Offshore wind turbine load scheduling method considering hydrogen production cost

Abstract

The invention relates to an offshore wind turbine load scheduling method considering hydrogen production cost. The method comprises the following steps: acquiring operation data of a wind turbine and a hydrogen production device in an offshore wind plant and preprocessing the data; constructing a multi-objective optimization model considering wind power utilization efficiency, load balance and hydrogen production cost based on the preprocessed data, and calculating expected average power; comparing the power of each fan with expected average power, and dividing the fans into a high-power group and a low-power group; calculating the similarity of the fan time power sequence in the high power group and the fan time power sequence in the low power group one by one, and matching according to the similarity; and setting a target average power and a soft threshold range in each matching group, and scheduling the fan load based on the target average power and the soft threshold range.

Description

Technical Field

[0001] The invention relates to an offshore wind turbine load scheduling method taking hydrogen production costs into consideration, and belongs to the technical field of offshore wind power. Background Art

[0002] As the global demand for renewable energy increases, wind power, as an important component of clean energy, is gradually expanding its installed capacity. However, due to the instability of wind energy itself, the output power of wind farms fluctuates greatly, and direct grid connection may lead to grid stability problems. Using excess wind power for hydrogen production has become an important technical approach to solve this problem.

[0003] Through water electrolysis hydrogen production technology, renewable electricity is converted into hydrogen storage, which not only improves the utilization rate of wind power, but also realizes flexible energy scheduling, especially in the field of green hydrogen, showing broad application prospects.

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

[0005] In order to solve the above problems existing in the prior art, the present invention proposes an offshore wind turbine load scheduling method taking hydrogen production cost into consideration.

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

[0007] On the one hand, the present invention provides an offshore wind turbine load scheduling method considering hydrogen production cost, comprising the following steps:

[0008] Collect the operating data of wind turbines and hydrogen production devices in offshore wind farms and pre-process 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] The fans are divided into high power group and low power group after comparing the power of each fan with the expected average power;

[0011] Take the wind turbine time power sequence in the high power group and the wind turbine time power sequence in the low power group and calculate the similarity one by one, and match them according to the similarity;

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

[0013] As a preferred embodiment of the present invention, a statistical method is used to identify and exclude abnormal values ​​from the operating data of wind turbines and hydrogen production devices in the offshore wind farm, and missing data is checked and filled with interpolation.

[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 life consumption of the fan; EC represents the electricity cost of hydrogen production; PU represents the utilization fluctuation of the fan; w1, w2, and w3 represent the weight coefficients respectively.

[0017] As a preferred embodiment of the present invention, the calculation formula for the consumption of the fan operating life is:

[0018]

[0019] Where: k f represents the fatigue damage coefficient; α represents the fatigue characteristic index of the fan structural component material; σ(t) represents the fan operating stress at time t; σ max Indicates the maximum allowable stress of the structural component material; k 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 represents the environmental impact coefficient; f represents the environmental impact function; T env Indicates ambient temperature; H env Indicates ambient humidity; S env Indicates the amount of environmental sand.

[0020] As a preferred embodiment of the present invention, the calculation formula of the hydrogen production electricity cost is:

[0021]

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

[0023] As a preferred embodiment of the present invention, the fan utilization rate fluctuation calculation formula is:

[0024]

[0025] Where: PAR represents the peak factor.

[0026] As a preferred embodiment of the present invention, the specific steps of calculating the similarity and matching according to the similarity are:

[0027] The fan time power series in the high power group and the fan time power series in the low power group are selected and the Pearson correlation coefficient is calculated according to 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; Represents the element means of X and Y respectively; x i represents the i-th element in X; y i 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] As a preferred embodiment of the present invention, a target average power T is set in each matching group. g and the soft threshold range α;

[0032] When the total power in any matching group is lower than (T g -α), start several high-power group fans or increase the load of the high-power fans in the current matching group;

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

[0034] On the other hand, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in any embodiment of the present invention is implemented.

[0035] In yet another aspect, the present invention further 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. The present invention introduces comprehensive life consumption and environmental impact functions, and introduces factors such as wind turbine life, temperature, humidity, wind and sand into life assessment and wind turbine scheduling decisions to extend equipment life and improve the reliability of long-term operation of wind farms.

[0038] 2. The present invention introduces an optimized electricity cost model, significantly reduces the unit hydrogen cost in the hydrogen production process, realizes the efficient utilization of wind power resources, and promotes the development of the green hydrogen economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

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

[0043] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0044] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0045] Embodiment 1:

[0046] See also Figure 1 , a method for offshore wind turbine load scheduling considering hydrogen production cost, comprising the following steps:

[0047] Collect the operating data of wind turbines and hydrogen production devices in offshore wind farms and pre-process 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 a high power group and a low power group. Specifically, the wind turbines with power lower than the expected average power are grouped as the low power group, and the wind turbines with power higher than the expected average power are grouped as the high power group.

[0050] Take the wind turbine time power sequence in the high power group and the wind turbine time power sequence in the low power group and calculate the similarity one by one, and match them according to the similarity;

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

[0052] As a preferred implementation of this embodiment, a statistical method is used to identify and exclude abnormal values ​​from the operating data of the wind turbines and the hydrogen production device in the offshore wind farm, and missing data is checked and filled with interpolation.

[0053] As a preferred implementation of this embodiment, 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 life consumption of the fan; EC represents the electricity cost of hydrogen production; PU represents the utilization fluctuation of the fan; w1, w2, and w3 represent the weight coefficients respectively.

[0056] As a preferred implementation of this embodiment, the operating life consumption of the fan is mainly determined by the fatigue accumulation of its structural components (such as blades, bearings, etc.), and the calculation formula for the operating life consumption of the fan is:

[0057]

[0058] Where: k f represents the fatigue damage coefficient, reflecting the sensitivity of material properties to fatigue damage; α represents the fatigue characteristic index of the material of the wind turbine structural component (usually 3-5); σ(t) represents the operating stress of the wind turbine 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 represents the load fluctuation coefficient, indicating the impact of load changes on life; 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 erepresents the environmental impact coefficient, which is used to quantify the impact of environmental conditions on life; F represents the environmental impact function; T env Indicates ambient temperature; H env Indicates ambient humidity; S env Indicates the amount of environmental sand;

[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 Respectively represent the weights of the effects of temperature, humidity, and wind and sand on life (determined based on actual experience or experimental data);

[0062] g Te (T evn ) represents the influence function of temperature on components, as shown in the following formula:

[0063]

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

[0065] g H (H evn ) represents the effect of humidity on corrosion or material properties, as 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 ) represents the erosion effect function of wind and sand, which is shown in the following formula:

[0069]

[0070] Where: γ S It represents the influence coefficient of wind-blown sand particles on life loss; ∈ represents the wear sensitivity index (usually takes a value of 1-3);

[0071] As a preferred implementation of this embodiment, the calculation formula for the hydrogen production electricity cost is:

[0072]

[0073] Where: P input，t represents the electric energy input power of the hydrogen production device at time t; C t represents the unit electricity price at time t; P buffer,t represents the output power of the energy storage system at time t; C buffer represents the unit cost of energy storage (including the depreciation cost 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 represents the wind power supply at time t; P grid,t represents the grid supplementary power at time t; C wind , C grid denote the unit costs of wind power and grid respectively;

[0076]

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

[0078] As a preferred implementation of this embodiment, the fan utilization rate fluctuation calculation formula is:

[0079]

[0080] Where: PAR stands for crest factor, which describes the ratio of peak to average power output:

[0081] As a preferred implementation of this embodiment, the specific steps of calculating the similarity and matching according to the similarity are:

[0082] The fan time power series in the high power group and the fan time power series in the low power group are selected and the Pearson correlation coefficient is calculated according to 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; Represents the element means of X and Y respectively; x i represents the i-th element in X; y i 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] As a preferred implementation of this embodiment, the target average power T is set in each matching group. g and the soft threshold range α;

[0087] When the total power in any matching group is lower than (T g -α), start several high-power group fans or increase the load of the high-power fans in the current matching group;

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

[0089] Embodiment 2:

[0090] This embodiment 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 implements the method described in any embodiment of the present invention when executing the program.

[0091] Embodiment three:

[0092] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in any embodiment of the present invention is implemented.

[0093] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated 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 single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

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

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

[0096] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0097] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for offshore wind turbine load scheduling considering hydrogen production cost, characterized in that: The following steps are involved: Collect the operating data of wind turbines and hydrogen production devices in offshore wind farms and pre-process 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 fans are divided into high power group and low power group after comparing the power of each fan with the expected average power; Take the wind turbine time power sequence in the high power group and the wind turbine time power sequence in the low power group and calculate the similarity one by one, and match them according to the similarity; A target average power and a soft threshold range are set in each matching group, and the wind turbine load is scheduled based on the target average power and the soft threshold range.

2. The offshore wind turbine load scheduling method considering hydrogen production demand according to claim 1, characterized in that: Statistical methods are used to identify and eliminate outliers in the operating data of wind turbines and hydrogen production devices in the offshore wind farm, and missing data are checked and filled with interpolation methods.

3. The offshore wind turbine load scheduling method considering hydrogen production demand according to claim 1, characterized in that: The multi-objective optimization model is specifically shown in the following formula: MinimizeZ=w1·EOL+w2·EC+w3·PU Where: Z represents the expected average power; EOL represents the life consumption of the fan; EC represents the electricity cost of hydrogen production; PU represents the utilization fluctuation of the fan; w1, w2, and w3 represent the weight coefficients respectively.

4. The offshore wind turbine load scheduling method considering hydrogen production demand according to claim 3 is characterized in that: The calculation formula for the fan operating life consumption is: Where: k f represents the fatigue damage coefficient; α represents the fatigue characteristic index of the fan structural component material; σ(t) represents the fan operating stress at time t; σ max Indicates the maximum allowable stress of the structural component material; k p represents the load fluctuation coefficient; P(t) represents the fan output power at time t; P avg represents the average output power of the fan at time T; P rated Indicates the rated power of the fan; k e represents the environmental impact coefficient; f represents the environmental impact function; T env Indicates ambient temperature; H env Indicates ambient humidity; S env Indicates the amount of environmental sand.

5. The offshore wind turbine load scheduling method considering hydrogen production demand according to claim 4 is characterized in that: The calculation formula of the hydrogen production electricity cost is: Where: P input，t represents the electric energy input power of the hydrogen production device at time t; C t represents the unit electricity price at time t; P buffer,t represents the output power of the energy storage system at time t; C buffer represents the unit cost of energy storage; η buffer Indicates the efficiency of the energy storage system; Indicates the hydrogen production.

6. The offshore wind turbine load scheduling method considering hydrogen production demand according to claim 5, characterized in that: The fan utilization rate fluctuation calculation formula is: Where: PAR represents the peak factor.

7. The offshore wind turbine load scheduling method considering hydrogen production demand according to claim 1, characterized in that: The specific steps of calculating the similarity and matching according to the similarity are: The fan time power series in the high power group and the fan time power series in the low power group are selected and the Pearson correlation coefficient is calculated according to the following formula: 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; Represents the element means of X and Y respectively; x i represents the i-th element in X; y i represents the i-th element in Y; The two wind turbines with the highest Pearson correlation coefficient were selected to form a matching group.

8. The method for offshore wind turbine load scheduling considering hydrogen production demand according to claim 1, characterized in that: Set the target average power T in each matching group g and the soft threshold range α; When the total power in any matching group is lower than (T g -α), start several high-power group fans or increase the load of the high-power fans in the current matching group; When the total power in any matching group is higher than (T g +α), shut down some high-power fans or reduce the load of high-power fans in the current matching group.

9. 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, the method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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