carbon emissions.

[0086] Scenario 6 adds a multi-interval uncertainty set to Scenario 5 to test the system's economy, low carbon emissions, and operational robustness under fluctuating conditions in real-world operating environments.

[0087] The above six scenario settings are shown in Table 5.

[0088] Table 5 Settings for Scenarios 1-6 Among them, the parameters of LCTM in scenarios 1-3 are set as follows: the basic carbon trading price is 0.26 yuan / kg, the carbon emission range is 150 kg, and the carbon trading price growth rate is 0.25%.

[0089] In scenarios 4-6, the ACTM parameters are set as follows: the basic carbon trading price range is (0, 0.5], the carbon emission range length is [100, 300], and the carbon trading price growth rate range is (0, 0.8).

[0090] In scenario 6, the distribution parameter of the source-load prediction power error is set as ( , The uncertainty set of wind and solar power generation is divided into three intervals with deviations of ±2%, ±5%, and ±10%, and the uncertainty set of load is divided into two intervals with deviations of ±2% and ±5%. Uncertainty budget parameters are set according to the probability of occurrence in each interval, as follows: , , The maximum deviation of IDR is set to 15%, and the budget parameter is... Set it to 12.

[0091] The optimal scheduling results for electricity, heat, and gas in scenario 5 are as follows: Figures 5 to 8 As shown. From Figure 5 It is evident that the system's power load is primarily supplied by WT and PV outputs, with any shortfall supplemented by HFC, the upstream grid, and CHP units. During the off-peak period from 23:00 to 6:00 the following day, WT output is sufficient, and the system utilizes surplus electricity to drive EL hydrogen production and supply power to CCS, reducing renewable energy curtailment and achieving CO2 capture. During the daytime period from 9:00 to 17:00, PV output is sufficient and the power load is at its peak. The system supplements the power shortage by generating electricity from hydrogen in HS through HFC and hydrogen-blended CHP units; simultaneously, ESS serves as a flexible dispatch resource, discharging power back to the grid, improving the margin and flexibility of system optimization scheduling.

[0092] Combination Figure 6 Analysis reveals that during periods of low electricity demand, heat load demand is higher. HFC equipment, as a low-carbon device, provides heat power to meet part of the heat load demand, while GB, CHP, and TS work together to meet the remaining heat demand. Notably, between 1:00 and 13:00, EL's electricity consumption increases, prompting CHP to increase its output to compensate for the insufficient wind and solar power generation. Therefore, the heat released by CHP also increases, and the excess heat is stored in TS.

[0093] Natural gas dispatch results are as follows Figure 7 As shown. Analysis Figure 7 It can be seen that since both GB and CHP adopt a hydrogen-blended combustion operation mode, natural gas consumption is somewhat alleviated. During peak nighttime electricity and heating load periods, CHP and GB consume a large amount of natural gas to meet heating load demands. However, during the early morning low electricity price period, the system produces natural gas through MR equipment, which can both meet the gas load demand during this period and store excess gas, thereby reducing the cost of purchasing natural gas from external sources.

[0094] Hydrogen scheduling results are as follows Figure 8 As shown. Analysis Figure 8 It is evident that the electro-hydrogen-carbon coupling mechanism enables refined management and efficient utilization of hydrogen energy within the system. Hydrogen is produced and consumed within the IES (Environmental Engineering System), while the HFC (Hydrogen Fuel Cell) generates electricity and provides heat through hydrogen combustion. The EL (Energy Energy Cell) can absorb more RE (Regenerative Reactors) to produce hydrogen, effectively reducing RE reductions. Furthermore, the hydrogen-blended operation of the GB (Gross Gasification) and CHP (Concentrated Hydrogen Production) units not only saves energy procurement costs but also effectively reduces carbon emissions because the emission from hydrogen combustion is water.

[0095] The optimized scheduling results for each scenario are shown in Table 6.

[0096] By comparing Scenario 1 and Scenario 2, the role of EHCC in the system is analyzed. Compared with Scenario 1, the total cost of Scenario 2 is reduced by RMB 558.96, a decrease of 7.43%. After introducing HS, EL significantly improves the absorption capacity of renewable energy power generation, resulting in a reduction of RMB 172 in curtailment costs. At the same time, GHP and GB hydrogen-blended operation effectively reduce system carbon emissions, and the carbon trading cost is reduced by RMB 59.42 accordingly. However, due to the increase in operation and maintenance costs, the overall improvement in system economics is somewhat limited. Although the purchase price of natural gas is lower than that of electricity, the cost savings from choosing to replace electricity with gas are still insufficient to fully offset the carbon trading costs brought about by natural gas combustion, therefore the reduction in system cost is limited.

[0097] By comparing Scenario 3 and Scenario 2, the role of IDR in the system is analyzed. Scenario 3 reduced the total cost by 929.68 yuan and carbon emissions by 232 kg, a reduction of 21.21%. This indicates that multi-mode utilization of hydrogen effectively improves the system's economics and emission reduction performance. Especially when flexible loads are involved in scheduling, the load curve becomes smoother. The reduction in operation and maintenance costs, energy purchase costs, and carbon trading costs outweighs the increase in IDR compensation costs, thus reducing the overall cost. This result proves that IDR can effectively improve the system's total cost and carbon emissions.

[0098] Scenario 4 and Scenario 2 are compared to analyze the role of ACTM in the system. Compared to Scenario 2, the total cost of Scenario 4 is reduced by RMB 486.50, a decrease of 6.98%. Carbon emissions are reduced by 304 kg, a decrease of 27.79%. Carbon emission costs are reduced by RMB 73.8. This indicates that the ACTM mechanism significantly enhances the incentive effect of carbon constraints by optimizing parameters, strengthening the system's low-carbon orientation. However, in this scenario, its emission reduction benefits are insufficient to fully cover the increased operating costs for deep emission reduction.

[0099] A comprehensive comparison was conducted between Scenario 5 and the remaining scenarios to analyze the synergistic effects of ACTM, IDR, and EHCC in the system. The optimized results for the three parameters of LCTM were 0.36 yuan / kg, 41%, and 150 kg, respectively. Compared with Scenario 1, the total cost of Scenario 5 was reduced by 1680.88 yuan, or 22.34%. Carbon emissions were reduced by 613 kg, or 44.01%. Carbon emission costs were reduced by 146.95 yuan, or 34.69%. This demonstrates that the synergistic effect of multiple mechanisms can help IES achieve low-carbon goals and improve economic efficiency.

[0100] Finally, to analyze the promoting effect of different mechanisms on the low-carbon economic dispatch of PIES, scenarios 4 and 3 were compared. Compared with scenario 3, the total cost of scenario 4 increased by 443.18 yuan, but carbon emissions decreased by 72 kg. This indicates that although the ACTM mechanism is better than the IDR mechanism in terms of emission reduction effect, it is difficult to maintain a balance in terms of economics. As can be seen from Table 6, the IDR mechanism focuses more on improving the economics of the system, while the ACTM mechanism is more inclined to reduce carbon emissions.

[0101] Table 6. Scheduling Costs and Carbon Emissions under Scenario 1-5 To verify the advantages of the two-stage RO model based on multi-interval uncertainty sets in dealing with source-load power uncertainty, the following three scheduling scenarios were set up for comparative analysis.

[0102] Scenario 6: Based on Scenario 5, source load uncertainty is considered, and a multi-interval uncertainty set is used for description; Scenario 7: Based on Scenario 5, stochastic optimization is considered to handle uncertainty; Scenario 8: Based on Scenario 5, traditional robust optimization is used, where the uncertainty budget parameters for WT and load are set to 12, and the uncertainty budget parameter for PV is set to 6. The operational optimization results for the three scenarios are shown in Table 7.

[0103] Table 7. Scheduling Costs and Carbon Emissions under Scenario 6-8 As shown in Table 7, Scenario 7 has the lowest operating cost at 5720.18 yuan, while Scenario 8 has the highest operating cost at 6565.39 yuan. Scenario 7 generates a specific scenario for solution based solely on the probability distribution function of uncertain variables. Although it has the best economic performance, its robustness is poor because it does not cover extreme scenarios. Scenario 8 adopts the worst-case scenario assumption, i.e., taking the lower limit of the deviation for renewable energy output and the upper limit of the deviation for load. Although this approach can ensure the safe operation of the system within all possible fluctuation ranges, its overly conservative approach and excessively high reserve capacity configuration result in poor economic efficiency. Compared with the traditional RO model, Scenario 6 further considers the distribution characteristics of uncertain parameters. By subdividing the fluctuation range of uncertain parameters, the optimized source-load power is closer to the actual situation, rather than always being at the extreme boundary. This method effectively reduces the economic cost caused by the overly conservative approach of traditional RO while ensuring the robustness of the scheduling results. Figure 9 The source-load power distribution of Scenario 6 is shown in both the prediction scenario and the multi-interval uncertainty set scenario.

[0104] like Figure 9As shown, in the worst-case scenario of traditional RO optimization, the output distributions of WT and PV are located at the lower limit of the uncertain fluctuation range, while the load power is at the upper limit. In this extremely conservative scenario, IES needs to purchase electricity from the grid or increase the output of CHP units to compensate for the insufficient renewable energy generation and meet the overestimated load demand. This assumption leads to a significant increase in operating costs. Although this method provides stronger robustness, in practical engineering, the probability of uncertain parameters being located at the interval boundaries is extremely small. Therefore, this invention introduces a multi-interval uncertainty set to keep the robustly optimized source-load power away from the fluctuation range boundaries, making it closer to reality and thus avoiding economic losses caused by setting excessive reserve capacity.

[0105] To analyze the impact of different IDR types on the results of IES, the following two scenarios were set up for comparative analysis.

[0106] Scenario 9: Scenario without IDR; Scenario 10: Scenario considering the participation of reduceable electrical load, transferable electrical load, and flexible thermal load in IDR. The scheduling cost under the worst-case scenario for each scenario is shown in Table 8.

[0107] Table 8. Scheduling costs for scenarios 9-10 As shown in Table 8, Scenario 9 does not consider IDR (Independent Energy Demand) participation in system scheduling, resulting in higher energy purchase costs and total costs. In contrast, Scenario 10 adjusts electricity and heat demand through IDR, generating an incentive cost of 389.25 yuan, but significantly reducing external energy purchase costs.

[0108] Compared with scenario 9, the total cost and energy purchase cost of scenario 10 decreased by RMB 456.47 and RMB 564.13 respectively, indicating that the proposed IDR strategy effectively utilizes load flexibility, smooths out fluctuations in wind and solar power generation and load demand, and thus improves the economics of the system.

[0109] Figure 10 The impact of flexible loads on the load curve is shown. It can be seen that under the IDR mechanism, the electricity load during high-price periods is reduced or shifted to off-peak periods, resulting in a smoother load curve and a significantly reduced peak-to-valley difference. The peak-to-valley difference is reduced to 49 kW, a decrease of 13.57%. Simultaneously, the heat load is also flexibly adjusted according to heating demand and incentive measures, with reduced fluctuations, helping to alleviate the energy supply pressure on the IES (Environmental Engineering System). This indicates that users can proactively optimize their energy consumption habits based on energy price signals and incentive subsidies, helping the system achieve peak shaving and valley filling while ensuring comfort, thereby improving overall operating efficiency and economy.

[0110] Considering the impact of different uncertain budget parameters on the IES scheduling results, this invention sets multiple sets of uncertain budget parameters for source loads for comparison, in order to analyze the impact of different uncertain budget parameters for source loads on the system optimization scheduling results. The results are shown in Table 9.

[0111] Table 9. The Influence of Uncertain Adjustment Parameters on Optimization Results As shown in Table 9, when all uncertainty budget parameters are set to 0, the RO optimization model degenerates into a deterministic optimization model, at which point the total system cost is lowest. As the uncertainty budget parameters gradually increase, the system's carbon emissions, carbon trading costs, and total cost all show an upward trend. This indicates that the scheduling scheme becomes more conservative. Specifically, an increased uncertainty budget parameter means an increase in the fluctuation range and number of periods in the source-load prediction interval, leading to greater uncertainty in the system's power balance. To ensure the reliability of system operation, it is necessary to increase reserve capacity or adjust the scheduling strategy, thereby increasing the system's operating costs. Therefore, in actual scheduling, the uncertainty budget parameters should be set reasonably to achieve a balance between system economy and robustness.

[0112] Different carbon trading mechanism parameters significantly affect IES scheduling results. This invention analyzes the impact of these parameters on total cost and carbon emissions based on the parameter settings of the adaptive carbon trading mechanism in Scenario 6, to verify the effectiveness of the proposed adaptive carbon trading mechanism. The results are as follows: Figure 11 , Figure 12 and Figure 13 As shown.

[0113] Figure 11 This study demonstrates the impact of different carbon trading base prices on the total system cost and carbon emissions, assuming a carbon emission range of 150 kg and a price growth rate of 41%. It shows that as the carbon trading base price increases, the total system cost exhibits a monotonically increasing trend, while carbon emissions gradually decrease, thus reducing carbon trading costs. Specifically, when the carbon trading base price is between 0.1 and 0.34 yuan / kg, the reduction in carbon emissions is relatively gradual. This is because the cost of IES energy purchase is significantly higher than the carbon trading cost at this point, lacking sufficient incentive for low-carbon scheduling. When the carbon trading base price is between 0.34 and 0.42 yuan / kg, carbon emissions drop sharply, indicating that carbon cost has become the dominant factor in scheduling decisions. The system reduces its dependence on high-carbon energy sources by adjusting the output of internal equipment to lower carbon emissions. When the carbon trading base price is between 0.42 and 0.5 yuan / kg, carbon emissions gradually stabilize, indicating that the output of each device in the system and carbon emissions have reached a stable state.

[0114] Figure 12This study demonstrates the impact of different price growth rates on total system cost and carbon emissions, assuming a carbon trading base price of 0.36 yuan / kg and a carbon emission range of 150 kg. It shows that as the carbon trading price growth rate increases, the total system cost rises continuously, while carbon emissions initially decrease rapidly and then gradually level off. When the price growth rate is below 40%, carbon emissions are highly sensitive to changes in the growth rate, resulting in a significant decrease; however, when the price growth rate exceeds 40%, the change in carbon emissions tends to level off. This is because carbon costs now constitute a large proportion of the total cost, further increases have a diminishing marginal impact on scheduling decisions, and carbon emissions enter a stable phase.

[0115] Figure 13 This study demonstrates the impact of different carbon emission interval lengths on the total system cost and carbon emissions, assuming a carbon trading base price of 0.36 yuan / kg and a price growth rate of 41%. The results show that as the carbon trading interval length increases, the total system cost gradually decreases, while carbon emissions initially rise slowly, then increase sharply, and eventually stabilize. When the carbon emission interval length is short, the carbon trading mechanism has a strong constraining effect, keeping carbon emissions at a low level. As the carbon emission interval length increases, the carbon constraint effect weakens, carbon emissions rise, and the total cost decreases due to the reduced carbon costs. With the continuous increase in the carbon emission interval length, the change in carbon emissions gradually approaches saturation, and the system eventually enters a high-emission stable state.

[0116] The above analysis shows that the carbon trading base price, price growth rate, and carbon emission range length can all effectively regulate the economic viability and low-carbon performance of IES (Environmentally Friendly Systems), and there is a clear synergistic and trade-off relationship among the three. By precisely setting these parameters, LCTM (Limited-Time Management) can effectively guide IES to achieve economical and low-carbon scheduling.

[0117] Finally, it should be noted that any parts of this invention not described in detail are prior art. Those skilled in the art will understand that the above descriptions are merely preferred embodiments of the invention and are not intended to limit the invention. Although the invention has been described in detail with reference to the foregoing examples, those skilled in the art can still modify the technical solutions described in the foregoing examples or make equivalent substitutions for some of the technical features. All modifications and equivalent substitutions made within the spirit and principles of the invention should be included within the scope of protection of the invention.

Claims

1. An IES scheduling method considering multiple uncertainties and electro-hydrogen-carbon coupling, characterized in that, Includes the following steps: S1. Establish an electricity-hydrogen-carbon coupling model to realize the conversion and storage of different energies; S2. Based on the electricity-hydrogen-carbon coupling model, an integrated demand response model is integrated to generate a coordinated scheduling strategy for the interaction between the integrated energy system and the load side, resulting in an improved electricity-hydrogen-carbon coupling model. S3. Based on the improved electricity-hydrogen-carbon coupling model in step S2, establish a two-layer scheduling model with minimizing the total operating cost of the system as the inner objective and minimizing the carbon emissions of the system as the outer objective, and solve the two-layer scheduling model to obtain the optimal scheduling model. The specific steps are as follows: S31. Establish an inner-layer model to obtain the minimum overall operating cost of IES; S32. Based on the inner model in step S31, introduce a multi-interval uncertainty set and establish a two-stage robust optimization model. S33. Establish an outer-layer optimization model to obtain the minimum total carbon emissions of the integrated energy system and define the parameters of the carbon trading mechanism; S34. Set up an iterative optimization mechanism for a two-layer model. The outer layer model passes carbon trading parameters to the inner layer model. The inner layer model solves the problem based on these parameters and feeds back the resulting system carbon emissions to the outer layer model. The outer layer model adjusts and optimizes the carbon trading parameters based on this feedback. S35. Repeat the iterative process of step S34 until the convergence condition is met, thereby obtaining the optimal carbon trading parameters and the final scheduling scheme. S4. Deploy the optimal scheduling model obtained in step S3 to the actual integrated energy system.

2. The IES scheduling method considering multiple uncertainties and electro-hydrogen-carbon coupling according to claim 1, characterized in that, The electricity-hydrogen-carbon coupling model in step S1 includes three stages: hydrogen production, hydrogen use, and hydrogen storage. In the hydrogen production stage, the electrolyzer utilizes surplus electricity from wind and solar power to produce hydrogen. In the hydrogen use stage, carbon capture and storage equipment captures CO2 emitted from cogeneration and gas-fired boiler equipment, and uses a methane reactor to react the captured CO2 with hydrogen to produce methane. The hydrogen is then directly used in hydrogen fuel cells for cogeneration or mixed with natural gas to supply a gas-fired hydrogen blending system. In the hydrogen storage stage, the remaining hydrogen is stored in hydrogen storage tanks.

3. The IES scheduling method considering multiple uncertainties and electro-hydrogen-carbon coupling according to claim 2, characterized in that, Hydrogen is incorporated into the combined heat and power (CHP) and gas-fired boiler equipment used in the hydrogen production process, with the proportions being 10%~20% and 0%~20%, respectively.

4. The IES scheduling method considering multiple uncertainties and electro-hydrogen-carbon coupling according to claim 1, characterized in that, The integrated demand response model in step S2 first models the flexible loads in the integrated energy system, then introduces a dynamic compensation pricing mechanism, and finally obtains the optimal flexible load scheduling strategy. The flexible loads include reduceable electrical loads, transferable electrical loads, and flexible heat loads. The expression for reducing electrical load is: In the formula: The electrical load power can be reduced during time period t. This is a Boolean variable indicating whether the electrical load is reduced during time period t. The maximum electrical load that can be reduced during time period t. This is the maximum number of times the electrical load response can be reduced; The expression for transferable electrical load is: In the formula: and These represent the incoming and outgoing power of the transferable electrical load during time period t, respectively. and This is a Boolean variable, indicating whether a load transfer response is performed during time period t. and These represent the upper limits of the incoming and outgoing electrical loads during time period t, respectively. Indicates the maximum number of times a transferable electrical load can respond; The expression for flexible heat load is: In the formula: and This represents the power of the flexible heat load adjusted upwards and downwards during time period t. This represents the maximum adjustable power of the flexible heat load during time period t. and This is a Boolean variable representing whether a heat load adjustment response is initiated. This represents the maximum number of responses to the flexible thermal load.

5. The IES scheduling method considering multiple uncertainties and electro-hydrogen-carbon coupling according to claim 1, characterized in that, The expression for the integrated demand response model is: In the formula: and These are the electrical load and thermal load after comprehensive demand response; and These are the initial electrical load and initial thermal load before the integrated demand response.

6. The IES scheduling method considering multiple uncertainties and electro-hydrogen-carbon coupling according to claim 1, characterized in that, The expression for the objective function of the inner model in step S31 is: In the formula: The cost of energy interaction between IES and the power grid. For carbon trading costs, For the operation and maintenance costs of IES, For gas purchase costs, To account for the cost of wind and solar power curtailment Cost of responding to demand.

7. An IES scheduling method considering multiple uncertainties and electro-hydrogen-carbon coupling according to claim 1 or 6, characterized in that, The expression for the multi-interval uncertain set mentioned in step S32 is: In the formula: For the set of uncertain variables of source load, For the source load uncertain variable, j (wt, pv, e, h), where each source charge uncertainty interval is divided into... Each interval The source load prediction value at time t. , These represent the positive and negative deviations of the source load at the endpoint of the k-th interval at time t, respectively. The uncertain budget parameters for each source load's uncertain interval are set according to the distribution of uncertain variables; These represent the state variables at time t where the uncertainty variable of the source load in the k-th interval is located in the positive and negative intervals, respectively. The expression for the two-stage robust optimization model is: In the formula: c represents the coefficient column variable corresponding to the objective function of the inner model; B, C, D, G, H, and J are the coefficient matrices of the variables under the corresponding constraints; b, d, f, and g are constant column vectors; and x and y are optimization variables. This is a set of uncertain variables in the overall demand response.

8. The IES scheduling method considering multiple uncertainties and electro-hydrogen-carbon coupling according to claim 1, characterized in that, The carbon trading mechanism parameters involved in the outer model described in step S33 include the basic carbon trading price, the growth rate of the carbon trading price, and the length of the carbon emission range, and their expressions are as follows: In the formula: For tiered carbon trading costs, This is the basic price for carbon trading; The length of the carbon emission range; This represents the growth rate of carbon trading prices.

9. The IES scheduling method considering multiple uncertainties and electro-hydrogen-carbon coupling according to claim 1, characterized in that, The solution steps for step S34 are as follows: Step 1: Input the unit operating parameters in IES, as well as the power of wind power, photovoltaic, electrical load, thermal load, and gas load; Step 2: Initialize the particle population and set the basic carbon trading price, the growth rate of the carbon trading price, and the length of the carbon emission range for the tiered carbon trading mechanism; Step 3: Given a set of uncertain variables as the initial worst-case scenario, set the lower bound LB, upper bound UB, maximum number of iterations, and maximum allowable error ε for the running cost of the final scheduling scheme; Step 4: Substitute the source-load power data under the worst-case operating condition into the main problem formula, solve and update the lower bound LB of the function, and output the optimized start-stop variable value x. k ; Step 5: Set x k Substitute the formulas of the subproblems to solve the problem, and update the source load power data and upper bound UB under the worst operating conditions; Step 6: Calculate the error UB-LB between the two-stage results. If the error is less than ε, exit the run; otherwise, add a new variable y. k+1 Update the main problem constraints and continue iterating until the conditions are met or the maximum number of iterations is reached; Step 7: Calculate the system's carbon emission fitness value and update the global optimal solution and particle parameters; Step 8: Determine if the algorithm termination condition is met. If not, return to step 2. If it is met, the iteration process ends. Step 9: Output the optimized parameters of the tiered carbon trading mechanism and the optimal operation scheduling strategy.

10. The IES scheduling method considering multiple uncertainties and electro-hydrogen-carbon coupling according to claim 9, characterized in that, The expression for the main problem is: In the formula: δ is the running cost of IES; k is the current iteration number. Let this be the return optimization variable for the subproblem after k-1 iterations. and These are the values ​​of the source load uncertainty variable and the comprehensive demand response uncertainty variable after k-1 iterations, respectively. This represents the maximum allowed number of iterations. The expression for the subproblem is: In the formula: It is the dual variable of the constraint related to the optimization variable y of the subproblem.

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