Distributed power generation maintenance arrangement optimization method and system considering multiple uncertainties

Abstract

The invention discloses a distributed power generation maintenance arrangement optimization method and system considering multiple uncertainties, and belongs to the technical field of electrical engineering, and the method specifically comprises the steps: arranging maintenance conditions of a sea wind turbine unit; the method comprises the following steps: acquiring sea wind overhaul arrangement data information based on set sea wind unit overhaul arrangement conditions, including sea wind unit parameters with overhaul requirements, an operation and maintenance ship rental price, the number of maintainers, work time salary and overtime time salary; and according to the obtained sea wind maintenance arrangement data information, a wind turbine generator maintenance mode arrangement plan considering multiple uncertain conditions is carried out. And various changing scenes can be coped, and the maintenance cost of the offshore wind turbine generator is reduced.

Description

Background Art

[0002] With the construction of offshore wind power in deep sea and offshore areas, the worse weather and lower accessibility faced by maintenance personnel further limit the choice of maintenance windows, bringing great challenges to the daily maintenance planning and scheduling of wind farms. According to statistics, the maintenance cost of offshore wind power accounts for about 30% of the total cost. Among them, the cost of technical workers and transportation accounts for 28-73% of the maintenance activity cost. In addition, the time for actual maintenance of wind turbines only accounts for 5% of the downtime, and the remaining 95% is caused by waiting for suitable weather and spare parts.

[0003] At present, the research on offshore wind power maintenance mainly focuses on the spare parts strategy of wind farms, the resource allocation of wind farm transport ships, and the formulation of maintenance cycles. Such research belongs to the long-term maintenance strategy of wind farms, and there are few studies on short-term maintenance strategies. In the field of offshore wind power, the maintenance scheduling strategy of short-term maintenance plans refers to the refinement of the start and stop status of generator sets, the allocation of maintenance resources and the arrangement of personnel under the conditions of meeting various maintenance constraints, so as to achieve the lowest cost of this maintenance activity. When scheduling maintenance tasks, factors such as wind and waves will affect the accessible window for maintenance personnel to go out to sea, and factors such as the availability of technicians and the leasing of maintenance ships will affect the execution efficiency of the work. Most of the existing research on short-term maintenance plans for offshore wind power is based on completely accurate prediction information, and maintenance scheduling optimization is performed on this basis, while the factors affecting the maintenance plan are not fully considered. Due to the uncertainty of wind speed, waves, wind power prices, etc., deterministic maintenance strategies cannot cope with a variety of situations. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a distributed power generation maintenance scheduling optimization method and system taking into account multiple uncertainties in view of the deficiencies in the above-mentioned prior art, so as to solve the technical problems that the existing factors affecting the maintenance plan are not considered comprehensively enough and the deterministic maintenance strategy cannot cope with various situations.

[0005] The present invention adopts the following technical solutions:

[0006] The distributed generation maintenance scheduling optimization method considering multiple uncertainties includes the following steps:

[0007] Set up maintenance scheduling conditions for sea wind turbines;

[0008] Based on the set sea wind turbine maintenance scheduling conditions, obtain sea wind maintenance scheduling data information, including sea wind turbine parameters with maintenance requirements, operation and maintenance ship rental prices, number of maintenance personnel, working hourly wages, and overtime hourly wages;

[0009] According to the acquired offshore wind maintenance scheduling data information, taking the maximum power generation benefit as the objective function, the wind turbine maintenance mode scheduling optimization plan is executed under multiple uncertain conditions.

[0010] Preferably, the maintenance conditions of the sea wind turbine include:

[0011] The offshore wind farm consists of I wind turbines. The maintenance task on each wind turbine requires τi hours of maintenance and must be completed before the Jth day.

[0012] In both short-term and long-term planning, the wind speed at the turbine hub, wave height, and wind power grid price are all uncertain;

[0013] The accessibility of wind turbines is limited by the safety thresholds of wind speed at the turbine hub and sea wave height.

[0014] Preferably, the sea wind turbine parameters requiring maintenance include rated power, cut-in wind speed, cut-out wind speed, and rated wind speed.

[0015] Preferably, the maintenance arrangement plan of the wind turbine generator set under multiple uncertain conditions is specifically as follows:

[0016] A two-stage optimization model for scheduling maintenance plans for offshore wind turbines was established with the goals of maximizing power generation revenue, minimizing maintenance costs, and minimizing downtime losses. The first stage is the scheduling plan for the 24 hours before the day, and the second stage is the scheduling plan for multiple days in the future after the short-term stage. Taking the maximization of power generation revenue as the objective function, the optimal scheduling of maintenance methods for offshore wind turbines is achieved by considering various constraints.

[0017] Preferably, the objective function is:

[0018]

[0019] Among them, r STS For short-term gains; is a set of days; d is the day number; For the future days’ long-term benefit, and l is the additional cost caused by delayed maintenance.

[0020] Preferably, in the short-term profit stage, the decision variable is m t,i 、n t,i and v, respectively, are the preventive maintenance, corrective maintenance and chartering instructions of wind turbine i at time t;

[0021] In the long-term benefit stage, the decision variables are and They are the preventive maintenance, corrective maintenance and chartering instruction of wind turbine i in scenario s on day d, respectively;

[0022] All decision variables in the short-term and long-term stages are binary variables.

[0023] Preferably, the short-term return r STS , long-term benefits The additional costs caused by delayed maintenance are as follows:

[0024]

[0025] in, Assemble for the fans; is the time set of STS; M, C are the maintenance costs of performing a preventive maintenance and a corrective maintenance respectively; R, E t are the leasing unit price of the operation and maintenance ship and the wind power sales price respectively; Ψ and Q are the hourly wage and overtime hourly wage of employees respectively; p t,i,s is the power generation of wind turbine i at time t in scenario s; x t,i,s ∈{0,1} is whether wind turbine i in scene s is under maintenance at time t; q s is the total overtime hours of the day; i ∈{0,1} is an input parameter, indicating whether the fan has been assigned a maintenance instruction; is the power generation on the dth day; τ i is the maintenance time of fan i; P rate is the rated power of the wind turbine; Estimated average power generation for the first day of LTS.

[0026] Preferably, the constraints are as follows:

[0027] Repair constraints:

[0028]

[0029] Among them, θ i ∈{0,1} is the maintenance demand of fan i, which is the input parameter; A t,i,s is the delay time of the maintenance command issued by scenario s to wind turbine i at time t in STS, and is the input parameter, I t,i,s ∈{0,1} indicates whether there is at least one maintenance window in the STS after time period t; b i,s The time required to transfer the maintenance tasks that have not been executed in STS to LTS for execution; i,s Indicates whether the maintenance instruction cannot be executed in the fan i of scene s in STS; B is the maximum value of employee dispatch; The demand for additional working groups, that is, if the maintenance order causes the number of maintenance working groups required to exceed the original wind farm configuration B, it is necessary to temporarily dispatch additional working groups to participate in the maintenance; g i,sIndicates whether the fan i fails to shut down after the maintenance command is delayed, g i,s When it is 0, it means a fault has occurred and the machine has stopped; d J is the last day of LTS; β is a small positive number, α d,i,s is an input parameter indicating whether wind turbine i in scenario s is accessible on day d, that is, whether there is at least one maintenance window on day d;

[0030] Wind turbine availability constraints:

[0031]

[0032] Among them, y t,i,s is whether wind turbine i is shut down at time t in scenario s; y d,i,s is whether wind turbine i in scenario s is shut down on day d; represents the corrective maintenance instruction of wind turbine i in scenario s on day d; θ i ∈{0,1} is the maintenance demand of fan i; M is a positive integer;

[0033] Constraints on operation and maintenance vessels and employees:

[0034]

[0035]

[0036] Among them, m t,i 、n t,i and v represent PM, CM and charter order of wind turbine i at time t, respectively; They represent the preventive maintenance, corrective maintenance and chartering instructions of wind turbine i in scenario s on day d; W represents the daily working hours of each employee; B is the maximum value of employee dispatch; x t,i,s ∈{0,1} is whether wind turbine i in scene s is under maintenance at time t; q s The total overtime hours for the day; The additional overtime hours for the day; The time required to transfer the maintenance tasks that have not been executed in STS to LTS for execution; τ i It indicates the continuous time that maintenance needs to meet; H indicates the number of hours that the total overtime time cannot exceed.

[0037] In a second aspect, an embodiment of the present invention provides a distributed generation maintenance scheduling optimization system taking into account multiple uncertainties, including:

[0038] Condition module, setting the maintenance scheduling conditions of sea wind turbines;

[0039] The parameter module obtains the sea wind maintenance scheduling data information based on the set sea wind unit maintenance scheduling conditions, including the parameters of the sea wind units with maintenance requirements, the rental price of the operation and maintenance ship, the number of maintenance personnel, the working hourly wage, and the overtime hourly wage;

[0040] The scheduling module, based on the acquired offshore wind maintenance scheduling data information, takes the maximum power generation benefit as the objective function and executes the optimization plan of wind turbine maintenance mode scheduling under multiple uncertain conditions.

[0041] Preferably, the maintenance scheduling conditions for sea wind turbines include:

[0042] The offshore wind farm consists of I wind turbines. The maintenance task on each wind turbine requires τi hours of maintenance and must be completed before the Jth day.

[0043] In both short-term and long-term planning, the wind speed at the turbine hub, wave height, and wind power grid price are all uncertain;

[0044] The accessibility of wind turbines is limited by the safety thresholds of wind speed at the turbine hub and sea wave height.

[0045] Preferably, the sea wind turbine parameters requiring maintenance include rated power, cut-in wind speed, cut-out wind speed, and rated wind speed.

[0046] Preferably, the maintenance arrangement plan of the wind turbine generator set under multiple uncertain conditions is specifically as follows:

[0047] A two-stage optimization model for scheduling maintenance plans for offshore wind turbines was established with the goals of maximizing power generation revenue, minimizing maintenance costs, and minimizing downtime losses. The first stage is the scheduling plan for the 24 hours before the day, and the second stage is the scheduling plan for multiple days in the future after the short-term stage. Taking the maximization of power generation revenue as the objective function, the optimal scheduling of maintenance methods for offshore wind turbines is achieved by considering various constraints.

[0048] Preferably, the objective function is:

[0049]

[0050] Among them, r STS For short-term gains; is a set of days; d is the day number; For the future days’ long-term benefit, and l is the additional cost caused by delayed maintenance.

[0051] Preferably, in the short-term profit stage, the decision variable is m t,i 、n t,i and v, respectively, are the preventive maintenance, corrective maintenance and chartering instructions of wind turbine i at time t;

[0052] In the long-term benefit stage, the decision variables are and They are the preventive maintenance, corrective maintenance and chartering instruction of wind turbine i in scenario s on day d, respectively;

[0053] All decision variables in the short-term benefit stage and the long-term benefit stage are binary variables;

[0054] Short-term benefits STS , long-term benefits The additional costs caused by delayed maintenance are as follows:

[0055]

[0056] in, Assemble for the fans; is the time set of STS; M, C are the maintenance costs of performing a preventive maintenance and a corrective maintenance respectively; R, E t are the leasing unit price of the operation and maintenance ship and the wind power sales price respectively; Ψ and Q are the hourly wage and overtime hourly wage of employees respectively; p t,i,s is the power generation of wind turbine i at time t in scenario s; x t,i,s ∈{0,1} is whether wind turbine i in scene s is under maintenance at time t; q s is the total overtime hours of the day; i ∈{0,1} is an input parameter, indicating whether the fan has been assigned a maintenance instruction; is the power generation on the dth day; τ i is the maintenance time of fan i; P rate is the rated power of the wind turbine; Estimated average power generation for the first day of LTS.

[0057] Preferably, the constraints are as follows:

[0058] Repair constraints:

[0059]

[0060] Among them, θ i ∈{0,1} is the maintenance demand of fan i, which is the input parameter; A t,i,s is the delay time of the maintenance command issued by scenario s to wind turbine i at time t in STS, and is the input parameter, I t,i,s ∈{0,1} indicates whether there is at least one maintenance window in the STS after time period t; b i,s The time required to transfer the maintenance tasks that have not been executed in STS to LTS for execution; i,sIndicates whether the maintenance instruction cannot be executed in the fan i of scene s in STS; B is the maximum value of employee dispatch; The demand for additional working groups, that is, if the maintenance order causes the number of maintenance working groups required to exceed the original wind farm configuration B, it is necessary to temporarily dispatch additional working groups to participate in the maintenance; g i,s Indicates whether the fan i fails to shut down after the maintenance command is delayed, g i,s When it is 0, it means a fault has occurred and the machine has stopped; d J is the last day of LTS; β is a small positive number, α d,i,s is an input parameter indicating whether wind turbine i in scenario s is accessible on day d, that is, whether there is at least one maintenance window on day d;

[0061] Wind turbine availability constraints:

[0062]

[0063] Among them, y t,i,s is whether wind turbine i is shut down at time t in scenario s; y d,i,s is whether wind turbine i in scenario s is shut down on day d; represents the corrective maintenance instruction of wind turbine i in scenario s on day d; θ i ∈{0,1} is the maintenance demand of fan i; M is a positive integer;

[0064] Constraints on operation and maintenance vessels and employees:

[0065]

[0066] Among them, m t,i 、n t,i and v represent PM, CM and charter order of wind turbine i at time t, respectively; They represent the preventive maintenance, corrective maintenance and chartering instructions of wind turbine i in scenario s on day d; W represents the daily working hours of each employee; B is the maximum value of employee dispatch; x t,i,s ∈{0,1} is whether wind turbine i in scene s is under maintenance at time t; q s The total overtime hours for the day; The additional overtime hours for the day; The time required to transfer the maintenance tasks that have not been executed in STS to LTS for execution; τ i It indicates the continuous time that maintenance needs to meet; H indicates the number of hours that the total overtime time cannot exceed.

[0067] In a third aspect, a computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for optimizing distributed generation maintenance scheduling taking into account multiple uncertainties when executing the computer program.

[0068] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned distributed power generation maintenance scheduling optimization method taking into account multiple uncertainties.

[0069] Compared with the prior art, the present invention has at least the following beneficial effects:

[0070] The distributed power generation maintenance scheduling optimization method taking into account multiple uncertainties makes conditional assumptions based on the characteristics of actual offshore wind maintenance, thereby improving the practical application value of subsequent scheduling of offshore wind unit maintenance plans; obtains offshore wind maintenance scheduling data information to provide boundary conditions for subsequent scheduling of offshore wind unit maintenance plans; fully considers the uncertainties of wind speed, waves, wind power prices, etc., and schedules wind turbine maintenance methods under multiple uncertain conditions, which can better cope with changing scenarios in actual scheduling.

[0071] Furthermore, in the maintenance condition setting of offshore wind turbines, setting maintenance conditions for the number of wind turbines in offshore wind farms, maintenance duration and time limit provides boundary parameters for subsequent maintenance scheduling; setting maintenance conditions with uncertainties in wind speed, wave height and wind power grid-connected electricity price can better cope with changing scenarios; setting maintenance conditions in which the accessibility of wind turbines is limited by safety thresholds of wind speed and sea wave height ensures the practicality of the maintenance scheduling plan. Therefore, setting the above offshore wind turbine maintenance conditions fully considers the actual characteristics of offshore wind turbine maintenance, and can improve the practical application value of subsequent scheduling of offshore wind turbine maintenance plans.

[0072] Furthermore, the maintenance performed before the remaining life of each wind turbine is set as preventive maintenance, and the maintenance performed thereafter is set as corrective maintenance, and the cost of corrective maintenance is higher than the cost of preventive maintenance. This is conducive to allowing wind turbines to undergo preventive maintenance as much as possible in the subsequent arrangement of offshore wind unit maintenance methods, thereby reducing the cost of arranging offshore wind unit maintenance methods and improving the economy of arranging offshore wind unit maintenance methods.

[0073] Furthermore, by establishing a two-stage offshore wind turbine maintenance plan scheduling optimization model with the goals of maximizing power generation revenue, minimizing maintenance cost and minimizing downtime loss, a wind turbine maintenance plan is scheduled under multiple uncertain conditions. Taking multiple uncertainties into account ensures the practicality of the maintenance schedule. Setting the objective function to maximize power generation revenue is conducive to reducing the cost of maintenance plan scheduling. Scheduling maintenance in two stages, short-term and long-term, makes the maintenance schedule more reasonable.

[0074] Furthermore, setting wind turbine preventive maintenance, corrective maintenance and chartering instructions as decision variables in the short-term and long-term stages respectively can take into account the influence of uncertainty factors at different time scales and effectively consider the different maintenance scheduling characteristics in the short-term and long-term stages, thereby obtaining targeted preventive maintenance, corrective maintenance and chartering instructions in the short-term and long-term stages, thereby improving the effectiveness and rationality of the optimization method for scheduling maintenance methods of offshore wind units.

[0075] Furthermore, maintenance constraints, wind turbine availability constraints, and maintenance ship and employee constraints are set as constraints for the offshore wind unit maintenance mode scheduling optimization method. The maintenance constraints take into account the actual characteristics of offshore wind unit maintenance, including maintenance requirements, maintenance time limit, maintenance continuity, etc. The wind turbine availability constraints describe whether the wind turbine is in an available state; the maintenance ship and employee constraints describe the maintenance ship rental requirements and employee working hour restrictions. Setting the above constraints is conducive to more refined modeling of offshore wind unit maintenance mode scheduling, making the maintenance scheduling closer to reality and improving the effectiveness of the maintenance mode scheduling optimization method.

[0076] It can be understood that the beneficial effects of the second aspect mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0077] In summary, the present invention can achieve refined scheduling and reduce offshore wind power maintenance costs.

[0078] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 Cost graphs for the three strategies in 40 test scenarios;

[0080] Figure 2 Result diagrams of the overhaul arrangement for the three strategies in scenario 1;

[0081] Figure 3 A schematic diagram of a computer device provided by an embodiment of the present invention;

[0082] Figure 4 A block diagram of an electronic device provided according to an embodiment of the present invention;

[0083] Figure 5 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0084] 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 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.

[0085] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0086] It should also 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.

[0087] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0088] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0089] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0090] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0091] The present invention provides a distributed power generation maintenance scheduling optimization method taking into account multiple uncertainties, and establishes a two-stage offshore wind turbine maintenance scheduling optimization model with the goals of maximizing power generation revenue, minimizing maintenance cost, and minimizing downtime loss; the first stage is a scheduling plan for 24 hours a day, called the short term stage (STS), and the second stage is a scheduling plan for multiple days in the future after the short term stage, called the long term stage (LTS); an optimization process method that can roll over input information daily is established to provide support for scheduling daily maintenance activities of wind farms, making the offshore wind turbine maintenance scheduling optimization method more scalable and practical.

[0092] See also Figure 5 The present invention provides a distributed generation maintenance scheduling optimization method taking into account multiple uncertainties, comprising the following steps:

[0093] S1. Three conditions are set for the maintenance arrangement of offshore wind turbines;

[0094] Condition 1: The offshore wind farm consists of I wind turbines. The maintenance task on each wind turbine requires τi hours of maintenance and must be completed before the Jth day.

[0095] The types of maintenance tasks considered by the present invention are small and medium-sized maintenance tasks, which usually take less than one day.

[0096] Condition 2: In both short-term and long-term planning, the wind speed at the turbine hub, wave height, and wind power grid price are all uncertain;

[0097] For fan failure, assume that each fan is equipped with a remaining life monitoring device, and the monitoring value is given by λ i Indicates that maintenance performed before the remaining life is called preventive maintenance (PM), and maintenance performed after that is called corrective maintenance (CM). In order to allow the fan to adopt PM as much as possible, the cost of CM is set higher than that of PM.

[0098] Condition 3: The accessibility of wind turbines is limited by the safety thresholds of wind speed at the turbine hub and sea wave height;

[0099] In the short-term planning, the transport ship can only carry out maintenance activities on the wind turbine when the weather and sea conditions are satisfied at the same time, and the maintenance activities must be carried out continuously without interruption in time;

[0100] In long-term planning, if there is at least one opportunity to arrange maintenance activities on day d, the day is considered accessible; at most one maintenance ship is rented per day to transport technicians into the wind farm, and work can only be carried out during the day.

[0101] S2. Obtain sea wind maintenance scheduling data information, including sea wind unit parameters (rated power, cut-in wind speed, cut-out wind speed, rated wind speed) that require maintenance, operation and maintenance ship rental prices, number of maintenance personnel, working hourly wages, and overtime hourly wages;

[0102] S3. According to the sea wind maintenance scheduling data information obtained in step S2, a maintenance mode scheduling plan for the wind turbines is carried out taking into account multiple uncertain conditions. The specific steps are as follows:

[0103] S301, taking the maximum power generation benefit as the objective function, and considering various constraints to achieve the optimal arrangement of the maintenance mode of the offshore wind turbine;

[0104] The following formula is used as the objective function:

[0105]

[0106] Among them, the first term r STS For short-term gains; is a set of days; d is the day number; the second item For the future The third item is the additional cost caused by delayed maintenance.

[0107] In STS, the decision variable is m t,i 、n t,i and v are PM, CM and charter order of wind turbine i at time t, respectively.

[0108] In LTS, the decision variables are and are the PM, CM and chartering instructions for wind turbine i in scenario s on day d, respectively.

[0109] All decision variables in both stages are binary variables.

[0110]

[0111] in, Assemble for the fans; is the time set of STS; M and C are the maintenance costs of performing PM and CM respectively; R and E t are the leasing unit price of the operation and maintenance ship and the wind power sales price respectively; Ψ and Q are the hourly wage and overtime hourly wage of employees respectively; p t,i,s is the power generation of wind turbine i at time t in scenario s; x t,i,s ∈{0,1} is whether wind turbine i in scene s is under maintenance at time t; q s is the total overtime hours of the day; i ∈{0,1} is an input parameter, indicating whether the fan has been assigned a maintenance instruction.

[0112] The benefits in LTS are similar to those in STS. is the power generation on the dth day; τ i is the maintenance time of fan i; P rate is the rated power of the wind turbine; Estimated average power generation for the first day of LTS.

[0113] S302, using the following formula as a constraint condition:

[0114] Repair constraints:

[0115]

[0116] Among them, θ i ∈{0,1} is the maintenance demand of fan i, which is the input parameter; A t,i,s is the delay time for the maintenance command issued by scenario s to wind turbine i at time t in STS, which is an input parameter. 8,i = 1, and in scenario s, wind turbine i is inaccessible in period 8 but accessible in period 9, then A 8,i,s =1, that is, the maintenance instruction will be executed one hour later. t,i,s ∈{0,1} indicates whether there is at least one maintenance window in the STS from time period t onwards; The time required to transfer the maintenance tasks that have not been executed in STS to LTS for execution; i,s ∈{0,1} indicates whether the wind turbine i in scene s is unable to execute the maintenance instruction in STS; B is the maximum value of employee dispatch; The demand for additional working groups, that is, if the maintenance order causes the number of maintenance working groups required to exceed the original wind farm configuration B, it is necessary to temporarily dispatch additional working groups to participate in the maintenance; g i,s ∈{0,1} indicates whether the fan i fails to shut down after the maintenance instruction is delayed, g i,s When it is 0, it means a fault has occurred and the machine has stopped; d J is the last day of LTS; β is a small positive number to avoid d=d JWhen the denominator is 0; α d,i,s ∈{0,1} is an input parameter indicating whether wind turbine i in scenario s is accessible on day d, that is, whether there is at least one maintenance window on day d.

[0117] Wind turbine availability constraints:

[0118]

[0119] Among them, y t,i,s ∈{0,1} is whether wind turbine i is shut down at time t in scenario s; y d,i,s ∈{0,1} is whether wind turbine i in scenario s stops working on day d; M is a large positive integer.

[0120] Constraints on operation and maintenance vessels and employees:

[0121]

[0122] Among them, W represents the daily working hours of each employee; H represents the number of hours that the total overtime time cannot exceed.

[0123] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0124] In another embodiment of the present invention, a distributed power generation maintenance scheduling optimization system taking into account multiple uncertainties is provided. The system can be used to implement the above-mentioned distributed power generation maintenance scheduling optimization method taking into account multiple uncertainties. Specifically, the distributed power generation maintenance scheduling optimization system taking into account multiple uncertainties includes a condition module, a parameter module and a scheduling module.

[0125] Among them, the condition module sets the maintenance scheduling conditions of the sea wind unit;

[0126] The maintenance scheduling conditions for offshore wind turbines include:

[0127] The offshore wind farm consists of I wind turbines. The maintenance task on each wind turbine requires τi hours of maintenance and must be completed before the Jth day.

[0128] In both short-term and long-term planning, the wind speed at the turbine hub, wave height, and wind power grid price are all uncertain;

[0129] The accessibility of wind turbines is limited by the safety thresholds of wind speed at the turbine hub and sea wave height.

[0130] The parameter module obtains the sea wind maintenance scheduling data information based on the set sea wind unit maintenance scheduling conditions, including the parameters of the sea wind units with maintenance requirements, the rental price of the operation and maintenance ship, the number of maintenance personnel, the working hourly wage, and the overtime hourly wage;

[0131] The parameters of offshore wind turbines that require maintenance include rated power, cut-in wind speed, cut-out wind speed, and rated wind speed.

[0132] The scheduling module, based on the acquired offshore wind maintenance scheduling data information, takes the maximum power generation benefit as the objective function and executes the optimization plan of wind turbine maintenance mode scheduling under multiple uncertain conditions.

[0133] The specific planning of wind turbine maintenance mode under multiple uncertain conditions is as follows:

[0134] A two-stage optimization model for scheduling maintenance plans for offshore wind turbines was established with the goals of maximizing power generation revenue, minimizing maintenance costs, and minimizing downtime losses. The first stage is the scheduling plan for the 24 hours before the day, and the second stage is the scheduling plan for multiple days in the future after the short-term stage. Taking the maximization of power generation revenue as the objective function, the optimal scheduling of maintenance methods for offshore wind turbines is achieved by considering various constraints.

[0135] The objective function is:

[0136]

[0137] Among them, r STS For short-term gains; is a set of days; d is the day number; For the future days’ long-term benefit, and l is the additional cost caused by delayed maintenance.

[0138] In the short-term profit stage, the decision variable is m t,i 、n t,i and v, respectively, are the preventive maintenance, corrective maintenance and chartering instructions of wind turbine i at time t;

[0139] In the long-term benefit stage, the decision variables are and They are the preventive maintenance, corrective maintenance and chartering instruction of wind turbine i in scenario s on day d, respectively;

[0140] All decision variables in the short-term benefit stage and the long-term benefit stage are binary variables;

[0141] Short-term benefits STS , long-term benefits The additional costs caused by delayed maintenance are as follows:

[0142]

[0143] in, Assemble for the fans; is the time set of STS; M, C are the maintenance costs of performing a preventive maintenance and a corrective maintenance respectively; R, E t are the leasing unit price of the operation and maintenance ship and the wind power sales price respectively; Ψ and Q are the hourly wage and overtime hourly wage of employees respectively; p t,i,s is the power generation of wind turbine i at time t in scenario s; x t,i,s ∈{0,1} is whether wind turbine i in scene s is under maintenance at time t; q s is the total overtime hours of the day; i ∈{0,1} is an input parameter, indicating whether the fan has been assigned a maintenance instruction; is the power generation on the dth day; τ i is the maintenance time of fan i; P rate is the rated power of the wind turbine; Estimated average power generation for the first day of LTS.

[0144] The constraints are as follows:

[0145] Repair constraints:

[0146]

[0147] Among them, θ i ∈{0,1} is the maintenance demand of fan i, which is the input parameter; A t,i,s is the delay time of the maintenance command issued by scenario s to wind turbine i at time t in STS, and is the input parameter, I t,i,s ∈{0,1} indicates whether there is at least one maintenance window in the STS after time period t; b i,s The time required to transfer the maintenance tasks that have not been executed in STS to LTS for execution; i,s Indicates whether the maintenance instruction cannot be executed in the fan i of scene s in STS; B is the maximum value of employee dispatch; The demand for additional working groups, that is, if the maintenance order causes the number of maintenance working groups required to exceed the original wind farm configuration B, it is necessary to temporarily dispatch additional working groups to participate in the maintenance; g i,s Indicates whether the fan i fails to shut down after the maintenance command is delayed, g i,s When it is 0, it means a fault has occurred and the machine has stopped; d J is the last day of LTS; β is a small positive number, α d,i,s is an input parameter indicating whether wind turbine i in scenario s is accessible on day d, that is, whether there is at least one maintenance window on day d;

[0148] Wind turbine availability constraints:

[0149]

[0150] Among them, y t,i,s is whether wind turbine i is shut down at time t in scenario s; y d,i,s is whether wind turbine i in scenario s is shut down on day d; represents the corrective maintenance instruction of wind turbine i in scenario s on day d; θ i ∈{0,1} is the maintenance demand of fan i; M is a positive integer;

[0151] Constraints on operation and maintenance vessels and employees:

[0152]

[0153] Among them, m t,i 、n t,i and v represent PM, CM and charter order of wind turbine i at time t, respectively; They represent the preventive maintenance, corrective maintenance and chartering instructions of wind turbine i in scenario s on day d; W represents the daily working hours of each employee; B is the maximum value of employee dispatch; x t,i,s ∈{0,1} is whether wind turbine i in scene s is under maintenance at time t; q s The total overtime hours for the day; The additional overtime hours for the day; The time required to transfer the maintenance tasks that have not been executed in STS to LTS for execution; τ i It indicates the continuous time that maintenance needs to meet; H indicates the number of hours that the total overtime time cannot exceed.

[0154] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the distributed generation maintenance scheduling optimization method taking into account multiple uncertainties, including:

[0155] Set the maintenance scheduling conditions for offshore wind turbines; based on the set maintenance scheduling conditions for offshore wind turbines, obtain offshore wind maintenance scheduling data information, including the parameters of offshore wind turbines with maintenance requirements, operation and maintenance ship rental prices, the number of maintenance personnel, working hourly wages, and overtime hourly wages; based on the acquired offshore wind maintenance scheduling data information, take the maximization of power generation revenue as the objective function, and execute the wind turbine maintenance mode scheduling optimization plan under multiple uncertain conditions.

[0156] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device, and can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples (non-exhaustive list) of the computer-readable storage medium here include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0157] Computer readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program codes. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0158] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0159] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the method for optimizing the maintenance scheduling of distributed generation considering multiple uncertainties in the above-mentioned embodiment; the processor may load and execute the following steps of one or more instructions in the computer-readable storage medium:

[0160] Set the maintenance scheduling conditions for offshore wind turbines; based on the set maintenance scheduling conditions for offshore wind turbines, obtain offshore wind maintenance scheduling data information, including the parameters of offshore wind turbines with maintenance requirements, operation and maintenance ship rental prices, the number of maintenance personnel, working hourly wages, and overtime hourly wages; based on the acquired offshore wind maintenance scheduling data information, take the maximization of power generation revenue as the objective function, and execute the wind turbine maintenance mode scheduling optimization plan under multiple uncertain conditions.

[0161] See also Figure 3 , the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the method for calculating the fluid composition in the reservoir transformation wellbore in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the distributed generation maintenance scheduling optimization system taking into account multiple uncertainties in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0162] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 3 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0163] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, central processing units, graphics processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, data processing logic based on quantum computing, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0164] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0165] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0166] Any reference to memory, database or other media used in the embodiments provided in the present application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0167] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0168] See also Figure 4 The terminal device 600 is an electronic device, and the electronic device is in the form of a general computing device. The components of the electronic device may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0169] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 5 Follow the steps shown in .

[0170] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0171] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0172] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0173] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0174] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0175] Next, a specific example is used to verify the feasibility of the proposed optimization method for scheduling maintenance modes of offshore wind turbines taking into account multiple uncertainties.

[0176] This example considers that there are 5 wind turbines that need maintenance. The rated power of the wind turbine is 12MW, the cut-in wind speed is 2.5m / s, the rated wind speed is 12m / s, and the cut-out wind speed is 25m / s. The rental price of the operation and maintenance ship is US$2,500 / day. The number of maintenance personnel is 4, divided into 2 working groups. Each working group is paid US$250 per hour, and the overtime hourly wage is US$125. The standard working time of each working group is 8 hours. The overtime time is over 8 hours, and the total overtime of all people per day is up to 8 hours. The cost of PM is US$4,000 / time, and the cost of CM is US$16,000 / time. Considering the time range of maintenance optimization is 20 days, that is, the wind turbine with a remaining life of less than 20 days is set to θ=1. The maximum wind speed for safe operation at sea is 15m / s, and the maximum wave height is 1.8m. The maintenance time of the five wind turbines is set to τ1 = 8h, τ2 = 7h, τ3 = 6h, τ4 = 7h, τ5 = 4h, and the remaining life is λ1 = 2d, λ2 = 8d, λ3 = 12d, λ4 = 16.2d, λ5 = 18d, respectively. The wind speed prediction error is 3m / s, the wave height prediction error is 0.5m, and the electricity price prediction error is 4 USD. The constructed maintenance scheduling model is solved by CPLEX.

[0177] The number of uncertain scenarios of random planning is set to 20 in total. In order to test the ability of the proposed maintenance scheduling model to cope with uncertainty, the following two strategies are set for comparison: 1) Assuming that the future wind speed, wave height and electricity price can be accurately predicted, that is, having perfect prediction information, and making maintenance scheduling planning on this basis, the purpose of this strategy is to compare the gap between the strategy considering uncertainty and the optimal strategy; 2) Only using the single wind speed, wave height and electricity price prediction results for maintenance scheduling planning, that is, single scenario planning, the prediction results of this scenario have the same error distribution as the results of multi-scenario prediction, this strategy is used to reflect the superiority of the maintenance scheduling strategy considering uncertainty.

[0178] The maintenance costs of the three strategies in 40 different weather and electricity price test scenarios are as follows: Figure 1 As shown. It can be seen that compared with random planning, the cost of single-scenario prediction planning is relatively high overall, and the cost of random planning is closer to perfect information planning. It can be seen that random planning that takes uncertainty into account has better economy and better ability to cope with predicted changes. More detailed results of the three strategies in these 40 test scenarios are shown in Table 1. It should be noted that the maintenance requirements of the present invention (remaining life of the fan, maintenance time) are unchanged in these 40 test scenarios. Different test scenarios mean different prediction information, so the maintenance scheduling results are also different.

[0179] Tab.1 Detailed performance comparison of three strategies in 40 test scenarios

[0180]

[0181] As shown in Table 1, the average maintenance cost of random planning in each scenario is about $35,732.6, which is only 1.5% lower than the $35,188.8 based on perfect information planning, while the cost of single-scenario prediction planning is $38,111.8, which is 8.3% lower than the cost of perfect information planning. In terms of power generation losses, the average loss per scenario of random planning is $2,063.9, which is $1,026.5 less than the $3,089.4 of single-scenario planning, reducing more downtime losses. In terms of additional costs, the additional required work group xa and its working hours qa of random planning are 0, which is consistent with perfect information planning. However, due to the inability to consider uncertainty, the maintenance scheduling plan given by the single-scenario prediction planning sometimes exceeds the original wind farm staffing, so there is an average of 0.125 additional work group requirements per scenario, and the corresponding additional work group working time is 0.8 hours. The more times the ship is rented, the more dispersed the maintenance scheduling tasks are.

[0182] Figure 2 The differences in maintenance scheduling of the three strategies in test scenario 1 are shown. It can be seen that the maintenance scheduling given by the single-scenario prediction planning exceeds the maintenance personnel configuration of the wind farm. There are overlapping maintenance periods for WT1, WT3 and WT4, but the wind farm was originally equipped with only two maintenance working groups, so it is necessary to urgently dispatch additional working groups to participate in the maintenance work. In contrast, the maintenance scheduling results of the random planning and the perfect information planning for the first three days are exactly the same, and wind turbines 1 and 4 are scheduled for maintenance on the second day. Due to the limited number of scenarios considered by the random planning and the existence of prediction errors, the maintenance scheduling of the remaining maintenance tasks, wind turbines 2, 3 and 5, is different from the maintenance scheduling results of the perfect information.

[0183] The example analysis shows that compared with the single-scenario maintenance scheduling plan without considering certainty, the maintenance scheduling plan given by the random planning considering uncertainty is lower in cost and more reasonable, and the cost difference with the optimal perfect information planning is only 1.5%. In addition, the model can be executed online and can provide support for the maintenance work of wind farms.

[0184] In summary, the present invention provides a method and system for optimizing maintenance scheduling of distributed power generation taking into account multiple uncertainties, which can cope with various changing scenarios in actual offshore wind turbine maintenance scheduling, reduce the maintenance cost of offshore wind turbines, and realize reasonable and effective maintenance scheduling of offshore wind turbines.

[0185] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0186] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0187] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of 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 the present invention.

[0188] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0189] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0190] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0191] If the integrated module / unit 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 present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0192] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0193] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0194] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0195] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A distributed generation maintenance scheduling optimization method taking into account multiple uncertainties, characterized in that: The following steps are involved: Set up maintenance scheduling conditions for sea wind turbines; Based on the set sea wind turbine maintenance scheduling conditions, obtain sea wind maintenance scheduling data information, including sea wind turbine parameters with maintenance requirements, operation and maintenance ship rental prices, number of maintenance personnel, working hourly wages, and overtime hourly wages; According to the acquired offshore wind maintenance scheduling data information, taking the maximum power generation benefit as the objective function, the wind turbine maintenance mode scheduling optimization plan is executed under multiple uncertain conditions.

2. The method for optimizing distributed generation maintenance scheduling taking into account multiple uncertainties according to claim 1 is characterized in that: The maintenance scheduling conditions for offshore wind turbines include: The offshore wind farm consists of I wind turbines. The maintenance task on each wind turbine requires τi hours of maintenance and must be completed before the Jth day. In both short-term and long-term planning, the wind speed at the turbine hub, wave height, and wind power grid price are all uncertain; The accessibility of wind turbines is limited by the safety thresholds of wind speed at the turbine hub and sea wave height.

3. The method for optimizing distributed generation maintenance scheduling taking into account multiple uncertainties according to claim 1 is characterized in that: The parameters of offshore wind turbines that require maintenance include rated power, cut-in wind speed, cut-out wind speed, and rated wind speed.

4. The method for optimizing distributed generation maintenance scheduling taking into account multiple uncertainties according to claim 1 is characterized in that: The specific planning of wind turbine maintenance mode under multiple uncertain conditions is as follows: A two-stage optimization model for scheduling maintenance plans for offshore wind turbines was established with the goals of maximizing power generation revenue, minimizing maintenance costs, and minimizing downtime losses. The first stage is the scheduling plan for the 24 hours before the day, and the second stage is the scheduling plan for multiple days in the future after the short-term stage. Taking the maximization of power generation revenue as the objective function, the optimal scheduling of maintenance methods for offshore wind turbines is achieved by considering various constraints.

5. The method for optimizing distributed generation maintenance scheduling taking into account multiple uncertainties according to claim 4 is characterized in that: The objective function is: Among them, r STS For short-term gains; is a set of days; d is the day number; For the future days’ long-term benefit, and l is the additional cost caused by delayed maintenance.

6. The method for optimizing distributed generation maintenance scheduling taking into account multiple uncertainties according to claim 5 is characterized in that: In the short-term profit stage, the decision variable is m t,i 、n t,i and v, respectively, are the preventive maintenance, corrective maintenance and chartering instructions of wind turbine i at time t; In the long-term benefit stage, the decision variables are and They are the preventive maintenance, corrective maintenance and chartering instructions of wind turbine i in scenario s on day d, respectively; All decision variables in the short-term benefit stage and the long-term benefit stage are binary variables.

7. The method for optimizing distributed generation maintenance scheduling taking into account multiple uncertainties according to claim 6 is characterized in that: Short-term benefits STS , long-term benefits The additional costs caused by delayed maintenance are as follows: in, Assemble for the fans; is the time set of STS; M, C are the maintenance costs of performing a preventive maintenance and a corrective maintenance respectively; R, E t are the leasing unit price of the operation and maintenance ship and the wind power sales price respectively; Ψ and Q are the hourly wage and overtime hourly wage of employees respectively; p t,i,s is the power generation of wind turbine i at time t in scenario s; x t,i,s ∈{0,1} is whether wind turbine i in scene s is under maintenance at time t; q s is the total overtime hours of the day; i ∈{0,1} is an input parameter, indicating whether the fan has been assigned a maintenance instruction; is the power generation on the dth day; τ i is the maintenance time of fan i; P rate is the rated power of the wind turbine; Estimated average power generation for the first day of LTS.

8. The method for optimizing distributed generation maintenance scheduling taking into account multiple uncertainties according to claim 5 is characterized in that: The constraints are as follows: Repair constraints: Among them, θ i ∈{0,1} is the maintenance demand of fan i, which is the input parameter; A t,i,s is the delay time of the maintenance command issued by scenario s to wind turbine i at time t in STS, and is the input parameter, I t,i,s ∈{0,1} indicates whether there is at least one maintenance window in the STS after time period t; b i,s The time required to transfer the maintenance tasks that have not been executed in STS to LTS for execution; i,s Indicates whether the maintenance instruction cannot be executed in the fan i of scene s in STS; B is the maximum value of employee dispatch; The demand for additional working groups, that is, if the maintenance order causes the number of maintenance working groups required to exceed the original wind farm configuration B, it is necessary to temporarily dispatch additional working groups to participate in the maintenance; g i,s Indicates whether the fan i fails to shut down after the maintenance command is delayed, g i,s When it is 0, it means a fault has occurred and the machine has stopped; d J is the last day of LTS; β is a small positive number, α d,i,s is an input parameter indicating whether wind turbine i in scenario s is accessible on day d, that is, whether there is at least one maintenance window on day d; Wind turbine availability constraints: Among them, y t,i,s is whether wind turbine i is shut down at time t in scenario s; y d,i,s is whether wind turbine i in scenario s is shut down on day d; represents the corrective maintenance instruction of wind turbine i in scenario s on day d; θ i ∈{0,1} is the maintenance demand of fan i; M is a positive integer; Constraints on operation and maintenance vessels and employees: Among them, m t,i 、n t,i and v represent PM, CM and charter order of wind turbine i at time t, respectively; They represent the preventive maintenance, corrective maintenance and chartering instructions of wind turbine i in scenario s on day d; W represents the daily working hours of each employee; B is the maximum value of employee dispatch; x t,i,s ∈{0,1} is whether wind turbine i in scene s is under maintenance at time t; q s The total overtime hours for the day; The additional overtime hours for the day; The time required to transfer the maintenance tasks that have not been executed in STS to LTS for execution; τ i It indicates the continuous time that maintenance needs to meet; H indicates the number of hours that the total overtime time cannot exceed.

9. A distributed generation maintenance scheduling optimization system taking into account multiple uncertainties, characterized in that: include: Condition module, setting the maintenance scheduling conditions of sea wind turbines; The parameter module obtains the sea wind maintenance scheduling data information based on the set sea wind unit maintenance scheduling conditions, including the parameters of the sea wind units with maintenance requirements, the rental price of the operation and maintenance ship, the number of maintenance personnel, the working hourly wage, and the overtime hourly wage; The scheduling module, based on the acquired offshore wind maintenance scheduling data information, takes the maximum power generation benefit as the objective function and executes the optimization plan of wind turbine maintenance mode scheduling under multiple uncertain conditions.

10. The distributed generation maintenance scheduling optimization system taking into account multiple uncertainties according to claim 9, characterized in that: The maintenance scheduling conditions for offshore wind turbines include: The offshore wind farm consists of I wind turbines. The maintenance task on each wind turbine requires τi hours of maintenance and must be completed before the Jth day. In both short-term and long-term planning, the wind speed at the turbine hub, wave height, and wind power grid price are all uncertain; The accessibility of wind turbines is limited by the safety thresholds of wind speed at the turbine hub and sea wave height.

11. The distributed generation maintenance scheduling optimization system taking into account multiple uncertainties according to claim 9, characterized in that: The parameters of offshore wind turbines that require maintenance include rated power, cut-in wind speed, cut-out wind speed, and rated wind speed.

12. The distributed generation maintenance scheduling optimization system taking into account multiple uncertainties according to claim 9, characterized in that: The specific planning of wind turbine maintenance mode under multiple uncertain conditions is as follows: A two-stage optimization model for scheduling maintenance plans for offshore wind turbines was established with the goals of maximizing power generation revenue, minimizing maintenance costs, and minimizing downtime losses. The first stage is the scheduling plan for the 24 hours before the day, and the second stage is the scheduling plan for multiple days in the future after the short-term stage. Taking the maximization of power generation revenue as the objective function, the optimal scheduling of maintenance methods for offshore wind turbines is achieved by considering various constraints.

13. The distributed generation maintenance scheduling optimization system taking into account multiple uncertainties according to claim 12, characterized in that: The objective function is: Among them, r STS For short-term gains; is a set of days; d is the day number; For the future days’ long-term benefit, and l is the additional cost caused by delayed maintenance.

14. The distributed generation maintenance scheduling optimization system taking into account multiple uncertainties according to claim 13, characterized in that: In the short-term profit stage, the decision variable is m t,i 、n t,i and v, respectively, are the preventive maintenance, corrective maintenance and chartering instructions of wind turbine i at time t; In the long-term benefit stage, the decision variables are and They are the preventive maintenance, corrective maintenance and chartering instructions of wind turbine i in scenario s on day d, respectively; All decision variables in the short-term benefit stage and the long-term benefit stage are binary variables; Short-term benefits STS , long-term benefits The additional costs caused by delayed maintenance are as follows: in, Assemble for the fans; is the time set of STS; M, C are the maintenance costs of performing a preventive maintenance and a corrective maintenance respectively; R, E t are the leasing unit price of the operation and maintenance ship and the wind power sales price respectively; Ψ and Q are the hourly wage and overtime hourly wage of employees respectively; p t,i,s is the power generation of wind turbine i at time t in scenario s; x t,i,s ∈{0,1} is whether wind turbine i in scene s is under maintenance at time t; q s is the total overtime hours of the day; i ∈{0,1} is an input parameter, indicating whether the fan has been assigned a maintenance instruction; is the power generation on the dth day; τ i is the maintenance time of fan i; P rate is the rated power of the wind turbine; Estimated average power generation for the first day of LTS.

15. The distributed generation maintenance scheduling optimization system taking into account multiple uncertainties according to claim 13, characterized in that: The constraints are as follows: Repair constraints: Among them, θ i ∈{0,1} is the maintenance demand of fan i, which is the input parameter; A t,i,s is the delay time of the maintenance command issued by scenario s to wind turbine i at time t in STS, and is the input parameter, I t,i,s ∈{0,1} indicates whether there is at least one maintenance window in the STS after time period t; b i,s The time required to transfer the maintenance tasks that have not been executed in STS to LTS for execution; i,s Indicates whether the maintenance instruction cannot be executed in the fan i of scene s in STS; B is the maximum value of employee dispatch; The demand for additional working groups, that is, if the maintenance order causes the number of maintenance working groups required to exceed the original wind farm configuration B, it is necessary to temporarily dispatch additional working groups to participate in the maintenance; g i,s Indicates whether the fan i fails to shut down after the maintenance command is delayed, g i,s When it is 0, it means a fault has occurred and the machine has stopped; d J is the last day of LTS; β is a small positive number, α d,i,s is an input parameter indicating whether wind turbine i in scenario s is accessible on day d, that is, whether there is at least one maintenance window on day d; Wind turbine availability constraints: Among them, y t,i,s is whether wind turbine i is shut down at time t in scenario s; y d,i,s is whether wind turbine i in scenario s is shut down on day d; represents the corrective maintenance instruction of wind turbine i in scenario s on day d; θ i ∈{0,1} is the maintenance demand of fan i; M is a positive integer; Constraints on operation and maintenance vessels and employees: Among them, m t,i 、n t,i and v represent PM, CM and charter order of wind turbine i at time t, respectively; They represent the preventive maintenance, corrective maintenance and chartering instructions of wind turbine i in scenario s on day d; W represents the daily working hours of each employee; B is the maximum value of employee dispatch; x t,i,s ∈{0,1} is whether wind turbine i in scene s is under maintenance at time t; q s The total overtime hours for the day; The additional overtime hours for the day; The time required to transfer the maintenance tasks that have not been executed in STS to LTS for execution; τ i It indicates the continuous time that maintenance needs to meet; H indicates the number of hours that the total overtime time cannot exceed.

16. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform the method of any one of claims 1 to 8.

17. A computing device, characterized in that include: One or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include steps for executing the method according to any one of claims 1 to 8.

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