Energy management module, system and method

Abstract

The invention relates to an energy management module, system and method. The energy management module comprises a photovoltaic productivity prediction layer, a system energy management optimization scheduling layer and an equipment optimization control layer. The photovoltaic productivity prediction layer establishes a multi-time scale photovoltaic productivity prediction model based on meteorological data and equipment operation state parameters; outputting a photovoltaic productivity prediction result based on the model; the system energy management optimization scheduling layer establishes an intra-day real-time rolling optimization scheduling model based on a photovoltaic productivity prediction result, equipment operation parameters and equipment running state parameters, and outputs a power distribution result of an electrolytic cell and an energy storage battery module; and the equipment optimization control layer establishes an electrolytic hydrogen production equipment and energy storage battery equipment prediction model based on the power distribution result, the equipment operation parameters and the equipment operation state parameters, so that target operation parameters are determined, and the electrolytic hydrogen production equipment and the energy storage battery equipment are regulated and controlled based on the target operation parameters.

Description

Technical Field

[0001] The present application relates to the field of new energy and hydrogen energy management technology, and in particular to an energy management module, system and method. Background Art

[0002] Hydrogen energy has the dual attributes of raw materials and fuels. It is green, low-carbon and widely used. It is an important part of the future energy system and is of great significance to achieving the dual carbon goals. According to research statistics, about 20-30% of carbon emission reduction in my country cannot be achieved through green electricity substitution, and fossil raw materials can only be replaced through the application of hydrogen energy. Therefore, with the rapid development of new energy such as wind power and photovoltaics, green electricity drives the electrolysis of water to produce "green hydrogen", which can not only play the advantages of hydrogen energy's long cycle and large-scale energy storage to support the large-scale development of new energy, but also support the deep decarbonization of terminal fields such as industry, transportation, and construction.

[0003] Photovoltaic green hydrogen projects provide the main technical support for electrolytic hydrogen production in the form of alkalinity as the main and proton exchange as the auxiliary. The efficient conversion of green electricity to green hydrogen requires that electrolytic hydrogen production can adapt to wide power fluctuations and fast response and flexible adjustment. However, under low power conditions, both alkaline and proton exchange membrane electrolysis hydrogen production have the phenomenon of hydrogen transmembrane transport, which limits the adjustable power range. Therefore, how to improve the adaptability of electrolytic hydrogen production to wide power fluctuations of new energy is one of the key challenges to improving the efficiency of electric hydrogen conversion. On the other hand, the current photovoltaic capacity prediction technology is mostly used to estimate the development potential of green hydrogen in a certain region or provide boundary conditions for green hydrogen project planning. There is a lack of technology that organically couples photovoltaic capacity prediction with electrolytic hydrogen production operation and regulation. Although most of the current photovoltaic green hydrogen projects are equipped with energy management systems, these systems generally have deficiencies in intelligence and informationization, and the functions of most systems are limited to monitoring rather than control. Due to the lack of a complete data monitoring framework, mature data analysis methods and flexible and effective regulation strategies, the potential value of the green hydrogen system has not been fully utilized. Summary of the invention

[0004] Based on this, it is necessary to provide an energy management module, system and method to address the above technical issues.

[0005] In a first aspect, an energy management module is provided, the energy management module comprising: a photovoltaic capacity prediction layer, a system energy management optimization scheduling layer and an equipment optimization control layer;

[0006] The photovoltaic capacity prediction layer is used to obtain meteorological data and equipment operating status parameters, establish a multi-time scale photovoltaic capacity prediction model based on the meteorological data and the equipment operating status parameters; and output a photovoltaic capacity prediction result based on the multi-time scale photovoltaic capacity prediction model;

[0007] The system energy management optimization scheduling layer is used to establish a daily real-time rolling optimization scheduling model based on the obtained photovoltaic capacity prediction results, equipment operating parameters and equipment operating status parameters; based on the daily real-time rolling optimization scheduling model, output the power allocation results, rolling time window scale, decision interval and update frequency of the electrolyzer and energy storage battery module;

[0008] The equipment optimization control layer is used to establish a prediction model for electrolytic hydrogen production equipment and energy storage battery equipment based on the acquired power allocation results, the equipment operating parameters and the equipment operating status parameters; determine target operating parameters based on the prediction model for electrolytic hydrogen production equipment and energy storage battery equipment; wherein the target operating parameters are used to regulate the electrolytic hydrogen production equipment and energy storage battery equipment.

[0009] In a second aspect, an energy management system is provided, the energy management system comprising: an equipment system, a parameter measurement system, a data acquisition system and the energy management module according to the first aspect;

[0010] The equipment system is used to convert solar energy into electrical energy, and electrical energy into hydrogen energy;

[0011] The parameter measurement system is used to measure the meteorological data, the equipment operating parameters of the equipment system and the equipment operating status parameters of the equipment system;

[0012] The data acquisition system is used to collect and store data from the parameter measurement system;

[0013] The energy management module is used to obtain data from the data acquisition system and determine target operating parameters based on the energy management module; wherein the target operating parameters are used to regulate the electrolytic hydrogen production equipment and the energy storage battery equipment.

[0014] In a third aspect, an energy management method is provided, the energy management method comprising:

[0015] Acquire meteorological data and equipment operating status parameters, and establish a multi-time-scale photovoltaic capacity prediction model based on the meteorological data and the equipment operating status parameters; output a photovoltaic capacity prediction result based on the multi-time-scale photovoltaic capacity prediction model;

[0016] Based on the obtained photovoltaic capacity prediction results, equipment operating parameters and equipment operating status parameters, a daily real-time rolling optimization scheduling model is established; based on the daily real-time rolling optimization scheduling model, power allocation results, rolling time window scale, decision interval and update frequency of electrolyzers and energy storage battery modules are output;

[0017] Based on the acquired power allocation results, the equipment operating parameters and the equipment operating status parameters, a prediction model for electrolytic hydrogen production equipment and energy storage battery equipment is established; based on the prediction model for electrolytic hydrogen production equipment and energy storage battery equipment, target operating parameters are determined; wherein the target operating parameters are used to regulate the electrolytic hydrogen production equipment and energy storage battery equipment.

[0018] The above-mentioned energy management module, system and method, the energy management module includes: a photovoltaic capacity prediction layer, a system energy management optimization scheduling layer and an equipment optimization control layer; the photovoltaic capacity prediction layer is used to obtain meteorological data and equipment operating status parameters, and establish a multi-time scale photovoltaic capacity prediction model based on the meteorological data and equipment operating status parameters; output the photovoltaic capacity prediction result based on the multi-time scale photovoltaic capacity prediction model; the system energy management optimization scheduling layer is used to establish a daily real-time rolling optimization scheduling model based on the acquired photovoltaic capacity prediction results, equipment operating parameters and equipment operating status parameters; output the power allocation results, rolling time window scale, decision interval and update frequency of the electrolyzer and energy storage battery module based on the daily real-time rolling optimization scheduling model; the equipment optimization control layer is used to establish an electrolytic hydrogen production equipment and energy storage battery equipment prediction model based on the acquired power allocation results, equipment operating parameters and equipment operating status parameters; determine the target operating parameters based on the electrolytic hydrogen production equipment and energy storage battery equipment prediction model; wherein the target operating parameters are used to regulate the electrolytic hydrogen production equipment and the energy storage battery equipment. Among them, through the above parameter control method, the photovoltaic capacity prediction results are predicted and output in advance, and the electrolysis hydrogen production system is controlled to adjust the operating parameters in advance, so as to guide the power distribution and energy management between electrolysis hydrogen production and auxiliary energy storage batteries in advance, so as to better follow the fluctuations of photovoltaic capacity, expand the low-power operation range and duration of the electrolysis hydrogen production system, and improve the overall efficiency of the electrolysis hydrogen production system and the adaptability to wide power fluctuations. In addition, by setting up the above energy management module, the function of the system is no longer limited to monitoring, but has a control function, so as to fully and comprehensively utilize the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is an architectural diagram of an energy management module in one embodiment;

[0020] Figure 2 is an architectural diagram of an energy management system in one embodiment;

[0021] Figure 3 is a flow chart of an energy management method in one embodiment;

[0022] Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0024] In one embodiment, Figure 1 As shown, an energy management module is provided, and the energy management module includes: a photovoltaic capacity prediction layer, a system energy management optimization scheduling layer and an equipment optimization control layer;

[0025] The photovoltaic capacity prediction layer is used to obtain meteorological data and equipment operating status parameters, establish a multi-time scale photovoltaic capacity prediction model based on the meteorological data and the equipment operating status parameters; and output a photovoltaic capacity prediction result based on the multi-time scale photovoltaic capacity prediction model;

[0026] The system energy management optimization scheduling layer is used to establish a daily real-time rolling optimization scheduling model based on the obtained photovoltaic capacity prediction results, equipment operating parameters and equipment operating status parameters; based on the daily real-time rolling optimization scheduling model, output the power allocation results, rolling time window scale, decision interval and update frequency of the electrolyzer and energy storage battery module;

[0027] The equipment optimization control layer is used to establish a prediction model for electrolytic hydrogen production equipment and energy storage battery equipment based on the acquired power allocation results, the equipment operating parameters and the equipment operating status parameters; determine target operating parameters based on the prediction model for electrolytic hydrogen production equipment and energy storage battery equipment; wherein the target operating parameters are used to regulate the electrolytic hydrogen production equipment and energy storage battery equipment.

[0028] Among them, through the collaborative work of model algorithms in the three layers of photovoltaic production capacity prediction layer, system energy management optimization scheduling layer and equipment optimization control layer, the photovoltaic green hydrogen system can achieve highly integrated and intelligent energy management, optimize energy utilization efficiency, reduce operating costs, and improve system reliability and stability.

[0029] Among them, by utilizing the time scale difference characteristics of the electric, mass and heat dynamic response of the electrolytic hydrogen production process and combining it with the technical idea of ​​high-precision prediction of photovoltaic production capacity, the adaptability of electrolytic hydrogen production to wide power fluctuations of new energy sources can be improved.

[0030] Among them, by utilizing the time scale difference characteristics of the dynamic response of electricity, mass and heat in hydrogen production by electrolysis, when the new energy fluctuates rapidly, the electrolyzer expands the power operation range in a short time while the hydrogen concentration in oxygen does not rise significantly, so that the electrolyzer can follow the random fluctuations of the new energy electricity in a short time. Then, by adjusting the electrolysis operation pressure, electrolyte circulation flow rate and electrolysis temperature, the accumulation of hydrogen on the oxygen production side can be controlled, further expanding the low-power operation range and duration of the hydrogen production system by electrolysis. Finally, predicting the fluctuation of photovoltaic production capacity in advance can win precious time for system pre-regulation, and guiding the power distribution and energy management between alkaline electrolysis hydrogen production, proton exchange membrane electrolysis hydrogen production and auxiliary energy storage batteries in advance, so as to better follow the fluctuation of photovoltaic production capacity.

[0031] Optionally, the above-mentioned prediction time scale may be a forecast time scale.

[0032] In an optional embodiment, the equipment operating status parameters include photovoltaic equipment operating parameters, and the photovoltaic production capacity prediction layer establishes a multi-time scale photovoltaic production capacity prediction model based on meteorological data and photovoltaic equipment operating parameters.

[0033] In an optional embodiment, the device operating parameters include photovoltaic device operating parameters, electrolysis hydrogen production device operating parameters and energy storage battery device operating parameters;

[0034] The photovoltaic device operating parameters include at least one of the following: photovoltaic panel orientation, inclination, and rated maximum power point of the photovoltaic panel;

[0035] The operating parameters of the electrolytic hydrogen production equipment include at least one of the following: electrolytic cell start and stop, hot standby mode state, cold standby mode state, voltage, current, electrolysis pressure, electrolysis temperature, electrolyte flow rate, and electrolyte circulation pump speed;

[0036] The energy storage battery equipment operating parameters include at least one of the following: energy storage battery charging power, energy storage battery discharging power, energy storage battery charging and discharging control strategy;

[0037] The equipment operating state parameters include photovoltaic equipment operating state parameters, electrolytic hydrogen production equipment operating state parameters, energy storage battery equipment operating state parameters and photovoltaic power generation equipment operating state parameters;

[0038] The photovoltaic equipment operating state parameter includes at least one of the following: photovoltaic power generation, photovoltaic temperature, inverter efficiency, photovoltaic module efficiency;

[0039] The operating state parameters of the electrolytic hydrogen production equipment include at least one of the following: hydrogen concentration in oxygen, oxygen concentration in hydrogen, hydrogen flow rate, energy conversion efficiency of electrolytic hydrogen production, and liquid level of the electrolyte storage tank;

[0040] The energy storage battery equipment operating state parameter includes at least one of the following: state of charge, temperature;

[0041] The operating status parameters of the photovoltaic power generation equipment include: power generation power.

[0042] In an optional embodiment, based on the multi-time scale photovoltaic capacity prediction model, historical meteorological data and real-time meteorological data are combined to predict and output historical photovoltaic capacity prediction results and real-time photovoltaic capacity prediction results;

[0043] Among them, the meteorological data includes surface measurement data and satellite remote sensing data; the surface measurement data includes at least one of the following: irradiance, air temperature, humidity, wind speed, wind direction, sky image; the satellite remote sensing data includes at least one of the following: spectral channel irradiance, cloud cover index, cloud height, cloud phase.

[0044] In an optional embodiment, the photovoltaic capacity prediction layer is used to obtain the power allocation result and the device operating parameters, and adjust the parameters of the multi-time scale photovoltaic capacity prediction model based on the power allocation result and the device operating parameters; wherein the parameters include at least one of the following: prediction time scale, prediction update frequency, prediction accuracy and prediction target.

[0045] In an optional embodiment, the prediction time scale of the multi-time scale photovoltaic capacity prediction model includes: an ultra-short-term prediction time scale, a short-term prediction time scale and a medium-term prediction time scale; the photovoltaic capacity prediction result output by the multi-time scale photovoltaic capacity prediction model includes: a short-term photovoltaic capacity prediction result and a long-term photovoltaic capacity prediction result;

[0046] The objective function of the multi-time scale photovoltaic capacity prediction model is: the minimum deviation from the hydrogen production plan value of the day-ahead optimal scheduling, or the lowest unit hydrogen production cost as the optimization target;

[0047] The constraints of the multi-time-scale photovoltaic capacity prediction model include: power balance constraints, output variable constraints, control variable constraints, and control variable increment constraints.

[0048] Among them, a multi-time scale photovoltaic capacity prediction model is established. Based on the multi-time scale photovoltaic capacity prediction model combined with time series analysis, machine learning algorithms (such as support vector machines, random forests, deep learning networks) and incremental learning technology, short-term and long-term predictions of photovoltaic power generation performance are achieved, and short-term photovoltaic capacity prediction results and long-term photovoltaic capacity prediction results are obtained. The multi-time scale photovoltaic capacity prediction model will adaptively adjust the prediction time scale, prediction update frequency, prediction accuracy and prediction target to adapt to the changing meteorological conditions and system status. The output photovoltaic capacity prediction results will serve as the key input for the optimization scheduling of the system energy management optimization scheduling layer.

[0049] In an optional embodiment, the target operating state parameters are output based on the prediction model of the electrolysis hydrogen production equipment and the energy storage battery equipment, and the equipment operating parameters are optimized based on the target operating state parameters to obtain the target operating parameters;

[0050] Among them, the constraints include: equipment operation constraints, safety restrictions, upper limit of hydrogen concentration in oxygen, and power allocation requirements of the system energy management optimization scheduling layer; the optimization goals include: maximizing hydrogen production efficiency and minimizing unit hydrogen production cost.

[0051] Among them, the target operating state parameters are the predicted operating state parameters of the electrolysis hydrogen production and energy storage battery equipment.

[0052] In an embodiment of the present invention, the energy management module includes: a photovoltaic capacity prediction layer, a system energy management optimization scheduling layer and an equipment optimization control layer; the photovoltaic capacity prediction layer is used to obtain meteorological data and equipment operating status parameters, and establish a multi-time scale photovoltaic capacity prediction model based on the meteorological data and equipment operating status parameters; output photovoltaic capacity prediction results based on the multi-time scale photovoltaic capacity prediction model; the system energy management optimization scheduling layer is used to establish a daily real-time rolling optimization scheduling model based on the acquired photovoltaic capacity prediction results, equipment operating parameters and equipment operating status parameters; output the power allocation results, rolling time window scale, decision interval and update frequency of the electrolyzer and energy storage battery module based on the daily real-time rolling optimization scheduling model; the equipment optimization control layer is used to establish a prediction model for electrolytic hydrogen production equipment and energy storage battery equipment based on the acquired power allocation results, equipment operating parameters and equipment operating status parameters; determine the target operating parameters based on the prediction model for electrolytic hydrogen production equipment and energy storage battery equipment; wherein the target operating parameters are used to regulate the electrolytic hydrogen production equipment and energy storage battery equipment.

[0053] In the embodiments of the present invention, the innovative technical idea of ​​utilizing the time scale difference characteristics of the dynamic response of electricity, mass and heat of hydrogen production by electrolysis and combining it with the high-precision prediction of photovoltaic production capacity has the potential to become an effective technical approach to improve the adaptability of hydrogen production by electrolysis to wide power fluctuations of new energy. Specifically:

[0054] 1) Hydrogen production by electrolysis has multi-time scale differences in milliseconds, seconds and minutes in the dynamic response of electricity, mass and heat.

[0055] In the electrolyzer, electric energy drives H2O to decompose into H2 and O2 through electrochemical reactions, while generating heat. When the power of new energy fluctuates, the response time of the electrolyzer current or voltage is in milliseconds; the response time of the electrolyte temperature is tens of seconds, and the change time lasts for several minutes to stabilize; the response time of the hydrogen concentration in the gas oxygen is tens of seconds, and the change time lasts for tens of minutes to stabilize.

[0056] In the present application, the time scale difference characteristics of the dynamic response of electricity, mass and heat in hydrogen production by electrolysis can be utilized. When the new energy fluctuates rapidly, the electrolyzer can expand the power operation range in a short time, and the hydrogen concentration in oxygen does not increase significantly, so that the electrolyzer can follow the random fluctuations of the new energy electricity in a short time, effectively absorb more new energy electricity and convert it into green hydrogen to improve the electricity-to-hydrogen conversion efficiency.

[0057] 2) Reasonably adjust parameters to suppress the rise of hydrogen in oxygen and further expand the operating range and duration of electrolysis power.

[0058] In the dynamic operation process of electrolyzer and new energy fluctuations to produce hydrogen in coordination, the operating parameters such as electrolysis operating pressure, electrolyte circulation flow and electrolysis temperature are precisely and reasonably regulated to control the increase in hydrogen concentration in oxygen. This parameter control strategy helps to improve system safety, further expand the low-power operation range and duration of the electrolysis hydrogen production system, and improve the overall efficiency and wide power adaptability of the system.

[0059] 3) The photovoltaic capacity forecast guidance system pre-regulates in advance, further improving the system's wide power adaptability

[0060] Combined with the hysteresis characteristics of the response of the thermal mass change of the hydrogen production system, it is possible to predict the fluctuation of photovoltaic production capacity in advance and gain valuable time for system pre-control. Based on the prediction of photovoltaic production capacity, instructions can be sent to the hydrogen production system to adjust the operating parameters in advance, and guide the power distribution and energy management between hydrogen production and auxiliary energy storage batteries in advance to better follow the fluctuation of photovoltaic production capacity.

[0061] In one embodiment, Figure 2 As shown, an energy management system is provided, the energy management system comprising: an equipment system, a parameter measurement system, a data acquisition system and an energy management module as claimed in any one of the above claims;

[0062] The equipment system is used to convert solar energy into electrical energy, and electrical energy into hydrogen energy;

[0063] The parameter measurement system is used to measure the meteorological data, the equipment operating parameters of the equipment system and the equipment operating status parameters of the equipment system;

[0064] The data acquisition system is used to collect and store data from the parameter measurement system;

[0065] The energy management module is used to obtain data from the data acquisition system and determine target operating parameters based on the energy management module; wherein the target operating parameters are used to regulate the electrolytic hydrogen production equipment and the energy storage battery equipment.

[0066] Among them, the data acquisition system includes data communication equipment, servers, and database management systems.

[0067] In an optional embodiment, the equipment system includes: photovoltaic power generation equipment, photovoltaic inverter, electric energy converter, energy storage battery unit, electrolysis hydrogen production unit;

[0068] The energy storage battery unit includes a plurality of battery modules;

[0069] The electrolytic hydrogen production unit comprises a plurality of electrolytic cells, which are in the form of one or more parallel configurations of alkaline electrolytic cells, proton exchange membrane electrolytic cells, solid oxide electrolytic cells, and solid polymer anion exchange membrane electrolytic cells.

[0070] The electrolytic cell capacity can be set to be isochoric or unequal capacity.

[0071] In an optional embodiment, the parameter measurement system includes:

[0072] Total solar radiation measuring device, direct solar radiation measuring device, geostationary meteorological satellite data measuring device, numerical weather forecast data measuring device, pressure gauge, thermometer, oxygen-hydrogen gas analyzer, hydrogen-oxygen gas analyzer, liquid level meter, voltmeter, ammeter, power meter, frequency meter, water pump tachometer, electrolyzer hot standby mode state measuring device, electrolyzer cold standby mode state measuring device, energy storage battery charge and discharge state measuring device.

[0073] In one embodiment, Figure 3 As shown, an energy management method is provided, the energy management method comprising:

[0074] Acquire meteorological data and equipment operating status parameters, and establish a multi-time-scale photovoltaic capacity prediction model based on the meteorological data and the equipment operating status parameters; output a photovoltaic capacity prediction result based on the multi-time-scale photovoltaic capacity prediction model;

[0075] Based on the obtained photovoltaic capacity prediction results, equipment operating parameters and equipment operating status parameters, a daily real-time rolling optimization scheduling model is established; based on the daily real-time rolling optimization scheduling model, power allocation results, rolling time window scale, decision interval and update frequency of electrolyzers and energy storage battery modules are output;

[0076] Based on the acquired power allocation results, the equipment operating parameters and the equipment operating status parameters, a prediction model for electrolytic hydrogen production equipment and energy storage battery equipment is established; based on the prediction model for electrolytic hydrogen production equipment and energy storage battery equipment, target operating parameters are determined; wherein the target operating parameters are used to regulate the electrolytic hydrogen production equipment and energy storage battery equipment.

[0077] It should be understood that although Figure 3The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 3 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0078] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an energy management method is implemented.

[0079] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0080] In one embodiment, an electronic device is provided, the device comprising: a processor and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the energy management method described in the above embodiment.

[0081] In one embodiment, a computer storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the energy management method described in the above embodiment is implemented.

[0082] In one embodiment, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the energy management method described in the above embodiment is implemented.

[0083] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0084] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0085] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. An energy management module, characterized in that: The energy management module includes: a photovoltaic capacity prediction layer, a system energy management optimization scheduling layer and an equipment optimization control layer; The photovoltaic capacity prediction layer is used to obtain meteorological data and equipment operating status parameters, establish a multi-time scale photovoltaic capacity prediction model based on the meteorological data and the equipment operating status parameters; and output a photovoltaic capacity prediction result based on the multi-time scale photovoltaic capacity prediction model; The system energy management optimization scheduling layer is used to establish a daily real-time rolling optimization scheduling model based on the obtained photovoltaic capacity prediction results, equipment operating parameters and equipment operating status parameters; based on the daily real-time rolling optimization scheduling model, output the power allocation results, rolling time window scale, decision interval and update frequency of the electrolyzer and energy storage battery module; The equipment optimization control layer is used to establish a prediction model for electrolytic hydrogen production equipment and energy storage battery equipment based on the acquired power allocation results, the equipment operating parameters and the equipment operating status parameters; determine target operating parameters based on the prediction model for electrolytic hydrogen production equipment and energy storage battery equipment; wherein the target operating parameters are used to regulate the electrolytic hydrogen production equipment and energy storage battery equipment.

2. The energy management module according to claim 1, characterized in that: Based on the multi-time-scale photovoltaic capacity prediction model, combined with historical meteorological data and real-time meteorological data, predict and output historical photovoltaic capacity prediction results and real-time photovoltaic capacity prediction results; Among them, the meteorological data includes surface measurement data and satellite remote sensing data; the surface measurement data includes at least one of the following: irradiance, air temperature, humidity, wind speed, wind direction, sky image; the satellite remote sensing data includes at least one of the following: spectral channel irradiance, cloud cover index, cloud height, cloud phase.

3. The energy management module according to claim 1, characterized in that: The photovoltaic capacity prediction layer is used to obtain the power allocation result and the device operating parameters, and adjust the parameters of the multi-time scale photovoltaic capacity prediction model based on the power allocation result and the device operating parameters; wherein the parameters include at least one of the following: prediction time scale, prediction update frequency, prediction accuracy and prediction target.

4. The energy management module according to claim 1, characterized in that: The prediction time scales of the multi-time-scale photovoltaic capacity prediction model include: an ultra-short-term prediction time scale, a short-term prediction time scale, and a medium-term prediction time scale; the photovoltaic capacity prediction results output by the multi-time-scale photovoltaic capacity prediction model include: a short-term photovoltaic capacity prediction result and a long-term photovoltaic capacity prediction result; The objective function of the multi-time scale photovoltaic capacity prediction model is: the minimum deviation from the hydrogen production plan value of the day-ahead optimal scheduling, or the lowest unit hydrogen production cost as the optimization target; The constraints of the multi-time-scale photovoltaic capacity prediction model include: power balance constraints, output variable constraints, control variable constraints, and control variable increment constraints.

5. The energy management module according to claim 1, characterized in that: Outputting target operating state parameters based on the prediction model of the electrolytic hydrogen production equipment and the energy storage battery equipment, optimizing the equipment operating parameters based on the target operating state parameters, and obtaining the target operating parameters; Among them, the constraints include: equipment operation constraints, safety restrictions, upper limit of hydrogen concentration in oxygen, and power allocation requirements of the system energy management optimization scheduling layer; the optimization goals include: maximizing hydrogen production efficiency and minimizing unit hydrogen production cost.

6. The energy management module according to claim 1, characterized in that: The equipment operating parameters include photovoltaic equipment operating parameters, electrolysis hydrogen production equipment operating parameters and energy storage battery equipment operating parameters; The photovoltaic device operating parameters include at least one of the following: photovoltaic panel orientation, inclination, and rated maximum power point of the photovoltaic panel; The operating parameters of the electrolytic hydrogen production equipment include at least one of the following: electrolytic cell start and stop, hot standby mode state, cold standby mode state, voltage, current, electrolysis pressure, electrolysis temperature, electrolyte flow rate, and electrolyte circulation pump speed; The energy storage battery equipment operating parameters include at least one of the following: energy storage battery charging power, energy storage battery discharging power, energy storage battery charging and discharging control strategy; The equipment operating state parameters include photovoltaic equipment operating state parameters, electrolytic hydrogen production equipment operating state parameters, energy storage battery equipment operating state parameters and photovoltaic power generation equipment operating state parameters; The photovoltaic equipment operating state parameter includes at least one of the following: photovoltaic power generation, photovoltaic temperature, inverter efficiency, photovoltaic module efficiency; The operating state parameters of the electrolytic hydrogen production equipment include at least one of the following: hydrogen concentration in oxygen, oxygen concentration in hydrogen, hydrogen flow rate, energy conversion efficiency of electrolytic hydrogen production, and liquid level of the electrolyte storage tank; The energy storage battery equipment operating state parameter includes at least one of the following: state of charge, temperature; The operating status parameters of the photovoltaic power generation equipment include: power generation power.

7. An energy management system, characterized in that: The energy management system comprises: an equipment system, a parameter measurement system, a data acquisition system and an energy management module according to any one of claims 1 to 6; The equipment system is used to convert solar energy into electrical energy, and electrical energy into hydrogen energy; The parameter measurement system is used to measure the meteorological data, the equipment operating parameters of the equipment system and the equipment operating status parameters of the equipment system; The data acquisition system is used to collect and store data from the parameter measurement system; The energy management module is used to obtain data from the data acquisition system and determine target operating parameters based on the energy management module; wherein the target operating parameters are used to regulate the electrolytic hydrogen production equipment and the energy storage battery equipment.

8. The energy management system according to claim 7, characterized in that: The equipment system includes: photovoltaic power generation equipment, photovoltaic inverter, electric energy converter, energy storage battery unit, and electrolytic hydrogen production unit; The energy storage battery unit includes a plurality of battery modules; The electrolytic hydrogen production unit comprises a plurality of electrolytic cells, which are in the form of one or more parallel configurations of alkaline electrolytic cells, proton exchange membrane electrolytic cells, solid oxide electrolytic cells, and solid polymer anion exchange membrane electrolytic cells.

9. The energy management system according to claim 7, characterized in that: The parameter measurement system comprises: Total solar radiation measuring device, direct solar radiation measuring device, geostationary meteorological satellite data measuring device, numerical weather forecast data measuring device, pressure gauge, thermometer, oxygen-hydrogen gas analyzer, hydrogen-oxygen gas analyzer, liquid level meter, voltmeter, ammeter, power meter, frequency meter, water pump tachometer, electrolyzer hot standby mode state measuring device, electrolyzer cold standby mode state measuring device, energy storage battery charge and discharge state measuring device.

10. An energy management method, characterized in that: The energy management method comprises: Acquire meteorological data and equipment operating status parameters, and establish a multi-time-scale photovoltaic capacity prediction model based on the meteorological data and the equipment operating status parameters; output a photovoltaic capacity prediction result based on the multi-time-scale photovoltaic capacity prediction model; Based on the obtained photovoltaic capacity prediction results, equipment operating parameters and equipment operating status parameters, a daily real-time rolling optimization scheduling model is established; based on the daily real-time rolling optimization scheduling model, power allocation results, rolling time window scale, decision interval and update frequency of electrolyzers and energy storage battery modules are output; Based on the acquired power allocation results, the equipment operating parameters and the equipment operating status parameters, a prediction model for electrolytic hydrogen production equipment and energy storage battery equipment is established; based on the prediction model for electrolytic hydrogen production equipment and energy storage battery equipment, target operating parameters are determined; wherein the target operating parameters are used to regulate the electrolytic hydrogen production equipment and energy storage battery equipment.

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