Unit maintenance-unit combination robust optimization method and system under source load uncertainty

Abstract

The invention discloses a unit maintenance-unit combination robust optimization method and system under source load uncertainty. The method comprises the following steps: acquiring data information of a power system, a load and wind power output; setting uncertainty parameters of the wind power and the load according to the acquired data information of the power system, the load and the wind power output; establishing an uncertainty set # imgabs0 # of wind power and load, and establishing a two-stage robust optimization model of unit maintenance-unit combination collaboration according to the acquired data information of the power system, the load and the wind power output and the acquired uncertainty set # imgabs1 # of the wind power and the load; and solving the established two-stage robust optimization model of unit maintenance-unit commitment collaboration, and completing plan formulation of unit maintenance-unit commitment according to an obtained result. According to the method, unit maintenance and unit combination plans can be reasonably arranged under source load uncertainty, and the operation cost of a power system is reduced.

Description

Background Art

[0002] Unit maintenance and unit combination are two key tasks in power system operation decision-making. Existing unit maintenance strategies usually expect high system reliability and low operation and maintenance costs when they are formulated. Unit combination scheduling pursues the minimum total system operation cost under the premise of system stability. The two are usually decoupled, that is, the unit maintenance results are used as the boundary conditions of unit combination scheduling, but this may lead to reduced operating efficiency and increased potential risks. Most of the existing joint strategy studies on unit maintenance and unit combination are based on the joint optimization of deterministic source and load output, ignoring the uncertainty and volatility of source and load output brought about by the large-scale integration of new energy and the access of flexible loads such as electric vehicles in the context of new power systems.

[0003] In view of the above problems, there is an urgent need for a method to solve the collaborative optimization problem of unit maintenance-unit combination strategy under source and load uncertainty. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a robust optimization method and system for unit maintenance-unit combination under source-load uncertainty in response to the deficiencies in the above-mentioned prior art, so as to solve the technical problem of potential power outage risks and higher costs caused by decoupling unit maintenance and unit combination in the context of new power systems.

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

[0006] A robust optimization method for unit maintenance-unit commitment under source-load uncertainty comprises the following steps:

[0007] S1. Obtaining power system, load and wind power output data information;

[0008] S2. According to the power system, load and wind power output data information obtained in step S1, the uncertainty parameters of wind power and load are set to establish the uncertainty set of wind power and load.

[0009] S3, based on the power system, load, and wind power output data information obtained in step S1 and the uncertain set of wind power and load obtained in step S2 Establish a two-stage robust optimization model for unit maintenance-unit commitment coordination;

[0010] S4. Solve the two-stage robust optimization model of unit maintenance-unit combination coordination established in step S3, and complete the planning of unit maintenance-unit combination according to the obtained results.

[0011] Preferably, in step S1, the power system and load, wind power output data information includes grid data, unit parameters, maintenance information, wind power and load forecast values ​​and fluctuation errors.

[0012] Preferably, in step S2, the uncertain set of wind power and load Specifically:

[0013]

[0014]

[0015]

[0016]

[0017] Among them, T represents the number of time periods divided in the research period; is the actual active power of the i-th load in time period t; is the predicted value of the i-th load in time period t; is the upper bound of the prediction error of the i-th load in time period t; is the lower bound of the prediction error of the i-th load in time period t; N D Indicates the total amount of system load; is a binary variable indicating whether the actual load reaches the upper limit of the prediction; Γ D is the conservative parameter of the load uncertainty set; is the actual active power that can be generated by the i-th wind power in time period t; is the predicted active power that can be generated by the i-th wind power in time period t; is the upper bound of the prediction error of the i-th wind power in time period t; is the lower bound of the prediction error of the i-th wind power in time period t; N W Represents the total number of wind turbines in the system; Γ W is the conservative parameter of the wind power uncertainty set.

[0018] Preferably, in step S3, the two-stage robust optimization model of unit maintenance-unit combination coordination is as follows:

[0019]

[0020] stAx≥e,

[0021] Ex+Hy+Gv≥g,

[0022] Fy+Mv=f,

[0023] x binary,y free

[0024] Among them, x is the first-stage variable of the model; y is the second-stage variable of the model; A, E, H, G, F, M, L are the corresponding coefficient matrices, and e, c, g, f, b, k are constant column vectors.

[0025] Preferably, the objective function of the two-stage robust optimization model of unit maintenance-unit commitment coordination is:

[0026]

[0027] Among them, F1 represents the costs related to unit maintenance and unit combination; F2 represents the costs related to economic dispatch and loss of load and wind curtailment; N M represents the number of units to be overhauled; T represents the number of time periods divided within the research cycle; represents the maintenance cost of unit n to be repaired in time period t; q n,t Indicates whether the unit $n$ to be overhauled starts overhaul in time period t; N G Indicates the number of conventional units in the system; represents the startup cost of unit g in period t; represents the cost of unit g at the minimum technical output; C LS represents the penalty cost of load loss; d i,t represents the load loss of load i in time period t; It represents the unit cost after the output of unit g exceeds the minimum technical output; p g,min Indicates the minimum technical output of unit g; C WS Represents the cost of wind power curtailment.

[0028] Preferably, the constraints include:

[0029] Power generation maintenance constraints:

[0030]

[0031]

[0032]

[0033]

[0034] Among them, m n,t S represents the maintenance status of the unit n to be repaired in time period t; n R represents the time required for one overhaul of the unit n to be overhauled; t represents the maintenance resources in time period t;

[0035] Unit Combination Constraints:

[0036]

[0037]

[0038] Among them, p g,maxIndicates the maximum output of unit g; RU g Indicates the upward climbing rate of unit g; RD g Indicates the downward climbing rate of unit g; RSU g Indicates the maximum startup rate of unit g; RSD g Indicates the maximum shutdown rate of unit g; UT g Indicates the minimum startup time of unit g; DT g represents the minimum downtime of unit g; ρ represents the system hot standby rate; D i,t represents the load of node i in time period t; M is a large number;

[0039] System constraints:

[0040]

[0041] Among them, f l,t represents the active power of line l in time period t; N L Indicates the number of lines in the system; represents the set of wind turbines connected to node i; represents the set of conventional units connected to node i; o(l) represents the set of starting points of line l; d(l) represents the set of end points of line l; x l represents the inductance of line l; δ i,t represents the phase angle of node i in time period t; f l,min Indicates the lower limit of active power of line l; f l,max Indicates the upper limit of active power of line l; δ i,min Indicates the lower limit of the phase angle of node i; δ i,max Represents the upper limit of the phase angle of node i.

[0042] Preferably, step S4 is specifically:

[0043] S401, set the parameters of the iC&CG algorithm for generating inexact columns and constraints, including the global optimization relative error δ, the inexact relative error Iteration factor α, initial master problem iteration relative error ∈ MP ;

[0044] S402, based on the relative error ∈ of the main problem iteration in step S401 MP , solve the main problem MP;

[0045] S403: Optimal solution obtained based on step S402 Solve the subproblem SP;

[0046] S404, based on the L obtained in step S402 in And the step S403 obtained Convergence determination is performed if Then the algorithm ends and the result is output; otherwise, jump to step S405;

[0047] S405, based on the U obtained in step S402 in And the step S403 obtained Backtracking, if Then set ∈ MP =α∈ MP , Jump to step S402; otherwise, k=k+1, the scene will be uncertain Adding to the main question, i.e. Jump to step S402.

[0048] Preferably, the main problem MP is as follows:

[0049]

[0050] stAx≥e

[0051]

[0052] Where k represents the number of iterations, is the lower bound of the main problem MP introduced by the iC&CG algorithm and updated in each iteration; α is the cutting plane of the subproblem; y s is the second-stage variable at the sth iteration; is the uncertainty scenario at the sth iteration, A is the coefficient matrix related to the first-stage variables in the first stage, L is the coefficient matrix related to the first- and second-stage variables in the second stage, F is the coefficient matrix related to the second-stage variables in the second stage, c is the constant column vector related to the first-stage variables in the objective function, and G is the coefficient matrix related to the uncertain vector.

[0053] Preferably, the subproblem SP is as follows:

[0054]

[0055] Among them, λ is the dual variable of Ex+Hy+Gv≥g, μ is the dual variable of Fy+Mv=f, is the objective function of the subproblem, c is the constant column vector related to the first-stage variables in the objective function, is the first-stage variable value given by the main problem, g is the constant column vector related to the inequality constraints in the second stage of the original problem, E is the coefficient matrix related to the first-stage variables in the second stage of the original problem, G is the coefficient matrix related to the uncertainty set in the original problem, f is the constant column vector in the second stage equality constraints of the original problem, M is the coefficient matrix related to the uncertainty set in the second stage equality constraints of the original problem, vis the uncertainty vector, k is the constant column vector related to the first-stage variables in the second-stage objective function of the original problem, H is the coefficient matrix related to the second-stage variables in the second-stage inequality constraints of the original problem, F is the coefficient matrix related to the second-stage variables in the second stage of the original problem, b is the constant column vector related to the second-stage variables in the objective function of the original problem, L is the coefficient matrix related to the first and second-stage variables in the second stage of the original problem, is an indeterminate set.

[0056] In a second aspect, an embodiment of the present invention provides a unit maintenance-unit combination robust optimization system under source-load uncertainty, comprising:

[0057] Data module, to obtain power system, load and wind power output data information;

[0058] The parameter module sets the uncertainty parameters of wind power and load according to the acquired power system, load and wind power output data information, and establishes the uncertainty set of wind power and load.

[0059] The construction module is based on the acquired power system and load, wind power output data information and the acquired wind power and load uncertainty sets. Establish a two-stage robust optimization model for unit maintenance-unit commitment coordination;

[0060] The optimization module solves the established two-stage robust optimization model of unit maintenance-unit combination coordination, and completes the planning of unit maintenance-unit combination based on the obtained results.

[0061] 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 robust optimization of unit maintenance-unit combination under source-load uncertainty when executing the computer program.

[0062] 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 unit maintenance-unit combination robust optimization method under source and load uncertainty.

[0063] In a fifth aspect, a chip 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 robust optimization of unit maintenance-unit combination under source-load uncertainty when executing the computer program.

[0064] In a sixth aspect, an embodiment of the present invention provides an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-mentioned unit maintenance-unit combination robust optimization method under source and load uncertainty.

[0065] Compared with the prior art, the present invention has at least the following beneficial effects:

[0066] A robust optimization method for unit maintenance-unit combination under source-load uncertainty, step S1 obtains power system and load, wind power output data information, and provides boundary conditions for subsequent coordinated robust optimization of unit combination-unit maintenance; step S2 sets uncertainty parameters of wind power and load according to the power system and load, wind power output data information obtained in step S1, establishes uncertainty sets of wind power and load, and can better cope with the changing wind power and load output scenarios under the background of new power systems; step S3 establishes a two-stage robust optimization model for unit maintenance-unit combination coordination, fully considers the uncertainty under the actual background, and can improve the actual application value of unit maintenance-unit combination; step S4 solves the two-stage robust optimization model for unit maintenance-unit combination coordination established in step S3, completes the planning of unit maintenance-unit combination according to the obtained results, and can obtain a unit maintenance and unit combination plan that is more suitable for the background of new power systems, more economical, and more reliable.

[0067] Furthermore, setting the power system and load, wind power output data information including grid data, unit parameters, maintenance information, wind power and load forecast values ​​and fluctuation errors can fully consider the uncertainty of wind power output and load forecasting under the background of new power system, can better cope with the changing scenarios in reality, and be more adaptable.

[0068] Furthermore, the uncertain set of wind power and load is set It can provide the range of uncertain parameters for the subsequent unit maintenance-unit combination two-stage robust optimization model, and better cope with the changing source and load output scenarios.

[0069] Furthermore, a two-stage robust optimization model of unit maintenance-unit combination coordination is set up, which can take into account the uncertainty of wind power output and load demand forecasting in the context of new power systems, coordinate unit maintenance and unit combination, and obtain more economical and reliable unit maintenance and unit combination plans.

[0070] Furthermore, a two-stage objective function with optimal economy is set. The first stage is the cost of unit maintenance and unit combination, and the second stage is the power generation cost under the worst source-load output scenario. This can take into account the needs of robustness and economy to conduct collaborative optimization of unit maintenance and unit combination.

[0071] Furthermore, constraints such as power generation maintenance, unit combination, and system constraints are set. Among them, power generation maintenance constraints take into account the actual characteristics of unit maintenance, including maintenance needs, maintenance duration, maintenance continuity, etc.; unit combination constraints describe the operating characteristics of conventional units, including upper and lower limits of output, minimum start and stop time, climbing constraints, etc.; system constraints use a DC power flow model, including restrictions on load shedding and wind power abandonment, etc. Setting the above constraints is conducive to more refined modeling, making unit maintenance and unit combination closer to reality and improving effectiveness.

[0072] Furthermore, step S4 uses the iC&CG algorithm to solve the two-stage robust optimization model of unit maintenance-unit combination collaboration. By iteratively setting the solution accuracy of the solver in the main problem, the main problem can be solved quickly. In the subsequent algorithm backtracking process, different steps are entered according to the gap between the solution and the optimal value. When the gap is small, the solution accuracy of the solver in the main problem is reduced. When the gap is large, new variables and constraints are generated in the main problem until the final convergence. This can ensure that the model solution has a high solution efficiency while ensuring accuracy.

[0073] Furthermore, setting the main problem MP is equivalent to relaxing the uncertainty set. Two min problems are solved independently under the given uncertainty set. Therefore, a lower bound of the global problem can be given in each iteration. Cooperating with the subproblem SP can accelerate the convergence process and improve the solution efficiency.

[0074] Furthermore, setting the subproblem SP is equivalent to giving the variables of the first stage and solving the subsequent problems when the variables of the first stage are determined. Therefore, an upper bound of the global problem can be given in each iteration. Cooperating with the main problem MP can accelerate the convergence process and improve the solution efficiency.

[0075] It can be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0076] In summary, the present invention can reduce the potential power outage risk and economic cost of the power system.

[0077] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0079] Figure 1 This is the system net load and available generating capacity diagram under Scheme 3 near the G24 maintenance period;

[0080] Figure 2 This is the system net load and available generating capacity diagram under Scheme 4 near the G24 maintenance period;

[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 a chip 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] See also Figure 5 The present invention provides a unit maintenance-unit combination robust optimization method under source-load uncertainty, comprising the following steps:

[0092] S1. Obtaining power system, load and wind power output data information, including grid data, unit parameters, maintenance information, wind power and load forecast values ​​and fluctuation errors;

[0093] S2. According to the data information obtained in step S1, set the uncertainty parameters of wind power and load, and establish the uncertainty set of wind power and load.

[0094] Uncertain set of wind power and load Specifically:

[0095]

[0096] Among them, T represents the number of time periods divided in the research period; is the actual active power of the i-th load in time period t; is the predicted value of the i-th load in time period t; is the upper bound of the prediction error of the i-th load in time period t; is the lower bound of the prediction error of the i-th load in time period t; N D Indicates the total amount of system load; is a binary variable indicating whether the actual load reaches the upper limit of the prediction; Γ D is the conservative parameter of the load uncertainty set; is the actual active power that can be generated by the i-th wind power in time period t; is the predicted active power that can be generated by the i-th wind power in time period t; is the upper bound of the prediction error of the i-th wind power in time period t; is the lower bound of the prediction error of the i-th wind power in time period t; N W Represents the total number of wind turbines in the system; Γ W is the conservative parameter of the wind power uncertainty set.

[0097] S3: Based on the data information obtained in step S1 and the uncertain set of wind power and load obtained in step S2 Establish a two-stage robust optimization model for unit maintenance-unit commitment coordination;

[0098] Taking the minimum system operation cost as the objective function, the optimal arrangement of unit maintenance and unit combination plan is realized by considering various constraints;

[0099] The following formula is used as the objective function:

[0100]

[0101] Among them, F1 represents the costs related to unit maintenance and unit combination; F2 represents the costs related to economic dispatch and loss of load and wind curtailment; N M represents the number of units to be overhauled; T represents the number of time periods divided within the research cycle; represents the maintenance cost of unit n to be repaired in time period t; q n,t Indicates whether the unit $n$ to be overhauled starts overhaul in time period t; N G Indicates the number of conventional units in the system; represents the startup cost of unit g in period t; represents the cost of unit g at the minimum technical output; C LS represents the penalty cost of load loss; d i,t represents the load loss of load i in time period t; It represents the unit cost after the output of unit g exceeds the minimum technical output; p g,min Indicates the minimum technical output of unit g; C WS Represents the cost of wind power curtailment.

[0102] The following formula is used as the constraint condition:

[0103] Power generation maintenance constraints:

[0104]

[0105] Among them, m n,t Indicates the maintenance status of the unit n to be repaired in time period t (0 means not under maintenance; 1 means under maintenance); S n R represents the time required for one overhaul of the unit n to be overhauled; t Represents the maintenance resources in time period t.

[0106] Unit Combination Constraints:

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] Among them, p g,max Indicates the maximum output of unit g; RU g Indicates the upward climbing rate of unit g; RD g Indicates the downward climbing rate of unit g; RSU g Indicates the maximum startup rate of unit g; RSD g Indicates the maximum shutdown rate of unit g; UT g Indicates the minimum startup time of unit g; DT g represents the minimum downtime of unit g; ρ represents the system hot standby rate; D i,t represents the load of node $i$ in time period t; M is a large number.

[0115] System constraints:

[0116]

[0117]

[0118] Among them, f l,t represents the active power of line l in time period t; N L Indicates the number of lines in the system; represents the set of wind turbines connected to node i; represents the set of conventional units connected to node i; o(l) represents the set of starting points of line l; d(l) represents the set of end points of line l; x l represents the inductance of line l; δ i,t represents the phase angle of node i in time period t; f l,min Indicates the lower limit of active power of line l; f l,max Indicates the upper limit of active power of line l; δ i,min Indicates the lower limit of the phase angle of node i; δ i,max Represents the upper limit of the phase angle of node i.

[0119] For the convenience of description, the above two-stage robust optimization model is expressed in the following matrix form:

[0120]

[0121] stAx≥e,

[0122] Ex+Hy+Gv≥g,

[0123] Fy+Mv=f,

[0124] xbinary,yfree

[0125] Among them, x is the first-stage variable of the above model; y is the second-stage variable of the above model; A, E, H, G, F, M, L are the corresponding coefficient matrices, and e, c, g, f, b, k are constant column vectors.

[0126] S4. Solve the two-stage robust optimization model of unit maintenance-unit combination coordination established in step S3, and complete the planning of unit maintenance-unit combination according to the obtained results.

[0127] S401, set the parameters of the iC&CG algorithm for generating inexact columns and constraints, including the global optimization relative error δ, the inexact relative error Iteration factor α, initial master problem iteration relative error ∈ MP ;

[0128] S402, relative error of the main problem iteration based on S401∈ MP , solve the main problem MP;

[0129] The main question MP is as follows:

[0130]

[0131] stAx≥e

[0132]

[0133]

[0134]

[0135]

[0136]

[0137] Where k represents the number of iterations, is the lower bound of the main problem MP introduced by the iC&CG algorithm and updated in each iteration; α is the cutting plane of the subproblem; y s is the second-stage variable at the sth iteration; is the uncertainty scenario at the sth iteration.

[0138] The optimal solution is obtained by solving the main problem Record upper bound and a lower bound

[0139] S403: Optimal solution obtained based on step S402 Solve the subproblem SP;

[0140] The sub-problem SP is as follows:

[0141]

[0142] Among them, λ is the dual variable of Ex+Hy+Gv≥g, and μ is the dual variable of Fy+Mv=f.

[0143] Solve the subproblem to get the optimal solution and an upper bound

[0144] S404, L obtained based on S402 in And S403 obtained Convergence determination is performed if Then the algorithm ends and the result is output; otherwise, jump to S405;

[0145] S405, U obtained based on S402 in And S403 obtained Backtracking, if Then set ∈ MP =α∈ MP , Jump to S402; otherwise, k=k+1, the scene will be uncertain Adding to the main question, i.e. Jump to S402.

[0146] 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".

[0147] In yet another embodiment of the present invention, a robust optimization system for unit maintenance-unit combination under source-load uncertainty is provided. The system can be used to implement the above-mentioned robust optimization method for unit maintenance-unit combination under source-load uncertainty. Specifically, the robust optimization system for unit maintenance-unit combination under source-load uncertainty includes a data module, a parameter module, a construction module and an optimization module.

[0148] Among them, the data module obtains the power system, load, and wind power output data information;

[0149] The parameter module sets the uncertainty parameters of wind power and load according to the acquired power system, load and wind power output data information, and establishes the uncertainty set of wind power and load.

[0150] The construction module is based on the acquired power system and load, wind power output data information and the acquired wind power and load uncertainty sets. Establish a two-stage robust optimization model for unit maintenance-unit commitment coordination;

[0151] The optimization module solves the established two-stage robust optimization model of unit maintenance-unit combination coordination, and completes the planning of unit maintenance-unit combination based on the obtained results.

[0152] 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, graphics processing units (GPU), tensor processing units (TPU), 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., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of the unit maintenance-unit combination robust optimization method under source-load uncertainty, including:

[0153] Obtain the power system, load, and wind power output data information; set the uncertainty parameters of wind power and load according to the obtained power system, load, and wind power output data information, and establish the uncertainty set of wind power and load. According to the acquired power system and load, wind power output data information and the acquired wind power and load uncertainty set A two-stage robust optimization model for unit maintenance-unit combination coordination is established; the established two-stage robust optimization model for unit maintenance-unit combination coordination is solved, and the unit maintenance-unit combination plan is completed according to the obtained results.

[0154] 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 robust optimization method for unit maintenance-unit combination under source-load uncertainty 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 robust optimization system for unit maintenance-unit combination under source-load uncertainty in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0155] 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.

[0156] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, graphics processing units (GPU), tensor processing units (TPU), 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, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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 .

[0161] 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 .

[0162] 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.

[0163] 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.

[0164] 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.

[0165] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It can be understood 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. 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. It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (Non-VolatileMemory), such as at least one disk memory.

[0166] One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the robust optimization method for unit maintenance-unit combination under source-load uncertainty in the above embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor as follows:

[0167] Obtain the power system, load, and wind power output data information; set the uncertainty parameters of wind power and load according to the obtained power system, load, and wind power output data information, and establish the uncertainty set of wind power and load. According to the acquired power system and load, wind power output data information and the acquired wind power and load uncertainty set A two-stage robust optimization model for unit maintenance-unit combination coordination is established; the established two-stage robust optimization model for unit maintenance-unit combination coordination is solved, and the unit maintenance-unit combination plan is completed according to the obtained results.

[0168] 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.

[0169] The IEEE RTS-96 high-voltage distribution network test system is used to verify the feasibility of the proposed adaptive robust optimization method for unit maintenance-unit commitment strategy collaboration.

[0170] This example consists of 24 nodes, 38 lines, 32 power sources and 17 loads. The maximum weekly load is 2800MW, and the ramp rate and decline rate of the unit are set to 40% of their respective capacities. The system includes three wind turbines with capacities of 100, 200 and 200MW, connected to nodes 1, 3 and 9 respectively. The prediction error of wind power and load is set to 10%, and the penalty costs for load shedding and wind power abandonment are 3000$ / kWh and 300$ / kWh respectively. The parameters of the units that need to be overhauled are detailed in Table 1. Assuming δ is 0.2%, Set to 0.15%, initial ∈ MP The constructed model is solved by using gurobi.

[0171] Table 1 Parameters of the units to be repaired

[0172]

[0173] In order to illustrate the effectiveness of the proposed adaptive robust optimization method for unit maintenance-unit commitment strategy coordination under source and load uncertainty, we set up the following four scenarios for analysis:

[0174] Solution 1: Without considering uncertainty, the unit maintenance strategy and unit combination strategy are separated. The unit maintenance plan is fixed at the reporting time, and the unit combination is carried out on this basis.

[0175] Solution 2: Uncertainty is not considered, but the unit maintenance strategy and unit combination strategy are considered in a coordinated manner. The unit maintenance-unit combination strategy coordinated method is based on the predicted expected values ​​of load and wind power output.

[0176] Scheme 3: Consider uncertainty, but separate the unit maintenance strategy and unit combination strategy. The uncertainty parameter is set as: Γ W =0.2N W ,Γ D =0.2N D .

[0177] Scheme 4: Considering uncertainty, the unit maintenance strategy and unit combination strategy are considered collaboratively, that is, the method proposed in this paper. The uncertainty parameters are the same as those of Scheme 3.

[0178] Table 2 Comparison of results of different schemes

[0179]

[0180]

[0181] As shown in Table 2, ignoring the uncertainty, the proposed method reduces the total cost from 1877.597k$ in Scheme 1 to 1876.813k$ in Scheme 2. This cost reduction is achieved by adjusting the unit maintenance plan and optimizing the unit combination method. It can be seen that the unit combination cost and scheduling cost generated by Schemes 3 and 4 are higher than those of Schemes 1 and 2. This is because the proposed strategy sacrifices some economy when considering uncertainty, but ensures robustness and can operate the system safely under certain circumstances. Compared with Scheme 3, Scheme 4 is more economical. In Scheme 4, the maintenance plans of G8 and G24 units are adjusted to the 2-13 period and the 145-168 period, respectively, so that the unit combination in the remaining periods has greater flexibility.

[0182] In order to better illustrate the advantages of the present invention, the unit maintenance strategy of the G24 unit under Scheme 3 and Scheme 4 is analyzed. Figure 2 , 3 As shown, the available power generation capacity of the system includes the available power generation capacity of the G24 unit, the available power generation capacity of the start-stop units, and the available power generation capacity of the base load units that remain in operation. It can be seen that in Scheme 3, the G24 unit cannot be started during the original maintenance period, so a large amount of start-stop unit capacity is required to meet the high load demand at this time, resulting in an increase in power generation costs. In Scheme 4, postponing the maintenance period to a period with lower net load can keep the G24 unit turned on during the high load period. The net load is lower during the adjusted maintenance period, and the required start-stop unit capacity is smaller, so it is more economical.

[0183] Table 3 Algorithm performance under different parameters

[0184]

[0185]

[0186] In addition, Table 3 compares the computational efficiency and accuracy of the C&CG algorithm and the iC&CG algorithm under different parameters, where δ is set to 0.2%. The optimal gap in the C&CG algorithm is set to 0.15%. To reduce the error, each case is calculated ten times and the average value is taken. The results show that the iC&CG algorithm improves the overall solution efficiency while maintaining an acceptable error range.

[0187] When ∈ MP When it is larger, a larger α will lead to a slower solution speed. This is because the larger α is, the more ∈ MP The slower the shrinking speed, the more likely it is to expand the scenario set in each iteration, thus limiting the solution efficiency. MPWhen the difference with δ is not large, the solution efficiency will be improved, because this method is more inclined to iterate ∈ MP Instead of adding new scenes. In this case, a larger α can promote convergence and thus slightly shorten the solution time. For example, when ∈ MP When =0.1% and α =0.9, the fastest solution time is reduced from 2061.5 seconds in C&CG to 930.2 seconds in iC&CG, an improvement of 54.88%.

[0188] In summary, the robust optimization method and system of unit maintenance-unit combination under source-load uncertainty of the present invention can take into account the uncertainty of wind power output and load demand, reduce the potential power outage risk and economic cost of the power system, and has higher solution efficiency than the conventional C&CG algorithm.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks 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 1 A device that provides the functions specified in a block or multiple blocks.

[0197] 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.

[0198] 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. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0199] 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 robust optimization method for unit maintenance and unit commitment under source and load uncertainty, characterized in that: The following steps are involved: S1. Obtaining power system, load and wind power output data information; S2. According to the power system, load and wind power output data information obtained in step S1, the uncertainty parameters of wind power and load are set to establish the uncertainty set of wind power and load. S3, based on the power system, load, and wind power output data information obtained in step S1 and the uncertain set of wind power and load obtained in step S2 Establish a two-stage robust optimization model for unit maintenance-unit commitment coordination; S4. Solve the two-stage robust optimization model of unit maintenance-unit combination coordination established in step S3, and complete the planning of unit maintenance-unit combination according to the obtained results.

2. The robust optimization method for unit maintenance and unit commitment under source-load uncertainty according to claim 1 is characterized in that: In step S1, the power system and load, wind power output data information includes grid data, unit parameters, maintenance information, wind power and load forecast values ​​and fluctuation errors.

3. The robust optimization method for unit maintenance and unit commitment under source-load uncertainty according to claim 1 is characterized in that: In step S2, the uncertain set of wind power and load Specifically: Among them, T represents the number of time periods divided in the research period; is the actual active power of the i-th load in time period t; is the predicted value of the i-th load in time period t; is the upper bound of the prediction error of the i-th load in time period t; is the lower bound of the prediction error of the i-th load in time period t; N D Indicates the total amount of system load; is a binary variable indicating whether the actual load reaches the upper limit of the prediction; Γ D is the conservative parameter of the load uncertainty set; is the actual active power that can be generated by the i-th wind power in time period t; is the predicted active power that can be generated by the i-th wind power in time period t; is the upper bound of the prediction error of the i-th wind power in time period t; is the lower bound of the prediction error of the i-th wind power in time period t; N W Represents the total number of wind turbines in the system; Γ W is the conservative parameter of the wind power uncertainty set.

4. The robust optimization method for unit maintenance and unit commitment under source-load uncertainty according to claim 1 is characterized in that: In step S3, the two-stage robust optimization model of unit maintenance-unit commitment coordination is as follows: stAx≥e, Ex+Hy+Gv≥g, Fy+Mv=f, x binary,y free Among them, x is the first-stage variable of the model; y is the second-stage variable of the model; A, E, H, G, F, M, L are the corresponding coefficient matrices, and e, c, g, f, b, k are constant column vectors.

5. The robust optimization method for unit maintenance and unit commitment under source-load uncertainty according to claim 4 is characterized in that: The objective function of the two-stage robust optimization model of unit maintenance-unit commitment coordination is: Among them, F1 represents the costs related to unit maintenance and unit combination; F2 represents the costs related to economic dispatch and loss of load and wind curtailment; N M represents the number of units to be overhauled; T represents the number of time periods divided within the research cycle; represents the maintenance cost of unit n to be repaired in time period t; q n,t Indicates whether the unit $n$ to be overhauled starts overhaul in time period t; N G Indicates the number of conventional units in the system; represents the startup cost of unit g in period t; represents the cost of unit g at the minimum technical output; C LS represents the penalty cost of load loss; d i,t represents the load loss of load i in time period t; It represents the unit cost after the output of unit g exceeds the minimum technical output; p g,min Indicates the minimum technical output of unit g; C WS Represents the cost of wind power curtailment.

6. The robust optimization method for unit maintenance and unit commitment under source-load uncertainty according to claim 4 is characterized in that: The constraints include: Power generation maintenance constraints: Among them, m n,t S represents the maintenance status of the unit n to be repaired in time period t; n R represents the time required for one overhaul of the unit n to be overhauled; t represents the maintenance resources in time period t; Unit Combination Constraints: Among them, p g,max Indicates the maximum output of unit g; RU g Indicates the upward climbing rate of unit g; RD g Indicates the downward climbing rate of unit g; RSU g Indicates the maximum startup rate of unit g; RSD g Indicates the maximum shutdown rate of unit g; UT g Indicates the minimum startup time of unit g; DT g represents the minimum downtime of unit g; ρ represents the system hot standby rate; D i,t represents the load of node i in time period t; M is a large number; System constraints: Among them, f l,t represents the active power of line l in time period t; N L Indicates the number of lines in the system; represents the set of wind turbines connected to node i; represents the set of conventional units connected to node i; o(l) represents the set of starting points of line l; d(l) represents the set of end points of line l; x l represents the inductance of line l; δ i,t represents the phase angle of node i in time period t; f l,min Indicates the lower limit of active power of line l; f l,max Indicates the upper limit of active power of line l; δ i,min Indicates the lower limit of the phase angle of node i; δ i,max Represents the upper limit of the phase angle of node i.

7. The robust optimization method for unit maintenance and unit commitment under source-load uncertainty according to claim 1 is characterized in that: Step S4 is specifically as follows: S401, set the parameters of the iC&CG algorithm for generating inexact columns and constraints, including the global optimization relative error δ, the inexact relative error Iteration factor α, initial master problem iteration relative error ∈ MP ; S402, based on the relative error ∈ of the main problem iteration in step S401 MP , solve the main problem MP; S403: Optimal solution obtained based on step S402 Solve the subproblem SP; S404, based on the L obtained in step S402 in And the step S403 obtained Convergence determination is performed if Then the algorithm ends and the result is output; Otherwise, jump to step S405; S405, based on the U obtained in step S402 in And the step S403 obtained Backtracking, if Then set ∈ MP =α MP , Jump to step S402; otherwise, k=k+1, the scene will be uncertain Adding to the main question, i.e. Jump to step S402.

8. The robust optimization method for unit maintenance and unit commitment under source-load uncertainty according to claim 7 is characterized in that: The main problem MP is as follows: stAx≥e Where k represents the number of iterations, is the lower bound of the main problem MP introduced by the iC&CG algorithm and updated in each iteration; α is the cutting plane of the subproblem; y s is the second-stage variable at the sth iteration; is the uncertainty scenario at the sth iteration, A is the coefficient matrix related to the first-stage variables in the first stage, L is the coefficient matrix related to the first- and second-stage variables in the second stage, F is the coefficient matrix related to the second-stage variables in the second stage, c is the constant column vector related to the first-stage variables in the objective function, and G is the coefficient matrix related to the uncertain vector.

9. The robust optimization method for unit maintenance and unit commitment under source-load uncertainty according to claim 7, characterized in that: The sub-problem SP is as follows: Among them, λ is the dual variable of Ex+Hy+Gv≥g, μ is the dual variable of Fy+Mv=f, is the objective function of the subproblem, c is the constant column vector related to the first-stage variables in the objective function, is the first-stage variable value given by the main problem, g is the constant column vector related to the inequality constraints in the second stage of the original problem, E is the coefficient matrix related to the first-stage variables in the second stage of the original problem, G is the coefficient matrix related to the uncertainty set in the original problem, f is the constant column vector in the second-stage equality constraints of the original problem, M is the coefficient matrix related to the uncertainty set in the second-stage equality constraints of the original problem, v is the uncertainty vector, k is the constant column vector related to the first-stage variables in the second-stage objective function of the original problem, H is the coefficient matrix related to the second-stage variables in the second-stage inequality constraints of the original problem, F is the coefficient matrix related to the second-stage variables in the second stage of the original problem, b is the constant column vector related to the second-stage variables in the objective function of the original problem, L is the coefficient matrix related to the first and second-stage variables in the second stage of the original problem, is an indeterminate set.

10. A unit maintenance-unit combination robust optimization system under source and load uncertainty, characterized in that: include: Data module, to obtain power system, load and wind power output data information; The parameter module sets the uncertainty parameters of wind power and load according to the acquired power system, load and wind power output data information, and establishes the uncertainty set of wind power and load. The construction module is based on the acquired power system and load, wind power output data information and the acquired wind power and load uncertainty sets. Establish a two-stage robust optimization model for unit maintenance-unit commitment coordination; The optimization module solves the established two-stage robust optimization model of unit maintenance-unit combination coordination, and completes the planning of unit maintenance-unit combination based on the obtained results.

Images