Multi-objective optimization method and system for cascade emission reduction of integrated energy system based on network configuration limiting and dynamic hydrogen-doped separation

Abstract

The present application relates to the field of energy optimization, and discloses a kind of based on distribution network limiting amplitude and dynamic hydrogen blending separation integrated energy system cascade emission reduction multi-objective optimization method and system, comprising: the dynamic hydrogen blending model considering mixed gas calorific value and safety constraint is constructed;Establish the operation constraint condition and decision variable of dynamic hydrogen blending model, using-constraint method, under the premise of meeting dynamic hydrogen blending proportion constraint condition and multi-energy balance constraint condition, respectively with system operation cost minimization and minimization total carbon emission as objective function, and with carbon emission constraint, energy purchase constraint and equipment model constraint as variable constraint boundary, the dynamic hydrogen blending model is carried out double objective optimization solution, and the pareto optimal solution set is generated;Pareto optimal solution set includes multiple different carbon emission upper limit corresponding system operation strategy;According to the pareto optimal solution set, output hydrogen blending proportion sequence and equipment output data under each system operation strategy, to determine optimal operation scheme.

Description

Technical Field

[0001] This invention relates to the field of energy optimization technology, and in particular to a multi-objective optimization method and system for cascade emission reduction in an integrated energy system based on distribution network limiting and dynamic hydrogen blending separation. Background Technology

[0002] Distributed Integrated Energy Systems (DIES) have become a key vehicle for energy transition due to their ability to achieve multi-energy complementarity and cascade utilization of cooling, heating, and electricity. Hydrogen, as a clean energy source, can effectively reduce carbon emissions and enhance the absorption capacity of renewable energy when blended into HCNG (High-Hydrogen Gas Generation) networks. However, hydrogen production costs are high, and different blending ratios significantly impact the operating efficiency, safety, and overall economic efficiency of gas-fired equipment. Current integrated energy system scheduling often neglects the dynamic adjustability of the hydrogen-to-natural gas blending ratio, resulting in the system's inability to find the optimal balance between economic costs and environmental benefits when facing electricity price fluctuations, changes in carbon tax policies, and uncertainties in load demand. Therefore, there is an urgent need to develop an evaluation and optimization method that considers dynamic hydrogen blending mechanisms to quantify the impact of different hydrogen blending strategies on system operating indicators and guide the planning and operation of low-carbon industrial parks.

[0003] Traditional methods for dealing with integrated hydrogen energy systems typically employ a "fixed hydrogen blending ratio" approach. This involves setting the hydrogen blending ratio as a constant and building an optimization model with the single objective of minimizing operating costs. In this model, the fuel calorific value and carbon emission coefficients of equipment such as gas turbines and gas boilers are treated as fixed parameters, and the output plan for each device is calculated using the Gurobi solver. While this traditional method simplifies the calculation process, it lacks flexibility. In actual operation, when renewable energy (wind and solar) is abundant and electricity prices are extremely low, increasing hydrogen production and the hydrogen blending ratio is beneficial for absorbing wind and solar curtailment; conversely, when hydrogen costs are too high or during peak load periods, reducing the hydrogen blending ratio may be more economical. The fixed ratio ignores this time-varying characteristic of "source-load-price," resulting in the system failing to achieve optimal economic and environmental performance, and potentially leading to unnecessary and expensive costs due to maintaining a high hydrogen blending ratio for extended periods. Summary of the Invention

[0004] To address the aforementioned problems, the purpose of this invention is to provide a multi-objective optimization method and system for cascade emission reduction in integrated energy systems based on the separation of distribution network limiting and dynamic hydrogen blending. By constructing a dynamic hydrogen blending model that considers the calorific value and safety constraints of the mixed gas, and combining it with a multi-objective optimization algorithm, the hydrogen blending ratio at different times can be flexibly adjusted under the premise of meeting the system's energy supply needs. The dual impact of this mechanism on the total system cost and carbon emissions is quantitatively evaluated, providing a scientific basis for the low-carbon economic dispatch of the system.

[0005] To achieve the above objectives, in a first aspect, the technical solution adopted by the present invention is as follows: a multi-objective optimization method for cascade emission reduction of an integrated energy system based on the separation of distribution network limiting and dynamic hydrogen blending, comprising: obtaining the operating parameters of the integrated energy system, constructing a dynamic hydrogen blending model considering the calorific value and safety constraints of the mixed gas; the system includes an energy supply side, an energy conversion side, an energy storage side, and a load side; establishing the operating constraints and decision variables of the dynamic hydrogen blending model, using the grid power purchase, natural gas purchase, hydrogen purchase, output power of each equipment, and energy storage status at each time period as decision variables; The operational constraints include dynamic hydrogen blending ratio constraints, multi-energy balance constraints, carbon emission constraints, energy purchase constraints, and equipment model constraints. The dynamic hydrogen blending ratio constraints configure the hydrogen blending ratio for each time period as a continuous decision variable limited by a safety upper limit, and perform terminal separation of the mixed gas purchased from the energy supply side to supply the separated pure hydrogen to pre-set zero-carbon power generation equipment. The multi-energy balance constraints are used to construct the coupling relationship between electricity, heat, and cooling energy sources, and constrain the power balance between the supply and load sides in each time period to determine the coordinated output scheme of each device. - The constraint method, under the premise of satisfying the dynamic hydrogen blending ratio constraint and the multi-energy balance constraint, takes minimizing system operating cost and minimizing total carbon emissions as objective functions, and uses carbon emission constraints, energy purchase constraints and equipment model constraints as variable constraint boundaries to perform bi-objective optimization on the dynamic hydrogen blending model, generating a Pareto optimal solution set; the Pareto optimal solution set contains multiple system operation strategies corresponding to different carbon emission upper limits; based on the Pareto optimal solution set, the hydrogen blending ratio sequence and equipment output data under each system operation strategy are output to determine the optimal operation scheme.

[0006] Furthermore, the dynamic hydrogen doping ratio constraint is as follows: ; in, The amount of hydrogen injected at time t; Let t be the amount of natural gas consumed at time t; This represents the upper limit of the maximum volumetric hydrogen doping ratio.

[0007] Furthermore, the multi-energy balance constraints include electrical power balance constraints, thermal power balance constraints, cold power balance constraints, and natural gas balance constraints; The power balance constraint is: ; The thermal power balance constraint is: ; ; The cold power balance constraint is: ; The natural gas balance constraint is: ; in, for Real-time power purchase capacity; for Always supplying CHP with grid power; for Battery power at all times; for Real-time user-side electrical load; for The power consumption of a ground source heat pump at all times; for The power consumption of the electric chiller; The heat exchange efficiency of the heat exchanger; for Total heating power at any given time; for User-side thermal load at any given time; for The heating power of the cogeneration system at all times; for The heating capacity of the gas boiler at all times; for The heating capacity of a ground source heat pump at all times; for The heat release capacity of the thermal storage tank at all times; for The cooling capacity of a constant-time absorption chiller; The energy efficiency ratio of an electric chiller; for The power consumption of the electric chiller; for Constant client-side cold load; for Total natural gas consumption at any given time; for Real-time user-end direct-fired natural gas consumption; for The amount of natural gas consumed by the gas-fired boiler at any given time.

[0008] Furthermore, the carbon emission constraints are: ; Purchase constraints are: ; in, For the first The upper limit of total carbon emissions is set for this iteration; T is the total number of time periods within the optimization cycle; for Real-time power purchase capacity; The maximum electrical power that can be purchased from the power grid; The average carbon emission factor of the power grid; Let t be the power purchased at time t; Carbon emission factors from natural gas combustion; This refers to the volume of natural gas consumed. For the purchase of natural gas; The maximum amount of natural gas that can be purchased.

[0009] The equipment model constraints include constraints on hydrogen storage tanks, hydrogen cogeneration units, electrical energy storage devices, boilers, thermal storage tanks, ground source heat pumps, and absorption chillers.

[0010] Furthermore, a dual-objective optimization solution is performed on the dynamic hydrogen doping model, including: The optimization objective is set as minimizing total carbon emissions. The decision variables are optimized across the entire time domain to calculate the theoretically lowest achievable carbon emission value of the system. And record the operating costs in this scenario; The optimization objective is switched to minimizing the total operating cost, and the constraint on total carbon emissions is removed from the constraint set. The optimization is then performed again to calculate the maximum carbon emissions of the system under the economically optimal mode. This establishes the search range for the multi-objective Pareto frontier in the carbon emission dimension. ; Based on the preset Pareto sampling point number parameter Divide the search interval into equal parts For each interval, the iteration step size of the carbon emission constraint is calculated, generating a sequence containing... A decreasing sequence of carbon emission ceilings for each carbon emission value; Iterate through the carbon emission limit sequence. For each carbon emission limit value in the sequence, add a carbon emission constraint to the original model that the total carbon emissions do not exceed the limit value, and lock the objective function to minimize the total operating cost. Call the Gurobi solver to solve the currently constructed single-objective subproblem and determine whether there is an optimal solution. If there is an optimal solution, extract the minimum objective function value and the actual total carbon emissions corresponding to the optimal solution, and store these two values ​​as a coordinate point of the Pareto solution set. If there is no optimal solution, skip the current carbon emission limit value and continue to traverse the next value. After the traversal is complete, all the collected coordinate points are sorted and connected according to carbon emissions from smallest to largest to form a discrete economic cost-carbon emission Pareto front curve.

[0011] Furthermore, the iteration step size for carbon emission constraints is: ; In the formula, This is the iteration step size.

[0012] Furthermore, based on the Pareto optimal solution set, the hydrogen doping ratio sequence and equipment output data under each system operating strategy are output to determine the optimal operating scheme, including: Based on the change in the slope of the Pareto frontier curve, the inflection point of the sudden change in marginal emission reduction cost is identified, and the upper limit of carbon emissions corresponding to the inflection point is used as the preset threshold. Extract the hourly hydrogen doping ratio sequence from each optimal solution, divide the normal operation stage and the deep emission reduction stage according to the preset threshold, and determine the hydrogen doping ratio range corresponding to each stage. Based on the Pareto frontier curve, the inflection point of marginal emission reduction costs, and the range of hydrogen blending ratios at each stage, an optimal operation strategy report is generated. The report includes hourly equipment output plans, energy storage charging and discharging plans, and hydrogen blending ratio sequences for each non-dominated solution, allowing decision-makers to select the appropriate operation scheme based on their actual carbon budget.

[0013] Secondly, the technical solution adopted by this invention is: a multi-objective optimization system for cascade emission reduction in a comprehensive energy system based on the separation of distribution network limiting and dynamic hydrogen doping, comprising: The model building module obtains the operating parameters of the integrated energy system and constructs a dynamic hydrogen-blending model that considers the calorific value of the mixed gas and safety constraints. The system includes the energy supply side, energy conversion side, energy storage side, and load side. The constraint module establishes the operational constraints and decision variables for the dynamic hydrogen blending model, using the grid purchase volume, natural gas purchase volume, hydrogen purchase volume, power output of each device, and energy storage status as decision variables for each time period. The operational constraints include dynamic hydrogen blending ratio constraints, multi-energy balance constraints, carbon emission constraints, energy purchase constraints, and device model constraints. The dynamic hydrogen blending ratio constraints are used to configure the hydrogen blending ratio for each time period as a continuous decision variable limited by a safety upper limit, and to perform terminal separation of the mixed gas purchased from the energy supply side so that the separated pure hydrogen can be supplied to the preset zero-carbon power generation equipment. The multi-energy balance constraints are used to construct the coupling relationship of multiple energy sources such as electricity, heat, and cooling, and to constrain the power balance between the supply side and the load side in each time period to determine the coordinated output scheme of each device. Optimize the solver module and adopt - Constraint method: Under the premise of satisfying dynamic hydrogen doping ratio constraints and multi-energy balance constraints, the dynamic hydrogen doping model is optimized by minimizing system operating cost and minimizing total carbon emissions as objective functions, respectively, and carbon emission constraints, energy purchase constraints and equipment model constraints are used as variable constraint boundaries to generate Pareto optimal solution set; Pareto optimal solution set contains system operation strategies corresponding to multiple different carbon emission upper limits. The output module outputs the hydrogen doping ratio sequence and equipment output data under each system operation strategy based on the Pareto optimal solution set, in order to determine the optimal operation scheme.

[0014] Thirdly, the technical solution adopted by the present invention is: a computer-readable storage medium for storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any of the methods described above.

[0015] Fourthly, the technical solution adopted by the present invention is: a computing device comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods described above.

[0016] The present invention has the following advantages due to the adoption of the above technical solutions: 1. This invention utilizes - The constraint method transforms the total carbon emissions into dynamic hard constraints for iterative solution, accurately constructs the Pareto frontier curve of "operating cost-carbon emissions", objectively quantifies the nonlinear trade-off between economic costs and environmental benefits, and enables decision-makers to flexibly choose the system operation scheme that best meets current needs.

[0017] 2. This invention breaks through the constraints of traditional fixed hydrogen blending, significantly reducing emission reduction costs through a dynamic hydrogen blending mechanism: In the traditional fixed-ratio hydrogen blending mode, whenever the system consumes natural gas, it must be forced to consume expensive hydrogen, resulting in extremely high ineffective fuel costs. This invention introduces a dynamic hydrogen blending continuous decision-making mechanism of 0-20% in the constraint settings. The system can autonomously determine the optimal hydrogen blending ratio for each time period based on time-of-use electricity prices and real-time carbon reduction needs: lowering the hydrogen blending ratio during normal periods to save costs, and increasing the hydrogen blending ratio to achieve zero-carbon support when carbon constraints tighten or the grid is constrained. This mechanism completely removes the rigid binding between hydrogen and natural gas consumption, avoiding the financial waste caused by blindly using hydrogen.

[0018] 3. Multi-energy Synergy and Staged Emission Reduction in this Invention: This invention reveals and utilizes a staged emission reduction mechanism of "ground source heat pump priority, hydrogen energy as a backup." Simulation results show that the introduction of ground source heat pumps brings excellent emission reduction benefits: in the initial stage of emission reduction, the system does not need to immediately start expensive hydrogen energy, but instead prioritizes scheduling heat pumps to utilize off-peak electricity at night for large-scale heating, directly replacing the fossil energy consumption of traditional gas boilers. The model can automatically identify differences in equipment energy efficiency, prioritizing the scheduling of high-COP ground source heat pumps for electrification in the initial stage of emission reduction, and only initiating the high-cost hydrogen blending mechanism in the deep emission reduction stage. This synergistic strategy avoids the premature introduction of expensive hydrogen energy, maximizing the tiered utilization of system energy efficiency.

[0019] 4. The model of this invention has high accuracy and closely matches engineering practice: The method takes into account the state of charge / discharge (SOC) of the battery, the loss characteristics of the thermal / hydrogen storage tanks, the start-up and shutdown logic of the CHP unit, and strict constraints on the balance of cold, heat, and electricity. This ensures that the generated scheduling scheme is not only theoretically optimal, but also feasible in engineering practice, avoiding the risks of wind and solar curtailment or insufficient power supply caused by simplified constraints. Attached Figure Description

[0020] Figure 1 This is a flowchart of the multi-objective optimization method for cascade emission reduction of an integrated energy system based on the separation of distribution network limiting and dynamic hydrogen doping in an embodiment of the present invention; Figure 2 This is the Pareto curve of total carbon emissions versus total operating cost in an embodiment of the present invention; Figure 3 This is a diagram showing the annual electrical and thermal energy supply structure in an embodiment of the present invention; Figure 4 This is a typical intraday electrical and thermal energy supply structure diagram in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0022] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0023] In one embodiment of the present invention, a multi-objective optimization method for cascade emission reduction in a comprehensive energy system based on the separation of distribution network limiting and dynamic hydrogen doping is provided. In this embodiment, as shown... Figure 1 As shown, the method includes the following steps: 1) Obtain the operating parameters of the integrated energy system and construct a dynamic hydrogen blending model that considers the calorific value of the mixed gas and safety constraints; the system includes the energy supply side, energy conversion side, energy storage side and load side.

[0024] 2) Establish the operational constraints and decision variables of the dynamic hydrogen blending model, using the grid purchase volume, natural gas purchase volume, hydrogen purchase volume, power output of each device, and energy storage status as decision variables for each time period. The operational constraints include dynamic hydrogen blending ratio constraints, multi-energy balance constraints, carbon emission constraints, energy purchase constraints, and device model constraints. The dynamic hydrogen blending ratio constraints are used to configure the hydrogen blending ratio for each time period as a continuous decision variable limited by a safety upper limit, and to perform terminal separation of the mixed gas purchased from the energy supply side so that the separated pure hydrogen can be supplied to the preset zero-carbon power generation equipment. The multi-energy balance constraints are used to construct the coupling relationship of multiple energy sources such as electricity, heat, and cooling, and to constrain the power balance between the supply side and the load side in each time period to determine the coordinated power output scheme of each device.

[0025] 3) Adopt - The constraint method, under the premise of satisfying the dynamic hydrogen doping ratio constraint and the multi-energy balance constraint, takes minimizing the system operating cost and minimizing the total carbon emissions as objective functions, respectively, and uses carbon emission constraints, energy purchase constraints and equipment model constraints as variable constraint boundaries to perform bi-objective optimization on the dynamic hydrogen doping model, generating a Pareto optimal solution set; the Pareto optimal solution set contains multiple system operation strategies corresponding to different carbon emission upper limits.

[0026] 4) Based on the Pareto optimal solution set, output the hydrogen doping ratio sequence and equipment output data under each operating strategy of the system to determine the optimal operating scheme.

[0027] In step 1) above, the energy supply side consists of: the power grid, natural gas grid, and hydrogen supply. The energy conversion side consists of: gas turbines, gas boilers, ground source heat pumps, electric chillers, and absorption chillers. The energy storage side consists of: batteries, thermal storage tanks, and cold storage tanks. The load side consists of: electrical, cooling, and heating loads.

[0028] In step 2) above, in order to ensure the safe operation of the gas equipment, a maximum upper limit for the hydrogen blending ratio by volume is set. (For example, 20%), then the dynamic hydrogen doping ratio constraint condition is: ; in, The amount of hydrogen injected at time t; Let t be the amount of natural gas consumed at time t; This is the maximum upper limit for the volumetric hydrogen blending ratio. This constraint ensures that the volumetric proportion of hydrogen in the blended fuel does not exceed the set threshold at any given time.

[0029] In step 2) above, the multi-energy balance constraints include electrical power balance constraints, thermal power balance constraints, cold power balance constraints, and natural gas balance constraints.

[0030] Electrical power includes load (user electrical load, heat pump, electric cooling) purchased from the grid, CHP generation (supplying the grid), and battery discharge. The power balance constraint is: ; The sum of heat generated by each piece of equipment (direct supply) and heat released by the heat storage tank, after being converted according to the heat exchanger efficiency, meets the heat load. The heat power balance constraint is: ; ; If the combined cooling output of the electric chiller and the absorption chiller satisfies the cooling load, then the cooling power balance constraint is: ; The purchased natural gas is divided into two parts: one for direct use by users and the other for boiler use. Therefore, the natural gas balance constraint is: ; in, for Real-time power purchase capacity; for Always supplying CHP with grid power; for Battery power at all times; for Real-time user-side electrical load; for The power consumption of a ground source heat pump at all times; for The power consumption of the electric chiller; The heat exchange efficiency of the heat exchanger; for Total heating power at any given time; for User-side thermal load at any given time; for The heating power of the cogeneration (CHP) system at any given time; for The heating capacity of the gas boiler at any given time; for The heating capacity of a ground source heat pump at all times; for The heat release capacity of the thermal storage tank at all times; for The cooling capacity of a constant-time absorption chiller; The energy efficiency ratio of an electric chiller; for The power consumption of the electric chiller; for Constant client-side cold load; for Total natural gas consumption at any given time; for Real-time user-end direct-fired natural gas consumption; for The amount of natural gas consumed by the gas-fired boiler at any given time.

[0031] In step 2) above, hydrogen combustion is considered to have zero carbon emissions, therefore the carbon emission constraint is: ; The energy purchase constraints are as follows: the amount of electricity purchased at any given time cannot exceed the maximum electricity purchase limit, and the amount of gas purchased cannot exceed the maximum download flow.

[0032] ; in, For the first The upper limit of total carbon emissions is set for this iteration; T is the total number of time periods within the optimization cycle; for Real-time power purchase capacity; The maximum electrical power that can be purchased from the power grid; The average carbon emission factor of the power grid; Let t be the power purchased at time t; Carbon emission factors from natural gas combustion; This refers to the volume of natural gas consumed. For the purchase of natural gas; The maximum amount of natural gas that can be purchased.

[0033] In this embodiment, the equipment model constraints include constraints on hydrogen storage tanks, hydrogen cogeneration units, electrical energy storage devices, boilers, thermal storage tanks, ground source heat pumps, and absorption chillers.

[0034] (1) Constraints of hydrogen storage tanks: Hydrogen is injected into the hydrogen storage tank at a certain proportion of the volume of natural gas, and the hydrogen storage tank must meet its own limitations at all times: (1) in, This refers to the amount of hydrogen injected. This represents the percentage of hydrogen relative to natural gas. The density of hydrogen gas, , , These represent the current hydrogen storage tank capacity, hydrogen injection volume, and hydrogen consumption, respectively. This represents the hydrogen storage tank volume at the previous moment. This represents the maximum hydrogen storage capacity of the hydrogen storage tank.

[0035] (2) Constraints of hydrogen cogeneration units: Hydrogen cogeneration units use hydrogen as fuel to produce both electricity and heat. Their operation is constrained by the state of the hydrogen storage system, their own operating characteristics, and the system's energy requirements.

[0036] (2) in, This represents the hydrogen consumption for CHP. The hydrogen consumption rate of CHP For the operation of a combined heat and power (CHP) unit, , These are the CHP power generation efficiency and heat generation efficiency, respectively. , They supply power to the grid and batteries respectively for CHP. , These are direct heating from CHP and heat from thermal storage tanks, respectively.

[0037] The CHP device should have corresponding startup logic and operational constraints, as follows: (3) in, and These represent the start or stop events of CHP, respectively, and the start or stop events may occur at different times.

[0038] To avoid damage to equipment lifespan caused by frequent start-stop cycles, the model sets fuel limits for CHP startup and minimum continuous operating time constraints.

[0039] (4) (3) Constraints on energy storage devices: In the model, the energy storage device acts as an "electricity buffer" or "time shifter." It can store electrical energy when there is a power surplus or low electricity prices, and release electrical energy when there is a power shortage or high electricity prices, thereby improving the system's flexibility and economy.

[0040] (5) in, , , These represent the current battery capacity, CHP storage capacity, and battery power supply, respectively. This represents the battery capacity at the previous moment. , , These are the battery's self-loss coefficient, charging efficiency, and discharging efficiency, respectively. , as well as These are the device's maximum energy storage capacity, the maximum energy storage capacity of the CHP, and the maximum discharge capacity of the battery, respectively. This is the minimum energy storage capacity of the device. The unit time step.

[0041] (4) Boiler constraints: The heat output of a boiler is determined by the amount of natural gas it consumes and its own thermal efficiency. The heat generated can be used directly or stored in a thermal storage tank.

[0042] (6) in, , These are the total heat generated by the boiler and the amount stored in the heat storage tank, respectively. Due to the low calorific value of natural gas, For boiler efficiency.

[0043] (5) Constraints of thermal storage tank: The thermal storage tank can receive heat from multiple heating devices in the system and has two heat outlets: one is for direct heating via a heat exchanger, and the other is for cooling by an absorption chiller.

[0044] (7) in, , These represent the heat stored in the heat storage tank and the heat output at the current moment, respectively. This represents the heat generated in the storage tank at the previous moment. The heat stored in the geothermal storage tank , These represent the heat transferred from the heat storage tank to the heat exchanger and the refrigeration unit, respectively. and These are the upper and lower limits for thermal storage tanks. A binary variable representing the heat release mode. It is a maximum value.

[0045] (6) Ground source heat pump constraints: A ground source heat pump is a high-efficiency electric heating device. It consumes electricity to "pump" low-grade heat energy from the ground into the system, achieving efficient heating.

[0046] (8) in, To provide heat for the ground source heat pump, The electrical power consumed by the ground source heat pump The heat supplied to the heat exchanger by the ground source heat pump The heat supplied to the storage tank by the ground source heat pump The coefficient of performance (COP) of a ground source heat pump. This represents the maximum heat output of the ground source heat pump.

[0047] (7) Constraints of absorption chillers: The constraints of absorption chillers are as follows: (9) in, The coefficient of performance (COP) of an absorption chiller. The heat is supplied to the heat storage tank by the absorption chiller.

[0048] In step 3) above, the dynamic hydrogen doping model is optimized using a dual-objective approach, which includes the following steps: 3.1) Set the optimization objective as minimizing total carbon emissions, call the mixed-integer linear programming solver to optimize the decision variables over the entire time domain, and calculate the theoretically lowest carbon emission value that the system can achieve. And record the operating costs in this scenario.

[0049] 3.2) Switch the optimization objective to minimizing the total operating cost, remove the constraint on total carbon emissions from the constraint set, and call the mixed-integer linear programming solver again to find the maximum carbon emissions of the system under the economically optimal mode. This establishes the search range for the multi-objective Pareto frontier in the carbon emission dimension. .

[0050] 3.3) Based on the preset Pareto sampling point number parameter Search range Divided into equal parts For each interval, the iteration step size of the carbon emission constraint is calculated, generating a sequence containing... A decreasing sequence of carbon emission caps ,in correspond , correspond .

[0051] In this embodiment, the iteration step size for carbon emission constraints is: ; In the formula, This is the iteration step size.

[0052] 3.4) Initialize a result list to store non-dominated solutions, and iterate through the carbon emission ceiling sequence. For each carbon emission ceiling value in the sequence... ( Add a carbon emission constraint to the original model that the total carbon emissions do not exceed the upper limit, and lock the objective function to minimize the total operating cost while keeping all physical operating constraints unchanged.

[0053] 3.5) Call the Gurobi solver to solve the currently constructed single-objective subproblem and determine whether an optimal solution exists; if an optimal solution exists, extract the minimum objective function value corresponding to the optimal solution. and actual total carbon emissions The two values ​​are stored as coordinates of a Pareto solution set and added to the results list; if no optimal solution is found, the current carbon emission limit is skipped and the process continues to the next value. value.

[0054] In this embodiment, the Gurobi solver meets strict carbon emission limits. Within the feasible domain, search for the optimal combination of all decision variables across the entire time domain, including the amount of natural gas purchased, the hydrogen blending ratio, the output of the ground source heat pump, and the charging and discharging strategy of the energy storage device at each moment.

[0055] 3.6) After the traversal is complete, collect all the coordinates. The carbon emissions are sorted from smallest to largest and connected to form a discrete economic cost-carbon emission Pareto front curve.

[0056] In step 4) above, based on the Pareto optimal solution set, the hydrogen doping ratio sequence and equipment output data under each system operation strategy are output to determine the optimal operation scheme, including the following steps: 4.1) Based on the change in the slope of the Pareto frontier curve, identify the inflection point of the sudden change in marginal emission reduction cost, and use the upper limit of carbon emissions corresponding to the inflection point as the preset threshold.

[0057] 4.2) Extract the hourly hydrogen doping ratio sequence from each optimal solution, divide the normal operation stage and the deep emission reduction stage according to the preset threshold, and determine the hydrogen doping ratio range corresponding to each stage.

[0058] 4.3) Based on the Pareto frontier curve, the inflection point of marginal emission reduction cost, and the range of hydrogen blending ratios at each stage, the optimal operation strategy report is output. The report includes the hourly equipment output plan, energy storage charging and discharging plan, and hydrogen blending ratio sequence corresponding to each non-dominated solution, so that decision-makers can select the corresponding operation scheme according to the actual carbon budget.

[0059] In summary, this invention is based on - A multi-objective joint optimization framework using constraint methods: An optimization model with system operating cost and total carbon emissions as dual objectives is constructed. By transforming carbon emissions into variable constraint boundaries, iterative solutions are obtained and a complete Pareto optimal solution set is output, replacing the subjective weighted summation method. This provides accurate marginal cost quantification data for different carbon reduction depths. The invention also includes a dynamic hydrogen blending constraint mechanism under grid-end hybrid transmission-terminal separation: The hydrogen blending ratio in the upstream pipeline is set as a continuous decision variable limited by a safe volume upper limit. At the terminal, physical constraints separate the mixed gas into natural gas and pure hydrogen, ensuring that pure hydrogen is exclusively supplied to CHP to achieve zero-carbon power generation. This constraint mechanism realizes on-demand hydrogen fuel allocation and dynamic evolution, effectively overcoming the high rigidity of hydrogen production costs in fixed hydrogen blending models.

[0060] Furthermore, this invention employs a highly efficient tiered emission reduction control strategy based on the electrothermal coupling of ground source heat pumps. The system's underlying model integrates the high energy efficiency ratio conversion constraints and multi-energy flow balance equations of the ground source heat pump. Through adaptive algorithm optimization, a tiered collaborative scheduling strategy is spontaneously formed, prioritizing electrification emission reduction using ground source heat pumps and dynamically adding hydrogen as a safety net during the deep decarbonization period. This maximizes the low-cost emission reduction potential of heat pumps in the initial stages of carbon emission mitigation.

[0061] Furthermore, this invention incorporates a boundary response mechanism that couples the physical limitations of the distribution network with time-of-use pricing: a rigid constraint on the maximum power purchase limit of the distribution network is added to the model. Based on normal peak shaving and valley filling guided by time-of-use pricing, the system can quickly respond to peak demand caused by extreme cold weather, ensuring energy supply security under stringent physical network constraints.

[0062] In one embodiment of the present invention, a multi-objective optimization system for cascade emission reduction in an integrated energy system based on the separation of distribution network limiting and dynamic hydrogen doping is provided, comprising: The model building module obtains the operating parameters of the integrated energy system and constructs a dynamic hydrogen-blending model that considers the calorific value of the mixed gas and safety constraints. The system includes the energy supply side, energy conversion side, energy storage side, and load side. The constraint module establishes the operational constraints and decision variables for the dynamic hydrogen blending model, using the grid purchase volume, natural gas purchase volume, hydrogen purchase volume, power output of each device, and energy storage status as decision variables for each time period. The operational constraints include dynamic hydrogen blending ratio constraints, multi-energy balance constraints, carbon emission constraints, energy purchase constraints, and device model constraints. The dynamic hydrogen blending ratio constraints are used to configure the hydrogen blending ratio for each time period as a continuous decision variable limited by a safety upper limit, and to perform terminal separation of the mixed gas purchased from the energy supply side so that the separated pure hydrogen can be supplied to the preset zero-carbon power generation equipment. The multi-energy balance constraints are used to construct the coupling relationship of multiple energy sources such as electricity, heat, and cooling, and to constrain the power balance between the supply side and the load side in each time period to determine the coordinated output scheme of each device. Optimize the solver module and adopt - Constraint method: Under the premise of satisfying dynamic hydrogen doping ratio constraints and multi-energy balance constraints, the dynamic hydrogen doping model is optimized by minimizing system operating cost and minimizing total carbon emissions as objective functions, respectively, and carbon emission constraints, energy purchase constraints and equipment model constraints are used as variable constraint boundaries to generate Pareto optimal solution set; Pareto optimal solution set contains system operation strategies corresponding to multiple different carbon emission upper limits. The output module outputs the hydrogen doping ratio sequence and equipment output data under each system operation strategy based on the Pareto optimal solution set, in order to determine the optimal operation scheme.

[0063] In the above embodiments, the dynamic hydrogen doping ratio constraint condition is as follows: ; in, The amount of hydrogen injected at time t; Let t be the amount of natural gas consumed at time t; This represents the upper limit of the maximum volumetric hydrogen doping ratio.

[0064] In the above embodiments, the multi-energy balance constraints include electrical power balance constraints, thermal power balance constraints, cold power balance constraints, and natural gas balance constraints. The power balance constraint is: ; The thermal power balance constraint is: ; ; The cold power balance constraint is: ; The natural gas balance constraint is: ; in, for Real-time power purchase capacity; for Always supplying CHP with grid power; for Battery power at all times; for Real-time user-side electrical load; for The power consumption of a ground source heat pump at all times; for The power consumption of the electric chiller; The heat exchange efficiency of the heat exchanger; for Total heating power at any given time; for User-side thermal load at any given time; for The heating power of the cogeneration system at all times; for The heating capacity of the gas boiler at all times; for The heating capacity of a ground source heat pump at all times; for The heat release capacity of the thermal storage tank at all times; for The cooling capacity of a constant-time absorption chiller; The energy efficiency ratio of an electric chiller; for The power consumption of the electric chiller; for Constant client-side cold load; for Total natural gas consumption at any given time; for Real-time user-end direct-fired natural gas consumption; for The amount of natural gas consumed by the gas-fired boiler at any given time.

[0065] In the above embodiments, the carbon emission constraints are: ; Purchase constraints are: ; in, For the first The upper limit of total carbon emissions is set for this iteration; T is the total number of time periods within the optimization cycle; for Real-time power purchase capacity; The maximum electrical power that can be purchased from the power grid; The average carbon emission factor of the power grid; Let t be the power purchased at time t; Carbon emission factors from natural gas combustion; This refers to the volume of natural gas consumed. For the purchase of natural gas; The maximum amount of natural gas that can be purchased.

[0066] The equipment model constraints include constraints on hydrogen storage tanks, hydrogen cogeneration units, electrical energy storage devices, boilers, thermal storage tanks, ground source heat pumps, and absorption chillers.

[0067] In the above embodiments, the dynamic hydrogen doping model is solved by a dual-objective optimization method, including: The optimization objective is set as minimizing total carbon emissions. The decision variables are optimized across the entire time domain to calculate the theoretically lowest achievable carbon emission value of the system. And record the operating costs in this scenario; The optimization objective is switched to minimizing the total operating cost, and the constraint on total carbon emissions is removed from the constraint set. The optimization is then performed again to calculate the maximum carbon emissions of the system under the economically optimal mode. This establishes the search range for the multi-objective Pareto frontier in the carbon emission dimension. ; Based on the preset Pareto sampling point number parameter Divide the search interval into equal parts For each interval, the iteration step size of the carbon emission constraint is calculated, generating a sequence containing... A decreasing sequence of carbon emission ceilings for each carbon emission value; Iterate through the carbon emission limit sequence. For each carbon emission limit value in the sequence, add a carbon emission constraint to the original model that the total carbon emissions do not exceed the limit value, and lock the objective function to minimize the total operating cost. Call the Gurobi solver to solve the currently constructed single-objective subproblem and determine whether there is an optimal solution. If there is an optimal solution, extract the minimum objective function value and the actual total carbon emissions corresponding to the optimal solution, and store these two values ​​as a coordinate point of the Pareto solution set. If there is no optimal solution, skip the current carbon emission limit value and continue to traverse the next value. After the traversal is complete, all the collected coordinate points are sorted and connected according to carbon emissions from smallest to largest to form a discrete economic cost-carbon emission Pareto front curve.

[0068] In the above embodiments, the iteration step size for carbon emission constraints is: ; In the formula, This is the iteration step size.

[0069] In the above embodiments, based on the Pareto optimal solution set, the hydrogen doping ratio sequence and equipment output data under each system operation strategy are output to determine the optimal operation scheme, including: Based on the change in the slope of the Pareto frontier curve, the inflection point of the sudden change in marginal emission reduction cost is identified, and the upper limit of carbon emissions corresponding to the inflection point is used as the preset threshold. Extract the hourly hydrogen doping ratio sequence from each optimal solution, divide the normal operation stage and the deep emission reduction stage according to the preset threshold, and determine the hydrogen doping ratio range corresponding to each stage. Based on the Pareto frontier curve, the inflection point of marginal emission reduction costs, and the range of hydrogen blending ratios at each stage, an optimal operation strategy report is generated. The report includes hourly equipment output plans, energy storage charging and discharging plans, and hydrogen blending ratio sequences for each non-dominated solution, allowing decision-makers to select the appropriate operation scheme based on their actual carbon budget.

[0070] The system provided in this embodiment is used to execute the above-described method embodiments. For specific processes and details, please refer to the above embodiments, which will not be repeated here.

[0071] In this example, since local climate conditions directly affect the energy characteristics of residential areas, a residential area located in a cold region is selected as the research object. In the original system, the main energy sources for the residential area are electricity and natural gas. Electricity is mainly used for cooling and power-consuming equipment (air conditioners, fans, and lighting), while natural gas is mainly used for heating and cooking. The assumptions made in the simulation are as follows: (1) Power supply is achieved through unified dispatch of power from integrated power grid, batteries and cogeneration equipment. Heat supply can come from different sources, such as boilers, thermal storage devices, cogeneration equipment and geothermal energy.

[0072] (2) There will be no simultaneous occurrence of cooling load and heating load. In fact, the cooling load accounts for a small proportion in this case.

[0073] The main parameters of the model are shown in Table 1.

[0074] Table 1 Residential Area and Modeling Parameters

[0075] Baseline scenario: Ground source heat pump power is set to 0, and the maximum allowable hydrogen blending ratio is set to 0 (pure natural gas + purchased electricity). The objective function is to minimize carbon emissions.

[0076] Optimized scenario: The maximum power of the ground source heat pump is increased to 150kW, and the maximum allowable hydrogen doping ratio is increased to 20%.

[0077] Comparison Scenario 1: Using the lowest operating cost as the objective function, solve for the total carbon emissions when the operating cost is lowest. This carbon emissions amount is the upper limit of the carbon emission range.

[0078] Comparison Scenario 2: Set the total carbon emission limit to 2,052,385 kg, and solve for the lowest operating cost as the objective function.

[0079] Comparison Scenario 3: Set the total carbon emission limit to 2,031,941 kg, and solve for the lowest operating cost as the objective function.

[0080] Comparison Scenario 4: Set the total carbon emission limit to 2,011,497 kg, and solve for the lowest operating cost as the objective function.

[0081] Comparison Scenario 5: Set the total carbon emission limit to 1,991,053 kg, and solve for the lowest operating cost as the objective function.

[0082] Comparison Scenario 6: Using the minimum carbon emissions as the objective function, solve for the total carbon emissions at the minimum carbon emissions, which is the lower limit of the carbon emissions range.

[0083] Using the Gurobi solver, through - The constraint method establishes six total carbon emission caps (Scenario 1 to Scenario 6), and the simulation results are shown in Table 2: Table 2 Simulation Results

[0084] This invention was verified through full-condition simulation in a demonstration park of a comprehensive energy system, utilizing... - The constraint method constructed a Pareto optimal solution set containing six typical scenarios. In-depth analysis of the solution results verified the significant advantages of the proposed dynamic hydrogen doping mechanism and multi-energy complementarity strategy in coordinating economic efficiency and environmental friendliness.

[0085] (1) Quantitative assessment of the economic-environmental Pareto frontier like Figure 2 As shown, this invention successfully constructed the Pareto frontier curve of the system's "economic cost-carbon emissions," quantitatively revealing the nonlinear trade-off between the two.

[0086] Cost and Emission Evolution Range: The system operation results cover the entire range from the "economically optimal mode" (Scenario 1) to the "deeply low-carbon mode" (Scenario 6). In the economically optimal mode, the system's annual operating cost is the lowest, at 2.3079 million yuan, with carbon emissions of 2102.99 tons, corresponding to a hydrogen blending ratio of 0%. During this stage, the system mainly meets the load by optimizing the output ratio of the ground source heat pump and the gas boiler, and the high-cost hydrogen blending mechanism has not yet been activated.

[0087] As carbon emission constraints tighten, the system operating point shifts to the lower right along the Pareto front. In the scenario with the lowest carbon emissions (Scenario 6), the system's carbon emissions drop to a minimum of 1972.73 tons, a reduction of 130.257 tons (approximately 6.1%) compared to the economically optimal model. Consequently, the system's annual operating cost increases to RMB 2.5213 million, an increase of approximately RMB 213,400.

[0088] Therefore, this invention accurately calculates that the average marginal emission reduction cost of achieving deep decarbonization in the park is approximately RMB 2,633 per ton of CO2, providing precise data support for users to formulate carbon budgets.

[0089] (2) A graded emission reduction mechanism of "ground source heat pumps as the priority and dynamic hydrogen doping as a backup". By comparing the power output structure of equipment in different scenarios, this invention reveals an efficient "two-stage" graded emission reduction mechanism, which effectively avoids the cost waste caused by traditional fixed hydrogen doping.

[0090] The first phase is the electrification replacement phase: During the transition from scenario 1 to scenario 3, with the initial introduction of carbon constraints, the system did not immediately use hydrogen on a large scale, but instead prioritized exploring the potential of ground-source heat pumps. Data shows that during the transition from scenario 1 to scenario 3, the proportion of hydrogen blending remained at a low level, but carbon emissions had already decreased significantly. This is because the model prioritizes the use of high-efficiency ground-source heat pumps to replace gas-fired boilers for heating, and uses electricity to replace fossil fuels. The emission reduction cost in this phase is extremely low, and the reduction in carbon emissions is basically linearly related to the increase in cost.

[0091] The second stage is the deep hydrogen energy substitution stage: In the deep emission reduction range of scenarios 3 to 6, relying solely on ground source heat pumps can no longer meet the stringent carbon quotas, and the system forces hydrogen energy as a fixed energy supply. In this stage, the average hydrogen blending ratio rises rapidly, reaching 4.2%, 12.4%, and even approaching the upper limit of 19.8%. At this point, the economic cost required to reduce carbon emissions by one ton rises rapidly.

[0092] like Figure 3 and Figure 4 As shown, the evolution of the energy supply structure verifies the aforementioned graded emission reduction and control mechanism from multiple scenario scales and intraday time series scales. Figure 3 As shown, with the gradual tightening of carbon emission constraints, the proportion of ground-source heat pumps in the heating network has continued to rise, replacing a large portion of the heating share from natural gas boilers and completing the electrification conversion of the heating system. Simultaneously, the dependence of the power grid on purchased electricity has shown a decreasing trend, and the share of electricity and heat supplied by hydrogen-blended cogeneration units has increased accordingly, demonstrating the carbon reduction effect of hydrogen energy. Figure 4 As shown, in the power grid, purchased grid electricity provides the system's base load, while hydrogen-blended cogeneration units operate primarily during peak daytime electricity consumption periods, reducing the system's demand for external conventional electricity during peak hours. In the heating network, natural gas boilers and ground source heat pumps jointly provide the base heat load, while thermal storage tanks release heat centrally during peak electricity price periods, playing a role in regulating the heat load across time periods.

[0093] In summary, the method proposed in this invention achieves... - The constraint method transforms the multi-objective optimization problem into a series of single-objective optimization problems, generating not only an intuitive Pareto front curve but also realizing a coordinated operation strategy of "ground source heat pump base load heating + dynamic proportional hydrogen doping peak shaving" through an embedded physical model. - The Pareto front curve generated by the constraint method clearly shows the dynamic evolution path of the hydrogen doping ratio (0% to 20%) as carbon emission reduction targets tighten, providing accurate quantitative basis for park managers to formulate optimal operation plans.

[0094] In one embodiment of the present invention, a computing device is provided, which may be a terminal and may include: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors, and the one or more programs include instructions for performing any of the methods in the above embodiments.

[0095] Furthermore, when the aforementioned instructions can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0096] In one embodiment of the present invention, a computer program product is provided, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, and when the program instructions are executed by a computer, the computer is able to perform the methods provided in the above-described method embodiments.

[0097] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided, which stores server instructions that cause a computer to perform the methods provided in the above embodiments.

[0098] The computer-readable storage medium provided in the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be described again here.

[0099] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0100] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-objective optimization method for cascade emission reduction in an integrated energy system based on distribution network limiting and dynamic hydrogen doping separation, characterized in that, include: The operating parameters of the integrated energy system are obtained, and a dynamic hydrogen doping model considering the calorific value of the mixed gas and safety constraints is constructed. The system includes the energy supply side, energy conversion side, energy storage side, and load side. The operational constraints and decision variables of the dynamic hydrogen blending model are established, with the power purchase volume of the grid, the purchase volume of natural gas, the purchase volume of hydrogen, the output power of each equipment, and the energy storage status of each time period as decision variables. The operational constraints include dynamic hydrogen blending ratio constraints, multi-energy balance constraints, carbon emission constraints, energy purchase constraints, and equipment model constraints. The dynamic hydrogen blending ratio constraints are used to configure the hydrogen blending ratio of each time period as a continuous decision variable limited by a safety upper limit, and to perform terminal separation of the mixed gas purchased by the energy supply side so that the separated pure hydrogen can be supplied to the preset zero-carbon power generation equipment. The multi-energy balance constraints are used to construct the coupling relationship of multiple energy sources such as electricity, heat, and cooling, and to constrain the power balance between the supply side and the load side in each time period to determine the coordinated output scheme of each equipment. use - Constraint method: Under the premise of satisfying dynamic hydrogen doping ratio constraints and multi-energy balance constraints, the dynamic hydrogen doping model is optimized by minimizing system operating cost and minimizing total carbon emissions as objective functions, respectively, and carbon emission constraints, energy purchase constraints and equipment model constraints are used as variable constraint boundaries to generate Pareto optimal solution set; Pareto optimal solution set contains system operation strategies corresponding to multiple different carbon emission upper limits. Based on the Pareto optimal solution set, output the hydrogen doping ratio sequence and equipment output data under each system operation strategy to determine the optimal operation scheme.

2. The multi-objective optimization method for cascade emission reduction in a comprehensive energy system based on the separation of distribution network limiting and dynamic hydrogen doping as described in claim 1, characterized in that, The dynamic hydrogen doping ratio constraint is: ； in, The amount of hydrogen injected at time t; Let t be the amount of natural gas consumed at time t; This represents the upper limit of the maximum volumetric hydrogen doping ratio.

3. The multi-objective optimization method for cascade emission reduction in a comprehensive energy system based on the separation of distribution network limiting and dynamic hydrogen doping as described in claim 1, characterized in that, Multi-energy balance constraints include electrical power balance constraints, thermal power balance constraints, cooling power balance constraints, and natural gas balance constraints; The power balance constraint is: ； The thermal power balance constraint is: ； ； The cold power balance constraint is: ； The natural gas balance constraint is: ； in, for Real-time power purchase capacity; for Always supplying CHP with grid power; for Battery power at all times; for Real-time user-side electrical load; for The power consumption of a ground source heat pump at all times; for The power consumption of the instantaneous refrigerator; The heat exchange efficiency of the heat exchanger; for Total heating power at any given time; for User-side thermal load at any given time; for The heating power of the cogeneration system at all times; for The heating capacity of the gas boiler at all times; for The heating capacity of a ground source heat pump at all times; for The heat release capacity of the thermal storage tank at all times; for The cooling capacity of a constant-time absorption chiller; The refrigeration efficiency ratio of an electric chiller; for The power consumption of the electric chiller; for Constant client-side cold load; for Total natural gas consumption at any given time; for Real-time user-end direct-fired natural gas consumption; for The amount of natural gas consumed by the gas-fired boiler at any given time.

4. The multi-objective optimization method for cascade emission reduction in a comprehensive energy system based on the separation of distribution network limiting and dynamic hydrogen doping as described in claim 1, characterized in that, Carbon emission constraints are: ； Purchase constraints are: ； in, For the first The upper limit of total carbon emissions is set for this iteration; T is the total number of time periods within the optimization cycle; for Real-time power purchase capacity; The maximum electrical power that can be purchased from the power grid; The average carbon emission factor of the power grid; Let t be the power purchased at time t; Carbon emission factors from natural gas combustion; This refers to the volume of natural gas consumed. For the purchase of natural gas; The maximum amount of natural gas that can be purchased; The equipment model constraints include constraints on hydrogen storage tanks, hydrogen cogeneration units, electrical energy storage devices, boilers, thermal storage tanks, ground source heat pumps, and absorption chillers.

5. The multi-objective optimization method for cascade emission reduction in a comprehensive energy system based on the separation of distribution network limiting and dynamic hydrogen doping as described in claim 1, characterized in that, A dual-objective optimization solution is performed on the dynamic hydrogen doping model, including: The optimization objective is set as minimizing total carbon emissions. The decision variables are optimized across the entire time domain to calculate the theoretically lowest achievable carbon emission value of the system. And record the operating costs in this scenario; The optimization objective is switched to minimizing the total operating cost, and the constraint on total carbon emissions is removed from the constraint set. The optimization is then performed again to calculate the maximum carbon emissions of the system under the economically optimal mode. This establishes the search range for the multi-objective Pareto frontier in the carbon emission dimension. ; Based on the preset Pareto sampling point number parameter Divide the search interval into equal parts For each interval, the iteration step size of the carbon emission constraint is calculated, generating a sequence containing... A decreasing sequence of carbon emission ceilings for each carbon emission value; Iterate through the carbon emission limit sequence. For each carbon emission limit value in the sequence, add a carbon emission constraint to the original model that the total carbon emissions do not exceed the limit value, and lock the objective function to minimize the total operating cost. Call the Gurobi solver to solve the currently constructed single-objective subproblem and determine whether there is an optimal solution. If there is an optimal solution, extract the minimum objective function value and the actual total carbon emissions corresponding to the optimal solution, and store these two values ​​as a coordinate point of the Pareto solution set. If there is no optimal solution, skip the current carbon emission limit value and continue to traverse the next value. After the traversal is complete, all the collected coordinate points are sorted and connected according to carbon emissions from smallest to largest to form a discrete economic cost-carbon emission Pareto front curve.

6. The multi-objective optimization method for cascade emission reduction in a comprehensive energy system based on the separation of distribution network limiting and dynamic hydrogen doping as described in claim 5, characterized in that, The iteration step size for carbon emission constraints is: ； In the formula, This is the iteration step size.

7. The multi-objective optimization method for cascade emission reduction in a comprehensive energy system based on the separation of distribution network limiting and dynamic hydrogen doping as described in claim 5, characterized in that, Based on the Pareto optimal solution set, the hydrogen doping ratio sequence and equipment output data under each system operating strategy are output to determine the optimal operating scheme, including: Based on the change in the slope of the Pareto frontier curve, the inflection point of the sudden change in marginal emission reduction cost is identified, and the upper limit of carbon emissions corresponding to the inflection point is used as the preset threshold. Extract the hourly hydrogen doping ratio sequence from each optimal solution, divide the normal operation stage and the deep emission reduction stage according to the preset threshold, and determine the hydrogen doping ratio range corresponding to each stage. Based on the Pareto frontier curve, the inflection point of marginal emission reduction costs, and the range of hydrogen blending ratios at each stage, an optimal operation strategy report is generated. The report includes hourly equipment output plans, energy storage charging and discharging plans, and hydrogen blending ratio sequences for each non-dominated solution, allowing decision-makers to select the appropriate operation scheme based on their actual carbon budget.

8. A multi-objective optimization system for cascade emission reduction in an integrated energy system based on distribution network limiting and dynamic hydrogen doping separation, characterized in that, include: The model building module obtains the operating parameters of the integrated energy system and constructs a dynamic hydrogen-blending model that considers the calorific value of the mixed gas and safety constraints. The system includes the energy supply side, energy conversion side, energy storage side, and load side. The constraint module establishes the operational constraints and decision variables for the dynamic hydrogen blending model, using the grid purchase volume, natural gas purchase volume, hydrogen purchase volume, power output of each device, and energy storage status as decision variables for each time period. The operational constraints include dynamic hydrogen blending ratio constraints, multi-energy balance constraints, carbon emission constraints, energy purchase constraints, and device model constraints. The dynamic hydrogen blending ratio constraints are used to configure the hydrogen blending ratio for each time period as a continuous decision variable limited by a safety upper limit, and to perform terminal separation of the mixed gas purchased from the energy supply side so that the separated pure hydrogen can be supplied to the preset zero-carbon power generation equipment. The multi-energy balance constraints are used to construct the coupling relationship of multiple energy sources such as electricity, heat, and cooling, and to constrain the power balance between the supply side and the load side in each time period to determine the coordinated output scheme of each device. Optimize the solver module and adopt - Constraint method: Under the premise of satisfying dynamic hydrogen doping ratio constraints and multi-energy balance constraints, the dynamic hydrogen doping model is optimized by minimizing system operating cost and minimizing total carbon emissions as objective functions, respectively, and carbon emission constraints, energy purchase constraints and equipment model constraints are used as variable constraint boundaries to generate Pareto optimal solution set; Pareto optimal solution set contains system operation strategies corresponding to multiple different carbon emission upper limits. The output module outputs the hydrogen doping ratio sequence and equipment output data under each system operation strategy based on the Pareto optimal solution set, in order to determine the optimal operation scheme.

9. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described in claims 1 to 7.

10. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described in claims 1 to 7.

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