Multi-target day-ahead scheduling optimization method, device and system, and storage medium

Abstract

The invention discloses a multi-target day-ahead scheduling optimization method, device and system and a storage medium. The method comprises the following steps: S1, constructing a multi-target day-ahead scheduling optimization model of a hydrogen-containing all-renewable combined heat and power generation system; and S2, according to the day-ahead predicted photovoltaic and wind turbine generator output and the electric heating load, through the multi-target day-ahead scheduling optimization model of the hydrogen-containing all-renewable combined heat and power generation system, the day-ahead scheduling of the hydrogen-containing all-renewable combined heat and power generation system taking economic and environmental benefits into consideration is realized. By the adoption of the technical scheme, the utilization way of hydrogen energy is expanded, consumption of renewable energy sources such as high-proportion wind and light is promoted, and then it is ensured that the system achieves economical and low-carbon operation in the environment where the electricity market and the hydrogen market participate.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy system optimization, and in particular relates to a multi-objective day-ahead scheduling optimization method and device, system, and storage medium for a hydrogen-containing fully renewable cogeneration system in an electricity market and a hydrogen market environment. Background Art

[0002] Faced with the current predicaments of fossil energy depletion and environmental pollution, the development of clean, low-carbon, safe and efficient energy has become the core of the energy transformation strategy of many countries. Hydrogen, as a clean and renewable energy, has significant advantages such as carbon-free combustion, high energy density, and convenient preparation, storage and transmission. It is considered to be an emerging and important way to promote energy transformation. However, the development and utilization of hydrogen energy are hindered by high costs and lack of infrastructure, which has led to more and more research focusing on how to carry and utilize hydrogen energy. The cogeneration system is a distributed integrated energy system that directly supplies electricity and heat energy to users. Under the action of multi-energy coupling and complementarity, it has the characteristics of high energy utilization and high renewable energy penetration. It is worth noting that there are fossil energy-driven equipment (such as gas turbines, gas boilers, etc.) in the traditional cogeneration system, which leads to a large amount of carbon dioxide emissions under large-scale promotion. If the production, storage and utilization of hydrogen are introduced into the cogeneration system to replace its fossil energy part, on the one hand, a fully renewable cogeneration system containing hydrogen can be formed to achieve lower carbon emissions (the interaction between the system and the upper power grid will indirectly cause carbon emissions); on the other hand, a new way based on an integrated energy system can be found for carrying and utilizing hydrogen, promoting the development of hydrogen energy.

[0003] At present, hydrogen energy storage, photovoltaics, wind power and cogeneration systems have been built in the same region. However, if hydrogen energy storage is introduced into the cogeneration system to replace the fossil energy part of the system, it is urgent to scientifically schedule the hydrogen energy storage, photovoltaics, wind power and the cogeneration system without the fossil energy part (hereinafter referred to as the hydrogen-containing fully renewable cogeneration system) in the region to obtain the best economic and environmental benefits. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a multi-objective day-ahead scheduling method and device, system, and storage medium for a hydrogen-containing fully renewable cogeneration system, aiming to expand the utilization of hydrogen energy and promote the consumption of a high proportion of renewable energy such as wind and light, thereby ensuring that the system achieves economical and low-carbon operation in an environment with the participation of the electricity market and the hydrogen market.

[0005] To achieve the above object, the present invention adopts the following technical solution:

[0006] A multi-objective day-ahead scheduling optimization method, comprising:

[0007] Step S1, constructing a multi-objective day-ahead dispatch optimization model for a hydrogen-containing fully renewable cogeneration system;

[0008] Step S2: based on the day-ahead forecast of the output of photovoltaic and wind turbines and the electric and thermal loads, a multi-objective day-ahead dispatch optimization model of the hydrogen-containing fully renewable cogeneration system is used to achieve day-ahead dispatch of the hydrogen-containing fully renewable cogeneration system that takes into account both economic and environmental benefits.

[0009] As a preferred method, the multi-objective function of the multi-objective day-ahead dispatch optimization model of the hydrogen-containing fully renewable cogeneration system is:

[0010]

[0011] Among them, F1 is the total daily operating income of the fully renewable cogeneration system containing hydrogen, including the sum of the income from participating in electricity market transactions, the income from participating in hydrogen market transactions, the inverse of the operation and maintenance costs of each equipment, and the inverse of the load shedding penalty cost; They represent the selling or purchasing price, the selling power and the purchasing power of the system at the t moment respectively; c HY and They represent the hydrogen selling price of the system and the hydrogen selling volume at the tth moment respectively; c EL and They represent the unit output operation and maintenance cost of the electrolyzer and the electrolyzer power at the tth moment respectively; c HC and They represent the unit output operation and maintenance cost of the hydrogen compressor and the electric power of the hydrogen compressor at the tth moment respectively; c HFC and They represent the unit output operation and maintenance cost of the hydrogen fuel cell and the electric power of the hydrogen fuel cell at the tth moment respectively; c HS , as well as They represent the unit charging and discharging operation and maintenance cost of the hydrogen storage tank and the discharging and charging power of the hydrogen storage tank at the tth moment respectively; c HR and They represent the unit output operation and maintenance cost of the waste heat recovery device and the waste heat recovery power of the waste heat recovery device at the tth moment respectively; c ASHP and Respectively represent the unit output operation and maintenance cost of the air heat pump and the thermal power of the air heat pump at the tth moment; M, and They represent the unit penalty cost of load shedding and the load shedding of electricity and heat at the tth moment respectively; F2 is the daily carbon emission of the fully renewable cogeneration system containing hydrogen, including the indirect carbon dioxide emissions caused by purchasing electricity from the electricity market; Indicates the mass of carbon dioxide emitted for every 1kWh of electricity sold to the system.

[0012] As a preferred option, the constraints of the multi-objective day-ahead scheduling optimization model of the hydrogen-containing fully renewable cogeneration system include: hydrogen energy storage system constraints, photovoltaic and wind turbine unit output constraints, air heat pump output constraints, market constraints, load shedding constraints, and system node balance constraints.

[0013] The present invention also provides a multi-objective day-ahead scheduling optimization device, comprising:

[0014] A construction module for constructing a multi-objective day-ahead dispatch optimization model for a fully renewable combined heat and power system containing hydrogen;

[0015] The scheduling module is used to predict the output of photovoltaic and wind turbines and electric heat loads according to the day-ahead scheduling optimization model of the hydrogen-containing fully renewable cogeneration system, so as to achieve the day-ahead scheduling of the hydrogen-containing fully renewable cogeneration system that takes into account both economic and environmental benefits.

[0016] As a preferred method, the multi-objective function of the multi-objective day-ahead dispatch optimization model of the hydrogen-containing fully renewable cogeneration system is:

[0017]

[0018] Among them, F1 is the total daily operating income of the fully renewable cogeneration system containing hydrogen, including the sum of the income from participating in electricity market transactions, the income from participating in hydrogen market transactions, the inverse of the operation and maintenance costs of each equipment, and the inverse of the load shedding penalty cost; They represent the selling or purchasing price, the selling power and the purchasing power of the system at the t moment respectively; c HY and They represent the hydrogen selling price of the system and the hydrogen selling volume at the tth moment respectively; c EL and They represent the unit output operation and maintenance cost of the electrolyzer and the electrolyzer power at the tth moment respectively; c HC and They represent the unit output operation and maintenance cost of the hydrogen compressor and the electric power of the hydrogen compressor at the tth moment respectively; c HFC and They represent the unit output operation and maintenance cost of the hydrogen fuel cell and the electric power of the hydrogen fuel cell at the tth moment respectively; c HS , as well as They represent the unit charging and discharging operation and maintenance cost of the hydrogen storage tank and the discharging and charging power of the hydrogen storage tank at the tth moment respectively; c HR and They represent the unit output operation and maintenance cost of the waste heat recovery device and the waste heat recovery power of the waste heat recovery device at the tth moment respectively; c ASHP and Respectively represent the unit output operation and maintenance cost of the air heat pump and the thermal power of the air heat pump at the tth moment; M, and They represent the unit penalty cost of load shedding and the load shedding of electricity and heat at the tth moment respectively; F2 is the daily carbon emission of the fully renewable cogeneration system containing hydrogen, including the indirect carbon dioxide emissions caused by purchasing electricity from the electricity market; Indicates the mass of carbon dioxide emitted for every 1kWh of electricity sold to the system.

[0019] As a preferred option, the constraints of the multi-objective day-ahead scheduling optimization model of the hydrogen-containing fully renewable cogeneration system include: hydrogen energy storage system constraints, photovoltaic and wind turbine unit output constraints, air heat pump output constraints, market constraints, load shedding constraints, and system node balance constraints.

[0020] The present invention also provides a multi-objective day-ahead scheduling optimization system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a multi-objective day-ahead scheduling optimization method when executed by the processor.

[0021] The present invention also provides a storage medium, on which a computer program is stored, and the computer program executes a multi-objective day-ahead scheduling optimization method when running.

[0022] The multi-objective day-ahead scheduling optimization of the hydrogen-containing fully renewable cogeneration system of the present invention can ensure that the system, on the basis of participating in the electricity and hydrogen markets, coordinates the scheduling of various internal facilities, achieves improved economic and environmental benefits, and promotes the consumption of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0024] Figure 1 The present invention is a flowchart of a multi-objective day-ahead scheduling optimization method for a hydrogen-containing fully renewable cogeneration system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] Embodiment 1:

[0028] like Figure 1 As shown, an embodiment of the present invention provides a multi-objective day-ahead scheduling optimization method, comprising:

[0029] Step S1, constructing a multi-objective day-ahead dispatch optimization model for a hydrogen-containing fully renewable cogeneration system;

[0030] Step S2: based on the day-ahead forecast of the output of photovoltaic and wind turbines and the electric and thermal loads, a multi-objective day-ahead dispatch optimization model of the hydrogen-containing fully renewable cogeneration system is used to achieve day-ahead dispatch of the hydrogen-containing fully renewable cogeneration system that takes into account both economic and environmental benefits.

[0031] As an implementation method of an embodiment of the present invention, the multi-objective function of the multi-objective day-ahead scheduling optimization model of the hydrogen-containing fully renewable cogeneration system is:

[0032]

[0033]

[0034] Among them, F1 is the maximum total daily operating income of the hydrogen-containing fully renewable cogeneration system, including the sum of the income from participating in electricity market transactions, the income from participating in hydrogen market transactions, the inverse of the operation and maintenance costs of each equipment, and the inverse of the load shedding penalty cost. Specifically, in F1, (constant), (variable), (variables) represent the system's electricity sales (purchase) price, electricity sales and electricity purchase at time t; c HY (constant) and (variables) represent the system's hydrogen selling price and hydrogen selling volume at time t respectively; c EL (constant) and (variables) represent the unit output operation and maintenance cost of the electrolyzer and the electrolyzer power at the tth moment respectively; c HC (constant) and (variables) represent the unit output operation and maintenance cost of the hydrogen compressor and the electric power of the hydrogen compressor at the tth moment respectively; c HFC (constant) and (variables) represent the unit output operation and maintenance cost of the hydrogen fuel cell and the electric power of the hydrogen fuel cell at the tth moment; c HS (constant), (variable) and (variables) represent the unit charging and discharging operation and maintenance cost of the hydrogen storage tank and the discharging and charging power of the hydrogen storage tank at time t; c HR (constant) and (variables) represent the unit output operation and maintenance cost of the waste heat recovery device and the waste heat recovery power of the waste heat recovery device at the tth moment respectively; c ASHP (constant) and (variables) represent the unit output operation and maintenance cost of the air heat pump and the thermal power of the air heat pump at the tth moment; M (constant), (variable) and (variables) represent the unit penalty cost of load shedding and the load shedding of electricity and heat at time t; F2 is the daily carbon emission of the fully renewable combined heat and power system containing hydrogen, including indirect carbon dioxide emissions caused by purchasing electricity from the electricity market. Specifically, Indicates the mass of carbon dioxide emitted for every 1kWh of electricity sold to the system.

[0035] As an implementation method of an embodiment of the present invention, the constraints of a multi-objective day-ahead scheduling optimization model for a hydrogen-containing fully renewable cogeneration system include: hydrogen energy storage system constraints, photovoltaic and wind turbine unit output constraints, air heat pump output constraints, market constraints, load shedding constraints, and system node balance constraints.

[0036] Furthermore, (1) Hydrogen energy storage system constraints

[0037] 1) Electrolyzer

[0038]

[0039] Formula (3) represents the hydrogen production (Variable) Input by electrical power (variable) is determined; Formula (4) represents The dispatching shall not exceed the rated power of the electrolyzer (constant), k EL (Constant) represents the hydrogen production coefficient of the electrolyzer.

[0040] 2) Hydrogen compressor

[0041]

[0042] Formula (5) represents the electrical power input required for hydrogen compression: (variable); Formula (6) represents The scheduling shall not exceed the planned capacity of HC (constant), k HC (Constant) represents the hydrogen compression coefficient of the hydrogen compressor.

[0043] 3) Hydrogen fuel cells

[0044]

[0045] Formula (7) indicates that a certain amount of hydrogen is consumed (Variable) can generate electricity (variable); Formula (8) refers to the heat generated by the hydrogen fuel cell (variable) is captured by HR; Formula (9) indicates that the difference in power production between two consecutive periods must not exceed the ramp rate limit, which is equal to the rated power of the hydrogen fuel cell. (constant) related to, (constant) and (constant) represents the downward and upward climbing coefficients respectively; Formula (10) represents The dispatch shall not exceed the rated power of the hydrogen fuel cell (constant).

[0046] 4) Hydrogen storage tank

[0047]

[0048] Φ0=Φ T (12)

[0049]

[0050] Formula (11) represents hydrogen charging (variable) or hydrogen release (Variable) Hydrogen storage caused by Φ t (variable) dynamic change; Formula (12) by specifying the hydrogen storage volume Φ at the end of the T period T (variable) is equal to the initial hydrogen storage capacity Φ0 (constant) to ensure the recycling of the hydrogen storage tank; Formulas (13) and (14) respectively represent the rated charging and discharging power (constant) determined and The feasible interval of ; Formula (15) indicates that hydrogen charging and de-hydrogenation cannot occur at the same time; Formula (16) indicates that the hydrogen storage capacity Φ t (variable) shall not exceed its planned capacity Φ max (constant).

[0051] (2) Output constraints of photovoltaic and wind turbine units

[0052]

[0053] Formulas (17) and (18) respectively represent the abandoned light (variable) and wind curtailment (Variable) shall not exceed the predicted output of photovoltaic and wind turbines (constant), (constant).

[0054] (3) Air heat pump output constraints

[0055]

[0056] Formula (19) shows that (variable), heat (Variable) conversion relationship, η ASHP (constant) represents the conversion coefficient; formula (20) represents the The dispatch shall not exceed the rated thermal power of the air heat pump (constant).

[0057] (4) Market constraints

[0058]

[0059] Formulas (21) and (22) represent the electricity sales of the fully renewable cogeneration system containing hydrogen participating in the electricity market transaction. (variable) and electricity purchase (variable) limit, (Constant) represents the maximum transmitted power; Formula (23) indicates that electricity purchase and electricity sales cannot occur at the same time. Formula (24) indicates that the fully renewable cogeneration system containing hydrogen participates in the hydrogen market to sell hydrogen (variable) limit, (Constant) represents the maximum amount of hydrogen sold.

[0060] (5) Load shedding constraints

[0061]

[0062] Formula (25) represents the load shedding Range constraints (of variables), Cannot exceed the predicted electrical load (constant). Formula (26) represents the thermal load Range constraints (of variables), Cannot exceed the predicted electrical load (constant).

[0063] (6) System node balance constraints

[0064]

[0065] Formula (27) is the electrical node balance constraint, formula (28) is the thermal node balance constraint, and formula (29) is the hydrogen node balance constraint.

[0066] In summary, the model {Formula (1)-Formula (29)} (Formula (1) and (2) are used as objective functions, and Formula (3)-(29) are used as constraints) is the multi-objective day-ahead dispatch optimization model of the hydrogen-containing fully renewable cogeneration system of the present invention. Based on the model construction, and given the premise of the day-ahead forecast of the output of photovoltaic and wind turbines and the electrical and thermal loads, the model can realize the day-ahead dispatch of the hydrogen-containing fully renewable cogeneration system that takes into account both economic and environmental benefits. The specific model solution process is as follows:

[0067] Step 1: Convert the multi-objective function in the model into a single objective function:

[0068] Add constraints:

[0069] ω1+ω2=1

[0070] 0≤ω1≤1

[0071] 0≤ω2≤1

[0072] Among them, ω1 and ω2 represent the corresponding weights of target F1 and target F2 respectively.

[0073] Invite the expert group to discuss the assignment weights ω1 and ω2, for example, ω1=0.6, ω2=0.4, on the premise of satisfying the above-mentioned constraints.

[0074] Based on the set specific weight values ​​ω1 and ω2, the multi-objective functions F1 and F2 in the model are merged into a single objective function F through the relationship F=ω1·F1+ω2·F2.

[0075] Step 2: Initialization:

[0076] The initialization time quantity T=24 means that a scheduling day is divided into 24 time periods.

[0077] Initialize the day-ahead forecast PV output data for the tth period Wind turbine output data Electric load data and heat load data

[0078] Initialize the parameter values ​​in the model objective function, including c HY , c EL , c HC , c HFC , c HS , c HR , c ASHP ,M,

[0079] Initialize the parameter values ​​in the model constraints, including k EL , k HC , η HFC,e , η HFC,h , Φ0, Φ max , η ASHP ,

[0080] Step 3: Set variables:

[0081] Set and define the real number variables for the tth period, including Φ t ,

[0082] Set and define the 0-1 type variable for the tth period. Includes

[0083] Step 4: Call the Yalmip toolkit to write and store constraints based on the built model:

[0084] A storage space Eq is set, and the equality and inequality constraints between variables and parameters in the constraints (3)-(29) in the constructed model are stored in the space Eq.

[0085] Step 5: Based on the single objective function converted in step 1, write and store the objective function:

[0086] Set the storage space Obj, and based on the variable setting and parameter assignment, write and store the single objective function converted in step 1 in Obj.

[0087] Step 6: Call the cplex solver to solve the model:

[0088] Call the cplex solver, input the constraint storage space Eq and the objective function storage space Obj, solve and obtain the optimal value of the variable.

[0089] The production, storage and utilization of hydrogen are introduced into the cogeneration system to replace its fossil energy part, and finally a fully renewable cogeneration system containing hydrogen is formed and the multi-objective day-ahead scheduling optimization of the system is further carried out, thereby expanding the existing theoretical system of energy system scheduling that introduces hydrogen energy. The multi-objective day-ahead scheduling optimization method for a fully renewable cogeneration system containing hydrogen in the embodiment of the present invention ensures that the coordinated operation of various devices in the system participates in electricity and carbon market transactions, realizes the satisfaction of the supply of electricity load and nearby heat load, and the high proportion of renewable energy consumption.

[0090] Embodiment 2:

[0091] The embodiment of the present invention further provides a multi-objective day-ahead scheduling optimization device, comprising:

[0092] A construction module for constructing a multi-objective day-ahead dispatch optimization model for a fully renewable combined heat and power system containing hydrogen;

[0093] The scheduling module is used to predict the output of photovoltaic and wind turbines and electric heat loads according to the day-ahead scheduling optimization model of the hydrogen-containing fully renewable cogeneration system, so as to achieve the day-ahead scheduling of the hydrogen-containing fully renewable cogeneration system that takes into account both economic and environmental benefits.

[0094] As an implementation method of an embodiment of the present invention, the multi-objective function of the multi-objective day-ahead scheduling optimization model of the hydrogen-containing fully renewable cogeneration system is:

[0095]

[0096] Among them, F1 is the total daily operating income of the fully renewable cogeneration system containing hydrogen, including the sum of the income from participating in electricity market transactions, the income from participating in hydrogen market transactions, the inverse of the operation and maintenance costs of each equipment, and the inverse of the load shedding penalty cost; They represent the selling or purchasing price, the selling power and the purchasing power of the system at the t moment respectively; c HY and They represent the hydrogen selling price of the system and the hydrogen selling volume at the tth moment respectively; c EL and They represent the unit output operation and maintenance cost of the electrolyzer and the electrolyzer power at the tth moment respectively; c HC and They represent the unit output operation and maintenance cost of the hydrogen compressor and the electric power of the hydrogen compressor at the tth moment respectively; c HFC and They represent the unit output operation and maintenance cost of the hydrogen fuel cell and the electric power of the hydrogen fuel cell at the tth moment respectively; c HS , as well as They represent the unit charging and discharging operation and maintenance cost of the hydrogen storage tank and the discharging and charging power of the hydrogen storage tank at the tth moment respectively; c HR and They represent the unit output operation and maintenance cost of the waste heat recovery device and the waste heat recovery power of the waste heat recovery device at the tth moment respectively; c ASHP and Respectively represent the unit output operation and maintenance cost of the air heat pump and the thermal power of the air heat pump at the tth moment; M, and They represent the unit penalty cost of load shedding and the load shedding of electricity and heat at the tth moment respectively; F2 is the daily carbon emission of the fully renewable cogeneration system containing hydrogen, including the indirect carbon dioxide emissions caused by purchasing electricity from the electricity market; Indicates the mass of carbon dioxide emitted for every 1kWh of electricity sold to the system.

[0097] As an implementation method of an embodiment of the present invention, the constraints of a multi-objective day-ahead scheduling optimization model for a hydrogen-containing fully renewable cogeneration system include: hydrogen energy storage system constraints, photovoltaic and wind turbine unit output constraints, air heat pump output constraints, market constraints, load shedding constraints, and system node balance constraints.

[0098] Embodiment 3:

[0099] An embodiment of the present invention also provides a multi-objective day-ahead scheduling optimization system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a multi-objective day-ahead scheduling optimization method when executed by the processor.

[0100] Embodiment 4:

[0101] An embodiment of the present invention further provides a storage medium, on which a computer program is stored, and the computer program executes a multi-objective day-ahead scheduling optimization method when running.

[0102] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A multi-objective day-ahead scheduling optimization method, characterized in that: include: Step S1, constructing a multi-objective day-ahead dispatch optimization model for a hydrogen-containing fully renewable cogeneration system; Step S2: based on the day-ahead forecast of the output of photovoltaic and wind turbines and the electric and thermal loads, a multi-objective day-ahead dispatch optimization model of the hydrogen-containing fully renewable cogeneration system is used to achieve day-ahead dispatch of the hydrogen-containing fully renewable cogeneration system that takes into account both economic and environmental benefits.

2. The multi-objective day-ahead scheduling optimization method according to claim 1, characterized in that: The multi-objective function of the multi-objective day-ahead dispatch optimization model of the fully renewable cogeneration system containing hydrogen is: Among them, F1 is the total daily operating income of the fully renewable cogeneration system containing hydrogen, including the sum of the income from participating in electricity market transactions, the income from participating in hydrogen market transactions, the inverse of the operation and maintenance costs of each equipment, and the inverse of the load shedding penalty cost; They represent the selling or purchasing price, the selling power and the purchasing power of the system at the t moment respectively; c HY and They represent the hydrogen selling price of the system and the hydrogen selling volume at the tth moment respectively; c EL and They represent the unit output operation and maintenance cost of the electrolyzer and the electrolyzer power at the tth moment respectively; c HC and They represent the unit output operation and maintenance cost of the hydrogen compressor and the electric power of the hydrogen compressor at the tth moment respectively; c HFC and They represent the unit output operation and maintenance cost of the hydrogen fuel cell and the electric power of the hydrogen fuel cell at the tth moment respectively; c HS , as well as They represent the unit charging and discharging operation and maintenance cost of the hydrogen storage tank and the discharging and charging power of the hydrogen storage tank at the tth moment respectively; c HR and They represent the unit output operation and maintenance cost of the waste heat recovery device and the waste heat recovery power of the waste heat recovery device at the tth moment respectively; c ASHP and Respectively represent the unit output operation and maintenance cost of the air heat pump and the thermal power of the air heat pump at the tth moment; M, and They represent the unit penalty cost of load shedding and the load shedding of electricity and heat at the tth moment respectively; F2 is the daily carbon emission of the fully renewable cogeneration system containing hydrogen, including the indirect carbon dioxide emissions caused by purchasing electricity from the electricity market; Indicates the mass of carbon dioxide emitted for every 1kWh of electricity sold to the system.

3. The multi-objective day-ahead scheduling optimization method according to claim 2, characterized in that: The constraints of the multi-objective day-ahead dispatch optimization model for a hydrogen-containing fully renewable cogeneration system include: hydrogen energy storage system constraints, photovoltaic and wind turbine unit output constraints, air heat pump output constraints, market constraints, load shedding constraints, and system node balance constraints.

4. A multi-objective day-ahead scheduling optimization device, characterized in that: include: A construction module for constructing a multi-objective day-ahead dispatch optimization model for a fully renewable combined heat and power system containing hydrogen; The scheduling module is used to predict the output of photovoltaic and wind turbines and electric heat loads according to the day-ahead scheduling optimization model of the hydrogen-containing fully renewable cogeneration system, so as to achieve the day-ahead scheduling of the hydrogen-containing fully renewable cogeneration system that takes into account both economic and environmental benefits.

5. The multi-objective day-ahead scheduling optimization device according to claim 4, characterized in that: The multi-objective function of the multi-objective day-ahead dispatch optimization model of the fully renewable cogeneration system containing hydrogen is: Among them, F1 is the total daily operating income of the fully renewable cogeneration system containing hydrogen, including the sum of the income from participating in electricity market transactions, the income from participating in hydrogen market transactions, the inverse of the operation and maintenance costs of each equipment, and the inverse of the load shedding penalty cost; They represent the selling or purchasing price, the selling power and the purchasing power of the system at the t moment respectively; c HY and They represent the hydrogen selling price of the system and the hydrogen selling volume at the tth moment respectively; c EL and They represent the unit output operation and maintenance cost of the electrolyzer and the electrolyzer power at the tth moment respectively; c HC and They represent the unit output operation and maintenance cost of the hydrogen compressor and the electric power of the hydrogen compressor at the tth moment respectively; c HFC and They represent the unit output operation and maintenance cost of the hydrogen fuel cell and the electric power of the hydrogen fuel cell at the tth moment respectively; c HS , as well as They represent the unit charging and discharging operation and maintenance cost of the hydrogen storage tank and the discharging and charging power of the hydrogen storage tank at the tth moment respectively; c HR and They represent the unit output operation and maintenance cost of the waste heat recovery device and the waste heat recovery power of the waste heat recovery device at the tth moment respectively; c ASHP and Respectively represent the unit output operation and maintenance cost of the air heat pump and the thermal power of the air heat pump at the tth moment; M, and They represent the unit penalty cost of load shedding and the load shedding of electricity and heat at the tth moment respectively; F2 is the daily carbon emission of the fully renewable cogeneration system containing hydrogen, including the indirect carbon dioxide emissions caused by purchasing electricity from the electricity market; Indicates the mass of carbon dioxide emitted for every 1kWh of electricity sold to the system.

6. The multi-objective day-ahead scheduling optimization device according to claim 5, characterized in that: The constraints of the multi-objective day-ahead dispatch optimization model for a hydrogen-containing fully renewable cogeneration system include: hydrogen energy storage system constraints, photovoltaic and wind turbine unit output constraints, air heat pump output constraints, market constraints, load shedding constraints, and system node balance constraints.

7. A multi-objective day-ahead scheduling optimization system, characterized in that: include: A memory and a processor, wherein the memory stores a computer program executed by the processor, and when the computer program is executed by the processor, the multi-objective day-ahead scheduling optimization method as described in any one of claims 1 to 3 is executed.

8. A storage medium, characterized in that: The storage medium stores a computer program, which, when running, executes the multi-objective day-ahead scheduling optimization method according to any one of claims 1 to 3.

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