Wind-solar-hydrogen multi-energy complementary integrated energy collaborative regulation method

Abstract

The application discloses a wind-solar-hydrogen multi-energy complementary comprehensive energy collaborative regulation method, and belongs to the technical field of multi-energy complementary comprehensive energy regulation. The method obtains current operation parameter sets of wind power, photovoltaic, energy storage and hydrogen energy multi-energy units, divides fast regulation parameter subsets and slow regulation parameter subsets according to unit power regulation response times, and establishes dynamic power coupling constraint boundaries of the energy units at a grid connection point. According to the dynamic power coupling constraint boundaries and the current operation parameter sets, the power regulation capacity sequences of the energy units within multiple prediction steps are calculated. With the minimum grid connection point power deviation as the optimization orientation, the power regulation capacity sequences are iteratively corrected by using a directional disturbance optimization method, and the power regulation instructions of the energy units at each prediction step are generated according to the corrected sequences and the collaborative regulation is performed. The method can reflect the dynamic adjustable range of fast and slow regulation resources, reduce the time consumption of optimization calculation and the grid connection point power deviation.

Description

Technical Field

[0001] This invention relates to the field of multi-energy complementary integrated energy regulation technology, specifically a method for coordinated regulation of wind, solar, storage, and hydrogen multi-energy complementary integrated energy. Background Technology

[0002] A multi-energy complementary integrated energy system combining wind, solar, energy storage, and hydrogen power mitigates renewable energy fluctuations and tracks grid dispatch power by coordinating the output of heterogeneous energy units such as wind power, solar power, energy storage, and hydrogen energy. Existing coordinated control methods typically incorporate each energy unit into an optimal dispatch model with a unified time scale and power constraints, employing centralized optimization, model predictive control, or intelligent optimization algorithms to calculate the output commands for each unit. During dispatch, power constraint boundaries are often determined based on the rated power of the equipment, maximum charging and discharging power, or fixed grid connection point limits, and the power regulation capacity of each energy unit is also approximated using a unified prediction step size.

[0003] Under conditions where wind and solar power output fluctuates rapidly and energy storage and hydrogen energy regulation response times differ significantly, the aforementioned processing method results in fast and slow regulation units being adjusted synchronously within the same scheduling cycle. However, slow regulation units experience tracking lag and lifespan loss due to frequent command changes, and the adjustable capacity of fast regulation units is not prioritized for smoothing rapid fluctuations. The overall adjustable power range at the grid connection point is limited by static boundaries, failing to accurately reflect the dynamic coupling relationship between fast and slow regulation resources under different prediction step sizes. The optimization process for multi-step power regulation capacity sequences relies on global optimization or intelligent search, which involves large computational loads and unclear perturbation directions, easily leading to slow convergence or regulation oscillations during online rolling operations. Therefore, it is necessary to construct dynamic power coupling constraint boundaries that adapt to the response time differences of heterogeneous energy units and to perform targeted and rapid correction of the multi-step power regulation capacity sequence under the objective of minimizing power deviation at the grid connection point. Summary of the Invention

[0004] To address the issues of insufficient differentiation in power regulation response time, static grid connection point power constraints, and inadequate real-time optimization of capacity sequences in existing wind-solar-hydrogen multi-energy complementary coordinated regulation methods, this paper proposes a comprehensive energy coordinated regulation method for wind-solar-hydrogen multi-energy complementary systems. This method establishes a dynamic power coupling constraint boundary by dividing the system into fast-adjustment and slow-adjustment parameter subsets, calculates a multi-prediction step-size power regulation capacity sequence, and performs directional perturbation iterative correction with the minimum grid connection point power deviation as the optimization guide. Finally, it generates and executes power regulation commands for each energy unit, achieving coordinated regulation across multiple time scales.

[0005] To achieve the above objectives, the present invention provides the following technical solution: The present invention provides a method for integrated energy coordination and regulation of wind, solar, energy storage, and hydrogen energy complementary systems, comprising: acquiring the current operating parameter set of multiple energy units including wind power, photovoltaic, energy storage, and hydrogen energy; dividing the current operating parameter set into a fast-adjustment parameter subset and a slow-adjustment parameter subset according to the power adjustment response time of each unit; establishing a dynamic power coupling constraint boundary for each energy unit at the grid connection point based on the fast-adjustment parameter subset and the slow-adjustment parameter subset; calculating the power adjustment capacity sequence of each energy unit within multiple prediction steps based on the dynamic power coupling constraint boundary and the current operating parameter set; iteratively correcting the power adjustment capacity sequence of each energy unit using a directional perturbation optimization method, with the minimum power deviation at the grid connection point as the optimization guide; generating power adjustment commands for each energy unit within the corresponding prediction steps based on the iteratively corrected power adjustment capacity sequence; and executing the coordinated regulation and regulation of each energy unit according to the power adjustment commands.

[0006] As a technical solution of the present invention, obtaining the current operating parameter set includes: collecting wind speed signals, pitch angle signals, and generator speed signals of the wind power unit; collecting irradiance signals and module temperature signals of the photovoltaic unit; collecting state of charge signals and terminal voltage signals of the energy storage unit; and collecting hydrogen storage tank pressure signals and electrolyzer temperature signals of the hydrogen energy unit. After aligning all the collected signals according to timestamps, the current operating parameter set is generated to simultaneously reflect the actual operating status of the fast-adjustment unit and the slow-adjustment unit, providing a unified data foundation for coordinated control.

[0007] Furthermore, based on the power regulation response time of each unit, the current operating parameter set is divided into a fast-adjustment parameter subset and a slow-adjustment parameter subset, including: obtaining a first response time threshold for the energy storage unit and a second response time threshold for the hydrogen energy unit, wherein the first response time threshold is less than the second response time threshold; classifying unit parameters in the current operating parameter set whose response time is less than or equal to the first response time threshold into the fast-adjustment parameter subset; and classifying unit parameters in the current operating parameter set whose response time is greater than the first response time threshold and less than or equal to the second response time threshold into the slow-adjustment parameter subset, so that subsequent constraint and regulation calculations are adapted to the dynamic characteristics of different energy units.

[0008] Furthermore, based on the fast-adjustment parameter subset and the slow-adjustment parameter subset, establishing the dynamic power coupling constraint boundary for each energy unit at the grid connection point includes: extracting the maximum charging power limit and maximum discharging power limit of the energy storage unit from the fast-adjustment parameter subset, and extracting the rated hydrogen production power and rated power generation power of the hydrogen energy unit from the slow-adjustment parameter subset; constructing an upper boundary constraint function and a lower boundary constraint function for the active power at the grid connection point based on the maximum charging power limit, maximum discharging power limit, rated hydrogen production power, and rated power generation power; and combining the upper boundary constraint function and the lower boundary constraint function to form the dynamic power coupling constraint boundary. This boundary changes with the current adjustable capabilities of energy storage and hydrogen energy, ensuring that the constraint conditions are consistent with the real-time operating state.

[0009] Furthermore, based on the dynamic power coupling constraint boundary and the current operating parameter set, calculating the power regulation capacity sequence of each energy unit within multiple prediction steps includes: extracting the current output value and adjustable capacity upper limit of each energy unit from the current operating parameter set; under the constraint of the dynamic power coupling constraint boundary, starting from the current output value, calculating the adjustable capacity and adjustable capacity of each energy unit within each prediction step according to a preset prediction step time interval; arranging the adjustable capacity and adjustable capacity within each prediction step in chronological order to generate the power regulation capacity sequence of each energy unit, providing a continuous time-dimensional adjustment space for optimizing the power deviation at the grid connection point.

[0010] Furthermore, based on the power regulation capacity sequence, and guided by minimizing the power deviation at the grid connection point, the iterative correction of the power regulation capacity sequence of each energy unit using a directional disturbance optimization method includes: calculating the initial deviation value between the current total power at the grid connection point and the grid dispatch target power based on the power regulation capacity sequence of each energy unit; using the initial deviation value as the initial optimization condition, sequentially selecting the power regulation capacity sequence of each energy unit as the current disturbance object, applying a positive disturbance to the current disturbance object, and recalculating the first deviation value between the total power at the grid connection point and the dispatch target power; comparing the first deviation value with the initial deviation value, and if the first deviation value is less than the initial deviation value, increasing the regulation amount of the current disturbance object along the positive disturbance direction to complete the iterative correction of the current energy unit. Through local directional disturbances and deviation comparison, the adjustment direction that is conducive to reducing the grid connection power deviation can be quickly determined.

[0011] Preferably, if the first deviation value is greater than the initial deviation value after recalculation, a reverse disturbance is applied to the current disturbance object and the second deviation value between the total power of the grid connection point and the scheduling target power is recalculated; the second deviation value is compared with the initial deviation value, and if the second deviation value is less than the initial deviation value, the adjustment amount of the current disturbance object is increased along the reverse disturbance direction to complete the iterative correction of the current energy unit; if the second deviation value is greater than the initial deviation value, the adjustment amount of the current disturbance object is kept unchanged to avoid ineffective adjustment and improve optimization efficiency.

[0012] Furthermore, generating power regulation instructions for each energy unit within the corresponding prediction step size based on the iteratively corrected power regulation capacity sequence includes: extracting the final regulation capacity value of each energy unit within each prediction step size from the iteratively corrected power regulation capacity sequence; calculating the target output value of each energy unit within each prediction step size based on the final regulation capacity value and the current output value of each energy unit; and encapsulating the target output value into a power regulation instruction sequence according to the time order of the prediction step size, wherein each instruction in the power regulation instruction sequence contains a corresponding prediction step size identifier, an energy unit identifier, and a target output value, so as to facilitate time-sharing and orderly distribution to each energy unit.

[0013] Furthermore, the coordinated control of each energy unit according to the power adjustment command includes: arranging the power adjustment command sequence into a control execution queue according to the prediction step size identifier in the power adjustment command sequence; reading the target output value of all energy units corresponding to each prediction step size at the start time of each prediction step size according to the order of the control execution queue; sending the target output value of each energy unit to the power controller of the corresponding energy unit, and having the power controller drive the energy unit to adjust its output to the target output value, thereby realizing the coordinated response of multiple energy units on a unified time scale and reducing power fluctuations at the grid connection point.

[0014] Preferably, in the directional disturbance optimization method, after each iteration correction is completed, the power regulation capacity sequence of each energy unit is updated, and the updated power regulation capacity sequence is used as the input for the next iteration correction. The iteration correction stops when the deviation between the total power at the grid connection point and the grid dispatch target power is less than the preset convergence threshold, so as to ensure that the final regulation of each energy unit meets the grid connection power deviation control requirements.

[0015] The technical effects and advantages provided by the present invention in the above technical solution are as follows: The current operating parameters of each energy unit are divided into fast-regulation parameter subsets and slow-regulation parameter subsets based on power regulation response time. Upper and lower boundary constraint functions at the grid connection point are constructed using the maximum charge / discharge power limits of the energy storage unit, the rated hydrogen production power, and the rated power generation power of the hydrogen energy unit, forming a dynamic power coupling constraint boundary. Under this boundary constraint, starting from the current output value of each energy unit, the adjustable capacity and adjustable capacity at each prediction step time interval are progressively calculated according to a preset prediction step time interval, resulting in a power regulation capacity sequence. This process allows fast-regulating resources such as energy storage and slow-regulating resources such as hydrogen energy to represent their adjustable capabilities at different time scales. The grid connection point constraint boundary changes with the operating state and prediction step time. Compared to static boundaries and uniform-scale scheduling, this reduces ineffective actions of slow-regulating units, releases short-term regulation space for fast-regulating units, and ensures that the power regulation capacity sequence matches the actual response time characteristics of each unit.

[0016] Guided by minimizing the power deviation at the grid connection point, this method sequentially applies directional perturbations to each energy unit in the power regulation capacity sequence. The deviation between the total power at the grid connection point and the target power is recalculated. Based on the direction of the deviation's decrease or increase, a positive perturbation, a negative perturbation, or maintaining the original regulation is selected. The updated power regulation capacity sequence is then used as the input for the next iteration until the deviation is less than a preset convergence threshold. This directional perturbation optimization method does not rely on global optimization or intelligent search; the perturbation direction is directly determined by changes in the power deviation at the grid connection point. The computational load for each correction is small, and the direction is clear. During multi-step rolling corrections, it can quickly approach the scheduling target, reducing the computational time and convergence oscillation amplitude of grid connection point power tracking, and improving the real-time performance and stability of the online coordinated regulation of the wind-solar-storage-hydrogen complementary system. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0018] Figure 1 This is a flowchart of a multi-energy complementary integrated energy synergistic regulation method for wind, solar, storage, and hydrogen; Figure 2 This is a flowchart for generating the power regulation capacity sequence of multiple energy units; Figure 3 It is the curve of dynamic power coupling constraint boundary and active power change at the grid connection point; Figure 4 It is the predicted step size target output curve of each unit of the wind, solar, storage and hydrogen multi-energy complementary integrated energy system. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] See Figure 1 This invention provides a method for integrated energy coordination and regulation of wind, solar, energy storage, and hydrogen energy complementary systems. It acquires the current operating parameter set of multiple energy units, including wind power, photovoltaics, energy storage, and hydrogen energy. Based on the power regulation response time of each unit, the current operating parameter set is divided into a fast-regulation parameter subset and a slow-regulation parameter subset. Based on these subsets, a dynamic power coupling constraint boundary is established for each energy unit at the grid connection point. Based on the dynamic power coupling constraint boundary and the current operating parameter set, the power regulation capacity sequence of each energy unit within multiple prediction steps is calculated. Based on the power regulation capacity sequence, guided by minimizing the power deviation at the grid connection point, a directional perturbation optimization method is used to iteratively correct the power regulation capacity sequence of each energy unit. Based on the iteratively corrected power regulation capacity sequence, a power regulation command is generated for each energy unit within the corresponding prediction step, and the coordinated regulation of each energy unit is executed according to the power regulation command.

[0021] In practical implementation, the current operating parameter sets of multiple energy units, including wind power, photovoltaics, energy storage, and hydrogen energy, are acquired. Wind power units are equipped with wind speed sensors, pitch angle sensors, and speed sensors to collect wind speed signals, pitch angle signals, and generator speed signals, respectively. Photovoltaic units are equipped with irradiance sensors and temperature sensors to collect irradiance signals and module temperature signals, respectively. Energy storage units are equipped with state-of-charge (SOC) monitoring circuits and voltage sampling circuits to collect SOC signals and terminal voltage signals, respectively. Hydrogen energy units are equipped with pressure sensors and temperature sensors to collect hydrogen storage tank pressure signals and electrolyzer temperature signals, respectively.

[0022] In some embodiments, the various signals described above are synchronously sampled by the data acquisition terminal according to a uniform sampling period. The data acquisition terminal adds a timestamp to each signal, with a timestamp accuracy in the millisecond range. Data acquisition terminals of different energy units are synchronized using a time synchronization device.

[0023] It's understandable that when aligning all acquired signals according to timestamps, a common timeline is first established. The common timeline is based on the master clock time of the data acquisition terminal, and the time interval is consistent with the sampling period. For each common timestamp, it's checked whether each signal has a sampled value at that timestamp. If a signal has a sampled value at the common timestamp, that value is retained; if a signal does not have a sampled value at the common timestamp, a linear interpolation method is used to calculate the corresponding signal's sampled value at the common timestamp using the sampled values ​​of the corresponding signal at two adjacent sampling times before and after the common timestamp.

[0024] In practical implementation, the combined and generated set of current running parameters is represented as follows: ; in, Indicates the current set of running parameters. This represents the sampled value of the wind speed signal of the wind power unit at a common timestamp. This represents the sampled value of the pitch angle signal of the wind turbine unit at a common timestamp. This represents the sampled value of the generator speed signal of the wind power unit at a common timestamp. This represents the sampled value of the photovoltaic unit's irradiance signal at a common timestamp. This represents the sampled value of the photovoltaic unit's module temperature signal at a common timestamp. This represents the sampled value of the energy storage unit's state of charge signal at a common timestamp. This represents the sampled value of the terminal voltage signal of the energy storage unit at a common timestamp. This represents the sampled value of the hydrogen storage tank pressure signal of the hydrogen energy unit at a common timestamp. This indicates the sampled value of the electrolyzer temperature signal of the hydrogen energy unit at a common timestamp, with the superscript indicating the sample value. This represents the transpose of a vector.

[0025] In practical implementation, based on the power regulation response time of each energy unit, the current operating parameter set is divided into a fast-adjustment parameter subset and a slow-adjustment parameter subset. The first response time threshold of the energy storage unit and the second response time threshold of the hydrogen energy unit are obtained, with the first response time threshold of the energy storage unit being less than the second response time threshold of the hydrogen energy unit. The first response time threshold of the energy storage unit is derived from the power response time constant specified in the factory parameters of the energy storage unit's power controller. The second response time threshold of the hydrogen energy unit is derived from the power response time constants of the hydrogen production system and the power generation system of the hydrogen energy unit, with the second response time threshold of the hydrogen energy unit being the larger of the power response time constants of the hydrogen production system and the power generation system. Parameters in the current operating parameter set whose response time is less than or equal to the first response time threshold of the energy storage unit are assigned to the fast-adjustment parameter subset; parameters in the current operating parameter set whose response time is greater than the first response time threshold of the energy storage unit but less than or equal to the second response time threshold of the hydrogen energy unit are assigned to the slow-adjustment parameter subset.

[0026] It can be understood that response time refers to the time elapsed from when an energy unit receives a power regulation command to when the actual power change reaches a preset proportion required by the power regulation command. This preset proportion is uniformly set to 0.95 for wind power units, photovoltaic units, energy storage units, and hydrogen energy units.

[0027] In some embodiments, the maximum charging power limit and the maximum discharging power limit of the energy storage unit are extracted from the fast-adjustment parameter subset, and the rated hydrogen production power and the rated power generation power of the hydrogen energy unit are extracted from the slow-adjustment parameter subset. The maximum charging power limit and the maximum discharging power limit of the energy storage unit are read from the energy management system of the energy storage unit; the rated hydrogen production power and the rated power generation power of the hydrogen energy unit are read from the equipment nameplate parameters of the hydrogen energy unit.

[0028] In practical implementation, based on the maximum charging power limit of the energy storage unit, the maximum discharging power limit of the energy storage unit, the rated hydrogen production power of the hydrogen energy unit, and the rated power generation power of the hydrogen energy unit, upper and lower boundary constraint functions for the active power at the grid connection point are constructed. The dynamic power coupling constraint boundary is expressed as: ; in, Indicates the active power at the grid connection point at time [time]. The instantaneous value, Indicates the lower boundary constraint function at time t. The value, Indicates the upper boundary constraint function at time t. The value of the upper boundary constraint function at time . The value is equal to the sum of the maximum discharge power limit of the energy storage unit and the rated power generation of the hydrogen energy unit. The lower boundary constraint function at time... The value is equal to the sum of the negative of the maximum charging power limit of the energy storage unit and the negative of the rated hydrogen production power of the hydrogen energy unit. The upper boundary constraint function and the lower boundary constraint function are combined to form a dynamic power coupling constraint boundary.

[0029] See Figure 3 In the figure, the horizontal axis represents time in minutes, and the vertical axis represents active power in kilowatts (kW). The curves and straight lines in the figure correspond to the established dynamic power coupling constraint boundary and the actual change of active power at the grid connection point, respectively.

[0030] The solid red line represents the upper boundary constraint function, with a constant value of approximately 800kW, representing the upper limit of the sum of the maximum discharge power limit of the energy storage unit and the rated power generation of the hydrogen energy unit; the dashed blue line represents the lower boundary constraint function, with a constant value of approximately -750kW, representing the lower limit of the sum of the maximum charging power limit (negative) of the energy storage unit and the rated hydrogen production power (negative) of the hydrogen energy unit. The two boundary curves form the allowable power range for dynamic power coupling.

[0031] The orange dashed line represents the maximum discharge power limit of the energy storage unit, which is stable at approximately 500kW; the cyan dashed line represents the maximum charging power limit of the energy storage unit (taken as a negative value), which is stable at approximately -500kW. The purple solid line represents the rated power generation of the hydrogen energy unit, which is constant at 300kW; the brown dotted line represents the rated hydrogen production power of the hydrogen energy unit (taken as a negative value), which is constant at approximately -250kW.

[0032] The green dashed line represents the actual change in active power at the grid connection point. Its trend shows a gradual decrease from approximately 200kW to nearly -100kW within 0 to 10 minutes, indicating a low initial active power. Between 10 and 30 minutes, the power fluctuation range widens, showing a gradual upward trend, transitioning from negative to positive fluctuations, gradually approaching and exceeding the 300kW rated power threshold of the hydrogen energy unit. Between 30 and 50 minutes, the active power at the grid connection point further increases and fluctuates, reaching a peak of approximately 470kW, approaching the maximum discharge power limit of 500kW for the energy storage unit, indicating that the system's power regulation capability is nearing its upper limit.

[0033] In specific implementation, please refer to Figure 2The current output value and adjustable capacity upper limit of each energy unit are extracted from the current operating parameter set. The current output value of the wind power unit is calculated from the wind speed signal, pitch angle signal, and generator speed signal of the wind power unit through the wind power unit power curve. The current output value of the photovoltaic unit is calculated from the irradiance signal and module temperature signal of the photovoltaic unit through the equivalent circuit model of the photovoltaic unit. The current output value of the energy storage unit is calculated from the terminal voltage signal and the current value output by the energy management system of the energy storage unit. The current value output by the energy management system of the energy storage unit is updated in real time through the terminal voltage signal and the state of charge signal of the energy storage unit. The current output value of the hydrogen energy unit is calculated from the hydrogen storage tank pressure signal and the electrolyzer temperature signal of the hydrogen energy unit through the hydrogen energy unit output model.

[0034] The adjustable capacity upper limit for wind turbine units is calculated based on the wind speed and generator speed signals of the wind turbine unit, determining the maximum mechanical power that the unit can capture at the current wind speed, and then multiplying this value by the unit's power generation efficiency. The adjustable capacity upper limit for photovoltaic (PV) units is calculated based on the irradiance and module temperature signals of the PV unit, determining the maximum power point power at the current irradiance. The adjustable capacity upper limit for energy storage units is taken from the maximum allowable discharge power and maximum allowable charging power output from the energy management system. The adjustable capacity upper limit for hydrogen energy units is taken from the parameters on the unit's nameplate; the upper limit is the greater of the unit's rated hydrogen production power and rated power generation power.

[0035] In practice, the preset prediction step interval is 5 minutes, the prediction duration is 30 minutes, and the total number of prediction steps, M, is 6. The 5-minute prediction step interval is chosen because the first response time threshold of the energy storage unit does not exceed 1 minute, and the second response time threshold of the hydrogen energy unit does not exceed 5 minutes. This 5-minute interval covers the time requirements for each energy unit to complete power regulation actions and matches the cycle of power plan instructions issued by the grid dispatch master station. The 30-minute prediction duration is chosen because it covers the slow dynamic processes of pressure changes in the hydrogen storage tank and temperature changes in the electrolyzer of the hydrogen energy unit, avoiding the introduction of numerous uncertainties due to an excessively long prediction range.

[0036] Under the constraints of dynamic power coupling, starting from the current output value of each energy unit, the calculation is performed progressively forward according to the predicted step time interval. For the th The energy unit in the first Within a prediction step, the output capacity can be adjusted upwards. The calculation method is as follows: (The remaining text appears to be incomplete and contains errors. A more accurate translation would require the full context The output of other energy units besides the first energy unit remains at the current output value. The output of the energy unit is from the first Starting from the current output value of each energy unit, the output gradually increases in increments of a preset power step size, where the preset power step size is taken as... , The power command resolution is consistent with that of each energy unit's power controller. The total power at the grid connection point is calculated for each increment of the preset power step. When the... The energy unit output reached the [number]th The increase will stop when the adjustable capacity of an energy unit reaches its upper limit, or when the total power at the grid connection point reaches the upper boundary constraint function value. The increase will stop when the [number]th [unit] stops increasing. The output value of the energy unit and the first The difference in the current output value of each energy unit is used as the first... The energy unit in the first The output capacity can be adjusted within a prediction step. Output capacity can be adjusted downwards. The calculation method is as follows: (The remaining text appears to be incomplete and contains errors. A more accurate translation would require the full context The output of other energy units besides the first energy unit remains at the current output value. The output of the energy unit is from the first Starting with the current output value of the energy unit, the output is gradually decreased in preset power step increments. The total power at the grid connection point is calculated after each preset power step increment decrease. When the [number]th [unit] [is mentioned]... The energy unit output reached the [number]th The reduction stops when the adjustable capacity of an energy unit reaches its lower limit, or when the total power at the grid connection point reaches the lower boundary constraint function value. The current output value of the energy unit is the same as that of the first energy unit. The difference in the output value of each energy unit is used as the first... The energy unit in the first The output capacity can be adjusted down within a prediction step. .

[0037] The above calculations are performed within each prediction step. During the forward stepwise calculation process, the... After each prediction step is completed, update the current output value of each energy unit to the [number]th [prediction step]. The output value at the end of the prediction step is used as the starting point, and then the updated current output value of each energy unit is used to calculate the output value of the predicted step. Within each prediction step, the output can be adjusted upwards or downwards. The output value at the end of the prediction step is the first... The current output value within the prediction step and the first prediction step The sum of the output changes of the energy units actually participating in power regulation within each prediction step is taken as zero when there are no other constraints.

[0038] The adjustable output capacity and adjustable output capacity within each prediction step are arranged in chronological order to generate a power regulation capacity sequence for each energy unit. The power regulation capacity sequence is represented as follows: ; in, This represents the power regulation capacity sequence of the a-th energy unit; Indicates the energy unit number, Take 1 as the corresponding wind power unit. Take 2 corresponding photovoltaic units, Take 3 corresponding energy storage units, Take 4 as the corresponding hydrogen energy unit; This indicates that the output capacity of the a-th energy unit can be increased within the m-th prediction step. This indicates that the output capacity of the a-th energy unit can be reduced within the m-th prediction step. Indicates the prediction step size number. The value range is 1 to ; This indicates the total number of prediction steps. Take 6; superscript This represents the transpose of a vector.

[0039] In practical implementation, based on the power regulation capacity sequence of each energy unit, the initial deviation between the current total power at the grid connection point and the grid dispatch target power is calculated. The formula for calculating the initial deviation is as follows: ; in, Indicates the initial deviation value; The total power at the grid connection point is represented by the sum of the current output value of each energy unit and the adjustment amount of the corresponding energy unit within the first prediction step. This represents the target power for power grid dispatch, which is determined by the planned active power value issued by the power grid dispatch master station to the grid connection point.

[0040] Before calculating the initial deviation value, the adjustment amount of each energy unit in each prediction step is initialized to 0. The current total power at the grid connection point is obtained by summing the current output values ​​of the wind power unit, photovoltaic unit, energy storage unit, and hydrogen energy unit, respectively, plus the adjustment amounts of the wind power unit, photovoltaic unit, energy storage unit, and hydrogen energy unit in the first prediction step.

[0041] It is understandable that the absolute value of the initial deviation is used to measure the degree to which the current total power at the grid connection point deviates from the grid dispatch target power. The larger the absolute value of the initial deviation, the greater the deviation of the current total power at the grid connection point from the grid dispatch target power.

[0042] In practice, the initial deviation value is used as the initial condition for optimization. The power regulation capacity sequence of each energy unit is selected as the current disturbance object in the order of energy storage unit, hydrogen energy unit, wind power unit, and photovoltaic unit. The energy storage unit has the shortest response time, followed by the hydrogen energy unit. The wind power unit and photovoltaic unit have fluctuating response times. Therefore, the current disturbance object is selected in the order of energy storage unit, hydrogen energy unit, wind power unit, and photovoltaic unit.

[0043] Before applying a positive disturbance to the current disturbance object, the adjustment amount of the current disturbance object within the first prediction step is the set value within the first prediction step of the power regulation capacity sequence corresponding to the current disturbance object. The positive disturbance amount is taken as 10% of the adjustable capacity of the output within the first prediction step of the power regulation capacity sequence corresponding to the current disturbance object. The basis for taking 10% of the adjustable capacity of the output in the positive disturbance amount is that the adjustable capacity of the output is the maximum adjustable increase of the current disturbance object within the first prediction step. Taking 10% can produce an observable change in the current total power at the grid connection point without exceeding the dynamic power coupling constraint boundary.

[0044] After applying a positive disturbance to the current disturbance target, the adjustment amount of the current disturbance target within the first prediction step increases by the positive disturbance amount, while the adjustment amounts of other energy units remain unchanged. The total power at the grid connection point is recalculated, and the recalculated current total power at the grid connection point is substituted into the initial deviation value calculation formula. Position, and obtain the first deviation value.

[0045] In some embodiments, when the absolute value of the first deviation value is less than the absolute value of the initial deviation value, it indicates that the positive disturbance direction can reduce the deviation between the current total power at the grid connection point and the grid dispatch target power. The adjustment amount of the current disturbance object is increased along the positive disturbance direction. The adjustment amount of the current disturbance object within the first prediction step remains the value after the positive disturbance amount is applied, thus completing the iterative correction of the current energy unit.

[0046] When the absolute value of the first deviation is greater than the absolute value of the initial deviation, it indicates that the forward disturbance direction cannot reduce the deviation between the current total power at the grid connection point and the grid dispatch target power. Therefore, a reverse disturbance is applied to the current disturbance object. The reverse disturbance is taken as 10% of the adjustable output capacity in the first prediction step of the power regulation capacity sequence corresponding to the current disturbance object. The basis for taking 10% of the adjustable output capacity is that the adjustable output capacity is the maximum adjustable decrease of the current disturbance object within the first prediction step. Taking 10% can produce an observable change in the current total power at the grid connection point without exceeding the dynamic power coupling constraint boundary.

[0047] After applying a reverse disturbance to the current disturbance target, recalculate the total power at the grid connection point, and substitute the recalculated total power at the grid connection point into the initial deviation value calculation formula. The position is used to obtain the second deviation value.

[0048] When the absolute value of the second deviation is less than the absolute value of the initial deviation, it indicates that the reverse disturbance direction can reduce the deviation between the current total power at the grid connection point and the grid dispatch target power. The adjustment amount of the current disturbance object is increased along the reverse disturbance direction. The adjustment amount of the current disturbance object within the first prediction step remains the value after the reverse disturbance amount is applied, thus completing the iterative correction of the current energy unit.

[0049] When the absolute value of the second deviation is greater than the absolute value of the initial deviation, it means that neither the forward disturbance direction nor the reverse disturbance direction can reduce the deviation between the current total power at the grid connection point and the grid dispatch target power, and the adjustment amount of the current disturbance object remains unchanged.

[0050] After completing the iterative correction of the current energy unit, the adjustment amount of the energy unit that has completed the iterative correction remains unchanged, and the perturbation optimization process is continued for the power regulation capacity sequence of the next energy unit. During the perturbation optimization process for the next energy unit, the adjustment amount of the energy unit that has completed the iterative correction is used as a fixed amount in the calculation of the current total power at the grid connection point.

[0051] In practical implementation, after each iteration of the directional perturbation optimization method, the power regulation capacity sequence of each energy unit is updated. When updating the power regulation capacity sequence, the regulation amount of the energy unit that completed the correction in this iteration within the first prediction step is first used as the current regulation amount within the first prediction step. Then, the current regulation amount of the energy unit within the first prediction step is added to the current output value of the energy unit to obtain the output value of the energy unit at the end of the first prediction step. Using the output value at the end of the first prediction step as the starting output value for subsequent prediction steps, the power regulation capacity sequence calculation process is re-executed to obtain the adjustable output capacity and adjustable output capacity of the energy unit in subsequent prediction steps. The current regulation amount within the first prediction step is combined with the updated adjustable output capacity and adjustable output capacity in subsequent prediction steps according to the time sequence of the prediction steps to form the updated power regulation capacity sequence of the energy unit.

[0052] It is understood that the updated power regulation capacity sequence of each energy unit serves as the input for the next iteration correction. The next iteration correction is performed according to the directional disturbance optimization method described in the above embodiment. The deviation between the current total power at the grid connection point and the grid dispatch target power is calculated. The iteration correction stops when the absolute value of the deviation is less than a preset convergence threshold. The preset convergence threshold is the larger of 0.5% of the absolute value of the grid dispatch target power and 1kW. Setting the preset convergence threshold to 0.5% of the absolute value of the grid dispatch target power satisfies the active power control accuracy requirements at the grid connection point. The lower limit of the preset convergence threshold is set to 1kW because 1kW is the power command resolution of the power controller of each energy unit, and power deviations below 1kW cannot be effectively eliminated by the power controller.

[0053] After stopping iterative correction, the final regulation capacity value is stored in the power regulation capacity sequence of each energy unit within each prediction step. The final regulation capacity value of each energy unit within each prediction step is extracted from the iteratively corrected power regulation capacity sequence. For the first prediction step, the final regulation capacity value is the regulation amount of the energy unit within the first prediction step when iterative correction stops. For subsequent prediction steps, the final regulation capacity value is the planned regulation amount within the corresponding prediction step in the updated power regulation capacity sequence. This planned regulation amount is consistent with the energy unit's adjustable output capacity (either up-adjustable or down-adjustable) within the corresponding prediction step when the power regulation capacity sequence is updated.

[0054] In practice, based on the final regulating capacity value and the current output value of each energy unit, the target output value of each energy unit within each prediction step is calculated. The formula for calculating the target output value is as follows: ; in, This represents the target output value of the q-th energy unit within the h-th prediction step; This represents the current output value of the q-th energy unit at the start of the h-th prediction step. When h is 1, To obtain the current output value of the q-th energy unit in the current operating parameter set, when h is greater than 1, Let q be the output value of the q-th energy unit at the end of the (h-1)-th prediction step; This represents the final adjustment capacity value of the q-th energy unit within the h-th prediction step; q represents the energy unit number, q1 corresponds to the wind power unit, q2 corresponds to the photovoltaic unit, q3 corresponds to the energy storage unit, and q4 corresponds to the hydrogen energy unit; h represents the prediction step number, with a value ranging from 1 to K, and K represents the total number of prediction steps, with K being 6.

[0055] In some embodiments, the target output value is encapsulated into a power regulation command sequence in chronological order according to the prediction step size. Each command in the power regulation command sequence includes a corresponding prediction step size identifier, an energy unit identifier, and a target output value. The prediction step size identifier is the prediction step size number h, the energy unit identifier is the energy unit number q, and the target output value is calculated according to the target output value calculation formula. Each instruction in the power regulation instruction sequence is arranged in order from 1 to K according to the prediction step number h. Within the same prediction step number h, they are arranged in order from 1 to 4 according to the energy unit number q.

[0056] In practice, based on the prediction step size identifier in the power regulation command sequence, the power regulation command sequence is arranged into a regulation execution queue according to chronological order. Each element in the regulation execution queue corresponds to a prediction step size, and each prediction step size contains the target output values ​​of all energy units corresponding to that prediction step size. Following the order of the regulation execution queue, at the start time of each prediction step size, the target output values ​​of all energy units corresponding to that prediction step size are read. The target output value of each energy unit is then sent to the power controller of the corresponding energy unit, which drives the energy unit to adjust its output to the target output value.

[0057] See Figure 4 In the graph, the horizontal axis represents time in minutes, ranging from 0 to 30 minutes, and the vertical axis represents the target output value in kilowatts (kW). The legend indicates that the four curves represent the target output value trends of the wind power unit (blue solid line), photovoltaic unit (orange dashed line), energy storage unit (green dotted line), and hydrogen energy unit (red dotted line), respectively.

[0058] As can be seen from the figure, the target output of the wind power unit generally remained between 195kW and 215kW, showing a trend of first rising and then falling: from 0 to 15 minutes, the target output of the wind power unit gradually increased from 200kW to a peak of 215kW, and from 15 to 30 minutes, it gradually decreased to about 195kW. This reflects that the target output of the wind power unit fluctuates slowly over time, which is consistent with the power regulation capacity sequence update mechanism based on iterative correction.

[0059] The target output of the photovoltaic unit is generally in the range of 150kW to 172kW. The curve shows a slow upward trend followed by a slight decline: from 0 to 20 minutes, the target output of the photovoltaic unit rises from 150kW to a peak of 172kW, and from 20 to 30 minutes, it drops slightly to 164kW, showing dynamic adjustment related to changes in illumination conditions, which is consistent with the description of the current output and adjustable capacity of the photovoltaic unit in the embodiment.

[0060] The target output of the energy storage unit fluctuates significantly, ranging from -15kW to 50kW. Its curve is characterized by a rapid initial rise followed by a slow decline and eventual negative value: the initial target output is 0 at 0 minutes, rapidly rising to a peak of 50kW between 5 and 10 minutes, then gradually decreasing to a low of -15kW at 25 minutes, before recovering to 5kW at 30 minutes. This trend reflects the energy storage unit's response characteristics as a fast-acting device and its charge / discharge power regulation capabilities, aligning with the iterative updates of the energy storage unit's rapid response and adjustment power regulation capacity sequence.

[0061] The target output of the hydrogen energy unit generally shows a slow upward trend followed by a decline, ranging from approximately 50kW to 92kW. From 0 to 20 minutes, the target output of the hydrogen energy unit increases from 50kW to a peak of 92kW, and then gradually decreases to 76kW. This change reflects the power regulation characteristics of the hydrogen energy unit as a slow-regulating entity over a longer timescale, consistent with the description of the hydrogen energy unit's response time and power regulation capacity sequence extrapolation.

[0062] 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 integrated energy synergy regulation of wind, solar, and hydrogen storage multi-energy complementarity, characterized in that, The method includes: The system acquires the current operating parameter set of various energy units, including wind power, photovoltaic, energy storage, and hydrogen energy, and divides the current operating parameter set into a fast-adjustment parameter subset and a slow-adjustment parameter subset based on the power regulation response time of each unit. Based on the fast-adjustment parameter subset and the slow-adjustment parameter subset, establish the dynamic power coupling constraint boundary of each energy unit at the grid connection point; Based on the dynamic power coupling constraint boundary and the current operating parameter set, calculate the power regulation capacity sequence of each energy unit within multiple prediction steps; Based on the power regulation capacity sequence, with the minimum power deviation at the grid connection point as the optimization guide, the power regulation capacity sequence of each energy unit is iteratively corrected using a directional perturbation optimization method. Based on the iteratively corrected power regulation capacity sequence, power regulation commands for each energy unit within the corresponding prediction step size are generated, and the coordinated regulation of each energy unit is executed according to the power regulation commands.

2. The integrated energy synergistic regulation method for wind, solar, and hydrogen storage multi-energy complementarity according to claim 1, characterized in that, The acquisition of the current operating parameter set for various energy units, including wind power, photovoltaics, energy storage, and hydrogen energy, includes: The system collects wind speed, pitch angle, and generator speed signals from wind power units; irradiance and module temperature signals from photovoltaic units; state of charge and terminal voltage signals from energy storage units; and hydrogen storage tank pressure and electrolyzer temperature signals from hydrogen energy units. After aligning all the collected signals according to their timestamps, they are combined to generate the current set of operating parameters.

3. The integrated energy synergistic regulation method for wind, solar, and hydrogen storage multi-energy complementarity according to claim 1, characterized in that, The process of dividing the current operating parameter set into a fast-adjustment parameter subset and a slow-adjustment parameter subset based on the power adjustment response time of each unit includes: Obtain a first response time threshold for the energy storage unit and a second response time threshold for the hydrogen energy unit, wherein the first response time threshold is less than the second response time threshold; The unit parameters whose response time in the current operating parameter set is less than or equal to the first response time threshold are included in the fast-tuning parameter subset; Unit parameters whose response time is greater than the first response time threshold and less than or equal to the second response time threshold in the current operating parameter set are classified into the slow-tuning parameter subset.

4. The integrated energy synergistic regulation method for wind, solar, and hydrogen storage multi-energy complementarity according to claim 1, characterized in that, The step of establishing the dynamic power coupling constraint boundary for each energy unit at the grid connection point based on the fast-adjustment parameter subset and the slow-adjustment parameter subset includes: Extract the maximum charging power limit and maximum discharging power limit of the energy storage unit from the fast-adjustment parameter subset, and extract the rated hydrogen production power and rated power generation power of the hydrogen energy unit from the slow-adjustment parameter subset; Based on the maximum charging power limit, maximum discharging power limit, rated hydrogen production power, and rated power generation power, construct the upper boundary constraint function and lower boundary constraint function of the active power at the grid connection point; The upper boundary constraint function and the lower boundary constraint function are combined to form the dynamic power coupling constraint boundary.

5. The integrated energy synergistic regulation method for wind, solar, and hydrogen storage multi-energy complementarity according to claim 1, characterized in that, The step of calculating the power regulation capacity sequence of each energy unit within multiple prediction steps based on the dynamic power coupling constraint boundary and the current operating parameter set includes: Extract the current output value and adjustable capacity limit of each energy unit from the current operating parameter set; Under the constraints of the dynamic power coupling boundary, starting from the current output value, the adjustable capacity and adjustable capacity of each energy unit in each prediction step are calculated progressively forward according to the preset prediction step time interval. The adjustable and de-adjustable output capacities within each prediction step are arranged in chronological order to generate the power regulation capacity sequence for each energy unit.

6. The integrated energy synergistic regulation method for wind, solar, and hydrogen storage multi-energy complementarity according to claim 1, characterized in that, The step of iteratively correcting the power regulation capacity sequence of each energy unit based on the power regulation capacity sequence, with the minimum power deviation at the grid connection point as the optimization guide, and using a directional perturbation optimization method, includes: Based on the power regulation capacity sequence of each energy unit, calculate the initial deviation between the current total power at the grid connection point and the grid dispatch target power; Using the initial deviation value as the initial condition for optimization, the power regulation capacity sequence of each energy unit is selected as the current disturbance object in sequence. After applying a positive disturbance to the current disturbance object, the first deviation value between the total power of the grid connection point and the scheduling target power is recalculated. Compare the first deviation value with the initial deviation value. If the first deviation value is less than the initial deviation value, increase the adjustment amount of the current disturbance object along the positive disturbance direction to complete the iterative correction of the current energy unit.

7. The integrated energy synergistic regulation method for wind, solar, and hydrogen storage multi-energy complementarity according to claim 6, characterized in that, After applying a positive disturbance to the current disturbance target and recalculating the first deviation between the total power of the grid connection point and the scheduling target power, the method further includes: If the first deviation value is greater than the initial deviation value, then after applying a reverse disturbance to the current disturbance object, the second deviation value between the total power of the grid connection point and the scheduling target power is recalculated. Compare the second deviation value with the initial deviation value. If the second deviation value is less than the initial deviation value, increase the adjustment amount of the current disturbance object along the reverse disturbance direction to complete the iterative correction of the current energy unit. If the second deviation value is greater than the initial deviation value, then the adjustment amount of the current disturbance object remains unchanged.

8. The integrated energy synergistic regulation method for wind, solar, and hydrogen storage multi-energy complementarity according to claim 1, characterized in that, The step of generating power regulation commands for each energy unit within the corresponding prediction step size based on the iteratively corrected power regulation capacity sequence includes: Extract the final regulation capacity value of each energy unit within each prediction step from the iteratively corrected power regulation capacity sequence; Based on the final regulation capacity value and the current output value of each energy unit, calculate the target output value of each energy unit within each prediction step. The target output value is encapsulated into a power regulation command sequence according to the time order of the prediction step size. Each command in the power regulation command sequence contains a corresponding prediction step size identifier, an energy unit identifier, and a target output value.

9. The integrated energy synergistic regulation method for wind, solar, and hydrogen storage multi-energy complementarity according to claim 8, characterized in that, The coordinated control of each energy unit according to the power adjustment command includes: Based on the predicted step size identifier in the power adjustment command sequence, the power adjustment command sequence is arranged into a control execution queue according to the chronological order; According to the order of the control execution queue, at the start time of each prediction step, the target output value of all energy units corresponding to that prediction step is read. The target output value of each energy unit is sent to the power controller of the corresponding energy unit, and the power controller drives the energy unit to adjust its output to the target output value.

10. The integrated energy synergistic regulation method for wind, solar, and hydrogen storage multi-energy complementarity according to claim 1, characterized in that, In the directional perturbation optimization method, after each iteration correction is completed, the power regulation capacity sequence of each energy unit is updated, and the updated power regulation capacity sequence is used as the input for the next iteration correction, until the deviation between the total power at the grid connection point and the grid dispatch target power is less than the preset convergence threshold, at which point the iteration correction stops.

Images