A method and device for optimizing scheduling of a micro-grid combined with photovoltaic and hydrogen production

Abstract

The application discloses a kind of photovoltaic and hydrogen production combined microgrid optimization scheduling method and device, method includes: establishing photovoltaic grid-connected microgrid topology structure and hydrogen production plant equipment model, based on photovoltaic grid-connected microgrid topology structure and hydrogen production plant equipment model, microgrid scheduling model is constructed, wherein, power load and hydrogen production plant equipment model are all absorbed the output of photovoltaic power supply, determine the equipment constraint condition of optimization scheduling objective function of microgrid scheduling model, under equipment constraint condition, optimization scheduling objective function is solved by particle swarm algorithm, and optimal scheduling solution result is obtained.Visible, power load and hydrogen production plant equipment model in microgrid scheduling model can absorb the output of photovoltaic power supply, photovoltaic power supply is efficiently utilized, improve the photovoltaic absorption capacity of microgrid, and the constraint condition of optimization target is defined for hydrogen production plant equipment model, so that microgrid scheduling model can simultaneously consider operation economy and hydrogen production cleanness when optimizing scheduling.

Description

Technical Field

[0001] This application relates to the field of power technology, and more specifically, to a microgrid optimization scheduling method and device that combines photovoltaic and hydrogen production. Background Technology

[0002] With the large-scale development of new energy sources and the increasing penetration rate of photovoltaic installations, the contradiction between the power consumption of microgrids with a high proportion of photovoltaic power is becoming increasingly prominent. This not only seriously threatens the safe and stable operation of microgrids but also leads to curtailment of solar power, resulting in energy waste. Currently, hydrogen energy has attracted widespread attention due to its high calorific value and zero pollution. One approach is to utilize the surplus electricity generated by distributed photovoltaic power for water electrolysis to produce hydrogen.

[0003] Currently, researchers can plan and optimize the operation of hydrogen production equipment in hydrogen production plants. However, after hydrogen production plants respond to the microgrid effect, there is a lack of simultaneous consideration of the operating economy of hydrogen production plants and microgrids and the cleanliness of hydrogen production. Furthermore, microgrids lack the capacity to absorb photovoltaic power.

[0004] How to achieve coordinated and optimized scheduling of microgrids for photovoltaic and hydrogen production is an issue that needs attention. Summary of the Invention

[0005] In view of the above problems, this application is made to provide a microgrid optimization scheduling method and device that combines photovoltaic and hydrogen production, so as to improve the photovoltaic absorption capacity of the microgrid and optimize the microgrid scheduling of hydrogen production plants in terms of operating economy and hydrogen production cleanliness.

[0006] To achieve the above objectives, the following specific solutions are proposed:

[0007] A microgrid optimization scheduling method combining photovoltaic and hydrogen production includes:

[0008] A photovoltaic grid-connected microgrid topology is established, which includes several user nodes, each of which is connected to electrical loads and photovoltaic power sources;

[0009] Establish a hydrogen production plant equipment model, which includes an electrolyzer model, a compressor model, and a hydrogen storage tank model.

[0010] Based on the photovoltaic grid-connected microgrid topology and the hydrogen production plant equipment model, a microgrid dispatch model is constructed. The microgrid dispatch model includes the electricity load, the photovoltaic power source, and the hydrogen production plant equipment model. The electricity load and the hydrogen production plant equipment model are both used to absorb the output of the photovoltaic power source.

[0011] Determine the optimal scheduling objective function for the microgrid scheduling model;

[0012] Based on the electrolyzer model, the compressor model, and the hydrogen storage tank model, the equipment constraints of the optimization scheduling objective function are determined;

[0013] Under the device constraints, the objective function of the optimization scheduling is solved to obtain the optimal scheduling solution, and the microgrid scheduling model is optimized based on the optimal scheduling solution.

[0014] Optionally, the microgrid dispatch model further includes a gas turbine;

[0015] The objective function for optimizing the microgrid scheduling model is:

[0016]

[0017] Among them, I eco For the target benefit of the microgrid dispatch model, I SH For the hydrogen sales revenue of the hydrogen production plant equipment model, I SO For the oxygen sales revenue of the hydrogen production plant equipment model, F MT F represents the consumption cost of the gas turbine. Grid Let k be the electricity purchase cost of the microgrid dispatch model. H For the price of hydrogen, M Hload k represents the total hydrogen production capacity of the hydrogen production plant equipment model. O For the price of oxygen, M Oload Let T be the total oxygen production of the hydrogen production plant equipment model, T be the total number of quarter-hours in a day, and k be the total oxygen production of the hydrogen production plant equipment model. MT This is the consumption cost coefficient of the gas turbine. Let k be the output cost of the gas turbine at the t-th minute. Grid The electricity purchase price for the microgrid dispatch model is... Let be the power purchased by the microgrid dispatch model at quarter-hour t.

[0018] Optionally, the operating mode of the electrolytic cell model satisfies the following formula:

[0019]

[0020] in, Let be the input power of the electrolytic cell model at the t-th minute. Let η be the hydrogen production rate of the electrolyzer model at minute t. H The hydrogen production efficiency of the electrolyzer model is given.

[0021] Optionally, the operating mode of the compressor model satisfies the following formula:

[0022]

[0023] in, Let T be the power consumption of the compressor model at quarter-t. in The temperature of the compressor model when hydrogen is input. Here is the specific heat constant of hydrogen. Let P be the hydrogen flow rate of the compressor model at clock t, k be the entropy exponent of hydrogen, and P be the hydrogen flow rate. out / P in The compression ratio of the compressor model for compressing hydrogen.

[0024] Optionally, the hydrogen production plant equipment model also includes a hydrogen load;

[0025] The operating mode of the hydrogen storage tank model satisfies the following equation:

[0026]

[0027] in, Let be the internal gas pressure of the hydrogen storage tank model at quarter-minute t+1. Let T be the internal gas pressure of the hydrogen storage tank model at the t-th minute. H V represents the internal temperature of the hydrogen storage tank in the hydrogen storage tank model. hs M is the volume of the hydrogen storage tank in the hydrogen storage tank model. hs Here is the molar mass of hydrogen. The hydrogen load required at the t-th quarter-hour mark is given.

[0028] Optionally, the microgrid dispatch model further includes a gas turbine;

[0029] The equipment constraints include the electrolyzer constraints of the electrolyzer model, the compressor constraints of the compressor model, the hydrogen storage tank constraints of the hydrogen storage tank model, the gas turbine constraints of the gas turbine, and system topology constraints, wherein...

[0030] The constraints of the electrolytic cell are:

[0031]

[0032] in, This represents the maximum input electrical power of the electrolytic cell model. This represents the maximum ramping power of the electrolytic cell model. The input power of the electrolytic cell model at the (t+1)th minute;

[0033] The constraints of the hydrogen storage tank are:

[0034]

[0035] in, The initial internal gas pressure of the hydrogen storage tank model is given. The internal gas pressure of the hydrogen storage tank model at its final state. The maximum internal gas pressure that the hydrogen storage tank model can withstand;

[0036] The compressor is constrained as follows:

[0037]

[0038] in, This represents the maximum input electrical power of the compressor model.

[0039] The gas turbine constraint is:

[0040]

[0041] in, Let be the output power of the gas turbine at the t-th minute. Let be the output power of the gas turbine at the (t-1)th minute. This represents the minimum output power of the gas turbine. This represents the maximum output power of the gas turbine. This refers to the maximum ramp power of the gas turbine.

[0042] The system topology constraints are as follows:

[0043]

[0044] Among them, M load This represents the predicted demand for hydrogen load over a given day. Let be the power purchased by the microgrid dispatch model at quarter hour t. Let be the load power of the hydrogen production plant equipment model at the t-th minute. Let t be the electrical load demand of the equipment in the microgrid scheduling model other than the hydrogen production plant equipment model at quarter hour t.

[0045] Optionally, under the device constraints, the optimal scheduling objective function is solved to obtain the optimal scheduling solution, including:

[0046] Under the aforementioned equipment constraints, the optimal scheduling objective function is solved using the particle swarm optimization algorithm to obtain the optimal scheduling solution.

[0047] Optionally, the photovoltaic grid-connected microgrid topology also includes a voltage source, which is connected in series with each user node;

[0048] The method also includes:

[0049] When a new user node is connected to the photovoltaic power source, the voltage affecting each user node in the photovoltaic grid-connected microgrid topology is determined using the following formula:

[0050]

[0051] Among them, U m0 U represents the voltage drop experienced by the m-th user node at a distance from the voltage source when a new user node is connected to the photovoltaic power source. m U0 is the node voltage at the m-th user node at a distance from the voltage source, U0 is the initial line voltage, N is the total number of user nodes in the photovoltaic grid-connected microgrid topology, and P is the node voltage at the m-th user node at a distance from the voltage source. n Q represents the active power load demand at the nth user node from the voltage source. n R represents the reactive load demand of the nth user node at a distance from the voltage source. i X is the line resistance value between the i-th user node and the (i-1)-th user node of the voltage source. i The line reactance is the distance between the i-th user node and the (i-1)-th user node of the voltage source. Let R be the influence coefficient of the change in active power at the j-th user node of the voltage source on the node voltage at the m-th user node of the voltage source, and (m∩j) be the intersection between each line segment from the 1-th user node of the voltage source to the m-th user node of the voltage source and each line segment from the 1-th user node of the voltage source to the j-th user node of the voltage source. l Let P be the line resistance value of the l-th line segment. j The active power load demand is located at the j-th user node at the distance from the voltage source. Q is the influence coefficient of the change in reactive power at a distance of j from the voltage source on the node voltage at a distance of m from the voltage source. j X represents the reactive load demand at the j-th user node from the voltage source. l The line reactance value of the l-th line segment, ΔP j ΔQ represents the active power load demand at the j-th user node from the voltage source, and is the phase difference before and after the photovoltaic power source is connected to the new user node. j The reactive load requirement of the j-th user node at the distance from the voltage source is the difference in value before and after the photovoltaic power source is connected to the new user node.

[0052] A microgrid optimization and dispatching device combining photovoltaic and hydrogen production, comprising:

[0053] A photovoltaic topology establishment unit is used to establish a photovoltaic grid-connected microgrid topology, which includes several user nodes, each of which is connected to electrical loads and photovoltaic power sources.

[0054] The hydrogen production model building unit is used to build a hydrogen production plant equipment model, which includes an electrolyzer model, a compressor model, and a hydrogen storage tank model.

[0055] The scheduling model establishment unit is used to construct a microgrid scheduling model based on the photovoltaic grid-connected microgrid topology and the hydrogen production plant equipment model. The microgrid scheduling model includes the electricity load, the photovoltaic power source, and the hydrogen production plant equipment model. The electricity load and the hydrogen production plant equipment model are both used to absorb the output of the photovoltaic power source.

[0056] An optimization function determination unit is used to determine the optimization scheduling objective function of the microgrid scheduling model;

[0057] The constraint determination unit is used to determine the equipment constraints of the optimization scheduling objective function based on the electrolyzer model, the compressor model, and the hydrogen storage tank model.

[0058] The optimal solution unit is used to solve the optimal scheduling objective function under the device constraints to obtain the optimal scheduling solution result, so as to optimize the scheduling of the microgrid scheduling model through the optimal scheduling solution result.

[0059] Optionally, the optimal solution unit includes:

[0060] The particle swarm optimization unit is used to solve the optimization scheduling objective function using the particle swarm algorithm under the device constraints to obtain the optimal scheduling solution.

[0061] Optionally, the photovoltaic grid-connected microgrid topology also includes a voltage source, which is connected in series with each user node;

[0062] The device also includes:

[0063] The voltage impact calculation unit is used to determine the voltage impact on each user node of the photovoltaic grid-connected microgrid topology when a new user node is connected to the photovoltaic power source, using the following formula:

[0064]

[0065] Among them, U m0 U represents the voltage drop experienced by the m-th user node at a distance from the voltage source when a new user node is connected to the photovoltaic power source. mU0 is the node voltage at the m-th user node at a distance from the voltage source, U0 is the initial line voltage, N is the total number of user nodes in the photovoltaic grid-connected microgrid topology, and P is the node voltage at the m-th user node at a distance from the voltage source. n Q represents the active power load demand at the nth user node from the voltage source. n R represents the reactive load demand of the nth user node at a distance from the voltage source. i X is the line resistance value between the i-th user node and the (i-1)-th user node of the voltage source. i The line reactance is the distance between the i-th user node and the (i-1)-th user node of the voltage source. Let R be the influence coefficient of the change in active power at the j-th user node of the voltage source on the node voltage at the m-th user node of the voltage source, and (m∩j) be the intersection between each line segment from the 1-th user node of the voltage source to the m-th user node of the voltage source and each line segment from the 1-th user node of the voltage source to the j-th user node of the voltage source. l Let P be the line resistance value of the l-th line segment. j The active power load demand is located at the j-th user node at the distance from the voltage source. Q is the influence coefficient of the change in reactive power at a distance of j from the voltage source on the node voltage at a distance of m from the voltage source. j X represents the reactive load demand at the j-th user node from the voltage source. l The line reactance value of the l-th line segment, ΔP j ΔQ represents the active power load demand at the j-th user node from the voltage source, and is the phase difference before and after the photovoltaic power source is connected to the new user node. j The reactive load requirement of the j-th user node at the distance from the voltage source is the difference in value before and after the photovoltaic power source is connected to the new user node.

[0066] Using the above technical solution, this application establishes a photovoltaic grid-connected microgrid topology, which includes several user nodes, each connected to both electrical loads and photovoltaic power sources. A hydrogen production plant equipment model is also established, comprising an electrolyzer model, a compressor model, and a hydrogen storage tank model. Based on the photovoltaic grid-connected microgrid topology and the hydrogen production plant equipment model, a microgrid scheduling model is constructed. This model includes the electrical loads, the photovoltaic power sources, and the hydrogen production plant equipment model, both of which are used to absorb the output of the photovoltaic power sources. An optimal scheduling objective function for the microgrid scheduling model is determined. Based on the electrolyzer model, the compressor model, and the hydrogen storage tank model, equipment constraints for the optimal scheduling objective function are determined. Under these constraints, the optimal scheduling objective function is solved to obtain the optimal scheduling solution. This optimal scheduling solution is then used to optimize the scheduling of the microgrid scheduling model. This demonstrates that the electricity load and hydrogen production plant equipment models in the microgrid dispatch model can absorb the output of photovoltaic power, making efficient use of photovoltaic power and improving the photovoltaic absorption capacity of the microgrid. Furthermore, the constraints on the optimization objectives defined for the hydrogen production plant equipment model enable the microgrid dispatch model to simultaneously consider operational economy and hydrogen production cleanliness during optimization. Attached Figure Description

[0067] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0068] Figure 1 A schematic diagram illustrating the optimized scheduling process of a microgrid combining photovoltaic and hydrogen production, provided as an embodiment of this application;

[0069] Figure 2 A schematic diagram of a photovoltaic grid-connected microgrid topology provided in an embodiment of this application;

[0070] Figure 3 A schematic diagram of a working operation mode of the microgrid corresponding to the microgrid scheduling model provided in the embodiments of this application;

[0071] Figure 4 A schematic diagram of a device structure for optimizing the scheduling of a microgrid combining photovoltaic and hydrogen production, provided as an embodiment of this application;

[0072] Figure 5 This is a schematic diagram of a microgrid optimization scheduling device that combines photovoltaic and hydrogen production, provided as an embodiment of this application. Detailed Implementation

[0073] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0074] The proposed solution can be implemented based on a terminal with data processing capabilities, such as a computer, server, or cloud platform.

[0075] Next, combined Figure 1 The microgrid optimization scheduling method combining photovoltaic and hydrogen production of this application may include the following steps:

[0076] Step S110: Establish the photovoltaic grid-connected microgrid topology.

[0077] Specifically, a photovoltaic grid-connected microgrid topology can include several user nodes, each of which is connected to both electrical loads and photovoltaic power sources.

[0078] For example Figure 2 In a photovoltaic grid-connected microgrid topology, the voltage source can be connected in series with each user node, and each user node can be connected to the electrical load and the photovoltaic power source.

[0079] Step S120: Establish a hydrogen production plant equipment model.

[0080] Specifically, the equipment model of a hydrogen production plant may include an electrolyzer model, a compressor model, and a hydrogen storage tank model.

[0081] Step S130: Based on the photovoltaic grid-connected microgrid topology and hydrogen production plant equipment model, construct a microgrid scheduling model.

[0082] Specifically, the microgrid dispatch model can include models of electricity load, photovoltaic power source, and hydrogen production plant equipment.

[0083] The electrical load and hydrogen production plant equipment model can both be used to absorb the output of photovoltaic power, so that the electrical load of the electrical load and the output of photovoltaic power can be balanced.

[0084] Step S140: Determine the optimal scheduling objective function of the microgrid scheduling model.

[0085] Specifically, the objective function for optimizing the microgrid scheduling model can be defined based on the combined operational benefits of the microgrid and hydrogen production plant equipment models.

[0086] It is understandable that, since the output of photovoltaic power is absorbed by the electricity load and the hydrogen production plant equipment model, the optimization scheduling objective function of the microgrid scheduling model can omit / cancel the load portion of photovoltaic power output and its corresponding electricity load, as well as the load portion of the hydrogen production plant equipment model.

[0087] Step S150: Based on the electrolyzer model, compressor model, and hydrogen storage tank model, determine the equipment constraints for optimizing the scheduling objective function.

[0088] It is understandable that the equipment constraints of the optimization scheduling objective function take into account the operation of the electrolyzer model, the compressor model, and the hydrogen storage tank model. Therefore, the equipment constraints of the optimization scheduling objective function can include constraints corresponding to the electrolyzer model, constraints corresponding to the compressor model, and constraints corresponding to the hydrogen storage tank model.

[0089] Step S160: Under equipment constraints, solve the optimization scheduling objective function to obtain the optimal scheduling solution, and use the optimal scheduling solution to optimize the microgrid scheduling model.

[0090] It is understandable that scheduling the microgrid scheduling model through the optimal scheduling solution can meet the requirements of equipment constraints while ensuring that the microgrid scheduling model has both operational economy and clean hydrogen production.

[0091] This embodiment provides a microgrid optimization scheduling method combining photovoltaic (PV) and hydrogen production. It establishes a PV-connected microgrid topology, including several user nodes, each connected to both electrical loads and PV power sources. A hydrogen production plant equipment model is also established, comprising an electrolyzer model, a compressor model, and a hydrogen storage tank model. Based on the PV-connected microgrid topology and the hydrogen production plant equipment model, a microgrid scheduling model is constructed. This model includes the electrical loads, the PV power sources, and the hydrogen production plant equipment model, all of which are used to absorb the output of the PV power sources. An optimization scheduling objective function for the microgrid scheduling model is determined. Based on the electrolyzer model, the compressor model, and the hydrogen storage tank model, equipment constraints for the optimization scheduling objective function are determined. Under these constraints, the optimization scheduling objective function is solved to obtain the optimal scheduling solution. This optimal solution is then used to optimize the microgrid scheduling model. This demonstrates that the electricity load and hydrogen production plant equipment models in the microgrid dispatch model can absorb the output of photovoltaic power, making efficient use of photovoltaic power and improving the photovoltaic absorption capacity of the microgrid. Furthermore, the constraints on the optimization objectives defined for the hydrogen production plant equipment model enable the microgrid dispatch model to simultaneously consider operational economy and hydrogen production cleanliness during optimization.

[0092] In some embodiments of this application, the microgrid dispatch model mentioned in the above embodiments is further described, wherein the microgrid dispatch model may also include a gas turbine.

[0093] Specifically, the microgrid scheduling model corresponds to the microgrid's operational architecture, such as... Figure 3 As shown, a portion of the photovoltaic power output is consumed by electrical loads, while another portion can be consumed by hydrogen production plants. The hydrogen produced by these plants can be consumed by hydrogen loads. Gas turbines can coordinate their output with photovoltaic power. Simultaneously, the microgrid can exchange power with the external grid via interconnects at a preset exchange capacity.

[0094] Based on this, the optimized scheduling objective function of the microgrid scheduling model mentioned in the above embodiments will be introduced. Specifically, the optimized scheduling objective function of the microgrid scheduling model can be:

[0095]

[0096] Among them, I eco For the target benefit of the microgrid dispatch model, I SH For the hydrogen sales revenue of the hydrogen production plant equipment model, I SO For the oxygen sales revenue of the hydrogen production plant equipment model, F MT F represents the consumption cost of the gas turbine. Grid Let k be the electricity purchase cost of the microgrid dispatch model. H This is the price of hydrogen, in yuan / kg, M Hload The total hydrogen production capacity of the hydrogen production plant equipment model is expressed in kg or kJ. O This is the price of oxygen, in yuan / kg, M Oload The total oxygen production of the hydrogen production plant equipment model is expressed in kg, and T is the total number of quarter-hours in a day, k. MT This is the consumption cost coefficient of the gas turbine, expressed in yuan / kW*h. Let k be the output cost of the gas turbine at the t-th minute. Grid The electricity purchase price for the microgrid dispatch model is expressed in yuan / kWh. Let be the power purchased by the microgrid dispatch model at quarter hour t, in kW.

[0097] like Figure 3 As shown, the external power grid can serve as the source of electricity for the microgrid corresponding to the microgrid dispatch model.

[0098] In some embodiments of this application, the electrolytic cell model mentioned in the above embodiments is described, and the working mode of the electrolytic cell model can satisfy the following formula:

[0099]

[0100] in, Let be the input power of the electrolytic cell model at the t-th minute. Let η be the hydrogen production rate of the electrolyzer model at minute t. H The hydrogen production efficiency of the electrolyzer model is given.

[0101] Understandably, the electrolyzer is the core device for hydrogen production through water electrolysis, and alkaline electrolyzers are used in local hydrogen production modules within microgrids. Alkaline water electrolysis is currently the most mature and commercially viable method for hydrogen production. An alkaline electrolyzer consists of electrodes, an electrolyte, and a diaphragm. The electrolyzer holds the electrolyte solution and is divided into anode and cathode chambers by the diaphragm, where the electrodes are assembled. By applying pressure between the electrodes, oxygen is generated at the anode and hydrogen at the cathode, thus achieving the goal of producing hydrogen through water electrolysis. The purity of the produced hydrogen is mostly above 99%, and further purification is determined based on the hydrogen load's purity requirements. Generally, the cost of an alkaline electrolyzer is related to its hydrogen production capacity; the higher the production capacity, the higher the cost.

[0102] In some embodiments of this application, the compressor model mentioned in the above embodiments is described, and the working mode of the compressor model can satisfy the following formula:

[0103]

[0104] in, Let T be the power consumption of the compressor model at quarter-t. in The temperature of the compressor model when hydrogen is input. Here is the specific heat constant of hydrogen. Let P be the hydrogen flow rate of the compressor model at clock t, k be the entropy exponent of hydrogen, and P be the hydrogen flow rate. out / P in The compression ratio of the compressor model for compressing hydrogen.

[0105] Understandably, considering the storage and transportation of hydrogen, compressors are generally used to compress the hydrogen. Based on their working principles, hydrogen compressors can be divided into mechanical compressors and non-mechanical compressors. Mechanical compressors mainly include diaphragm compressors, liquid-driven reciprocating compressors, and ionic liquid compressors. Non-mechanical compressors include metal hydride compressors and electrochemical hydrogen compressors. Selecting the right hydrogen compressor model should be based on the current production needs of the hydrogen production plant, choosing a model that matches its operating characteristics. Appropriate selection is particularly important for hydrogen production plants.

[0106] In some embodiments of this application, the hydrogen production plant equipment model mentioned in the above embodiments is further described. The hydrogen production plant equipment model may also include a hydrogen load for consuming the hydrogen produced by the hydrogen production plant.

[0107] Based on this, the hydrogen storage tank model mentioned in the above embodiments will be introduced. The working mode of this hydrogen storage tank model can satisfy the following formula:

[0108]

[0109] in, Let be the internal gas pressure of the hydrogen storage tank model at quarter-minute t+1. Let T be the internal gas pressure of the hydrogen storage tank model at the t-th minute. H V represents the internal temperature of the hydrogen storage tank in the hydrogen storage tank model. hs M is the volume of the hydrogen storage tank in the hydrogen storage tank model. hs Here is the molar mass of hydrogen. The hydrogen load required at the t-th quarter-hour mark is given.

[0110] Understandably, current hydrogen storage methods mainly include high-pressure gaseous hydrogen storage, cryogenic hydraulic hydrogen storage, solid-state hydrogen storage, and organic liquid hydrogen storage, which utilize compression, liquefaction, and other technologies to store hydrogen. Among these, high-pressure gaseous hydrogen storage has advantages such as simple equipment structure, low energy consumption for hydrogen compression production, fast filling and discharge speeds, and a wide temperature adaptability range. Considering that hydrogen is stored after being compressed using a hydrogen compressor in this hydrogen production module, a high-pressure hydrogen storage tank is used to store the compressed hydrogen. Since the internal gas pressure of the hydrogen storage tank can indirectly represent the amount of hydrogen stored, an internal gas pressure model of the hydrogen storage tank model can be established, and this internal gas pressure model can be used as the working mode of the hydrogen storage tank model.

[0111] In some embodiments of this application, the equipment characteristics of the hydrogen production plant equipment model mentioned in the above embodiments are described.

[0112] First, the electrical characteristics of the equipment in the hydrogen production plant equipment model will be introduced.

[0113] Specifically, when the electrolytic cell model is in production, its input power should fluctuate within the load margin, generally ranging from 20% to 100% of its rated power. Even when the operating power exceeds the rated power, the electrolytic cell model can still operate under overload for a short period. At high current densities, the current density may exceed the rated current density, potentially damaging the equipment. At low current densities, the hydrogen density in the produced oxygen may reach the lower limit of the explosion limit.

[0114] When the electrolyzer model operates in cold standby mode, it shuts down, ceases hydrogen production, depressurizes, and cools. This requires only low power consumption from the control unit and antifreeze system, and cold start-up is relatively long, typically 1-2 hours. Conversely, in hot standby mode, the electrolyzer model is also off, but no cooling is required. Higher standby power consumption is needed to maintain the necessary cell temperature and pressure. Hot start-up is shorter, typically 1-5 minutes, and the response rate meets grid requirements. Shutting down the electrolyzer model involves reducing current, disconnecting power, venting and depressurizing, and stopping alkali circulation after cooling. Furthermore, after shutdown, the entire system, including the attached cooling, purification, and compressor models, must vent air and check for airtightness, typically by purging with nitrogen. This process takes several hours before the system can be restarted.

[0115] Furthermore, the hydrogen production characteristics of the equipment model of the hydrogen production plant are introduced.

[0116] Specifically, the largest domestically produced electrolyzer has a hydrogen output of 1000m³. 3 Two types of electrolytic cells with a capacity of / h, both models are 1000m 3 The main parameters of the alkaline electrolyzer with a capacity of / h are shown in Table 1.

[0117]

[0118]

[0119] Understandably, a 1000m 3 Two 500m³ electrolytic cells per hour 3 The cost of a single-hour electrolyzer accounts for 70% to 50% of the total cost, which indicates that developing a higher-capacity electrolyzer can further reduce the cost of hydrogen production.

[0120] In some embodiments of this application, the equipment constraints mentioned in the above embodiments are described. Specifically, the equipment constraints may include electrolyzer constraints of the electrolyzer model, compressor constraints of the compressor model, hydrogen storage tank constraints of the hydrogen storage tank model, gas turbine constraints of the gas turbine, and system topology constraints.

[0121] The constraints of the electrolytic cell can be:

[0122]

[0123] in, This represents the maximum input electrical power of the electrolytic cell model. This represents the maximum ramping power of the electrolytic cell model. Let be the input power of the electrolytic cell model at the (t+1)th minute.

[0124] It is understandable that the electrolyzer constraint is a constraint on the operating characteristics of the electrolyzer model in the hydrogen production plant equipment model for the optimization scheduling objective function. When optimizing the scheduling of the microgrid scheduling model, the operating characteristics of the electrolyzer model need to be satisfied.

[0125] The constraints of the hydrogen storage tank can be:

[0126]

[0127] in, The initial internal gas pressure of the hydrogen storage tank model is given. The internal gas pressure of the hydrogen storage tank model at its final state. This represents the maximum internal gas pressure that the hydrogen storage tank model can withstand.

[0128] It is understandable that the hydrogen storage tank constraint is a constraint on the operating characteristics of the hydrogen storage tank model in the hydrogen production plant equipment model for the optimization scheduling objective function. When optimizing the scheduling of the microgrid scheduling model, the operating characteristics of the hydrogen storage tank model need to be satisfied.

[0129] Compressor constraints can be:

[0130]

[0131] in, This represents the maximum input electrical power of the compressor model.

[0132] It is understandable that the compressor constraint is a constraint on the operating characteristics of the compressor model in the hydrogen production plant equipment model for the optimization scheduling objective function. When optimizing the scheduling of the microgrid scheduling model, the operating characteristics of the compressor model need to be satisfied.

[0133] Gas turbine constraints can be:

[0134]

[0135] in, Let be the output power of the gas turbine at the t-th minute. Let be the output power of the gas turbine at the (t-1)th minute. This represents the minimum output power of the gas turbine. This represents the maximum output power of the gas turbine. This represents the maximum ramp power of the gas turbine.

[0136] It is understandable that the gas turbine constraint is a constraint on the operating characteristics of the gas turbine in the hydrogen production plant equipment model for the optimization scheduling objective function. When optimizing the scheduling of the microgrid scheduling model, the operating characteristics of the gas turbine need to be satisfied.

[0137] System topology constraints can be:

[0138]

[0139] Among them, M load This represents the predicted demand for hydrogen load over a given day. Let be the power purchased by the microgrid dispatch model at quarter hour t. Let be the load power of the hydrogen production plant equipment model at the t-th minute. Let t be the electrical load demand of the equipment in the microgrid scheduling model other than the hydrogen production plant equipment model at quarter hour t.

[0140] It is understandable that the system topology constraints are constraints on the overall operating characteristics of the microgrid corresponding to the optimal scheduling objective function for the microgrid in the hydrogen production plant equipment model. When optimizing the microgrid scheduling model, the overall operating characteristics of the microgrid must be satisfied.

[0141] In some embodiments of this application, the process of solving the optimization scheduling objective function under equipment constraints to obtain the optimal scheduling solution, as mentioned in the above embodiments, is described. This process may include:

[0142] Under equipment constraints, the optimal scheduling solution is obtained by solving the objective function of the optimization scheduling using the particle swarm optimization algorithm.

[0143] Understandably, the Particle Swarm Optimization (PSO) algorithm leverages information sharing among individuals within a swarm to drive the overall swarm's movement through an evolutionary process from disorder to order in the problem space, thereby finding the optimal solution. PSO has broad application prospects and significant importance in power systems, primarily in load forecasting, electricity market bidding, and optimal power system dispatching. In optimal power system dispatching, PSO can help achieve optimal power allocation and scheduling. By analyzing factors such as load demand, generator capacity, and power balance, PSO can find the optimal power allocation scheme, further improving the efficiency and stability of the power system.

[0144] Specifically, under equipment constraints, the process of solving the optimization scheduling objective function using the particle swarm optimization algorithm to obtain the optimal scheduling solution can include:

[0145] S1. Initialize population processing.

[0146] Specifically, within the constraints of solving the optimization scheduling objective function, a particle swarm is determined and initialized. The velocity and position of each solution are randomly initialized in the search space. The inertia factor, acceleration constant, maximum number of iterations, and termination condition of the algorithm are also initialized. The algorithm terminates when the number of iterations reaches a predetermined value or when the computational error reaches a required level. Simultaneously, the fitness function values ​​of the particles are calculated. The initial fitness value is taken as the optimal solution for the current particle, and the position corresponding to each fitness value is taken as the globally optimal position of the particle. The best fitness value among the particles is taken as the globally optimal value, and the position corresponding to each fitness value is taken as the globally optimal position of the particle.

[0147] S2, Update particle velocity and position.

[0148] Specifically, the particle velocity and position can be updated according to the velocity update formula and position update formula of the particle swarm algorithm. During this process, the preset maximum velocity cannot be exceeded, and corrections should be made for positions that exceed the limits.

[0149] S3. Evaluate the fitness function value of the particle.

[0150] Specifically, the historical best position and the global best position of a particle can be updated by evaluating its fitness function value.

[0151] For example, we can compare the fitness value of each particle with the historical best value. If it is better, we replace it. If the global best fitness value of a particle is better than the historical best value, we replace it.

[0152] Furthermore, S1-S3 of this embodiment are repeated until the number of iterations reaches the set maximum number of iterations or the set minimum error. The global optimal value of the optimal particle and its corresponding position, as well as the local optimal value and its corresponding position of each particle, are output to obtain the optimal scheduling solution.

[0153] In some embodiments of this application, for Figure 2 The illustrated photovoltaic grid-connected microgrid topology describes the process of determining the voltage impact on each user node when a new user node connects to the photovoltaic power supply. This process may include:

[0154] When a new user node is connected to the photovoltaic power source, the voltage affecting each user node in the photovoltaic grid-connected microgrid topology is determined using the following formula:

[0155]

[0156] Among them, U m0 U represents the voltage drop experienced by the m-th user node at a distance from the voltage source when a new user node is connected to the photovoltaic power source. mU0 is the node voltage at the m-th user node at a distance from the voltage source, U0 is the initial line voltage, N is the total number of user nodes in the photovoltaic grid-connected microgrid topology, and P is the node voltage at the m-th user node at a distance from the voltage source. n Q represents the active power load demand at the nth user node from the voltage source. n R represents the reactive load demand of the nth user node at a distance from the voltage source. i X is the line resistance value between the i-th user node and the (i-1)-th user node of the voltage source. i The line reactance is the distance between the i-th user node and the (i-1)-th user node of the voltage source. Let R be the influence coefficient of the change in active power at the j-th user node of the voltage source on the node voltage at the m-th user node of the voltage source, and (m∩j) be the intersection between each line segment from the 1-th user node of the voltage source to the m-th user node of the voltage source and each line segment from the 1-th user node of the voltage source to the j-th user node of the voltage source. l Let P be the line resistance value of the l-th line segment. j The active power load demand is located at the j-th user node at the distance from the voltage source. Q is the influence coefficient of the change in reactive power at a distance of j from the voltage source on the node voltage at a distance of m from the voltage source. j X represents the reactive load demand at the j-th user node from the voltage source. l The line reactance value of the l-th line segment, ΔP j ΔQ represents the active power load demand at the j-th user node from the voltage source, and is the phase difference before and after the photovoltaic power source is connected to the new user node. j The reactive load requirement of the j-th user node at the distance from the voltage source is the difference in value before and after the photovoltaic power source is connected to the new user node.

[0157] Understandably, considering the length of the microgrid lines, the line losses in this topology can be ignored. Using the line voltage drop formula, the node voltage for each user node can be obtained as follows:

[0158]

[0159] Furthermore, considering that the per-unit value of each voltage node is close to 1.0 pu, we have the following formula:

[0160]

[0161] Based on this, we analyze the impact of active power changes of any user node on the node voltage of the m-th user node at a distance from the voltage source, and the impact of reactive power changes of any user node on the node voltage of the m-th user node at a distance from the voltage source, and we have the following equation:

[0162]

[0163] Therefore, when a new user node is connected to the photovoltaic power source, the voltage affecting each user node is:

[0164]

[0165] It is understandable that the voltage of each user node in this photovoltaic grid-connected microgrid topology is affected to different degrees. The farther the user node is from the feeder head, the greater the impact on the global node voltage. When the user node is improperly located or the installed capacity is too large, voltage over-limit problems may occur.

[0166] Based on this, the electrical load in the photovoltaic grid-connected microgrid topology can respond flexibly and absorb the photovoltaic power output in a timely manner, and the net load of the m-th user node at the distance from the voltage source can be calculated.

[0167] Specifically, the net load of a user node can represent the photovoltaic (PV) power absorption capacity of that node. When PV power cannot be absorbed in a timely manner, line overload problems will occur. Prolonged voltage exceeding limits or line overload problems can have serious consequences, causing irreversible damage to the power system. Therefore, it is important to utilize the electricity load to absorb PV power output in a timely manner.

[0168] The apparatus for optimizing the scheduling of a microgrid combining photovoltaic and hydrogen production, provided in the embodiments of this application, is described below. The apparatus for optimizing the scheduling of a microgrid combining photovoltaic and hydrogen production described below can be referred to in correspondence with the method for optimizing the scheduling of a microgrid combining photovoltaic and hydrogen production described above.

[0169] See Figure 4 , Figure 4 This is a schematic diagram of a device structure for optimizing the scheduling of a microgrid that combines photovoltaic and hydrogen production, as disclosed in an embodiment of this application.

[0170] like Figure 4 As shown, the device may include:

[0171] The photovoltaic topology establishment unit 11 is used to establish a photovoltaic grid-connected microgrid topology, which includes several user nodes, each of which is connected to electrical loads and photovoltaic power sources.

[0172] The hydrogen production model establishment unit 12 is used to establish a hydrogen production plant equipment model, which includes an electrolyzer model, a compressor model, and a hydrogen storage tank model.

[0173] The scheduling model establishment unit 13 is used to construct a microgrid scheduling model based on the photovoltaic grid-connected microgrid topology and the hydrogen production plant equipment model. The microgrid scheduling model includes the electricity load, the photovoltaic power source and the hydrogen production plant equipment model. The electricity load and the hydrogen production plant equipment model are both used to absorb the output of the photovoltaic power source.

[0174] The optimization function determination unit 14 is used to determine the optimization scheduling objective function of the microgrid scheduling model;

[0175] The constraint determination unit 15 is used to determine the equipment constraints of the optimization scheduling objective function based on the electrolyzer model, the compressor model, and the hydrogen storage tank model.

[0176] The optimal solution unit 16 is used to solve the optimal scheduling objective function under the equipment constraints to obtain the optimal scheduling solution result, so as to optimize the scheduling of the microgrid scheduling model through the optimal scheduling solution result.

[0177] The specific implementation logic of each of the above units can be found in the relevant introduction of the microgrid optimization scheduling method combining photovoltaic and hydrogen production mentioned above, and will not be repeated here.

[0178] The microgrid optimization and scheduling device combining photovoltaic and hydrogen production provided in this application embodiment can be applied to equipment for microgrid optimization and scheduling combining photovoltaic and hydrogen production, such as terminals: mobile phones, computers, etc. Optionally, Figure 5 This diagram shows the hardware structure of a microgrid optimization scheduling device that combines photovoltaics and hydrogen production. (Refer to...) Figure 5 The hardware structure of the microgrid optimization scheduling equipment that combines photovoltaics and hydrogen production may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4.

[0179] In this embodiment of the application, the number of processor 1, communication interface 2, memory 3, and communication bus 4 is at least one, and processor 1, communication interface 2, and memory 3 communicate with each other through communication bus 4;

[0180] Processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0181] Memory 3 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device;

[0182] The memory stores a program, which the processor can call. The program is used for:

[0183] A photovoltaic grid-connected microgrid topology is established, which includes several user nodes, each of which is connected to electrical loads and photovoltaic power sources;

[0184] A hydrogen production plant equipment model is established, which includes an electrolyzer model, a compressor model, and a hydrogen storage tank model.

[0185] Based on the photovoltaic grid-connected microgrid topology and the hydrogen production plant equipment model, a microgrid dispatch model is constructed. The microgrid dispatch model includes the electricity load, the photovoltaic power source, and the hydrogen production plant equipment model. The electricity load and the hydrogen production plant equipment model are both used to absorb the output of the photovoltaic power source.

[0186] Determine the optimal scheduling objective function for the microgrid scheduling model;

[0187] Based on the electrolyzer model, the compressor model, and the hydrogen storage tank model, the equipment constraints of the optimization scheduling objective function are determined;

[0188] Under the device constraints, the objective function of the optimization scheduling is solved to obtain the optimal scheduling solution, and the microgrid scheduling model is optimized based on the optimal scheduling solution.

[0189] Optionally, the refined and extended functions of the program can be found in the description above.

[0190] This application embodiment also provides a storage medium that can store a program suitable for execution by a processor, the program being used for:

[0191] A photovoltaic grid-connected microgrid topology is established, which includes several user nodes, each of which is connected to electrical loads and photovoltaic power sources;

[0192] Establish a hydrogen production plant equipment model, which includes an electrolyzer model, a compressor model, and a hydrogen storage tank model.

[0193] Based on the photovoltaic grid-connected microgrid topology and the hydrogen production plant equipment model, a microgrid dispatch model is constructed. The microgrid dispatch model includes the electricity load, the photovoltaic power source, and the hydrogen production plant equipment model. The electricity load and the hydrogen production plant equipment model are both used to absorb the output of the photovoltaic power source.

[0194] Determine the optimal scheduling objective function for the microgrid scheduling model;

[0195] Based on the electrolyzer model, the compressor model, and the hydrogen storage tank model, the equipment constraints of the optimization scheduling objective function are determined;

[0196] Under the device constraints, the objective function of the optimization scheduling is solved to obtain the optimal scheduling solution, and the microgrid scheduling model is optimized based on the optimal scheduling solution.

[0197] Optionally, the refined and extended functions of the program can be found in the description above.

[0198] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0199] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0200] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A microgrid optimization scheduling method combining photovoltaic and hydrogen production, characterized in that, include: A photovoltaic grid-connected microgrid topology is established, which includes several user nodes, each of which is connected to electrical loads and photovoltaic power sources; A hydrogen production plant equipment model is established, which includes an electrolyzer model, a compressor model, and a hydrogen storage tank model. Based on the photovoltaic grid-connected microgrid topology and the hydrogen production plant equipment model, a microgrid dispatch model is constructed. The microgrid dispatch model includes the electricity load, the photovoltaic power source, and the hydrogen production plant equipment model. The electricity load and the hydrogen production plant equipment model are both used to absorb the output of the photovoltaic power source. Determine the optimal scheduling objective function for the microgrid scheduling model; Based on the electrolyzer model, the compressor model, and the hydrogen storage tank model, the equipment constraints of the optimization scheduling objective function are determined; Under the device constraints, the optimization scheduling objective function is solved to obtain the optimal scheduling solution, and the microgrid scheduling model is optimized using the optimal scheduling solution. The microgrid dispatch model also includes a gas turbine; The objective function for optimizing the microgrid scheduling model is: in, For the target benefits of the microgrid dispatch model, The revenue from hydrogen sales for the hydrogen production plant equipment model. The oxygen sales revenue of the hydrogen production plant equipment model. The consumption cost of the gas turbine, The electricity purchase cost of the microgrid dispatch model is... For the price of hydrogen, The total hydrogen production capacity of the hydrogen production plant equipment model is [missing information]. The price of oxygen. The total oxygen production of the hydrogen production plant equipment model is T, where T is the total number of quarter-hours in a day. This is the consumption cost coefficient of the gas turbine. Let $t$ be the output cost of the gas turbine at the quarter-minute t. The electricity purchase price for the microgrid dispatch model is... Let be the power purchased by the microgrid dispatch model at quarter hour t; The hydrogen production plant equipment model also includes hydrogen load; The operating mode of the hydrogen storage tank model satisfies the following equation: in, Let be the internal gas pressure of the hydrogen storage tank model at quarter-minute t+1. Let be the internal gas pressure of the hydrogen storage tank model at the t-th minute. The internal temperature of the hydrogen storage tank in the hydrogen storage tank model is given. The hydrogen storage tank volume of the hydrogen storage tank model is given. Here is the molar mass of hydrogen. This represents the hydrogen load requirement at the t-th quarter-hour mark; The microgrid dispatch model also includes a gas turbine; The equipment constraints include the electrolyzer constraints of the electrolyzer model, the compressor constraints of the compressor model, the hydrogen storage tank constraints of the hydrogen storage tank model, the gas turbine constraints of the gas turbine, and system topology constraints, wherein... The constraints of the electrolytic cell are: in, This represents the maximum input electrical power of the electrolytic cell model. This represents the maximum ramping power of the electrolytic cell model. The input power of the electrolytic cell model at the (t+1)th minute; The constraints of the hydrogen storage tank are: in, The initial internal gas pressure of the hydrogen storage tank model is given. The internal gas pressure of the hydrogen storage tank model at its final state. The maximum internal gas pressure that the hydrogen storage tank model can withstand; The compressor is constrained as follows: in, This represents the maximum input electrical power of the compressor model. The gas turbine constraint is: in, Let be the output power of the gas turbine at the t-th minute. Let be the output power of the gas turbine at the (t-1)th minute. This represents the minimum output power of the gas turbine. This represents the maximum output power of the gas turbine. This refers to the maximum ramp power of the gas turbine. The system topology constraints are as follows: in, This represents the predicted demand for hydrogen load over a given day. Let be the power purchased by the microgrid dispatch model at quarter hour t. Let be the load power of the hydrogen production plant equipment model at the t-th minute. Let t be the electrical load demand of the equipment in the microgrid scheduling model other than the hydrogen production plant equipment model at quarter hour t.

2. The method according to claim 1, characterized in that, The working mode of the electrolytic cell model satisfies the following equation: in, Let be the input power of the electrolytic cell model at the t-th minute. Let be the hydrogen production rate of the electrolyzer model at minute t. The hydrogen production efficiency of the electrolyzer model is given.

3. The method according to claim 2, characterized in that, The operating mode of the compressor model satisfies the following equation: in, Let be the power consumption of the compressor model at quarter hour t. The temperature of the compressor model when hydrogen is input. Here is the specific heat constant of hydrogen. Let be the hydrogen flow rate of the compressor model compressing hydrogen at quarter hour t, and k be the entropy exponent of hydrogen. The compression ratio of the compressor model for compressing hydrogen.

4. The method according to claim 1, characterized in that, Under the aforementioned equipment constraints, the optimal scheduling objective function is solved to obtain the optimal scheduling solution, including: Under the aforementioned equipment constraints, the optimal scheduling objective function is solved using the particle swarm optimization algorithm to obtain the optimal scheduling solution.

5. The method according to any one of claims 1-4, characterized in that, The photovoltaic grid-connected microgrid topology also includes a voltage source, which is connected in series with each user node; The method also includes: When a new user node is connected to the photovoltaic power source, the voltage affecting each user node in the photovoltaic grid-connected microgrid topology is determined using the following formula: in, This refers to the voltage drop experienced by the m-th user node at a distance from the voltage source when a new user node is connected to the photovoltaic power source. The node voltage is the distance from the m-th user node to the voltage source. Where is the initial voltage of the line, and N is the total number of user nodes in the photovoltaic grid-connected microgrid topology. The active power load demand is located at the nth user node at a distance from the voltage source. The reactive load demand is the distance from the nth user node to the voltage source. The line resistance value between the i-th user node and the (i-1)-th user node of the voltage source is given. The line reactance is the distance between the i-th user node and the (i-1)-th user node of the voltage source. Let be the influence coefficient of the change in active power at a distance of j from the voltage source on the node voltage at a distance of m from the voltage source. The intersection of each line segment from the first user node of the voltage source to the m-th user node of the voltage source, and each line segment from the first user node of the voltage source to the j-th user node of the voltage source. For the first The line resistance value of each line segment. The active power load demand is located at the j-th user node at the distance from the voltage source. Let be the influence coefficient of the change in reactive power at a distance of j from the voltage source on the node voltage at a distance of m from the voltage source. The reactive load demand is the distance from the j-th user node of the voltage source. No. The line reactance value of each line segment, The active power load demand at the j-th user node from the voltage source is the phase difference before and after the photovoltaic power source is connected to the new user node. The reactive load requirement of the j-th user node at the distance from the voltage source is the difference in value before and after the photovoltaic power source is connected to the new user node.

6. A microgrid optimization and dispatching device combining photovoltaic and hydrogen production, characterized in that, The device, applied to the microgrid optimization scheduling method combining photovoltaic and hydrogen production as described in claim 1, comprises: A photovoltaic topology establishment unit is used to establish a photovoltaic grid-connected microgrid topology, which includes several user nodes, each of which is connected to electrical loads and photovoltaic power sources. The hydrogen production model building unit is used to build a hydrogen production plant equipment model, which includes an electrolyzer model, a compressor model, and a hydrogen storage tank model. The scheduling model establishment unit is used to construct a microgrid scheduling model based on the photovoltaic grid-connected microgrid topology and the hydrogen production plant equipment model. The microgrid scheduling model includes the electricity load, the photovoltaic power source, and the hydrogen production plant equipment model. The electricity load and the hydrogen production plant equipment model are both used to absorb the output of the photovoltaic power source. An optimization function determination unit is used to determine the optimization scheduling objective function of the microgrid scheduling model; The constraint determination unit is used to determine the equipment constraints of the optimization scheduling objective function based on the electrolyzer model, the compressor model, and the hydrogen storage tank model. The optimal solution unit is used to solve the optimal scheduling objective function under the device constraints to obtain the optimal scheduling solution result, so as to optimize the scheduling of the microgrid scheduling model through the optimal scheduling solution result.

7. The apparatus according to claim 6, characterized in that, The microgrid dispatch model also includes a gas turbine; The objective function for optimizing the microgrid scheduling model is: in, For the target benefits of the microgrid dispatch model, The revenue from hydrogen sales for the hydrogen production plant equipment model. The oxygen sales revenue of the hydrogen production plant equipment model. The consumption cost of the gas turbine, The electricity purchase cost of the microgrid dispatch model is... For the price of hydrogen, The total hydrogen production capacity of the hydrogen production plant equipment model is [missing information]. The price of oxygen. The total oxygen production of the hydrogen production plant equipment model is T, where T is the total number of quarter-hours in a day. This is the consumption cost coefficient of the gas turbine. Let $t$ be the output cost of the gas turbine at the quarter-minute t. The electricity purchase price for the microgrid dispatch model is... Let be the power purchased by the microgrid dispatch model at quarter-hour t.

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