Multi-target adjustable load management method and device based on micro-energy network

Abstract

The invention relates to the technical field of adjustable load management, in particular to a multi-target adjustable load management method based on a micro-energy grid, which can establish a micro-energy grid operation model comprising a mathematical model of a hydrogen system and a solar wind power generation mathematical model; establishing an adjustable load management strategy and an uncertainty model; a multi-objective function is established by taking minimization of system cost, minimization of carbon emission and minimization of peak load as objectives, and an MIN-MAX conversion model is established in combination with a micro energy grid operation model, an adjustable load management strategy and an uncertainty model to perform multi-objective adjustable load management. It can be understood that the micro-energy grid operation model combines a hydrogen system and solar wind power generation, the flexibility of the micro-energy grid to uncertainty of solar energy, wind energy and the like is increased through adjustable load management, and the total cost, carbon emission and peak load can be reduced at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of adjustable load management, and in particular to a multi-objective adjustable load management method and device based on a micro energy network. Background Art

[0002] Carbon emissions have always been a major challenge facing our society. Developing renewable energy sources (RES) is one of the main solutions to reduce carbon emissions in the energy system. In the distribution system, the application of microgrids (MGS) in a local manner is the most appropriate way of resource solutions in the power system. Microgrids (MGS) incorporate distributable resources and energy storage systems into the entire system to control the uncertainty of the entire system.

[0003] With the continuous advancement of technology, microgrids have considered more interactions with other energy sources and developed micro energy networks (MES). Energy storage systems and demand response (DR) are considered to be two main tools to manage and distribute the uncertainty of renewable energy sources (RES) in the system.

[0004] One of the effective ways to increase the flexibility of micro energy grids (MES) is to integrate them with hydrogen systems. By adding hydrogen to micro energy grids (MES), they can achieve greater flexibility and higher resilience. Hydrogen systems can be used as power resources (hydrogen-electricity) or energy storage systems (hydrogen storage tanks). This integration helps provide power balance between power supply and load while reducing emissions generated by fuel.

[0005] Most current research efforts focus on the economic operation of multi-load systems and attempt to reduce the total operating cost of the system. However, little attention has been paid to modeling effective demand response (DR) programs and reducing the peak load of the system. An effective DR program can prevent the creation of new peaks in the load. This can reduce power losses, increase power flexibility, and improve equipment utilization. In addition, most studies have ignored the issue of incorporating BSS, renewable energy sources (RES), and non-renewable energy into the energy system, and have also neglected the integration of hydrogen-power-power-hydrogen systems. Summary of the invention

[0006] In view of this, the purpose of the present invention is to provide a multi-objective adjustable load management method and device based on a micro energy network, so as to minimize operating costs, reduce emissions and peak loads while taking into account factors such as renewable output uncertainty, adjustable load response and the basic operating principles of a hybrid electric-hydrogen micro energy network.

[0007] According to a first aspect of an embodiment of the present invention, a multi-objective adjustable load management method based on a micro energy network is provided, comprising:

[0008] Establish mathematical models of hydrogen system and solar wind power generation, and establish micro energy network operation model based on the mathematical models of hydrogen system and solar wind power generation;

[0009] Establishing an adjustable load management strategy and an uncertainty model according to a load transfer scheme of preset adjustable load management;

[0010] A first objective function is established with the goal of minimizing system cost, a second objective function is established with the goal of minimizing carbon emissions, and a third objective function is established with the goal of minimizing peak load;

[0011] According to the first objective function, the second objective function and the third objective function, a MIN-MAX conversion model is established in combination with the micro energy grid operation model, the adjustable load management strategy and the uncertainty model;

[0012] Multi-objective adjustable load management is carried out using the MIN-MAX transformation model.

[0013] Preferably, the mathematical model of the hydrogen system is established, including the following formula:

[0014] Formula (1):

[0015] in, is the electrolyzer input power; h,t is the electricity-to-hydrogen conversion coefficient; is the power input to the hydrogen storage system;

[0016] Formula (2):

[0017] Among them, HL h,t is the capacity of the hydrogen storage tank at time t; is the capacity of the hydrogen storage tank at the initial moment; is the efficiency of hydrogen charging and discharging; is the power of hydrogen output from the hydrogen storage tank;

[0018] Formula (3):

[0019] in, is the amount of hydrogen used for electricity generation; is the amount of hydrogen available for sale;

[0020] Formula (4):

[0021] Formula (5):

[0022] Formula (6):

[0023] in, and is the maximum and minimum storage level of the hydrogen storage tank; h,t a binary variable indicating whether the hydrogen storage is used for hydrogen power generation; The maximum amount of hydrogen used for power generation; σ h,t A binary variable indicating whether the stored hydrogen is for sale; is the maximum amount of hydrogen available for sale;

[0024] Formula (7):

[0025] Formula (8): σ h,t +χ h,t ≤1

[0026] Among them, ψ h It represents the power constant coefficient of hydrogen power generation; It is the power of converting hydrogen into electricity.

[0027] Preferably, the established solar wind power generation mathematical model includes the following formula:

[0028] Formula (9):

[0029] Formula (10):

[0030] in, Represents photovoltaic power generation; η PV is the photovoltaic panel efficiency; is the number of photovoltaic panels; The area of ​​a single photovoltaic panel; I t,s It is solar radiation; is the ambient temperature; is the wind power; v t,s is the real-time wind speed; v ci is the cut-in wind speed; v co is the cut-out wind speed; v i,r is the rated wind speed; is the rated power;

[0031] Formula (11):

[0032] Formula (12):

[0033] Formula (13):

[0034] Among them, PDF() is the beta probability distribution function; Γ is a specific common function; α and β are specific parameters; μ and δ represent the mean and variance respectively;

[0035] Formula (14):

[0036] Formula (15):

[0037] Here, the parameters c and k represent the scale and shape index of the Weibull PDF, respectively.

[0038] Preferably, the power balance constraint formula of the established micro energy grid operation model is expressed as follows:

[0039] Formula (16):

[0040]

[0041] in, It is the amount of electricity purchased from the grid; is the power generation of the micro-turbine; ρ s is the confidence factor; and are the discharge and charge amounts of the energy storage battery respectively; It is the amount of electricity sold to the grid; It is the electricity load after demand response.

[0042] Preferably, the load transfer scheme of the preset adjustable load management is expressed as follows:

[0043] Formula (17): DR min ≤DR l,t ≤DR max

[0044] Formula (18):

[0045] Formula (19):

[0046] Among them, DR l,t is the demand response level of the load; DR min and DR max are the minimum and maximum levels of demand response, respectively; ldr l,t is a transferable load; It is the electricity load after demand response.

[0047] Preferably, the formula for establishing the first objective function with the goal of minimizing system cost is expressed as follows:

[0048] Formula (20):

[0049] in, is the electricity price; α i and β iis the fuel cost coefficient of the micro-turbine; γ i is the fixed cost coefficient of the micro-turbine; u i,t is a binary variable representing the operation of the micro-turbine; It is the price of hydrogen.

[0050] Preferably, the formula for establishing the second objective function with the goal of minimizing carbon emissions is as follows:

[0051] Formula (21):

[0052] in, is the carbon emission coefficient from the electricity generated by the grid; is the emission coefficient of the micro-turbine; is the emission coefficient of the hydrogen engine.

[0053] Preferably, the formula for establishing the third objective function with the goal of minimizing the peak load is as follows:

[0054] Formula (22):

[0055] in, It is the electricity load after demand response.

[0056] Preferably, a MIN-MAX conversion model is established according to the first objective function, the second objective function and the third objective function, including:

[0057] The mixed standardized Min-Max method is used to transform the first objective function, the second objective function and the third objective function into a single objective function, and the MIN-MAX transformation model is established. The formula is as follows:

[0058] Formula (23):

[0059] Among them, w1, w2 and w3 represent the importance of the objective function, w1+w2+w3=1; cost * and Emission * Represents the optimal result of operating cost and emission intensity.

[0060] According to a second aspect of an embodiment of the present invention, there is provided a multi-objective adjustable load management device based on a micro energy network, comprising:

[0061] A main controller, and a memory connected to the main controller;

[0062] a memory in which program instructions are stored;

[0063] The main controller is used to execute program instructions stored in the memory to perform any of the above methods.

[0064] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0065] It is understandable that the technical solution shown in the present invention can establish a micro-energy network operation model including a mathematical model of a hydrogen system and a mathematical model of solar wind power generation; establish an adjustable load management strategy and an uncertainty model; establish a multi-objective function with the goal of minimizing system cost, minimizing carbon emissions, and minimizing peak load, and combine the micro-energy network operation model with the adjustable load management strategy and uncertainty model to establish a MIN-MAX conversion model to perform multi-objective adjustable load management. It is understandable that the micro-energy network operation model combines the hydrogen system with solar wind power generation, and increases the flexibility of the micro-energy network to uncertainties such as solar energy and wind energy through adjustable load management, and can simultaneously reduce total costs, carbon emissions, and peak loads.

[0066] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0068] Figure 1 It is a schematic diagram of the steps of a multi-objective adjustable load management method based on a micro energy network according to an exemplary embodiment. DETAILED DESCRIPTION

[0069] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0070] In one embodiment, Figure 1 is a schematic diagram of the steps of a multi-objective adjustable load management method based on a micro energy network according to an exemplary embodiment, see Figure 1 , a multi-objective adjustable load management method based on a micro energy network is provided, comprising:

[0071] Step S11, establishing a mathematical model of the hydrogen system and a mathematical model of solar wind power generation, and establishing a micro energy network operation model based on the mathematical model of the hydrogen system and the mathematical model of solar wind power generation.

[0072] Step S12: establishing an adjustable load management strategy and an uncertainty model according to a preset load transfer scheme of adjustable load management.

[0073] Step S13: Establish a first objective function with the goal of minimizing system cost, establish a second objective function with the goal of minimizing carbon emissions, and establish a third objective function with the goal of minimizing peak load.

[0074] Step S14: Establish a MIN-MAX conversion model based on the first objective function, the second objective function and the third objective function, in combination with the micro energy grid operation model, the adjustable load management strategy and the uncertainty model.

[0075] Step S15: Use the MIN-MAX conversion model to perform multi-objective adjustable load management.

[0076] It is understandable that the technical solution shown in this embodiment can establish a micro-energy network operation model including a mathematical model of a hydrogen system and a mathematical model of solar wind power generation; establish an adjustable load management strategy and an uncertainty model; establish a multi-objective function with the goal of minimizing system costs, minimizing carbon emissions, and minimizing peak loads, and combine the micro-energy network operation model with the adjustable load management strategy and uncertainty model to establish a MIN-MAX conversion model to perform multi-objective adjustable load management. It is understandable that the micro-energy network operation model combines the hydrogen system with solar wind power generation, and increases the flexibility of the micro-energy network to uncertainties such as solar energy and wind energy through adjustable load management, and can simultaneously reduce total costs, carbon emissions, and peak loads.

[0077] In specific applications, a multi-objective adjustable load management method based on micro energy network is implemented, which can reduce total cost, carbon emissions and peak load at the same time. Micro energy network (MES) combines electricity-hydrogen, hydrogen-electricity and hydrogen storage tanks in the system, and uses the electricity-hydrogen system to convert the remaining wind energy and photovoltaic energy into hydrogen; the produced hydrogen can be stored in hydrogen storage tanks and converted from hydrogen to power units to supply loads, and the flexibility of micro energy network (MES) to uncertainties such as solar energy and wind energy can be further increased through adjustable load management.

[0078] It should be noted that in step S11, the mathematical model of the hydrogen system is established, including the following formula:

[0079] Formula (1):

[0080] This formula is a calculation formula for converting electricity into hydrogen in an electrolyzer.

[0081] in, is the electrolyzer input power; h,t is the electricity-to-hydrogen conversion coefficient; is the power input into the hydrogen storage system.

[0082] Formula (2):

[0083] This formula indicates the level of hydrogen stored in the storage tank.

[0084] Among them, HL h,t is the capacity of the hydrogen storage tank at time t; is the capacity of the hydrogen storage tank at the initial moment; is the efficiency of hydrogen charging and discharging; It is the power of hydrogen output from the hydrogen storage tank.

[0085] Formula (3):

[0086] The formula suggests that the stored hydrogen could be sold to industry or used by hydrogen-powered combustion turbines to generate electricity.

[0087] in, is the amount of hydrogen used for electricity generation; is the amount of hydrogen available for sale.

[0088] The capacity limit of the hydrogen storage tank and the scale of hydrogen energy that can be used to generate electricity and sell can be calculated using equations (4) to (6).

[0089] Formula (4):

[0090] Formula (5):

[0091] Formula (6):

[0092] in, and is the maximum and minimum storage level of the hydrogen storage tank; X h,t a binary variable indicating whether the hydrogen storage is used for hydrogen power generation; The maximum amount of hydrogen used for power generation; σ h,t A binary variable indicating whether the stored hydrogen is for sale; is the maximum amount of hydrogen available for sale.

[0093] Formula (7):

[0094] This formula can calculate the power generation of a hydrogen gas engine generator set.

[0095] Among them, ψ h It represents the power constant coefficient of hydrogen power generation; It is the power of converting hydrogen into electricity.

[0096] Formula (8): σ h,t+X h,t ≤1

[0097] The formula shows that the hydrogen system can only choose one of the following methods at the same time: selling the generated hydrogen or consuming the hydrogen through the hydrogen power plant.

[0098] It should be noted that in step S11, the solar wind power generation mathematical model established includes the following formula:

[0099] Solar and wind power generation capacity depends on weather conditions and is determined by the following formula:

[0100] Formula (9):

[0101] Formula (10):

[0102] in, Represents photovoltaic power generation; η PV is the photovoltaic panel efficiency; is the number of photovoltaic panels; The area of ​​a single photovoltaic panel; I t,s It is solar radiation; is the ambient temperature; is the wind power; v t,s is the real-time wind speed; v ci is the cut-in wind speed; v co is the cut-out wind speed; v i,r is the rated wind speed; is the rated power.

[0103] In this embodiment, the uncertainty of solar radiation and wind speed changes can be simulated. For solar radiation, the beta probability distribution function (PDF) is considered to be the possible conditions for generating different solar radiations, which can be expressed as the following formula:

[0104] Formula (11):

[0105] Formula (12):

[0106] Formula (13):

[0107] Among them, PDF() is the beta probability distribution function; Γ is a specific common function; α and β are specific parameters; μ and δ represent the mean and variance respectively.

[0108] For wind speed conditions, different possible wind speed scenarios can be generated using the Weibull PDF, see the following formula:

[0109] Formula (14):

[0110] Formula (15):

[0111] Here, the parameters c and k represent the scale and shape index of the Weibull PDF, respectively.

[0112] It should be noted that in step S11, the formula of the power balance constraint of the micro energy grid operation model established is expressed as follows:

[0113] Formula (16):

[0114]

[0115] in, It is the amount of electricity purchased from the grid; is the power generation of the micro-turbine; ρ s is the confidence factor; and are the discharge and charge amounts of the energy storage battery respectively; It is the amount of electricity sold to the grid; It is the electricity load after demand response.

[0116] It should be noted that in step S12, when establishing the adjustable load management strategy and uncertainty model, the adjustable load management needs to limit the number of transferred loads and prevent the occurrence of load loss. In addition, it is also necessary to simulate the load profile after the load transfer occurs. The formula of the load transfer scheme of the adjustable load management is as follows:

[0117] Formula (17): DR min ≤DR l,t ≤DR max

[0118] Formula (18):

[0119] Formula (19):

[0120] Among them, DR l,t is the demand response level of the load; DR min and DR max are the minimum and maximum levels of demand response, respectively; ldr l,t is a transferable load; It is the electricity load after demand response.

[0121] It should be noted that, in step S13, the formula for establishing the first objective function with the goal of minimizing the system cost is expressed as follows:

[0122] Formula (20):

[0123] in, is the electricity price; α i and β i is the fuel cost coefficient of the micro-turbine; γ i is the fixed cost coefficient of the micro-turbine; u i,t is a binary variable representing the operation of the micro-turbine; It is the price of hydrogen.

[0124] In specific practice, one of the main goals of this embodiment is to minimize the cost of the system. It mainly includes the cost of purchasing energy from the main grid and the power generation cost of the distributed micro-turbine, minus the profit of energy trading with the main grid and the profit of selling hydrogen to industry. Therefore, the above formula (20) can be obtained.

[0125] It should be noted that in step S13, the second goal of the system is to provide a sustainable mechanism for its operation with the lowest carbon emissions. Therefore, the formula for establishing the second objective function with the goal of minimizing carbon emissions is as follows:

[0126] Formula (21):

[0127] in, is the carbon emission coefficient from the electricity generated by the grid; is the emission coefficient of the micro-turbine; is the emission coefficient of the hydrogen engine.

[0128] It should be noted that in step S13, the Min-MAX method can be used for modeling to reduce the peak load. Therefore, the formula for establishing the third objective function with the goal of minimizing the peak load is as follows:

[0129] Formula (22):

[0130] in, It is the electricity load after demand response.

[0131] It should be noted that, in step S14, in order to transform the three-objective problem into a single-objective problem, this embodiment adopts a hybrid standardized Min-Max method to transform it, and establishes a MIN-MAX transformation model according to the first objective function, the second objective function and the third objective function, including:

[0132] The mixed standardized Min-Max method is used to transform the first objective function, the second objective function and the third objective function into a single objective function, and the MIN-MAX transformation model is established. The formula is as follows:

[0133] Formula (23):

[0134] Among them, w1, w2 and w3 represent the importance of the objective function, w1+w2+w3=1; cost * and Emission * Represents the optimal result of operating cost and emission intensity.

[0135] Preferably, in formula (23) It can be replaced by the following formula:

[0136] Formula (24):

[0137] Among them, λ l,t Refers to a free variable.

[0138] Then, formula (23) can be converted into the following formula (25):

[0139] Formula (25):

[0140] According to a second aspect of an embodiment of the present invention, there is provided a multi-objective adjustable load management device based on a micro energy network, comprising:

[0141] A main controller, and a memory connected to the main controller;

[0142] a memory in which program instructions are stored;

[0143] The main controller is used to execute program instructions stored in the memory to perform any of the above methods.

[0144] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0145] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0146] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0147] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0148] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0149] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0150] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0151] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0152] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A multi-objective adjustable load management method based on micro energy grid, characterized in that: include: Establish mathematical models of hydrogen system and solar wind power generation, and establish micro energy network operation model based on the mathematical models of hydrogen system and solar wind power generation; Establishing an adjustable load management strategy and an uncertainty model according to a load transfer scheme of preset adjustable load management; A first objective function is established with the goal of minimizing system cost, a second objective function is established with the goal of minimizing carbon emissions, and a third objective function is established with the goal of minimizing peak load; According to the first objective function, the second objective function and the third objective function, a MIN-MAX conversion model is established in combination with the micro energy grid operation model, the adjustable load management strategy and the uncertainty model; Multi-objective adjustable load management is carried out using the MIN-MAX transformation model.

2. The method according to claim 1, characterized in that The mathematical model of the hydrogen system established includes the following formulas: Formula (1): in, is the electrolyzer input power; h,t is the electricity-to-hydrogen conversion coefficient; is the power input to the hydrogen storage system; Formula (2): Among them, HL h,t is the capacity of the hydrogen storage tank at time t; is the capacity of the hydrogen storage tank at the initial moment; is the efficiency of hydrogen charging and discharging; is the power of hydrogen output from the hydrogen storage tank; Formula (3): in, is the amount of hydrogen used for electricity generation; is the amount of hydrogen available for sale; Formula (4): Formula (5): Formula (6): in, and is the maximum and minimum storage level of the hydrogen storage tank; h,t a binary variable indicating whether the hydrogen storage is used for hydrogen power generation; The maximum amount of hydrogen used for power generation; σ h,t A binary variable indicating whether the stored hydrogen is for sale; is the maximum amount of hydrogen available for sale; Formula (7): Formula (8): σ h,t + χ h,t ≤ 1 Among them, ψ h It represents the power constant coefficient of hydrogen power generation; It is the power of converting hydrogen into electricity.

3. The method according to claim 2, characterized in that The established mathematical model of solar wind power generation includes the following formulas: Formula (9): Formula (10): in, Represents photovoltaic power generation; η PV is the photovoltaic panel efficiency; is the number of photovoltaic panels; The area of ​​a single photovoltaic panel; I t,s It is solar radiation; is the ambient temperature; is the wind power; v t,s is the real-time wind speed; v ci is the cut-in wind speed; v co is the cut-out wind speed; v i,r is the rated wind speed; is the rated power; Formula (11): Formula (12): Formula (13): Among them, PDF() is the beta probability distribution function; Γ is a specific common function; α and β are specific parameters; μ and δ represent the mean and variance respectively; Formula (14): Formula(15): Here, the parameters c and k represent the scale and shape index of the Weibull PDF, respectively.

4. The method according to claim 3, characterized in that The formula of power balance constraint of the established micro energy grid operation model is as follows: Formula (16): in, It is the amount of electricity purchased from the grid; is the power generation of the micro-turbine; ρ s is the confidence factor; and are the discharge and charge amounts of the energy storage battery respectively; It is the amount of electricity sold to the grid; It is the electricity load after demand response.

5. The method according to claim 4, characterized in that The formula of the load transfer scheme of the preset adjustable load management is as follows: Formula (17): DR min ≤DR l,t ≤DR max Formula(18): Formula(19): Among them, DR l,t is the demand response level of the load; DR min and DR max are the minimum and maximum levels of demand response, respectively; ldr l,t is a transferable load; It is the electricity load after demand response.

6. The method according to claim 5, characterized in that The formula for establishing the first objective function with the goal of minimizing system cost is as follows: Formula(20): in, is the electricity price; α i and β i is the fuel cost coefficient of the micro-turbine; γ i is the fixed cost coefficient of the micro-turbine; u i,t is a binary variable representing the operation of the micro-turbine; It is the price of hydrogen.

7. The method according to claim 6, characterized in that The formula for establishing the second objective function with the goal of minimizing carbon emissions is as follows: Formula(21): in, is the carbon emission coefficient from the electricity generated by the grid; is the emission coefficient of the micro-turbine; is the emission coefficient of the hydrogen engine.

8. The method according to claim 7, characterized in that The formula for establishing the third objective function with the goal of minimizing the peak load is as follows: Formula(22): in, It is the electricity load after demand response.

9. The method according to claim 8, characterized in that According to the first objective function, the second objective function and the third objective function, a MIN-MAX conversion model is established, including: The mixed standardized Min-Max method is used to transform the first objective function, the second objective function and the third objective function into a single objective function, and the MIN-MAX transformation model is established. The formula is as follows: Formula(23): Among them, w1, w2 and w3 represent the importance of the objective function, w1+w2+w3=1; cost * and Emission * Represents the optimal result of operating cost and emission intensity.

10. A multi-objective adjustable load management device based on a micro energy network, characterized in that: include: A main controller, and a memory connected to the main controller; a memory in which program instructions are stored; The main controller is used to execute program instructions stored in the memory to perform the method according to any one of claims 1 to 9.

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