A microgrid hybrid energy storage optimization method, system, equipment, product and medium

Abstract

The present invention relates to the field of energy management technology and provides a microgrid hybrid energy storage optimization method, system, device, product, and medium, including: determining upper-level constraints; determining the energy supply unit and system loss costs to construct a minimum cost constraint, calculating the rated power and rated capacity based on the minimum cost constraint, and building a microgrid energy storage system; obtaining operating parameters based on the rated power and rated capacity, and determining lower-level constraints of the microgrid energy storage system; obtaining the system operating state of the microgrid energy storage system to calculate the system deviation value, and in a normal state, calculating the penalty coefficient and obtaining a first system deviation value; in an extreme state, constructing a minimum time function and obtaining a second system deviation value; constructing an operating objective function, solving the operating objective function, and obtaining the energy storage system operating parameters to control the operation of the microgrid energy storage system. The present invention improves the operating efficiency of the microgrid energy storage system.

Description

Technical Field

[0001] The present invention relates to the field of energy management technology, and in particular to a microgrid hybrid energy storage optimization method, system, equipment, product and medium. Background Art

[0002] As renewable energy sources increasingly contribute to today's energy supply, microgrid systems comprised of multiple renewable energy sources are attracting increasing attention. However, the reliability and economic efficiency of current microgrid systems face significant challenges, particularly in optimizing the configuration of energy storage systems. Existing technical solutions primarily focus on optimizing single energy storage systems and hybrid energy storage systems. Among single energy storage systems, pumped hydro is the most widely used and technologically mature technology, but it has high requirements for location. Supercapacitor energy storage offers higher output power, faster response, and longer lifespans than traditional electrochemical energy storage technologies, but supercapacitors have lower energy density and are more expensive. Lithium-ion batteries are technologically mature and compact, but their low power density makes their short cycle life more pronounced during the frequent charge and discharge cycles required for frequency modulation. Therefore, a single type of energy storage system can no longer meet the high power, large capacity, high-frequency charge and discharge, and economic efficiency requirements of new microgrid systems. Optimization of hybrid energy storage systems explores the optimal configuration of various single energy storage systems combined with microgrids, but these solutions often lack adaptability under extreme operating conditions and, in particular, fail to fully consider the flexibility of multiple energy storage systems complementing each other.

[0003] Existing optimization methods do not fully consider optimization under extreme operating conditions, making it difficult to ensure system stability and economy under special conditions. Furthermore, existing hybrid energy storage system optimization solutions lack in-depth exploration of the comprehensive complementary characteristics of batteries and hydrogen storage systems, failing to fully leverage their complementary advantages between short-term rapid response and long-term energy storage. Furthermore, existing hybrid energy storage system optimization solutions lack in-depth exploration of the comprehensive complementary characteristics of batteries and hydrogen storage systems, failing to fully leverage their complementary advantages between short-term rapid response and long-term energy storage. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the related art. To this end, the present invention provides a microgrid hybrid energy storage optimization method, system, device, product and medium to effectively optimize the energy storage system operating parameters of the hybrid energy storage system in the microgrid system.

[0005] The present invention provides a microgrid hybrid energy storage optimization method, comprising:

[0006] S1: Determine the upper constraints of the microgrid energy storage system;

[0007] S2: Determine an energy supply unit of a microgrid energy storage system, determine a system loss cost of the energy supply unit, construct a minimum cost constraint using the system loss cost, calculate the rated power and rated capacity of the energy supply unit according to the minimum cost constraint under the constraints of the upper-level constraints, and build the microgrid energy storage system;

[0008] S3: Obtaining operating parameters based on the rated power and the rated capacity, and determining lower-level constraints of the microgrid energy storage system according to the operating parameters;

[0009] S4: Obtaining a system operating state of the microgrid energy storage system, and calculating a system deviation value based on the system operating state, wherein the system deviation value includes a first system deviation value and a second system deviation value, wherein when the system operating state is normal, a penalty coefficient is calculated based on parameters of the hydrogen storage device and the battery, and the first system deviation value is obtained by the penalty coefficient; when the system operating state is extreme, a minimum time function is constructed based on the parameters of the hydrogen storage device and the battery, and the second system deviation value is obtained by the minimum time function;

[0010] S5: Constructing an operation objective function through the system deviation value, solving the operation objective function under the constraints of the lower-level constraints to obtain energy storage system operation parameters, and controlling the operation of the microgrid energy storage system through the energy storage system operation parameters.

[0011] According to a microgrid hybrid energy storage optimization method provided by the present invention, step S2 further includes:

[0012] S21: Determine the energy supply unit of the microgrid energy storage system, wherein the energy supply unit includes a hydrogen storage device, a fuel cell, a battery, and an electrolyzer;

[0013] S22: Acquire historical data of the microgrid energy storage system, determine the system loss cost of the energy supply unit based on the historical data, calculate the initial investment cost, annual operation and maintenance cost, and replacement cost of the microgrid energy storage system based on the system loss cost, and calculate the minimum cost constraint based on the initial investment cost, the annual operation and maintenance cost, and the replacement cost;

[0014] S23: Constructing a system objective function based on the minimum cost constraint, and solving the system objective function under the constraints of the upper-level constraints to obtain the rated power and the rated capacity of the energy supply unit; wherein the rated power includes the rated power of the electrolyzer, the rated power of the fuel cell, and the rated power of the battery, and the rated capacity includes the rated capacity of the hydrogen storage device and the rated capacity of the battery;

[0015] S24: Select an electrolyzer, a fuel cell, a hydrogen storage device and a battery according to the rated power and the rated capacity, connect the electrolyzer, the hydrogen storage device and the fuel cell in sequence to obtain a fuel cell charging and discharging unit, and build the microgrid energy storage system through the fuel cell charging and discharging unit and the battery.

[0016] According to a microgrid hybrid energy storage optimization method provided by the present invention, step S3 further includes:

[0017] S31: acquiring battery operating parameters and fuel cell operating parameters from the rated power and the rated capacity, constructing battery constraints based on the battery operating parameters, and constructing fuel cell constraints based on the fuel cell operating parameters;

[0018] S32: Determine a battery allowable deviation coefficient, a battery initial capacity, and a system cycle period, and construct a battery cycle endpoint constraint based on the system cycle period, the battery initial capacity, and the battery allowable deviation coefficient;

[0019] S33: Obtain the allowable deviation coefficient of the hydrogen storage and the initial capacity of the hydrogen storage, construct the fuel cell cycle endpoint constraint through the system cycle period, the allowable deviation coefficient of the hydrogen storage and the initial capacity of the hydrogen storage, and use the battery cycle endpoint constraint, the fuel cell cycle endpoint constraint, the fuel cell constraint condition and the battery constraint condition as the lower-level constraint condition.

[0020] According to a microgrid hybrid energy storage optimization method provided by the present invention, a battery detection module and a hydrogen storage device detection module are obtained, the battery detection module is connected to the battery, and the hydrogen storage device detection module is connected to the hydrogen storage device. When the system cycle ends, the battery detection module detects the battery, and the hydrogen storage device detection module detects the hydrogen storage device.

[0021] According to a microgrid hybrid energy storage optimization method provided by the present invention, in step S4, when the system working state is normal, the penalty coefficient includes a first penalty coefficient and the second penalty coefficient :

[0022]

[0023]

[0024] in, is the first adjustment parameter, is the second adjustment parameter, is the current battery charge, is the maximum allowable battery capacity, is the minimum allowable battery capacity, is the current hydrogen storage capacity of the hydrogen storage device, is the maximum allowable hydrogen storage capacity of the hydrogen storage device, The minimum allowable hydrogen storage capacity of the hydrogen storage device;

[0025] The first system deviation value is calculated by using the first penalty coefficient and the second penalty coefficient. The expression is:

[0026]

[0027] Where t is the system working time, is the system cycle, is the system power difference when the system working time is t, is the instantaneous power of the electrolyzer when the system works for t time, is the instantaneous charging power of the battery when the system works for t time, is the instantaneous discharge power of the battery when the system working time is t, is the instantaneous discharge power of the fuel cell when the system working time is t.

[0028] According to a microgrid hybrid energy storage optimization method provided by the present invention, when the system working state is an extreme state, the minimum time function for:

[0029]

[0030]

[0031]

[0032] Among them, min{} means taking the minimum value in the brackets. is the fuel cell working time, is the battery working time, is the current battery capacity, is the rated power of the battery, is the equivalent battery capacity of the hydrogen storage device, is the fuel cell rated power;

[0033] The second system deviation value is calculated by the first penalty coefficient, the second penalty coefficient and the minimum time function. The expression is:

[0034] .

[0035] The present invention also provides a microgrid hybrid energy storage optimization system, comprising:

[0036] Upper-level constraint condition module: used to determine the upper-level constraint conditions of the microgrid energy storage system;

[0037] Energy storage system construction module: used to determine the energy supply unit of the microgrid energy storage system, determine the system loss cost of the energy supply unit, construct a minimum cost constraint based on the system loss cost, calculate the rated power and rated capacity of the energy supply unit according to the minimum cost constraint under the constraints of the upper-level constraints, and build the microgrid energy storage system;

[0038] A lower constraint condition module is configured to obtain operating parameters based on the rated power and the rated capacity, and determine lower constraint conditions of the microgrid energy storage system according to the operating parameters;

[0039] System deviation value module: used to obtain the system working state of the microgrid energy storage system, calculate the system deviation value according to the system working state, and the system deviation value includes a first system deviation value and a second system deviation value. When the system working state is normal, a penalty coefficient is calculated according to the parameters of the hydrogen storage device and the battery, and the first system deviation value is obtained by the penalty coefficient; when the system working state is extreme, a minimum time function is constructed according to the parameters of the hydrogen storage device and the battery, and the second system deviation value is obtained by the minimum time function;

[0040] Energy storage system operation parameter module: used to construct an operation objective function through the system deviation value, solve the operation objective function under the constraints of the lower-level constraints, obtain energy storage system operation parameters, and control the operation of the microgrid energy storage system through the energy storage system operation parameters.

[0041] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a microgrid hybrid energy storage optimization method as described above are implemented.

[0042] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described microgrid hybrid energy storage optimization methods.

[0043] The present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can perform the steps of any of the microgrid hybrid energy storage optimization methods described above.

[0044] The above one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects:

[0045] The present invention provides a microgrid hybrid energy storage optimization method, system, device, product, and medium that can effectively reduce electricity costs by optimizing the operating parameters of the energy storage system in the microgrid. In particular, when power deviations are small, the system's electricity costs can be effectively reduced. Furthermore, when faced with extreme operating conditions, the present invention uses different system deviation values ​​under different conditions to effectively reduce electricity costs when power deviations are small. While improving system stability, the present invention also enables the system to demonstrate higher economic returns and greater operability when combined with different types of energy storage technologies.

[0046] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 It is a flow chart of a microgrid hybrid energy storage optimization method provided by the present invention.

[0049] Figure 2 It is a structural schematic diagram of a microgrid hybrid energy storage optimization system provided by the present invention.

[0050] Figure 3 It is a structural schematic diagram of a microgrid hybrid energy storage optimization device provided by the present invention.

[0051] Reference numerals:

[0052] 100, upper-level constraint module; 200, energy storage system construction module; 300, lower-level constraint module; 400, system deviation value module; 500, energy storage system operation parameter module; 810, processor; 820, communication interface; 830, memory; 840, communication bus. DETAILED DESCRIPTION

[0053] To make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below. Obviously, the embodiments described are part of the embodiments of the present invention, rather than all 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. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0054] In the description of the embodiments of the present invention, it should be noted that the terms “first”, “second” and “third” are used for descriptive purposes only and should not be understood as indicating or implying relative importance.

[0055] In the description of the embodiments of the present invention, it should be noted that, unless otherwise specified or limited, the terms "connected" and "connection" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections, electrical connections; and direct connections or indirect connections through an intermediary. Those skilled in the art will understand the specific meanings of the above terms in the embodiments of the present invention based on the specific circumstances.

[0056] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0057] The following combination Figures 1 to 3 Describing embodiments of the present invention

[0058] Figure 1 This is a flow chart of a microgrid hybrid energy storage optimization method provided by the present invention. This method includes constructing upper-level constraints, determining the energy supply unit and system loss costs, then constructing a minimum cost constraint to construct and solve the system objective function, thereby obtaining the rated power and rated capacity to build the microgrid energy storage system. Next, lower-level constraints are constructed to determine the system operating state and obtain the system deviation value, thereby constructing and solving the operating objective function, ultimately obtaining the energy storage system operating parameters.

[0059] The present invention provides a microgrid hybrid energy storage optimization method, comprising:

[0060] S1: Determine the upper constraints of the microgrid energy storage system;

[0061] Furthermore, the purpose of this stage is to determine the upper-level constraints of the microgrid energy storage system, thereby providing constraints for the subsequent calculation of the rated power and rated capacity of the energy supply unit. Specifically, the microgrid served by the microgrid energy storage system is determined and the upper-level constraints are determined. The upper-level constraints specifically include:

[0062]

[0063]

[0064]

[0065]

[0066] in, is the rated power of the electrolyzer. Here, since the electrolyzer consumes electrical energy, and the purpose of the energy storage system is to compensate for the system power difference between the power generation capacity of the microgrid and the external power demand, the contribution of the electrolyzer to compensating the system power difference is negative, so it takes a negative value; is the rated power of the battery, is the fuel cell rated power, is the maximum instantaneous system power difference when the system working time is t, is the minimum instantaneous system power difference when the system working time is t, and It can be determined based on the changes in the microgrid's power generation capacity and the changes in the external power demand; is the rated capacity of the hydrogen storage tank, is the rated capacity of the battery, is the system cycle, here we take 24 hours, is the system power difference when the system working time is t, It is the allowable deviation coefficient of the battery determined based on experience. is the discharge time of the battery and fuel cell in the system cycle estimated based on the system power difference of the microgrid, is the maximum allowable deviation of the system, which is 10% here.

[0067] S2: Determine an energy supply unit of a microgrid energy storage system, determine a system loss cost of the energy supply unit, construct a minimum cost constraint using the system loss cost, calculate the rated power and rated capacity of the energy supply unit according to the minimum cost constraint under the constraints of the upper-level constraints, and build the microgrid energy storage system;

[0068] Furthermore, the purpose of this stage is to select the energy supply unit in the microgrid energy storage system and change the unit rated power and unit rated capacity of the energy supply unit while satisfying the constraints, so as to minimize the cost of the microgrid energy storage system and finally build the microgrid energy storage system. Among them, step S2 further includes:

[0069] S21: Determine the energy supply unit of the microgrid energy storage system, wherein the energy supply unit includes a hydrogen storage device, a fuel cell, a battery, and an electrolyzer;

[0070] S22: Acquire historical data of the microgrid energy storage system, determine the system loss cost of the energy supply unit based on the historical data, calculate the initial investment cost, annual operation and maintenance cost, and replacement cost of the microgrid energy storage system based on the system loss cost, and calculate the minimum cost constraint based on the initial investment cost, the annual operation and maintenance cost, and the replacement cost;

[0071] S23: Constructing a system objective function based on the minimum cost constraint, and solving the system objective function under the constraints of the upper-level constraints to obtain the rated power and the rated capacity of the energy supply unit; wherein the rated power includes the rated power of the electrolyzer, the rated power of the fuel cell, and the rated power of the battery, and the rated capacity includes the rated capacity of the hydrogen storage device and the rated capacity of the battery;

[0072] S24: Select an electrolyzer, a fuel cell, a hydrogen storage device and a battery according to the rated power and the rated capacity, connect the electrolyzer, the hydrogen storage device and the fuel cell in sequence to obtain a fuel cell charging and discharging unit, and build the microgrid energy storage system through the fuel cell charging and discharging unit and the battery.

[0073] Regarding the above steps, the specific implementation methods in this embodiment are as follows:

[0074] First, the energy supply units that make up the microgrid energy storage system are selected. Here, the energy supply units include hydrogen storage devices, fuel cells, batteries, and electrolyzers. The hydrogen storage device is used to store hydrogen and can be in the form of hydrogen storage tanks, solid-state hydrogen storage materials, etc. The electrolyzer is used to use the microgrid's surplus electricity to produce hydrogen through electrolysis when the microgrid's power generation capacity exceeds the external power demand, and the resulting hydrogen is stored in the hydrogen storage device. The fuel cell is used to generate electricity using the hydrogen in the hydrogen storage device as fuel, thereby compensating for the system power difference when the microgrid's power generation capacity is less than the external power demand. The battery can be charged and discharged, and can also be charged according to the microgrid's power generation capacity and the external power demand, or compensate for the microgrid's system power difference.

[0075] Then, the historical data of the microgrid energy storage system is obtained, and the system loss cost of the energy supply unit in the microgrid energy storage system and related data are determined based on the historical data, including the service life of the microgrid energy storage system, the replacement age of the equipment in the microgrid energy storage system, the power replacement cost of the equipment, the capacity replacement cost, the discharge time of the fuel cell and the battery, etc. Then, the initial investment cost of the microgrid energy storage system can be calculated based on the system loss cost. , annual operation and maintenance costs and replacement costs :

[0076]

[0077]

[0078]

[0079] in, is the power replacement cost of the electrolyzer, is the power replacement cost of the fuel cell, in yuan / W, is the capacity replacement cost of the hydrogen storage device, in yuan / kg, is the power replacement cost of the battery, in yuan / W, is the capacity replacement cost of the battery, in yuan / Wh; here, the power replacement cost refers to the equipment cost required to obtain each unit of power, and the capacity replacement cost refers to the equipment cost required to obtain each unit of hydrogen storage or electricity storage. is the annual maintenance cost per unit power of the electrolyzer, is the annual maintenance cost per unit power of the fuel cell, and the unit of both is RMB / W / year; is the annual maintenance cost per unit weight of the hydrogen storage device, in yuan / kg / year; is the annual maintenance cost per unit capacity of the battery, in RMB / Wh / year. The above data can be obtained from the historical data of the microgrid energy storage system.

[0080] Then the minimum cost constraint of the microgrid energy storage system can be constructed :

[0081]

[0082]

[0083] in, is the annual energy storage output, n is the service life of the microgrid energy storage system in years, i is the discount rate of the microgrid energy storage system, is the expected service life of the microgrid energy storage system, is the expected equipment replacement time for the microgrid energy storage system. The discount rate, expected service life, and expected equipment replacement time can all be obtained from historical data.

[0084] Then the system objective function is constructed by the minimum cost constraint :

[0085]

[0086] in, The parentheses () indicate that the values ​​of the parameters within the brackets are changed to minimize the minimum cost constraint. Solving the system objective function under the constraints of the upper-level constraints yields the rated power and rated capacity of the energy supply unit. The rated power includes the rated power of the electrolyzer, the rated power of the fuel cell, and the rated power of the battery. The rated capacity includes the rated capacity of the hydrogen storage device and the rated capacity of the battery. During the solution process, it is important to note that the relationship between the rated power of the electrolyzer and the rated power of the fuel cell is fixed. Furthermore, when the rated power is constant, the rated capacity must not fall below the minimum rated capacity threshold determined empirically to ensure the proper operation of the fuel cell and battery.

[0087] Finally, based on the rated power and rated capacity, an appropriate electrolyzer, fuel cell, hydrogen storage device, and battery are selected and connected in sequence to form a fuel cell charging and discharging unit. The electrolyzer and fuel cell are connected to the microgrid. The electrolyzer draws electricity from the microgrid and produces hydrogen through electrolysis, which is then stored in the hydrogen storage device to complete energy storage. The fuel cell burns the hydrogen in the hydrogen storage device and outputs electricity to the microgrid to compensate for the system power difference. The battery is also connected to the microgrid and draws electricity from the microgrid to complete energy storage or outputs electricity to the microgrid, thus completing the construction of the microgrid energy storage system.

[0088] S3: Obtaining operating parameters based on the rated power and the rated capacity, and determining lower-level constraints of the microgrid energy storage system according to the operating parameters;

[0089] Furthermore, the purpose of this stage is to formulate the lower-level constraints of the microgrid energy storage system, thereby providing constraints for solving the operation objective function. Specifically, step S3 further includes:

[0090] S31: acquiring battery operating parameters and fuel cell operating parameters from the rated power and the rated capacity, constructing battery constraints based on the battery operating parameters, and constructing fuel cell constraints based on the fuel cell operating parameters;

[0091] S32: Determine a battery allowable deviation coefficient, a battery initial capacity, and a system cycle period, and construct a battery cycle endpoint constraint based on the system cycle period, the battery initial capacity, and the battery allowable deviation coefficient;

[0092] S33: Obtain the allowable deviation coefficient of the hydrogen storage and the initial capacity of the hydrogen storage, construct the fuel cell cycle endpoint constraint through the system cycle period, the allowable deviation coefficient of the hydrogen storage and the initial capacity of the hydrogen storage, and use the battery cycle endpoint constraint, the fuel cell cycle endpoint constraint, the fuel cell constraint condition and the battery constraint condition as the lower-level constraint condition.

[0093] Obtain a battery detection module and a hydrogen storage device detection module, wherein the battery detection module is connected to the battery, and the hydrogen storage device detection module is connected to the hydrogen storage device. When the system cycle ends, the battery detection module detects the battery, and the hydrogen storage device detection module detects the hydrogen storage device.

[0094] Regarding the above steps, the specific implementation methods in this embodiment are as follows:

[0095] First, the battery and fuel cell operating parameters are obtained from the rated power and rated capacity. Battery operating parameters include the battery rated power, rated capacity, and the battery self-discharge rate determined based on the actual battery conditions. Fuel cell operating parameters include the fuel cell rated power, hydrogen storage device rated capacity, and the fuel cell operating efficiency and electrolyzer operating efficiency determined based on actual conditions.

[0096] Then construct the battery constraints:

[0097]

[0098]

[0099]

[0100] in, is the instantaneous charging power of the battery when the system works for t time, is the instantaneous discharge power of the battery when the system working time is t, To charge the battery, run the variable, = is the battery discharge operating variable. Since the battery consumes energy when charging, the instantaneous charging power of the battery takes a negative value. When the battery is charging, the battery charge operating variable is 1, and the battery discharge operating variable is 0. The opposite is true when the battery is discharging. The battery charge and discharge operating variables are used to ensure that the battery is not in both the charging and discharging states simultaneously.

[0101] Battery constraints also include:

[0102]

[0103]

[0104] in, is the battery capacity when the system is working for t, that is, the amount of electrical energy stored in the battery, is the battery capacity when the system working time is t-1, is the self-discharge rate of the battery, is the battery charging efficiency, is the battery discharge efficiency, is the minimum allowable capacity of the battery, The maximum allowable capacity of the battery. The minimum allowable capacity and the maximum allowable capacity of the battery are determined according to the rated capacity of the battery.

[0105] Then construct the fuel cell constraints:

[0106]

[0107]

[0108]

[0109] in, is the instantaneous power of the electrolyzer when the system works for t time, is the instantaneous discharge power of the fuel cell when the system works for t time, For electrolyzer operation variables, is the fuel cell operating variable. Here, because the electrolyzer consumes electricity during operation, the instantaneous power of the electrolyzer takes a negative value. When the electrolyzer is operating, the electrolyzer operating variable is 1, and the fuel cell operating variable is 0. The opposite is true when the fuel cell is discharging. The electrolyzer and fuel cell operating variables are used to ensure that the fuel cell's charging and discharging units are not simultaneously in the hydrogen production and discharging states.

[0110] Fuel cell constraints also include:

[0111]

[0112]

[0113] in, is the capacity of the hydrogen storage device when the system is working for t, that is, the amount of hydrogen stored in the hydrogen storage device, is the hydrogen storage capacity when the system working time is t-1, is the heat of combustion of hydrogen, The working efficiency of the electrolytic cell is For fuel cell efficiency, is the minimum allowable capacity of the hydrogen storage tank, is the maximum allowable capacity of the hydrogen storage device. The minimum allowable capacity of the hydrogen storage device and the maximum allowable capacity of the hydrogen storage device are determined according to the rated capacity of the hydrogen storage device.

[0114] Then determine the battery allowable deviation coefficient, battery initial capacity, and system cycle period. Here, the battery allowable deviation coefficient and system cycle period have been determined before. The battery capacity at t = 0 is used as the battery initial capacity to construct the battery cycle endpoint constraint:

[0115]

[0116] in, is the initial capacity of the battery, For t= The battery capacity at this time, similarly, obtain the allowable deviation coefficient of the hydrogen storage device , taking the hydrogen storage capacity at t = 0 as the initial capacity of the hydrogen storage, thus constructing the fuel cell cycle endpoint constraint:

[0117]

[0118] in, is the initial capacity of the hydrogen storage tank, For t= The battery cycle endpoint constraint, fuel cell cycle endpoint constraint, fuel cell constraint condition and battery constraint condition are taken as the lower level constraint conditions.

[0119] Here, the fuel cell cycle endpoint constraints and battery cycle endpoint constraints are intended to ensure that the hydrogen storage capacity and battery capacity of the fuel cell and battery are approximately the same at the beginning and end of a system cycle, thereby facilitating management. Furthermore, the parameters of the hydrogen storage and battery are approximately the same at the beginning of each system cycle. Therefore, a battery detection module and a hydrogen storage detection module can be obtained and connected to the battery, and the hydrogen storage detection module can be connected to the hydrogen storage. Thus, at the end of the system cycle, since the parameters of the hydrogen storage and battery are approximately the same at the beginning of each system cycle, the hydrogen storage detection module and the battery detection module can be conveniently used to detect various parameters of the hydrogen storage and battery, respectively, such as battery voltage, electrolyte concentration, and gas pressure within the hydrogen storage, and compare them with standard parameters, thereby monitoring the status of the hydrogen storage and battery.

[0120] S4: Obtaining a system operating state of the microgrid energy storage system, and calculating a system deviation value based on the system operating state, wherein the system deviation value includes a first system deviation value and a second system deviation value, wherein when the system operating state is normal, a penalty coefficient is calculated based on parameters of the hydrogen storage device and the battery, and the first system deviation value is obtained by the penalty coefficient; when the system operating state is extreme, a minimum time function is constructed based on the parameters of the hydrogen storage device and the battery, and the second system deviation value is obtained by the minimum time function;

[0121] Furthermore, the purpose of this stage is to determine whether the working state of the microgrid energy storage system is normal or extreme, and then calculate the system deviation value under the corresponding state according to different states. Among them, when the system working state is normal, the penalty coefficient includes the first penalty coefficient and the second penalty coefficient :

[0122]

[0123]

[0124] in, is the first adjustment parameter, is the second adjustment parameter, is the current battery charge, is the maximum allowable battery capacity, is the minimum allowable battery capacity, is the current hydrogen storage capacity of the hydrogen storage device, is the maximum allowable hydrogen storage capacity of the hydrogen storage device, The minimum allowable hydrogen storage capacity of the hydrogen storage device;

[0125] The first system deviation value is calculated by the first penalty coefficient and the second penalty coefficient. The expression is:

[0126]

[0127] Where t is the system working time, is the system cycle, is the system power difference when the system working time is t, is the instantaneous power of the electrolyzer when the system works for t time, is the instantaneous charging power of the battery when the system works for t time, is the instantaneous discharge power of the battery when the system working time is t, is the instantaneous discharge power of the fuel cell when the system working time is t.

[0128] When the system working state is extreme, the minimum time function for:

[0129]

[0130]

[0131]

[0132] Among them, min{} means taking the minimum value in the brackets. is the fuel cell working time, is the battery working time, is the current battery capacity, is the rated power of the battery, is the equivalent battery capacity of the hydrogen storage device, is the fuel cell rated power;

[0133] The second system deviation value is calculated by the first penalty coefficient, the second penalty coefficient and the minimum time function. The expression is:

[0134] .

[0135] Regarding the above steps, the specific implementation methods in this embodiment are as follows:

[0136] In the actual working process, under normal working conditions, it is hoped that the amount of electricity in the battery and the amount of hydrogen stored in the hydrogen storage device are maintained in a reasonable range. In this embodiment, the range is 20% to 80% of the rated capacity. This can ensure the reliability and service life of the hydrogen storage device and the battery, thereby reducing the operating costs of the microgrid energy storage system. To this end, in addition to controlling the charging amount and the amount of hydrogen stored during the charging and hydrogen storage processes, under normal circumstances, the discharge process of the fuel cell and the battery also needs to be controlled. Therefore, here, the system working state of the microgrid energy storage system is obtained. When the system working state is normal, it is necessary to set a penalty coefficient for the battery and the fuel cell. The penalty coefficient includes a first penalty coefficient and the second penalty coefficient :

[0137]

[0138]

[0139] in, is the first adjustment parameter, is the second adjustment parameter, and the first adjustment parameter and the second adjustment parameter are both set based on experience. is the current battery charge, The maximum allowable battery capacity is set to 80%. The minimum allowable battery capacity is set to 20%. is the current hydrogen storage capacity of the hydrogen storage device, is the maximum allowable hydrogen storage capacity of the hydrogen storage device, which is set to 80% here. The minimum allowable hydrogen storage capacity of the hydrogen storage device is set to 20% here. The power is the percentage of the battery capacity relative to the rated capacity of the battery, and the hydrogen storage capacity is the percentage of the hydrogen storage device capacity relative to the rated capacity of the hydrogen storage device.

[0140] Then, the first system deviation value is calculated by the first penalty coefficient and the second penalty coefficient. The expression is:

[0141]

[0142] When the system is operating in an extreme state, that is, when the battery and fuel cell are discharged for a long time, the power of the battery and fuel cell may be exhausted. To avoid this situation, it is necessary to set a minimum time function:

[0143]

[0144]

[0145]

[0146] Among them, min{} means taking the minimum value in the brackets. is the fuel cell working time, is the battery working time, is the current battery capacity, is the rated power of the battery, is the equivalent battery capacity of the hydrogen storage device, that is, the equivalent battery capacity after the hydrogen in the hydrogen storage device is converted into electrical energy. is the fuel cell rated power.

[0147] Then the second system deviation value is calculated by the minimum time function. The expression is:

[0148] .

[0149] S5: Constructing an operation objective function through the system deviation value, solving the operation objective function under the constraints of the lower-level constraints to obtain energy storage system operation parameters, and controlling the operation of the microgrid energy storage system through the energy storage system operation parameters.

[0150] Furthermore, the purpose of this stage is to construct an operation objective function and solve the operation objective function to obtain the energy storage system operation parameters to control the operation of the microgrid energy storage system. Specifically, the operation objective function is first constructed by the system deviation value. When the system working state is normal, the operation objective function is the first operation objective function. :

[0151]

[0152] in, () indicates changing the parameters in the brackets of the first system deviation value to make the first system deviation value take the minimum value. When the system working state is extreme, the operating objective function is the second operating objective function :

[0153]

[0154] in, () indicates changing the parameters in the brackets of the second system deviation value to minimize the first system deviation value. Solve the operation objective function under the constraints of the lower level constraints to obtain the following: The energy storage system operating parameters, including the energy storage system operating parameters, are used to control the operation of the microgrid energy storage system.

[0155] A microgrid hybrid energy storage optimization device provided by the present invention is described below. The microgrid hybrid energy storage optimization device described below and the microgrid hybrid energy storage optimization method described above can be referenced to each other.

[0156] Figure 2 The following is an example of a structural diagram of a microgrid hybrid energy storage optimization system: Figure 2 As shown, a microgrid hybrid energy storage optimization method for executing the above-mentioned method includes:

[0157] Upper constraint condition module 100: used to determine the upper constraint conditions of the microgrid energy storage system;

[0158] Energy storage system construction module 200: used to determine the energy supply unit of the microgrid energy storage system, determine the system loss cost of the energy supply unit, construct a minimum cost constraint based on the system loss cost, calculate the rated power and rated capacity of the energy supply unit according to the minimum cost constraint under the constraints of the upper-level constraints, and construct the microgrid energy storage system;

[0159] A lower constraint condition module 300 is configured to obtain operating parameters based on the rated power and the rated capacity, and determine lower constraint conditions of the microgrid energy storage system according to the operating parameters;

[0160] System deviation value module 400: used to obtain the system operating state of the microgrid energy storage system, and calculate the system deviation value according to the system operating state, wherein the system deviation value includes a first system deviation value and a second system deviation value. When the system operating state is normal, a penalty coefficient is calculated according to the parameters of the hydrogen storage device and the battery, and the first system deviation value is obtained by the penalty coefficient; when the system operating state is extreme, a minimum time function is constructed according to the parameters of the hydrogen storage device and the battery, and the second system deviation value is obtained by the minimum time function;

[0161] Energy storage system operating parameter module 500: used to construct an operating objective function based on the system deviation value, solve the operating objective function under the constraints of the lower-level constraints, obtain energy storage system operating parameters, and control the operation of the microgrid energy storage system through the energy storage system operating parameters.

[0162] on the other hand, Figure 3 An example of a physical structure diagram of an electronic device is shown below. Figure 3 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute a microgrid hybrid energy storage optimization method, which includes:

[0163] S1: Determine the upper constraints of the microgrid energy storage system;

[0164] S2: Determine an energy supply unit of a microgrid energy storage system, determine a system loss cost of the energy supply unit, construct a minimum cost constraint using the system loss cost, calculate the rated power and rated capacity of the energy supply unit according to the minimum cost constraint under the constraints of the upper-level constraints, and build the microgrid energy storage system;

[0165] S3: Obtaining operating parameters based on the rated power and the rated capacity, and determining lower-level constraints of the microgrid energy storage system according to the operating parameters;

[0166] S4: Obtaining a system operating state of the microgrid energy storage system, and calculating a system deviation value based on the system operating state, wherein the system deviation value includes a first system deviation value and a second system deviation value, wherein when the system operating state is normal, a penalty coefficient is calculated based on parameters of the hydrogen storage device and the battery, and the first system deviation value is obtained by the penalty coefficient; when the system operating state is extreme, a minimum time function is constructed based on the parameters of the hydrogen storage device and the battery, and the second system deviation value is obtained by the minimum time function;

[0167] S5: Constructing an operation objective function through the system deviation value, solving the operation objective function under the constraints of the lower-level constraints to obtain energy storage system operation parameters, and controlling the operation of the microgrid energy storage system through the energy storage system operation parameters.

[0168] Furthermore, the logic instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0169] On the other hand, the present invention further provides a computer program product, comprising a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program comprises program instructions. When the program instructions are executed by a computer, the computer is capable of performing a microgrid hybrid energy storage optimization method provided by the above methods, the method comprising:

[0170] S1: Determine the upper constraints of the microgrid energy storage system;

[0171] S2: Determine an energy supply unit of a microgrid energy storage system, determine a system loss cost of the energy supply unit, construct a minimum cost constraint using the system loss cost, calculate the rated power and rated capacity of the energy supply unit according to the minimum cost constraint under the constraints of the upper-level constraints, and build the microgrid energy storage system;

[0172] S3: Obtaining operating parameters based on the rated power and the rated capacity, and determining lower-level constraints of the microgrid energy storage system according to the operating parameters;

[0173] S4: Obtaining a system operating state of the microgrid energy storage system, and calculating a system deviation value based on the system operating state, wherein the system deviation value includes a first system deviation value and a second system deviation value, wherein when the system operating state is normal, a penalty coefficient is calculated based on parameters of the hydrogen storage device and the battery, and the first system deviation value is obtained by the penalty coefficient; when the system operating state is extreme, a minimum time function is constructed based on the parameters of the hydrogen storage device and the battery, and the second system deviation value is obtained by the minimum time function;

[0174] S5: Constructing an operation objective function through the system deviation value, solving the operation objective function under the constraints of the lower-level constraints to obtain energy storage system operation parameters, and controlling the operation of the microgrid energy storage system through the energy storage system operation parameters.

[0175] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform a microgrid hybrid energy storage optimization method provided by the above methods, the method comprising:

[0176] S1: Determine the upper constraints of the microgrid energy storage system;

[0177] S2: Determine an energy supply unit of a microgrid energy storage system, determine a system loss cost of the energy supply unit, construct a minimum cost constraint using the system loss cost, calculate the rated power and rated capacity of the energy supply unit according to the minimum cost constraint under the constraints of the upper-level constraints, and build the microgrid energy storage system;

[0178] S3: Obtaining operating parameters based on the rated power and the rated capacity, and determining lower-level constraints of the microgrid energy storage system according to the operating parameters;

[0179] S4: Obtaining a system operating state of the microgrid energy storage system, and calculating a system deviation value based on the system operating state, wherein the system deviation value includes a first system deviation value and a second system deviation value, wherein when the system operating state is normal, a penalty coefficient is calculated based on parameters of the hydrogen storage device and the battery, and the first system deviation value is obtained by the penalty coefficient; when the system operating state is extreme, a minimum time function is constructed based on the parameters of the hydrogen storage device and the battery, and the second system deviation value is obtained by the minimum time function;

[0180] S5: Constructing an operation objective function through the system deviation value, solving the operation objective function under the constraints of the lower-level constraints to obtain energy storage system operation parameters, and controlling the operation of the microgrid energy storage system through the energy storage system operation parameters.

[0181] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0182] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0183] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A microgrid hybrid energy storage optimization method, characterized in that: include: S1: Determine the upper constraints of the microgrid energy storage system; S2: Determine an energy supply unit of a microgrid energy storage system, determine a system loss cost of the energy supply unit, construct a minimum cost constraint using the system loss cost, calculate the rated power and rated capacity of the energy supply unit according to the minimum cost constraint under the constraints of the upper-level constraints, and build the microgrid energy storage system; S3: Obtaining operating parameters based on the rated power and the rated capacity, and determining lower-level constraints of the microgrid energy storage system according to the operating parameters; S4: Obtain the system working state of the microgrid energy storage system, calculate the system deviation value according to the system working state, the system deviation value includes a first system deviation value and a second system deviation value, wherein, when the system working state is normal, calculate the penalty coefficient according to the parameters of the hydrogen storage device and the battery, and obtain the first system deviation value through the penalty coefficient; when the system working state is extreme, construct a minimum time function according to the parameters of the hydrogen storage device and the battery, and obtain the second system deviation value through the minimum time function; in step S4, when the system working state is normal, the penalty coefficient includes a first penalty coefficient and the second penalty coefficient : in, is the first adjustment parameter, is the second adjustment parameter, is the current battery charge, is the maximum allowable battery capacity, is the minimum allowable battery capacity, is the current hydrogen storage capacity of the hydrogen storage device, is the maximum allowable hydrogen storage capacity of the hydrogen storage device, The minimum allowable hydrogen storage capacity of the hydrogen storage device; The first system deviation value is calculated by using the first penalty coefficient and the second penalty coefficient. The expression is: Where t is the system working time, is the system cycle, is the system power difference when the system working time is t, is the instantaneous power of the electrolyzer when the system works for t time, is the instantaneous charging power of the battery when the system works for t time, is the instantaneous discharge power of the battery when the system working time is t, is the instantaneous discharge power of the fuel cell when the system working time is t; When the system working state is an extreme state, the minimum time function for: Among them, min{} means taking the minimum value in the brackets. is the fuel cell working time, is the battery working time, is the current battery capacity, is the rated power of the battery, is the equivalent battery capacity of the hydrogen storage device, is the fuel cell rated power; The second system deviation value is calculated by the first penalty coefficient, the second penalty coefficient and the minimum time function. The expression is: ； S5: Constructing an operation objective function through the system deviation value, solving the operation objective function under the constraints of the lower-level constraints to obtain energy storage system operation parameters, and controlling the operation of the microgrid energy storage system through the energy storage system operation parameters.

2. A microgrid hybrid energy storage optimization method according to claim 1, characterized in that: Step S2 further comprises: S21: Determine the energy supply unit of the microgrid energy storage system, wherein the energy supply unit includes a hydrogen storage device, a fuel cell, a battery, and an electrolyzer; S22: Acquire historical data of the microgrid energy storage system, determine the system loss cost of the energy supply unit based on the historical data, calculate the initial investment cost, annual operation and maintenance cost, and replacement cost of the microgrid energy storage system based on the system loss cost, and calculate the minimum cost constraint based on the initial investment cost, the annual operation and maintenance cost, and the replacement cost; S23: Constructing a system objective function based on the minimum cost constraint, and solving the system objective function under the constraints of the upper-level constraints to obtain the rated power and the rated capacity of the energy supply unit; wherein the rated power includes the rated power of the electrolyzer, the rated power of the fuel cell, and the rated power of the battery, and the rated capacity includes the rated capacity of the hydrogen storage device and the rated capacity of the battery; S24: Select an electrolyzer, a fuel cell, a hydrogen storage device and a battery according to the rated power and the rated capacity, connect the electrolyzer, the hydrogen storage device and the fuel cell in sequence to obtain a fuel cell charging and discharging unit, and build the microgrid energy storage system through the fuel cell charging and discharging unit and the battery.

3. A microgrid hybrid energy storage optimization method according to claim 1, characterized in that: Step S3 further comprises: S31: acquiring battery operating parameters and fuel cell operating parameters from the rated power and the rated capacity, constructing battery constraints based on the battery operating parameters, and constructing fuel cell constraints based on the fuel cell operating parameters; S32: Determine a battery allowable deviation coefficient, a battery initial capacity, and a system cycle period, and construct a battery cycle endpoint constraint based on the system cycle period, the battery initial capacity, and the battery allowable deviation coefficient; S33: Obtain the allowable deviation coefficient of the hydrogen storage and the initial capacity of the hydrogen storage, construct the fuel cell cycle endpoint constraint through the system cycle period, the allowable deviation coefficient of the hydrogen storage and the initial capacity of the hydrogen storage, and use the battery cycle endpoint constraint, the fuel cell cycle endpoint constraint, the fuel cell constraint condition and the battery constraint condition as the lower-level constraint condition.

4. A microgrid hybrid energy storage optimization method according to claim 3, characterized in that: Obtain a battery detection module and a hydrogen storage device detection module, wherein the battery detection module is connected to the battery, and the hydrogen storage device detection module is connected to the hydrogen storage device. When the system cycle ends, the battery detection module detects the battery, and the hydrogen storage device detection module detects the hydrogen storage device.

5. A microgrid hybrid energy storage optimization system, used to execute a microgrid hybrid energy storage optimization method according to any one of claims 1 to 4, characterized in that: include: Upper-level constraint condition module: used to determine the upper-level constraint conditions of the microgrid energy storage system; Energy storage system construction module: used to determine the energy supply unit of the microgrid energy storage system, determine the system loss cost of the energy supply unit, construct a minimum cost constraint based on the system loss cost, calculate the rated power and rated capacity of the energy supply unit according to the minimum cost constraint under the constraints of the upper-level constraints, and build the microgrid energy storage system; A lower constraint condition module is configured to obtain operating parameters based on the rated power and the rated capacity, and determine lower constraint conditions of the microgrid energy storage system according to the operating parameters; System deviation value module: used to obtain the system working state of the microgrid energy storage system, calculate the system deviation value according to the system working state, and the system deviation value includes a first system deviation value and a second system deviation value. When the system working state is normal, a penalty coefficient is calculated according to the parameters of the hydrogen storage device and the battery, and the first system deviation value is obtained by the penalty coefficient; when the system working state is extreme, a minimum time function is constructed according to the parameters of the hydrogen storage device and the battery, and the second system deviation value is obtained by the minimum time function; Energy storage system operation parameter module: used to construct an operation objective function through the system deviation value, solve the operation objective function under the constraints of the lower-level constraints, obtain energy storage system operation parameters, and control the operation of the microgrid energy storage system through the energy storage system operation parameters.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the microgrid hybrid energy storage optimization method according to any one of claims 1 to 4 are implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a microgrid hybrid energy storage optimization method as described in any one of claims 1 to 4 are implemented.

8. A computer program product, comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, characterized in that: When the program instructions are executed by a computer, the computer can execute the steps of a microgrid hybrid energy storage optimization method as described in any one of claims 1 to 4.

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