Multi-microgrid photovoltaic storage dual-layer optimization configuration method and system

Abstract

The present invention provides a method and system for optimizing the dual-layer configuration of photovoltaic and energy storage in multiple microgrids. The method comprises: establishing an electricity-hydrogen coupled operation architecture and constructing a model and constraints based on the electricity-hydrogen coupled operation architecture; expanding a single microgrid into multiple microgrids and collaboratively optimizing the power balance and power constraints of the i-th microgrid based on the power interaction of electricity and hydrogen energy between the multiple microgrids; constructing an upper-layer optimization model with the goal of minimizing the sum of the microgrid's equal annual investment cost and annual maintenance cost, and constructing a lower-layer optimization model with the goal of maximizing the microgrid's operating revenue, thereby obtaining a dual-layer model; and solving the dual-layer model to obtain the final capacity configuration results for the multiple microgrids. The present invention can effectively reduce microgrids' dependence on fossil fuels and reduce fuel costs.

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 a more resilient energy structure, microgrids (MGs) are gaining widespread attention as a new energy management and distribution model. Optimizing the capacity of MGs can reduce losses and increase their reliability, flexibility, and energy efficiency, making them a key component of MG design and planning.

[0003] Current research on microgrid capacity optimization focuses primarily on the coordinated integration of wind power, photovoltaics, and energy storage batteries. Micro-turbines (MTs) are often used as emergency backup equipment to improve microgrid power reliability, but their polluting emissions conflict with the vision of developing clean energy. Therefore, cleaner forms of renewable energy generation are needed to replace micro-turbines as backup equipment. Furthermore, traditional electrochemical energy storage cannot flexibly dispatch electricity to meet load demands at different times, requiring external power purchases 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 problems of high fuel cost and large environmental pollution during the operation of traditional microgrids, and the inability of traditional electrochemical energy storage to flexibly dispatch electricity to meet the load requirements of different time periods, and the need 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 method for optimizing the configuration of a multi-microgrid photovoltaic and storage dual-layer structure, the method comprising:

[0006] Establishing an electric-hydrogen coupled operation framework, and constructing a power capacity calculation model for the battery and energy storage charge and discharge 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 based on the electric-hydrogen coupled operation framework;

[0007] 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 electricity and hydrogen energy among the multiple microgrids;

[0008] 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 operating income, thus obtaining a two-layer model.

[0009] The two-layer model is solved to obtain a final capacity configuration result of the multi-microgrid.

[0010] Furthermore, a power capacity calculation model for the battery is constructed according to the following formula:

[0011] ;

[0012] 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 battery energy loss rate;

[0013] The energy storage charging and discharging constraint conditions corresponding to the power capacity calculation model are constructed according to the following formula:

[0014] ;

[0015] in, is the state variable of the battery charging and discharging power in time period t, is the maximum charge and discharge power of the battery;

[0016] The relationship between the maximum charge and discharge power of the battery and the battery capacity is expressed as:

[0017] ;

[0018] Battery state of charge during time period t Expressed as:

[0019] ;

[0020] in, Indicates the fixed proportional coefficient between the battery power upper limit and capacity. Indicates the battery capacity.

[0021] Furthermore, an output power model of the electrolyzer is constructed according to the following formula:

[0022] ;

[0023] in, is the power consumption of the electrolytic cell, The hydrogen power converted by the electrolyzer, is the output efficiency of the electrolyzer;

[0024] The output power model of the fuel cell is constructed according to the following formula:

[0025] ;

[0026] in, is the output electrical power of the fuel cell, is the hydrogen power consumed by the fuel cell, Output efficiency for hydrogen fuel cells;

[0027] A hydrogen storage model for hydrogen storage tanks is constructed according to the following formula:

[0028] ;

[0029] in, is the hydrogen storage capacity of the hydrogen storage tank in time period t, is the hydrogen charging 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.

[0030] Furthermore, the electric-hydrogen coupling model is constructed according to the following formula:

[0031] ;

[0032] in, is the hydrogen power converted by the electrolyzer in time period t, is the power converted from the electrolyzer directly into the fuel cell during time period t, 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;

[0033] The hydrogen power constraints include hydrogen power balance constraints and hydrogen energy storage charging and discharging power upper and lower limit constraints;

[0034] The hydrogen power balance constraint is constructed according to the following formula:

[0035] ;

[0036] The upper and lower limit constraints of hydrogen energy storage charging and discharging power are constructed according to the following formula:

[0037] ;

[0038] in, is the hydrogen charging and discharging state variable in time period t, is the maximum value that the hydrogen charging and discharging power can reach, and , Configure the capacity for the hydrogen storage tank, is the power energy conversion coefficient of the hydrogen storage tank.

[0039] Furthermore, the power balance constraint of the i-th microgrid is constructed according to the following formula:

[0040] ;

[0041] 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 during 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, In order to use historical data to predict the load results, the load demand power of the i-th microgrid in the t time period 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 the t time period, is the charging power of the battery in the i-th microgrid during 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 during time period t, is the hydrogen discharge power of the hydrogen storage tank in the i-th microgrid during 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 during time period t, represents the hydrogen power purchased by the jth microgrid from the ith microgrid in time period t;

[0042] The power constraint of the i-th microgrid is constructed according to the following formula:

[0043]

[0044]

[0045] ;

[0046] 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 hydrogen power purchase and sale 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.

[0047] Furthermore, the steps of constructing an upper-layer 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-layer optimization model with the goal of maximizing the microgrid operating income, to obtain the two-layer model include:

[0048] The upper optimization model is constructed according to the following formula:

[0049] ;

[0050] 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-year investment cost and annual maintenance cost of the i-th microgrid;

[0051] The lower-level optimization model is constructed according to the following formula:

[0052] ;

[0053] in, The profit from selling electricity to users within the unit power microgrid, and are the unit electricity and hydrogen energy generated by 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.

[0054] Furthermore, the step of solving the two-layer model to obtain a final capacity configuration result of the multi-microgrid includes:

[0055] Step 1: Initialize and assign values ​​to the microgrid;

[0056] 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 results are transmitted to the lower optimization model as the upper limit of operation;

[0057] 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's profit, and return the operation results to the upper optimization model;

[0058] Step 4: Return the optimized variables of the lower optimization model to the upper optimization model. The maximum value of the optimized variables of the lower optimization model is used as the minimum value of the optimized variable constraint of the upper optimization model. The upper optimization model again determines the capacity with the goal of minimizing the internal cost of the microgrid.

[0059] 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.

[0060] Furthermore, the equal annual investment cost of the i-th microgrid is calculated according to the following formula:

[0061] ;

[0062] 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 device, 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;

[0063] The operating cost of the i-th microgrid is calculated according to the following formula:

[0064] ;

[0065] ;

[0066] 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 in 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;

[0067] The constraints of each device in the microgrid are constructed according to the following formula:

[0068] ;

[0069] 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.

[0070] Furthermore, various profits are calculated according to the following formula:

[0071] ;

[0072] in, is the profit brought by the unit electricity of the power and external microgrid interaction in time period t, 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;

[0073] The upper and lower limit constraints of the lower-level decision variables are constructed according to the following formula:

[0074] ;

[0075] in, is the per-unit 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 in 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;

[0076] The final capacity configuration result includes 、 、 、 、 、 .

[0077] 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:

[0078] an electric-hydrogen coupling architecture construction module, configured to establish an electric-hydrogen coupling operation architecture and, based on the electric-hydrogen coupling operation architecture, construct a power capacity calculation model for the battery and energy storage charge and discharge 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;

[0079] The microgrid expansion module is used to 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 electricity and hydrogen energy among multiple microgrids;

[0080] A two-layer model construction module is used to construct an upper-layer 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 construct a lower-layer optimization model with the goal of maximizing the microgrid operating income, thereby obtaining a two-layer model;

[0081] A solution module is used to solve the two-layer model to obtain a final capacity configuration result of the multi-microgrid.

[0082] Compared with the prior art, the present invention has the following advantages:

[0083] 1. To address the high fuel costs and high emissions associated with traditional micro-turbines as backup energy sources, a basic model for a microgrid hydrogen energy storage system was proposed. The method for hydrogen generation, storage, and hydrogen energy conversion was studied, leading to a hydrogen energy storage system consisting of an electrolyzer, a hydrogen storage tank, and a hydrogen fuel cell. This approach effectively reduces the microgrid's reliance on fossil fuels and reduces fuel costs.

[0084] 2. In response to the supply and demand imbalance problem of traditional microgrids, a basic model of electricity-hydrogen coupling was established. Electric energy is converted into hydrogen energy through an electrolyzer and stored in a hydrogen storage tank. When the load demand is greater than the power generation, the hydrogen energy is input into the hydrogen fuel cell for combustion and power generation, effectively solving the problem of supply and demand imbalance in microgrids at different times.

[0085] 3. To address the poor economic efficiency of small and micro-parks, a method for optimizing the configuration of hydrogen-containing energy storage under a multi-park collaborative optimization operation model is proposed. By leveraging the interactive characteristics of hydrogen energy and electricity in multi-park collaborative optimization, a multi-park collaborative optimization configuration model for hydrogen-containing energy storage is constructed with the goal of increasing the economic efficiency of small and micro-parks. The upper layer sizing the internal equipment of the small and micro-park is targeted at minimizing the overall cost within the park, while the lower layer simulates typical daily scenarios with the goal of maximizing the internal benefits of the park. The results are obtained through multiple iterative calculations of the upper and lower layers. This method effectively improves the economic efficiency and flexibility of small and micro-parks and ensures their stable operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 This is a flow chart of a multi-microgrid photovoltaic-storage dual-layer optimization configuration method proposed in one embodiment of the present invention;

[0087] Figure 2 A schematic diagram of the structure of a microgrid according to an embodiment of the present invention;

[0088] Figure 3 This is a multi-microgrid collaborative optimization operation framework diagram according to an embodiment of the present invention;

[0089] Figure 4 This is a schematic diagram of a typical spring day power operation scenario of a microgrid according to scenario 4 of an embodiment of the present invention;

[0090] Figure 5 This is a schematic diagram of a typical spring electricity operation scenario of a microgrid according to scenario 5 of an example of an embodiment of the present invention;

[0091] 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;

[0092] Figure 7 This is a structural diagram of a multi-microgrid photovoltaic-storage dual-layer optimization configuration system proposed in one embodiment of the present invention.

[0093] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0094] 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 making 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 the common meanings understood by people with ordinary skills in the field to which the invention belongs. The words "including" and similar words used in this article mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0095] like Figure 1 As shown, an embodiment of the present invention provides a multi-microgrid photovoltaic storage dual-layer optimization configuration method, the method comprising steps S101 to S104, wherein:

[0096] Step S101: Establishing an electric-hydrogen coupled operation framework, and constructing a power capacity calculation model for the battery and energy storage charge and discharge 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 based on the electric-hydrogen coupled operation framework;

[0097] 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 mainly consists 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.

[0098] First, if the system's power generation exceeds the load demand, the excess power is stored in the energy storage device. If the load demand exceeds the system's power generation, power is drawn from the energy storage device to meet the load demand. Energy storage devices can effectively solve the problem of uneven distribution of renewable energy output and effectively improve the economic efficiency of microgrid internal operations. Specifically, a power capacity calculation model for batteries is constructed according to the following formula:

[0099] ;

[0100] 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.

[0101] The energy storage charging and discharging constraint conditions corresponding to the power capacity calculation model are constructed according to the following formula:

[0102] ;

[0103] in, is the state variable of the battery charging and discharging power in time period t, is the maximum charge and discharge power of the battery;

[0104] The relationship between the maximum charge and discharge power of the battery and the battery capacity is expressed as:

[0105] ;

[0106] Battery state of charge during time period t Expressed as:

[0107] ;

[0108] in, Indicates the fixed proportional coefficient between the battery power upper limit and capacity. Indicates the battery capacity.

[0109] In addition, in some embodiments, the electrolyzer-hydrogen storage tank-hydrogen fuel cell model has 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 sunlight 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:

[0110] ;

[0111] in, is the power consumption of the electrolytic cell, The hydrogen power converted by the electrolyzer, is the output efficiency of the electrolyzer.

[0112] A hydrogen fuel cell is a proton exchange membrane fuel cell that uses hydrogen and oxygen as fuel to convert chemical energy into electrical energy for storage. Its output power is:

[0113] ;

[0114] in, is the output electrical power of the fuel cell, is the hydrogen power consumed by the fuel cell, Output efficiency of hydrogen fuel cells.

[0115] Hydrogen storage tanks are used to collect and store hydrogen, providing hydrogen to fuel cells when the load demand is high. Compared with batteries, hydrogen storage tanks are safer and have lower energy loss. The hydrogen storage model of hydrogen storage tanks is shown as follows:

[0116] ;

[0117] in, is the hydrogen storage capacity of the hydrogen storage tank in time period t, is the hydrogen charging 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.

[0118] Part of the hydrogen energy converted by the electrolyzer is stored in a hydrogen storage tank. When the system's electrical energy is insufficient to meet load demand, the hydrogen energy is transferred to the hydrogen fuel cell for combustion and power generation. The remaining part is directly transferred to the hydrogen fuel cell, and the discarded hydrogen is sold. At the same time, when hydrogen energy is insufficient to meet load demand, the system purchases hydrogen energy from the outside to maintain the supply and demand balance within the microgrid. Specifically, the electric-hydrogen coupling model is constructed according to the following formula:

[0119] ;

[0120] in, is the hydrogen power converted by the electrolyzer in time period t, is the power converted from the electrolyzer directly into the fuel cell during time period t, 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;

[0121] The hydrogen power constraints include hydrogen power balance constraints and hydrogen energy storage charging and discharging power upper and lower limit constraints;

[0122] The hydrogen power balance constraint is constructed according to the following formula:

[0123] ;

[0124] The upper and lower limit constraints of hydrogen energy storage charging and discharging power are constructed according to the following formula:

[0125] ;

[0126] in, is the hydrogen charging and discharging state variable during time period t, is the maximum value that the hydrogen charging and discharging power can reach, and , Configure the capacity for the hydrogen storage tank, is the power energy conversion coefficient of the hydrogen storage tank.

[0127] Step S102: Expanding a single microgrid into multiple microgrids, and collaboratively optimizing 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;

[0128] 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 of 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.

[0129] Specifically, the power balance constraint of the i-th microgrid is constructed according to the following formula:

[0130] ;

[0131] 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 during 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, In order to use historical data to predict the load results, the load demand power of the i-th microgrid in the t time period 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 the t time period, is the charging power of the battery in the i-th microgrid during 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 during time period t, is the hydrogen discharge power of the hydrogen storage tank in the i-th microgrid during 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 during time period t, represents the hydrogen power purchased by the jth microgrid from the ith microgrid in time period t;

[0132] The power constraint of the i-th microgrid is constructed according to the following formula:

[0133]

[0134]

[0135] ;

[0136] 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 hydrogen power purchase and sale 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.

[0137] Step S103: constructing an upper-layer 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-layer optimization model with the goal of maximizing the microgrid operating income, thereby obtaining a two-layer model;

[0138] It should be noted that, based on the microgrid structure, the upper-level optimization involves capacity configuration optimization, aiming to minimize the microgrid's equivalent annual investment cost and annual maintenance cost. This involves factoring the microgrid's capacity configuration into a typical day's operation. The decision variables are the configured capacities 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:

[0139] ;

[0140] 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-year investment cost and annual maintenance cost of the i-th microgrid.

[0141] In some embodiments, the equivalent annual investment cost of the i-th microgrid is calculated according to the following formula:

[0142] ;

[0143] 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 device, 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;

[0144] The operating cost of the i-th microgrid is calculated according to the following formula:

[0145] ;

[0146] ;

[0147] 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 in 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;

[0148] The constraints of each device in the microgrid are constructed according to the following formula:

[0149] ;

[0150] 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.

[0151] Furthermore, the lower-level optimization model aims to maximize the microgrid's operating revenue. Based on the optimal installed capacity obtained in the upper-level model, it calculates the scenario that maximizes operating revenue. This worst-case scenario is then incorporated into the upper-level model's constraints, completing the iteration of the upper and lower-level models. Microgrid operating revenue includes: revenue from self-generated electricity generated by the microgrid using photovoltaic power generation devices and hydrogen energy storage systems; revenue from electricity sales through participation in electricity market transactions; and revenue from hydrogen sales through participation in hydrogen energy market transactions.

[0152] Specifically, the lower-level optimization model is constructed according to the following formula:

[0153] ;

[0154] in, The profit from selling electricity to users within the unit power microgrid, and are the unit electricity and hydrogen energy generated by 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.

[0155] In some embodiments, various profits are calculated according to the following formula:

[0156] ;

[0157] in, is the profit brought by the unit electricity of the power and external microgrid interaction in time period t, 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;

[0158] The upper and lower limit constraints of the lower-level decision variables are constructed according to the following formula:

[0159] ;

[0160] in, is the per-unit 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 unit time capacity of the hydrogen storage tank in the i-th microgrid, is the state of charge of the i-th microgrid battery per unit time, is the lower limit of the battery state of charge, The upper limit of the battery state of charge.

[0161] Step S104: Solve the two-layer model to obtain a final capacity configuration result of the multi-microgrid.

[0162] 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 more carefully. And through the upper and lower layer iterative model calculation mode, the intermediate solution of the model can be effectively extracted, making the 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:

[0163] Step 1: Initialize and assign values ​​to the microgrid;

[0164] ;

[0165] 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.

[0166] 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 results are transmitted to the lower optimization model as the upper limit of operation;

[0167] 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's profit, and return the operation results to the upper optimization model;

[0168] Step 4: Return the optimized variables of the lower optimization model to the upper optimization model. The maximum value of the optimized variables of the lower optimization model is used as the minimum value of the optimized variable constraint of the upper optimization model. The upper optimization model again determines the capacity with the goal of minimizing the internal cost of the microgrid.

[0169] 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.

[0170] In addition, the final capacity configuration results include 、 、 、 、 、 .

[0171] 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 size Δt is set to 1h. This embodiment considers three scenarios, among which scenario 1 is the optimized configuration of independent operation of multiple microgrids based on photovoltaics and energy storage, scenario 2 is the optimized configuration of independent operation of multiple microgrids considering the addition of traditional micro gas turbines under the condition of scenario 1, scenario 3 is the optimized configuration of microgrids considering the interaction of traditional micro gas turbines and multiple microgrids under the condition of scenario 2, scenario 4 replaces the traditional gas turbine in scenario 3 with the hydrogen-containing multi-microgrid photovoltaic storage optimized configuration of the 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 optimized configuration model of multi-microgrids containing hydrogen energy storage.

[0172] According to the optimization configuration method mentioned in this article, the number of iterations for scenarios 1 and 2 is set to 20, the number of iterations for scenario 3 is set to 30, and the number of iterations for scenarios 4 and 5 is set to 40. 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 microgrid under all scenarios.

[0173] Table 1 Microgrid configuration results for scenario 5

[0174] ;

[0175] Table 2 Comparison of microgrid operation results under five scenarios

[0176] ;

[0177] Table 2 shows that Scenario 1 offers the lowest benefits compared to the other scenarios. Scenario 2, with the addition of gas turbines, sees both costs and benefits increase significantly compared to Scenario 1. Scenario 3, by adding power exchange between multiple microgrids, reduces costs and significantly increases benefits. Scenario 4, by replacing the gas turbines with a hydrogen energy storage system, significantly reduces costs and slightly increases benefits. Hydrogen energy is a greener and more efficient energy source than microturbines. Hydrogen energy eliminates the fuel costs associated with gas turbines and instead generates hydrogen through the electrolysis of water using renewable energy, creating a closed-loop renewable energy system. This significantly reduces the microgrid's annual operating costs, and the generated hydrogen can be traded externally, increasing the microgrid's annual operating revenue. Scenario 5 builds on Scenario 4 by adding power and hydrogen exchange between microgrids. Comparison shows that the multi-microgrid hydrogen energy storage optimized interactive system considered in Scenario 5 outperforms traditional microgrid optimization configurations in terms of optimal microgrid configuration and stable operation.

[0178] Taking the typical daily operation scenario of microgrid 1 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 4 As shown, the optimization configuration model proposed in this embodiment can effectively distribute power. When microgrid 1 is in sufficient sunlight during the period of 11:00-14:00, the photovoltaic output is relatively high. The hydrogen fuel cell output is significantly reduced during the period of 10:00-13:00, and the electrolyzer output also reaches its peak during this period. Therefore, during the period of sufficient sunlight during the day, the photovoltaic output exceeds the load demand, and the abandoned photovoltaic power is used to produce hydrogen in the electrolyzer. Taking advantage of the long-term storage of hydrogen in the hydrogen storage tank, the produced hydrogen is stored in the hydrogen storage tank for combustion and power generation during periods when electricity is insufficient to meet the load demand, thereby increasing the internal revenue of the microgrid. During the nighttime period, when the photovoltaic output is insufficient to meet the load demand, the hydrogen energy in the hydrogen storage tank, 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 photovoltaic power, improve energy utilization and the stability of the microgrid system.

[0179] 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 the multi-microgrid with hydrogen energy storage optimization in each period are analyzed. Figure 5 and Figure 4Comparative analysis shows that the output of the electrolyzer fuel cell fluctuates more significantly than in Scenario 4, and the output is relatively high. This shows that Scenario 5, based on Scenario 4, requires coordinated optimization among multiple microgrids. While meeting the internal load requirements of the microgrid, it is also necessary to consider the load requirements of other microgrids to achieve a balanced multi-microgrid system and reduce the purchase of electricity and hydrogen from external sources.

[0180] like Figure 6 As 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 between 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 during the night period greatly reduces the power purchased from the distribution network. Scenario 3 considers the power interaction between multiple microgrids, and adds power scheduling between multiple microgrids in addition to the output of micro gas turbines. This makes power system dispatch more flexible, allowing for further replenishment of power shortfalls during different time periods, further reducing the amount of power purchased from the distribution grid per unit time period. In Scenario 4, the micro-turbine is replaced with a hydrogen energy storage system. This system can store hydrogen produced during the day in a hydrogen storage tank for use at night. Analysis shows that the microgrid's power purchase from the distribution grid is significantly reduced. Scenario 5 builds on Scenario 4 by adding energy and hydrogen interaction between multiple microgrids. When the power generated by a microgrid and stored in the energy storage system is insufficient to meet the load demand during that period, the microgrid can interact with two other microgrids to purchase energy and hydrogen to meet the load demand. This further reduces the power purchased from the distribution grid compared to Scenario 4. This shows that coupling the hydrogen energy storage system with the power system can significantly improve energy efficiency and enhance the stability of the system's power output over multiple time periods. Energy interaction between multiple microgrids enhances the microgrid's flexibility and the stability of its power supply over different time periods.

[0181] In summary, the above-mentioned multi-microgrid photovoltaic storage dual-layer optimization configuration method has the following advantages:

[0182] 1. To address the high fuel costs and high emissions associated with traditional micro-turbines as backup energy sources, a basic model for a microgrid hydrogen energy storage system was proposed. The method for hydrogen generation, storage, and hydrogen energy conversion was studied, leading to a hydrogen energy storage system consisting of an electrolyzer, a hydrogen storage tank, and a hydrogen fuel cell. This approach effectively reduces the microgrid's reliance on fossil fuels and reduces fuel costs.

[0183] 2. In response to the supply and demand imbalance problem of traditional microgrids, a basic model of electricity-hydrogen coupling was established. Electric energy is converted into hydrogen energy through an electrolyzer and stored in a hydrogen storage tank. When the load demand is greater than the power generation, the hydrogen energy is input into the hydrogen fuel cell for combustion and power generation, effectively solving the problem of supply and demand imbalance in microgrids at different times.

[0184] 3. To address the poor economic efficiency of small and micro-parks, a method for optimizing the configuration of hydrogen-containing energy storage under a multi-park collaborative optimization operation model is proposed. By leveraging the interactive characteristics of hydrogen energy and electricity in multi-park collaborative optimization, a multi-park collaborative optimization configuration model for hydrogen-containing energy storage is constructed with the goal of increasing the economic efficiency of small and micro-parks. The upper layer sizing the internal equipment of the small and micro-park is targeted at minimizing the overall cost within the park, while the lower layer simulates typical daily scenarios with the goal of maximizing the internal benefits of the park. The results are obtained through multiple iterative calculations of the upper and lower layers. This method effectively improves the economic efficiency and flexibility of small and micro-parks and ensures their stable operation.

[0185] 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:

[0186] An electric-hydrogen coupling architecture construction module 10 is used to establish an electric-hydrogen coupling operation architecture and, based on the electric-hydrogen coupling operation architecture, construct a power capacity calculation model for the battery and energy storage charge and discharge 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;

[0187] A microgrid expansion module 20 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 the multiple microgrids;

[0188] A two-layer model construction module 30 is used to construct an upper-layer 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 construct a lower-layer optimization model with the goal of maximizing the operating income of the microgrid, thereby obtaining a two-layer model;

[0189] The solving module 40 is used to solve the two-layer model to obtain a final capacity configuration result of the multi-microgrid.

[0190] On the other hand, the present invention also proposes a storage medium on which one or more programs are stored. When the program is executed by a processor, the above-mentioned multi-microgrid photovoltaic storage dual-layer optimization configuration method is implemented.

[0191] 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.

[0192] While the embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations of these embodiments are possible. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as set forth in the claims. Furthermore, the invention described herein is susceptible to other embodiments and may be practiced or implemented 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 coupled operation framework, and constructing a power capacity calculation model for the battery and energy storage charge and discharge 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 based on the electric-hydrogen coupled operation framework; 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 from the electrolyzer directly into the fuel cell during time period t, 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 constraints include hydrogen power balance constraints and hydrogen energy storage charging and discharging power upper and lower limit constraints; 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 hydrogen charging and discharging state variable during time period t, is the maximum value that the hydrogen charging and discharging power can reach, and , Configure the capacity for the hydrogen storage tank, is the power energy conversion coefficient of the hydrogen storage tank; 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 electricity and hydrogen energy among the multiple microgrids; 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 during 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, In order to use historical data to predict the load results, the load demand power of the i-th microgrid in the t time period 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 the t time period, is the charging power of the battery in the i-th microgrid during 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 during time period t, is the hydrogen discharge power of the hydrogen storage tank in the i-th microgrid during 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 during time period t, represents the hydrogen power purchased by the jth microgrid from the ith 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 hydrogen power purchase and sale 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 for the i-th microgrid; 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 operating income, thus obtaining a two-layer model. The upper optimization model is constructed according to the following formula: ； in, For the i The equivalent annual investment cost of a microgrid is For the i The operating cost of a microgrid, Indicates the i The sum of the annual investment cost and annual maintenance cost of each 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 Respectively i The profit from the unit electricity and hydrogen energy generated by the interaction between the microgrid and the external microgrid is for n The number of typical days in a season, A collection of four seasons, is the operating income of the i-th microgrid; Solving the two-layer model to obtain a final capacity configuration result for the multi-microgrid, specifically including: 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; 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 results are 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's profit, and return the operation results to the upper optimization model; Step 4: Return the optimized variables of the lower optimization model to the upper optimization model. The maximum value of the optimized variables of the lower optimization model is used as the minimum value of the optimized variable constraint 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 、 ε Represents the convergence threshold value of the upper optimization model and the lower optimization model.

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, 、 The battery is t Time period, reserves in time period t-1, For the battery t Charging power during the time period, For the battery t Discharge power during the time period, is the charge and discharge rate of the battery, is the time step, is the battery energy loss rate; 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 charge and discharge power of the battery; The relationship between the maximum charge and discharge power of the battery and the battery capacity is expressed as: ； Battery in t State of charge during the time period 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 electrical power of the fuel cell, is the hydrogen power consumed by the fuel cell, Output efficiency for hydrogen fuel cells; A hydrogen storage model for hydrogen storage tanks is constructed according to the following formula: ； in, for t The hydrogen storage capacity of the hydrogen storage tank during the time period, is the hydrogen charging and discharging efficiency of the hydrogen storage tank, For hydrogen storage tanks t The hydrogen charging power in the time period, 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 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, For the k The operating life of the equipment, For equipment k Unit power investment cost, including unit capacity investment cost of energy storage 、 Investment cost per unit power of photovoltaic power generation 、 Investment cost per unit power of electrolyzer 、 Investment cost per unit capacity of hydrogen storage tanks and investment cost per unit power of hydrogen fuel cells ， For the i The capacity of k devices in the microgrid; The first i The operating cost of a microgrid is: ； ； in, For the i The operating costs within a microgrid, For the i The cost of electricity purchase and sale for a microgrid, For the i The cost of purchasing and selling hydrogen for a microgrid, is the unit electricity price of the microgrid, is the hydrogen price in the microgrid, To maintain the proportionality factor, for w The operation and maintenance cost coefficient of each component, For the i Microgrid No. w The operating power of each component; The constraints of each device in the microgrid are constructed according to the following formula: ； in, Respectively i The maximum values ​​of batteries, photovoltaic arrays, electrolyzers, hydrogen energy storage, and hydrogen storage tanks in each microgrid are obtained after the lower-level optimization model calculations. 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.

5. The multi-microgrid photovoltaic storage dual-layer optimization configuration method according to claim 4 is characterized in that: Various profits are calculated according to the following formula: ； in, is the profit brought by the unit electricity of the power and external microgrid interaction in time period t, The profit of unit hydrogen energy generated by the interaction between power and external microgrid in time period t, in units of , for w The operation and maintenance profit of each component per unit of electricity; The upper and lower limit constraints of the lower-level decision variables are constructed according to the following formula: ； in, is the per-unit 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 unit time capacity of the hydrogen storage tank in the i-th microgrid, 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 、 、 、 、 、 .

6. A multi-microgrid photovoltaic storage dual-layer optimization configuration system, characterized in that: The system comprises: an electric-hydrogen coupling architecture construction module, configured to establish an electric-hydrogen coupling operation architecture and, based on the electric-hydrogen coupling operation architecture, construct a power capacity calculation model for the battery and energy storage charge and discharge 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; 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 from the electrolyzer directly into the fuel cell during time period t, 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 constraints include hydrogen power balance constraints and hydrogen energy storage charging and discharging power upper and lower limit constraints; 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 hydrogen charging and discharging state variable in time period t, is the maximum value that the hydrogen charging and discharging power can reach, and , Configure the capacity for the hydrogen storage tank, is the power energy conversion coefficient of the hydrogen storage tank; The microgrid expansion module is used to 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 electricity and hydrogen energy among multiple microgrids; 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 during 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, In order to use historical data to predict the load results, the load demand power of the i-th microgrid in the t time period 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 the t time period, is the charging power of the battery in the i-th microgrid during 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 during time period t, is the hydrogen discharge power of the hydrogen storage tank in the i-th microgrid during 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 during time period t, represents the hydrogen power purchased by the jth microgrid from the ith 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 hydrogen power purchase and sale 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 for the i-th microgrid; A two-layer model construction module is used to construct an upper-layer 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 construct a lower-layer optimization model with the goal of maximizing the microgrid operating income, thereby obtaining a two-layer model; The upper optimization model is constructed according to the following formula: ； in, For the i The equivalent annual investment cost of a microgrid is For the i The operating cost of a microgrid, Indicates the i The sum of the annual investment cost and annual maintenance cost of each 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 Respectively i The profit from the unit electricity and hydrogen energy generated by the interaction between the microgrid and the external microgrid is for n The number of typical days in a season, A collection of four seasons, is the operating income of the i-th microgrid; A solution module is used to solve the two-layer model to obtain a final capacity configuration result of the multi-microgrid, specifically including: 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; 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 results are 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's profit, and return the operation results to the upper optimization model; Step 4: Return the optimized variables of the lower optimization model to the upper optimization model. The maximum value of the optimized variables of the lower optimization model is used as the minimum value of the optimized variable constraint 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 、 ε Represents the convergence threshold value of the upper optimization model and the lower optimization model.

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