Optimization methods, devices, equipment, and media for control strategies of new energy grid-connected hydrogen production

Abstract

This application relates to the field of new energy technology and discloses a method, apparatus, equipment, and medium for optimizing the control strategy of grid-connected hydrogen production from new energy sources. The method includes: determining predicted data for new energy power generation and predicted data for load demand based on historical data of new energy power generation, historical data of load demand, and a prediction model; determining multiple hydrogen production scenarios based on the multiple hydrogen production scenarios and the surplus / deficit of the grid-connected bus power of the grid-connected hydrogen production system; constructing a rolling optimization scheduling model by combining a preset rolling optimization scheduling strategy; and solving the rolling optimization scheduling model based on the predicted data of new energy power generation, predicted data of load demand, rated parameters, and real-time state parameters of the grid-connected hydrogen production system to obtain the target control strategy. This avoids relying on manual experience to formulate control strategies, thereby improving the overall energy utilization rate and enhancing the economic benefits of the new energy internet.

Description

Technical Field

[0001] This application relates to the field of new energy technology, and in particular to a method, apparatus, equipment and medium for optimizing the control strategy of new energy grid-connected hydrogen production. Background Technology

[0002] Based on the future energy development direction of my country, the proportion of renewable energy power generation will become increasingly larger, and its large-scale storage, transportation and consumption will gradually become key issues for my country.

[0003] Hydrogen energy is a high-energy-density, green, clean, and pollution-free energy source with a strong market presence in current industrial systems and the future Industry 4.0. Grid-connected hydrogen production from renewable energy sources not only generates large quantities of clean hydrogen, creating economic benefits, but also possesses unparalleled flexibility and energy storage advantages. It can effectively offset the adverse effects of random fluctuations in renewable energy generation such as wind and solar power, making a significant contribution to the flexibility and security of the power system. However, current control strategies for grid-connected hydrogen production from renewable energy sources are typically formulated manually based on past experience. Furthermore, renewable energy generation is highly random, and control strategies based on manual experience cannot accurately achieve the ideal comprehensive energy utilization rate. The overall energy utilization rate is low, and the economic benefits of the energy internet still have considerable room for improvement. Summary of the Invention

[0004] In view of this, the purpose of this application is to overcome the shortcomings of the prior art and provide a method for optimizing the control strategy of new energy grid-connected hydrogen production, the method comprising:

[0005] Acquire historical data of renewable energy power generation and load demand power of renewable energy grid-connected hydrogen production system, and determine the predicted data of renewable energy power generation and load demand power based on the historical data of renewable energy power generation, the historical data of load demand power and the prediction model.

[0006] Based on the new energy power generation forecast data and the load demand power forecast data, multiple hydrogen production scenarios are identified.

[0007] Based on the various hydrogen production scenarios and the surplus or deficit of grid-connected bus power in the new energy grid-connected hydrogen production system, the state-space model, multi-objective function, and constraints are determined.

[0008] A rolling optimization scheduling model is constructed based on the state space model, the multi-objective function, the constraints, and the preset rolling optimization scheduling strategy.

[0009] Based on the predicted power generation data of the new energy source, the predicted power demand data of the load, and the rated parameters and real-time status parameters of the new energy grid-connected hydrogen production system, the rolling optimization scheduling model is solved to obtain the target control strategy of the new energy grid-connected hydrogen production system.

[0010] In one embodiment, the new energy power generation forecast data includes predicted new energy power generation values ​​for multiple future time periods, and the load demand power forecast data includes predicted load demand power values ​​for multiple future time periods. The step of determining multiple hydrogen production scenarios based on the new energy power generation forecast data and the load demand power forecast data includes:

[0011] Based on preset combination rules, the predicted load demand power values ​​for multiple future time periods are combined to determine multiple hydrogen production scenarios.

[0012] In one embodiment, the step of determining the state-space model, multi-objective function, and constraints based on multiple hydrogen production scenarios and the grid-connected bus power surplus / deficit of the new energy grid-connected hydrogen production system includes:

[0013] Constraints are determined based on multiple hydrogen production scenarios, and a multi-objective function is determined based on the constraints.

[0014] Based on the power surplus or deficit of the grid-connected bus of the new energy grid-connected hydrogen production system, a dispatching instruction is determined, and a state-space model is determined based on the dispatching instruction.

[0015] In one embodiment, the constraints include: system power balance constraints, new energy power generation capacity and load power consumption constraints, power absorption range constraints of the electrolyzer hydrogen production system, electrolyzer ramp rate constraints, and power fluctuation constraints of the system grid connection tie line.

[0016] In one embodiment, the multi-objective function includes: a renewable energy curtailment function, a load shedding function under power grid deficit conditions, a function to minimize power fluctuations in the system's grid-connected tie line, and a function to maximize hydrogen production from the electrolyzer.

[0017] In one embodiment, the step of determining the state-space model according to the scheduling instruction includes:

[0018] Based on the scheduling instructions, determine the state vector, control variables, and output vector of the new energy grid-connected hydrogen production system;

[0019] The state-space model is determined based on the state vector, the control variables, and the output vector.

[0020] In one embodiment, the step of solving the rolling optimization scheduling model based on the new energy power generation forecast data, the load demand power forecast data, the rated parameters and real-time status parameters of the new energy grid-connected hydrogen production system, to obtain the target control strategy of the new energy grid-connected hydrogen production system, includes:

[0021] Based on the predicted power generation data of the new energy source, the predicted power demand data of the load, the rated parameters and real-time status parameters of the new energy grid-connected hydrogen production system, the rolling optimization scheduling model is solved to obtain the initial control strategy of the new energy grid-connected hydrogen production system.

[0022] The real-time status parameters of the new energy grid-connected hydrogen production system are corrected, and the new energy power generation prediction data and the load demand power prediction data are updated based on the corrected real-time status parameters.

[0023] Based on the updated new energy power generation forecast data, the updated load demand power forecast data, the rated parameters, and the corrected real-time status parameters, the rolling optimization scheduling model is solved to obtain the optimized control strategy of the new energy grid-connected hydrogen production system.

[0024] The optimized control strategy is repeatedly updated and iterated until it converges, thus obtaining the target control strategy for the new energy grid-connected hydrogen production system.

[0025] This application also provides a new energy grid-connected hydrogen production control strategy optimization device, the new energy grid-connected hydrogen production control strategy optimization device comprising:

[0026] The prediction module is used to acquire historical data of new energy power generation and historical data of load demand power of the new energy grid-connected hydrogen production system, and to determine the predicted data of new energy power generation and predicted data of load demand power based on the historical data of new energy power generation, the historical data of load demand power and the prediction model.

[0027] The first determining module is used to determine multiple hydrogen production scenarios based on the new energy power generation forecast data and the load demand power forecast data;

[0028] The second determining module is used to determine the state-space model, multi-objective function and constraints based on the multiple hydrogen production scenarios and the grid-connected bus power surplus and deficit of the new energy grid-connected hydrogen production system.

[0029] The construction module is used to construct a rolling optimization scheduling model based on the state space model, the multi-objective function, the constraints, and the preset rolling optimization scheduling strategy.

[0030] The solution module is used to solve the rolling optimization scheduling model based on the new energy power generation forecast data, the load demand power forecast data, the rated parameters and real-time status parameters of the new energy grid-connected hydrogen production system, and obtain the target control strategy of the new energy grid-connected hydrogen production system.

[0031] This application also provides a computer device, which includes a processor and a memory. The memory stores a computer program, and the processor is used to execute the computer program to implement the above-described optimization method for the control strategy of new energy grid-connected hydrogen production.

[0032] This application also provides a computer-readable storage medium storing a computer program that, when run on a processor, executes the above-described optimization method for new energy grid-connected hydrogen production control strategy.

[0033] The embodiments of this application have the following beneficial effects:

[0034] This application embodiment acquires historical data on renewable energy power generation and load demand power of a renewable energy grid-connected hydrogen production system. Based on this historical data and a prediction model, it determines predicted renewable energy power generation and load demand power. Based on these predicted data, multiple hydrogen production scenarios are identified. According to these scenarios and the grid-connected bus power surplus / deficit of the renewable energy grid-connected hydrogen production system, a state-space model, a multi-objective function, and constraints are determined. A rolling optimization scheduling model is constructed based on the state-space model, the multi-objective function, the constraints, and a preset rolling optimization scheduling strategy. The rolling optimization scheduling model is solved based on the predicted renewable energy power generation, the predicted load demand power, the rated parameters of the renewable energy grid-connected hydrogen production system, and the real-time state parameters, yielding the target control strategy for the renewable energy grid-connected hydrogen production system. By combining various parameters of the renewable energy grid-connected hydrogen production system with multiple hydrogen production scenarios, the target control strategy for the renewable energy grid-connected hydrogen production system is determined, avoiding the need to formulate control strategies based on manual experience, thereby improving overall energy utilization and the economic benefits of the renewable energy internet. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and therefore should not be considered as a limitation on the scope of protection of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1A flowchart illustrating the first embodiment of the new energy grid-connected hydrogen production control strategy optimization method provided in this application;

[0037] Figure 2 A schematic diagram of the topology of the new energy grid-connected hydrogen production system provided in this application;

[0038] Figure 3 A flowchart illustrating the second embodiment of the new energy grid-connected hydrogen production control strategy optimization method provided in this application;

[0039] Figure 4 A flowchart illustrating the third embodiment of the new energy grid-connected hydrogen production control strategy optimization method provided in this application;

[0040] Figure 5 A flowchart illustrating the fourth embodiment of the new energy grid-connected hydrogen production control strategy optimization method provided in this application;

[0041] Figure 6 A flowchart illustrating the fifth embodiment of the new energy grid-connected hydrogen production control strategy optimization method provided in this application;

[0042] Figure 7 A schematic diagram of the structure of the new energy grid-connected hydrogen production control strategy optimization device provided in this application. Detailed Implementation

[0043] The technical solutions in 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.

[0044] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0045] In the following, the terms “comprising,” “having,” and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as excluding, firstly, the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations thereof.

[0046] Furthermore, the terms "first," "second," and "third" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0047] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0048] It is understood that the method of this application is applied to the new energy grid-connected hydrogen production control strategy optimization equipment, which can be a smart terminal, PC terminal, mobile terminal, etc., and is not limited here.

[0049] The purpose of this invention is to provide a control strategy optimization method for a new energy grid-connected hydrogen production system, which greatly reduces the impact of uncertainties in the power grid and ensures the rationality of the short-term rolling scheduling plan of the electrolyzer hydrogen production system in the new energy grid-connected hydrogen production system and the stability of system operation.

[0050] The new energy grid-connected hydrogen production system of this invention includes renewable energy power generation equipment such as wind and solar power, and electrolyzer hydrogen production equipment. The main idea of ​​this invention is model predictive control. It makes short-term predictions on the power generation and load consumption patterns of new energy in the new energy grid-connected hydrogen production system, and obtains the upper and lower limits of power supply and load consumption at different times by predicting the intervals within N time periods in the future, so as to obtain hydrogen production scenarios under various power supply and consumption ranges. Then, it performs multi-objective optimization on the electrolyzer hydrogen production system under different power supply and consumption scenarios to obtain the electrolyzer power optimization control sequence, and finally forms an optimized control strategy for the new energy grid-connected hydrogen production system that is adapted to multiple scenarios.

[0051] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0052] Please refer to Figure 1 , Figure 1 A flowchart illustrating the first embodiment of the new energy grid-connected hydrogen production control strategy optimization method provided in this application, the method comprising:

[0053] Step S101: Obtain historical data of new energy power generation and historical data of load demand power of the new energy grid-connected hydrogen production system, and determine the predicted data of new energy power generation and predicted data of load demand power based on the historical data of new energy power generation, the historical data of load demand power and the prediction model.

[0054] In this embodiment, the renewable energy grid-connected hydrogen production control strategy optimization equipment acquires historical data on renewable energy power generation and load demand power from the renewable energy grid-connected hydrogen production system. Based on this historical data and a prediction model, it determines the predicted data on renewable energy power generation and load demand power. It should be noted that, please refer to... Figure 2 , Figure 2 This is a schematic diagram of the topology of a new energy grid-connected hydrogen production system. The new energy grid-connected hydrogen production system includes: renewable energy power generation equipment such as wind and solar power, power system load, electrolyzer hydrogen production equipment, and a large power grid. Among them, the new energy grid-connected hydrogen production system is a system obtained by connecting the electrolyzer hydrogen production equipment to the power system of new energy power generation through grid connection.

[0055] It is understandable that historical power generation data for new energy sources refers to historical power generation data for renewable energy power generation equipment such as wind and solar power, while historical power demand data refers to historical power demand data for loads in the system. Load is a general term for other equipment in the power system that needs electricity.

[0056] In one embodiment, the new energy grid-connected hydrogen production control strategy optimization device acquires basic data and topology information within the new energy grid-connected hydrogen production system. The basic data includes dispatchable power information for wind power, photovoltaic power, and other dispatchable new energy power generation systems within the system, power system load demand forecast information, rated power, rated power and rated operating parameters of the electrolyzer hydrogen production system, and the maximum hydrogen production capacity of the electrolyzer, etc. The topology information includes system connection methods and power supply bus configurations, etc. Based on the topology information and basic data, the new energy grid-connected hydrogen production control strategy optimization device determines the power supply and demand balance status of the power system and performs big data training on the prediction model neural network to obtain the prediction model. The new energy grid-connected hydrogen production control strategy optimization device inputs historical data of new energy power generation and historical data of load demand power into the prediction model to obtain predicted data of new energy power generation and predicted data of load demand power.

[0057] Step S102: Based on the new energy power generation forecast data and the load demand power forecast data, determine multiple hydrogen production scenarios.

[0058] In this embodiment, the new energy grid-connected hydrogen production control strategy optimization device randomly selects points from the upper and lower limit power matrices of the new energy power generation forecast data and load demand power forecast data to form multiple hydrogen production scenarios. It should be noted that the number of hydrogen production scenarios can be determined based on a pre-set threshold or by the new energy grid-connected hydrogen production control strategy optimization device according to the state of the new energy grid-connected hydrogen production system. For example, the multiple hydrogen production scenarios include: the maximum hydrogen production scenario formed when the peak time of new energy power generation coincides with the off-peak time of load consumption; the minimum hydrogen production scenario formed when the off-peak time of new energy power generation coincides with the peak time of load consumption; scenarios with anisotropic fluctuations in new energy and load; scenarios with synchronous fluctuations in new energy and load; and scenarios with sudden load increases. Determining multiple hydrogen production scenarios is to cover the uncertainty space, thereby improving the robustness and adaptability of the new energy grid-connected hydrogen production system.

[0059] Step S103: Based on the multiple hydrogen production scenarios and the surplus or deficit of grid-connected bus power in the new energy grid-connected hydrogen production system, determine the state-space model, multi-objective function, and constraints.

[0060] In this embodiment, the new energy grid-connected hydrogen production control strategy optimization equipment determines the state-space model, multi-objective function, and constraints based on multiple hydrogen production scenarios and the surplus or shortage of grid-connected bus power in the new energy grid-connected hydrogen production system.

[0061] Step S104: Construct a rolling optimization scheduling model based on the state space model, the multi-objective function, the constraints, and the preset rolling optimization scheduling strategy.

[0062] In this embodiment, the new energy grid-connected hydrogen production control strategy optimization equipment constructs a rolling optimization scheduling model based on a state-space model, a multi-objective function, constraints, and a preset rolling optimization scheduling strategy.

[0063] Step S105: Based on the new energy power generation forecast data, the load demand power forecast data, the rated parameters and real-time status parameters of the new energy grid-connected hydrogen production system, the rolling optimization scheduling model is solved using a multi-objective swarm intelligent optimization algorithm to obtain the target control strategy of the new energy grid-connected hydrogen production system.

[0064] In this embodiment, the new energy grid-connected hydrogen production control strategy optimization equipment solves the rolling optimization scheduling model based on the new energy power generation forecast data, load demand power forecast data, rated parameters and real-time status parameters of the new energy grid-connected hydrogen production system to obtain the initial control strategy of the new energy grid-connected hydrogen production system. It then corrects the current state of each subsystem of the new energy grid-connected hydrogen production system, samples the real-time system state, and updates the new energy power generation forecast data and load demand power forecast data. The initial control strategy is continuously updated and optimized until convergence, and finally the target control strategy of the new energy grid-connected hydrogen production system is obtained.

[0065] The renewable energy grid-connected hydrogen production control strategy optimization device in this embodiment acquires historical data of renewable energy power generation and load demand power of the renewable energy grid-connected hydrogen production system. Based on the historical data of renewable energy power generation and load demand power, and a prediction model, it determines the predicted data of renewable energy power generation and load demand power. Based on the predicted data of renewable energy power generation and load demand power, it determines multiple hydrogen production scenarios. According to the multiple hydrogen production scenarios and the surplus / deficit of the grid-connected bus power of the renewable energy grid-connected hydrogen production system, it determines the state-space model, multi-objective function, and constraints. Based on the state-space model, multi-objective function, constraints, and a preset rolling optimization scheduling strategy, it constructs a rolling optimization scheduling model. Based on the predicted data of renewable energy power generation, load demand power, rated parameters, and real-time state parameters of the renewable energy grid-connected hydrogen production system, it solves the rolling optimization scheduling model to obtain the target control strategy of the renewable energy grid-connected hydrogen production system. By combining various parameters of the renewable energy grid-connected hydrogen production system and multiple hydrogen production scenarios, the target control strategy of the renewable energy grid-connected hydrogen production system is determined, avoiding the need to formulate control strategies based on human experience, thereby improving the overall energy utilization rate and the economic benefits of the renewable energy internet.

[0066] Please refer to Figure 3 , Figure 3 This is a flowchart illustrating a second embodiment of the new energy grid-connected hydrogen production control strategy optimization method provided in this application. The difference between the second and first embodiments is that the new energy power generation forecast data includes predicted new energy power generation values ​​for multiple future time periods, and the load demand power forecast data includes predicted load demand power values ​​for multiple future time periods. The step of determining multiple hydrogen production scenarios based on the new energy power generation forecast data and the load demand power forecast data includes:

[0067] Step S201: Based on preset combination rules, combine the predicted load demand power values ​​for multiple future time periods to determine multiple hydrogen production scenarios.

[0068] In this embodiment, the new energy grid-connected hydrogen production control strategy optimization device acquires historical data of new energy power generation and historical data of load demand power of the new energy grid-connected hydrogen production system, and determines the predicted data of new energy power generation and predicted data of load demand power based on the historical data of new energy power generation, the historical data of load demand power and the prediction model.

[0069] In one embodiment, the prediction model is:

[0070]

[0071] in, Forecast data on new energy power generation; This provides the power demand forecast data. By analyzing the forecast error, the distribution variance σ of renewable energy power generation and load power consumption can be obtained. Based on the renewable energy power generation forecast data and variance, the upper and lower limits of the forecasted power for renewable energy generation range can be determined. Similarly, based on the load demand power forecast data and variance, the upper and lower limits of the forecasted power for load demand can be determined.

[0072]

[0073] in, The upper limit of the predicted power output for new energy power generation range. This represents the lower limit of the predicted power output for new energy power generation. Predict the upper limit of power consumption for the load range. This sets the lower limit for predicted power for the load consumption range. Finally, the predicted power range data is matrixed to obtain new energy power generation prediction data including predicted values ​​for new energy power generation over multiple future time periods, and load demand power prediction data including predicted values ​​for load demand over multiple future time periods, as follows:

[0074]

[0075]

[0076] Equipment for optimizing control strategies for grid-connected hydrogen production from new energy sources is randomly selected. The combination of row p and column m The q-th row and m-th column constitute different hydrogen production scenarios at the same time, where p, q = [1, 2]. For example, multiple hydrogen production scenarios include: Scenario 1: Scene 2: Scenario 1 represents the maximum hydrogen production scenario where the peak of renewable energy power generation coincides with the off-peak of electricity demand. Scenario 2 represents the minimum hydrogen production scenario where the off-peak of renewable energy power generation coincides with the peak of electricity demand. It is understandable that many more hydrogen production scenarios can be derived from this, which will not be illustrated here.

[0077] The renewable energy grid-connected hydrogen production control strategy optimization device in this embodiment combines multiple predicted load demand power values ​​for multiple future time periods based on preset combination rules to determine multiple hydrogen production scenarios. This can cover the uncertainty space of the renewable energy grid-connected hydrogen production system, helping to improve the accuracy of subsequent control strategy optimization, thereby enhancing the robustness and adaptability of the renewable energy grid-connected hydrogen production system.

[0078] Please refer to Figure 4 , Figure 4 This is a flowchart illustrating a third embodiment of the new energy grid-connected hydrogen production control strategy optimization method provided in this application. The difference between the third embodiment and the first to second embodiments lies in the step of determining the state-space model, multi-objective function, and constraints based on multiple hydrogen production scenarios and the grid-connected bus power surplus / deficit of the new energy grid-connected hydrogen production system, including:

[0079] Step S301: Determine constraints based on multiple hydrogen production scenarios, and determine a multi-objective function based on the constraints.

[0080] In this embodiment, the new energy grid-connected hydrogen production control strategy optimization equipment determines multiple hydrogen production scenarios, determines constraints based on these scenarios, and then determines a multi-objective function based on these constraints.

[0081] In one embodiment, the constraints include: system power balance constraints, new energy power generation capacity and load power consumption constraints, power absorption range constraints of the electrolyzer hydrogen production system, electrolyzer ramp rate constraints, and power fluctuation constraints of the system grid connection tie line transmission power; wherein, the power fluctuation constraints of the system grid connection tie line transmission power are set according to the specific conditions of the power system for new energy power generation.

[0082] In one embodiment, the system power balance constraint is:

[0083]

[0084]

[0085] and The upper and lower limits of the predicted power of the new energy power generation system at time k+i are given; and Predict the upper and lower limits of the power consumption range for the load at time k+i. N el This refers to the installed capacity of the electrolyzer hydrogen production system; At time k+i, the... l Hydrogen production capacity per electrolyzer during charging; and Let be the power exchanged between the new energy grid-connected hydrogen production system interconnection lines at time k+i under scenarios one and two.

[0086] In one embodiment, the constraints on new energy power generation capacity and load power consumption are as follows:

[0087]

[0088]

[0089]

[0090]

[0091] in, Corresponding predicted power range matrix The second line List, Corresponding predicted power range matrix The 1 Line 1 Column, among which =[1,N], where N represents the prediction time length; This refers to the output power of new energy sources in the first power supply and consumption scenario. The output power of new energy in the second power supply and consumption scenario; This represents the power consumption of the load in the first power supply and consumption scenario. This represents the power consumption of the load in the second power supply scenario.

[0092] In one embodiment, the electrolyzer hydrogen production system can absorb the following power range constraints:

[0093]

[0094] in, For the first The rated power of each electrolytic cell, For the first The actual power of each electrolytic cell at time k+i.

[0095] In one embodiment, the hydrogen production power ramp-up rate of the electrolyzer is constrained:

[0096]

[0097] Among them, the appropriate ramp rate β needs to be determined in the electrolyzer hydrogen production process to ensure that the electrolyzer system operates in the optimal operating range and maintains a stable and high-speed hydrogen production process.

[0098] In one embodiment, the multi-objective function includes: a renewable energy curtailment function, a load shedding function under grid power shortage conditions, a function to minimize the power fluctuation of the system's grid-connected tie line, and a function to maximize the hydrogen production capacity of the electrolyzer.

[0099] Among them, multi-objective function for:

[0100]

[0101] Wherein, J1 is the function for the amount of renewable energy abandoned; J2 is the function for the load shedding under the power grid shortage state; J3 is the function for minimizing the power fluctuation of the system's grid-connected tie line transmission; and J4 is the function for maximizing the hydrogen production of the electrolyzer.

[0102] Step S302: Determine the scheduling instruction based on the power surplus or shortage of the grid-connected bus of the new energy grid-connected hydrogen production system, and determine the state-space model based on the scheduling instruction.

[0103] In this embodiment, the new energy grid-connected hydrogen production control strategy optimization equipment determines the scheduling instructions based on the surplus or shortage of power on the grid-connected bus of the new energy grid-connected hydrogen production system, and constructs the state-space model corresponding to the new energy grid-connected hydrogen production system based on the scheduling instructions and model predictive control theory.

[0104] The new energy grid-connected hydrogen production control strategy optimization equipment in this embodiment formulates a set of constraints based on multiple hydrogen production scenarios to ensure that the system operates within resource limitations and safety boundaries. At the same time, it adopts a multi-objective function to jointly optimize multiple objectives such as task scheduling time, energy consumption, and load balancing, which provides support for the subsequent construction of a rolling optimization scheduling model and helps to improve the accuracy of the rolling optimization scheduling model.

[0105] Please refer to Figure 5 , Figure 5 This is a flowchart illustrating the fourth embodiment of the new energy grid-connected hydrogen production control strategy optimization method provided in this application. The difference between the fourth embodiment and the first to third embodiments is that the step of determining the state-space model according to the scheduling instruction includes:

[0106] Step S401: Determine the state vector, control variables, and output vector of the new energy grid-connected hydrogen production system according to the scheduling instruction.

[0107] Step S402: Determine the state-space model based on the state vector, the control variables, and the output vector.

[0108] In this embodiment, the new energy grid-connected hydrogen production control strategy optimization equipment determines the state vector, control variables, and output vector of the new energy grid-connected hydrogen production system according to the scheduling instructions. The scheduling instructions are issued based on the changes in the source-load imbalance within the system, specifically through the following process to analyze the energy imbalance relationship of the green hydrogen production system at each moment: This represents the power imbalance between the required source and load. If... This indicates that the power grid has sufficient electricity and there is a surplus of renewable energy generation. To maintain system power balance, some renewable energy needs to be curtailed. In this case, an electrolysis hydrogen production system is connected, requiring the electrolysis hydrogen production unit to absorb the produced hydrogen to compensate for the power imbalance and minimize the curtailment of renewable energy. If Therefore, priority should be given to ensuring power supply to the load to avoid load shedding in the power grid due to large-scale hydrogen production, which would affect production and daily life. If the system is in equilibrium, there is no need to abandon power or cut off loads to ensure power balance between the grids. The electrolyzer hydrogen production system only needs to produce hydrogen as a normal load or be shut down to maintain system power balance and give priority to power supply to other loads.

[0109] In practical applications, based on model predictive control theory, a corresponding state-space model is constructed for a new energy grid-connected hydrogen production system. Multi-objective optimization is then performed using a multi-objective function to solve the problem, establishing a time-domain rolling optimization scheduling strategy within the system. The model in this optimization scheduling method is characterized by including the following input and output information:

[0110] Based on the power balance equation of the system for each time period and the hydrogen production status of the electrolyzer, the dispatchable new energy output power obtained from the preliminary forecast data is... Power consumption of the electrolyzer hydrogen production system and microgrid interconnect switching power The resulting vector serves as the state vector:

[0111]

[0112] Increased output of dispatchable new energy power generation systems and adjustable load and the change in power consumption of the adjustable electrolytic cell system The vector formed As a control variable.

[0113] A vector consisting of ultra-short-term predicted power increments of wind power, solar power, and microgrid load demand. As a perturbation input in the time-domain rolling optimization process.

[0114] The system output vector is a vector composed of the power exchanged via the tie line between the microgrid and the main grid, and the SOC and SOHR of the energy storage system. .

[0115] The new energy grid-connected hydrogen production control strategy optimization equipment determines the state-space model based on state vectors, control variables, and output vectors. In one embodiment, the state-space model is as follows:

[0116]

[0117]

[0118] in, For the state vector in The state at any given moment; For new energy power generation systems Power state value at any given time; For the electrolysis hydrogen production system in Power state value at any given time; Exchange power status values ​​for grid interconnection lines; To optimize the duration of each step during scrolling; This provides an increase in the dispatchable output of new energy power generation systems; Incremental change in load demand power; This refers to the amount of power consumed by the adjustable electrolytic cell system. Power exchange for the interconnection line of the new energy grid-connected hydrogen production system; The system output variable is a vector consisting of the power output of the new energy system, the electrolysis hydrogen production system, and the load absorption.

[0119] The new energy grid-connected hydrogen production control strategy optimization device in this embodiment constructs a state-space model, abstracts the internal operating state of the system into a set of state variables, and realizes the modeling of the dynamic behavior of the system based on the state transition equation and observation equation. It quantifies the current state of the system, provides support for the subsequent construction of a rolling optimization scheduling model, and helps to improve the accuracy of the rolling optimization scheduling model.

[0120] Please refer to Figure 6 , Figure 6 This is a flowchart illustrating the fifth embodiment of the new energy grid-connected hydrogen production control strategy optimization method provided in this application. The difference between the fifth embodiment and the first to fourth embodiments is that the step of solving the rolling optimization scheduling model based on the new energy power generation forecast data, the load demand power forecast data, the rated parameters and real-time status parameters of the new energy grid-connected hydrogen production system to obtain the target control strategy of the new energy grid-connected hydrogen production system includes:

[0121] Step S401: Based on the new energy power generation forecast data, the load demand power forecast data, the rated parameters and real-time status parameters of the new energy grid-connected hydrogen production system, solve the rolling optimization scheduling model to obtain the initial control strategy of the new energy grid-connected hydrogen production system.

[0122] In this embodiment, the new energy grid-connected hydrogen production control strategy optimization equipment solves a rolling optimization scheduling model based on new energy power generation forecast data, load demand power forecast data, rated parameters and real-time status parameters of the new energy grid-connected hydrogen production system, to obtain the initial control strategy of the new energy grid-connected hydrogen production system. The obtained initial control strategy includes the initial power output value, initial load shedding amount, and initial electrolyzer hydrogen production power output sequence of the new energy grid-connected hydrogen production system.

[0123] In one embodiment, the rolling optimization scheduling model is as follows:

[0124]

[0125] Among them, among them, The fusion objective function is a synthesis of multiple objective functions; For the first A vector composed of the output values ​​of each subsystem optimized under each objective function; For the first A vector reference value composed of the output values ​​of each subsystem optimized under each objective function; and It is obtained by substituting the predicted power generation data of new energy sources, the predicted power demand data of load, and the rated parameters and real-time status parameters of the new energy grid-connected hydrogen production system into a fusion objective function of multiple objective functions.

[0126] Step S402: Correct the real-time status parameters of the new energy grid-connected hydrogen production system, and update the new energy power generation prediction data and the load demand power prediction data based on the corrected real-time status parameters.

[0127] In this embodiment, the new energy grid-connected hydrogen production control strategy optimization equipment corrects the real-time state parameters of the new energy grid-connected hydrogen production system based on the initial electrolyzer hydrogen production power output sequence in the initial control strategy, and updates the new energy power generation prediction data and load demand power prediction data based on the corrected real-time state parameters.

[0128] Step S403: Based on the updated new energy power generation forecast data, the updated load demand power forecast data, the rated parameters, and the corrected real-time state parameters, solve the rolling optimization scheduling model to obtain the optimized control strategy of the new energy grid-connected hydrogen production system.

[0129] In this embodiment, the new energy grid-connected hydrogen production control strategy optimization equipment solves the rolling optimization scheduling model based on the updated new energy power generation prediction data, the updated load demand power prediction data, the rated parameters, and the corrected real-time state parameters, to obtain the optimized control strategy of the new energy grid-connected hydrogen production system.

[0130] Step S404: Repeatedly update and iterate the optimized control strategy until the optimized control strategy converges to obtain the target control strategy of the new energy grid-connected hydrogen production system.

[0131] In this embodiment, the new energy grid-connected hydrogen production control strategy optimization equipment repeatedly updates and iterates the optimization control strategy until the optimized control strategy converges, thus obtaining the target control strategy for the new energy grid-connected hydrogen production system. The obtained target control strategy includes the target power output value, target load shedding amount, and target electrolyzer hydrogen production power output sequence of the new energy grid-connected hydrogen production system.

[0132] In one embodiment, the target power output, target load shedding, and target electrolyzer hydrogen production power output sequence obtained by the new energy grid-connected hydrogen production control strategy optimization equipment are all sequence values ​​for multiple future time periods. The new energy grid-connected hydrogen production control strategy optimization equipment only uses the target power output, target load shedding, and target electrolyzer hydrogen production power output values ​​for the current time period to control the operation of the new energy grid-connected hydrogen production system. Optionally, for the remaining power output, target load shedding, and target electrolyzer hydrogen production power output values ​​for other future time periods, the new energy grid-connected hydrogen production control strategy optimization equipment uses them as a reference to perform the next control strategy optimization operation. Optionally, the new energy grid-connected hydrogen production control strategy optimization equipment can directly discard the remaining power output, target load shedding, and target electrolyzer hydrogen production power output values ​​for other future time periods. It should be noted that the process steps of the new energy grid-connected hydrogen production control strategy optimization equipment performing the next control strategy optimization operation are exactly the same as the process steps in the above embodiment, and will not be repeated here.

[0133] The renewable energy grid-connected hydrogen production control strategy optimization device in this embodiment obtains the target control strategy for the renewable energy grid-connected hydrogen production system by solving a rolling optimization scheduling model constructed based on constraints, multi-objective functions, and state-space models under multiple hydrogen production scenarios. This target control strategy can reduce the curtailment rate of renewable energy power generation systems in microgrids, improve the grid-connected stability of renewable energy power generation systems, enhance the grid's ability to guarantee power supply to loads, and ensure the rationality of the system's short-term rolling scheduling plan and the stability of system operation. This contributes to improving the overall energy utilization rate and enhancing the economic benefits of the renewable energy internet.

[0134] refer to Figure 7 , Figure 7 This is a schematic diagram of the structure of the new energy grid-connected hydrogen production control strategy optimization device provided in this application. The new energy grid-connected hydrogen production control strategy optimization device includes:

[0135] Prediction module 10 is used to acquire historical data of new energy power generation and historical data of load demand power of the new energy grid-connected hydrogen production system, and to determine the predicted data of new energy power generation and predicted data of load demand power based on the historical data of new energy power generation, the historical data of load demand power and the prediction model.

[0136] The first determining module 20 is used to determine multiple hydrogen production scenarios based on the new energy power generation prediction data and the load demand power prediction data;

[0137] The second determining module 30 is used to determine the state-space model, multi-objective function and constraints based on the multiple hydrogen production scenarios and the power surplus or deficit of the grid-connected bus of the new energy grid-connected hydrogen production system.

[0138] Construction module 40 is used to construct a rolling optimization scheduling model based on the state space model, the multi-objective function, the constraints, and the preset rolling optimization scheduling strategy;

[0139] The solution module 50 is used to solve the rolling optimization scheduling model based on the new energy power generation forecast data, the load demand power forecast data, the rated parameters and real-time status parameters of the new energy grid-connected hydrogen production system, and obtain the target control strategy of the new energy grid-connected hydrogen production system.

[0140] This application also provides a computer device, exemplary of which includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to enable the computer device to perform the functions of the various modules in the above-described new energy grid-connected hydrogen production control strategy optimization method or the above-described new energy grid-connected hydrogen production control strategy optimization device.

[0141] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including at least one of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Network Processor (NP), Digital Signal Processor (DSP), Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0142] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory is used to store computer programs, and the processor can execute the computer programs accordingly after receiving execution instructions.

[0143] This application also provides a computer storage medium for storing the computer program used in the aforementioned computer device. The computer storage medium can be a readable storage medium, a non-volatile storage medium, or a volatile storage medium. For example, the computer storage medium may include, but is not limited to, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0144] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0145] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0146] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0147] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for optimizing control strategies for grid-connected hydrogen production from new energy sources, characterized in that, The method includes: Acquire historical data of renewable energy power generation and load demand power of renewable energy grid-connected hydrogen production system, and determine the predicted data of renewable energy power generation and load demand power based on the historical data of renewable energy power generation, the historical data of load demand power and the prediction model. Based on the new energy power generation forecast data and the load demand power forecast data, multiple hydrogen production scenarios are identified. Based on the various hydrogen production scenarios and the surplus or deficit of grid-connected bus power in the new energy grid-connected hydrogen production system, the state-space model, multi-objective function, and constraints are determined. A rolling optimization scheduling model is constructed based on the state space model, the multi-objective function, the constraints, and the preset rolling optimization scheduling strategy. Based on the predicted power generation data of the new energy source, the predicted power demand data of the load, and the rated parameters and real-time status parameters of the new energy grid-connected hydrogen production system, the rolling optimization scheduling model is solved to obtain the target control strategy of the new energy grid-connected hydrogen production system.

2. The method for optimizing the control strategy of new energy grid-connected hydrogen production according to claim 1, characterized in that, The new energy power generation forecast data includes predicted new energy power generation values ​​for multiple future time periods, and the load demand power forecast data includes predicted load demand power values ​​for multiple future time periods. The step of determining multiple hydrogen production scenarios based on the new energy power generation forecast data and the load demand power forecast data includes: Based on preset combination rules, the predicted load demand power values ​​for multiple future time periods are combined to determine multiple hydrogen production scenarios.

3. The method for optimizing the control strategy of new energy grid-connected hydrogen production according to claim 1, characterized in that, The step of determining the state-space model, multi-objective function, and constraints based on multiple hydrogen production scenarios and the grid-connected bus power surplus / deficit of the new energy grid-connected hydrogen production system includes: Constraints are determined based on multiple hydrogen production scenarios, and a multi-objective function is determined based on the constraints. Based on the power surplus or deficit of the grid-connected bus of the new energy grid-connected hydrogen production system, a dispatching instruction is determined, and a state-space model is determined based on the dispatching instruction.

4. The method for optimizing the control strategy of new energy grid-connected hydrogen production according to claim 3, characterized in that, The constraints include: system power balance constraints, new energy power generation capacity and load power consumption constraints, power absorption range constraints of the electrolyzer hydrogen production system, electrolyzer ramp rate constraints, and power fluctuation constraints of the system grid connection line transmission power.

5. The method for optimizing the control strategy of new energy grid-connected hydrogen production according to claim 3, characterized in that, The multi-objective functions include: the renewable energy curtailment function, the load shedding function under power grid deficit conditions, the function for minimizing power fluctuations in the system's grid-connected tie line transmission, and the function for maximizing hydrogen production from the electrolyzer.

6. The method for optimizing the control strategy of new energy grid-connected hydrogen production according to claim 3, characterized in that, The step of determining the state-space model according to the scheduling instruction includes: Based on the scheduling instructions, determine the state vector, control variables, and output vector of the new energy grid-connected hydrogen production system; The state-space model is determined based on the state vector, the control variables, and the output vector.

7. The method for optimizing the control strategy of new energy grid-connected hydrogen production according to claim 1, characterized in that, The step of solving the rolling optimization scheduling model based on the new energy power generation forecast data, the load demand power forecast data, and the rated parameters and real-time status parameters of the new energy grid-connected hydrogen production system to obtain the target control strategy of the new energy grid-connected hydrogen production system includes: Based on the predicted power generation data of the new energy source, the predicted power demand data of the load, the rated parameters and real-time status parameters of the new energy grid-connected hydrogen production system, the rolling optimization scheduling model is solved to obtain the initial control strategy of the new energy grid-connected hydrogen production system. The real-time status parameters of the new energy grid-connected hydrogen production system are corrected, and the new energy power generation prediction data and the load demand power prediction data are updated based on the corrected real-time status parameters. Based on the updated new energy power generation forecast data, the updated load demand power forecast data, the rated parameters, and the corrected real-time status parameters, the rolling optimization scheduling model is solved to obtain the optimized control strategy of the new energy grid-connected hydrogen production system. The optimized control strategy is repeatedly updated and iterated until it converges, thus obtaining the target control strategy for the new energy grid-connected hydrogen production system.

8. A device for optimizing control strategies for grid-connected hydrogen production from new energy sources, characterized in that, The new energy grid-connected hydrogen production control strategy optimization device includes: The prediction module is used to acquire historical data of new energy power generation and historical data of load demand power of the new energy grid-connected hydrogen production system, and to determine the predicted data of new energy power generation and predicted data of load demand power based on the historical data of new energy power generation, the historical data of load demand power and the prediction model. The first determining module is used to determine multiple hydrogen production scenarios based on the new energy power generation forecast data and the load demand power forecast data; The second determining module is used to determine the state-space model, multi-objective function and constraints based on the multiple hydrogen production scenarios and the grid-connected bus power surplus or deficit of the new energy grid-connected hydrogen production system. The construction module is used to construct a rolling optimization scheduling model based on the state space model, the multi-objective function, the constraints, and the preset rolling optimization scheduling strategy. The solution module is used to solve the rolling optimization scheduling model based on the new energy power generation forecast data, the load demand power forecast data, the rated parameters and real-time status parameters of the new energy grid-connected hydrogen production system, and obtain the target control strategy of the new energy grid-connected hydrogen production system.

9. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the new energy grid-connected hydrogen production control strategy optimization method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when run on a processor, executes the new energy grid-connected hydrogen production control strategy optimization method as described in any one of claims 1-7.

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