An economic optimization scheduling method for wind-solar-storage microgrid considering hydrogen production by electrolysis

Abstract

The application discloses to the technical field of micro-grid optimal scheduling, and particularly relates to a wind-solar-fuel-storage micro-grid economic optimal scheduling method considering hydrogen production by electricity, which combines an electricity-hydrogen comprehensive energy micro-grid system, constructs a power model of a micro-power unit, and qualitatively analyzes the output characteristics of the micro-power unit; obtains renewable energy and load data of the electricity-hydrogen comprehensive energy micro-grid assumed to be installed at a site, performs typical scene analysis by means of the original data, extracts the electricity-hydrogen comprehensive energy micro-grid typical scene capable of representing the regional wind-solar-electricity characteristics through scene reduction; establishes an electricity-hydrogen comprehensive energy micro-grid optimal scheduling model; based on the electricity-hydrogen comprehensive energy micro-grid optimal scheduling model, solves the operation state of the electricity-hydrogen comprehensive energy micro-grid, and obtains scheduling decision results for guiding the operation of the micro-grid. An effective control mechanism and an energy storage system are introduced to balance supply and demand, and ensure the stability and efficient operation of the power grid.

Description

Technical Field

[0001] This invention belongs to the field of microgrid optimization scheduling technology, and specifically relates to an economic optimization scheduling method for wind, solar, gas and energy storage microgrids that considers hydrogen production by electricity. Background Technology

[0002] Vigorously promoting locally adapted renewable energy power generation technologies such as wind and solar power can not only effectively address the increasingly severe energy crisis and reduce air pollution, but also serve as distributed power sources embedded in microgrids to improve energy efficiency. Renewable energy sources like wind and solar power are typically random and intermittent. Without timely energy management and effective control, and without developing appropriate microgrid dispatch plans, the uncertainty in wind and solar power output will lead to power fluctuations in the microgrid system, posing a significant challenge to the safe operation of the microgrid.

[0003] Therefore, there is an urgent need for an economically optimized dispatch method for wind, solar, gas, and energy storage microgrids that takes into account the advantages of renewable energy sources such as wind and solar, to formulate a scientific microgrid dispatch plan, and to introduce effective control mechanisms and energy storage systems to balance supply and demand, thereby ensuring the stability and efficient operation of the power grid. Summary of the Invention

[0004] The purpose of this invention is to provide an economically optimized dispatch method for wind-solar-gas-storage microgrids that considers hydrogen production by electricity, comprising the following steps:

[0005] In conjunction with the integrated electric-hydrogen energy microgrid system, a power model of the micro power unit is constructed, and the output characteristics of the micro power unit are qualitatively analyzed. The micro power unit includes: wind turbine generator set, photovoltaic generator set, electrolysis hydrogen production unit, hydrogen fuel cell unit, micro gas turbine and battery energy storage unit.

[0006] Acquire renewable energy and load data at the assumed installation site of the integrated electric-hydrogen energy microgrid, conduct typical scenario analysis using the raw data, and extract typical scenarios of the integrated electric-hydrogen energy microgrid that can represent the regional wind and solar characteristics through scenario reduction.

[0007] Under the constraints of the power model of the micro power unit and the typical scenario of the integrated electric-hydrogen energy microgrid, an optimal scheduling model for the integrated electric-hydrogen energy microgrid is established.

[0008] Based on the aforementioned optimized scheduling model for the integrated electric-hydrogen energy microgrid, the operating status of the integrated electric-hydrogen energy microgrid is solved to obtain scheduling decision results that guide the operation of the microgrid.

[0009] The power models of the micro power supply unit include: the output power model of the wind turbine, the output power model of the photovoltaic cell, the electrolysis hydrogen production model, the output power model of the fuel cell, the output power model of the micro gas turbine, and the charging and discharging model of the battery.

[0010] The output power model of the wind turbine is as follows:

[0011] (1)

[0012] In the formula: It is air density. It is the area swept by the wind turbine blades. It's wind speed. It is the wind energy utilization coefficient;

[0013] The photovoltaic cell output power model is as follows:

[0014] (2)

[0015] In the formula: This is the output power under standard conditions. and These represent standard solar radiation intensity and standard ambient temperature, respectively. and These represent the actual solar radiation intensity and the actual ambient temperature, respectively. It is the temperature coefficient. It refers to the area of ​​the photovoltaic panel. It refers to the production capacity efficiency of photovoltaic panels;

[0016] The electrolytic hydrogen production model is as follows:

[0017] (3)

[0018] In the formula: The electrical power output of the fuel cell. The volume of hydrogen consumed by the fuel cell. The energy conversion coefficient of a fuel cell;

[0019] The fuel cell output power model is as follows:

[0020] (4)

[0021] In the formula: It is the volume of hydrogen gas produced by the electrolyzer. It refers to the working efficiency of the electrolytic cell. It is the power consumption of the electrolytic cell. and It has a low calorific value and density;

[0022] The output electric power model of the micro gas turbine is as follows:

[0023] (5)

[0024] In the formula: To output electrical power for the micro gas turbine, This refers to the volume of natural gas consumed by the micro gas turbine. The lower calorific value of natural gas is taken as 9.7 kW·h / m³. 3 , To improve the power generation efficiency of micro gas turbines, For time intervals;

[0025] The battery charging and discharging model is as follows:

[0026] During charging:

[0027] (6)

[0028] During discharge:

[0029] (7)

[0030] In the formula: express The battery capacity status at all times. express The battery capacity status at any time: SOC=0 indicates that the battery is in a fully discharged state, and SOC=1 indicates that the battery is in a fully charged state. It is the self-discharge rate; Indicates the battery capacity; , Indicates the charging and discharging power of the battery; , It refers to the charging and discharging efficiency of the battery.

[0031] The acquisition of renewable energy and load data at the assumed installation location of the integrated electric-hydrogen microgrid includes:

[0032] After collecting historical data of the microgrid, the historical data is preprocessed. The data preprocessing includes: detecting the data integrity of each time period within a day according to a preset data missing judgment rule; removing the data of the corresponding time period for a period in which the length of consecutive missing data exceeds a preset threshold; and using an interpolation method to complete the missing data for a period in which the length of consecutive missing data does not exceed the preset threshold.

[0033] The analysis of typical scenarios using raw data includes:

[0034] The cumulative probability distribution function of the integrated electric-hydrogen energy microgrid is calculated based on the uncertainties, and the cumulative probability distribution function is divided into N non-overlapping sub-intervals, with the width of each sub-interval being 1 / N.

[0035] For any subinterval i among N intervals, a random number with a value in the range [0,1] is randomly generated within the subinterval to select sample values:

[0036] (8)

[0037] In the formula: It is a sampled value within sub-region i; It is a random number within sub-region i;

[0038] Assuming the cumulative probability distribution function The inverse function is ,Will Substituting the values ​​into the inverse function allows us to calculate the specific sample values ​​of the load data. :

[0039] (9)

[0040] After obtaining the cumulative probability distribution function using the probability density function of wind speed and light intensity, the cumulative probability distribution function of wind speed and light intensity is sampled using the Latin hypercube sampling method to obtain the field sets of wind speed and light intensity.

[0041] The typical scenarios for integrated electric-hydrogen energy microgrids that can represent the regional wind and solar power characteristics, extracted through scenario reduction, include:

[0042] Use K-means clustering algorithm to reduce the scene size;

[0043] The objective of the K-means clustering algorithm is:

[0044] (10)

[0045] The formula for calculating the new cluster centers in the K-means clustering algorithm is as follows:

[0046] (11)

[0047] The evaluation metric for the number of clusters in the K-means clustering algorithm is:

[0048] (12)

[0049] In the formula, Let be the objective function of the clustering algorithm. for The i-th sample, if it belongs to the i-th sample If there are 1 cluster, then the sample value is used; otherwise, it is not counted. Here, m is the total number of samples in the dataset, and K is the number of cluster centers. For the first Cluster centers, For belonging to the first The number of samples in each class C is a clustering effectiveness indicator. k For the first A sample set of classes.

[0050] The objective function of the optimized scheduling model for the integrated electric-hydrogen microgrid is:

[0051] (13)

[0052] In the formula, For the energy purchase cost of microgrid systems, The cost of wind and solar power curtailment penalties for microgrid systems, For the operation and maintenance costs of microgrid systems, The total cost of the microgrid;

[0053] Energy purchase cost of microgrid systems for:

[0054] (14)

[0055] In the formula: , , These are the unit costs of electricity purchase, gas purchase, and hydrogen purchase for microgrids; , , yes The power, gas, and hydrogen purchased by the microgrid during the specified time period; The scheduling period is set to 24, indicating that the scheduling period is 24 hours.

[0056] Curtailment penalties for wind and solar power in microgrid systems for:

[0057] (15)

[0058] In the formula: and It is the unit penalty cost for wind and solar power curtailment in microgrids; and yes Predicted power output of wind and solar power in microgrids during specific time periods; and yes The actual output power of wind and solar power in the microgrid during the time period;

[0059] Operation and maintenance costs of microgrid systems for:

[0060] (16)

[0061] In the formula: and This refers to the unit operation and maintenance cost of wind turbines and photovoltaic panels; and This refers to the unit operation and maintenance cost of micro-turbines and fuel cells; and Micro engines and fuel cells are in Output power during the time period; This refers to the unit operation and maintenance cost of an energy storage system; It is the power used to calculate the equivalent cost of an energy storage system.

[0062] The optimized scheduling model for the integrated electric-hydrogen microgrid normalizes all energy storage units and treats them as a single energy storage system when calculating operation and maintenance costs. This energy storage system includes battery units, gas storage units, and hydrogen storage tank units. The equivalent cost power of the energy storage system is calculated as follows:

[0063] (17)

[0064] In the formula: and It refers to the charging and discharging power of the battery; and It refers to the gas filling and releasing power of the gas storage unit; and It refers to the hydrogen filling and discharging power of the hydrogen storage tank.

[0065] The power balance constraint of the microgrid system in the optimized scheduling model of the integrated electric-hydrogen energy microgrid is as follows:

[0066] (18)

[0067] In the formula: yes The electrical power consumed by the electrolytic cell during a given time period; yes Power demand of microgrid systems during specific time periods;

[0068] The output power constraints for the wind turbine generator set and the photovoltaic generator set are as follows:

[0069] (19)

[0070] (20)

[0071] In the formula: This is the maximum output power of the wind turbine. This is the maximum output power of the photovoltaic unit.

[0072] The energy conversion constraint between the natural gas power consumed and the electrical power generated by the micro gas turbine is:

[0073] (twenty one)

[0074] In the formula: It is the gas-to-electric conversion coefficient of the micro-gas engine, with a value of 0.92;

[0075] The natural gas power consumed by the micro-turbine at a certain time period The constraints are:

[0076] (twenty two)

[0077] In the formula: yes The maximum gas power consumed by the micro gas turbine during a given period.

[0078] The ramp-up constraint for the micro gas turbine unit is:

[0079] (twenty three)

[0080] In the formula: yes The gas power consumed by the micro gas turbine during a given period. and These are the upper and lower limits for the ramp speed of a micro gas turbine;

[0081] The energy conversion constraint between the electrical power consumed by the electrolyzer and the hydrogen power generated is:

[0082] (twenty four)

[0083] In the formula: This is the electro-hydrogen conversion coefficient of the electrolyzer, with a value of 0.87. The hydrogen power generated by the electrolyzer;

[0084] The power consumption constraint of the electrolytic cell during a certain period is:

[0085] (25)

[0086] In the formula: yes The maximum electrical power consumed by the electrolytic cell during a given time period;

[0087] The ramp-up constraint for the change in electrolytic cell output is:

[0088] (26)

[0089] In the formula: yes The electrical power consumed by the electrolytic cell during the time period. and These are the upper and lower limits of the slope for the electrolytic cell;

[0090] The energy conversion constraint between the hydrogen power consumed and the electrical power generated by the fuel cell is:

[0091] (27)

[0092] In the formula: This is the hydrogen-to-electric conversion coefficient of the fuel cell, with a value of 0.95. The hydrogen power consumed by the fuel cell during time period t;

[0093] The hydrogen power consumption constraint of the fuel cell at a certain time period is:

[0094] (28)

[0095] In the formula: yes The maximum hydrogen power consumed by the fuel cell during a given time period;

[0096] The ramp-up constraint for the output variation of the fuel cell in each time period is:

[0097] (29)

[0098] In the formula: yes Hydrogen power consumed by fuel cells during a given period and These are the upper and lower limits of fuel cell ramp rate;

[0099] The constraints of a battery energy storage system during operation are:

[0100] (30)

[0101] In the formula: This is the maximum charging and discharging power of the battery; This is the battery charging / discharging indicator; 0 indicates charging the battery, and 1 indicates discharging the battery. and This represents the charging and discharging efficiency of the battery. It refers to the battery capacity; and The upper and lower limits represent the remaining capacity of the battery.

[0102] The constraints for natural gas energy storage units during operation are:

[0103] (31)

[0104] In the formula: This is the maximum charging and discharging power of the gas storage unit; This is the gas storage unit's filling and releasing indicator. A value of 0 indicates the gas storage unit is filling, and a value of 1 indicates the gas storage unit is releasing. and This represents the gas filling and discharging efficiency of the gas storage unit; It refers to the capacity of the natural gas energy storage unit; and These represent the upper and lower limits of the remaining capacity of the gas storage unit. This represents the remaining capacity of the gas storage unit at time 0. The remaining capacity of the gas storage unit at time 1. This represents the remaining capacity of the gas storage unit at 24 hours.

[0105] The constraints for hydrogen storage tanks during operation are as follows:

[0106] (32)

[0107] In the formula: This is the maximum hydrogen filling and discharging power of the hydrogen storage tank; This is the indicator for filling and discharging hydrogen from the hydrogen storage tank. 0 indicates filling the hydrogen storage tank, and 1 indicates discharging the hydrogen storage tank. and This represents the hydrogen filling and discharging efficiency of the hydrogen storage tank; This refers to the capacity of the hydrogen storage tank; and The upper and lower limits represent the remaining capacity of the hydrogen storage tank. This represents the remaining capacity of the hydrogen storage tank at time 0. This represents the remaining capacity status of the hydrogen storage tank at time 1. This represents the remaining capacity status of the hydrogen storage tank at 24 hours.

[0108] The power purchase constraint for microgrid systems is:

[0109] (33)

[0110] In the formula: It is the maximum value of the electrical power exchanged between the microgrid and the distribution network. Power purchased for microgrid systems;

[0111] The gas purchase power constraint for the microgrid system is:

[0112] (34)

[0113] In the formula: This is the maximum power that the microgrid can purchase from natural gas. Gas purchase capacity for microgrid systems;

[0114] The hydrogen purchase power constraint for the microgrid system is:

[0115] (35)

[0116] In the formula: This is the maximum power that the microgrid can purchase hydrogen for. Hydrogen purchase capacity for microgrid systems.

[0117] Another object of the present invention is to provide a computer device including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the economic optimization scheduling method for wind-solar-gas-storage microgrids that takes into account hydrogen production by electricity, as described in the present invention.

[0118] Another object of the present invention is to provide a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the processor performs the economic optimization scheduling method for wind-solar-gas-storage microgrids that takes into account hydrogen production by electricity, as described in the present invention.

[0119] The beneficial effects of this invention are as follows:

[0120] This invention discloses an economic optimization scheduling method for wind-solar-gas-storage microgrids that considers hydrogen production via electricity. It establishes various common micro-power source power models for integrated electric-hydrogen energy microgrid systems. The k-means algorithm is used to extract typical scenarios of integrated electric-hydrogen energy microgrids that represent the regional wind and solar power characteristics. An optimized scheduling model for the integrated electric-hydrogen energy microgrid is established based on the objective function and various constraints. The proposed model is solved and analyzed using the CPLEX solver in MATLAB. The results verify the effectiveness and superiority of the proposed method. This demonstrates that the economic optimization scheduling method for wind-solar-gas-storage microgrids that considers hydrogen production via electricity disclosed in this invention can fully utilize the advantages of renewable energy sources such as wind and solar power, formulate scientific microgrid scheduling plans, introduce effective control mechanisms and energy storage systems to balance supply and demand, and ensure the stability and efficient operation of the power grid. Attached Figure Description

[0121] Figure 1 This is a flowchart illustrating an economic optimization scheduling method for a wind-solar-gas-storage microgrid that considers hydrogen production by electricity, according to the present invention.

[0122] Figure 2 This is a schematic diagram of the scheduling optimization results without introducing the energy curtailment penalty cost in an embodiment of the present invention;

[0123] Figure 3 This is a schematic diagram of the wind curtailment situation in the system when no energy curtailment penalty cost is introduced according to an embodiment of the present invention;

[0124] Figure 4 This is a schematic diagram of the system's curtailment situation when no energy curtailment penalty cost is introduced according to an embodiment of the present invention;

[0125] Figure 5 This is a schematic diagram of the scheduling optimization results after introducing the energy curtailment penalty cost in an embodiment of the present invention;

[0126] Figure 6 This is a schematic diagram of the wind curtailment situation in the system after introducing the energy curtailment penalty cost in an embodiment of the present invention;

[0127] Figure 7 This is a schematic diagram of the system's curtailment situation after introducing the curtailment penalty cost in an embodiment of the present invention. Detailed Implementation

[0128] Against the backdrop of the rapid development of distributed power generation and clean energy generation technologies, the construction of integrated energy microgrids has significant environmental and economic implications. Addressing the optimization and scheduling problem of grid-connected integrated energy microgrids, this invention proposes an economically optimized scheduling method for wind-solar-gas-storage microgrids that considers hydrogen production via electricity. The invention will be further described in detail below with reference to the accompanying drawings.

[0129] like Figure 1 The embodiment of the present invention disclosed presents an economically optimized dispatch method for a wind-solar-gas-storage microgrid considering hydrogen production by electricity, comprising the following steps:

[0130] In conjunction with the integrated electric-hydrogen energy microgrid system, a power model of the micro power unit is constructed, and the output characteristics of the micro power unit are qualitatively analyzed. The micro power unit includes: wind turbine generator set, photovoltaic generator set, electrolysis hydrogen production unit, hydrogen fuel cell unit, micro gas turbine and battery energy storage unit.

[0131] Acquire renewable energy and load data at the assumed installation site of the integrated electric-hydrogen energy microgrid, conduct typical scenario analysis using the raw data, and extract typical scenarios of the integrated electric-hydrogen energy microgrid that can represent the regional wind and solar characteristics through scenario reduction.

[0132] Under the constraints of the power model of the micro power unit and the typical scenario of the integrated electric-hydrogen energy microgrid, an optimal scheduling model for the integrated electric-hydrogen energy microgrid is established.

[0133] Based on the aforementioned optimized scheduling model for the integrated electric-hydrogen energy microgrid, the operating status of the integrated electric-hydrogen energy microgrid is solved to obtain scheduling decision results that guide the operation of the microgrid.

[0134] In this embodiment, power models of wind turbines, photovoltaic generators, hydrogen fuel cells, electrolyzers, micro gas turbines, and batteries are established in conjunction with the integrated electric-hydrogen energy microgrid system, and their output characteristics are qualitatively analyzed. After obtaining renewable energy and load data for the assumed installation location of the integrated electric-hydrogen energy microgrid, typical scenario analysis is performed using the original data. Through scenario reduction, typical scenarios of the integrated electric-hydrogen energy microgrid that represent the regional wind and solar power characteristics are extracted. An optimized scheduling model for the integrated electric-hydrogen energy microgrid is established according to the optimization objective function and various constraints. Applying the economic optimization scheduling method for wind-solar-gas-storage microgrids that considers hydrogen production by electricity disclosed in this invention can fully utilize the advantages of renewable energy sources such as wind and solar, formulate scientific microgrid scheduling plans, introduce effective control mechanisms and energy storage systems to balance supply and demand, and ensure the stability and efficient operation of the power grid.

[0135] The following sections provide a detailed explanation of each step in the economic optimization scheduling method for a wind-solar-gas-storage microgrid that considers hydrogen production by electricity, as disclosed in this invention.

[0136] Step A: Combine the integrated electric-hydrogen energy microgrid system to construct a power model for the micro power supply unit and perform a qualitative analysis of the output characteristics of the micro power supply unit; the micro power supply unit includes: wind turbine generator set, photovoltaic generator set, electrolysis hydrogen production unit, hydrogen fuel cell unit, micro gas turbine and battery energy storage unit;

[0137] In an integrated electric-hydrogen microgrid system, wind turbines, photovoltaic generators, hydrogen fuel cells, electrolyzers, micro gas turbines, and batteries each possess distinct power output characteristics. Wind and photovoltaic power output is significantly affected by natural environmental conditions, exhibiting marked intermittency and uncertainty. Hydrogen fuel cells provide stable power output and possess strong adjustment flexibility; electrolyzers rely on grid load for hydrogen production, serving as energy storage. Micro gas turbines provide stable backup power, compensating for the deficiencies caused by renewable energy fluctuations. Batteries regulate power supply and demand through charging and discharging, ensuring system balance and stability. Qualitative analysis of the output characteristics of these devices can optimize microgrid scheduling strategies, improving system efficiency and stability.

[0138] In this embodiment, power models of wind turbine generators, photovoltaic generators, hydrogen fuel cells, electrolyzers, micro gas turbines, and batteries are established in conjunction with the integrated electric-hydrogen energy microgrid system. A qualitative analysis of the output characteristics of the micro-power units is then conducted, specifically including the following steps:

[0139] Step A1: The basic working principle of a wind turbine is to convert wind energy into mechanical energy through a wind turbine, then convert the mechanical energy into electrical energy through a generator, and finally transmit it to the power grid via an inverter and transformer. Based on the wind power equation, establish the output power model of the wind turbine.

[0140] The output power model of the wind turbine is as follows:

[0141] (1)

[0142] In the formula: It is air density. It is the area swept by the wind turbine blades. It's wind speed. It is the wind energy utilization coefficient;

[0143] Step A2: A photovoltaic power generation system mainly consists of photovoltaic modules, a controller, an inverter, batteries, and other accessories. The photovoltaic modules convert sunlight into direct current (DC), which is then converted into alternating current (AC) by the inverter, ultimately achieving grid-connected operation. The power output of a photovoltaic power generation system is closely related to temperature and sunlight.

[0144] The photovoltaic cell output power model is as follows:

[0145] (2)

[0146] In the formula: This is the output power under standard conditions. and These represent standard solar radiation intensity and standard ambient temperature, respectively. and These represent the actual solar radiation intensity and the actual ambient temperature, respectively. It is the temperature coefficient. It refers to the area of ​​the photovoltaic panel. It refers to the production capacity efficiency of photovoltaic panels;

[0147] Step A3: Hydrogen energy has broad application prospects in many fields such as public transportation, heavy industry, and power generation. Hydrogen can be used as fuel to power fuel cells. Among them, proton exchange membrane fuel cells are widely used in microgrids due to their ability to achieve rapid low-temperature start-up, extremely high power density, and good reliability. The electrical power output of a fuel cell is closely related to the amount of fuel.

[0148] The electrolytic hydrogen production model is as follows:

[0149] (3)

[0150] In the formula: The electrical power output of the fuel cell. The volume of hydrogen consumed by the fuel cell. The energy conversion coefficient of a fuel cell;

[0151] Step A4: Hydrogen production and storage via water electrolysis has the advantages of large storage range, long storage time, wide application range, and simple operation, making it very suitable for building energy storage systems with renewable energy sources. This improves the power supply stability and power generation efficiency of renewable energy power generation systems. There is a quantitative relationship between the power consumption and hydrogen production of the electrolyzer.

[0152] The fuel cell output power model is as follows:

[0153] (4)

[0154] In the formula: It is the volume of hydrogen gas produced by the electrolyzer. It refers to the working efficiency of the electrolytic cell. It is the power consumption of the electrolytic cell. and It has a low calorific value and density;

[0155] Step A5: The micro-engine mainly consists of three core components: a compressor, a combustion chamber, and a turbine. After being pressurized by the compressor, air is burned with fuel in the combustion chamber. The resulting high-temperature, high-pressure gas drives the turbine to do work and generates electricity.

[0156] The output electric power model of the micro gas turbine is as follows:

[0157] (5)

[0158] In the formula: To output electrical power for the micro gas turbine, This refers to the volume of natural gas consumed by the micro gas turbine. The lower calorific value of natural gas is taken as 9.7 kW·h / m³. 3 , For micro gas turbine power generation efficiency, For time intervals;

[0159] Step A6: A storage battery is a device for storing electrical energy. It consists of one or more battery cells, each containing a positive electrode, a negative electrode, and an electrolyte. When a storage battery is charged, chemical reactions store electrical energy in the battery, and when it discharges, these reactions reverse, converting the stored chemical energy into electrical energy and releasing it.

[0160] The battery charging and discharging model is as follows:

[0161] During charging:

[0162] (6)

[0163] During discharge:

[0164] (7)

[0165] In the formula: express The battery capacity status at all times. express The battery capacity status at any time: SOC=0 indicates that the battery is in a fully discharged state, and SOC=1 indicates that the battery is in a fully charged state. It is the self-discharge rate; Indicates the battery capacity; , Indicates the charging and discharging power of the battery; , It refers to the charging and discharging efficiency of the battery.

[0166] Step B: Obtain renewable energy and load data for the assumed installation site of the integrated electric-hydrogen energy microgrid; conduct typical scenario analysis using the raw data; and extract typical scenarios of the integrated electric-hydrogen energy microgrid that can represent the regional wind and solar power characteristics through scenario reduction.

[0167] After acquiring renewable energy data (such as wind and solar power data) and load data for the assumed installation location of the integrated electric-hydrogen microgrid, the first step is to conduct in-depth analysis of this raw data. Using typical scenario analysis, the raw data is analyzed under different time periods, weather conditions, and seasonal fluctuations to extract typical scenarios that fully represent the characteristics of wind and solar power generation and load fluctuations in the region. This process not only helps reveal the changing patterns of renewable energy and load within the region but also identifies representative days and periods, forming a scenario model that accurately reflects actual operation. Subsequently, a scenario reduction method is used to further reduce the size of the scenario set, making it more concise and operable. During the reduction process, scenarios with strong repetition or minor impact are removed, retaining only those that are typical and representative. These typical scenarios will comprehensively reflect the characteristics of wind and solar power generation, load fluctuations, and their impact on the operation of the integrated electric-hydrogen microgrid, providing a scientific basis for microgrid scheduling and optimization. Finally, through the extracted typical scenarios, the scheduling strategy of the electric-hydrogen microgrid can be optimized more accurately, ensuring the system's efficiency, reliability, and sustainability.

[0168] In this embodiment, after acquiring renewable energy and load data for the assumed installation location of the integrated electric-hydrogen energy microgrid, typical scenario analysis is performed using the raw data. Through scenario reduction, typical scenarios representing the regional wind and solar power characteristics of the integrated electric-hydrogen energy microgrid are extracted. The specific process is as follows:

[0169] Step B1: After collecting historical data of the microgrid, perform data preprocessing on the historical data;

[0170] The acquisition of renewable energy and load data at the assumed installation location of the integrated electric-hydrogen microgrid includes:

[0171] After collecting historical data of the microgrid, the historical data is preprocessed. The data preprocessing includes: detecting the data integrity of each time period within a day according to a preset data missing judgment rule; removing the data of the corresponding time period for a period in which the length of consecutive missing data exceeds a preset threshold; and using an interpolation method to complete the missing data for a period in which the length of consecutive missing data does not exceed the preset threshold.

[0172] In this embodiment, the length of the continuously missing data exceeds a preset threshold of 1 hour.

[0173] Because the sampling data sources are not singular, and the sensors used are prone to data transmission anomalies during data acquisition, bad data (such as measurement errors) or even data loss are inevitable during the collection of historical microgrid data. Therefore, it is necessary to preprocess the data before forming the final typical scenario. Data preprocessing generally involves removing periods with a large number of consecutive data losses within a day, while periods with smaller data losses are supplemented using average interpolation.

[0174] The analysis of typical scenarios using raw data includes:

[0175] Step B2: Calculate the cumulative probability distribution function of the integrated electric-hydrogen energy microgrid based on its uncertainties, and divide the cumulative probability distribution function into N non-overlapping sub-intervals, with each sub-interval having a width of 1 / N;

[0176] For any subinterval i among N intervals, a random number with a value in the range [0,1] is randomly generated within the subinterval to select sample values:

[0177] (8)

[0178] In the formula: It is a sampled value within sub-region i; It is a random number within sub-region i;

[0179] Assuming the cumulative probability distribution function The inverse function is ,Will Substituting the values ​​into the inverse function allows us to calculate the specific sample values ​​of the load data. :

[0180] (9)

[0181] After obtaining the cumulative probability distribution function using the probability density function of wind speed and light intensity, the cumulative probability distribution function of wind speed and light intensity is sampled using the Latin hypercube sampling method to obtain the field sets of wind speed and light intensity.

[0182] Step B3: The K-means clustering algorithm divides a set of data points into k clusters based on the pre-given number of clusters k. It randomly selects k samples belonging to different clusters as initial cluster centers, then calculates the distance between the remaining data samples and each initial cluster center, assigning the remaining data samples to the nearest cluster. As shown in equation (10), the goal of the K-means clustering algorithm is to minimize the squared error between the sample and the cluster center. If the result after two iterations does not satisfy this formula, the mean value within each cluster is recalculated as the new cluster center. The sum of squared distance errors between the sample and the newly generated cluster center is recalculated, and this process is repeated iteratively until the objective function shown in equation (10) converges. The formula for calculating the new cluster center is shown in equation (11).

[0183] Use K-means clustering algorithm to reduce the scene size;

[0184] The objective of the K-means clustering algorithm is:

[0185] (10)

[0186] The formula for calculating the new cluster centers in the K-means clustering algorithm is as follows:

[0187] (11)

[0188] The evaluation metric for the number of clusters in the K-means clustering algorithm is:

[0189] (12)

[0190] In the formula, Let be the objective function of the clustering algorithm. for The i-th sample, if it belongs to the i-th sample If there are 1 cluster, then the sample value is used; otherwise, it is not counted. Here, m is the total number of samples in the dataset, and K is the number of cluster centers. For the first Cluster centers, For belonging to the first The number of samples in each class C is a clustering effectiveness indicator. k For the first A sample set of classes.

[0191] To determine the number of clusters, this paper adopts V. alidity(k) index is used to find the optimal number of clusters and is used as an evaluation index for selecting the number of clusters in the clustering algorithm.

[0192] In equation (12), the numerator represents the average spacing between particles within a class, and the denominator represents the minimum spacing between classes. Based on this characteristic, a smaller numerator indicates a more compact cluster structure, while a larger denominator indicates a looser inter-cluster structure. Therefore, V alidity (k) The smaller the index, the better the classification effect.

[0193] Step C: Under the constraints of the power model of the micro power unit and the typical scenario of the integrated electric-hydrogen energy microgrid, establish an optimized scheduling model for the integrated electric-hydrogen energy microgrid.

[0194] An optimal scheduling model for an integrated electric-hydrogen energy microgrid is established based on the objective function and various constraints. According to the operational requirements and objectives of the integrated electric-hydrogen energy microgrid, the system's objective function must first be defined. These objectives typically include minimizing total operating costs, maximizing energy efficiency, ensuring system stability and security, and reducing carbon emissions. Based on this, and considering the actual needs of the system and the characteristics of various devices, such as wind power, photovoltaic power, hydrogen fuel cells, energy storage devices, and micro gas turbines, the objective function is set by comprehensively considering their output characteristics, operating costs, regulation capabilities, and response speeds. The specific process is as follows:

[0195] Step C1: The objective function of the integrated electric-hydrogen energy microgrid optimization scheduling model designed in this invention consists of three parts: the energy purchase cost of the microgrid system, the wind and solar curtailment penalty cost of the microgrid system, and the operation and maintenance cost of the microgrid system;

[0196] The objective function of the optimized scheduling model for the integrated electric-hydrogen microgrid is:

[0197] (13)

[0198] In the formula, For the energy purchase cost of microgrid systems, The cost of wind and solar power curtailment penalties for microgrid systems, For the operation and maintenance costs of microgrid systems, This represents the total cost of the microgrid.

[0199] Energy purchase cost of microgrid systems for:

[0200] (14)

[0201] In the formula: , , These are the unit costs of electricity purchase, gas purchase, and hydrogen purchase for microgrids; , , yes The power, gas, and hydrogen purchased by the microgrid during the specified time period; The scheduling period is set to 24, indicating that the scheduling period is set to 24 hours.

[0202] Curtailment penalties for wind and solar power in microgrid systems for:

[0203] (15)

[0204] In the formula: and It is the unit penalty cost for wind and solar power curtailment in microgrids; and yes Predicted power output of wind and solar power in microgrids during specific time periods; and yes The actual output power of wind and solar power in the microgrid during the time period;

[0205] Operation and maintenance costs of microgrid systems for:

[0206] (16)

[0207] In the formula: and This refers to the unit operation and maintenance cost of wind turbines and photovoltaic panels; and This refers to the unit operation and maintenance cost of micro-turbines and fuel cells; and Micro engines and fuel cells are in Output power during the time period; This refers to the unit operation and maintenance cost of an energy storage system; It is the power used to calculate the equivalent cost of an energy storage system.

[0208] The optimized scheduling model for the integrated electric-hydrogen microgrid normalizes all energy storage units when calculating operation and maintenance costs, treating them as a single energy storage system. The energy storage system includes battery units, gas storage units, and hydrogen storage tank units.

[0209] The equivalent cost calculation power of an energy storage system is a linear combination of the charging and discharging power of each unit;

[0210] The equivalent cost power of the energy storage system is calculated as follows:

[0211] (17)

[0212] In the formula: and It refers to the charging and discharging power of the battery; and It refers to the gas filling and releasing power of the gas storage unit; and It refers to the hydrogen filling and discharging power of the hydrogen storage tank.

[0213] Step C2: The power balance constraint of the microgrid system in the optimized scheduling model of the integrated electric-hydrogen energy microgrid is:

[0214] (18)

[0215] In the formula: yes The electrical power consumed by the electrolytic cell during a given time period; yes Power demand of microgrid systems during specific time periods;

[0216] Step C3: The output power of renewable energy sources such as wind power and photovoltaic power must not exceed their maximum limits.

[0217] The output power constraints for the wind turbine generator set and the photovoltaic generator set are as follows:

[0218] (19)

[0219] (20)

[0220] In the formula: This is the maximum output power of the wind turbine. This is the maximum output power of the photovoltaic unit.

[0221] Step C4: The following energy conversion constraints exist between the natural gas power consumed by the micro gas turbine and the electrical power generated.

[0222] The energy conversion constraint between the natural gas power consumed and the electrical power generated by the micro gas turbine is:

[0223] (twenty one)

[0224] In the formula: It is the gas-to-electric conversion coefficient of the micro-gas engine, with a value of 0.92;

[0225] The power consumption of natural gas by the micro gas turbine must not exceed the upper and lower limits during a certain period of time, hence the following constraints:

[0226] The natural gas power consumed by the micro-turbine at a certain time period The constraints are:

[0227] (twenty two)

[0228] In the formula: yes The maximum gas power consumed by the micro gas turbine during a given period.

[0229] When the load changes, the micro-turbine unit needs to change from one output value to another at a certain slope. Due to the mechanical and thermodynamic constraints of the micro-turbine unit, the power response speed is constant, and the output change in each time period cannot exceed the specified maximum limit.

[0230] The ramp-up constraint for the micro gas turbine unit is:

[0231] (twenty three)

[0232] In the formula: yes The gas power consumed by the micro-turbine during a given period. and These are the upper and lower limits for the ramp speed of a micro gas turbine;

[0233] Step C5: The following energy conversion constraints exist between the electrical power consumed by the electrolyzer and the hydrogen power generated;

[0234] The energy conversion constraint between the electrical power consumed by the electrolyzer and the hydrogen power generated is:

[0235] (twenty four)

[0236] In the formula: This is the electro-hydrogen conversion coefficient of the electrolyzer, with a value of 0.87. The hydrogen power generated by the electrolyzer;

[0237] To ensure the high stability and durability of the electrolytic cell in high-temperature and high-humidity working environments and to prevent material corrosion, the electrical power consumed by the electrolytic cell must not exceed the upper and lower limits during a certain period of time.

[0238] The power consumption constraint of the electrolytic cell during a certain period is:

[0239] (25)

[0240] In the formula: yes The maximum electrical power consumed by the electrolytic cell during a given time period;

[0241] When the load changes, the power response speed of the electrolytic cell is constant, and the output change in each time period cannot exceed the specified maximum limit. The ramp-up constraint for the output change of the electrolytic cell is as follows:

[0242] (26)

[0243] In the formula: yes The electrical power consumed by the electrolytic cell during the time period. and These are the upper and lower limits of the slope for the electrolytic cell;

[0244] Step C6: There is an energy conversion constraint between the hydrogen power consumed by the fuel cell and the electrical power generated. The energy conversion constraint between the hydrogen power consumed by the fuel cell and the electrical power generated is as follows:

[0245] (27)

[0246] In the formula: This is the hydrogen-to-electric conversion coefficient of the fuel cell, with a value of 0.95. The hydrogen power consumed by the fuel cell during time period t;

[0247] The power density of a fuel cell is closely related to its current density. In fact, the higher the output current of a fuel cell, the lower the voltage output, thus limiting the total power that can be released. The hydrogen power consumed by a fuel cell within a certain time period must not exceed upper or lower limits. These hydrogen power consumption constraints within a certain time period are as follows:

[0248] (28)

[0249] In the formula: yes The maximum hydrogen power consumed by the fuel cell during a given time period;

[0250] When the load changes, the power response speed of the fuel cell is constant, and the output change in each time period cannot exceed the specified maximum limit. The ramp-up constraint for the output change of the fuel cell in each time period is as follows:

[0251] (29)

[0252] In the formula: yes Hydrogen power consumed by fuel cells during a given period and These are the upper and lower limits of fuel cell ramp rate;

[0253] Step C7: There is an energy conversion constraint between the hydrogen power consumed and the electrical power generated by the fuel cell:

[0254] The battery energy storage system is subject to the following constraints during operation:

[0255] The battery energy storage system should satisfy the constraint of equation (30) during operation:

[0256] The constraints of a battery energy storage system during operation are:

[0257] (30)

[0258] In the formula: This is the maximum charging and discharging power of the battery; This is the battery charging / discharging indicator; 0 indicates charging the battery, and 1 indicates discharging the battery. and This represents the charging and discharging efficiency of the battery. It refers to the battery capacity; and The upper and lower limits represent the remaining capacity of the battery.

[0259] Natural gas energy storage units should meet the constraints of formula (31) during operation:

[0260] The natural gas energy storage unit is subject to the following constraints during operation:

[0261] (31)

[0262] In the formula: This is the maximum charging and discharging power of the gas storage unit; This is the gas storage unit's filling and releasing indicator. A value of 0 indicates the gas storage unit is filling, and a value of 1 indicates the gas storage unit is releasing. and This represents the gas filling and discharging efficiency of the gas storage unit; It refers to the capacity of the natural gas energy storage unit; and These represent the upper and lower limits of the remaining capacity of the gas storage unit. This represents the remaining capacity of the gas storage unit at time 0. The remaining capacity of the gas storage unit at time 1. This represents the remaining capacity of the gas storage unit at 24 hours.

[0263] The hydrogen storage tank should meet the constraints of formula (32) during operation:

[0264] The hydrogen storage tank is subject to the following constraints during operation:

[0265] (32)

[0266] In the formula: This is the maximum hydrogen filling and discharging power of the hydrogen storage tank; This is the indicator for filling and discharging hydrogen from the hydrogen storage tank. 0 indicates filling the hydrogen storage tank, and 1 indicates discharging the hydrogen storage tank. and This represents the hydrogen filling and discharging efficiency of the hydrogen storage tank; This refers to the capacity of the hydrogen storage tank; and The upper and lower limits represent the remaining capacity of the hydrogen storage tank. This represents the remaining capacity of the hydrogen storage tank at time 0. This represents the remaining capacity status of the hydrogen storage tank at time 1. This represents the remaining capacity status of the hydrogen storage tank at 24 hours.

[0267] Step C8: The power purchase constraint for the microgrid system is:

[0268] (33)

[0269] In the formula: It is the maximum value of the electrical power exchanged between the microgrid and the distribution network. Power purchased for microgrid systems;

[0270] The gas purchase power constraint for the microgrid system is:

[0271] (34)

[0272] In the formula: This is the maximum power that the microgrid can purchase from natural gas. Gas purchase capacity for microgrid systems;

[0273] The hydrogen purchase power constraint for the microgrid system is:

[0274] (35)

[0275] In the formula: This is the maximum power that the microgrid can purchase hydrogen from. Hydrogen purchase capacity for microgrid systems.

[0276] Step D: Based on the optimized scheduling model of the integrated electric-hydrogen energy microgrid, solve the operating status of the integrated electric-hydrogen energy microgrid to obtain scheduling decision results to guide the operation of the microgrid.

[0277] In this embodiment, the optimal scheduling model of the integrated electric-hydrogen energy microgrid is solved and analyzed using the CPLEX solver in MATLAB. Scheduling decision results are obtained to guide the operation of the microgrid.

[0278] In this embodiment, various common micro-power source power models are established in conjunction with the integrated electric-hydrogen energy microgrid system. A typical scenario of the integrated electric-hydrogen energy microgrid, representing the regional wind and solar power characteristics, is extracted using the k-means algorithm. An optimal scheduling model for the integrated electric-hydrogen energy microgrid is established based on the objective function and various constraints. The proposed model is solved and analyzed using the CPLEX solver in MATLAB, and the results verify the effectiveness and superiority of the proposed method. By fully utilizing the advantages of renewable energy sources such as wind and solar power, a scientific microgrid scheduling plan is formulated, and effective control mechanisms and energy storage systems are introduced to balance supply and demand, ensuring the stability and efficient operation of the power grid.

[0279] To enable those skilled in the art to better understand the present invention and its advantages over the prior art, the applicant provides further explanation in conjunction with specific embodiments.

[0280] 1. Comprehensive comparative analysis of the two scenarios

[0281] Table 1 shows a comparison of microgrid operation results. Introducing the curtailment penalty cost reduces various costs of the microgrid and promotes wind and solar power consumption. The total cost of the microgrid after introducing the curtailment penalty cost is reduced by RMB 3374.64 compared to without it, with the wind curtailment rate decreasing from 15.2% to 1.19% and the solar curtailment rate decreasing from 5.5% to 0. Due to the introduction of the curtailment penalty cost, when the system has surplus renewable energy output, considering the high curtailment cost, the system can prioritize the consumption of new energy sources, preventing the extreme situation of almost complete wind power curtailment during midday due to high wind power operation and maintenance costs. Since midday is also the peak wind power output period, if a large amount of wind power is curtailed during this period, the system cannot utilize the surplus electricity to charge batteries to cope with power supply during other power shortage periods and to produce hydrogen to supply hydrogen load. This undoubtedly increases the operation and maintenance costs of micro-turbines, batteries, electricity purchases, and gas purchases during periods of power shortage, thus increasing the overall system cost. Without the introduction of curtailment penalty costs, a small amount of wind and solar curtailment still occurred during off-peak hours of renewable energy output. This was due to constraints such as the power ramp-up of electrolyzers and the charging and discharging power of batteries and hydrogen storage tanks, which prevented the microgrid system from quickly absorbing excess energy, resulting in unavoidable curtailment. However, this small amount of curtailment was permissible. As shown in Table 1, even with the introduction of curtailment penalty costs, which increased the system's cost by 27.41 yuan, the full utilization of renewable energy and the effective coordination of various energy sources during the dispatch period resulted in a lower overall cost for the microgrid system and a higher renewable energy absorption rate. Therefore, introducing curtailment penalty costs into the optimized dispatch model improved the economics of the integrated energy microgrid, effectively promoted the absorption of clean energy by the microgrid, and enhanced the overall efficiency of the integrated energy system.

[0282] Table 1 Comparison of Microgrid Operation Results

[0283] system Energy purchase cost / yuan Energy curtailment penalty cost / yuan Maintenance cost / yuan Total cost / yuan Wind curtailment rate Discard rate No penalty cost for energy curtailment was introduced 3342.62 0 6405.43 9748.05 15.2% 5.5% Introducing the cost of energy curtailment penalties 2193.01 27.41 4152.99 6373.41 1.19% 0

[0284] 2. Scheduling results without introducing energy curtailment penalty costs

[0285] Without introducing the cost of energy abandonment penalty, the scheduling optimization results are as follows: Figure 2 As shown, in Figure 2The data shows the optimized scheduling results of the wind-solar-gas-storage microgrid. At 1:00, due to insufficient renewable energy output and a small load gap, the system compensated for the deficit by discharging batteries. From 7:00 to 15:00, wind and solar output exceeded the load, and the electrolyzers operated to produce hydrogen from the surplus electricity. The surplus electricity at 10:00 and 14:00 was also stored in batteries for backup. A power shortage occurred from 16:00 to 23:00. At 19:00, 20:00, and 22:00, the load was supplemented through the coordinated operation of micro-gas turbines and batteries. At 17:00, 18:00, and 23:00, a small amount of electricity needed to be purchased to fill the gap. Figure 3 and Figure 4 The system's wind curtailment and solar curtailment scenarios are presented separately. The system's wind curtailment scenario without incorporating curtailment penalty costs is as follows: Figure 3 As shown, the system's light curtailment situation without introducing energy curtailment penalty costs is as follows: Figure 4 As shown, the results indicate that significant wind curtailment occurs at 1:00, 7:00, 11:00, and 15:00, with a maximum curtailment of 350kW. Solar curtailment also occurs during periods of strong daytime sunlight, with a peak curtailment of 350kW. Overall, energy curtailment is mainly concentrated during peak renewable energy output periods.

[0286] 3. Scheduling results after introducing the energy curtailment penalty cost

[0287] The scheduling optimization results after introducing the energy curtailment penalty cost are as follows: Figure 5 As shown, in Figure 5 The diagram illustrates the optimized scheduling results of a wind-solar-gas-storage microgrid. At 1:00, the power shortage is met by battery discharge. From 2:00 to 5:00, wind and solar output is insufficient, and energy storage has no backup, requiring a small amount of electricity to be purchased to supplement the supply. At 6:00, energy storage has output capacity and begins to participate in regulation. From 7:00 to 14:00, wind and solar output exceeds the load, and the electrolyzer operates to produce hydrogen. From 10:00 to 13:00, surplus electricity is further stored in the battery for backup. From 15:00 to 22:00, a large-scale power shortage occurs, and the micro-gas turbine and battery work together to ensure power supply. During some periods, the micro-gas turbine can meet the load by operating alone. Figure 6 and Figure 7 The text illustrates the situation regarding wind and solar power curtailment. It then describes the system's wind power curtailment situation after introducing the energy curtailment penalty cost, as shown below. Figure 6 As shown, the system's curtailment situation after introducing the energy curtailment penalty cost is illustrated below. Figure 7 As shown, the results indicate that only a small amount of wind curtailment occurred around 10:00 AM, thanks to the system absorbing excess wind power through water electrolysis and battery storage. Meanwhile, due to the higher cost of solar curtailment penalties, the system prioritized solar utilization during optimization, resulting in no solar curtailment throughout the day. Overall, the introduction of the penalty mechanism significantly improved the absorption capacity of renewable energy and reduced energy waste.

[0288] The above embodiments have provided a detailed description of the technical solution of the present invention. Obviously, the present invention is not limited to the described embodiments. Based on the embodiments of the present invention, those skilled in the art can make various modifications, but any modifications that are equivalent to or similar to the present invention fall within the scope of protection of the present invention.

[0289] Another embodiment of the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the economic optimization scheduling method for wind, solar, gas, and energy storage microgrids that takes into account hydrogen production by electricity, according to the present invention.

[0290] Another embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the processor executes the economic optimization scheduling method for wind-solar-gas-storage microgrids that takes into account hydrogen production by electricity, as described in the present invention.

Claims

1. An economically optimized dispatch method for wind-solar-gas-storage microgrids considering hydrogen production by electricity, characterized in that, Includes the following steps: In conjunction with the integrated electric-hydrogen energy microgrid system, a power model of the micro power unit is constructed, and the output characteristics of the micro power unit are qualitatively analyzed. The micro power unit includes: wind turbine generator set, photovoltaic generator set, electrolysis hydrogen production unit, hydrogen fuel cell unit, micro gas turbine and battery energy storage unit. Acquire renewable energy and load data at the assumed installation site of the integrated electric-hydrogen energy microgrid, conduct typical scenario analysis using the raw data, and extract typical scenarios of the integrated electric-hydrogen energy microgrid that can represent the regional wind and solar characteristics through scenario reduction. Under the constraints of the power model of the micro power unit and the typical scenario of the integrated electric-hydrogen energy microgrid, an optimal scheduling model for the integrated electric-hydrogen energy microgrid is established. Based on the aforementioned optimized scheduling model for the integrated electric-hydrogen energy microgrid, the operating status of the integrated electric-hydrogen energy microgrid is solved to obtain scheduling decision results that guide the operation of the microgrid.

2. The economic optimization dispatch method for wind-solar-gas-storage microgrids considering hydrogen production by electricity as described in claim 1, characterized in that, The power models of the micro power supply unit include: the output power model of the wind turbine, the output power model of the photovoltaic cell, the electrolysis hydrogen production model, the output power model of the fuel cell, the output power model of the micro gas turbine, and the charging and discharging model of the battery. The output power model of the wind turbine is as follows: （1） In the formula: It is air density. It is the area swept by the wind turbine blades. It's wind speed. It is the wind energy utilization coefficient; The photovoltaic cell output power model is as follows: （2） In the formula: This is the output power under standard conditions. and These represent standard solar radiation intensity and standard ambient temperature, respectively. and These represent the actual solar radiation intensity and the actual ambient temperature, respectively. It is the temperature coefficient. It refers to the area of ​​the photovoltaic panel. It refers to the production capacity efficiency of photovoltaic panels; The electrolytic hydrogen production model is as follows: （3） In the formula: The electrical power output of the fuel cell. The volume of hydrogen consumed by the fuel cell. The energy conversion coefficient of a fuel cell; The fuel cell output power model is as follows: （4） In the formula: It is the volume of hydrogen gas produced by the electrolyzer. It refers to the working efficiency of the electrolytic cell. It is the power consumption of the electrolytic cell. and It has a low calorific value and density; The output electric power model of the micro gas turbine is as follows: （5） In the formula: To output electrical power for the micro gas turbine, This refers to the volume of natural gas consumed by the micro gas turbine. The lower calorific value of natural gas is taken as 9.7 kW·h / m³. 3 , For micro gas turbine power generation efficiency, For time intervals; The battery charging and discharging model is as follows: During charging: （6） During discharge: （7） In the formula: express The battery capacity status at all times. express The battery capacity status is displayed at any time. SOC=0 indicates that the battery is in a fully discharged state, and SOC=1 indicates that the battery is in a fully charged state. It is the self-discharge rate; Indicates the battery capacity; , Indicates the charging and discharging power of the battery; , It refers to the charging and discharging efficiency of the battery.

3. The economic optimization dispatch method for wind-solar-gas-storage microgrids considering hydrogen production by electricity as described in claim 1, characterized in that, The acquisition of renewable energy and load data at the assumed installation location of the integrated electric-hydrogen microgrid includes: After collecting historical data of the microgrid, the historical data is preprocessed. The data preprocessing includes: detecting the data integrity of each time period within a day according to a preset data missing judgment rule; removing the data of the corresponding time period for a period in which the length of consecutive missing data exceeds a preset threshold; and using an interpolation method to complete the missing data for a period in which the length of consecutive missing data does not exceed the preset threshold.

4. The economic optimization dispatch method for wind-solar-gas-storage microgrids considering hydrogen production by electricity as described in claim 1, characterized in that, The analysis of typical scenarios using raw data includes: The cumulative probability distribution function of the integrated electric-hydrogen energy microgrid is calculated based on the uncertainties, and the cumulative probability distribution function is divided into N non-overlapping sub-intervals, with the width of each sub-interval being 1 / N. For any subinterval i among N intervals, a random number with a value in the range [0,1] is randomly generated within the subinterval to select sample values: （8） In the formula: It is a sampled value within sub-region i; It is a random number within sub-region i; Assuming the cumulative probability distribution function The inverse function is ,Will Substituting the values ​​into the inverse function allows us to calculate the specific sample values ​​of the load data. : （9） After obtaining the cumulative probability distribution function using the probability density function of wind speed and light intensity, the cumulative probability distribution function of wind speed and light intensity is sampled using the Latin hypercube sampling method to obtain the field sets of wind speed and light intensity.

5. The economic optimization dispatch method for wind-solar-gas-storage microgrids considering hydrogen production by electricity as described in claim 1, characterized in that, The typical scenarios for integrated electric-hydrogen energy microgrids that represent the regional wind, solar, and solar characteristics, extracted through scenario reduction, include: Use K-means clustering algorithm to reduce the scene size; The objective of the K-means clustering algorithm is: （10） The formula for calculating the new cluster centers in the K-means clustering algorithm is as follows: （11） The evaluation metric for the number of clusters in the K-means clustering algorithm is: （12） In the formula, Let be the objective function of the clustering algorithm. for The i-th sample, if it belongs to the i-th sample If there are 1 cluster, then the sample value is used; otherwise, it is not counted. Here, m is the total number of samples in the dataset, and K is the number of cluster centers. For the first Cluster centers, For belonging to the first The number of samples in each class C is a clustering effectiveness indicator. k For the first A sample set of classes.

6. The economic optimization dispatch method for wind-solar-gas-storage microgrids considering hydrogen production by electricity as described in claim 1, characterized in that, The objective function of the optimized scheduling model for the integrated electric-hydrogen microgrid is: （13） In the formula, For the energy purchase cost of microgrid systems, The cost of wind and solar power curtailment penalties for microgrid systems, For the operation and maintenance costs of microgrid systems, This represents the total cost of the microgrid. Energy purchase cost of microgrid systems for: （14） In the formula: , , These are the unit costs of electricity purchase, gas purchase, and hydrogen purchase for microgrids; , , yes The power, gas, and hydrogen purchased by the microgrid during the specified time period; The scheduling period is set to 24, indicating that the scheduling period is 24 hours. Curtailment penalties for wind and solar power in microgrid systems for: （15） In the formula: and It is the unit penalty cost for wind and solar power curtailment in microgrids; and yes Predicted power output of wind and solar power in microgrids during specific time periods; and yes The actual output power of wind and solar power in the microgrid during the time period; Operation and maintenance costs of microgrid systems for: （16） In the formula: and This refers to the unit operation and maintenance cost of wind turbines and photovoltaic panels; and This refers to the unit operation and maintenance cost of micro-turbines and fuel cells; and Micro engines and fuel cells are in Output power during the time period; This refers to the unit operation and maintenance cost of an energy storage system; It is the power used to calculate the equivalent cost of an energy storage system.

7. The economic optimization dispatch method for wind-solar-gas-storage microgrids considering hydrogen production by electricity as described in claim 6, characterized in that, The optimized scheduling model for the integrated electric-hydrogen microgrid normalizes all energy storage units and treats them as a single energy storage system when calculating operation and maintenance costs. This energy storage system includes battery units, gas storage units, and hydrogen storage tank units. The equivalent cost power of the energy storage system is calculated as follows: （17） In the formula: and It refers to the charging and discharging power of the battery; and It refers to the gas filling and releasing power of the gas storage unit; and It refers to the hydrogen filling and discharging power of the hydrogen storage tank.

8. The economic optimization dispatch method for wind-solar-gas-storage microgrids considering hydrogen production by electricity as described in claim 1, characterized in that, The power balance constraint of the microgrid system in the optimized scheduling model of the integrated electric-hydrogen energy microgrid is as follows: （18） In the formula: yes The electrical power consumed by the electrolytic cell during a given time period; yes Power demand of microgrid systems during specific time periods; The output power constraints for the wind turbine generator set and the photovoltaic generator set are as follows: （19） （20） In the formula: This is the maximum output power of the wind turbine. This is the maximum output power of the photovoltaic unit. The energy conversion constraint between the natural gas power consumed and the electrical power generated by the micro gas turbine is: （21） In the formula: It is the gas-to-electric conversion coefficient of the micro-gas engine, with a value of 0.92; The natural gas power consumed by the micro-turbine at a certain time period The constraints are: （22） In the formula: yes The maximum gas power consumed by the micro gas turbine during a given period. The ramp-up constraint for the micro gas turbine unit is: （23） In the formula: yes The gas power consumed by the micro gas turbine during a given period. and These are the upper and lower limits for the ramp speed of a micro gas turbine; The energy conversion constraint between the electrical power consumed by the electrolyzer and the hydrogen power generated is: （24） In the formula: This is the electro-hydrogen conversion coefficient of the electrolyzer, with a value of 0.

87. The hydrogen power generated by the electrolyzer; The power consumption constraint of the electrolytic cell during a certain period is: （25） In the formula: yes The maximum electrical power consumed by the electrolytic cell during a given time period; The ramp-up constraint for the change in electrolytic cell output is: （26） In the formula: yes The electrical power consumed by the electrolytic cell during the time period. and These are the upper and lower limits of the slope for the electrolytic cell; The energy conversion constraint between the hydrogen power consumed and the electrical power generated by the fuel cell is: （27） In the formula: This is the hydrogen-to-electric conversion coefficient of the fuel cell, with a value of 0.

95. The hydrogen power consumed by the fuel cell during time period t; The hydrogen power consumption constraint of the fuel cell at a certain time period is: （28） In the formula: yes The maximum hydrogen power consumed by the fuel cell during a given time period; The ramp-up constraint for the output variation of the fuel cell in each time period is: （29） In the formula: yes Hydrogen power consumed by fuel cells during a given period and These are the upper and lower limits of fuel cell ramp rate; The constraints of a battery energy storage system during operation are: （30） In the formula: This is the maximum charging and discharging power of the battery; This is the battery charging / discharging indicator; 0 indicates charging the battery, and 1 indicates discharging the battery. and This represents the charging and discharging efficiency of the battery. It refers to the battery capacity; and The upper and lower limits represent the remaining capacity of the battery. The constraints for natural gas energy storage units during operation are: （31） In the formula: This is the maximum charging and discharging power of the gas storage unit; This is the gas storage unit's filling and releasing indicator. A value of 0 indicates the gas storage unit is filling, and a value of 1 indicates the gas storage unit is releasing. and This represents the gas filling and discharging efficiency of the gas storage unit; It refers to the capacity of the natural gas energy storage unit; and These represent the upper and lower limits of the remaining capacity of the gas storage unit. This represents the remaining capacity of the gas storage unit at time 0. The remaining capacity of the gas storage unit at time 1. This represents the remaining capacity of the gas storage unit at 24 hours. The constraints for hydrogen storage tanks during operation are as follows: （32） In the formula: This is the maximum hydrogen filling and discharging power of the hydrogen storage tank; This is the indicator for filling and discharging hydrogen from the hydrogen storage tank. 0 indicates filling the hydrogen storage tank, and 1 indicates discharging the hydrogen storage tank. and This represents the hydrogen filling and discharging efficiency of the hydrogen storage tank; This refers to the capacity of the hydrogen storage tank; and The upper and lower limits represent the remaining capacity of the hydrogen storage tank. This represents the remaining capacity of the hydrogen storage tank at time 0. This represents the remaining capacity status of the hydrogen storage tank at time 1. This represents the remaining capacity status of the hydrogen storage tank at 24 hours. The power purchase constraint for microgrid systems is: （33） In the formula: It is the maximum value of the electrical power exchanged between the microgrid and the distribution network. Power purchased for microgrid systems; The gas purchase power constraint for the microgrid system is: （34） In the formula: This is the maximum power that the microgrid can purchase from natural gas. Gas purchase capacity for microgrid systems; The hydrogen purchase power constraint for the microgrid system is: （35） In the formula: This is the maximum power that the microgrid can purchase hydrogen from. Hydrogen purchase capacity for microgrid systems.

9. A computer device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the economic optimization scheduling method for wind-solar-gas-storage microgrids considering hydrogen production by electricity according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the processor executes the economic optimization scheduling method for wind-solar-gas-storage microgrids that takes into account hydrogen production by electricity, as described in any one of claims 1 to 8.

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