An optimal scheduling method for electric-thermal-hydrogen system considering flexibility constraints

Abstract

The application provides a kind of electric-thermal-hydrogen system optimization scheduling method considering flexibility constraint, and relates to the field of integrated energy system scheduling.The application comprises: establishing hydrogen energy multi-mode utilization equipment;Quantify the flexibility adjustment capability of hydrogen energy equipment, supply side and demand side;Build a double-layer optimization model, the upper layer is an improved step carbon trading model optimized by differential evolution algorithm, and the lower layer is an economic dispatching model considering flexibility constraint;Obtain the optimal scheduling scheme by iterative solution.The application guides low-carbon scheduling by optimizing carbon trading parameters, and ensures that the scheduling scheme meets the flexibility supply-demand balance of each subsystem of electricity, heat, gas and hydrogen by using hard constraints, so as to realize the collaborative optimization of economy, low carbon and operation flexibility under the premise of ensuring the safe and stable operation of the system.

Description

Technical Field

[0001] This invention belongs to the field of energy dispatching technology and relates to an optimized dispatching method for an electric-thermal-hydrogen system that takes into account flexibility constraints. Background Technology

[0002] Guided by the "dual carbon" goals, the electricity-heat-hydrogen system, by optimizing and integrating multiple energy forms such as electricity, heat, and hydrogen, has enormous potential in improving energy efficiency and reducing carbon emissions. Against this backdrop, various emission reduction mechanisms have emerged. Among them, carbon trading mechanisms, as an effective emission reduction method, are widely used in different energy systems, especially tiered carbon trading mechanisms, which, by setting different carbon emission ranges and corresponding carbon prices, can more effectively incentivize low-carbon emissions.

[0003] However, most existing studies adopt a tiered carbon trading mechanism with fixed parameters, which lacks the ability to adaptively optimize the parameters of the mechanism itself and makes it difficult to accurately match the operating characteristics of different systems, thus limiting the maximization of its emission reduction incentive effect. Currently, there is a lack of systematic research and improvement on the structure and parameter adjustment strategies of carbon trading mechanisms.

[0004] Especially with the integration of a high proportion of renewable energy, the volatility and uncertainty of energy systems have increased significantly, placing higher demands on the flexibility of system operation. Flexibility refers to the system's ability to adjust to fluctuations in power generation and load. Existing research often considers the flexibility of the power system or individual equipment in isolation, or fails to explicitly include flexibility as a constraint in economic dispatch. This may lead to dispatch schemes that are theoretically economically optimal but cannot cope with real-time fluctuations in actual operation due to a lack of sufficient flexibility, affecting the safe and stable operation of the system. Particularly for deeply coupled electricity-heat-hydrogen systems, the multi-mode utilization of hydrogen energy (such as hydrogen production, hydrogen storage, hydrogen-to-electricity, hydrogen-to-heat, and hydrogen-to-gas) brings new potential for flexible adjustment. However, quantifying these potentials and systematically incorporating them into dispatch models that consider both economic efficiency and low carbon emissions remains a challenge.

[0005] Therefore, existing technologies have the following shortcomings: First, the parameters of the carbon trading mechanism are fixed and cannot be adaptively optimized, resulting in insufficient precision in emission reduction incentives; Second, the scheduling models often neglect or fail to systematically characterize the flexible supply and demand balance constraints among multiple subsystems such as electricity, heat, gas, and hydrogen, leading to insufficient robustness of the scheduling scheme; Third, they fail to fully explore and quantify the cross-energy flexibility adjustment value brought about by multi-mode utilization of hydrogen energy and to synergistically optimize it with economic and low-carbon goals. Summary of the Invention

[0006] The purpose of this invention is to address the problems existing in the prior art by providing an optimized scheduling method for an electric-thermal-hydrogen system that considers flexibility constraints. This method solves the problems of fixed parameters in the current carbon trading mechanism, which cannot be adaptively optimized and lacks precise emission reduction incentives.

[0007] This invention proposes a two-layer low-carbon economic scheduling model, comprising an upper-layer model and a lower-layer model. The upper-layer model aims to minimize carbon trading volume by constructing an improved tiered carbon trading system. It uses a differential evolution algorithm to adaptively optimize the tiered carbon prices and their corresponding trading ranges, thereby achieving dynamic adjustment of carbon trading costs. The lower-layer model aims to minimize operating costs. Considering the operating characteristics and flexibility constraints of various types of equipment, it establishes an electricity-heat-hydrogen system scheduling model that covers the coupling relationships of multiple energy sources such as electricity, heat, gas, and hydrogen. The optimal scheduling strategy is then solved using a Cplex solver.

[0008] Therefore, the present invention adopts the following technical solution:

[0009] An optimal scheduling method for an electric-thermal-hydrogen system considering flexibility constraints includes the following steps:

[0010] Step 1: Obtain the basic operating parameters of the multi-energy system, which includes an electrical subsystem, a thermal subsystem, and a hydrogen subsystem.

[0011] Step 2: Establish a mathematical model for hydrogen energy multi-mode utilization equipment, which includes: an electrolyzer, a hydrogen storage tank, a methanation reactor, and a hydrogen fuel cell.

[0012] Hydrogen energy multi-mode utilization equipment can realize "electricity-hydrogen", "hydrogen-electricity / heat" and "hydrogen-gas" energy conversion paths, thereby enriching the operation mode and regulation means of multi-energy systems.

[0013] Specifically:

[0014] An electrolyzer is used to convert surplus electrical energy into hydrogen energy. Its mathematical model is as follows:

[0015]

[0016] In the formula: Let be the hydrogen power output of the i-th electrolyzer at time t; The hydrogen production efficiency of the electrolyzer; Let t be the input power of the i-th electrolytic cell; The lower limit of the electrical power of the i-th electrolytic cell is input. Input the upper limit of the electrical power of the i-th electrolytic cell; Let be the maximum downward ramp rate of the i-th electrolytic cell; Input the electrical power of the i-th electrolytic cell at time t+1; Let be the maximum upward ramp rate of the i-th electrolytic cell.

[0017] Hydrogen storage tanks are used to store hydrogen produced by electrolyzers. Their mathematical model is as follows:

[0018]

[0019] In the formula: Let i be the hydrogen charging power of the i-th hydrogen storage tank at time t; The hydrogen charging status parameters of the i-th hydrogen storage tank at time t; The maximum hydrogen charging power of the i-th hydrogen storage tank; Let be the hydrogen release power of the i-th hydrogen storage tank at time t; Here are the hydrogen release status parameters for the i-th hydrogen storage tank at time t; Let i be the maximum hydrogen release power of the i-th hydrogen storage tank; Let be the equivalent electrical power of the i-th hydrogen storage tank interacting with the system at time t; Let be the hydrogen filling efficiency of the i-th hydrogen storage tank; Let be the hydrogen release efficiency of the i-th hydrogen storage tank; Let be the capacity of the i-th hydrogen storage tank at time t; Let be the amount of hydrogen stored in the i-th hydrogen storage tank at time t-1; Let be the output power of the i-th hydrogen storage tank at time t; This refers to the rated capacity of the hydrogen storage tank. Let be the capacity of the i-th hydrogen storage tank at time t=1; This is the lower limit of the hydrogen storage tank capacity; This is the upper limit of the hydrogen storage tank capacity.

[0020] A methanation reactor is used to react hydrogen with carbon dioxide to produce methane, thus achieving carbon recycling. Its mathematical model is as follows:

[0021]

[0022] In the formula: Let be the natural gas power output of the i-th methanation reactor during time period t; The gas production efficiency of the methanation reactor; Let t be the hydrogen power input to the i-th methanation reactor; The lower limit of hydrogen energy input for the i-th methanation reactor; To input the upper limit of hydrogen energy for the i-th methanation reactor; The maximum down ramp rate of the i-th methanation reactor; Input the hydrogen power of the i-th methanation reactor at time t+1; denoted as the maximum uphill ramp rate of the i-th methanation reactor.

[0023] Hydrogen fuel cells are used to convert hydrogen into electrical and thermal energy, enabling combined heat and power (CHP), reducing system carbon emissions, and improving wind power integration. The mathematical model for this is as follows:

[0024]

[0025] In the formula: Let be the power output of the i-th hydrogen fuel cell at time t; The power generation efficiency of hydrogen fuel cells; Let be the hydrogen consumption power of the i-th hydrogen fuel cell at time t; Let be the heat output of the i-th hydrogen fuel cell at time t; For the heat generation efficiency of hydrogen fuel cells; Let be the hydrogen consumption power of the i-th hydrogen fuel cell at time t; This is the lower limit of the hydrogen consumption power of the i-th hydrogen fuel cell; This represents the upper limit of hydrogen consumption power of the i-th hydrogen fuel cell; Let be the maximum downhill ramp rate of the i-th hydrogen fuel cell; Let be the hydrogen consumption power of the i-th hydrogen fuel cell at time t+1; Let be the maximum uphill ramp rate of the i-th hydrogen fuel cell.

[0026] Step 3: Set constraints for the flexibility requirements and flexibility supply of hydrogen energy multi-mode utilization equipment.

[0027] The flexibility requirement refers to the amount of upward or downward power adjustment that the equipment needs to provide in order to cope with load fluctuations; it includes upward flexibility requirement and downward flexibility requirement.

[0028] The so-called flexible supply refers to the upward or downward power adjustment capability that each device can provide, including increasing the flexible supply and decreasing the flexible supply.

[0029] Specifically:

[0030] The flexibility requirement for adjusting the electrolytic cell must meet the following constraints:

[0031]

[0032] In the formula: Let be the power consumption of the i-th electrolytic cell at time t; To increase flexibility requirements; This represents the upper limit of power consumption for the i-th electrolytic cell; The maximum upward ramp rate of the power consumption of the i-th electrolytic cell; The time step is the discrete time unit of the scheduling cycle; For t+ The power consumption of the i-th electrolytic cell at time i.

[0033] The flexibility requirement for adjusting the electrolytic cell size must meet the following constraints:

[0034]

[0035] In the formula: To reduce flexibility requirements; This is the lower limit of the power consumption of the i-th electrolytic cell; The maximum downward ramp rate is the power consumption of the i-th electrolytic cell.

[0036] The increased flexibility in the supply of electrolytic cells must meet the following constraints:

[0037]

[0038] In the formula: For the i-th electrolytic cell t∼t+ The increased flexibility in supply generated within the hydrogen energy subsystem during period t; The hydrogen production efficiency of the electrolyzer; For the i-th electrolytic cell t∼t+ The reduced flexibility supply generated within the hydrogen energy subsystem during period t.

[0039] The constraints that satisfy the downward adjustment of the supply flexibility of electrolyzers are the same as those that satisfy the upward adjustment of the supply flexibility.

[0040] The flexibility requirement for adjusting the hydrogen storage tank size must meet the following constraints:

[0041]

[0042] In the formula, For the i-th hydrogen storage tank in time period t∼t+ The increased flexibility required within t; Let be the hydrogen release power of the i-th hydrogen storage tank at time t; For t+ The hydrogen release power of the i-th hydrogen storage tank at time t; t is the time step.

[0043] The flexibility requirement for adjusting the hydrogen storage tank size must meet the following constraints:

[0044]

[0045] In the formula, For the i-th hydrogen storage tank in time period t∼t+ The need for reduced flexibility generated within t; Let be the hydrogen release power of the i-th hydrogen storage tank at time t; For t+ The hydrogen release power of the i-th hydrogen storage tank at time t.

[0046] The increased flexibility of hydrogen storage tank supply is subject to the following constraints.

[0047]

[0048] In the formula, For the i-th hydrogen storage tank at t∼t+ The increased flexibility supply generated during period t; Let be the hydrogen release efficiency of the i-th hydrogen storage tank; Let be the amount of hydrogen stored in the i-th hydrogen storage tank at the previous time (t-1); Let be the lower limit of the hydrogen storage capacity of the i-th hydrogen storage tank at time t; Let be the maximum hydrogen release power of the i-th hydrogen storage tank at time t.

[0049] The flexible supply of hydrogen storage tanks is subject to the following constraints:

[0050]

[0051] In the formula, For the i-th hydrogen storage tank at t∼t+ The reduced flexibility supply generated during period t; Let be the hydrogen filling efficiency of the i-th hydrogen storage tank; Let be the upper limit of hydrogen storage capacity of the i-th hydrogen storage tank at time t; Let be the maximum hydrogen charging power of the i-th hydrogen storage tank at time t.

[0052] The upscaling flexibility requirement of the methanation reactor must meet the following constraints:

[0053]

[0054] In the formula: Let be the hydrogen consumption power of the i-th methanation reactor at time t; To meet the increased flexibility requirements of the methanation reactor; This represents the upper limit of hydrogen consumption power for the i-th methanation reactor; The maximum upward ramp rate of the hydrogen consumption power of the i-th methanation reactor; For t+ The hydrogen consumption power of the i-th methanation reactor at time i.

[0055] The downsizing flexibility requirement of the methanation reactor must meet the following constraints:

[0056]

[0057] In the formula: To meet the flexibility requirements of methanation reactors; This represents the lower limit of hydrogen consumption power for the i-th methanation reactor; The maximum downhill ramp rate of the hydrogen consumption power of the i-th methanation reactor.

[0058] The upsizing flexibility of the methanation reactor is subject to the following constraints:

[0059]

[0060] In the formula: For the i-th methanation reactor unit t∼t+ The increased flexibility supply generated within the natural gas subsystem during period t; This represents the efficiency coefficient of the methanation reactor.

[0061] The downsizing flexibility of the methanation reactor is subject to the following constraints:

[0062]

[0063] In the formula: For the i-th methanation reactor unit t∼t+ The reduced flexibility supply generated within the natural gas subsystem during period t.

[0064] The upscaling flexibility requirements of hydrogen fuel cells must meet the following constraints:

[0065]

[0066] In the formula: Let be the hydrogen consumption power of the i-th hydrogen fuel cell at time t; To meet the increased flexibility requirements of hydrogen fuel cells; This represents the upper limit of hydrogen consumption power of the i-th hydrogen fuel cell; is the maximum ramp rate of the hydrogen power consumption of the i-th hydrogen fuel cell. For t+ The hydrogen consumption power of the i-th hydrogen fuel cell at time i.

[0067] The downsizing flexibility requirements of hydrogen fuel cells must meet the following constraints:

[0068]

[0069] In the formula: To reduce the flexibility requirements of hydrogen fuel cells; Let be the maximum downhill ramp rate of the hydrogen power consumption of the i-th hydrogen fuel cell.

[0070] The up- and down-regulation flexibility provided by hydrogen fuel cells in the power subsystem meets the following constraints:

[0071]

[0072] In the formula: For the i-th hydrogen fuel cell t∼t+ The increased flexibility supply generated within the power system during period t; The power generation efficiency of hydrogen fuel cells; For the i-th hydrogen fuel cell t∼t+ The reduced flexibility supply generated within the power system during period t.

[0073] The up- and down-adjustment flexibility of the hydrogen fuel cell in the thermal energy subsystem satisfies the following constraints:

[0074]

[0075] In the formula: For the i-th hydrogen fuel cell t∼t+ The increased flexibility in supply generated within the thermal energy system during period t; For the heat generation efficiency of hydrogen fuel cells; For the i-th hydrogen fuel cell t∼t+ The reduced flexibility supply generated within the thermal energy system during period t.

[0076] Step 4: Based on the stochasticity of system load, calculate the flexibility supply on the energy supply side and the flexibility demand on the energy demand side; where:

[0077] The energy supply side includes: purchased electricity, purchased natural gas, wind power, and solar power;

[0078] Energy demand includes: electricity load, heat load, and natural gas load.

[0079] Specifically:

[0080] The formula for the upward flexibility supply provided by the purchased electricity to the power subsystem is as follows:

[0081]

[0082] In the formula, For the power grid at t∼t+ The increased flexibility supply generated during period t; The maximum power capacity to be purchased from the power grid; Let be the power supplied by the power grid at time t; The maximum upward ramp rate for purchasing electricity from the grid.

[0083] The formula for the downsizing flexibility provided by the purchased electricity to the power subsystem is as follows:

[0084]

[0085] In the formula, For the power grid at t∼t+ The reduced flexibility supply generated during period t; The minimum power capacity to be purchased from the power grid; This represents the maximum downhill ramp rate for purchasing electricity from the grid.

[0086] The formula for calculating the increased flexibility supply provided by the purchased natural gas to the natural gas subsystem is as follows:

[0087]

[0088] In the formula: For the i-th gas source at t∼t+ The increased flexibility supply generated during period t; This represents the upper limit of the gas production capacity of the gas source; For t– The gas production power of the i-th gas source at time i; The maximum upward ramp rate for the gas source to produce gas power.

[0089] The formula for calculating the downsizing flexibility supply provided by the purchased natural gas to the natural gas subsystem is as follows:

[0090]

[0091] In the formula: For the i-th gas source at t∼t+ The reduced flexibility supply generated during period t; This is the lower limit of the gas production capacity of the gas source; The maximum downward ramp rate for the gas source's gas production power.

[0092] Considering the randomness and fluctuation of wind power output, it is necessary to adjust the wind power output by setting a maximum error coefficient.

[0093] The quantization model is shown in the equation:

[0094]

[0095] In the formula, , These represent the upward and downward flexibility requirements of wind power at time t; This represents the error coefficient for wind power. Let t be the wind power output at time t.

[0096] To quantify the demand generated by load fluctuations, upper and lower limits of load fluctuations are defined.

[0097] The quantitative calculation formula considering the maximum fluctuation error of electrical load can be expressed as:

[0098]

[0099] In the formula, This represents the upper limit of the electrical load fluctuation during time period t; This is the prediction error coefficient for electrical load; Let t be the electrical load power at time t; This represents the lower limit of the electrical load fluctuation during time period t.

[0100] like This indicates that there is only upward demand during time period t;

[0101] This indicates that there is only downward demand during time period t;

[0102] like This indicates that there is both upward and downward demand during time period t.

[0103] Based on this, the formula for the upward flexibility requirement of electrical load is:

[0104]

[0105] In the formula, The upward flexibility requirement of the electrical load at time t; Let be the electrical load power at time t-1.

[0106] The formula for the downward flexibility requirement of electrical load is:

[0107]

[0108] In the formula, Let t be the downward flexibility requirement of the electrical load.

[0109] The calculation method for the flexibility requirements of heat load and natural gas load is the same as that for the flexibility requirements of electricity load.

[0110] Step 5: Construct a two-layer low-carbon economic dispatch model, which includes an upper-layer model and a lower-layer model.

[0111] The upper-level model aims to minimize the carbon trading volume of the system and uses a differential evolution algorithm to optimize the tiered carbon trading parameters.

[0112] In the upper-level model, the optimization parameters refer to the length of the carbon trading volume interval and the carbon trading price coefficient for each interval.

[0113] The differential evolution algorithm is used to adaptively adjust the length of the carbon trading volume range and the carbon trading price coefficient.

[0114] Specifically:

[0115] The upper-level models include carbon emission quota models and actual carbon emission models.

[0116] The expression for the carbon emission quota model is as follows:

[0117]

[0118]

[0119]

[0120]

[0121] In the formula: The carbon emission allowance for the system to purchase electricity from the grid; Carbon quota coefficient per unit of electricity purchased; The amount of electricity purchased by the superior during period t; The time step is the length of a single discrete time interval within the scheduling period. Carbon emission allowances for combined heat and power units; Carbon quota coefficient per unit of calorific value; The conversion coefficient of electricity generated by a combined heat and power (CHP) unit into heat output. The scheduling period; This refers to the number of combined heat and power (CHP) units in the system. Let be the electrical power output of the i-th combined heat and power unit at time t; Let be the thermal power output of the i-th cogeneration unit at time t; Carbon emission allowances for gas-fired boilers; Let be the thermal power output of the i-th gas-fired boiler at time t; For system carbon emission quotas.

[0122] The expression for the actual carbon emissions model is as follows:

[0123]

[0124]

[0125]

[0126]

[0127]

[0128]

[0129] In the formula: This refers to the actual carbon emissions from electricity purchases. Carbon emission coefficient per unit of electricity purchased; This represents the actual carbon emissions from combined heat and power (CHP) units. The carbon emission coefficient per unit of calorific value; For the actual absorption by the methanation reactor quantity; Absorption of hydrogen energy for natural gas conversion in methanation reactor equipment Parameters; The hydrogen power consumed by the i-th methanation reactor during time period t (hydrogen energy input participating in the methanation reaction); This represents the actual carbon emissions from gas-fired boilers. This represents the total carbon emissions of the actual system. This refers to the actual carbon emissions from electricity purchases. This represents the actual carbon emissions from combined heat and power (CHP) units. This represents the actual carbon emissions from gas-fired boilers. For carbon trading volume; This represents the total carbon emissions of the actual system.

[0130] The lower-level model is a scheduling simulation model that takes into account flexibility constraints, with the goal of minimizing system operating costs;

[0131] Specifically:

[0132] The objective function of the lower-level model is expressed as:

[0133]

[0134] In the formula, For the system's operating costs; For electricity purchase costs; Gas purchase cost; The cost of curtailing wind and solar power; For equipment operation and maintenance costs; Cost of carbon emissions.

[0135] in:

[0136] The formula for calculating the cost of electricity is:

[0137]

[0138] In the formula: This refers to the unit price of electricity purchased. Electricity purchase.

[0139] The formula for calculating the cost of gas purchase is:

[0140]

[0141] In the formula: This refers to the unit price of gas. Gas purchase volume.

[0142] The formula for calculating the penalty cost of wind and solar power curtailment is:

[0143]

[0144] In the formula: Costs for wind curtailment penalties; This refers to the wind power output value; This refers to the actual wind power absorbed. Fees for penalties for abandoning light; Photovoltaic power output; This represents the actual photovoltaic power absorbed.

[0145] The formula for calculating operation and maintenance costs is:

[0146]

[0147] In the formula: The overall operation and maintenance unit price for a combined heat and power (CHP) unit; This refers to the unit price for the operation and maintenance of gas-fired boilers. This refers to the unit price for the operation and maintenance of electric boilers. Let be the thermal power output of the i-th electric boiler at time t; The unit price for the operation and maintenance of the methanation reactor; This refers to the unit price for the operation and maintenance of hydrogen fuel cells; Let be the power output of the i-th hydrogen fuel cell at time t; Let be the heat output of the i-th hydrogen fuel cell at time t; This refers to the unit price for the operation and maintenance of electrolytic cells; Let be the hydrogen production power of the i-th electrolyzer at time t.

[0148] The cost of carbon emissions is:

[0149]

[0150] In the formula: , , , , Price coefficient; This represents the length of the carbon emission range. When... A value less than 0 indicates that carbon trading revenue has been obtained.

[0151] The constraints that the objective function of the lower-level model must satisfy include power balance constraints and flexibility constraints; among which:

[0152] Power balance constraints include electrical power balance constraints, thermal power balance constraints, natural gas balance constraints, and hydrogen power balance constraints.

[0153] Specifically:

[0154] The power balance constraints are as follows:

[0155]

[0156] In the formula: Let t be the power generation capacity of the photovoltaic power source at time t; Let t be the total discharge power of N batteries at time t; The electrical load during time period t; Let t be the total power consumption of N electrolytic cells at time t; Let N be the total charging power of N batteries at time t; Let be the power output of the distributed generator set at time t; Let N be the total power generation capacity of N combined heat and power units at time t; Let t be the total power generation of N hydrogen fuel cells at time t; Let t be the total power consumption of N electric boilers at time t; The electricity purchase limit for time period t.

[0157] The thermal power balance constraints are as follows:

[0158]

[0159] In the formula: Let t be the heat load at time t; Let N be the total heat release power of N heat storage tanks at time t; Let t be the total heat output of N hydrogen fuel cells at time t; Let N be the total heat output of N combined heat and power units at time t; Let t be the total heat output of N gas-fired boilers at time t; Let N be the total heat output of N electric boilers at time t; Let t be the total heat charging power of N heat storage tanks.

[0160] The natural gas balance constraints are as follows:

[0161]

[0162] In the formula: Let N be the total hydrogen release power of N gas storage tanks at time t; Let t be the natural gas load. Let N be the total hydrogen charging power of the gas storage tanks at time t; Let N be the total gas consumption power of N combined heat and power units at time t; Let t be the total gas consumption of N gas-fired boilers at time t; The total gas consumption power of N methanation reactors at time t; Let t be the gas purchase limit.

[0163] The hydrogen power balance conditions are as follows:

[0164]

[0165] In the formula: Let N be the total hydrogen production power of N electrolyzers at time t; Let t be the total hydrogen release power of N hydrogen storage tanks at time t; The total hydrogen consumption power of N methanation reactors at time t; Let t be the total hydrogen consumption power of N hydrogen fuel cells at time t; Let t be the total hydrogen charging power of N hydrogen storage tanks.

[0166] Flexibility constraints include flexibility constraints for electricity, natural gas, thermal energy, and hydrogen energy subsystems.

[0167] The flexibility requirements of the power subsystem mainly come from net load, electric boilers, and electrolyzer equipment, while the flexibility supply comes from purchased electricity, combined heat and power units, hydrogen fuel cells, and batteries. The system flexibility requirements and supply can be calculated by the following formulas respectively.

[0168]

[0169]

[0170] In the formula: To meet the increased flexibility requirements of the power subsystem at time t; To meet the flexibility requirements for adjusting net load fluctuations in the power subsystem at time t; The upward adjustment flexibility requirement of the electrolytic cell unit in the power subsystem at time t; The upward adjustment flexibility requirement of the electric boiler unit at time t in the power subsystem; The reduced flexibility requirement of the power subsystem at time t; To reduce the flexibility required for adjusting the net load fluctuation of the power subsystem at time t; The down-adjustment flexibility requirement of the electrolytic cell unit at time t in the power subsystem; The down-adjustment flexibility requirement of the electric boiler unit at time t in the power subsystem; Provides upward flexibility for the power system at time t; To provide the increased flexibility of the power subsystem generated by the battery at time t; To provide upward adjustment flexibility for the power subsystem generated by the cogeneration unit at time t; To provide the upward flexibility supply generated by the hydrogen fuel cell unit within the power subsystem at time t; Provides flexibility for power system downsizing at time t; To provide the down-adjustment flexibility of the battery in the power subsystem at time t; To provide down-regulation flexibility for the power subsystem generated by the cogeneration unit at time t; Provides downsizing flexibility for the power subsystem generated by the hydrogen fuel cell unit at time t.

[0171] The flexibility constraints of the power subsystem are:

[0172]

[0173] The flexibility requirements of the thermal energy subsystem mainly stem from the heat load, while the supply of flexibility comes from combined heat and power units, hydrogen fuel cells, gas boilers, electric boilers, and thermal storage tanks. The overall flexibility requirements and supply of the system can be calculated using the following formulas:

[0174]

[0175]

[0176] In the formula: To meet the flexibility requirements of the thermal energy subsystem during time period t; To meet the flexibility requirements of the thermal energy subsystem during time period t; To meet the flexibility requirements for adjusting the net load fluctuation of the thermal energy subsystem at time t; To reduce the flexibility required for adjusting the net load fluctuation of the thermal energy subsystem at time t; Provides flexibility for the upward adjustment of the thermal energy subsystem at time t; To provide upward flexibility for the thermal energy subsystem generated by the heat storage tank at time t; To provide the upward adjustment flexibility of the thermal energy subsystem generated by the cogeneration unit at time t; To provide upward adjustment flexibility for GB unit at time t in the thermal energy subsystem; To provide upward flexibility supply for the thermal energy subsystem generated by the hydrogen fuel cell unit at time t; To provide the upward adjustment flexibility of the thermal energy subsystem generated by the electric boiler unit at time t; Provides flexibility for downsizing of the thermal subsystem at time t; To provide flexibility for the reduction generated by the thermal energy subsystem at time t in the heat storage tank; To provide flexibility for the reduction generated by the thermal energy subsystem at time t in a combined heat and power unit; To provide flexibility for the reduction generated by the thermal energy subsystem at time t of GB unit; To provide flexibility for the thermal energy subsystem of the hydrogen fuel cell unit at time t; Provides flexibility for the thermal energy subsystem at time t of the electric boiler unit.

[0177] The flexibility constraints of the thermal subsystem are:

[0178]

[0179] The flexibility requirements of the natural gas subsystem mainly come from natural gas load, cogeneration units, and gas-fired boilers, while the flexibility supply comes from MR and gas storage tanks. The overall flexibility requirements and supply of the system can be calculated by the following formulas.

[0180]

[0181]

[0182] In the formula: The upward adjustment flexibility requirement of the natural gas subsystem at time t; To meet the flexibility requirements for adjusting the net load fluctuation of the natural gas subsystem at time t; The upward adjustment flexibility requirement of the natural gas subsystem generated by the cogeneration unit at time t; The upward adjustment flexibility requirement generated by the GB unit in the natural gas subsystem at time t; The reduced flexibility requirement of the natural gas subsystem at time t; To reduce the flexibility required for adjusting the net load fluctuation of the natural gas subsystem at time t; The down-adjustment flexibility requirement of the combined heat and power unit at time t in the natural gas subsystem; The downsizing flexibility requirement of the GB unit at time t in the natural gas subsystem; Provides flexibility for the natural gas subsystem at time t; To provide flexibility for the upward adjustment generated by the gas storage tank at time t; To provide the upward adjustment flexibility supply generated by the MR unit in the natural gas subsystem at time t; Provide flexibility for the natural gas subsystem at time t; To provide flexibility for the reduction generated by the gas storage tank at time t; This provides downsizing flexibility for the MR unit at time t in the natural gas subsystem.

[0183] The flexibility constraints of the natural gas subsystem are:

[0184]

[0185] The flexibility constraints of the hydrogen energy subsystem in the lower-level system are as follows:

[0186]

[0187] in: Provides flexibility for the upscaling of the hydrogen energy subsystem at time t; To meet the flexibility requirements of the hydrogen energy subsystem at time t; Provides flexibility for downsizing at time t in the hydrogen energy subsystem; This is to reduce the flexibility requirements of the hydrogen energy subsystem at time t.

[0188] The flexibility of the hydrogen energy subsystem mainly comes from the electrolyzer and hydrogen storage tank, while the flexibility requirement comes from the hydrogen fuel cell and methanation reactor.

[0189] Specifically:

[0190] The formula for adjusting the flexibility requirements of the hydrogen energy subsystem is as follows:

[0191]

[0192] In the formula: This represents the total upward flexibility demand generated on the hydrogen load side during period t. This represents the total amount of upward flexibility required by the methanation reactor during time period t.

[0193] The formula for the downsizing flexibility requirement of the hydrogen energy subsystem is:

[0194]

[0195] In the formula: This represents the total downward flexibility demand generated on the hydrogen load side during period t. This represents the total amount of down-adjustment flexibility required by the methanation reactor during time period t.

[0196] The formula for adjusting the flexibility of hydrogen energy supply in the subsystem is as follows:

[0197]

[0198] In the formula: The total amount of increased flexibility supply provided by the electrolytic cell during time period t; This represents the total amount of increased flexibility supply provided by the hydrogen storage tanks during period t.

[0199] The formula for adjusting the flexible supply of the hydrogen energy subsystem is as follows:

[0200]

[0201] In the formula: The total amount of reduced flexibility supply provided by the electrolytic cell during time period t; This represents the total amount of reduced flexibility supply provided by the hydrogen storage tank during period t.

[0202] Step 6, Model Solving, includes the following steps:

[0203] Step 6a: Set the initial parameters in the lower-level model, including:

[0204] Scheduling period T, time step t, various equipment operating parameters, system load and renewable energy forecast data, and initial parameters of the improved tiered carbon trading mechanism in the upper-level model; among which:

[0205] Equipment operating parameters should include at least power upper and lower limits, ramp rate, and efficiency parameters;

[0206] The initial parameters of the improved tiered carbon trading mechanism in the upper-level model include the length d of the carbon emission range and the carbon trading price coefficient corresponding to each tier.

[0207] Step 6b: Substitute the initial parameters into the upper-level model and use the differential evolution algorithm to solve the improved tiered carbon trading mechanism to obtain a set of updated carbon trading rule parameters; the carbon trading rule parameters include the length of the carbon emission range and the corresponding price coefficient.

[0208] Step 6c: Input the carbon trading rule parameters output by the upper-level model into the lower-level scheduling model. Considering the power balance constraints, equipment operation constraints, and flexibility supply and demand balance constraints of each subsystem (electricity, heat, and hydrogen), use the Cplex solver to solve the lower-level model, obtain the system operation results under the current scheduling scheme, and calculate the corresponding carbon trading volume. E.

[0209] Step 6d, using carbon trading volume E is used as the evaluation index of the upper-level model, and its minimization is used as the optimization objective to determine whether the preset convergence conditions are met.

[0210] Step 6e: If the convergence condition is not met, then based on the current carbon trading volume... E, the differential evolution algorithm continues to generate new carbon trading rule parameters, and steps 2 to 4 are repeated for iterative calculation until the convergence condition is met.

[0211] Step 6f: When the convergence condition is met, output the optimal carbon trading rule parameters and the corresponding lower-level optimal scheduling scheme; under this optimal scheduling scheme, the electricity-heat-hydrogen system can simultaneously meet the power balance constraint, equipment operation constraint and flexibility supply and demand balance constraint, and minimize the total operating cost of the system.

[0212] The final output includes: parameters of the improved tiered carbon trading rules, total system operating costs, and carbon trading volume.

[0213] The beneficial effects of this invention are as follows:

[0214] This invention enables adaptive optimization of carbon trading costs and ensures that the scheduling scheme, while pursuing economic and low-carbon goals, meets the system's flexibility requirements across multiple time scales, thereby enhancing the ability of multi-energy systems to cope with uncertainties; specifically:

[0215] 1. This invention constructs a multi-mode hydrogen energy utilization device, covering multiple energy conversion paths such as electrolytic hydrogen production, hydrogen storage, hydrogen-to-electricity / heat conversion, and hydrogen-to-gas conversion. It fully explores the cross-energy flexibility adjustment potential of hydrogen energy as an energy carrier and storage medium, and improves the renewable energy absorption capacity and the overall operational flexibility of the system.

[0216] 2. This invention constructs a two-layer low-carbon economic scheduling model, utilizing the iteration of the upper and lower layers to combine adaptive carbon cost with economic scheduling under flexibility constraints. This achieves synergistic optimization of economic efficiency, low carbon emissions, and operational flexibility, enabling the finding of the optimal scheduling strategy while ensuring the safe and stable operation of the system. Wherein:

[0217] The upper-level model of this invention is used to achieve adaptive parameter optimization of the improved tiered carbon trading mechanism. It dynamically adjusts the carbon trading range and price through differential evolution algorithm, so that the carbon cost can more accurately reflect the marginal benefits of system emission reduction, thereby guiding the lower-level scheduling to be more inclined to low-carbon solutions, and overcoming the shortcomings of the rigid incentive effect of the fixed parameter carbon trading mechanism.

[0218] The lower-level model of this invention introduces flexible supply and demand balance constraints for each subsystem of electricity, heat, gas, and hydrogen to ensure the obtained economically optimal scheduling scheme, while also possessing the practical adjustment capability to cope with source load uncertainties, thereby significantly enhancing the robustness and practicality of the scheduling scheme. Attached Figure Description

[0219] Figure 1 This is a schematic diagram of the electro-thermal-hydrogen system structure in this embodiment.

[0220] Figure 2 This is a schematic diagram of the solution process for the two-layer optimization model in this embodiment.

[0221] Figure 3 This is intended to illustrate the comparison of operating costs in different scenarios.

[0222] Figure 4 This is a schematic diagram of the net load curve of the power subsystem in this embodiment.

[0223] Figure 5 This is a schematic diagram of the supply and demand curves for the power subsystem flexibility when considering flexibility constraints in this embodiment.

[0224] Figure 6 This is a schematic diagram of the supply and demand curves of the thermal energy subsystem considering flexibility constraints in this embodiment.

[0225] Figure 7 This is a schematic diagram of the supply and demand curves of the natural gas subsystem considering flexibility constraints in this embodiment.

[0226] Figure 8 This is a schematic diagram of the supply and demand curves of the hydrogen energy subsystem considering flexibility constraints in this embodiment.

[0227] Figure 9 This is a schematic diagram of the supply and demand curves of the power subsystem flexibility when flexibility constraints are not considered in this embodiment.

[0228] Figure 10 This is a schematic diagram of the supply and demand curves of the hydrogen energy subsystem without considering flexibility constraints in this embodiment.

[0229] Figure 11 This is a schematic diagram of the supply and demand curves of the thermal energy subsystem without considering flexibility constraints in this embodiment.

[0230] Figure 12 This is a schematic diagram of the power balance in this embodiment.

[0231] Figure 13 This is a schematic diagram of natural gas power balance in this embodiment.

[0232] Figure 14 This is a schematic diagram of the thermal power balance in this embodiment.

[0233] Figure 15 This is a schematic diagram of hydrogen power balance in this embodiment. Detailed Implementation

[0234] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0235] like Figure 1 and Figure 2 As shown, this embodiment provides an optimized scheduling method for an electric-thermal-hydrogen system considering flexibility constraints, including the following steps:

[0236] Step 1: Obtain the basic operating parameters of the multi-energy system, which includes an electrical subsystem, a thermal subsystem, and a hydrogen subsystem.

[0237] Step 2: Establish a mathematical model for hydrogen energy multi-mode utilization equipment, which includes: an electrolyzer, a hydrogen storage tank, a methanation reactor, and a hydrogen fuel cell.

[0238] An electrolyzer is used to convert surplus electrical energy into hydrogen energy. Its mathematical model is as follows:

[0239]

[0240] Hydrogen storage tanks are used to store hydrogen produced by electrolyzers. Their mathematical model is as follows:

[0241]

[0242] A methanation reactor is used to react hydrogen with carbon dioxide to produce methane, thus achieving carbon recycling. Its mathematical model is as follows:

[0243]

[0244] Hydrogen fuel cells are used to convert hydrogen into electrical and thermal energy, enabling combined heat and power (CHP), reducing system carbon emissions, and improving wind power integration. The mathematical model for this is as follows:

[0245]

[0246] Step 3: Set constraints for the flexibility requirements and flexibility supply of hydrogen energy multi-mode utilization equipment.

[0247] The flexibility requirement for adjusting the electrolytic cell must meet the following constraints:

[0248]

[0249] The flexibility requirement for adjusting the electrolytic cell size must meet the following constraints:

[0250]

[0251] The increased flexibility in the supply of electrolytic cells must meet the following constraints:

[0252]

[0253] The constraints that satisfy the downward adjustment of the supply flexibility of electrolyzers are the same as those that satisfy the upward adjustment of the supply flexibility.

[0254] The flexibility requirement for adjusting the hydrogen storage tank size must meet the following constraints:

[0255]

[0256] The flexibility requirement for adjusting the hydrogen storage tank size must meet the following constraints:

[0257]

[0258] The increased flexibility of hydrogen storage tank supply must meet the following constraints.

[0259]

[0260] The flexible supply of hydrogen storage tanks is subject to the following constraints:

[0261]

[0262] The upscaling flexibility requirement of the methanation reactor must meet the following constraints:

[0263]

[0264] The downsizing flexibility requirement of the methanation reactor must meet the following constraints:

[0265]

[0266] The upsizing flexibility of the methanation reactor is subject to the following constraints:

[0267]

[0268] The downsizing flexibility of the methanation reactor is subject to the following constraints:

[0269]

[0270] The upscaling flexibility requirements of hydrogen fuel cells must meet the following constraints:

[0271]

[0272] The downsizing flexibility requirements of hydrogen fuel cells must meet the following constraints:

[0273]

[0274] The up- and down-regulation flexibility provided by hydrogen fuel cells in the power subsystem meets the following constraints:

[0275]

[0276] The up- and down-adjustment flexibility of hydrogen fuel cells in the thermal energy subsystem satisfies the following constraints:

[0277]

[0278] Step 4: Based on the stochasticity of system load, calculate the flexibility supply on the energy supply side and the flexibility demand on the energy demand side; where:

[0279] The energy supply side includes: purchased electricity, purchased natural gas, wind power, and solar power;

[0280] Energy demand includes: electricity load, heat load, and natural gas load.

[0281] Specifically:

[0282] The formula for the upward flexibility supply provided by the purchased electricity to the power subsystem is as follows:

[0283]

[0284] The formula for the downsizing flexibility provided by the purchased electricity to the power subsystem is as follows:

[0285]

[0286] The formula for calculating the increased flexibility supply provided by the purchased natural gas to the natural gas subsystem is as follows:

[0287]

[0288] The formula for calculating the downsizing flexibility supply provided by the purchased natural gas to the natural gas subsystem is as follows:

[0289]

[0290] Taking wind power generation as an example, considering the significant randomness and fluctuations in its output power, it is necessary to adjust the wind power output by setting a maximum error coefficient. The quantification model is shown in the equation:

[0291]

[0292] The formula for the upward flexibility requirement of electrical load is:

[0293]

[0294] The formula for the downward flexibility requirement of electrical load is:

[0295]

[0296] The calculation method for the flexibility requirements of heat load and natural gas load is the same as that for the flexibility requirements of electricity load.

[0297] Step 5: Construct a two-layer low-carbon economic dispatch model, which includes an upper-layer model and a lower-layer model.

[0298] The upper-level models include carbon emission quota models and actual carbon emission models.

[0299] The expression for the carbon emission quota model is as follows:

[0300]

[0301]

[0302]

[0303]

[0304] The expression for the actual carbon emissions model is as follows:

[0305]

[0306]

[0307]

[0308]

[0309]

[0310]

[0311] The objective function of the lower-level model is expressed as:

[0312]

[0313] in:

[0314] The formula for calculating the cost of electricity is:

[0315]

[0316] In the formula: This refers to the unit price of electricity purchased. Electricity purchased.

[0317] The formula for calculating the cost of gas purchase is:

[0318]

[0319] The formula for calculating the penalty cost of wind and solar power curtailment is:

[0320]

[0321] The formula for calculating operation and maintenance costs is:

[0322]

[0323] The cost of carbon emissions is:

[0324]

[0325] The constraints that the objective function of the lower-level model must satisfy include power balance constraints and flexibility constraints; among which:

[0326] Power balance constraints include electrical power balance constraints, thermal power balance constraints, natural gas balance constraints, and hydrogen power balance constraints.

[0327] Specifically:

[0328] The power balance constraints are as follows:

[0329]

[0330] The thermal power balance constraints are as follows:

[0331]

[0332] The natural gas balance constraints are as follows:

[0333]

[0334] The hydrogen power balance conditions are as follows:

[0335]

[0336] Flexibility constraints include flexibility constraints for electricity, natural gas, thermal energy, and hydrogen energy subsystems.

[0337] The flexibility requirements of the power subsystem mainly come from net load, electric boilers, and electrolyzer equipment, while the flexibility supply comes from purchased electricity, combined heat and power units, hydrogen fuel cells, and batteries. The overall flexibility requirements and supply of the system can be calculated by the following formulas.

[0338]

[0339]

[0340] The flexibility constraints of the power subsystem are:

[0341]

[0342] The flexibility requirements of the thermal energy subsystem mainly come from the heat load, while the supply of flexibility comes from cogeneration units, hydrogen fuel cells, gas boilers, electric boilers and thermal storage tanks. The overall flexibility requirements and supply of the system can be calculated by the following formulas.

[0343]

[0344]

[0345] The flexibility constraints of the thermal subsystem are:

[0346]

[0347] The flexibility requirements of the natural gas subsystem mainly come from natural gas load, cogeneration units, and gas-fired boilers, while the flexibility supply comes from MR and gas storage tanks. The overall flexibility requirements and supply of the system can be calculated by the following formulas.

[0348]

[0349]

[0350] The flexibility constraints of the natural gas subsystem are:

[0351]

[0352] The flexibility constraints of the hydrogen energy subsystem in the lower-level system are as follows:

[0353]

[0354] The flexibility of the hydrogen energy subsystem mainly comes from the electrolyzer and hydrogen storage tank, while the flexibility requirement comes from the hydrogen fuel cell and methanation reactor.

[0355] Specifically:

[0356] The formula for adjusting the flexibility requirements of the hydrogen energy subsystem is as follows:

[0357]

[0358] The formula for the downsizing flexibility requirement of the hydrogen energy subsystem is:

[0359]

[0360] The formula for adjusting the flexibility of hydrogen energy supply in the subsystem is as follows:

[0361]

[0362] The formula for adjusting the flexible supply of the hydrogen energy subsystem is as follows:

[0363]

[0364] Step 6, Model Solving, includes the following steps:

[0365] Solving the model involves the following steps:

[0366] Step 6a: Set the initial parameters in the lower-level model, including:

[0367] Scheduling period T, time step t, various equipment operating parameters, system load and renewable energy forecast data, and initial parameters of the improved tiered carbon trading mechanism in the upper-level model; among which:

[0368] Equipment operating parameters should include at least power upper and lower limits, ramp rate, and efficiency parameters;

[0369] The initial parameters of the improved tiered carbon trading mechanism in the upper-level model include the length d of the carbon emission range and the carbon trading price coefficient corresponding to each tier.

[0370] Step 6b: Substitute the above initial parameters into the upper-level model and use the differential evolution algorithm to solve the improved tiered carbon trading mechanism to obtain a set of updated carbon trading rule parameters; the carbon trading rule parameters include the length of the carbon emission range and the corresponding price coefficient.

[0371] Step 6c: Input the carbon trading rule parameters output by the upper-level model into the lower-level scheduling model. Considering the power balance constraints, equipment operation constraints, and flexibility supply and demand balance constraints of each subsystem (electricity, heat, and hydrogen), use the Cplex solver to solve the lower-level model, obtain the system operation results under the current scheduling scheme, and calculate the corresponding carbon trading volume. E.

[0372] Step 6d, using carbon trading volume E is used as the evaluation index of the upper-level model, and its minimization is used as the optimization objective to determine whether the preset convergence conditions are met.

[0373] Step 6e: If the convergence condition is not met, then based on the current carbon trading volume... E, the differential evolution algorithm continues to generate new carbon trading rule parameters, and steps 2 to 4 are repeated for iterative calculation until the convergence condition is met.

[0374] Step 6f: When the convergence condition is met, output the optimal carbon trading rule parameters and the corresponding lower-level optimal scheduling scheme; under this optimal scheduling scheme, the electricity-heat-hydrogen system can simultaneously meet the power balance constraint, equipment operation constraint and flexibility supply and demand balance constraint, and minimize the total operating cost of the system.

[0375] The final output includes: parameters of the improved tiered carbon trading rules, total system operating costs, and carbon trading volume.

[0376] Taking an electric-thermal-hydrogen system in Northwest China as an example, with a scheduling cycle of 24 hours and a step size of 1 hour, the system operation scheduling is studied.

[0377] Table 1-3 shows the equipment parameters and electricity prices for cogeneration units, gas-fired boilers, hydrogen fuel cells, electrolyzers, methanation reactors, etc., in the multi-energy system. Wind power and photovoltaic output and daily load data are shown in Table 3. Figure 3 As shown.

[0378] Table 1 Energy Coupling Equipment Parameters

[0379]

[0380] Table 2 Energy Storage Equipment Parameters

[0381]

[0382] Table 3 Time-of-use electricity prices

[0383]

[0384] To verify the effectiveness of this embodiment, the following three scenarios were set up for simulation analysis:

[0385] Comparative Example 1: Without considering flexibility constraints, a traditional tiered carbon trading mechanism with fixed parameters is adopted.

[0386] Comparative Example 2: Without considering flexibility constraints, an improved tiered carbon trading mechanism optimized by the upper-level model of this invention is adopted.

[0387] This embodiment adopts an improved tiered carbon trading mechanism optimized by the upper-level model, taking into account flexibility constraints.

[0388] The analysis of economic efficiency and low carbon emissions is as follows.

[0389] The operating costs for different scenarios are shown in Table 4.

[0390] Table 4 Operating Costs for Each Scenario

[0391]

[0392] from Figure 3 According to the data in Table 4:

[0393] Without considering flexibility constraints, Comparative Example 2 showed a 9.3% decrease in carbon trading volume compared to Comparative Example 1, but a 60.59% increase in carbon trading costs. This indicates that optimized carbon trading parameters can guide the system to utilize more low-carbon equipment and reduce carbon emissions, but this increases carbon costs and the overall cost rises.

[0394] This embodiment uses the same improved carbon trading mechanism as Comparative Example 2, but this embodiment introduces additional flexibility constraints. The carbon trading volume increases by 4.16% compared to Comparative Example 2, and the electricity purchase cost increases by 15.68%. It can be seen that there is an inherent contradiction between system flexibility and intermittent renewable energy consumption. In order to cope with the uncertainty of renewable energy output, the system needs to purchase peak-shaving electricity at a higher price to ensure operational safety and supply-demand balance.

[0395] The following is an analysis of the supply and demand for flexibility.

[0396] like Figure 4As shown, during the 5:00-6:00 period, the system's demand for upward adjustment flexibility of net load significantly exceeds its demand for downward adjustment flexibility. During this period, the demand for upward adjustment flexibility of electrical load is much higher than the demand for downward adjustment flexibility, the demand for upward adjustment flexibility of wind power is higher than the demand for downward adjustment flexibility, while the demand for downward adjustment flexibility of photovoltaic power is higher than the demand for upward adjustment flexibility. Since the flexibility demands of wind power and photovoltaic power cancel each other out, the fluctuation characteristics of net load during this period are determined by the fluctuation characteristics of electrical load.

[0397] At 10:00, the downward adjustment flexibility requirement of net load is significantly higher than the upward adjustment flexibility requirement. At this time, the upward adjustment flexibility requirement of both electric load and wind power is higher than the downward adjustment flexibility requirement. Since the upward adjustment flexibility requirement of wind power is much higher than the downward adjustment flexibility requirement, the fluctuation characteristics of net load are determined by the fluctuation characteristics of wind power.

[0398] Between 11:00 and 12:00, the demand for downward adjustment flexibility for both electricity load and photovoltaic power is higher than the demand for upward adjustment flexibility, while the demand for upward adjustment flexibility for wind power is higher than the demand for downward adjustment flexibility. Since the demand for downward adjustment flexibility for photovoltaic power is relatively small, the superposition of wind power and electricity load during this period results in a higher demand for downward adjustment flexibility for net load.

[0399] At 16:00, the upward adjustment flexibility demand for net load is higher than the downward adjustment flexibility demand. During this period, the upward adjustment flexibility demand for electrical load is higher than the downward adjustment flexibility demand, the upward adjustment flexibility demand for photovoltaic power is higher than the downward adjustment flexibility demand, and the downward adjustment flexibility demand for wind power is higher than the upward adjustment flexibility demand. Although the upward adjustment flexibility demand for photovoltaic power is not zero during this period, the demand is relatively small and cannot determine the fluctuation characteristics of net load. Therefore, the fluctuation characteristics of net load during this period are jointly determined by the fluctuation characteristics of electrical load and wind power.

[0400] like Figures 5 to 8 As shown, this embodiment ensures that, within the scheduling cycle, the upward flexibility supply of the four subsystems—electricity, heat, natural gas, and hydrogen—is never lower than the upward flexibility demand, and the downward flexibility supply is never lower than the downward flexibility demand; although the flexibility margin may be zero at certain times (e.g., Figure 5 The fact that the upward adjustment margin is 0 at 4:00 and 6:00 indicates that the system has made full use of all economically feasible adjustment resources to meet the fluctuation demand, thus proving that this embodiment can effectively ensure the flexibility of system operation.

[0401] according to Figure 9 Analysis shows that during periods of significant net load fluctuation in the power subsystem, the optimization results cannot meet the subsystem's requirements for flexible dispatching.

[0402] Figure 10 The data shows that at 1:00, a flexibility supply gap emerged in the hydrogen energy system.

[0403] Figure 11 This indicates that there is also a shortage of flexible supply in the thermal energy subsystem.

[0404] The above analysis shows that considering only the economically optimal scheduling objective cannot guarantee that each energy subsystem has sufficient flexibility to cope with potential power fluctuations in the system.

[0405] In Comparative Example 1, without considering flexibility constraints, when using the ordinary tiered carbon trading mechanism, the power, hydrogen, and thermal energy subsystems experience situations where the supply of flexibility is lower than the demand in multiple periods. That is, when operating according to the economically optimal plan, the system does not have sufficient adjustment capacity to cope with fluctuations in actual source load, which may lead to insufficient power supply, increased wind and solar curtailment, or equipment exceeding limits, thus triggering operational risks.

[0406] The analysis of the scheduling scheme is as follows.

[0407] like Figure 12 As shown, the power balance is as follows: During the period from 1:00 to 6:00, when wind power output is at its peak, the electrolyzer, through coupling with the hydrogen fuel cell, provides both electrical and thermal energy to the system, while also providing flexible supply to the thermal and natural gas subsystems. During the period from 7:00 to 20:00, due to the reduced wind power output, the combined heat and power (CHP) unit is limited by its output power. In this embodiment, electricity can only be purchased externally to meet the electrical load demand. Although the price of electricity is higher than that of natural gas at this time, the system's economic efficiency must be sacrificed to ensure a flexible supply-demand balance.

[0408] like Figure 13 As shown, during the periods of 1:00-9:00 and 20:00-24:00, MR, through coupling with the electrolyzer, converts the hydrogen generated by the electrolyzer using wind power into natural gas, reducing the need for external natural gas purchases and improving the wind power utilization rate.

[0409] To meet the system's electricity and heat load demands, combined heat and power (CHP) units and gas-fired boilers maintain high output power, resulting in a high demand for natural gas. As shown in the graph, the amount of natural gas purchased has remained at a high level. Although purchasing large quantities of natural gas is beneficial for the system's flexibility and supply-demand balance, the large-scale use of natural gas also increases the system's carbon emissions.

[0410] like Figure 14 As shown, the heat power balance is as follows: During the entire scheduling cycle, the heat load is provided by the combined heat and power unit, gas boiler, electric boiler, hydrogen fuel cell and thermal storage. During this period, the flexible supply of heat energy can meet the flexibility requirements.

[0411] Between 8:00 and 21:00, the flexible supply of heat energy is provided by gas-fired boilers, hydrogen fuel cells, electric boilers, and thermal energy storage. Since the output power of the cogeneration units is close to the maximum value, no further flexible supply can be provided. Between 23:00 and 24:00, the output power of the cogeneration units is reduced, so flexible supply of heat energy can also be provided during this period.

[0412] like Figure 15 As shown, the hydrogen power balance is as follows: During the periods of 2:00-3:00 and 22:00-24:00, since the power of the electrolyzer does not reach its maximum, the upward flexibility supply of hydrogen is provided jointly by the electrolyzer and the hydrogen storage tank during these periods. During the period of 10:00-15:00, the downward flexibility demand for hydrogen is 0, therefore, the electrolyzer and the hydrogen storage tank do not provide flexible supply during this period.

[0413] In summary, this embodiment achieves precise low-carbon incentives through multi-mode hydrogen energy utilization equipment and "upper-level optimization" based on improved tiered carbon trading, ensures operational robustness through "lower-level flexibility constraints," and finally obtains the optimal electricity-heat-hydrogen system scheduling scheme that synergistically optimizes economy, low carbon emissions, and flexibility through a two-level iterative solution; specifically:

[0414] 1. This embodiment introduces a multi-mode utilization mechanism for hydrogen energy into the electricity-heat-hydrogen system scheduling model, which effectively increases the participation of hydrogen energy in the system, thereby reducing the overall carbon emission level and enhancing the absorption capacity of renewable energy sources such as wind power.

[0415] 2. This embodiment constructs an improved tiered carbon trading mechanism, which uses price incentives to divide carbon emission ranges and guides the system to prioritize the scheduling of low-carbon equipment. This achieves a stronger carbon emission reduction effect while ensuring the system's economic efficiency, and is significantly better than the traditional tiered carbon trading mechanism.

Claims

1. A method for optimizing the scheduling of an electric-thermal-hydrogen system considering flexibility constraints, characterized in that, Includes the following steps: Obtain the basic operating parameters of the multi-energy system; the multi-energy system includes an electric subsystem, a thermal subsystem, a hydrogen subsystem, and a natural gas subsystem; Establish a mathematical model for hydrogen energy utilization equipment, which includes an electrolyzer, a hydrogen storage tank, a methanation reactor, and a hydrogen fuel cell; The flexibility and adjustability of hydrogen energy utilization equipment are calculated, including: The flexibility requirements generated by electrolyzers in the power subsystem, and the flexibility supply provided in the hydrogen energy subsystem; Hydrogen storage tanks provide flexible supply in hydrogen energy subsystems; The flexibility required by the methanation reactor in the hydrogen subsystem, and the flexibility provided in the natural gas subsystem; The flexibility requirements generated by hydrogen fuel cells in hydrogen energy subsystems, and the flexibility provided in power and thermal energy subsystems; Calculate the flexible adjustment capabilities on the energy supply and demand sides; these capabilities include flexible demand and flexible supply; flexible demand includes increasing and decreasing flexible demand; flexible supply includes increasing and decreasing flexible supply; wherein: The flexible adjustment capability on the energy supply side includes: The purchased electricity provides flexibility in the supply of power subsystems; The purchased natural gas provides a flexible supply for the natural gas subsystem; The flexible supply of wind and solar power in the power subsystem; The flexible adjustment capability on the energy demand side includes: The flexibility requirements of electrical load in the power subsystem; The flexibility requirements of heat load generation in thermal energy subsystems; The flexibility requirements of natural gas load in the natural gas subsystem; A two-layer low-carbon economic dispatch model is constructed, which includes an upper-layer model and a lower-layer model; The upper-level model aims to minimize the carbon trading volume of the system, and it uses a differential evolution algorithm to optimize carbon trading parameters. The optimized carbon trading parameters in the upper-level model include: the length of the carbon trading volume interval and the tiered carbon trading price coefficient. The upper-level model includes a carbon emission quota model and an actual carbon emission model. The expression for the carbon emission quota model is as follows: ； In the formula: The carbon emission allowance for the system to purchase electricity from the grid; Carbon emission allowances for combined heat and power units; Carbon emission allowances for gas-fired boilers; For system carbon emission quotas; The expression for the actual carbon emission model is as follows: ； ； In the formula: This refers to the actual carbon emissions from electricity purchases. This represents the actual carbon emissions from combined heat and power (CHP) units. For the actual absorption by the methanation reactor quantity; This represents the actual carbon emissions from gas-fired boilers. This represents the total carbon emissions of the actual system. For carbon trading volume; The lower-level model aims to minimize the system operating cost, and it is solved using a solver. The system operating cost includes electricity purchase cost, gas purchase cost, wind and solar curtailment penalty cost, equipment operation and maintenance cost, and carbon trading cost. The objective function of the lower-level model is expressed as: ； In the formula, For the system's operating costs; For electricity purchase costs; Gas purchase cost; The cost of curtailing wind and solar power; For equipment operation and maintenance costs; Cost of carbon emissions; The constraints of the lower-level model include power balance constraints and flexibility supply and demand balance constraints. The flexibility supply and demand balance constraint means that, during any scheduling period, the upward flexibility supply of the subsystems of the multi-energy system is not less than the upward flexibility demand, and the downward flexibility supply of the subsystems is not less than the downward flexibility demand. The optimal scheduling scheme of the electric-thermal-hydrogen system is obtained through iterative solutions of the upper-level model and the lower-level model.

2. The method according to claim 1, characterized in that, In the hydrogen energy utilization equipment: Electrolyzers are used to convert electrical energy into hydrogen energy; Hydrogen fuel cells are used to convert hydrogen energy into electrical and thermal energy; Methanation reactors are used to convert hydrogen and carbon dioxide into natural gas; Hydrogen storage tanks are used to store hydrogen gas.

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