A scheduling optimization method for hydrogen-electricity coupling system considering comprehensive utilization of multiple energies

Abstract

The present invention relates to a method for scheduling and optimizing a hydrogen-electricity coupling system taking into account the comprehensive utilization of multiple energies, and belongs to the field of renewable energy power generation. The method of the present invention divides the scheduling of the multi-energy system into two stages: day-ahead and real-time. Scheduling optimization is performed based on the forecast data of wind and solar power on the day-ahead: the day-ahead output of each device is used to construct a multi-objective function with the goal of minimizing operating costs, minimizing wind and solar power abandonment, and minimizing carbon emissions to determine the optimal solution for the day-ahead output of each device; in the real-time stage, the optimal solution for the change in the operating output of each device is solved with the goal of minimizing the intra-day operation adjustment cost. The method of the present invention performs scheduling optimization on the hydrogen-electricity coupling system at multiple time scales, which solves the problem that the existing scheduling method does not consider the impact of wind and solar uncertainty on the real-time scheduling of the system, and ensures the overall economy of the scheduling optimization method.

Description

Technical Field

[0001] The present invention belongs to the technical field of renewable energy power generation, and in particular relates to a method for optimizing the scheduling of a hydrogen-electricity coupling system taking into account comprehensive utilization of multiple energies. Background Art

[0002] The hydrogen-electric coupling system is a comprehensive energy system formed by the coordination of multiple systems such as new energy power generation system, water electrolysis hydrogen production system, and power-heating system. It aims to use new energy electricity to electrolyze water to produce green hydrogen, and then apply green hydrogen to electricity, industry, transportation, heating and other fields, so as to achieve efficient utilization of green hydrogen and value derivation of new energy electricity. Among them, the degree of coupling between hydrogen energy and the power field is the deepest. The two-way conversion between electricity and hydrogen is one of the typical characteristics of the hydrogen-electric coupling system and an important way to utilize hydrogen energy. On this basis, through hydrogen storage technology, large-scale storage of new energy electricity can be achieved using hydrogen energy as a medium. However, with the development of the industry and the advancement of related technologies, the value of hydrogen energy in the fields of industry, transportation, heating and so on is constantly being discovered. Therefore, the diversified, comprehensive and efficient utilization of green hydrogen will be another typical feature of the future hydrogen-electric coupling system.

[0003] Regarding the research on hydrogen-electric systems, the current analysis of hydrogen-electric systems mainly focuses on the coordination between clean energy such as wind power and photovoltaics, as well as electricity, heat and other energy systems and hydrogen-electricity, and evaluates the feasibility of different hydrogen-electric systems from the aspects of economic benefits, social benefits, clean energy absorption capacity, and auxiliary capacity of hydrogen-electric systems. However, at present, the research on hydrogen-electric systems that integrate multiple fields such as industry, transportation, and heating is relatively scarce, so it is urgent to explore the multi-energy comprehensive utilization research of hydrogen-electric coupling systems in multiple fields.

[0004] Regarding the research on multi-energy system dispatch optimization methods, the current dispatch optimization analysis is mainly focused on the stage where the system participates in electricity or other energy market transactions in the day-ahead stage, and does not consider the impact of wind and solar uncertainty on the system's real-time dispatch strategy. How to make dispatch adjustments based on wind and solar uncertainty and ensure the optimal overall economic benefits is an issue that needs to be resolved. Summary of the invention

[0005] In view of the above analysis, the present invention aims to provide a scheduling optimization method for a hydrogen-electric coupling system taking into account the comprehensive utilization of multiple energies. The scheduling of the multiple energy system is divided into two stages: day-ahead and real-time. In the day-ahead stage, scheduling optimization is performed based on wind and solar forecast data. In the real-time stage, the uncertainty of wind and solar is considered with the goal of minimizing adjustment costs, thereby ensuring that the overall economic benefits of the scheduling optimization method are optimal.

[0006] A method for optimizing the scheduling of a hydrogen-electricity coupling system taking into account the comprehensive utilization of multiple energies comprises the following steps:

[0007] The first objective function is constructed based on the day-ahead output of each device in the hydrogen-electricity coupling system, with the goal of minimizing operating costs. The second objective function is constructed based on the day-ahead output of wind power and photovoltaic power and the predicted output of wind power and photovoltaic power the next day, with the goal of minimizing wind and solar power abandonment. The third objective function is constructed based on the day-ahead hydrogen purchase volume, with the goal of minimizing carbon emissions.

[0008] Based on hydrogen load regulation constraints, power balance constraints, and equipment operation constraints, the first, second, and third objective functions are solved by multi-function objectives to obtain the optimal solution for the output of each device on the previous day;

[0009] Based on the output changes of each device during the day, the fourth objective function is constructed with the goal of minimizing the adjustment cost of the day's operation;

[0010] The fourth objective function is solved based on the power balance constraint, equipment operation constraint, and the optimal solution of the output of each device the day before, and the optimal solution for the change of the operating output of each device during the day's operation is obtained.

[0011] Furthermore, the equipment of the hydrogen-electricity coupling system includes wind power, photovoltaic power, gas turbine, gas boiler, hydrogen fuel cell and alkaline electrolyzer; the first objective function based on the day-ahead output of each equipment of the hydrogen-electricity coupling system with the goal of minimizing the operating cost includes:

[0012] Determine maintenance costs based on the day-ahead output and operating cost coefficients of wind power, photovoltaics, gas turbines, gas boilers, hydrogen fuel cells, and alkaline electrolyzers;

[0013] Determine the gas purchase cost based on the hydrogen purchase volume during the day-ahead period;

[0014] Determine the electricity purchase cost based on the electricity purchased and sold in the day-ahead phase;

[0015] Determine the interaction cost between the system and the hydrogen network based on the amount of hydrogen purchased and sold in the day-ahead phase;

[0016] A first objective function with the goal of minimizing the operating cost is constructed based on the maintenance cost, the gas purchase cost, the electricity purchase cost and the interaction cost.

[0017] Furthermore, the first objective function is expressed as: minF1 = C gas +C ope +C grid +C other ;

[0018] Where minF1 represents the first objective function, C gas , C ope , C grid , C other They are gas purchase cost, maintenance cost, electricity purchase cost and interaction cost between the system and the hydrogen grid;

[0019]

[0020]

[0021]

[0022]

[0023] in, is the selling price / purchase price of hydrogen; and They represent the amount of hydrogen purchased and sold in the day-ahead phase respectively; T represents the number of moments in the day-ahead phase; c WPP 、c PV 、c GT 、c GB 、c HFC 、c EL represent the operating cost coefficients of wind power, photovoltaic, gas turbine, gas boiler, hydrogen fuel cell and alkaline electrolyzer respectively; They represent the output of wind power, photovoltaic power, gas turbine, gas boiler, hydrogen fuel cell and alkaline electrolyzer at time t in the day-ahead phase respectively; and They represent the electricity purchase price and electricity sales price in the day-ahead period respectively; and They represent the purchased and sold electricity in the day-ahead period respectively; c HT The unit hydrogen transportation cost.

[0024] Furthermore, the second objective function for minimizing wind and solar power abandonment is constructed based on the wind power and photovoltaic power output the day before and the predicted values ​​of wind power and photovoltaic power the next day, including:

[0025] The second objective function is expressed as

[0026] in, They represent the output of wind power and photovoltaic power at time t in the day-ahead phase respectively; and They represent the predicted output of wind power and photovoltaic power at time t the next day respectively.

[0027] Furthermore, the third objective function is constructed based on the hydrogen purchase amount in the day-ahead phase with the goal of minimizing carbon emissions, including:

[0028] The third objective function is expressed as

[0029] Among them, e g Represents the carbon emission cost coefficient per unit of hydrogen production.

[0030] Furthermore, the fourth objective function is constructed based on the output change of each device during the daily operation with the goal of minimizing the daily operation adjustment cost, including:

[0031] The fourth objective function is expressed as minF4=c θ ΔP θ,t ,θ∈Ω;

[0032] Among them, minF4 represents the fourth objective function, c θ is the adjustment cost constant of the unit power change of the equipment θ, Ω is the equipment set of the hydrogen-electric coupling system, ΔP θ,t is the power change value of device θ at time t, is the real-time output of the device θ during the day, is the output of equipment θ at time t in the day-ahead phase.

[0033] Furthermore, the multi-function objective solution of the first, second and third objective functions to obtain the optimal solution for the output of each device a day ago includes:

[0034] De-dimensionalize the values ​​of the first, second, and third objective functions;

[0035] Assign weights to each objective function to obtain a multi-objective model;

[0036] The multi-objective model is solved to obtain the optimal solution for the output of each device on the previous day.

[0037] Furthermore, the values ​​of the first, second and third objective functions are dimensionless using a membership function; the membership function is expressed as:

[0038]

[0039] Among them, F i is the value of the i-th objective function; F i min and F i max are the minimum and maximum values ​​of the objective function.

[0040] Furthermore, a subjective and objective integrated weighting method is used to assign weights to each objective function, including:

[0041]

[0042] Among them, w i The weight obtained by integrating subjective and objective factors; v i 、u i are the weights obtained by the hierarchical analysis method and the entropy weight method respectively; r i is the decision maker’s preference coefficient for subjective and objective factors.

[0043] Further, the hydrogen load adjustment constraint includes a transferable hydrogen load constraint and a reducible hydrogen load constraint;

[0044] The power balance constraints include electric power balance constraints, hydrogen power balance constraints and thermal power balance constraints.

[0045] The present invention can achieve at least one of the following beneficial effects:

[0046] By dividing the multi-energy system scheduling into two stages: day-ahead and real-time, the day-ahead output of each device is used to construct a multi-objective function with the goals of minimizing operating costs, minimizing wind and solar power abandonment, and minimizing carbon emissions to determine the optimal solution for the day-ahead output of each device. In the real-time stage, the optimal solution for the change in the operating output of each device is solved with the goal of minimizing the intraday operation adjustment cost. This takes into account the scheduling optimization of the system in the day-ahead stage and the impact of wind and solar power uncertainty on the real-time scheduling of the system, while ensuring that the adjustment cost of intraday operation is minimized and achieving the best economic benefits.

[0047] When solving multi-objective functions, each optimization objective is weighted by using a subjective and objective integrated weighting method based on the analytic hierarchy process and the entropy weight method. This avoids excessive reliance on experts and reflects the actual importance of each optimization objective, further improving the scientific nature of the solution process.

[0048] Other features and advantages of the present invention will be described in the following description, and some advantages may become apparent from the description, or may be understood by practicing the present invention. The purpose and other advantages of the present invention may be realized and obtained through the contents particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components;

[0050] Figure 1 This is a flow chart of the scheduling optimization method of the present invention;

[0051] Figure 2 This is a schematic diagram of the framework for comprehensive utilization of green hydrogen in the hydrogen-electricity coupling system of the present invention. DETAILED DESCRIPTION

[0052] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0053] A specific embodiment of the present invention discloses a method for optimizing the scheduling of a hydrogen-electricity coupling system taking into account the comprehensive utilization of multiple energies, which specifically includes the following steps:

[0054] Step S01: construct a first objective function with the goal of minimizing the operating cost based on the day-ahead output of each device in the hydrogen-electricity coupling system; construct a second objective function with the goal of minimizing the wind and solar power abandonment based on the day-ahead output of wind power and photovoltaic power and the predicted output of wind power and photovoltaic power for the next day; construct a third objective function with the goal of minimizing carbon emissions based on the hydrogen purchase amount in the day-ahead stage;

[0055] Step S02: Based on the hydrogen load regulation constraint, the power balance constraint, and the equipment operation constraint, the first, second, and third objective functions are solved for multi-function objectives to obtain the optimal solution for the output of each device the day before; when solving the multi-function objectives, the membership function is used to de-dimensionalize the values ​​of each objective function, and a subjective and objective integrated weighting method is used to assign weights to each objective function;

[0056] Step S03, based on the output changes of each device during daily operation, a fourth objective function is constructed with the goal of minimizing the daily operation adjustment cost;

[0057] Step S04, solving the fourth objective function based on the power balance constraint, the equipment operation constraint, and the optimal solution of the output of each device a day ago, to obtain the optimal solution for the change of the operating output of each device during the day's operation.

[0058] The method for optimizing the scheduling of a hydrogen-electric coupling system taking into account the comprehensive utilization of multiple energies disclosed in this embodiment divides the scheduling of the multiple energy system into two stages: the day-ahead and the real-time stage. The day-ahead output of each device is used to construct a multi-objective function with the goal of minimizing operating costs, minimizing wind and solar power abandonment, and minimizing carbon emissions to determine the optimal solution for the day-ahead output of each device. In the real-time stage, the optimal solution for the change in the operating output of each device is solved with the goal of minimizing the intraday operation adjustment cost. This method takes into account the scheduling optimization of the system in the day-ahead stage and the impact of wind and solar power uncertainty on the scheduling of the system in the real-time stage, while ensuring that the adjustment cost of intraday operation is minimized and achieving optimal economic benefits.

[0059] In one embodiment of the present invention, the equipment of the hydrogen-electricity coupling system includes wind power, photovoltaic, gas turbine, gas boiler, hydrogen fuel cell and alkaline electrolyzer. Figure 2 Shown is a schematic diagram of the framework for comprehensive utilization of green hydrogen in a hydrogen-electricity coupling system.

[0060] Specifically, in step S01, a first objective function with the goal of minimizing the operating cost is constructed based on the day-ahead output of each device in the hydrogen-electricity coupling system, including:

[0061] Determine maintenance costs based on the day-ahead output and operating cost coefficients of wind power, photovoltaics, gas turbines, gas boilers, hydrogen fuel cells, and alkaline electrolyzers;

[0062] Determine the gas purchase cost based on the hydrogen purchase volume during the day-ahead period;

[0063] Determine the electricity purchase cost based on the electricity purchased and sold in the day-ahead phase;

[0064] Determine the interaction cost between the system and the hydrogen network based on the amount of hydrogen purchased and sold in the day-ahead phase;

[0065] A first objective function with the goal of minimizing the operating cost is constructed based on the maintenance cost, the gas purchase cost, the electricity purchase cost and the interaction cost.

[0066] Furthermore, the first objective function is expressed as: minF1 = C gas +C ope +C grid +C other ;

[0067] Where minF1 represents the first objective function, C gas , C ope , C grid , C other They are gas purchase cost, maintenance cost, electricity purchase cost and interaction cost between the system and the hydrogen grid;

[0068] Gas purchase cost

[0069] Maintenance costs

[0070] Power purchase cost

[0071] Interaction Cost

[0072] in, is the selling price / purchasing price of hydrogen, the value is known; and They represent the amount of hydrogen purchased and sold in the day-ahead phase, which are parameters to be solved; T represents the number of moments in the day-ahead phase, and its value is known; c WPP 、c PV 、c GT 、c GB 、c HFC 、c EL They represent the operating cost coefficients of wind power, photovoltaic, gas turbine, gas boiler, hydrogen fuel cell and alkaline electrolyzer respectively, and their values ​​are known; They represent the output of wind power, photovoltaic power, gas turbine, gas boiler, hydrogen fuel cell and alkaline electrolyzer at time t in the day-ahead stage, respectively, and are the parameters to be solved; and They represent the electricity purchase price and electricity sales price in the day-ahead period, respectively, and their values ​​are known; and They represent the purchased and sold electricity in the day-ahead phase respectively, and are parameters to be solved; c HT is the unit hydrogen transportation cost, and its value is known.

[0073] Specifically, in step S01, the second objective function constructed with the goal of minimizing wind and solar power abandonment is expressed as:

[0074] in, They represent the output of wind power and photovoltaic power at time t in the day-ahead phase, respectively, and are the parameters to be solved; and They represent the predicted output of wind power and photovoltaic power at time t the next day, and their values ​​are known.

[0075] Specifically, in step S01, the third objective function constructed with the goal of minimizing carbon emissions is expressed as

[0076] Among them, e g represents the carbon emission cost coefficient per unit of hydrogen production, It represents the amount of hydrogen purchased in the day-ahead period, which is the parameter to be solved.

[0077] Specifically, in step S02, the hydrogen load adjustment constraint includes a transferable hydrogen load constraint and a reducible hydrogen load constraint.

[0078] It should be noted that the hydrogen load in the industrial park is an adjustable load. By adjusting or reducing the demand for hydrogen, the wind and solar power output can be maximized and the abandonment of wind and solar power can be reduced. In terms of adjustment methods, adjustable hydrogen loads are divided into two categories: transferable and reducible. Among them, adjustable hydrogen loads include hydrogen metallurgy and hydrogen energy transportation, and reducible hydrogen loads include hydrogen chemical industry and natural gas blending with hydrogen.

[0079] Furthermore, the transferable hydrogen load model is expressed as:

[0080]

[0081]

[0082] In the formula, Ω che A collection of hydrogen chemical equipment types, which may include, for example, the production of polysilicon, synthetic ammonia, synthetic methanol, and the production of green aviation kerosene; trA collection of hydrogen load types in the transportation field, which may include hydrogen buses, hydrogen public transportation, hydrogen heavy trucks, hydrogen cruise ships, and hydrogen refueling stations, for example; and They are respectively the hydrogen metallurgy type θ at time t che and traffic domain type θ tr Hydrogen load; the subscript new indicates the new hydrogen load after transfer; They are the hydrogen loads in the metallurgical and transportation sectors at time t after the transfer; They are the hydrogen loads in the metallurgical and transportation fields transferred at time t. A value greater than 0 indicates that the hydrogen load is transferred in at time t, and a value less than 0 indicates that the hydrogen load is transferred out at time t.

[0083] Furthermore, the transferable hydrogen load must meet the constraints that the load amount before and after the transfer remains unchanged and the transfer ratio is within the allowable range. The transferable hydrogen load constraints include:

[0084]

[0085]

[0086]

[0087]

[0088] In the formula, T represents the number of moments in the day-ahead phase; are the transferability coefficients for hydrogen metallurgy and transportation, respectively.

[0089] Further, the hydrogen load constraints that can be reduced include:

[0090]

[0091]

[0092] In the formula, They are the reduction in hydrogen load of hydrogen chemical industry and natural gas blending with hydrogen at time t respectively; They are the maximum values ​​of the reduction in hydrogen load for hydrogen chemical industry and natural gas blending at time t respectively.

[0093] Specifically, in step S02, the power balance constraints include electric power balance constraints, hydrogen power balance constraints and thermal power balance constraints.

[0094] Furthermore, the electric power balance constraints include:

[0095]

[0096] in, They represent the output of wind power, photovoltaic power, gas turbine, and hydrogen fuel cell at time t in the day-ahead phase respectively; and They represent the electricity purchase and sales in the day-ahead period respectively; represents the system electrical load and is a known constant; It represents the output of the alkaline electrolyzer at time t in the previous stage, that is, the power consumption of hydrogen production by electrolyzing water.

[0097] Furthermore, the thermal power balance constraints include:

[0098]

[0099] in, and Represent the thermal power of gas turbine and gas boiler respectively, Indicates the system heat load.

[0100] Furthermore, the hydrogen power balance constraints include:

[0101]

[0102]

[0103] Wherein, T represents the number of measurement moments (for example, if one year is used as the measurement period and month is used as the time scale, then T is 12); It represents the flow rate of hydrogen-mixed natural gas in the system at time t; and They represent the hydrogen-mixed natural gas flow rates of the gas turbine and gas boiler at time t in the day-ahead phase respectively; It represents the total hydrogen flow rate of the water electrolysis hydrogen production system at time t; Indicates the hydrogen load of natural gas blended with hydrogen; and Indicates the amount of hydrogen purchased and sold in the previous period; represents the hydrogen consumption of hydrogen fuel cells at time t; k represents hydrogen metallurgy, hydrogen transportation and hydrogen chemical industry, It represents the hydrogen load of hydrogen metallurgy, hydrogen transportation or hydrogen chemical industry at time t, N k It represents the quantity of hydrogen load, which is 3 in the solution of the present invention.

[0104] Specifically, in step S02, the equipment operation constraints include output constraints of each equipment and operation constraints of each equipment.

[0105] Furthermore, the output constraints of each device include:

[0106]

[0107]

[0108] Where, θ∈Ω, Ω is the set of hydrogen-electric coupling system equipment; and are the outputs of the equipment at time t and time t-1 in the day-ahead phase respectively; They are the lower and upper output limits of the equipment θ respectively; They are the maximum downward climbing power and the maximum upward climbing power of the device θ respectively.

[0109] Furthermore, the operating constraints of wind power and photovoltaic equipment are expressed as:

[0110]

[0111] in, and is the day-ahead output of wind power and photovoltaic power, i.e. the day-ahead planned output; the day-ahead predicted wind power output and photovoltaic power output are and

[0112] Furthermore, the operating constraints of each device in the water electrolysis hydrogen production system are described as follows:

[0113] The water electrolysis hydrogen production system consists of an alkaline electrolyzer, a high-pressure hydrogen storage tank and a compressor.

[0114] When the regional power grid cannot absorb new energy, the hydrogen-electricity coupling system uses alkaline electrolyzer equipment to convert excess new energy power into hydrogen energy. The equipment operation constraints of the conversion process are:

[0115]

[0116] In the formula, is the hydrogen flow rate produced by the alkaline electrolyzer at time t; is the electric power consumed by the alkaline electrolytic cell at time t; η EL is the energy conversion efficiency of the alkaline electrolyzer; It is the lower calorific value of hydrogen, which is 12.75MJ / m3.

[0117] The hydrogen energy produced by the alkaline electrolyzer can be directly supplied to the hydrogen energy demand within the system through the hydrogen pipeline, or it can be stored in a high-pressure hydrogen storage tank. The specific operating constraints of the equipment are as follows:

[0118]

[0119] Where: is the hydrogen flow rate from the alkaline electrolyzer into the high-pressure hydrogen storage tank at time t; is the hydrogen flow rate directly input from the alkaline electrolyzer into the hydrogen pipeline at time t.

[0120] In addition, whether hydrogen is introduced into the hydrogen transmission pipeline or stored in a high-pressure hydrogen storage tank, it must be compressed to a specific pressure. The operating constraints are expressed as:

[0121]

[0122] Where: co represents compression (compress), is the electrical power consumed to compress hydrogen at time t; and The electrical energy required to compress a unit of hydrogen to the specified pressure in the hydrogen transmission pipeline and high-pressure hydrogen storage tank.

[0123] The hydrogen flow operating constraints of the water electrolysis hydrogen production system are expressed as:

[0124]

[0125] Where: is the total hydrogen flow rate output by the water electrolysis hydrogen production system at time t; is the hydrogen flow rate output from the high-pressure hydrogen storage tank at time t; is the hydrogen flow rate directly input from the alkaline electrolyzer into the hydrogen pipeline at time t.

[0126] The operating constraints of the hydrogen storage process of the high-pressure hydrogen storage tank are expressed as follows:

[0127]

[0128] Where: is the hydrogen stored in the high-pressure hydrogen storage tank at time t, and Δt represents the time variable of the hydrogen storage process.

[0129] Furthermore, the operating constraints of each device in the electric heating system are described as follows:

[0130] The electricity-heating system receives hydrogen energy generated by the water electrolysis hydrogen production system through a hydrogen pipeline. The system mainly includes hydrogen fuel cells, gas turbines and gas boilers.

[0131] Among them, hydrogen fuel cells use hydrogen as a power source to generate electricity, gas turbines use hydrogen-blended natural gas as a power source to generate electricity and provide heat, and gas boilers use hydrogen-blended natural gas as a power source to provide heat.

[0132] Hydrogen fuel cells generate electricity through the movement of electrons during redox reactions, thereby providing power generation for the system. The hydrogen supply pressure of hydrogen fuel cells is usually between 0.03 and 0.06 MPa, and its operating constraints are:

[0133]

[0134] Where: is the hydrogen flow rate consumed by the hydrogen fuel cell at time t; Indicates the lower heating value of hydrogen; is the discharge power of the hydrogen fuel cell at time t; α HFC is the energy conversion efficiency of hydrogen fuel cells.

[0135] Gas turbines and gas boilers: Gas turbines and gas boilers are the main ways to utilize hydrogen-mixed natural gas, and are also important sources of heat and power in hydrogen-electric systems. Within a certain range of hydrogen blending ratios, hydrogen-mixed natural gas has little impact on equipment operation. The equipment operation constraints are expressed as:

[0136]

[0137]

[0138] Where: is the flow rate of hydrogen-mixed natural gas consumed by the gas turbine at time t; is the power generation of the gas turbine at time t; α GT is the energy conversion efficiency of the gas turbine; is the flow rate of hydrogen-mixed natural gas consumed by the gas boiler at time t; is the heating power of the gas boiler at time t; α GB The energy conversion efficiency of the gas boiler; Indicates the lower calorific value of hydrogen-mixed natural gas.

[0139] Further, the adjustable hydrogen load operation constraints are described as follows:

[0140] Adjustable hydrogen loads include industrial hydrogen loads, transportation hydrogen loads and electricity-heating hydrogen loads.

[0141] The hydrogen load in the industrial field mainly includes hydrogen chemical industry (synthetic ammonia, methanol, etc.) and hydrogen metallurgy (hydrogen reduction of iron, etc.). The operation constraints are expressed as:

[0142]

[0143] In the formula, is the total hydrogen load in the industrial sector at time t; is the hydrogen load of type hydrogen metallurgy at time t; θ che ∈Ω che ,Ω che It is a collection of hydrogen chemical industries, including the production of polysilicon, synthetic ammonia, synthetic methanol, and the production of green aviation kerosene; Type θ at time t chem of hydrogen loading.

[0144] The hydrogen load in the transportation field mainly includes land hydrogen transportation (buses, public transportation, heavy trucks) and water hydrogen transportation (cruise ships). The operation constraints are expressed as:

[0145]

[0146] In the formula, is the total hydrogen load in the transportation sector at time t; θ tr ∈Ω tr ,Ω tr It is a collection of hydrogen load types in the transportation field, including hydrogen buses, hydrogen public transportation, hydrogen heavy trucks, hydrogen cruise ships, and hydrogen refueling stations; Type θ at time t tr of hydrogen loading.

[0147] The hydrogen load in the power-heating sector is mainly natural gas mixed with hydrogen. The operating constraints of the hydrogen mixing process are as follows:

[0148]

[0149]

[0150] Where: is the flow rate of hydrogen-mixed natural gas in the pipeline at time t; is the hydrogen flow rate injected into the pipeline; The flow of natural gas purchased. It is the ratio of hydrogen blended in natural gas pipelines. Since adding too much hydrogen will cause "hydrogen embrittlement", it should not be higher than 20% in general. In addition, the calorific value of hydrogen-mixed natural gas is related to the ratio of hydrogen blended, which can be calculated by the following formula:

[0151]

[0152] Where: H mix is the calorific value of hydrogen-mixed natural gas; H gas is the calorific value of natural gas.

[0153] Specifically, in step S03, the first, second and third objective functions are solved based on the hydrogen load regulation constraint, the power balance constraint and the equipment operation constraint to obtain the optimal solution for the output of each device on the previous day, including:

[0154] De-dimensionalize the values ​​of the first, second, and third objective functions;

[0155] Assign weights to each objective function to obtain a multi-objective model;

[0156] The multi-objective model is solved to obtain the optimal solution for the output of each device on the previous day.

[0157] Furthermore, the values ​​of the first, second and third objective functions are dimensionless using a membership function; the membership function is expressed as:

[0158]

[0159] Among them, F i is the value of the i-th objective function; F i min and F i max are the minimum and maximum values ​​of the objective function.

[0160] Furthermore, when solving the multi-objective function, it is necessary to assign weights to each objective function. The present invention adopts a subjective and objective integrated weighting method to assign weights to each objective function, including:

[0161]

[0162] Among them, w i The weight obtained by integrating subjective and objective factors; v i 、u i are the weights obtained by the hierarchical analysis method and the entropy weight method respectively; r i is the decision maker’s preference coefficient for subjective and objective factors. It should be noted that r i The value of is related to the emphasis of the decision maker on the objectives of each objective function. For example, if the most important concern is the optimal wind and solar curtailment, the value of the second objective function can be increased accordingly. i The value is usually the mean, which is 1 / 3.

[0163] Furthermore, after the objective function is dimensionless and the subjective and objective integrated weights are assigned, the multi-objective model for day-ahead scheduling optimization can be converted into the following form: And the solution is based on hydrogen load regulation constraints, power balance constraints, and equipment operation constraints.

[0164] Furthermore, during implementation, CPLEX can be used to solve multi-function objectives and obtain the optimal solution for the output of each device on the previous day.

[0165] Specifically, in step S03, the fourth objective function constructed with the goal of minimizing the intraday operation adjustment cost is expressed as minF4=c θ ΔP θ,t ,θ∈Ω;

[0166] Among them, minF4 represents the fourth objective function, c θ is the adjustment cost constant of the unit power change of the equipment θ, Ω is the equipment set of the hydrogen-electric coupling system, ΔP θ,t is the power change value of device θ at time t, and is the parameter to be solved; is the real-time output of the device θ during the intraday period, and is the parameter to be solved; is the output of equipment θ at time t in the day-ahead phase, which is determined based on the solution results of the first, second, and third objective functions. Where θ∈Ω, Ω is the set of equipment in the hydrogen-electricity coupling system.

[0167] Specifically, in step S04, the fourth objective function minF4=c is calculated based on the optimal solution of the power balance constraint, the equipment operation constraint, and the output of each equipment a day ago. θ ΔP θ,t ,θ∈Ω, Solve and obtain the optimal solution for the change of operating output of each device during daily operation.

[0168] This embodiment discloses a method for optimizing the scheduling of a hydrogen-electric coupling system taking into account the comprehensive utilization of multiple energies. By considering the multi-time scale scheduling of the hydrogen-electric coupling system, the scheduling is divided into two stages: day-ahead and real-time. Multiple objective functions are constructed to solve and determine the optimal solution for the day-ahead output of each device and the optimal solution for the change in the operating output of the device in the real-time stage. When solving the multi-objective function, a subjective and objective integrated weighting method based on the analytic hierarchy process and the entropy weight method is used to weight each optimization target, avoiding excessive reliance on experts while reflecting the actual importance of each optimization target, further improving the scientific nature of the solution process.

[0169] It should be noted that the above embodiments are based on the same inventive concept and parts not described repeatedly can be used as reference for each other.

[0170] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for optimizing the scheduling of a hydrogen-electricity coupling system taking into account the comprehensive utilization of multiple energies, characterized in that: The steps include: A first objective function with the goal of minimizing the operating cost is constructed based on the day-ahead output of each device in the hydrogen-electric coupling system; each device in the hydrogen-electric coupling system includes wind power, photovoltaic power, gas turbine, gas boiler, hydrogen fuel cell and alkaline electrolyzer; the first objective function with the goal of minimizing the operating cost is constructed based on the day-ahead output of each device in the hydrogen-electric coupling system, including: determining the maintenance cost based on the day-ahead output and operating cost coefficient of wind power, photovoltaic power, gas turbine, gas boiler, hydrogen fuel cell and alkaline electrolyzer; determining the gas purchase cost based on the hydrogen purchase amount in the day-ahead stage; determining the electricity purchase cost based on the electricity purchase amount and electricity sales in the day-ahead stage; determining the interaction cost between the system and the hydrogen network based on the hydrogen purchase amount and sales amount in the day-ahead stage; constructing the first objective function with the goal of minimizing the operating cost based on the maintenance cost, gas purchase cost, electricity purchase cost and interaction cost; the first objective function is expressed as: min F1=C gas +C ope +C grid +C other ; Where min F1 represents the first objective function, C gas , C ope , C grid , C other They are gas purchase cost, maintenance cost, electricity purchase cost and interaction cost between the system and the hydrogen grid; in, is the selling price / purchase price of hydrogen; and They represent the amount of hydrogen purchased and sold in the previous period respectively; T represents the amount at the time of measurement; c WPP 、c PV 、c GT 、c GB 、c HFC 、c EL represent the operating cost coefficients of wind power, photovoltaic, gas turbine, gas boiler, hydrogen fuel cell and alkaline electrolyzer respectively; They represent the output of wind power, photovoltaic power, gas turbine, gas boiler, hydrogen fuel cell and alkaline electrolyzer at time t in the day-ahead phase respectively; and They represent the electricity purchase price and electricity sales price in the day-ahead period respectively; and They represent the purchased and sold electricity in the day-ahead period respectively; c HT is the unit hydrogen transportation cost; Based on the wind power and photovoltaic power output the day before and the predicted output of wind power and photovoltaic power the next day, a second objective function with the goal of minimizing wind and solar power abandonment is constructed; the second objective function is expressed as in, They represent the output of wind power and photovoltaic power at time t in the day-ahead phase respectively; and They represent the predicted output of wind power and photovoltaic power at time t the next day respectively; The third objective function is constructed based on the hydrogen purchase amount in the day-ahead phase with the goal of minimizing carbon emissions; the third objective function is expressed as Among them, e g represents the carbon emission cost coefficient per unit of hydrogen production; Based on hydrogen load regulation constraints, power balance constraints, and equipment operation constraints, the first, second, and third objective functions are solved by multi-function objectives to obtain the optimal solution for the output of each device on the previous day; Based on the output changes of each device during the day, the fourth objective function is constructed with the goal of minimizing the adjustment cost of the day's operation; The fourth objective function is solved based on the power balance constraint, equipment operation constraint, and the optimal solution of the output of each device the day before, and the optimal solution for the change of the operating output of each device during the day's operation is obtained.

2. The scheduling optimization method according to claim 1, characterized in that: The fourth objective function is constructed based on the output change of each device during the daily operation with the goal of minimizing the adjustment cost of the daily operation, and includes: The fourth objective function is expressed as min F4 = c θ ΔP θ,t ,θ∈Ω; Among them, min F4 represents the fourth objective function, c θ is the adjustment cost constant of the unit power change of the equipment θ, Ω is the equipment set of the hydrogen-electric coupling system, ΔP θ,t is the power change value of device θ at time t, is the real-time output of the device θ during the day, is the output of equipment θ at time t in the day-ahead phase.

3. The scheduling optimization method according to claim 2, characterized in that: The multi-function objective solution of the first, second and third objective functions to obtain the optimal solution of the output of each device on the previous day includes: De-dimensionalize the values ​​of the first, second, and third objective functions; Assign weights to each objective function to obtain a multi-objective model; The multi-objective model is solved to obtain the optimal solution for the output of each device on the previous day.

4. The scheduling optimization method according to claim 3, characterized in that: The values ​​of the first, second and third objective functions are dimensionless processed using the membership function; the membership function is expressed as: Among them, F i is the value of the i-th objective function; F i min and F i max are the minimum and maximum values ​​of the objective function.

5. The scheduling optimization method according to claim 3 or 4, characterized in that: The subjective and objective integrated weighting method is used to assign weights to each objective function, including: Among them, w i The weight obtained by integrating subjective and objective factors; v i 、u i are the weights obtained by the analytic hierarchy process and the entropy weight method respectively; ri is the preference coefficient of the decision maker for subjective and objective factors.

6. The scheduling optimization method according to claim 1, characterized in that: The hydrogen load adjustment constraints include transferable hydrogen load constraints and reducible hydrogen load constraints; The power balance constraints include electric power balance constraints, hydrogen power balance constraints and thermal power balance constraints.

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