Multi-target scheduling optimization method for wind and light hydrogen storage and production comprehensive energy device

Abstract

The invention relates to a wind and light hydrogen storage and production energy device scheduling optimization method, which comprises the following steps: S100, acquiring operation constraint and operation data of each energy device, and identifying an operation boundary of each energy device to obtain system bearing capacity; the operation constraints comprise online and offline operation constraints; s200, obtaining the output constraint of the wind-light system and the wind-light prediction output data of the wind-light system, correcting the output constraint condition of the wind-light system based on the system bearing capacity of all the energy devices, and correcting the wind-light prediction output data based on the corrected output constraint condition of the wind-light system to obtain planned maximum wind-light output data; and S300, obtaining a future planning duration and an optimization target, matching a corresponding multi-target optimization plan from the preset library, determining the optimization configuration of the multi-target optimization plan, applying the optimization configuration to obtain a plan curve of each energy device, and sending the plan curve to the corresponding energy device. According to the method, the adaptability, reliability and comprehensive benefits under different working conditions are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of integrated energy device scheduling optimization or energy-saving technology, and in particular to a multi-objective scheduling optimization method for integrated wind, solar, energy storage and hydrogen production energy devices. Background Technology

[0002] With the rapid development of renewable energy, the proportion of clean energy sources such as wind and solar power in the energy structure is constantly increasing. However, these energy sources have inherent volatility and uncertainty, posing challenges to the stable operation of the power system. To improve the utilization efficiency of new energy sources and achieve synergistic optimization of "source, grid, load, and storage," a wind-solar-storage hydrogen production integrated energy device has emerged. This system uses wind and solar power to drive an electrolyzer to produce green hydrogen and leverages an energy storage system to balance energy fluctuations, thereby achieving efficient energy conversion and local consumption.

[0003] An existing method for optimizing the control of integrated energy devices includes the following steps: S1: Solving a mixed-integer linear programming model to obtain initial estimates of the preset values ​​of the equipment within the system; S2: Passing the obtained integrated energy device variables to the network optimization operation model, and estimating the integrated cost using stochastic mixed-integer linear programming; S3: Determining whether the preset values ​​of the equipment have converged. If so, outputting the specific preset value combination and energy network data for the optimization model operation; otherwise, returning to the optimization model and performing linear approximation calculations until the optimized operating state parameters of the integrated energy devices are obtained. This method can achieve optimized control of the integrated energy network while considering the uncertainties of different energy devices in the integrated energy network and the models of the power grid, heating grid, and natural gas grid. It is highly accurate, safe, and effective.

[0004] An existing integrated energy optimization control method for industrial parks belongs to the field of energy control technology. It includes five control methods: bathing water replenishment control, bathing heating circulation control, primary pipeline circulation heating control, heating pipeline circulation heating control, and heating pipeline water replenishment control. An electric boiler is used as a shared heat source for both the integrated bathing hot water supply system and the heating system. Through the installation of solenoid valves, water replenishment pumps, circulation pumps, and frequency converters, the system functions can be fully utilized and maximized. It also provides water replenishment for bathing and heating. When the liquid level or pressure falls below the limit values, an alarm will be triggered. This method effectively optimizes the integrated energy control system for industrial parks, saving equipment costs and conserving energy.

[0005] However, the existing methods mentioned above suffer from poor scenario adaptability, coarse constraint handling, insufficient multi-objective coordination, and weak dynamic adjustment capabilities. For example, the first method mentioned above does not fully consider the impact of uncertainties on the system during the optimization control process, which limits the accuracy and reliability of the optimization results, and lacks a mechanism for dynamically adjusting constraints. The second method mainly focuses on bath water replenishment control, bath heating cycle control, primary pipeline circulation heating control, heating pipeline circulation heating control, and heating pipeline water replenishment control, which are relatively limited in function, have a narrow scope of application, lack versatility and universality, and are difficult to promote and apply to a wider range of integrated energy device scenarios. Therefore, there is an urgent need for a multi-objective scheduling optimization method for integrated wind, solar, energy storage, and hydrogen production energy devices that can be applied to multiple scenarios and take into account multiple dimensions of objectives, so as to achieve coordinated optimization scheduling of multiple links such as wind and solar power output, electrolysis load, and energy storage charging and discharging. Summary of the Invention

[0006] (a) Technical problems to be solved

[0007] To address the technical problems of existing methods, such as poor scenario adaptability, coarse constraint handling, insufficient multi-objective coordination, and weak dynamic adjustment capabilities, this invention provides a multi-objective scheduling optimization method for integrated wind, solar, energy storage, and hydrogen production energy devices. This method combines the fluctuation characteristics of wind and solar power generation, the adjustment capability of energy storage systems, and hydrogen production technology, organically integrating each link. Through dynamic identification of system carrying capacity and matching of multi-objective optimization plans, it achieves energy balance and efficient allocation, and can perform multi-objective scheduling optimization for energy devices in multiple scenarios.

[0008] (II) Technical Solution

[0009] To achieve the above objectives, the main technical solutions adopted by the present invention include:

[0010] Firstly, a method for optimizing the scheduling of wind, solar, energy storage, and hydrogen production energy devices includes:

[0011] S100. Obtain the operating constraints and operating data of each energy device. For each energy device, identify its operating boundary based on its current operating constraints and current operating data to obtain the system carrying capacity.

[0012] The operational constraints include electrolyzer operational constraints, hydrogen storage and transportation constraints, energy storage operational constraints, energy balance constraints, and grid connection and supply constraints.

[0013] The operational data includes electrolyzer operational data, hydrogen storage and transportation operational data, energy storage operational data, and grid connection and off-grid operational data;

[0014] S200: Obtain the power output constraints of the wind and solar system and the predicted power output data of the wind and solar system for the future cycle; correct the power output constraints of the wind and solar system based on the system carrying capacity of all energy devices; correct the predicted power output data of the wind and solar system based on the corrected power output constraints; and obtain the planned maximum power output data of the wind and solar system.

[0015] S300: Obtain the future planning duration and optimization objectives; based on the optimization objectives, match the corresponding multi-objective optimization plan from the preset library and determine its optimization configuration.

[0016] The optimization configuration includes solving constraints, which include only running constraints, or a combination of running constraints and pre-set constraints. The optimization configuration is applied to perform iterative calculations based on the planned maximum wind and solar power output data to obtain the planned curves of each optimization object within each energy device in the future planning period, and the planned curves are sent to their corresponding energy devices.

[0017] This invention's method achieves efficient operation across multiple scenarios and objectives through hierarchical collaboration and multi-dimensional constraints. S100 accurately identifies the operational boundaries of each energy device by collecting operational constraints and data, laying a solid foundation for safe operation. S200 corrects the output and predicted data of the wind and solar system based on system carrying capacity, effectively solving the intermittent problem of wind and solar power generation and improving the efficiency of wind and solar energy consumption. S300 matches multi-objective optimization plans from a preset library based on multi-dimensional optimization objectives, generating planned curves for each energy device through iterative calculation, achieving dynamic optimization scheduling of the system. Specifically, the grid connection and off-grid operation constraints ensure that the power interaction between the energy device and the grid is within a preset range. When connected to the grid, bidirectional power flow is allowed; when disconnected from the grid, the power interaction is zero; and when connected to the grid in one direction, only grid connection is allowed. This allows the method to flexibly adapt to off-grid, grid-connected, and one-way grid-connected scenarios. By considering multiple objectives such as safety, economy, and efficiency, this method achieves full-process collaboration of wind, solar, energy storage, and hydrogen production energy devices, significantly improving adaptability, reliability, and overall benefits under different operating conditions.

[0018] Optionally, obtaining the predicted wind and solar power output data for future cycles of the wind and solar system in step S200 includes:

[0019] Obtain the medium-term wind and solar power forecast output data updated at the first frequency, the short-term wind and solar power forecast output data updated at the second frequency, and the ultra-short-term wind and solar power forecast output data updated at the third frequency at the current moment, wherein the first frequency is less than the second frequency, and the second frequency is less than the third frequency.

[0020] The medium-term, short-term, and ultra-short-term wind and solar power forecast output data are aligned according to the time axis. The ultra-short-term wind and solar power forecast output data is used to cover the short-term wind and solar power forecast output data of the same period, and the covered short-term wind and solar power forecast output data is used to cover the medium-term wind and solar power forecast processing data of the same period.

[0021] The data for the period of wind and solar power output that is consistent with the future planning duration is extracted from the medium-term wind and solar power output data after coverage.

[0022] Optionally, the grid connection and disconnection constraints are that the interaction power between the energy device and the power grid is within a preset power range.

[0023] Interactive power includes internet access power and offline access power, with internet access power being a positive value and offline access power being a negative value.

[0024] When connected to the grid, the lower limit of the preset power range is negative, and the upper limit is positive.

[0025] When the energy device is off-grid, the interaction power between the energy device and the grid is 0.

[0026] When connecting to a single network in a unidirectional manner that only allows internet access, the lower limit of the preset power range is 0.

[0027] Optionally, the optimization configuration may further include: optimization object, objective function, initial optimization value, solution method, and solution accuracy.

[0028] Optionally, S300 includes:

[0029] When grid connection is achieved and AGC is in operation, the optimization objective is to minimize hydrogen production cost. A corresponding multi-objective optimization plan is matched from a pre-set library, and the optimization objects are determined as electrolyzer power, energy storage power, hydrogen storage tank flow rate, and abandoned power. The objective function is the difference between hydrogen production revenue and hydrogen production cost divided by the total hydrogen production. Here, hydrogen production revenue, hydrogen production cost, and total hydrogen production are obtained based on the optimization objects. The solution constraint for the objective function is an operational constraint. Under this constraint, the SLSQP algorithm is used to iteratively calculate the objective function based on the planned maximum wind and solar power output data until the solution accuracy is met (0.1), thus obtaining the planned curves for each optimization object within the energy unit over the future planning period.

[0030] Optionally, S300 includes:

[0031] When grid connection is achieved and AGC (Automatic Generator Collection) is not in operation, priority is given to grid connection to achieve the lowest wind and solar curtailment rate and the highest overall economic benefits. However, the grid-connected power should not exceed 40% of the real-time hydrogen production power in the electrolyzer's operating data, and system stability is the optimization objective. A corresponding multi-objective optimization plan is matched from a pre-set library to determine the optimization targets: electrolyzer power, grid-connected power, energy storage power, hydrogen storage tank flow rate, and curtailed power.

[0032] The objective function is the sum of the revenue associated with the wind and solar curtailment rate and the total economic benefit, adjusted for weights. Both the revenue associated with the wind and solar curtailment rate and the total economic benefit are derived from the optimization object.

[0033] The constraints for solving the objective function include operational constraints and pre-set constraints. The pre-set constraints include the standard deviation constraint on the time-period variation of the total power of the electrolyzer, the standard deviation constraint on the time-period variation of the hydrogen flow rate of the hydrogen storage tank, and the constraint that the grid-connected power does not exceed 40% of the real-time hydrogen production power in the electrolyzer's operating data.

[0034] Under the aforementioned constraints, the SLSQP algorithm is used to iteratively calculate based on the planned maximum wind and solar power output data until the required accuracy is met, thereby obtaining the planned curves of each optimization object within the energy device over the future planning period.

[0035] Optionally, the revenue associated with the wind and solar curtailment rate is a function of the wind and solar curtailment rate multiplied by the total cumulative wind and solar power generation and the system's power generation cost.

[0036] The function for the abandonment rate of wind and solar power is 1 minus the kth power of the abandonment rate of wind and solar power.

[0037] The curtailment rate is equal to the sum of the solar curtailment rate and the wind curtailment rate, where,

[0038] The solar curtailment rate is the ratio of the total curtailed solar power within the planned future period to the theoretical total power generation of solar power. The wind curtailment rate is the ratio of the total curtailed wind power within the planned future period to the theoretical total power generation of wind power.

[0039] Optionally, the total economic benefit is the sum of hydrogen production revenue and grid connection revenue within the planned future period, minus operation and maintenance costs, grid connection costs, and operation and maintenance costs.

[0040] Optionally, a file is generated based on the planned maximum wind and solar power output data and transmitted to the wind and solar power plants as an adjustment suggestion for wind and solar operation.

[0041] In a second aspect, embodiments of the present invention provide an electronic device, including: a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program in the memory, specifically executing the steps of the scheduling optimization method for a wind, solar, energy storage and hydrogen production energy device described in any of the first aspects above.

[0042] (III) Beneficial Effects

[0043] The beneficial effects of this invention are as follows: This invention achieves efficient operation across multiple scenarios and objectives through hierarchical collaboration and multi-dimensional constraints. S100 accurately identifies the operating boundaries of each energy device by collecting operational constraints and data, laying a solid foundation for safe operation. S200 corrects the output and predicted data of the wind and solar system based on system carrying capacity, effectively solving the intermittent problem of wind and solar power generation and improving the efficiency of wind and solar energy consumption. S300 matches multi-objective optimization plans from a preset library based on multi-dimensional optimization objectives, and generates planned curves for each energy device through iterative calculation, achieving dynamic optimization scheduling of the system. Specifically, the grid connection and off-grid operation constraints ensure that the interaction power between the energy device and the grid is within a preset range. When connected to the grid, bidirectional power flow is allowed; when disconnected from the grid, the interaction power is zero; and when connected to the grid in one direction, only grid connection is allowed. This allows the method to flexibly adapt to three scenarios: off-grid, grid-connected, and one-way grid-connected. By considering multiple objectives such as safety, economy, and efficiency, this method achieves full-process collaboration of wind, solar, energy storage, and hydrogen production energy devices, significantly improving adaptability, reliability, and overall benefits under different operating conditions.

[0044] This invention presents a multi-objective scheduling and control method for integrated wind, solar, energy storage, and hydrogen production energy systems, which differs significantly from the two existing methods mentioned in the background section. Regarding application scenarios, the first method is suitable for grid-connected scenarios on the renewable energy generation side, and the second method is suitable for off-grid circulating water systems in industrial parks. This invention, however, can meet the needs of various integrated energy system scenarios, including off-grid, grid-connected, and unidirectional grid-connected systems. In terms of implementation, the first method deals with linear problems, the second method primarily addresses operational safety issues without involving optimization, while the solution process of this invention mainly involves nonlinear problems. In terms of functionality, the first method aims to improve the economic efficiency of grid-connected energy systems, the second method aims to improve the operational safety of energy systems, while this invention comprehensively considers various needs of off-grid integrated energy systems, including economic efficiency, system operational stability, and downstream load demand matching.

[0045] Compared with existing methods, the advantages and challenges of this invention are as follows: First, this invention can meet the scheduling optimization needs of multiple scenarios, including off-grid, grid-connected, and unidirectional grid-connected (allowing only internet access or only power outage); Second, this invention optimizes the operation plan of each device in the integrated energy device over a future planning period, rather than solving for the operation status at a single moment, making the solution more difficult but offering greater potential for improving operating efficiency; Third, this invention pre-sets a multi-objective optimization scheme, which can achieve stable solutions to multi-objective problems by adjusting the optimization configuration (including the constraints, optimization objects, objective functions, initial optimization values, solution methods, and solution accuracy) to meet different actual coupled optimization needs (covering economic efficiency, operational stability, system efficiency, hydrogen production costs, wind and solar power rates, etc.). Attached Figure Description

[0046] Figure 1A flowchart illustrating a method for optimizing the scheduling of a wind, solar, energy storage, and hydrogen production energy device, provided in an embodiment of the present invention.

[0047] Figure 2 This is a flowchart illustrating a method for optimizing the scheduling of a wind, solar, energy storage, and hydrogen production energy device, as provided in an embodiment of the present invention. Detailed Implementation

[0048] To better explain and facilitate understanding of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] Dispatch optimization requires comprehensive consideration of multiple factors, including wind and solar power generation forecasts, energy storage charging and discharging strategies, and electrolyzer start-up and shutdown planning. Optimization objectives generally include minimizing wind and solar curtailment rates, improving economic efficiency, and reducing hydrogen production costs. Constraints involve the operating characteristics of power generation and energy storage equipment, grid load demand, energy storage capacity limitations, the rate of change and load fluctuation limits of the hydrogen production system, and the need for stable hydrogen transportation. Therefore, a multi-objective dispatch control method is urgently needed to balance hydrogen production efficiency, electricity costs, equipment lifespan, load tracking capability, and other multi-dimensional objectives, achieving coordinated and optimized dispatch of wind and solar power output, electrolysis load, and energy storage charging and discharging.

[0050] This embodiment aims to maximize the utilization rate of renewable energy by comprehensively scheduling wind power, photovoltaic power, hydrogen production, and energy storage systems, solving the coordination and scheduling problem of wind power, photovoltaic power, and hydrogen energy storage systems, and improving the overall economy and stability of the system. The method of this embodiment is not only an important support for promoting the deep integration of renewable energy, but also a key technological path for building a safe, efficient, and low-carbon energy system.

[0051] Specifically, this embodiment combines the fluctuation characteristics of wind and solar power generation, the regulation capability of energy storage systems, and hydrogen production technology, organically integrating each link to achieve balanced energy distribution and efficient utilization through intelligent scheduling.

[0052] This embodiment applies to integrated wind, solar, energy storage, and hydrogen production energy systems, including off-grid, grid-connected, and single-phase grid-connected systems. It uses real-time splicing of ultra-short-term, short-term, and medium-term wind and solar power output forecasts, combined with wind and solar system output constraints, to correct and output planned maximum wind and solar power output data for future periods (e.g., 4h, 12h, 24h). Based on this planned maximum output data, under constraints such as electrolyzer operation, energy storage operation, grid connection and off-grid operation, and hydrogen storage and transportation, multi-objective scheduling optimization (e.g., minimum wind and solar curtailment rate, minimum system operating cost, minimum hydrogen production cost, minimum system maintenance cost, and operational stability) is performed to solve for the scheduling strategy of the energy system for future planned durations (corresponding to 4h, 12h, 24h, etc.). After the solution is completed, the planned curves for each device are output, and data files are generated, providing data support for subsequent device operation such as energy storage scheduling and electrolyzer rotation.

[0053] As can be understood, off-grid refers to energy devices that operate completely independently of the public power grid, typically relying on renewable energy sources (such as solar and wind power) and energy storage devices to achieve autonomous power supply to loads. Grid-connected refers to energy devices whose power generation system is connected to the public power grid, enabling bidirectional flow of electricity; that is, energy devices that draw power from the grid when demand is insufficient and send surplus power back to the grid when generation is excessive. Unidirectional grid connection refers to energy devices whose power generation system is connected to the grid, but where electricity is only allowed to flow in one direction (usually only sending power to the grid, not drawing power from it). Operational constraints refer to the operational parameter constraints that equipment must meet during operation, including but not limited to maximum power limits, minimum power limits, maximum energy flow limits, power change rate limits, start-stop frequency limits, power fluctuation limits, power fluctuation rate limits, and fluctuation power operating duration limits.

[0054] The scheduling optimization method for wind, solar, energy storage and hydrogen production energy devices in this embodiment will be described in detail below with reference to the accompanying drawings. It should be noted that the execution subject of this embodiment is a remote or cloud server, which can perform scheduling optimization for multiple energy devices based on multiple objectives. Of course, if only a single energy device is targeted for scheduling optimization, the execution subject of this embodiment can also be the local hardware of the energy device (such as an industrial control computer).

[0055] Example 1

[0056] See Figure 1 A method for optimizing the scheduling of wind, solar, energy storage, and hydrogen production energy devices, comprising:

[0057] S100. Obtain the operating constraints and operating data of each energy device. For each energy device, identify its operating boundary based on its current operating constraints and current operating data to obtain the system carrying capacity.

[0058] The operational constraints include electrolyzer operational constraints, hydrogen storage and transportation constraints, energy storage operational constraints, energy balance constraints, and grid connection and supply constraints.

[0059] It should be noted that the core components of the integrated wind, solar, storage and hydrogen production energy device in this embodiment include a wind and solar system (including a photovoltaic power generation system and a wind power generation system), an electrolyzer, a hydrogen storage tank, and energy storage equipment.

[0060] The following examples illustrate the operational constraints.

[0061] 1. Electrolytic cell operating constraints

[0062] In the construction of the electrolyzer model for the integrated wind, solar, energy storage and hydrogen production energy device, in order to clearly distinguish the characteristics of different configuration types of electrolyzers, this embodiment takes "one-to-one electrolyzer" and "one-to-four electrolyzer" as examples to elaborate on the constraints of the electrolyzer.

[0063] (1) For a single-unit electrolyzer (i.e., only one set of electrolyzers, which includes only one electrolyzer) in a wind-solar-storage-hydrogen production integrated energy device, the characteristic model of its input hydrogen production power and hydrogen production can be:

[0064]

[0065] Electrolyzer operating constraints can be:

[0066]

[0067] Among them, P H2，1 (t) represents the hydrogen production input power of a one-to-one electrolyzer at time t, in MW; Q H2，1 (t) represents the hydrogen production of a one-to-one electrolyzer at time t, in Nm³. 3 / h; This is the minimum input power for a one-to-one electrolytic cell, expressed in MW. R is the maximum input power of a one-to-one electrolytic cell, measured in MW; adjust,1 (t) represents the power regulation rate of a one-to-one electrolyzer at time t, in MW / s; The maximum power adjustment rate for a one-to-one electrolytic cell is expressed in MW / s. This represents the maximum power reduction rate for a one-to-one electrolyzer, expressed in MW / s.

[0068] (2) For a wind-solar-storage-hydrogen production integrated energy device with 3 sets of one-to-four electrolyzers (i.e., 3 sets of electrolyzers, each set including four electrolyzers), the characteristic model of the input hydrogen production power and hydrogen production of the electrolyzers can be:

[0069]

[0070] When n=1, the operating constraints of its electrolytic cell are the same as those of the electrolytic cell operating constraints of the above group of one-to-one electrolytic cells.

[0071] For m = 2, 3, 4, the operating constraints of the electrolytic cell can be:

[0072]

[0073] Where m and n are the cell numbers of each electrolytic cell group; P H2,n / m (t) represents the hydrogen production input power of a pair of four electrolyzers at time t, in MW; P H2,n,start P represents the starting threshold power of the electrolyzer when n=1; H2,m，start The starting threshold power of the electrolytic cell when m = 2, 3, 4; Q H2，i (t) represents the hydrogen production of a pair of four electrolyzers at time t, in MW, where i = 2, 3, 4 represent the number of electrolyzer units; This is the minimum input power for a pair of four electrolytic cells; This is the maximum input power of a pair of four electrolytic cells; R adjust,m (t) represents the power regulation rate of a pair of four electrolyzers at time t, in MW / s; The maximum power adjustment rate for a pair of four electrolytic cells is expressed in MW / s. This represents the maximum power reduction regulation rate for a pair of four electrolyzers, expressed in MW / s.

[0074] The total hydrogen production capacity of the two types of electrolyzers above can be expressed as:

[0075] Q H2，total (t)=f(P H2,total (t)).

[0076] The operational constraints of an electrolytic cell may also include total power regulation constraints, specifically:

[0077]

[0078] The operational constraints of an electrolyzer can also include constraints on the rate of change of total power, specifically:

[0079]

[0080] Among them, Q H2，total (t) represents the total hydrogen production of the energy unit at time t, in MW; P H2，total (t) represents the total hydrogen production power of the energy device at time t, in MW; The minimum input power of an energy device, measured in MW; R is the maximum input power of the energy device, measured in MW; adjust,total(t) represents the power regulation rate of the energy device at time t, in MW / s; The maximum power adjustment rate of the energy device, expressed in MW / s; The maximum power reduction regulation rate of the energy device, expressed in MW / s.

[0081] The start-up and stop times of the electrolytic cell are:

[0082]

[0083] Among them, T start T represents the start-up time of the electrolytic cell, indicating the time required from the start-up of the electrolytic cell to its full operational state, measured in seconds (s). em T represents the initial start-up temperature of the electrolytic cell, expressed in °C. pressur This is the initial pressure for starting up the electrolytic cell, expressed in MPa. The start-up rate of the electrolytic cell is expressed in MW / s. The starting power of the electrolytic cell is expressed in MW; T end The stopping time of the electrolytic cell is expressed in seconds (s). The descent rate of the electrolytic cell is expressed in MW / s. The power at the end of the electrolytic cell's operation is expressed in MW.

[0084] (2) Constraints on hydrogen storage and transportation

[0085] The hydrogen storage tank model in a wind-solar-storage-hydrogen production integrated energy device can be:

[0086] Q hs (t)=Q hs (t-1)+(F H2,in (t)-F H2,out (t))·Δt

[0087]

[0088] The constraints on hydrogen storage and transportation can be:

[0089]

[0090] In the formula: Q hs (t) represents the hydrogen storage capacity of the hydrogen storage tank at time t, in Nm³. 3 Q hs (t-1) represents the amount of hydrogen stored in the hydrogen storage tank at time t-1, in Nm³. 3 ;F H2,in (t) represents the hydrogen production flow rate at time t, in Nm³. 3 / h;F H2,out (t) represents the hydrogen flow rate at time t, in Nm³.3 / h; Δt is the time step, i.e., the time interval from time t to t-1, in hours; SOC hs (t) represents the percentage (%) of the current hydrogen volume in the hydrogen storage tank relative to its maximum capacity; This refers to the maximum hydrogen storage capacity of the hydrogen storage tank, expressed in Nm³. 3 ; This represents the maximum permissible hydrogen production flow rate, in Nm³. 3 / h; This represents the minimum permissible hydrogen production flow rate, in Nm³. 3 / h; This represents the maximum permissible hydrogen flow rate, in Nm³. 3 / h; This represents the minimum allowable hydrogen flow rate, in Nm³. 3 / h;P pressure_hs (t) represents the pressure of the hydrogen storage tank at time t, in MPa; This is the minimum safe operating pressure allowed for the hydrogen storage tank, expressed in MPa. This is the maximum safe operating pressure allowed for the hydrogen storage tank, expressed in MPa.

[0091] (3) Energy storage operation constraints

[0092] The charging and discharging power model of the energy storage equipment in the integrated wind, solar, energy storage and hydrogen production energy device can be:

[0093]

[0094] In the formula: P ES,in (t) represents the actual charging power of the energy storage device at time t, in MW; η represents the rated input power of the energy storage device at time t, in MW; in,ES The charging efficiency of energy storage devices; P ES,out (t) represents the actual discharge power of the energy storage device at time t, in MW; η is the rated output power of the energy storage device at time t, in MW; out,ES R represents the discharge efficiency of the energy storage device. ES,out (t) represents the rate of change of the energy storage device's discharge power, in MW / s; R ES,in (t) represents the rate of change of the charging power of the energy storage device, in MW / s.

[0095] The capacity model for energy storage devices can be:

[0096] E ES (t)=E ES (t-1)+(P ES,in (t)-P ES,out (t))·Δt

[0097]

[0098] In the formula, E ES (t) represents the energy storage capacity of the energy storage device at time t, in MWh; E ES (t-1) represents the energy storage capacity of the energy storage device at time t-1, in MWh; State of Charge (SOC) is the maximum energy storage capacity of an energy storage device when fully charged, measured in MWh. ES (t) represents the charging state of the energy storage device at time t, indicating the percentage (%) of the current stored energy relative to the total capacity.

[0099] Energy storage operation constraints can be:

[0100]

[0101] P ES,in (t)·P ES,out (t)=0

[0102]

[0103] in, This refers to the maximum charging power that the energy storage device can withstand, measured in MW. This refers to the maximum discharge power that the energy storage device can withstand, measured in MW. The minimum rate of change of power for charging energy storage devices, expressed in MW / s; This is the minimum power change rate of the energy storage device, expressed in MW / s. The maximum rate of change of charging power for energy storage devices, expressed in MW / s; The rate of change of the maximum discharge power of the energy storage device, expressed in MW / s; The minimum permissible SOC (%) for energy storage devices; The maximum allowable SOC (%) for energy storage devices.

[0104] (4) Energy balance constraint

[0105] The energy balance constraint for a wind-solar-storage-hydrogen production integrated energy plant can be:

[0106] P PV (t)+P WD (t)+P ES (t)=P H2 (t)+P grid (t)+P oth (t).

[0107] In the formula, P PV(t) represents the output power of the photovoltaic power generation system at time t, in MW; P WD (t) represents the output power of the wind power system at time t, in MW; P ES (t) represents the charging and discharging power of the energy storage device at time t, with a positive value indicating that the energy storage device is discharging and a negative value indicating that the energy storage device is charging; P H2 (t) represents the power consumed by the hydrogen production system (mainly an electrolyzer) at time t, in MW; P grid ,(t) represents the power exchange of the power grid at time t, with positive values ​​indicating the purchase of electricity from the grid and negative values ​​indicating the transmission of electricity to the grid, in MW; P oth (t) represents the power consumption of other load equipment in the energy unit, in MW.

[0108] (5) Constraints on network operation

[0109] The grid connection and disconnection operation constraints are that the interaction power between the energy device and the grid is within a preset power range. The interaction power includes grid connection power and grid disconnection power. Grid connection power is a positive value, and grid disconnection power is a negative value. When connected to the grid, the lower limit of the preset power range is negative and the upper limit is positive. When disconnected from the grid, the interaction power between the energy device and the grid is 0. When connected to the grid in a unidirectional grid connection where only grid connection is allowed, the lower limit of the preset power range is 0.

[0110] Specifically, the constraints for network uplink and downlink operation can be:

[0111]

[0112] in: The interaction power between the energy device and the power grid at time t (positive for grid connection, negative for grid disconnection), in MW; The unit for the maximum power interaction between energy devices and the power grid is MW; This is the minimum power required for an energy device to interact with the power grid, measured in MW.

[0113] The operational data includes electrolyzer operational data, hydrogen storage and transportation operational data, energy storage operational data, and grid connection and off-grid operational data.

[0114] This embodiment starts from the scheduling and optimization needs of energy devices, and first obtains pre-collected multi-source data, including but not limited to the operation data mentioned above.

[0115] Step S100 accurately identifies the boundary conditions for stable operation of each energy device by acquiring its operational constraints and data in real time. Then, through the synergistic coupling of these device boundaries, it comprehensively assesses the system's operational limits under current conditions, including energy interaction, hydrogen production, and hydrogen storage, thus obtaining the system's carrying capacity. This step provides a foundation for real-time scheduling, ensuring that each device operates within safe constraints. Furthermore, by quantifying the system's carrying capacity, it helps optimize energy allocation strategies, achieving efficient matching between wind and solar power consumption, hydrogen production and storage, and energy demand, supporting the stable, economical, and efficient operation of the integrated energy system. Step S100 is the core guarantee of scheduling reliability, preventing equipment from operating beyond its limits and improving the device's adaptability to renewable energy fluctuations.

[0116] S200: Obtain the power output constraints of the wind and solar system and the predicted power output data of the wind and solar system for future cycles. Correct the power output constraints of the wind and solar system based on the system carrying capacity of all energy devices. Correct the predicted power output data of the wind and solar system based on the corrected power output constraints of the wind and solar system to obtain the planned maximum power output data of the wind and solar system.

[0117] Step S200 first obtains the output constraints of the wind and solar power systems and the predicted output data for the future period, with the predicted output data generally coming from wind and solar power plants. Then, combining this with the previously obtained system carrying capacity of all energy devices, the output constraints of the wind and solar power systems are corrected. For example, if the energy storage capacity is insufficient or the hydrogen production power of the electrolyzer is limited, the maximum output of the wind and solar power systems needs to be lowered to avoid power surplus that cannot be absorbed. Finally, based on the corrected output constraints, the predicted output data is adjusted, eliminating predicted values ​​that exceed the system carrying capacity, to obtain the planned maximum wind and solar power output data. This step, by closely integrating the output constraints of the wind and solar power systems with the overall carrying capacity of the energy devices, effectively avoids problems such as low operating efficiency and equipment wear caused by overcapacity or undercapacity of wind and solar power generation. It achieves synergistic optimization of wind and solar power generation with hydrogen production and energy storage, improving the overall stability and economy of the system operation, and providing a reliable planning basis for subsequent energy dispatch. Meanwhile, this step calibrates the original wind and solar power output data through system load capacity feedback, providing a high-precision wind and solar power output benchmark for subsequent multi-objective solutions, and improving the reliability and feasibility of wind and solar power output plans.

[0118] For example, the wind power constraints in wind power constraints can be:

[0119]

[0120] in, Let be the rate of change of wind power generation at time t; This represents the maximum rate of change in wind power generation, expressed in MW / s. P represents the maximum rate of increase in wind power generation, expressed in MW / s.WD Let t represent the wind power generation capacity at time t, in MW. This represents the minimum power output of wind power generation (typically 0 MW). This is the maximum output power of wind power generation, also known as the rated power of wind power, measured in MW.

[0121] The photovoltaic power generation constraint model in the wind power generation constraint can be:

[0122]

[0123] In the formula: Let be the rate of change of photovoltaic power generation at time t; This represents the maximum rate of change in photovoltaic power generation, expressed in MW / s. P represents the maximum rate of increase in photovoltaic power generation, expressed in MW / s. PV Let t be the photovoltaic power generation at time t, in MW; This represents the minimum power output of photovoltaic power generation (typically 0 MW). This represents the maximum power output of photovoltaic power generation, i.e., the rated power of the photovoltaic system, measured in MW.

[0124] Specifically, the acquisition of wind and solar power forecasting output data for future cycles in S200 includes: acquiring medium-term wind and solar power forecasting output data updated at a first frequency, short-term wind and solar power forecasting output data updated at a second frequency, and ultra-short-term wind and solar power forecasting output data updated at a third frequency, wherein the first frequency is less than the second frequency, and the second frequency is less than the third frequency; aligning the medium-term, short-term, and ultra-short-term wind and solar power forecasting output data according to the time axis, covering the short-term wind and solar power forecasting output data of the same period with the ultra-short-term wind and solar power forecasting output data, and covering the medium-term wind and solar power forecasting data of the same period with the covered short-term wind and solar power forecasting output data; and extracting time period data consistent with the future planning duration from the covered medium-term wind and solar power forecasting output data as wind and solar power forecasting output data.

[0125] When acquiring wind and solar power output forecasts for future cycles, accurate predictions are achieved through dynamic fusion and stitching of data across multiple time scales and update frequencies, ensuring the real-time nature and effectiveness of the forecast data. Specifically, medium-term wind and solar power output forecasts are updated at a lower first frequency (e.g., once a week, updating data for the next week each time), predicting power generation trends for the next week based on weather forecasts and historical power generation patterns. Short-term wind and solar power output forecasts are updated at a medium second frequency (e.g., once a day, updating data for one to three days each time), combining more detailed weather changes and equipment status to predict output for the next few days. Ultra-short-term wind and solar power output forecasts are acquired at a higher third frequency (e.g., once every 15 minutes, updating data for one day each time), capturing recent fluctuations in wind and solar power in real time. The three types of data are aligned along a timeline. The high real-time performance of ultra-short-term wind and solar power output forecasts is used to overlay concurrent short-term wind and solar power output forecasts, correcting deviations in the ultra-short-term forecasts. Then, the corrected ultra-short-term forecasts are used to overlay concurrent medium-term forecasts, optimizing forecast accuracy layer by layer. Finally, data from the overlaid ultra-short-term forecasts, aligning with future planning durations, are extracted as the final wind and solar power output forecasts. By integrating data at different time scales and accuracies, both the trends of medium- and long-term planning and the details of real-time fluctuations are considered, significantly improving the accuracy of wind and solar forecasts. This provides precise data support for correcting wind and solar system output constraints, optimizing system capacity matching, reducing the impact of wind and solar intermittency on system operation, and improving the reliability and economy of integrated energy system scheduling.

[0126] Files are generated based on the planned maximum output data of wind and solar power plants and transmitted to them as adjustment suggestions for wind and solar power operation.

[0127] Based on the revised planned maximum wind and solar power output data, a file is generated containing suggested output values ​​for each time period, adjustment strategies, and constraints. This file covers the optimal output curves of the wind and solar system over future cycles. After this file is transmitted to wind and solar power plants, the plants can adjust the operating status of wind and solar power accordingly, such as through wind turbine pitch control and photovoltaic inverter power regulation, to match the wind and solar power output with the overall system capacity. This process achieves coordinated scheduling of wind and solar power generation with energy storage and hydrogen production, avoiding power curtailment or equipment overload caused by wind and solar power output exceeding the system's absorption capacity. It also maximizes the utilization of wind and solar energy within the system's capacity, improves green hydrogen production efficiency, ensures the stable and efficient operation of integrated energy devices, and provides key execution basis for the optimal energy allocation of the entire system.

[0128] S300. Obtain the future planning duration and optimization objectives. Based on the optimization objectives, match the corresponding multi-objective optimization plan from the preset library and determine its optimization configuration. The optimization configuration includes solving constraints, which may include only operational constraints or a combination of operational constraints and plan-set constraints. Apply the optimization configuration and perform iterative calculations based on the planned maximum wind and solar power output data to obtain the planned curves for each optimization object within each energy device during the future planning duration. Send the planned curves to their corresponding energy devices. Based on the received planned curves, the energy devices adjust their own operating states to achieve optimal control of the energy system.

[0129] After obtaining the planned curves for each device, they can be saved and archived. The optimization results can be output and displayed in a visual manner, thereby realizing intelligent and efficient scheduling and control of the integrated energy device for wind, solar, energy storage and hydrogen production, and providing decision support for energy synergy and low-carbon operation.

[0130] Specifically, the optimization configuration also includes: the optimization object, the objective function, the initial value for optimization, the solution method, and the solution accuracy.

[0131] In step S300, the future planning duration (e.g., 4h, 12h, or 24h) and optimization objectives (e.g., minimum curtailment rate of solar and wind power, lowest system operating cost, lowest hydrogen production cost, lowest system maintenance cost, and system stability) are first defined. Then, a matching multi-objective optimization plan is retrieved from a preset library based on these objectives. After determining the multi-objective optimization plan, its corresponding optimization configuration is defined. The constraint solving is divided into two categories: one considers only operational constraints; the other combines operational constraints and plan-set constraints. Based on this optimization configuration, the maximum planned output data of wind and solar power is used as input to iteratively calculate the optimization objects. By repeatedly adjusting various parameters, the planned curves of each optimization object within each energy device are obtained within the future planning duration. Finally, these planned curves are sent to the corresponding energy devices as instructions for their operation and control, realizing refined and customized control of integrated energy devices. This ensures that each energy device operates within safety constraints and allows for flexible adjustment of operating strategies based on different optimization objectives, improving overall operating efficiency and economic benefits, and providing core decision support for the intelligent scheduling of wind, solar, energy storage, and hydrogen production devices.

[0132] This embodiment also provides an electronic device, including: a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program in the memory, specifically performing the steps of the above-described method for scheduling optimization of a wind, solar, energy storage and hydrogen production energy device.

[0133] This embodiment's method achieves efficient operation across multiple scenarios and objectives through hierarchical collaboration and multi-dimensional constraints. S100 accurately identifies the operational boundaries of each energy device by collecting operational constraints and data, laying a solid foundation for safe operation. S200 corrects the output and predicted data of the wind and solar system based on system carrying capacity, effectively solving the intermittent problem of wind and solar power generation and improving the efficiency of wind and solar energy consumption. S300 matches multi-objective optimization plans from a preset library based on multi-dimensional optimization objectives, generating planned curves for each energy device through iterative calculation, achieving dynamic optimization scheduling of the system. Specifically, the grid connection and off-grid operation constraints ensure that the power interaction between the energy device and the grid is within a preset range. When connected to the grid, bidirectional power flow is allowed; when disconnected from the grid, the power interaction is zero; and when connected to the grid in a unidirectional manner, only grid connection is allowed. This allows the method to flexibly adapt to off-grid, grid-connected, and unidirectional grid-connected scenarios. By considering multiple objectives such as safety, economy, and efficiency, this method achieves full-process collaboration of wind, solar, energy storage, and hydrogen production energy devices, significantly improving adaptability, reliability, and overall benefits under different operating conditions.

[0134] See Figure 2 Before optimizing the scheduling, this embodiment first determines the system carrying capacity of the energy devices, calculates the planned maximum wind and solar power output based on the predicted wind and solar power output, and returns the planned maximum wind and solar power output data to the wind and solar power plants, thereby improving the reliability of integrated energy device scheduling.

[0135] This embodiment, by setting up a multi-objective optimization plan, allows for flexible switching of optimization configurations to achieve scheduling optimization of integrated energy devices under different scenarios, with different optimization objectives, and different planning durations. Furthermore, during the optimization process, the scheduling characteristics of the energy device itself are fully considered, comprehensively taking into account the impact of factors such as the electrolyzer start-up and shutdown process, start-up and shutdown time, and safe operating range on the planned power output.

[0136] Example 2

[0137] To better understand the above embodiment 1, this embodiment will further explain step S300 in conjunction with a specific application scenario.

[0138] S300 includes: when connected to the grid and Automatic Generation Control (AGC) is in operation, with the lowest hydrogen production cost as the optimization objective, matching the corresponding multi-objective optimization plan from a preset library, determining the optimization objects as electrolyzer power, energy storage power, hydrogen storage tank flow rate, and abandoned power, and the objective function is the difference between hydrogen production revenue and hydrogen production cost divided by the total hydrogen production, wherein hydrogen production revenue, hydrogen production cost, and total hydrogen production are obtained based on the optimization objects, and the solution constraint of the objective function is the operational constraint. Under the solution constraint, the Sequential Least Squares Programming (SLSQP) algorithm is used to iteratively calculate the objective function based on the planned maximum wind and solar power output data until the solution accuracy is satisfied to 0.1, and the planned curves of each optimization object in the energy device within the future planning time are obtained.

[0139] In a grid-connected scenario with AGC (Automatic Gaming Capacitor) in operation, the optimization objective is to minimize hydrogen production cost. A corresponding multi-objective optimization plan is matched from a pre-defined library. In this scenario, there is only one optimization objective, and the portion connected to the grid does not require optimization due to AGC operation. Therefore, the overall optimization difficulty of the corresponding multi-objective optimization plan is low. The solution method is the SLSQP algorithm, with a solution precision set to 0.1. The objective function is S1 = (Hydrogen Production Revenue - Hydrogen Production Cost) / Total Hydrogen Production. Hydrogen production revenue is calculated based on hydrogen production and market price, while hydrogen production cost includes electrolyzer power consumption costs, equipment operation and maintenance expenses, etc. The total hydrogen production is obtained in real-time from the electrolyzer power.

[0140] This embodiment achieves the dual objectives of meeting grid dispatch requirements and controlling hydrogen production costs. While satisfying grid-connected power regulation needs, it maximizes the utilization rate of wind and solar energy and reduces the electricity consumption cost of hydrogen production. On the other hand, relying on multivariate coupling optimization and high-precision solutions, it ensures the coordinated and unified effect of energy storage equipment on the smoothing effect of wind and solar fluctuations, hydrogen storage tank on the buffering effect of hydrogen supply and demand, and efficient operation of electrolyzer. This enables the energy device to maintain economical and efficient operation in a dynamic grid environment, significantly improving the grid-connected adaptability and cost control capability of the integrated wind, solar, energy storage and hydrogen production energy system.

[0141] This embodiment uses the SLSQP algorithm from the SciPy library as the solver. SLSQP is an algorithm for nonlinear optimization that provides the ability to handle constrained optimization problems. It belongs to the `minimize` method in the `scipy.optimize` module and is suitable for continuous variable optimization problems with equality and inequality constraints. The SLSQP algorithm is suitable for nonlinear constrained optimization (supporting both equality constraints `eq` and inequality constraints `ineq`), optimizes based on gradient information, and is suitable for solving constrained optimization problems such as engineering optimization and economic modeling.

[0142] Example 3

[0143] To better understand the above embodiment 1, this embodiment will further explain step S300 in conjunction with another specific application scenario.

[0144] S300 includes: when connected to the grid and AGC is not in operation, prioritizing grid connection with the lowest wind and solar curtailment rate and the highest total economic benefit, but ensuring that the grid-connected power does not exceed 40% of the real-time hydrogen production power in the electrolyzer's operating data, and maintaining system stability as the optimization objective. A corresponding multi-objective optimization plan is matched from a pre-set library, determining the optimization objects as electrolyzer power, grid-connected power, energy storage power, hydrogen storage tank flow rate, and curtailed power. The objective function is the sum of the wind and solar curtailment rate-related revenue and the total economic benefit, after weight adjustment. Both the wind and solar curtailment rate-related revenue and the total economic benefit are derived from the optimization objects. The constraints for solving the objective function include operational constraints and plan setting constraints. The plan setting constraints include the standard deviation constraint of the total power variation of the electrolyzer over time, the standard deviation constraint of the hydrogen flow rate variation of the hydrogen storage tank over time, and ensuring that the grid-connected power does not exceed 40% of the real-time hydrogen production power in the electrolyzer's operating data. Under these constraints, the SLSQP algorithm is used to iteratively calculate based on the planned maximum wind and solar power output data until the solution accuracy is met, obtaining the planned curves for each optimization object within the energy device over the future planning period.

[0145] This scenario involves a multi-objective optimization problem, which is broken down according to the corresponding multi-objective optimization plan. In this scenario, different weights are assigned to the objectives of minimizing wind and solar curtailment rate and maximizing total economic benefit based on actual needs, forming an objective function to achieve a globally optimal scheduling strategy. The objective function consists of a weighted average of the revenue associated with wind and solar curtailment rate and total economic benefit.

[0146] The revenue associated with the curtailment rate is calculated by multiplying the curtailment rate function by the total cumulative wind and solar power generation and the system's power generation cost. The curtailment rate function is 1 minus the curtailment rate raised to the power of k. The curtailment rate is equal to the sum of the solar curtailment rate and the wind curtailment rate. The solar curtailment rate is the ratio of the total curtailed solar power to the theoretical total solar power generation within the future planned period, and the wind curtailment rate is the ratio of the total curtailed wind power to the theoretical total wind power generation within the future planned period.

[0147] The total economic benefit is the sum of hydrogen production revenue and grid connection revenue over the planned future period, minus operation and maintenance costs, grid disconnection costs, and operation and maintenance costs. Since this embodiment does not involve grid disconnection, grid disconnection costs are not considered and are assumed to be 0.

[0148] Therefore, the objective function is S2 = f(curtailment rate of wind and solar power) × total cumulative wind and solar power generation × system power generation cost × a + total economic benefit × b, where a and b are weights.

[0149] f(curtailment rate of wind and solar power) = 1 - x k x represents the rate of wind and solar power abandonment.

[0150] One of the optimization objectives in this embodiment is to minimize the wind and solar curtailment rate in order to improve the utilization efficiency of wind and solar resources. The wind and solar curtailment rate can be obtained by the following expression:

[0151]

[0152] Where f1(x) is the wind and solar curtailment rate; P pv (t) represents the actual photovoltaic power utilized at time t, in MW; The theoretical maximum power output of photovoltaic power generation at time t, in MW; P wd (t) Actual wind power generation utilization at time t; The theoretical maximum generating capacity of wind power at time t, in MW; T is the time interval of the entire optimization process from time t=0 to time t=T, that is, the time interval of the future planning period.

[0153] Another optimization objective in this embodiment is to maximize total economic benefit. This is achieved by subtracting operation and maintenance costs, grid connection costs, and water fees from the sum of hydrogen production revenue and grid connection revenue, thus obtaining the net benefit. The total economic benefit can be obtained using the following expression:

[0154]

[0155] R H2 (t)=P H2,actual (t)×R H / P ×p H2

[0156] R grid (t)=P grid (t)×p grid

[0157]

[0158] C water (t)=Q water (t)×p water .

[0159]

[0160] Where f2(x) represents the total economic benefit; R H2 (t) represents the hydrogen production revenue at time t, in yuan / h; P H2,actual Hydrogen production capacity (MW) for each time period, R H / P The volume of hydrogen produced per megawatt-hour of electrical energy, expressed in Nm³. 3 / MWh;p H2 The unit price of hydrogen is yuan / Nm³. 3 ;Rgrid (t) represents internet access revenue, in yuan / hour; P grid (t) represents the power supplied to the grid for each time period, in MW; p grid The electricity price for grid connection is generally based on the standard unit price, expressed in yuan / MWh; C tol Total operating and maintenance cost, expressed in yuan / hour; The average annual depreciation on the power generation side is expressed in yuan / year. The average annual operation and maintenance cost for the power generation side is expressed in yuan / year. The average annual depreciation and financials for hydrogen production are expressed in yuan per year. The average annual operation and maintenance cost for hydrogen production is expressed in yuan / year; C tol , These four variable values ​​are set via the manual interface; C water The cost of water consumed in the hydrogen production process, expressed in yuan / h; Q water (t) represents the amount of water consumed within time t during the hydrogen production process, in units of t / h; p water The price per ton of water is expressed in yuan / ton; F H2 (t) The amount of hydrogen produced per hour, in Nm³. 3 / h;C elec (t) represents the total cost of electricity supplied to the grid, in yuan / h; T peak The peak time range (e.g., 8:00–12:00 and 18:00–22:00); P elec,peak (t) represents the power consumption during peak hours, in MW; p peak The electricity price during peak hours is expressed in yuan / MWh; T flat This refers to the usual time range (e.g., 12:00–18:00); P elec,flat (t) represents the power consumption during normal periods, in MW; p flat This refers to the electricity price during normal periods, expressed in yuan / MWh; T valley For the time range of the valley period (e.g., 22:00–8:00), P elec,valley (t) represents the power consumption during off-peak hours, in MW; p valley This is the electricity price during normal periods, expressed in yuan / MWh.

[0161] If the optimization objective in this embodiment includes the minimum hydrogen production cost, a weighted term for hydrogen production cost can be added to the objective function.

[0162] The cost of hydrogen production can be obtained using the following expression:

[0163]

[0164]

[0165] Where f3(x) is the cost of hydrogen production; The electricity cost required for hydrogen production is expressed in yuan / Nm³; p UEC The cost of electricity generation is expressed in yuan / MWh; The unit power consumption for hydrogen production is expressed in MWh / Nm³. The average annual depreciation on the power generation side is expressed in yuan / year. E represents the average annual operation and maintenance cost on the power generation side, expressed in yuan / year. daily This refers to the total daily power generation, expressed in MWh / day. The cumulative electricity consumption for hydrogen production within time t, expressed in MWh; The cumulative hydrogen production within time t, expressed in Nm³. The cost of hydrogen production operation and maintenance is expressed in yuan / Nm³. The average annual depreciation and financial cost of hydrogen production equipment are expressed in yuan / year. The annual operating and maintenance cost of the hydrogen production equipment is expressed in yuan per year. and These two variable values ​​are set via the manual interface; H daily (t) represents the total cumulative hydrogen production per day, in Nm³ / day; For every 1 Nm produced within time t... 3 The water cost required for hydrogen production is expressed in yuan / Nm³. 3 Q water (t) represents the amount of water consumed within time t during the hydrogen production process, in tons (t); p water Q represents the unit price of water, expressed in yuan / ton; H2 (t) represents the amount of hydrogen produced within time t, in Nm³. 3 .

[0166] The constraints of ensuring that the grid-connected power does not exceed 40% of the real-time hydrogen production power and that the system stabilizes are converted into a set of pre-set constraints, which, together with the operational constraints, constitute the solution constraints. Specifically, the pre-set constraints for the system stabilization transition include the standard deviation constraints on the time-period variation of the total power of the electrolyzer and the standard deviation constraints on the time-period variation of the hydrogen flow rate of the hydrogen storage tank.

[0167] The standard deviation constraint for the time-period variation of the total power of the electrolyzer can be:

[0168]

[0169] Among them, P H2，i Let i be the hydrogen production power of the electrolyzer at time i; This represents the average hydrogen production power of the electrolyzer during the hydrogen production power period (from time t to time t+N). This represents the minimum standard deviation of hydrogen production power over a given period. is the maximum standard deviation of hydrogen production power over a given period; N is the number of time intervals contained in the period used to calculate the standard deviation of flow rate over a given period.

[0170] The standard deviation constraint for the time-limited variation of hydrogen flow rate in the hydrogen storage tank can be:

[0171]

[0172] Among them, F out，i Let i be the output flow rate of the hydrogen storage tank at time i; This represents the average output flow rate of the hydrogen storage tank during the hydrogen transport flow period (from time t to time t+N). This represents the minimum standard deviation of hydrogen transport flow rate over a given period. is the maximum standard deviation of hydrogen transport flow rate over a given period, and N is the number of time intervals contained in the period used to calculate the standard deviation of flow rate over a given period.

[0173] In a grid-connected scenario without AGC (Automatic Generation Control) intervention, this embodiment aims to minimize wind and solar curtailment rates, maximize overall economic benefits, and ensure system stability. It prioritizes matching suitable multi-objective optimization plans from a pre-defined library, identifying electrolyzer power, grid-connected power (≤40% of real-time hydrogen production power), energy storage charging and discharging power, hydrogen storage tank flow rate, and curtailment power as optimization targets. When solving the objective function, both operational constraints and plan-defined constraints are satisfied. The SLSQP algorithm is used to iteratively calculate based on planned maximum wind and solar power output data until the required accuracy is achieved, generating planned curves for each energy unit. Through multi-objective weighting and precise constraints, the system simultaneously reduces wind and solar curtailment rates and maximizes grid-connected and hydrogen production revenue. Standard deviation constraints ensure stable operation of the electrolyzer and hydrogen storage tank, significantly improving the overall energy efficiency and economic benefits of the integrated wind, solar, energy storage, and hydrogen production energy system in a grid-connected, non-AGC scenario, achieving synergistic optimization of efficient renewable energy consumption and stable operation.

[0174] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0175] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions.

[0176] It should be noted that any reference numerals placed between parentheses in the claims should not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In claims that enumerate several means, several of these means may be embodied by the same hardware. The use of the terms first, second, third, etc., is merely for convenience of expression and does not indicate any order. These terms can be understood as part of the component names.

[0177] Furthermore, it should be noted that in the description of this specification, the terms "one embodiment," "some embodiments," "embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0178] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the claims should be interpreted to include both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0179] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, then this invention should also include these modifications and variations.

Claims

1. A method for optimizing the scheduling of wind, solar, energy storage, and hydrogen production energy devices, characterized in that, include: S100. Obtain the operating constraints and operating data of each energy device. For each energy device, identify its operating boundary based on its current operating constraints and current operating data to obtain the system carrying capacity. The operational constraints include electrolyzer operational constraints, hydrogen storage and transportation constraints, energy storage operational constraints, energy balance constraints, and grid connection and supply constraints. The operational data includes electrolyzer operational data, hydrogen storage and transportation operational data, energy storage operational data, and grid connection and off-grid operational data; S200: Obtain the power output constraints of the wind and solar system and the predicted power output data of the wind and solar system for the future cycle; correct the power output constraints of the wind and solar system based on the system carrying capacity of all energy devices; correct the predicted power output data of the wind and solar system based on the corrected power output constraints; and obtain the planned maximum power output data of the wind and solar system. S300: Obtain the future planning duration and optimization objectives; based on the optimization objectives, match the corresponding multi-objective optimization plan from the preset library and determine its optimization configuration. The optimization configuration includes solving constraints, which include only running constraints, or a combination of running constraints and pre-set constraints. The optimization configuration is applied to perform iterative calculations based on the planned maximum wind and solar power output data to obtain the planned curves of each optimization object within each energy device in the future planning period, and the planned curves are sent to their corresponding energy devices.

2. The method according to claim 1, characterized in that, The wind and solar power output data for future cycles obtained in S200 include: Obtain the medium-term wind and solar power forecast output data updated at the first frequency, the short-term wind and solar power forecast output data updated at the second frequency, and the ultra-short-term wind and solar power forecast output data updated at the third frequency at the current moment, wherein the first frequency is less than the second frequency, and the second frequency is less than the third frequency. The medium-term, short-term, and ultra-short-term wind and solar power forecast output data are aligned according to the time axis. The ultra-short-term wind and solar power forecast output data is used to cover the short-term wind and solar power forecast output data of the same period, and the covered short-term wind and solar power forecast output data is used to cover the medium-term wind and solar power forecast processing data of the same period. The data for the period of wind and solar power output that is consistent with the future planning duration is extracted from the medium-term wind and solar power output data after coverage.

3. The method according to claim 1, characterized in that, The grid connection and grid operation constraints are that the interaction power between the energy device and the power grid is within a preset power range. Interactive power includes internet access power and offline access power, with internet access power being a positive value and offline access power being a negative value. When connected to the grid, the lower limit of the preset power range is negative, and the upper limit is positive. When the energy device is off-grid, the interaction power between the energy device and the grid is 0. When connecting to a single network in a unidirectional manner that only allows internet access, the lower limit of the preset power range is 0.

4. The method according to claim 3, characterized in that, The optimization configuration also includes: optimization object, objective function, initial optimization value, solution method, and solution accuracy.

5. The method according to claim 4, characterized in that, The S300 includes: When grid connection is achieved and AGC is in operation, the optimization objective is to minimize hydrogen production cost. A corresponding multi-objective optimization plan is matched from a pre-set library, and the optimization objects are determined as electrolyzer power, energy storage power, hydrogen storage tank flow rate, and abandoned power. The objective function is the difference between hydrogen production revenue and hydrogen production cost divided by the total hydrogen production. Here, hydrogen production revenue, hydrogen production cost, and total hydrogen production are obtained based on the optimization objects. The solution constraint for the objective function is an operational constraint. Under this constraint, the SLSQP algorithm is used to iteratively calculate the objective function based on the planned maximum wind and solar power output data until the solution accuracy is met (0.1), thus obtaining the planned curves for each optimization object within the energy unit over the future planning period.

6. The method according to claim 4, characterized in that, The S300 includes: When grid connection is achieved and AGC (Automatic Generator Collection) is not in operation, priority is given to grid connection to achieve the lowest wind and solar curtailment rate and the highest overall economic benefits. However, the grid-connected power should not exceed 40% of the real-time hydrogen production power in the electrolyzer's operating data, and system stability is the optimization objective. A corresponding multi-objective optimization plan is matched from a pre-set library to determine the optimization targets: electrolyzer power, grid-connected power, energy storage power, hydrogen storage tank flow rate, and curtailed power. The objective function is the sum of the revenue associated with the wind and solar curtailment rate and the total economic benefit, adjusted for weights. Both the revenue associated with the wind and solar curtailment rate and the total economic benefit are derived from the optimization object. The constraints for solving the objective function include operational constraints and pre-set constraints. The pre-set constraints include the standard deviation constraint on the time-period variation of the total power of the electrolyzer, the standard deviation constraint on the time-period variation of the hydrogen flow rate of the hydrogen storage tank, and the constraint that the grid-connected power does not exceed 40% of the real-time hydrogen production power in the electrolyzer's operating data. Under the aforementioned constraints, the SLSQP algorithm is used to iteratively calculate based on the planned maximum wind and solar power output data until the required accuracy is met, thereby obtaining the planned curves of each optimization object within the energy device over the future planning period.

7. The method according to claim 6, characterized in that, The revenue associated with the curtailment rate is calculated as a function of the curtailment rate multiplied by the total cumulative wind and solar power generation and the system's power generation cost. The function for the abandonment rate of wind and solar power is 1 minus the kth power of the abandonment rate of wind and solar power. The curtailment rate is equal to the sum of the solar curtailment rate and the wind curtailment rate, where, The solar curtailment rate is the ratio of the total curtailed solar power within the planned future period to the theoretical total power generation of solar power. The wind curtailment rate is the ratio of the total curtailed wind power within the planned future period to the theoretical total power generation of wind power.

8. The method according to claim 6, characterized in that, The total economic benefit is the sum of hydrogen production revenue and grid connection revenue within the planned future period, minus operation and maintenance costs, grid connection costs, and operation and maintenance costs.

9. The method according to claim 1, characterized in that, A file is generated based on the planned maximum wind and solar power output data and transmitted to the wind and solar power plants as adjustment suggestions for wind and solar power operation.

10. An electronic device, characterized in that, include: The device includes a memory and a processor, wherein the memory stores a computer program and the processor executes the computer program in the memory, specifically performing the steps of the scheduling optimization method for a wind, solar, energy storage and hydrogen production energy device as described in any one of claims 1 to 8.

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