An offshore wind turbine load scheduling method considering hydrogen production cost

Abstract

The present application relates to a kind of offshore wind turbine load scheduling method considering hydrogen production cost, comprising the following steps: collecting the operation data of offshore wind farm and hydrogen production device and pre-processing data;Based on the data after pre-processing, construct multi-objective optimization model considering wind power utilization efficiency, load balancing and hydrogen production cost to calculate expected average power;The power of each wind turbine is compared with the expected average power, and the wind turbine is divided into high-power group and low-power group;Take the time power sequence of wind turbine in high-power group and the time power sequence of wind turbine in low-power group to calculate similarity one by one, and match according to similarity;In each matching group, set target average power and soft threshold range, based on target average power and soft threshold range, wind turbine load is scheduled.

Description

Technical Field

[0001] This invention relates to a method for scheduling offshore wind turbine loads that takes into account the cost of hydrogen production, and belongs to the field of offshore wind power technology. Background Technology

[0002] With the increasing global demand for renewable energy, wind power, as an important component of clean energy, is gradually expanding its installed capacity. However, due to the inherent instability of wind energy, the output power of wind farms fluctuates significantly, and direct grid connection may lead to grid stability issues. Utilizing surplus wind power for hydrogen production has become an important technological approach to address this problem.

[0003] Hydrogen production technology through water electrolysis converts renewable electrical energy into hydrogen for storage, which not only improves the utilization rate of wind power but also enables flexible energy dispatch, showing broad application prospects, especially in the field of green hydrogen.

[0004] Although wind power hydrogen production technology has made some progress in experimental and small-scale applications, the following major problems still exist: wind power output is random and intermittent; hydrogen production devices usually have high requirements for the stability of input power, and fluctuations will lead to a decrease in efficiency; long-term high load or frequent fluctuations will aggravate the fatigue wear of wind turbine components and shorten their service life; and the off-peak electricity pricing strategy is not fully utilized in wind power hydrogen production systems. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention proposes a method for scheduling offshore wind turbine loads that takes into account hydrogen production costs.

[0006] The technical solution of the present invention is as follows:

[0007] On the one hand, the present invention provides a method for offshore wind turbine load scheduling that takes into account hydrogen production costs, comprising the following steps:

[0008] Collect operational data from wind turbines and hydrogen production units in offshore wind farms and preprocess the data;

[0009] Based on the preprocessed data, a multi-objective optimization model considering wind power utilization efficiency, load balance, and hydrogen production cost is constructed to calculate the expected average power.

[0010] After comparing the power of each wind turbine with the expected average power, the wind turbines are divided into high-power group and low-power group.

[0011] The similarity between the time-power sequences of wind turbines in the high-power group and the time-power sequences of wind turbines in the low-power group is calculated one by one, and matching is performed based on the similarity.

[0012] Within each matching group, a target average power and a soft threshold range are set, and the wind turbine load is scheduled based on the target average power and the soft threshold range.

[0013] In a preferred embodiment of the present invention, statistical methods are used to identify and eliminate outliers in the operating data of wind turbines and hydrogen production units in the offshore wind farm, while missing data is checked and interpolation is used to fill in the missing data.

[0014] As a preferred embodiment of the present invention, the multi-objective optimization model is specifically shown in the following formula:

[0015] MinimizeZ=w1·EOL+w2·EC+w3·PU

[0016] Where: Z represents the expected average power; EOL represents the wind turbine's operating life consumption; EC represents the cost of hydrogen production electricity; PU represents the wind turbine utilization rate fluctuation; w1, w2, and w3 represent the weighting coefficients, respectively.

[0017] In a preferred embodiment of the present invention, the formula for calculating the service life consumption of the wind turbine is as follows:

[0018]

[0019] Where: k f α represents the fatigue damage coefficient; α represents the fatigue characteristic index of the material of the wind turbine structural components; σ(t) represents the operating stress of the wind turbine at time t; σ max Indicates the maximum allowable stress of the structural component material; k p P represents the load fluctuation coefficient; P(t) represents the fan output power at time t; P avg P represents the average output power of the fan at time T; rated Indicates the rated power of the fan; k e T represents the environmental impact coefficient; f represents the environmental impact function; T env Indicates ambient temperature; H env Indicates ambient humidity; S env This indicates the amount of wind and sand in the environment.

[0020] In a preferred embodiment of the present invention, the formula for calculating the cost of hydrogen production electricity is as follows:

[0021]

[0022] Where: P input，t C represents the electrical input power of the hydrogen production unit at time t; t P represents the unit electricity price at time t; buffer,t C represents the output power of the energy storage system at time t; buffer Indicates the unit cost of energy storage; η buffer Indicates the efficiency of the energy storage system; This indicates the amount of hydrogen produced.

[0023] In a preferred embodiment of the present invention, the formula for calculating the fluctuation of wind turbine utilization rate is as follows:

[0024]

[0025] Where PAR represents the peak factor.

[0026] In a preferred embodiment of the present invention, the specific steps for calculating similarity and matching based on similarity are as follows:

[0027] The Pearson correlation coefficient is calculated by successively selecting the time power series of wind turbines in the high-power group and the time power series of wind turbines in the low-power group according to the following formula, as shown in the following formula:

[0028]

[0029] Where: X and Y represent the time power series of the two wind turbines, respectively; r represents the Pearson correlation coefficient; n represents the total number of elements in the time power series; Let x and y represent the mean of elements in X and Y, respectively; x i Represents the i-th element in X; y i This represents the i-th element in Y;

[0030] The two wind turbines with the highest Pearson correlation coefficient were selected to form a matching group.

[0031] In a preferred embodiment of the present invention, a target average power T is set within each matching group. g And the soft threshold range α;

[0032] When the total power within any matched group is detected to be lower than (T) g When -α), start several high-power fan groups or increase the load of high-power fan groups in the current matching group;

[0033] When the total power within any matched group is detected to be higher than (T) g When +α), shut down some high-power fans or reduce the load on the high-power fans in the current matching group.

[0034] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method as described in any embodiment of the present invention.

[0035] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0036] The present invention has the following beneficial effects:

[0037] 1. This invention introduces a comprehensive lifespan consumption and environmental impact function, incorporating factors such as wind turbine lifespan, temperature, humidity, and wind and sand into lifespan assessment and wind turbine scheduling decisions to extend equipment lifespan and improve the reliability of long-term wind farm operation.

[0038] 2. This invention introduces an optimized electricity cost model, which significantly reduces the unit hydrogen cost in the hydrogen production process, realizes the efficient utilization of wind power resources, and contributes to the development of the green hydrogen economy. Attached Figure Description

[0039] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0040] 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, and 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.

[0041] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0042] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0043] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0044] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0045] Example 1:

[0046] See Figure 1 A method for scheduling the load of offshore wind turbines, taking into account the cost of hydrogen production, includes the following steps:

[0047] Collect operational data from wind turbines and hydrogen production units in offshore wind farms and preprocess the data;

[0048] Based on the preprocessed data, a multi-objective optimization model considering wind power utilization efficiency, load balance, and hydrogen production cost is constructed to calculate the expected average power.

[0049] After comparing the power of each wind turbine with the expected average power, the wind turbines are divided into high-power group and low-power group. Specifically, wind turbines with power lower than the expected average power are classified as low-power group, and wind turbines with power higher than the expected average power are classified as high-power group.

[0050] The similarity between the time-power sequences of wind turbines in the high-power group and the time-power sequences of wind turbines in the low-power group is calculated one by one, and matching is performed based on the similarity.

[0051] Within each matching group, a target average power and a soft threshold range are set, and the wind turbine load is scheduled based on the target average power and the soft threshold range.

[0052] In a preferred embodiment of this invention, statistical methods are used to identify and eliminate outliers in the operating data of the wind turbines and hydrogen production units in the offshore wind farm, while missing data is checked and interpolation is used to fill in the missing data.

[0053] As a preferred embodiment of this invention, the multi-objective optimization model is specifically shown in the following formula:

[0054] MinimizeZ=w1·EOL+w2·EC+w3·PU

[0055] Where: Z represents the expected average power; EOL represents the wind turbine's operating life consumption; EC represents the cost of hydrogen production electricity; PU represents the wind turbine utilization rate fluctuation; w1, w2, and w3 represent the weighting coefficients, respectively.

[0056] In a preferred embodiment of this invention, the service life of the wind turbine is mainly determined by the cumulative fatigue of its structural components (such as blades and bearings). The formula for calculating the service life of the wind turbine is as follows:

[0057]

[0058] Where: k f α represents the fatigue damage coefficient, reflecting the sensitivity of material properties to fatigue damage; α represents the fatigue characteristic index of the wind turbine structural component material (usually 3-5); σ(t) represents the wind turbine operating stress at time t (affected by wind speed, blade vibration, and other loads); σ max Indicates the maximum allowable stress of the structural component material; k p P(t) represents the load fluctuation coefficient, indicating the impact of load changes on the lifespan; P(t) represents the fan output power at time t; P avg P represents the average output power of the fan at time T; rated Indicates the rated power of the fan; k eThe environmental impact coefficient represents the environmental impact factor, used to quantify the impact of environmental conditions on lifespan; F represents the environmental impact function; T env Indicates ambient temperature; H env Indicates ambient humidity; S env Indicates the amount of wind and sand in the environment;

[0059] The environmental impact function is specifically shown in the following formula:

[0060] F(T env H env S env ) = C T ·g Te (T evn )+C H ·g H (H evn )+C S ·g S (S env )

[0061] Where: C T C H C S These represent the weights of temperature, humidity, and wind and sand on lifespan (determined based on practical experience or experimental data);

[0062] g Te (T evn The function representing the effect of temperature on the component is shown in the following formula:

[0063]

[0064] Where: E a The activation energy represents the fatigue or aging of the component material; R represents the gas constant (8.314 J / m0l·K).

[0065] g H (H evn The function representing the effect of humidity on corrosion or material properties is shown in the following formula:

[0066] g H (H evn )=57.31+β H ·H evn

[0067] Where: β H Indicates the humidity influence coefficient (determined by material properties);

[0068] g S (S env The function representing the erosion effect of wind and sand is shown in the following formula:

[0069]

[0070] Wherein: γ S The coefficient representing the influence of wind-blown sand particles on lifespan loss; ∈ represents the wear sensitivity index (usually a value of 1-3);

[0071] In a preferred embodiment of this invention, the formula for calculating the cost of hydrogen production electricity is as follows:

[0072]

[0073] Where: P input，t C represents the electrical input power of the hydrogen production unit at time t; t P represents the unit electricity price at time t; buffer,t C represents the output power of the energy storage system at time t; buffer This represents the unit cost of energy storage (including depreciation costs of energy storage equipment); η buffer Indicates the efficiency of the energy storage system (0-1); Indicates hydrogen production;

[0074]

[0075] Where: P wind，t P represents the wind power supply at time t; grid,t C represents the supplementary power of the power grid at time t; wind C grid These represent the unit costs of wind power and the power grid, respectively.

[0076]

[0077] in: This represents the theoretical electrical energy required to produce 1 kg of hydrogen (39.4 kWh / kg); η elect Δt represents the efficiency of the hydrogen electrolyzer (typically 60%-80%); Δt represents the duration of a single time period.

[0078] In a preferred embodiment of this invention, the formula for calculating the fluctuation of the wind turbine utilization rate is as follows:

[0079]

[0080] Where: PAR represents the peak factor, used to describe the ratio of the peak value to the average value of the power output:

[0081] In a preferred embodiment of this invention, the specific steps for calculating similarity and matching based on similarity are as follows:

[0082] The Pearson correlation coefficient is calculated by successively selecting the time power series of wind turbines in the high-power group and the time power series of wind turbines in the low-power group according to the following formula, as shown in the following formula:

[0083]

[0084] Where: X and Y represent the time power series of the two wind turbines, respectively; r represents the Pearson correlation coefficient; n represents the total number of elements in the time power series; Let x and y represent the mean of elements in X and Y, respectively; x i Represents the i-th element in X; y i This represents the i-th element in Y;

[0085] The two wind turbines with the highest Pearson correlation coefficient were selected to form a matching group.

[0086] In a preferred embodiment of this invention, a target average power T is set within each matching group. g And the soft threshold range α;

[0087] When the total power within any matched group is detected to be lower than (T) g When -α), start several high-power fan groups or increase the load of high-power fan groups in the current matching group;

[0088] When the total power within any matched group is detected to be higher than (T) g When +α), shut down some high-power fans or reduce the load on the high-power fans in the current matching group.

[0089] Example 2:

[0090] This embodiment proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method described in any embodiment of the present invention.

[0091] Example 3:

[0092] This embodiment proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0093] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0094] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0095] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0096] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0097] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for scheduling offshore wind turbine loads considering hydrogen production costs, characterized in that, Includes the following steps: Collect operational data from wind turbines and hydrogen production units in offshore wind farms and preprocess the data; Based on the preprocessed data, a multi-objective optimization model considering wind power utilization efficiency, load balance, and hydrogen production cost is constructed to calculate the expected average power. The multi-objective optimization model is specifically shown in the following equation: in: Indicates the expected average power; This indicates the consumption of the wind turbine's operating life; This indicates the cost of electricity used to produce hydrogen; This indicates fluctuations in wind turbine utilization. , , These represent the weighting coefficients; The formula for calculating the service life consumption of the wind turbine is as follows: in: Indicates the fatigue damage coefficient; Indicates the fatigue characteristic index of the materials used in the structural components of the wind turbine; express Constant wind turbine operating stress; Indicates the maximum allowable stress of the structural component material; Indicates the load fluctuation coefficient; express The output power of the fan at all times; express Average output power of the fan within a given time period; Indicates the rated power of the fan; Indicates the environmental impact coefficient; Represents the environmental impact function; Indicates ambient temperature; Indicates ambient humidity; Indicates the amount of wind and sand in the environment; The formula for calculating the cost of hydrogen production electricity is as follows: in: Indicates that the hydrogen production unit is in The electrical energy input power at any given moment; express The unit price of electricity at any given time; Indicates that the energy storage system is in Output power at any given moment; Indicates the unit cost of energy storage; Indicates the efficiency of the energy storage system; Indicates hydrogen production; The formula for calculating the fluctuation of wind turbine utilization rate is as follows: in: Indicates the peak factor; After comparing the power of each wind turbine with the expected average power, the wind turbines are divided into high-power group and low-power group. The similarity between the time-power sequences of wind turbines in the high-power group and the time-power sequences of wind turbines in the low-power group is calculated one by one, and matching is performed based on the similarity. Within each matching group, a target average power and a soft threshold range are set, and the wind turbine load is scheduled based on the target average power and the soft threshold range.

2. The method for offshore wind turbine load scheduling considering hydrogen production costs according to claim 1, characterized in that, Statistical methods were used to identify and eliminate outliers in the operating data of wind turbines and hydrogen production units in the offshore wind farm. At the same time, missing data was checked and interpolation was used to fill in the missing data.

3. The method for offshore wind turbine load scheduling considering hydrogen production costs according to claim 1, characterized in that, The specific steps for calculating similarity and matching based on similarity are as follows: The Pearson correlation coefficient is calculated by successively selecting the time power series of wind turbines in the high-power group and the time power series of wind turbines in the low-power group according to the following formula, as shown in the following formula: in: , These represent the time power sequences of the two wind turbines, respectively. This represents the Pearson correlation coefficient; This represents the total number of elements in the time power sequence; , They represent , The mean of the elements; express The first in One element; express The first in One element; The two wind turbines with the highest Pearson correlation coefficient were selected to form a matching group.

4. The method for offshore wind turbine load scheduling considering hydrogen production costs according to claim 1, characterized in that, Set target average power within each matching group and soft threshold range ; When the total power within any matched group is detected to be lower than At the same time, start several high-power fan groups or increase the load of high-power fan groups in the current matching group; When the total power within any matched group is detected to be higher than At this time, shut down some high-power fan groups or reduce the load on the high-power fans in the current matching group.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 4.

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