Multi-microgrid optical storage double-layer optimal configuration method and system

Abstract

The invention provides a multi-microgrid optical storage double-layer optimization configuration method and system, and the method comprises the steps: building an electricity-hydrogen coupling operation architecture, and constructing a model and constraint conditions according to the electricity-hydrogen coupling operation architecture; expanding a single micro-grid into multiple micro-grids, and optimizing the power balance constraint and the power constraint of the ith micro-grid based on power interaction and cooperation of electric energy and hydrogen energy among the multiple micro-grids; constructing an upper-layer optimization model by taking the minimum sum of the equivalent annual value investment cost and the annual maintenance cost of the micro-grid as a target, and constructing a lower-layer optimization model by taking the maximization of the operating income of the micro-grid as a target to obtain a double-layer model; and solving the double-layer model to obtain a final capacity configuration result of the multi-microgrid. According to the invention, the dependence of the micro-grid on fossil fuel can be effectively reduced, and the fuel cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid optimization configuration, and in particular to a multi-microgrid photovoltaic storage dual-layer optimization configuration method and system. Background Art

[0002] With the development of renewable energy and the urgent need for energy structure, microgrid (MG) as a new energy management and distribution mode has gradually attracted widespread attention. The capacity configuration optimization of microgrid can reduce loss costs, increase the reliability, flexibility and energy efficiency of microgrid, and is an important part of microgrid design and planning.

[0003] At present, most of the research on microgrid capacity optimization focuses on the coordination of wind power, photovoltaic power, and energy storage batteries. Micro-turbines (MT) are usually used as emergency backup equipment to improve the power supply reliability of microgrids, but their polluting gas emissions are contrary to the vision of developing clean energy. Therefore, it is necessary to replace micro-turbines with cleaner new energy power generation as backup equipment. In addition, traditional electrochemical energy storage cannot flexibly dispatch electricity to meet the load requirements of different periods, and it is necessary to purchase electricity from the outside to achieve a balance between supply and demand within the microgrid. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-microgrid photovoltaic storage dual-layer optimization configuration method and system, aiming to solve the problem that the fuel cost consumed in the operation of traditional microgrids is high and the environmental pollution is large, and the traditional electrochemical energy storage cannot flexibly dispatch electricity to meet the load demand in different time periods, and needs to purchase electricity from the outside to achieve supply and demand balance within the microgrid.

[0005] In a first aspect, the present invention provides a multi-microgrid photovoltaic storage dual-layer optimization configuration method, the method comprising: Establishing an electric-hydrogen coupling operation framework, and constructing a power capacity calculation model for the battery and energy storage charging and discharging constraints corresponding to the power capacity calculation model, an output power model for the electrolyzer, an output power model for the fuel cell, a hydrogen storage model for the hydrogen storage tank, an electric-hydrogen coupling model, and hydrogen power constraints corresponding to the electric-hydrogen coupling model according to the electric-hydrogen coupling operation framework; Expand a single microgrid into multiple microgrids, and collaboratively optimize the power balance constraints and power constraints of the i-th microgrid based on the power interaction of electric energy and hydrogen energy among multiple microgrids; The upper optimization model is constructed with the goal of minimizing the sum of the equal annual investment cost and the annual maintenance cost of the microgrid, and the lower optimization model is constructed with the goal of maximizing the microgrid operation income, thus obtaining a two-layer model. The two-layer model is solved to obtain a final capacity configuration result of the multi-microgrid.

[0006] Furthermore, a power capacity calculation model for the battery is constructed according to the following formula: ; in, , are the storage capacity of the battery in time period t and time period t-1 respectively, is the charging power of the battery in time period t, is the discharge power of the battery in time period t, is the charge and discharge rate of the battery, is the time step, is the energy loss rate of the battery; The energy storage charging and discharging constraint conditions corresponding to the power capacity calculation model are constructed according to the following formula: ; in, is the state variable of the battery charging and discharging power in time period t, is the maximum charging and discharging power of the battery; The relationship between the maximum charging and discharging power of the battery and the battery capacity is expressed as: ; The battery's state of charge during time period t It is expressed as: ; in, Indicates the fixed proportional coefficient between the battery power upper limit and capacity. Indicates the battery capacity.

[0007] Furthermore, the output power model of the electrolyzer is constructed according to the following formula: ; in, is the power consumption of the electrolytic cell, The hydrogen power converted by the electrolyzer, is the output efficiency of the electrolyzer; The output power model of the fuel cell is constructed according to the following formula: ; in, is the output power of the fuel cell, is the hydrogen power consumed by the fuel cell, Output efficiency for hydrogen fuel cells; The hydrogen storage model for the hydrogen storage tank is constructed according to the following formula: ; in, is the hydrogen storage capacity of the hydrogen storage tank in time period t, is the hydrogen filling and discharging efficiency of the hydrogen storage tank, is the hydrogen energy charging power of the hydrogen storage tank in time period t, is the hydrogen discharge power of the hydrogen storage tank in time period t.

[0008] Furthermore, the electric-hydrogen coupling model is constructed according to the following formula: ; in, is the hydrogen power converted by the electrolyzer in time period t, is the power converted directly from the electrolyzer into the fuel cell during time period t, is the hydrogen energy purchased per unit time, is the hydrogen power consumed by the fuel cell in time period t, is the hydrogen energy sold per unit time; The hydrogen power constraint conditions include hydrogen power balance constraint conditions and hydrogen energy storage charging and discharging power upper and lower limit constraint conditions; The hydrogen power balance constraint is constructed according to the following formula: ; The upper and lower limit constraints of hydrogen energy storage charging and discharging power are constructed according to the following formula: ; in, is the state variable of hydrogen charging and discharging during time period t, is the maximum value that can be achieved during hydrogen charging and discharging, and , Assign capacity to the hydrogen storage tank. is the power energy conversion coefficient of the hydrogen storage tank.

[0009] Furthermore, the power balance constraint of the i-th microgrid is constructed according to the following formula: ; in, is the discharge power of the battery in the i-th microgrid during the t time period, is the power purchased by the i-th microgrid from the main grid during time period t, is the output power of the fuel cell in the i-th microgrid in time period t, is the photovoltaic output of the i-th microgrid in time period t, is the electric power purchased by the jth microgrid from the ith microgrid in time period t, To use historical data to predict the load results, the load demand power of the i-th microgrid in time period t is: is the power sold by the i-th microgrid to the main grid during time period t, is the power consumption of the electrolyzer in the i-th microgrid during time period t, is the charging power of the battery in the i-th microgrid in time period t, represents the electric power purchased by the i-th microgrid from the j-th microgrid in time period t, n represents the total number of microgrids, is the hydrogen power converted by the bottom electrolyzer in the i-th microgrid in time period t, is the hydrogen discharge power of the hydrogen storage tank in the i-th microgrid in time period t, represents the hydrogen power purchased by the i-th microgrid from the j-th microgrid in time period t, is the hydrogen energy charging power of the hydrogen storage tank in the i-th microgrid during the t-time period, is the hydrogen power consumed by the fuel cell in the i-th microgrid in time period t, represents the hydrogen power purchased by the jth microgrid from the i-th microgrid in time period t; The power constraint of the i-th microgrid is constructed according to the following formula:

[0010]

[0011] ; in, represents the state variable of the power purchased and sold between the i-th microgrid and the main grid in period t, is the upper limit of the power of the grid tie line, is the state variable of the purchase and sale of hydrogen power between the i-th microgrid and the main grid in period t, Import hydrogen power limit into hydrogen transmission pipe, is the hydrogen energy sold per unit time by the i-th microgrid, is the hydrogen energy purchased per unit time by the i-th microgrid.

[0012] Furthermore, the steps of constructing an upper optimization model with the goal of minimizing the sum of the equal annual investment cost and the annual maintenance cost of the microgrid, and constructing a lower optimization model with the goal of maximizing the operating income of the microgrid, to obtain a two-layer model include: The upper optimization model is constructed according to the following formula: ; in, is the annual investment cost of the i-th microgrid, is the operating cost of the i-th microgrid, represents the sum of the equal annual investment cost and annual maintenance cost of the i-th microgrid; The lower-level optimization model is constructed according to the following formula: ; in, The profit from selling electricity to users within the unit power microgrid. and are the unit electricity and hydrogen energy profits of the interaction between the i-th microgrid and the external microgrid, is the number of typical days in season n, A collection of four seasons, is the operating income of the i-th microgrid.

[0013] Furthermore, the step of solving the two-layer model to obtain a final capacity configuration result of the multi-microgrid includes: Step 1: Initialize and assign values ​​to the microgrid; Step 2: In the upper optimization model, the capacity of each device is determined with the goal of minimizing the internal cost of the microgrid, and the capacity determination result is transmitted to the lower optimization model as the upper limit of operation; Step 3: Input the fixed capacity value of the upper optimization model and the predicted value of the photovoltaic output load demand, simulate the typical daily operation of the microgrid with the goal of maximizing the microgrid profit, and return the operation results to the upper optimization model; Step 4: Return the optimization variables of the lower optimization model to the upper optimization model. The maximum value of the optimization variables of the lower optimization model is used as the minimum value of the optimization variable constraints of the upper optimization model. The upper optimization model again determines the capacity with the goal of minimizing the internal cost of the microgrid. Step 5: Repeat steps 2 to 4 for multiple iterations. The optimization target of the kth operation of the upper optimization model is recorded as , the optimization goal of the kth iteration of the lower optimization model is ,when ,and The iteration stops when , and the final capacity configuration result is obtained, where η and ε represent the convergence domain values ​​of the upper optimization model and the lower optimization model.

[0014] Furthermore, the equal annual investment cost of the i-th microgrid is calculated according to the following formula: ; in, To invest in and build a collection of equipment, including photovoltaic arrays, wind turbines, hydrogen generators, electrolyzers, and fuel cells; is the capital recovery coefficient, is the discount rate, is the operating life of the kth equipment, For equipment k The unit power investment cost includes the unit capacity investment cost of energy storage, the unit power investment cost of photovoltaic power generation, the unit power investment cost of electrolyzer, the unit capacity investment cost of hydrogen storage tank and the unit power investment cost of hydrogen fuel cell. is the capacity of the kth device in the i-th microgrid; The operating cost of the i-th microgrid is calculated according to the following formula: ; ; in, is the operating cost within the i-th microgrid, is the electricity purchase and sales cost of the i-th microgrid, is the cost of purchasing and selling hydrogen for the i-th microgrid, is the unit electricity price of the microgrid, is the hydrogen price of the microgrid, To maintain the proportionality factor, is the operation and maintenance cost coefficient of w components, is the operating power of the wth component of the i-th microgrid; The constraints of each device in the microgrid are constructed according to the following formula: ; in, are the maximum values ​​of the battery, photovoltaic array, electrolyzer, hydrogen energy storage, and hydrogen storage tank in the i-th microgrid after the lower optimization model calculation, They represent the maximum values ​​that can be achieved for the installed capacity of batteries, photovoltaic arrays, electrolyzers, hydrogen energy storage, and hydrogen storage tanks. , The rated capacity and rated power are configured for the i-th microgrid battery, photovoltaic cell, electrolyzer, fuel cell, and hydrogen storage tank respectively.

[0015] Furthermore, various profits are calculated according to the following formula: ; in, is the profit brought by the unit electricity of the power interacting with the external microgrid during time period t, is the profit of unit hydrogen energy generated by the interaction between power and external microgrid in time period t, in units of , is the operation and maintenance cost coefficient of w components; The upper and lower limit constraints of the lower-level decision variables are constructed according to the following formula: ; in, is the per-unit value of photovoltaic output predicted by microgrid i, is the lower limit of the hydrogen storage tank’s energy storage capacity, is the upper limit of the hydrogen storage tank’s energy storage capacity, is the capacity of the hydrogen storage tank of the i-th microgrid per unit time, is the state of charge of the i-th microgrid battery per unit time, is the lower limit of the battery state of charge, is the upper limit of the battery state of charge; The final capacity configuration result includes , , , , , .

[0016] In a second aspect, an embodiment of the present invention further proposes a multi-microgrid photovoltaic storage dual-layer optimization configuration system, the system comprising: An electric-hydrogen coupling architecture construction module, used to establish an electric-hydrogen coupling operation architecture, and to construct a power capacity calculation model for a battery and energy storage charging and discharging constraints corresponding to the power capacity calculation model, an output power model for an electrolyzer, an output power model for a fuel cell, a hydrogen storage model for a hydrogen storage tank, an electric-hydrogen coupling model, and hydrogen power constraints corresponding to the electric-hydrogen coupling model according to the electric-hydrogen coupling operation architecture; The microgrid expansion module is used to expand a single microgrid into multiple microgrids, and to collaboratively optimize the power balance constraints and power constraints of the i-th microgrid based on the power interaction of electric energy and hydrogen energy among multiple microgrids; A two-layer model building module is used to build an upper optimization model with the goal of minimizing the sum of the equal annual investment cost and the annual maintenance cost of the microgrid, and to build a lower optimization model with the goal of maximizing the operating income of the microgrid, thereby obtaining a two-layer model; A solution module is used to solve the two-layer model to obtain a final capacity configuration result of the multi-microgrid.

[0017] Compared with the prior art, the present invention has the following advantages: 1. Aiming at the high fuel cost and high emission pollution of traditional micro gas turbines as backup energy sources, a basic model of microgrid hydrogen energy storage system is proposed. The method of hydrogen production, storage and hydrogen energy conversion is studied, and a hydrogen energy storage system in the form of electrolyzer-hydrogen storage tank-hydrogen fuel cell is established. This method can effectively reduce the microgrid's dependence on fossil fuels and reduce fuel costs.

[0018] 2. In response to the problem of imbalanced supply and demand in traditional microgrids, a basic model of electricity-hydrogen coupling was established. Electric energy was converted into hydrogen energy through an electrolyzer and stored in a hydrogen storage tank. When the load demand was greater than power generation, the hydrogen energy was input into the hydrogen fuel cell for combustion and power generation, effectively solving the problem of imbalanced supply and demand in microgrids at different times.

[0019] 3. Aiming at the problem of poor economic efficiency of small and micro parks, an optimization configuration method of hydrogen energy storage based on the multi-park collaborative optimization operation mode is proposed. Taking advantage of the characteristics of multi-park collaborative optimization of hydrogen energy and electric energy interaction, a multi-park collaborative optimization configuration model of hydrogen energy storage is constructed with the goal of increasing the economic efficiency of small and micro parks. The upper layer determines the capacity of the internal equipment of the park with the goal of minimizing the comprehensive cost within the small and micro park, and the lower layer performs typical daily simulation scenario operation with the goal of maximizing the internal profit of the park. The results are obtained through multiple iterative calculations of the upper and lower layers. This method effectively improves the economy and flexibility of small and micro parks and ensures the stable operation of the park. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flowchart of a multi-microgrid photovoltaic-storage dual-layer optimization configuration method proposed in one embodiment of the present invention; Figure 2 A schematic diagram of the structure of a microgrid according to an example of an embodiment of the present invention; Figure 3 This is a multi-microgrid collaborative optimization operation framework diagram of an example of an embodiment of the present invention; Figure 4 This is a schematic diagram of a typical day power operation scenario of a microgrid in spring according to scenario 4 of an example of an embodiment of the present invention; Figure 5 This is a schematic diagram of a typical spring electricity operation scenario of a microgrid in scenario 5 according to an example of an embodiment of the present invention; Figure 6 This is a schematic diagram of a typical daily total purchased power operation scenario of a multi-microgrid hydrogen energy distribution network according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of a multi-microgrid photovoltaic storage dual-layer optimization configuration system proposed in one embodiment of the present invention.

[0021] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be understood by people with general skills in the field to which the present invention belongs. "Including" and similar words used in this article mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0023] like Figure 1 As shown, an embodiment of the present invention provides a multi-microgrid photovoltaic storage dual-layer optimization configuration method, the method includes steps S101 to S104, wherein: Step S101: Establishing an electric-hydrogen coupling operation framework, and constructing a power capacity calculation model for a battery and energy storage charging and discharging constraints corresponding to the power capacity calculation model, an output power model for an electrolyzer, an output power model for a fuel cell, a hydrogen storage model for a hydrogen storage tank, an electric-hydrogen coupling model, and hydrogen power constraints corresponding to the electric-hydrogen coupling model according to the electric-hydrogen coupling operation framework; It should be noted that the electric-hydrogen coupled hybrid energy storage microgrid structure considered in this embodiment Figure 2 As shown in the figure, it is mainly composed of photovoltaic arrays, energy storage batteries, fuel cells, electrolyzers, hydrogen storage tanks, loads and DC-DC converters. The energy storage system includes an electric energy storage system based on batteries and a hydrogen energy storage system based on electrolyzers, hydrogen storage tanks and fuel cells.

[0024] First, if the power generation in the system is greater than the load demand, the excess power is stored in the energy storage device; if the load demand in the system is greater than the power generation, the power is called from the energy storage device to supply the load demand. The energy storage device can effectively solve the problem of uneven distribution of the output of new energy, and effectively improve the economic efficiency of the internal operation of the microgrid. Specifically, the power capacity calculation model of the battery is constructed according to the following formula: ; in, , are the storage capacity of the battery in time period t and time period t-1 respectively, is the charging power of the battery in time period t, is the discharge power of the battery in time period t, is the charge and discharge rate of the battery, is the time step, is the energy loss rate of the battery.

[0025] The energy storage charging and discharging constraint conditions corresponding to the power capacity calculation model are constructed according to the following formula: ; in, is the state variable of the battery charging and discharging power in time period t, is the maximum charging and discharging power of the battery; The relationship between the maximum charging and discharging power of the battery and the battery capacity is expressed as: ; The battery's state of charge during time period t It is expressed as: ; in, Indicates the fixed proportional coefficient between the battery power upper limit and capacity. Indicates the battery capacity.

[0026] In addition, in some embodiments, the electrolyzer-hydrogen storage tank-hydrogen fuel cell and other related models established have the same function as battery energy storage. That is, during the period of sufficient sunlight, the excess electrical energy is used to produce hydrogen in the electrolyzer, and the produced hydrogen is stored in the hydrogen storage tank; when the light is insufficient, the fuel cell uses the hydrogen in the hydrogen storage tank as fuel to generate electricity to meet the load demand. Specifically, the electrolyzer can electrolyze water into hydrogen and oxygen. It is a device that can convert electrical energy into hydrogen energy, and its output power can be expressed as: ; in, is the power consumption of the electrolytic cell, The hydrogen power converted by the electrolyzer, is the output efficiency of the electrolyzer.

[0027] Hydrogen fuel cells are proton exchange membrane fuel cells that use hydrogen and oxygen as fuel to convert chemical energy into electrical energy for storage. Their output power is: ; in, is the output power of the fuel cell, is the hydrogen power consumed by the fuel cell, Output efficiency of hydrogen fuel cells.

[0028] The hydrogen storage tank is used to collect and store hydrogen, and provide hydrogen for the fuel cell when the load demand is large. Compared with batteries, the hydrogen storage tank is safer and has lower energy loss. The hydrogen storage model of the hydrogen storage tank is expressed as follows: ; in, is the hydrogen storage capacity of the hydrogen storage tank in time period t, is the hydrogen filling and discharging efficiency of the hydrogen storage tank, is the hydrogen energy charging power of the hydrogen storage tank in time period t, is the hydrogen discharge power of the hydrogen storage tank in time period t.

[0029] Part of the hydrogen energy converted by the electrolyzer is stored in the hydrogen storage tank. When the system power is insufficient to meet the load demand, the hydrogen energy is transferred to the hydrogen fuel cell for combustion and power generation. The other part is directly transferred to the hydrogen fuel cell, and the abandoned hydrogen is sold. At the same time, when the hydrogen energy is insufficient to meet the load demand, the system purchases hydrogen energy from the outside to maintain the balance of supply and demand in the microgrid. Specifically, the electric-hydrogen coupling model is constructed according to the following formula: ; in, is the hydrogen power converted by the electrolyzer in time period t, is the power converted directly from the electrolyzer into the fuel cell during time period t, is the hydrogen energy purchased per unit time, is the hydrogen power consumed by the fuel cell in time period t, is the hydrogen energy sold per unit time; The hydrogen power constraint conditions include hydrogen power balance constraint conditions and hydrogen energy storage charging and discharging power upper and lower limit constraint conditions; The hydrogen power balance constraint is constructed according to the following formula: ; The upper and lower limit constraints of hydrogen energy storage charging and discharging power are constructed according to the following formula: ; in, is the state variable of hydrogen charging and discharging during time period t, is the maximum value that can be achieved during hydrogen charging and discharging, and , Assign capacity to the hydrogen storage tank. is the power energy conversion coefficient of the hydrogen storage tank.

[0030] Step S102: Expand a single microgrid into multiple microgrids, and collaboratively optimize the power balance constraint and power constraint of the i-th microgrid based on the power interaction of electric energy and hydrogen energy among the multiple microgrids; It should be pointed out that when expanding a single microgrid into multiple microgrids, this step takes into account the power interaction of electricity and hydrogen energy between multiple microgrids, and establishes a multi-microgrid energy collaborative optimization model. If the electricity and hydrogen energy generated within the microgrid cannot meet the load demand, it is necessary to purchase energy from other microgrids. On the contrary, if the energy generated is greater than the load demand, it can be sold to other microgrids with larger load demand. Figure 3 As shown, there are n multi-microgrids in total, among which, , , They represent the first, second, and nth microgrids respectively. The multi-microgrid collaborative optimization model can not only realize the diversified utilization of electricity-hydrogen coupling in a single microgrid, but also realize the interaction between electricity and hydrogen between different microgrids, making full use of the flexible characteristics of microgrids and improving the energy flexibility, complementarity and mutual assistance capabilities of microgrids.

[0031] Specifically, the power balance constraint of the i-th microgrid is constructed according to the following formula: ; in, is the discharge power of the battery in the i-th microgrid during the t time period, is the power purchased by the i-th microgrid from the main grid during time period t, is the output power of the fuel cell in the i-th microgrid in time period t, is the photovoltaic output of the i-th microgrid in time period t, is the electric power purchased by the jth microgrid from the ith microgrid in time period t, To use historical data to predict the load results, the load demand power of the i-th microgrid in time period t is: is the power sold by the i-th microgrid to the main grid during time period t, is the power consumption of the electrolyzer in the i-th microgrid during time period t, is the charging power of the battery in the i-th microgrid in time period t, represents the electric power purchased by the i-th microgrid from the j-th microgrid in time period t, n represents the total number of microgrids, is the hydrogen power converted by the bottom electrolyzer in the i-th microgrid in time period t, is the hydrogen discharge power of the hydrogen storage tank in the i-th microgrid in time period t, represents the hydrogen power purchased by the i-th microgrid from the j-th microgrid in time period t, is the hydrogen energy charging power of the hydrogen storage tank in the i-th microgrid during the t-time period, is the hydrogen power consumed by the fuel cell in the i-th microgrid in time period t, represents the hydrogen power purchased by the jth microgrid from the i-th microgrid in time period t; The power constraint of the i-th microgrid is constructed according to the following formula:

[0032]

[0033] ; in, represents the state variable of the power purchased and sold between the i-th microgrid and the main grid in period t, is the upper limit of the power of the grid tie line, is the state variable of the purchase and sale of hydrogen power between the i-th microgrid and the main grid in period t, Import hydrogen power limit into hydrogen transmission pipe, is the hydrogen energy sold per unit time by the i-th microgrid, is the hydrogen energy purchased per unit time by the i-th microgrid.

[0034] Step S103: constructing an upper optimization model with the goal of minimizing the sum of the equal annual value investment cost and the annual maintenance cost of the microgrid, and constructing a lower optimization model with the goal of maximizing the microgrid operation income, thereby obtaining a two-layer model; It should be noted that according to the microgrid structure, the upper-level optimization is capacity configuration optimization, and the goal is to minimize the equal annual investment cost and annual maintenance cost of the microgrid. It is to add the capacity configuration in the microgrid to the typical day of microgrid operation. The decision variables are the configuration capacity of the photovoltaic array, battery, electrolyzer, hydrogen storage tank, and fuel cell. Based on this, the upper-level optimization model is constructed according to the following formula: ; in, is the annual investment cost of the i-th microgrid, is the operating cost of the i-th microgrid, It represents the sum of the equal annual investment cost and annual maintenance cost of the i-th microgrid.

[0035] In some embodiments, the equal annual investment cost of the i-th microgrid is calculated according to the following formula: ; in, To invest in and build a collection of equipment, including photovoltaic arrays, wind turbines, hydrogen generators, electrolyzers, and fuel cells; is the capital recovery coefficient, is the discount rate, is the operating life of the kth equipment, For equipment kThe unit power investment cost includes the unit capacity investment cost of energy storage, the unit power investment cost of photovoltaic power generation, the unit power investment cost of electrolyzer, the unit capacity investment cost of hydrogen storage tank and the unit power investment cost of hydrogen fuel cell. is the capacity of the kth device in the i-th microgrid; The operating cost of the i-th microgrid is calculated according to the following formula: ; ; in, is the operating cost within the i-th microgrid, is the electricity purchase and sales cost of the i-th microgrid, is the cost of purchasing and selling hydrogen for the i-th microgrid, is the unit electricity price of the microgrid, is the hydrogen price of the microgrid, To maintain the proportionality factor, is the operation and maintenance cost coefficient of w components, is the operating power of the wth component of the i-th microgrid; The constraints of each device in the microgrid are constructed according to the following formula: ; in, are the maximum values ​​of the battery, photovoltaic array, electrolyzer, hydrogen energy storage, and hydrogen storage tank in the i-th microgrid after the lower optimization model calculation, They represent the maximum values ​​that can be achieved for the installed capacity of batteries, photovoltaic arrays, electrolyzers, hydrogen energy storage, and hydrogen storage tanks. , The rated capacity and rated power are configured for the i-th microgrid battery, photovoltaic cell, electrolyzer, fuel cell, and hydrogen storage tank respectively.

[0036] In addition, the lower optimization model aims to maximize the operating income of the microgrid. It calculates the scenario of maximizing the operating income based on the optimal installed capacity obtained in the upper model, and brings this worst scenario of the day back into the constraints of the upper model to complete the iteration of the upper and lower models. Among them, the operating income of the microgrid includes: the income obtained by the microgrid using photovoltaic power generation devices and hydrogen energy storage system devices for self-use; the income from electricity sales obtained by participating in electricity market transactions; the income from hydrogen sales obtained by participating in hydrogen energy market transactions, etc.

[0037] Specifically, the lower layer optimization model is constructed according to the following formula: ; in, The profit from selling electricity to users within the unit power microgrid. and are the unit electricity and hydrogen energy profits of the interaction between the i-th microgrid and the external microgrid, is the number of typical days in season n, A collection of four seasons, is the operating income of the i-th microgrid.

[0038] In some embodiments, various profits are calculated according to the following formula: ; in, is the profit brought by the unit electricity of the power interacting with the external microgrid during time period t, is the profit of unit hydrogen energy generated by the interaction between power and external microgrid in time period t, in units of , is the operation and maintenance cost coefficient of w components; The upper and lower limit constraints of the lower-level decision variables are constructed according to the following formula: ; in, is the per-unit value of photovoltaic output predicted by microgrid i, is the lower limit of the hydrogen storage tank’s energy storage capacity, is the upper limit of the hydrogen storage tank’s energy storage capacity, is the capacity of the hydrogen storage tank of the i-th microgrid per unit time, is the state of charge of the i-th microgrid battery per unit time, is the lower limit of the battery state of charge, It is the upper limit of battery state of charge.

[0039] Step S104: Solving the two-layer model to obtain a final capacity configuration result of the multi-microgrid.

[0040] It should be noted that the model proposed in this embodiment is a two-layer optimization model. The two-layer model can effectively solve the complexity of decision-making at the planning and operation levels of the microgrid. The iterative solution method can achieve a global solution to the microgrid configuration problem, provide more control and flexibility for the solution process, and handle the complexity of decisions at each level in a more detailed manner. And through the upper and lower layer iterative model calculation mode, the intermediate solution of the model can be effectively extracted, making data preprocessing and result analysis simpler and more intuitive. However, the upper and lower layer model calculation process is relatively complicated, and there are a large number of mixed integer linear programming problems in the process, which require linearization and multiple iterative calculations to obtain the final result. Therefore, this embodiment uses The model is transformed into a linear model using Platform and The mathematical optimization solver solves the model. The specific implementation process is as follows: Step 1: Initialize and assign values ​​to the microgrid; ; in, Configure the initial value for the photovoltaic cell, Configure the initial values ​​for the electrolyzer, Configure the initial value for the hydrogen fuel cell, Configure the initial value for the battery, Configure initial values ​​for the hydrogen storage tank.

[0041] Step 2: In the upper optimization model, the capacity of each device is determined with the goal of minimizing the internal cost of the microgrid, and the capacity determination result is transmitted to the lower optimization model as the upper limit of operation; Step 3: Input the fixed capacity value of the upper optimization model and the predicted value of the photovoltaic output load demand, simulate the typical daily operation of the microgrid with the goal of maximizing the microgrid profit, and return the operation results to the upper optimization model; Step 4: Return the optimization variables of the lower optimization model to the upper optimization model. The maximum value of the optimization variables of the lower optimization model is used as the minimum value of the optimization variable constraints of the upper optimization model. The upper optimization model again determines the capacity with the goal of minimizing the internal cost of the microgrid. Step 5: Repeat steps 2 to 4 for multiple iterations. The optimization target of the kth operation of the upper optimization model is recorded as , the optimization goal of the kth iteration of the lower optimization model is ,when ,and The iteration stops when , and the final capacity configuration result is obtained, where η and ε represent the convergence domain values ​​of the upper optimization model and the lower optimization model.

[0042] In addition, the final capacity configuration results include , , , , , .

[0043] According to the microgrid capacity configuration model established above, the load and light data of a certain area are selected as input data. The simulation step length Δt is set to 1h. This embodiment considers three scenarios, among which scenario 1 is the optimal configuration of independent operation of multiple microgrids based on photovoltaics and energy storage, scenario 2 is the optimal configuration of independent operation of multiple microgrids considering the addition of traditional micro gas turbines under scenario 1, scenario 3 is the optimal configuration of microgrids considering the interaction of traditional micro gas turbines and multiple microgrids under scenario 2, scenario 4 replaces the traditional gas turbine in scenario 3 with the optimal configuration of hydrogen-containing multi-microgrid photovoltaic storage of hydrogen energy storage system, and scenario 5 adds the coordinated operation between multiple microgrids on the basis of scenario 4, and constructs a two-layer optimal configuration model of multi-microgrids containing hydrogen energy storage.

[0044] According to the optimization configuration method mentioned in this paper, the number of iterations is set to 20 for scenario 1 and scenario 2, 30 for scenario 3, and 40 for scenario 4 and scenario 5. Finally, the configuration scheme and operation result values ​​of the microgrid are obtained. Table 1 lists the configuration results of the three microgrids, and Table 2 lists the operation results of the microgrids under all scenarios.

[0045] Table 1 Microgrid configuration results for scenario 5 ; Table 2 Comparison of microgrid operation results under five scenarios ; As shown in Table 2, the benefits in scenario 1 are the lowest compared to the other scenarios. After the gas turbine is configured in scenario 2, the cost and benefits are greatly increased compared to scenario 1. After the interactive power between multiple microgrids is increased in scenario 3 compared to scenario 2, the cost is reduced and the benefits are significantly increased. After the gas turbine is replaced with a hydrogen energy storage system in scenario 4, the cost is significantly reduced and the benefits are slightly increased. Compared with micro gas turbines, hydrogen energy is a green and efficient energy. Hydrogen energy does not need to consider the fuel cost in the gas turbine, but forms a closed-loop renewable energy system by electrolyzing water with renewable energy to produce hydrogen, which greatly reduces the annual operating cost of the microgrid, and the generated hydrogen can be traded to the outside world to increase the annual operating income of the microgrid. Scenario 5 adds the interaction of electricity and hydrogen energy between microgrids on the basis of scenario 4. By comparison, it can be found that for the reasonable configuration and stable operation of the microgrid, the multi-microgrid hydrogen energy storage optimization interaction system considered in scenario 5 is better than the traditional microgrid optimization configuration.

[0046] Taking the typical daily operation scenario of microgrid 1 in scenario 4 in spring as an example, the photovoltaic output, electrolyzer, fuel cell, battery and load output of the electricity-hydrogen coupling system in each period are analyzed. Figure 4As shown, the optimization configuration model proposed in this embodiment can effectively distribute power. When the microgrid 1 is in the period of 11:00-14:00 with sufficient sunlight, the photovoltaic output is relatively large, and the output of the hydrogen fuel cell is greatly reduced in the period of 10:00-13:00, and the output of the electrolyzer also reaches a peak during this period. It can be seen that during the period of sufficient sunlight during the day, the photovoltaic output is higher than the load demand, and the abandoned light is used for hydrogen production by the electrolyzer. Taking advantage of the characteristics of the hydrogen storage tank that can store hydrogen for a long time, the prepared hydrogen is stored in the hydrogen storage tank for combustion and power generation during the period when the electricity is not enough to meet the load demand, thereby increasing the internal benefits of the microgrid. When the photovoltaic output is not enough to meet the load demand during the night period, the hydrogen energy in the hydrogen storage tank and the hydrogen energy produced by the electrolyzer during this period and the hydrogen energy purchased from the outside will be used to generate electricity through the hydrogen fuel cell to meet the load demand. Therefore, the complementary characteristics of the electric energy storage system and the hydrogen energy storage system greatly reduce the amount of abandoned light, improve the energy utilization rate and the stability of the microgrid system.

[0047] Taking a typical spring day in scenario 5 microgrid 1 as an example, the photovoltaic output, electrolyzer, hydrogen fuel cell output and power interaction of multiple microgrids with hydrogen energy storage optimization are analyzed at different time periods. Figure 5 and Figure 4 Comparative analysis shows that the output of the electrolyzer fuel cell fluctuates more and has a relatively higher output than that of scenario 4. It can be seen that scenario 5 requires coordinated optimization among multiple microgrids based on scenario 4. While meeting the internal load demand of the microgrid, it is necessary to consider the load demand of other microgrids to achieve a balance of the multi-microgrid system and reduce the purchase of electricity and hydrogen energy from the outside.

[0048] like Figure 6As shown in the figure, in scenario 1, battery storage cannot meet the load demand of the power system in multiple time periods. The microgrid needs to purchase a large amount of electricity to meet the load demand during the periods of 5:00-8:00 and 17:00-19:00. In scenario 2, micro gas turbines are considered on the basis of scenario 1. Sustainable power generation at night greatly reduces the power purchased from the distribution network. In scenario 3, power interaction between multiple microgrids is considered, and power scheduling between multiple microgrids is added in addition to the output of micro gas turbines. This makes the power dispatch of the power system more flexible, and the power shortage in different time periods can be further supplemented, so that the power purchased from the distribution network in a unit time period is further reduced; in scenario 4, the micro gas turbine is replaced by a hydrogen energy storage system, and the hydrogen energy storage system can store the hydrogen produced during the day in the hydrogen storage tank for use at night. Analysis shows that the power purchased by the microgrid from the distribution network is greatly reduced; in scenario 5, on the basis of scenario 4, the interaction of electricity and hydrogen energy between multiple microgrids is added. When the power generation of the microgrid and the power storage system are not enough to meet the load demand during this period, the microgrid can purchase electricity and hydrogen energy through interaction with the other two microgrids to meet the load demand during this period. As a result, the power purchased from the distribution network is reduced again compared with scenario 4. It can be seen that the mutual coupling of the hydrogen energy storage system and the power system can greatly improve the energy utilization efficiency and improve the stability of the power system output in multiple time periods. The energy interaction between multiple microgrids can enhance the flexibility of the microgrid and the stability of power supply in different time periods.

[0049] In summary, the above-mentioned multi-microgrid photovoltaic storage dual-layer optimization configuration method has the following advantages: 1. Aiming at the high fuel cost and high emission pollution of traditional micro gas turbines as backup energy sources, a basic model of microgrid hydrogen energy storage system is proposed. The method of hydrogen production, storage and hydrogen energy conversion is studied, and a hydrogen energy storage system in the form of electrolyzer-hydrogen storage tank-hydrogen fuel cell is established. This method can effectively reduce the microgrid's dependence on fossil fuels and reduce fuel costs.

[0050] 2. In response to the problem of imbalanced supply and demand in traditional microgrids, a basic model of electricity-hydrogen coupling was established. Electric energy was converted into hydrogen energy through an electrolyzer and stored in a hydrogen storage tank. When the load demand was greater than power generation, the hydrogen energy was input into the hydrogen fuel cell for combustion and power generation, effectively solving the problem of imbalanced supply and demand in microgrids at different times.

[0051] 3. Aiming at the problem of poor economic efficiency of small and micro parks, an optimization configuration method of hydrogen energy storage based on the multi-park collaborative optimization operation mode is proposed. Taking advantage of the characteristics of multi-park collaborative optimization of hydrogen energy and electric energy interaction, a multi-park collaborative optimization configuration model of hydrogen energy storage is constructed with the goal of increasing the economic efficiency of small and micro parks. The upper layer determines the capacity of the internal equipment of the park with the goal of minimizing the comprehensive cost within the small and micro park, and the lower layer performs typical daily simulation scenario operation with the goal of maximizing the internal profit of the park. The results are obtained through multiple iterative calculations of the upper and lower layers. This method effectively improves the economy and flexibility of small and micro parks and ensures the stable operation of the park.

[0052] like Figure 7 As shown, an embodiment of the present invention further proposes a multi-microgrid photovoltaic storage dual-layer optimization configuration system, the system comprising: The electric-hydrogen coupling architecture construction module 10 is used to establish an electric-hydrogen coupling operation architecture, and construct a power capacity calculation model for the battery and energy storage charging and discharging constraints corresponding to the power capacity calculation model, an output power model for the electrolyzer, an output power model for the fuel cell, a hydrogen storage model for the hydrogen storage tank, an electric-hydrogen coupling model, and hydrogen power constraints corresponding to the electric-hydrogen coupling model according to the electric-hydrogen coupling operation architecture; A microgrid expansion module 20 is used to expand a single microgrid into multiple microgrids, and to collaboratively optimize the power balance constraint and power constraint of the i-th microgrid based on the power interaction of electric energy and hydrogen energy between the multiple microgrids; A two-layer model building module 30 is used to build an upper optimization model with the goal of minimizing the sum of the equal annual value investment cost and the annual maintenance cost of the microgrid, and to build a lower optimization model with the goal of maximizing the operating income of the microgrid, thereby obtaining a two-layer model; The solution module 40 is used to solve the two-layer model to obtain the final capacity configuration result of the multi-microgrid.

[0053] On the other hand, the present invention further proposes a storage medium on which one or more programs are stored, which, when executed by a processor, implement the above-mentioned multi-microgrid photovoltaic storage dual-layer optimization configuration method.

[0054] On the other hand, the present invention also proposes an electronic device, including a memory and a processor, wherein the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the above-mentioned multi-microgrid photovoltaic storage dual-layer optimization configuration method.

[0055] Although the embodiments of the present invention are described in detail above, it is obvious to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as described in the claims. Moreover, the present invention described herein may have other embodiments and may be implemented or realized in a variety of ways.

Claims

1. A multi-microgrid photovoltaic storage dual-layer optimization configuration method, characterized in that: The method comprises: Establishing an electric-hydrogen coupling operation framework, and constructing a power capacity calculation model for the battery and energy storage charging and discharging constraints corresponding to the power capacity calculation model, an output power model for the electrolyzer, an output power model for the fuel cell, a hydrogen storage model for the hydrogen storage tank, an electric-hydrogen coupling model, and hydrogen power constraints corresponding to the electric-hydrogen coupling model according to the electric-hydrogen coupling operation framework; Expand a single microgrid into multiple microgrids, and collaboratively optimize the power balance constraints and power constraints of the i-th microgrid based on the power interaction of electric energy and hydrogen energy among multiple microgrids; The upper optimization model is constructed with the goal of minimizing the sum of the equal annual investment cost and the annual maintenance cost of the microgrid, and the lower optimization model is constructed with the goal of maximizing the microgrid operation income, thus obtaining a two-layer model. The two-layer model is solved to obtain a final capacity configuration result of the multi-microgrid.

2. The multi-microgrid photovoltaic storage dual-layer optimization configuration method according to claim 1 is characterized in that: The power capacity calculation model of the battery is constructed according to the following formula: ； in, , are the storage capacity of the battery in time period t and time period t-1 respectively, is the charging power of the battery in time period t, is the discharge power of the battery in time period t, is the charge and discharge rate of the battery, is the time step, is the energy loss rate of the battery; The energy storage charging and discharging constraint conditions corresponding to the power capacity calculation model are constructed according to the following formula: ； in, is the state variable of the battery charging and discharging power in time period t, is the maximum charging and discharging power of the battery; The relationship between the maximum charging and discharging power of the battery and the battery capacity is expressed as: ； The battery's state of charge during time period t It is expressed as: ； in, Indicates the fixed proportional coefficient between the battery power upper limit and capacity. Indicates the battery capacity.

3. The multi-microgrid photovoltaic storage dual-layer optimization configuration method according to claim 2 is characterized in that: The output power model of the electrolyzer is constructed according to the following formula: ； in, is the power consumption of the electrolytic cell, The hydrogen power converted by the electrolyzer, is the output efficiency of the electrolyzer; The output power model of the fuel cell is constructed according to the following formula: ； in, is the output power of the fuel cell, is the hydrogen power consumed by the fuel cell, Output efficiency for hydrogen fuel cells; The hydrogen storage model for the hydrogen storage tank is constructed according to the following formula: ； in, is the hydrogen storage capacity of the hydrogen storage tank in time period t, is the hydrogen filling and discharging efficiency of the hydrogen storage tank, is the hydrogen energy charging power of the hydrogen storage tank in time period t, is the hydrogen discharge power of the hydrogen storage tank in time period t.

4. The multi-microgrid photovoltaic storage dual-layer optimization configuration method according to claim 3 is characterized in that: The electric-hydrogen coupling model is constructed according to the following formula: ； in, is the hydrogen power converted by the electrolyzer in time period t, is the power converted directly from the electrolyzer into the fuel cell during time period t, is the hydrogen energy purchased per unit time, is the hydrogen power consumed by the fuel cell in time period t, is the hydrogen energy sold per unit time; The hydrogen power constraint conditions include hydrogen power balance constraint conditions and hydrogen energy storage charging and discharging power upper and lower limit constraint conditions; The hydrogen power balance constraint is constructed according to the following formula: ； The upper and lower limit constraints of hydrogen energy storage charging and discharging power are constructed according to the following formula: ； in, is the state variable of hydrogen charging and discharging during time period t, is the maximum value that can be achieved during hydrogen charging and discharging, and , Assign capacity to the hydrogen storage tank. is the power energy conversion coefficient of the hydrogen storage tank.

5. The multi-microgrid photovoltaic storage dual-layer optimization configuration method according to claim 4 is characterized in that: The power balance constraint of the i-th microgrid is constructed according to the following formula: ； in, is the discharge power of the battery in the i-th microgrid during the t time period, is the power purchased by the i-th microgrid from the main grid during time period t, is the output power of the fuel cell in the i-th microgrid in time period t, is the photovoltaic output of the i-th microgrid in time period t, is the electric power purchased by the jth microgrid from the ith microgrid in time period t, To use historical data to predict the load results, the load demand power of the i-th microgrid in time period t is: is the power sold by the i-th microgrid to the main grid during time period t, is the power consumption of the electrolyzer in the i-th microgrid during time period t, is the charging power of the battery in the i-th microgrid in time period t, represents the electric power purchased by the i-th microgrid from the j-th microgrid in time period t, n represents the total number of microgrids, is the hydrogen power converted by the bottom electrolyzer in the i-th microgrid in time period t, is the hydrogen discharge power of the hydrogen storage tank in the i-th microgrid in time period t, represents the hydrogen power purchased by the i-th microgrid from the j-th microgrid in time period t, is the hydrogen energy charging power of the hydrogen storage tank in the i-th microgrid during the t-time period, is the hydrogen power consumed by the fuel cell in the i-th microgrid in time period t, represents the hydrogen power purchased by the jth microgrid from the i-th microgrid in time period t; The power constraint of the i-th microgrid is constructed according to the following formula: ； ； ； in, represents the state variable of the power purchased and sold between the i-th microgrid and the main grid in period t, is the upper limit of the power of the grid tie line, is the state variable of the purchase and sale of hydrogen power between the i-th microgrid and the main grid in period t, Import hydrogen power limit into hydrogen transmission pipe, is the hydrogen energy sold per unit time by the i-th microgrid, is the hydrogen energy purchased per unit time by the i-th microgrid.

6. The multi-microgrid photovoltaic storage dual-layer optimization configuration method according to claim 5 is characterized in that: The steps of constructing an upper optimization model with the goal of minimizing the sum of the equal annual investment cost and the annual maintenance cost of the microgrid, and constructing a lower optimization model with the goal of maximizing the operating income of the microgrid, to obtain a two-layer model include: The upper optimization model is constructed according to the following formula: ； in, is the annual investment cost of the i-th microgrid, is the operating cost of the i-th microgrid, represents the sum of the equal annual investment cost and annual maintenance cost of the i-th microgrid; The lower-level optimization model is constructed according to the following formula: ； in, The profit from selling electricity to users within the unit power microgrid. and are the unit electricity and hydrogen energy profits of the interaction between the i-th microgrid and the external microgrid, is the number of typical days in season n, A collection of four seasons, is the operating income of the i-th microgrid.

7. The multi-microgrid photovoltaic storage dual-layer optimization configuration method according to claim 6 is characterized in that: The step of solving the two-layer model to obtain a final capacity configuration result of the multi-microgrid includes: Step 1: Initialize and assign values ​​to the microgrid; Step 2: In the upper optimization model, the capacity of each device is determined with the goal of minimizing the internal cost of the microgrid, and the capacity determination result is transmitted to the lower optimization model as the upper limit of operation; Step 3: Input the fixed capacity value of the upper optimization model and the predicted value of the photovoltaic output load demand, simulate the typical daily operation of the microgrid with the goal of maximizing the microgrid profit, and return the operation results to the upper optimization model; Step 4: Return the optimization variables of the lower optimization model to the upper optimization model. The maximum value of the optimization variables of the lower optimization model is used as the minimum value of the optimization variable constraints of the upper optimization model. The upper optimization model again determines the capacity with the goal of minimizing the internal cost of the microgrid. Step 5: Repeat steps 2 to 4 for multiple iterations. The optimization target of the kth operation of the upper optimization model is recorded as , the optimization goal of the kth iteration of the lower optimization model is ,when ,and The iteration stops when , and the final capacity configuration result is obtained, where η and ε represent the convergence domain values ​​of the upper optimization model and the lower optimization model.

8. The multi-microgrid photovoltaic storage dual-layer optimization configuration method according to claim 7 is characterized in that: The equal annual investment cost of the i-th microgrid is calculated according to the following formula: ； in, To invest in and build a collection of equipment, including photovoltaic arrays, wind turbines, hydrogen generators, electrolyzers, and fuel cells; is the capital recovery coefficient, is the discount rate, is the operating life of the kth equipment, For equipment k The unit power investment cost includes the unit capacity investment cost of energy storage, the unit power investment cost of photovoltaic power generation, the unit power investment cost of electrolyzer, the unit capacity investment cost of hydrogen storage tank and the unit power investment cost of hydrogen fuel cell. is the capacity of the kth device in the i-th microgrid; The operating cost of the i-th microgrid is calculated according to the following formula: ； ； in, is the operating cost within the i-th microgrid, is the electricity purchase and sales cost of the i-th microgrid, is the cost of purchasing and selling hydrogen for the i-th microgrid, is the unit electricity price of the microgrid, is the microgrid hydrogen price, To maintain the proportionality factor, is the operation and maintenance cost coefficient of w components, is the operating power of the wth component of the i-th microgrid; The constraints of each device in the microgrid are constructed according to the following formula: ； in, are the maximum values ​​of the battery, photovoltaic array, electrolyzer, hydrogen energy storage, and hydrogen storage tank in the i-th microgrid after the lower optimization model calculation, They represent the maximum values ​​that can be achieved for the installed capacity of batteries, photovoltaic arrays, electrolyzers, hydrogen energy storage, and hydrogen storage tanks. , The rated capacity and rated power are configured for the i-th microgrid battery, photovoltaic cell, electrolyzer, fuel cell, and hydrogen storage tank respectively.

9. The multi-microgrid photovoltaic storage dual-layer optimization configuration method according to claim 8 is characterized in that: The various profits are calculated according to the following formula: ； in, is the profit brought by the unit electricity of the power interacting with the external microgrid during time period t, is the profit of unit hydrogen energy generated by the interaction between power and external microgrid in time period t, in units of , is the operation and maintenance cost coefficient of w components; Construct the upper and lower limit constraints of the lower-level decision variables according to the following formula: ； in, is the per-unit value of photovoltaic output predicted by microgrid i, is the lower limit of the hydrogen storage tank’s energy storage capacity, is the upper limit of the hydrogen storage tank’s energy storage capacity, is the capacity of the hydrogen storage tank of the i-th microgrid per unit time, is the state of charge of the i-th microgrid battery per unit time, is the lower limit of the battery state of charge, is the upper limit of the battery state of charge; The final capacity configuration result includes , , , , , .

10. A multi-microgrid photovoltaic storage dual-layer optimization configuration system, characterized in that: The system comprises: An electric-hydrogen coupling architecture construction module, used to establish an electric-hydrogen coupling operation architecture, and to construct a power capacity calculation model for a battery and energy storage charging and discharging constraints corresponding to the power capacity calculation model, an output power model for an electrolyzer, an output power model for a fuel cell, a hydrogen storage model for a hydrogen storage tank, an electric-hydrogen coupling model, and hydrogen power constraints corresponding to the electric-hydrogen coupling model according to the electric-hydrogen coupling operation architecture; The microgrid expansion module is used to expand a single microgrid into multiple microgrids, and to collaboratively optimize the power balance constraints and power constraints of the i-th microgrid based on the power interaction of electric energy and hydrogen energy among multiple microgrids; A two-layer model building module is used to build an upper optimization model with the goal of minimizing the sum of the equal annual investment cost and the annual maintenance cost of the microgrid, and to build a lower optimization model with the goal of maximizing the operating income of the microgrid, thereby obtaining a two-layer model; A solution module is used to solve the two-layer model to obtain a final capacity configuration result of the multi-microgrid.

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