Sagomean new energy base safety economic environment multi-target bilevel planning method and device

Abstract

The invention provides a multi-target double-layer planning method and device for the safe and economic environment of a new energy base of Sagomean, and the method comprises the steps: building mathematical models of various types of energy equipment according to the architecture of a comprehensive energy system in the new energy base; the method comprises the following steps: establishing a double-layer planning model, taking a maximum new energy base guaranteed power supply capability and a maximum new energy consumption rate as safety and economy objective functions by an upper-layer capacity planning model, and converting a multi-objective function of an upper-layer model into a single-objective function by adopting a minimum-maximum normalization method; the lower-layer planning model performs analysis by applying a carbon pinch point technology and is used for determining the minimum clean energy usage amount required for meeting the energy demand and controlling the carbon emission requirement; and solving the model, and outputting an optimal capacity configuration scheme of the new energy base under the conditions of ensuring comprehensive optimization of the power supply capability and the new energy consumption rate and considering the carbon emission. According to the method, a more efficient and feasible solution can be provided in an actual planning process, and sustainable development of new energy base construction is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of energy system planning, and in particular to a multi-objective double-layer planning method and device for the safety and economic environment of a Shagohuang new energy base. Background Art

[0002] With the increasing global demand for renewable energy, deserts, Gobi and wasteland (abbreviated as "Sagohuang") have become important areas for the construction of new energy bases due to their rich solar and wind energy resources. However, these areas are far away from the load center and have fragile ecological environment. The construction of the Sagohuang new energy base faces the constraints of the "safety-economy-environment" impossible triangle, which needs to be solved urgently. In this context, how to effectively and scientifically plan the new energy base to achieve multi-objective optimization has become a core issue of concern to the academic and engineering communities.

[0003] Traditional new energy base planning methods often focus on optimizing a single goal, such as simply pursuing economic benefits or maximizing resource utilization, while ignoring other key factors such as safety and environmental protection. This planning method can easily lead to local optimization and poor overall results, and may even cause safety hazards or environmental damage, thus affecting the sustainable development of new energy bases.

[0004] In order to overcome the above problems, in recent years, research methods based on multi-objective optimization and two-level planning have gradually been introduced into the planning process of new energy bases. The two-level planning method can flexibly respond to complex multi-objective constraints by establishing an interactive relationship between the upper and lower levels. Through reasonable two-level structural optimization, the interaction and trade-off between different objectives can be taken into account, thereby achieving coordinated optimization of safety, economy, and environment.

[0005] However, the existing two-level planning methods still have problems such as high computational complexity and difficulty in coordinating conflicting objectives, which lead to certain challenges in their application in actual new energy bases. Therefore, it is of great theoretical value and application prospect to develop a new energy base safety, economic and environmental multi-objective two-level planning method that can simultaneously consider multiple objectives such as economic benefits, safety assurance and environmental impact. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a multi-objective two-level planning method and device for the safety and economic environment of the Shagohuang new energy base. By optimizing the objective function design, the reasonable setting of constraints and the improvement of the algorithm model, the shortcomings of the existing methods can be overcome, and a more efficient and feasible solution can be provided in the actual planning process, thereby promoting the sustainable development of the new energy base construction.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is:

[0008] A multi-objective bi-level programming method for the safety and economic environment of a Shagohuang new energy base comprises the following steps:

[0009] Step S1: According to the comprehensive energy system architecture in the new energy base, a mathematical model of wind power equipment, photovoltaic equipment, gas turbines, hydrogen energy storage and conversion devices, and energy storage devices in the new energy base is established to provide data support for the subsequent two-layer planning model;

[0010] Step S2: A two-level planning model is established based on the mathematical model of step S1. The upper capacity planning model of the two-level planning model takes the maximum power supply capacity of the new energy base and the maximum new energy consumption rate as the safety and economic objective functions, and uses the minimum-maximum normalization method to convert the multi-objective function of the upper model into a single objective function. The decision variables are the capacities of various units.

[0011] Step S3: Apply the carbon pinch technique to analyze the lower-level operation planning model of the two-level planning model, taking the minimum clean energy supply to meet the regional energy demand as the environmental objective function, considering the carbon emission constraints, to determine the minimum clean energy usage required to meet the energy demand and control the carbon emission requirements;

[0012] Step S4: Use a genetic algorithm to solve the upper-level capacity planning model, carry out capacity planning of the new energy base under consideration of safety and economy, and pass the planned capacity of each type of unit to the lower level. The lower-level operation model formulates the lower-level optimal solution under its optimization objectives and constraints, and feeds back the optimal solution to the upper-level planning model. The upper-level planning model adjusts the solution according to the feedback, and optimizes the planned capacity of each type of equipment again. Repeat this process until both the upper and lower layer solutions meet the conditions or reach the maximum number of iterations, and output the optimal capacity configuration plan for the new energy base under the condition of optimal power supply capacity and new energy consumption rate while taking into account carbon emissions.

[0013] The present invention also provides a multi-objective double-layer planning device for the safety and economic environment of a Shagohuang new energy base, including the following modules:

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned multi-objective two-layer planning method for the safety and economic environment of the Shagohuang new energy base are implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned multi-objective two-level planning method for the safety and economic environment of the Shagohuang new energy base.

[0016] Beneficial effects:

[0017] In order to solve the problem that it is difficult to take into account multiple objectives of safety, economy and environment in the current capacity planning of Shagohuang new energy base, the present invention proposes a two-level planning model for the capacity configuration of Shagohuang new energy base. The upper level takes the maximum power supply capacity of the new energy base and the maximum new energy consumption rate as the objective function, and the lower level planning fully considers the carbon emission constraints, and uses genetic algorithms and carbon pinch analysis methods to solve and output the optimal capacity configuration plan of Shagohuang new energy base under the conditions of optimal power supply capacity and new energy consumption rate while taking into account carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of the multi-objective double-layer planning method for the safety and economic environment of the Shagohuang new energy base of the present invention;

[0019] Figure 2 It is a flow chart for solving the bi-level programming model in the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in each embodiment of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0021] The present invention provides a multi-objective two-level planning method for the safety and economic environment of the Shagohuang new energy base. By optimizing the objective function design, the reasonable setting of constraint conditions and the improvement of the algorithm model, the shortcomings of the existing methods are overcome, a more efficient and feasible solution is provided in the actual planning process, and the sustainable development of the new energy base construction is promoted.

[0022] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below through specific implementations and in conjunction with the accompanying drawings.

[0023] like Figure 1 As shown, a multi-objective two-level planning method for the safety and economic environment of a Shagohuang new energy base provided by an embodiment of the present invention includes the following steps:

[0024] Step S1: According to the comprehensive energy system architecture in the new energy base, a mathematical model of wind power equipment, photovoltaic equipment, gas turbines, hydrogen energy storage and conversion devices, and energy storage devices in the new energy base is established to provide data support for the subsequent two-layer planning model;

[0025] Step S2: A two-level planning model is established based on the mathematical model of step S1. The upper capacity planning model of the two-level planning model takes the maximum power supply capacity of the new energy base and the maximum new energy consumption rate as the safety and economic objective functions, and uses the minimum-maximum normalization method to convert the multi-objective function of the upper model into a single objective function. The decision variables are the capacities of various units.

[0026] Step S3: Apply the carbon pinch technique to analyze the lower-level operation planning model of the two-level planning model, taking the minimum clean energy supply to meet the regional energy demand as the environmental objective function, considering the carbon emission constraints, to determine the minimum clean energy usage required to meet the energy demand and control the carbon emission requirements;

[0027] Step S4: Use a genetic algorithm to solve the upper-level capacity planning model, carry out capacity planning of the new energy base under consideration of safety and economy, and pass the planned capacity of each type of unit to the lower level. The lower-level operation model formulates the lower-level optimal solution under its optimization objectives and constraints, and feeds back the optimal solution to the upper-level planning model. The upper-level planning model adjusts the solution according to the feedback, and optimizes the planned capacity of each type of equipment again. Repeat this process until both the upper and lower layer solutions meet the conditions or reach the maximum number of iterations, and output the optimal capacity configuration plan for the new energy base under the condition of optimal power supply capacity and new energy consumption rate while taking into account carbon emissions.

[0028] Specifically, the step S1 includes:

[0029] Step S11: Establishing a mathematical model of wind power equipment:

[0030] ;

[0031] in, is the actual power generated by the wind power equipment, is the rated power of the wind power equipment; is the undisturbed wind speed, is the fan cut-in wind speed, is the rated wind speed of the fan, Cut out wind speed for the fan; is the air density, is the swept area of ​​the wind wheel; is the wind energy utilization rate, which is related to the tip speed ratio and the blade pitch angle;

[0032] Step S12: Establishing a mathematical model of the photovoltaic device:

[0033] ;

[0034] in, is the actual power generation of the photovoltaic panel, is the nominal power and installed capacity of the photovoltaic modules under standard test conditions. is the power temperature coefficient, is the actual operating temperature of the photovoltaic module, is the reference temperature under standard test conditions, is the power decay coefficient, is the actual light intensity, is the light intensity under standard conditions.

[0035] Step S13: Establishing a mathematical model of the gas turbine:

[0036] ;

[0037] in, Output electrical power to the gas turbine, is the gas turbine power generation efficiency, Consumes natural gas power for gas turbines.

[0038] Step S14: Establishing the mathematical model of the electrolytic cell:

[0039] ;

[0040] in, is the hydrogen production power input from the electrolyzer to the hydrogen storage tank, is the electrical power input to the electrolyzer, is the electrolysis efficiency of the electrolytic cell.

[0041] Step S15: Establish a mathematical model of the fuel cell:

[0042] ;

[0043] in, Output electrical power to the fuel cell, The power delivered from the hydrogen tank to the fuel cell, The power generation efficiency of the fuel cell.

[0044] Step S16: Establishing the mathematical model of the hydrogen storage tank:

[0045] ;

[0046] in, for The energy stored in the hydrogen tank at all times, For the hydrogen tank charging efficiency, The working efficiency of hydrogen storage tank; and They represent the operating status of the electrolyzer and hydrogen fuel cell at time t respectively, 0 represents the shutdown state, and 1 represents the operating state.

[0047] Step S17: Establishing the mathematical model of the battery:

[0048] ;

[0049] in, For the battery The energy stored in is the battery self-discharge rate; , are the battery charging and discharging power, respectively. , are the battery charging and discharging efficiencies respectively; and Respectively represent the energy storage The charging and discharging status at all times.

[0050] After step S1, a mathematical model of each energy device in the new energy base is constructed to provide data support for subsequent model planning and help optimize system configuration.

[0051] Specifically, the step S2 includes:

[0052] Step S21: Establish an upper-level capacity planning model. The new energy base has the maximum power supply capacity, that is, the maximum power supply reliability. The optimization target is as follows:

[0053] ;

[0054] in, is the security objective function; Provide power supply reliability for new energy bases; , , They are the annual power shortage ratio of new energy bases, new energy output fluctuations, and comprehensive failure rate; It is the weight coefficient, which is used to adjust the relative importance of each indicator in the evaluation of the power supply capacity of the new energy base.

[0055] Annual power shortage ratio of new energy bases The calculation is as follows:

[0056] ;

[0057] in, , For the system The power shortage and power load at the moment; New energy base The amount of electricity generated at any time, For energy storage devices Discharge amount at any time, The amount of wind and solar power abandoned at time t for the new energy base.

[0058] Fluctuation of renewable energy output in renewable energy bases The calculation is as follows:

[0059] ;

[0060] in, , They are respectively the maximum and minimum power generation of new energy in a typical day at the new energy base.

[0061] Comprehensive failure rate of new energy base The calculation is as follows:

[0062] ;

[0063] in, , Respectively The failure rate and installed capacity of the equipment.

[0064] Step S22: The new energy base has the largest new energy consumption rate, that is, the new energy base has the smallest power abandonment rate, which can be expressed as:

[0065] ;

[0066] in, is the economic objective function, is the power abandonment rate of new energy bases, New energy base Renewable energy generation at all times.

[0067] Step S23: Use the minimum-maximum normalization method to convert the multi-objective optimization problem of the upper model into a single-objective optimization problem, as shown in the following formula:

[0068] ;

[0069] in, is the normalized total objective function, and are the maximum and minimum values ​​of the power supply reliability of the new energy base based on operating experience, and They are respectively the maximum and minimum values ​​of the power abandonment rate of the new energy base based on operating experience.

[0070] Step S24: construct the upper-level capacity planning model constraint conditions, as shown in the following formula:

[0071] The output constraints of wind power equipment, photovoltaic equipment, gas turbines and other equipment and the system power balance constraints are:

[0072] ;

[0073] in, is the maximum output of the gas turbine.

[0074] The constraints of the hydrogen energy storage system, including the power constraints of the electrolyzer and fuel cell and the capacity constraints of the hydrogen storage tank, are:

[0075] ;

[0076] in, , are the minimum and maximum output power of the electrolyzer, , are the minimum and maximum output power of the fuel cell, , They are the minimum and maximum values ​​of the energy stored in the hydrogen storage tank.

[0077] Through step S2, the safety and economy of the capacity planning of the Shagohuang new energy base are taken into consideration, providing capacity support for the lower-level planning.

[0078] Specifically, step S3 includes:

[0079] Step S31: construct the lower carbon pinch analysis optimization objective function as shown below:

[0080] ;

[0081] in, is the environmental objective function, i.e., the minimum renewable energy supply required to meet the region’s electricity demand and carbon emission limit targets; The supply of renewable energy.

[0082] Step S32: Construct the lower carbon pinch analysis constraint condition, as shown in the following formula:

[0083] The energy supply balance condition is:

[0084] ;

[0085] in, For energy The maximum supply of , The energy sources that meet the regional energy needs are Supply and energy of the remaining supply.

[0086] The energy demand balance condition is:

[0087] ;

[0088] in, is the total regional energy demand.

[0089] The carbon emission limits are:

[0090] ;

[0091] in, For energy The supply emission factor is is the regional energy demand emission factor, For Region Emission constraints.

[0092] The non-negative constraints are:

[0093] ;

[0094] After step S3, by introducing carbon pinch analysis and considering carbon emission constraints, the minimum clean energy supply that meets regional energy demand is solved, while taking into account the environmental protection of the Shagohuang New Energy Base.

[0095] Specifically, Figure 2 As shown, step S4 includes:

[0096] Step S41: Initialize, set genetic algorithm parameters, and input relevant data;

[0097] Step S42: Optimize the upper-layer capacity planning model, iterate through the genetic algorithm, solve the new energy base capacity planning scheme, and pass it into the lower-layer operation planning model;

[0098] Step S43: The lower-level operation planning model is optimized, and the minimum supply of renewable energy is solved through carbon pinch technology analysis, and the minimum supply of renewable energy is returned to the upper-level planning model;

[0099] Step S44: The upper-layer planning model adjusts the solution according to the feedback, optimizes the planned capacity of each type of equipment again, and determines whether the solutions between the upper and lower layers conflict. If there is a conflict, return to step S42 and repeat this process until the maximum number of upper and lower layer iterations is reached or the upper and lower layer solutions do not conflict, and outputs the optimal capacity configuration plan.

[0100] In step S42, the specific steps of the genetic algorithm include:

[0101] Step 42.1: Initialize and set genetic algorithm parameters;

[0102] Step 42.2: Generate initial population ;

[0103] Step 42.3: Bring the generated individuals into the upper capacity planning model one by one to solve the corresponding fitness values;

[0104] Step 42.4: Generate a child population from the parent population through crowding selection, crossover, and mutation operations ;

[0105] Step 42.5: Initial Parent Population and progeny population Synthesize a new population , follow step 42.3 to solve the new population The expected value of the upper fitness of each individual, and then prioritize the solution set; use the new population The top ranked solutions generate the new generation of parent population ;

[0106] Step 42.6: Populate the new generation parent population The elements in are sorted by congestion, and if the number of iterations reaches the maximum value, the optimal solution is output;

[0107] Otherwise go to step 42.4.

[0108] In step S43, the carbon pinch analysis includes:

[0109] Step 43.1: Based on the energy demand and Emissions restrictions draw a composite curve for energy demand;

[0110] Step 43.2: Based on the energy supply and Emission levels draw a composite energy supply curve;

[0111] Step 43.3: Superimpose the two composite curves in the same coordinate system, and move the energy supply curve horizontally to the right so that the energy supply curve intersects with the rightmost side of the energy demand curve. The intersection point is the carbon pinch point;

[0112] Step 43.4: The horizontal distance between the initial position of the energy supply curve and the shifted position is the amount of clean energy required to meet the regional emission limits;

[0113] Through step S4, the optimal capacity configuration plan of the Shagohuang New Energy Base is obtained under the condition of optimal power supply capacity and new energy consumption rate while taking into account carbon emission restrictions.

[0114] The present invention also provides a multi-objective double-layer planning device for the safety and economic environment of a Shagohuang new energy base, including the following modules:

[0115] The mathematical model building module builds mathematical models of wind power equipment, photovoltaic equipment, gas turbines, hydrogen energy storage and conversion devices, and energy storage devices in the new energy base according to the comprehensive energy system architecture in the new energy base, providing data support for the subsequent two-level planning model;

[0116] The objective function construction module establishes a two-level planning model based on the mathematical model. The upper capacity planning model of the two-level planning model takes the maximum power supply capacity of the new energy base and the maximum new energy consumption rate as the safety and economic objective functions, and uses the minimum-maximum normalization method to convert the multi-objective function of the upper model into a single objective function. The decision variables are the capacities of various units.

[0117] The constraint module applies the carbon pinch technique to analyze the lower-level operation planning model of the two-level planning model, taking the minimum clean energy supply to meet the regional energy demand as the environmental objective function, and considering the carbon emission constraints to determine the minimum clean energy usage required to meet the energy demand and control carbon emission requirements;

[0118] The configuration module uses a genetic algorithm to solve the upper-level capacity planning model, conducts capacity planning of the new energy base under consideration of safety and economy, and passes the planned capacity of each type of unit to the lower level. The lower-level operation model formulates the lower-level optimal solution under its optimization objectives and constraints, and feeds the optimal solution back to the upper-level planning model. The upper-level planning model adjusts the solution based on the feedback, and optimizes the planned capacity of each type of equipment again. This process is repeated until the upper and lower solutions meet the conditions or the maximum number of iterations is reached, and the optimal capacity configuration plan for the new energy base is output under the condition of optimal power supply capacity and new energy consumption rate while taking into account carbon emissions.

[0119] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned multi-objective two-layer planning method for the safety and economic environment of the Shagohuang new energy base are implemented.

[0120] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned multi-objective two-level planning method for the safety and economic environment of the Shagohuang new energy base.

[0121] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and interpreted scripting language JavaScript, etc.

[0122] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0123] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0124] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0125] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0126] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A multi-objective two-level programming method for the safety and economic environment of the Shagohuang new energy base, characterized in that: The steps include: Step S1: According to the comprehensive energy system architecture in the new energy base, a mathematical model of wind power equipment, photovoltaic equipment, gas turbines, hydrogen energy storage and conversion devices, and energy storage devices in the new energy base is established to provide data support for the subsequent two-layer planning model; Step S2: A two-level planning model is established based on the mathematical model of step S1. The upper capacity planning model of the two-level planning model takes the maximum power supply capacity of the new energy base and the maximum new energy consumption rate as the safety and economic objective functions, and uses the minimum-maximum normalization method to convert the multi-objective function of the upper model into a single objective function. The decision variables are the capacities of various units. Step S3: Apply the carbon pinch technique to analyze the lower-level operation planning model of the two-level planning model, taking the minimum clean energy supply to meet the regional energy demand as the environmental objective function, considering the carbon emission constraints, to determine the minimum clean energy usage required to meet the energy demand and control the carbon emission requirements; Step S4: Use a genetic algorithm to solve the upper-level capacity planning model, carry out capacity planning of the new energy base under consideration of safety and economy, and pass the planned capacity of each type of unit to the lower level. The lower-level operation model formulates the lower-level optimal solution under its optimization objectives and constraints, and feeds back the optimal solution to the upper-level planning model. The upper-level planning model adjusts the solution according to the feedback, and optimizes the planned capacity of each type of equipment again. Repeat this process until both the upper and lower layer solutions meet the conditions or reach the maximum number of iterations, and output the optimal capacity configuration plan for the new energy base under the condition of optimal power supply capacity and new energy consumption rate while taking into account carbon emissions.

2. According to claim 1, a multi-objective two-level planning method for the safety and economic environment of the Shagohuang new energy base is characterized in that: The step S1 comprises: Step S11: Establishing a mathematical model of wind power equipment: ； in, is the actual power generated by the wind power equipment, is the rated power of the wind power equipment; is the undisturbed wind speed, is the fan cut-in wind speed, is the rated wind speed of the fan, Cut out wind speed for the fan; is the air density, is the swept area of ​​the wind wheel; is the wind energy utilization rate, which is related to the tip speed ratio and the blade pitch angle; Step S12: Establishing a mathematical model of the photovoltaic device: ； in, is the actual power generation of the photovoltaic panel, is the nominal power and installed capacity of the photovoltaic modules under standard test conditions. is the power temperature coefficient, is the actual operating temperature of the photovoltaic module, is the reference temperature under standard test conditions, is the power decay coefficient, is the actual light intensity, is the light intensity under standard conditions; Step S13: Establishing a mathematical model of the gas turbine: ； in, Output electrical power to the gas turbine, is the gas turbine power generation efficiency, Consume natural gas power for gas turbines; Step S14: Establishing the mathematical model of the electrolytic cell: ； in, is the hydrogen production power input from the electrolyzer to the hydrogen storage tank, is the electrical power input to the electrolyzer, is the electrolysis efficiency of the electrolytic cell; Step S15: Establish a mathematical model of the fuel cell: ； in, Output electrical power to the fuel cell, The power delivered from the hydrogen tank to the fuel cell, The power generation efficiency of the fuel cell; Step S16: Establishing the mathematical model of the hydrogen storage tank: ； in, for The energy stored in the hydrogen tank at all times, For the hydrogen tank charging efficiency, The working efficiency of hydrogen storage tank; and They represent the operating status of the electrolyzer and the hydrogen fuel cell at time t, 0 represents the shutdown status, and 1 represents the operating status; Step S17: Establishing the mathematical model of the battery: ； in, For the battery The energy stored in is the battery self-discharge rate, , are the battery charging and discharging power, respectively. , are the battery charging and discharging efficiencies respectively; and Respectively represent the energy storage The charging and discharging status at all times.

3. According to claim 2, a multi-objective two-level programming method for the safety and economic environment of the Shagohuang new energy base is characterized in that: The step S2 comprises: Step S21: Establish an upper-level capacity planning model. The new energy base has the maximum power supply capacity, that is, the maximum power supply reliability. The optimization target is as follows: ； in, is the security objective function; Provide power supply reliability for new energy bases; , , They are the annual power shortage ratio of new energy bases, new energy output fluctuations, and comprehensive failure rate; , , is the weight coefficient, which is used to adjust the relative importance of each indicator in the evaluation of the power supply guarantee capability of the new energy base; Annual power shortage ratio of new energy bases The calculation of is as follows: ； in, , For the system The power shortage and power load at the moment; New energy base The amount of electricity generated at any time, For energy storage devices Discharge amount at any time, The amount of wind and solar power abandoned at time t for the new energy base; Fluctuation of renewable energy output in renewable energy bases The calculation of is as follows: ； in, , They are the maximum and minimum power generation of new energy in a typical day at the new energy base; Comprehensive failure rate of new energy base The calculation of is as follows: ； in, , Respectively Failure rate and installed capacity of each type of equipment; Step S22: The new energy base has the largest new energy consumption rate, that is, the new energy base has the smallest power abandonment rate, which is expressed as: ； in, is the economic objective function, is the power abandonment rate of new energy bases, New energy base Renewable energy generation at every moment; Step S23: Use the minimum-maximum normalization method to convert the multi-objective optimization problem of the upper model into a single-objective optimization problem as shown in the following formula: ； in, is the normalized total objective function, and are the maximum and minimum values ​​of the power supply reliability of the new energy base based on operating experience, and They are the maximum and minimum values ​​of the power abandonment rate of the new energy base based on operating experience; Step S24: The upper layer capacity planning model constraint condition is as follows: The output constraints of wind power equipment, photovoltaic equipment, gas turbines and other equipment and the system power balance constraints are: ； in, is the maximum output of the gas turbine; The constraints of the hydrogen energy storage system, including the power constraints of the electrolyzer and fuel cell and the capacity constraints of the hydrogen storage tank, are: ； in, , are the minimum and maximum output power of the electrolyzer, , are the minimum and maximum output power of the fuel cell, , They are the minimum and maximum values ​​of the energy stored in the hydrogen storage tank.

4. According to claim 3, a multi-objective two-level programming method for the safety and economic environment of the Shagohuang new energy base is characterized in that: The step S3 comprises: Step S31: construct the lower carbon pinch analysis optimization objective function as follows: ； in, is the environmental objective function, i.e., the minimum renewable energy supply required to meet the region’s electricity demand and carbon emission limit targets; The amount of renewable energy supplied; Step S32: Construct the lower carbon pinch analysis constraint condition as shown in the formula: Establishing energy supply balance conditions: ； in, For energy The maximum supply of , The energy sources that meet the regional energy needs are Supply and energy The remaining supply of Constructing energy demand balance conditions: ； in, is the total regional energy demand; Constructing carbon emission constraints: ； in, For energy The supply emission factor is is the regional energy demand emission factor, For Region Emissions constraints; Construct non-negative constraints: 。 5. According to claim 4, a multi-objective two-level programming method for the safety and economic environment of the Shagohuang new energy base is characterized in that: The step S4 comprises: Step S41: Initialize, set genetic algorithm parameters, and input relevant data; Step S42: Optimizing the upper-layer capacity planning model, solving the new energy base capacity planning scheme through genetic algorithm iteration, and transferring it to the lower-layer operation planning model; Step S43: Optimize the lower-level operation planning model, analyze the carbon pinch technology, solve the minimum supply of renewable energy, and feed the lower-level solution back to the upper level; Step S44: The upper-layer planning model adjusts the solution according to the feedback, optimizes the planned capacity of each type of equipment again, and determines whether the solutions between the upper and lower layers conflict. If there is a conflict, return to step S42 and repeat this process until the maximum number of upper and lower layer iterations is reached or the upper and lower layer solutions do not conflict, and outputs the optimal capacity configuration plan.

6. According to claim 5, a multi-objective two-level programming method for the safety and economic environment of the Shagohuang new energy base is characterized in that: The steps of the genetic algorithm in step S42 include: Step 42.1: Initialize and set genetic algorithm parameters; Step 42.2: Generate initial population ; Step 42.3: Bring the generated individuals into the upper capacity planning model one by one to solve the corresponding fitness values; Step 42.4: Generate a child population from the parent population through crowding selection, crossover, and mutation operations ; Step 42.5: Initial Population and population Synthesize a new population , solve according to step 42.3 to obtain the population The expected value of the upper fitness of each individual, and then prioritize the solution set; use The top ranked solutions generate the new generation of parent population ; Step 42.6: Populate the new generation parent population The elements in are sorted by congestion, and if the number of iterations reaches the maximum value, the optimal solution is output; Otherwise go to step 42.

4.

7. The multi-objective two-level programming method for the safety and economic environment of the Shagohuang new energy base according to claim 6 is characterized in that: The steps of carbon pinch analysis in step S43 include: Step 43.1: Based on the energy demand and Emissions restrictions draw a composite curve for energy demand; Step 43.2: Based on the energy supply and Emission levels draw a composite energy supply curve; Step 43.3: Superimpose the two composite curves in the same coordinate system, and move the energy supply curve horizontally to the right so that the energy supply curve intersects with the rightmost side of the energy demand curve. The intersection point is the carbon pinch point; Step 43.4: The horizontal distance between the initial position of the energy supply curve and the shifted position is the amount of clean energy required to meet the regional emission limits.

8. A multi-objective two-level planning device for the safety and economic environment of the Shagohuang new energy base, characterized in that: Includes the following modules: The mathematical model building module builds mathematical models of wind power equipment, photovoltaic equipment, gas turbines, hydrogen energy storage and conversion devices, and energy storage devices in the new energy base according to the comprehensive energy system architecture in the new energy base, providing data support for the subsequent two-level planning model; The objective function construction module establishes a two-level planning model based on the mathematical model. The upper capacity planning model of the two-level planning model takes the maximum power supply capacity of the new energy base and the maximum new energy consumption rate as the safety and economic objective functions, and uses the minimum-maximum normalization method to convert the multi-objective function of the upper model into a single objective function. The decision variables are the capacities of various units. The constraint module applies the carbon pinch technique to analyze the lower-level operation planning model of the two-level planning model, taking the minimum clean energy supply to meet the regional energy demand as the environmental objective function, and considering the carbon emission constraints to determine the minimum clean energy usage required to meet the energy demand and control carbon emission requirements; The configuration module uses a genetic algorithm to solve the upper-level capacity planning model, conducts capacity planning of the new energy base under consideration of safety and economy, and passes the planned capacity of each type of unit to the lower level. The lower-level operation model formulates the lower-level optimal solution under its optimization objectives and constraints, and feeds the optimal solution back to the upper-level planning model. The upper-level planning model adjusts the solution based on the feedback, and optimizes the planned capacity of each type of equipment again. This process is repeated until the upper and lower solutions meet the conditions or the maximum number of iterations is reached, and the optimal capacity configuration plan for the new energy base is output under the condition of optimal power supply capacity and new energy consumption rate while taking into account carbon emissions.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of a multi-objective two-level planning method for the safety and economic environment of the Shagohuang new energy base described in one of claims 1-7 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a multi-objective two-level planning method for the safety and economic environment of the Shagohuang new energy base described in one of claims 1-7 are implemented.

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