Power system unit commitment acceleration method and system based on dynamic interaction of main problem and sub-problem

Abstract

The invention discloses an electric power system unit commitment acceleration method and system based on dynamic interaction of a main problem and a sub problem, and the method comprises the steps: 1, building an electric power system unit commitment mathematical model, and obtaining a main problem; 2, solving the unit commitment mathematical model of the power system by adopting a branch and bound method, and collecting N relaxation solutions at the initial stage of solving; 3, evaluating the N slack solutions, and predicting a unit start-stop state variable value; 4, the start-stop states of part of units are fixed, a small-scale auxiliary unit combination model is constructed, and sub-problems are obtained; and step 5, solving the sub-problem, sending the obtained sub-optimal solution to a main problem solving process, accelerating a branch and bound convergence process by updating a global upper bound, and finally obtaining an optimal solution of the main problem. According to the method, the heuristic method based on the physical characteristics of the power system is deeply embedded in the solving process of the general solver with the branch and bound as the core algorithm, and customized efficient and accurate solving of the unit combination of the power system is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of power system operation optimization, and in particular to a power system unit combination acceleration method and system based on dynamic interaction of main and sub-problems. Background Art

[0002] The unit commitment of power system is one of the most important technologies for formulating short-term power dispatching plans, and it is also a key link in the day-ahead market clearing in the power spot market environment. The unit commitment generally aims to minimize the total operating cost of the system or maximize social welfare. Under the premise of ensuring operational safety, the start-up and shutdown plans and power generation plans of the units in the system are arranged to meet the load and backup requirements of the system. In mathematical models, the unit commitment problem is a non-convex large-scale mixed integer programming problem. With the development of commercial mixed integer linear programming (MILP) solvers, MILP commercial solvers based on the branch-and-bound method have become the mainstream tool for solving unit commitment problems and are widely used in actual provincial dispatching systems.

[0003] With the expansion of the scale of power grids and the increase in the types and number of resources in the system, the model scale of unit combination has increased significantly. Under the constraints of practical engineering requirements such as power market clearing, unit combination faces the problem of difficulty in balancing solution accuracy and solution efficiency. Among the existing unit combination solution methods, the branch and bound method continuously reduces the search space through enumerated branching, pruning, and bounding operations until the optimal solution is found. As an accurate algorithm, the branch and bound method can guarantee the optimality of the calculation results, but the convergence efficiency is low on large-scale problems. The main idea of ​​unit combination acceleration is to reduce the problem scale based on the knowledge of the power system domain, such as reducing the model scale by fixing some decision variables or removing ineffective constraints, thereby improving the solution efficiency. However, this type of heuristic method can only obtain a feasible solution to the original problem, but cannot guarantee the optimality of the solution.

[0004] In summary, among the existing unit combination solution methods, the branch and bound method can ensure the optimality of the solution results, but the convergence efficiency for large-scale problems is low and it is difficult to meet the requirements of practical engineering for solution efficiency; the heuristic method uses domain knowledge to achieve efficient solution of the problem, but cannot guarantee the solution accuracy of the problem. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention provides a method and system for accelerating the combination of power system units based on dynamic interaction of main and sub-problems. The present invention deeply embeds a heuristic method based on the physical characteristics of the power system into the solution process of a general solver with branch and bound as the core algorithm, thereby realizing customized, efficient and accurate solution of the combination of power system units.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A power system unit commitment acceleration method based on dynamic interaction of master and sub-problems, comprising:

[0008] Step 1, establish a mathematical model of the power system unit combination to obtain the main problem;

[0009] Step 2, using the branch and bound method to solve the mathematical model of the power system unit combination, and collecting N relaxed solutions in the initial stage of the solution;

[0010] Step 3, evaluate N relaxation solutions and predict the values ​​of the start-stop state variables of the unit;

[0011] Step 4, fix the start and stop status of some units, build a small-scale auxiliary unit combination model, and obtain the sub-problem;

[0012] Step 5, solve the sub-problem and send the obtained sub-optimal solution to the main problem solving process, accelerate the branch and bound convergence process by updating the global upper bound, and finally obtain the optimal solution to the main problem.

[0013] Preferably, in step 1, the power system unit combination mathematical model aims to minimize the system operation cost, and the objective function is specifically:

[0014]

[0015] In the formula, set T represents the dispatch period; set G represents the dispatch unit; constant C g represents the unit coal consumption cost of power generation of unit g; variable p g,t Indicates the output of unit g during period t; constant and Respectively represent the startup cost and shutdown cost of unit g; variable y g, t and z g, t represents the start command and shutdown command of unit g at time t respectively.

[0016] Preferably, in step 1, the constraints of the power system unit combination mathematical model are specifically:

[0017]

[0018]

[0019] In the formula, set D represents the system load; set L represents the network line; constant D d,t represents the load size of load node d at time t; constant r t represents the backup demand of the system at time t; the constant F l max represents the transmission power limit of line l; H PTDFis the line power flow transfer distribution factor matrix; variable u g,t Indicates the start and stop status of unit g at time t; constant and Indicates the upper and lower limits of the output of unit g; constant and Indicates the minimum start and stop time of unit g; constant S g and R g Indicates the start / stop rate and ramp rate of the unit.

[0020] Preferably, in step 2, a general solver of the branch and bound method is used to solve the mathematical model of the power system unit combination, and N relaxation solutions of the unit start and stop state variables are collected at the beginning of the solution process, and the i-th relaxation solution is recorded as

[0021] Preferably, in step 3, N relaxed solutions are evaluated, and the values ​​of the start-stop state variables of the predicted unit are specifically including:

[0022] The threshold method is used to identify the normally open and normally closed units, and determine the values ​​of the start and stop state variables of the units. The specific judgment criteria are:

[0023] for like but

[0024] for like but

[0025] In the formula, the parameter α 1 and β 1 is the judgment threshold, usually 0≤β 1 <α 1 ≤1; represents the i-th predicted value of the start / stop state variable of unit g in period t;

[0026] Taking the minimum start-stop time as the time window, the threshold method is used to identify the start-stop period of the start-stop unit, and the value of the start-stop state variable of the unit is determined. The specific judgment criteria are:

[0027] for like but

[0028] for like but

[0029] In the formula, the parameter α 2 and β 2 is the judgment threshold, usually 0≤β 2 <α2 ≤1;

[0030] The current prediction results are corrected according to the minimum start-stop time constraint to determine the values ​​of more unit start-stop state variables. The correction criteria are specifically expressed as:

[0031] for like but

[0032] for like but

[0033] The prediction results of N relaxed solutions are intersected to obtain the final prediction values ​​of the start-stop state variables of some units, which are recorded as

[0034] for like but

[0035] for like but

[0036] Preferably, in step 4, the small-scale auxiliary unit combination model aims to minimize the system operation cost, and the objective function is specifically:

[0037]

[0038] In the formula, set T represents the dispatch period; set G represents the dispatch unit; constant C g represents the unit coal consumption cost of power generation of unit g; variable p g,t Indicates the output of unit g during period t; constant and Respectively represent the startup cost and shutdown cost of unit g; variable y g,t and z g, t represents the start command and shutdown command of unit g at time t respectively.

[0039] Preferably, in step 4, the constraints of the small-scale auxiliary unit combination model are specifically:

[0040]

[0041]

[0042] In the formula, set D represents the system load; set L represents the network line; constant D d,t represents the load size of load node d at time t; constant r trepresents the backup demand of the system at time t; the constant F l max represents the transmission power limit of line l; H PTDF is the line power flow transfer distribution factor matrix; variable u g,t Indicates the start and stop status of unit g at time t; constant and Indicates the upper and lower limits of the output of unit g; constant and Indicates the minimum start and stop time of unit g; constant S g and R g Indicates the start / stop rate and ramp rate of the unit.

[0043] A power system unit combination acceleration system based on dynamic interaction of main and sub-problems, comprising a power system unit combination mathematical model module, a relaxation solution solving module, a prediction module, an auxiliary unit combination model module, and an optimal solution module;

[0044] The power system unit combination mathematical model module is used to construct the main problem;

[0045] The relaxation solution solving module is used to solve the power system unit combination mathematical model, and output the solution to the power system unit combination mathematical model, and collect N relaxation solutions in the initial stage of the solution;

[0046] The prediction module is used to evaluate the N relaxation solutions in the relaxation solution solving module and predict the values ​​of the start-stop state variables of the unit;

[0047] The auxiliary unit combination model module is used to construct a small-scale auxiliary unit combination model according to the unit start-stop state variable values ​​to obtain a sub-problem;

[0048] The optimal solution module is used to solve the sub-problem and send the obtained sub-optimal solution to the main problem solving process, and accelerate the branch and bound convergence process by updating the global upper bound to obtain the optimal solution to the main problem.

[0049] Compared with the prior art, the present invention has the following beneficial technical effects:

[0050] The present invention provides a method for accelerating the combination of power system units based on the dynamic interaction of main and sub-problems. Through the dynamic interaction between the main problem (original unit combination problem) and the sub-problem (small-scale auxiliary unit combination problem), the heuristic method is deeply embedded in the solution process of the general solver with the branch and bound method as the core algorithm, so as to realize customized, efficient and accurate solution of the problem. The present invention is a heuristic variable prediction method based on the physical characteristics of the power system. In the actual large-scale power system, there are a large number of normally open and normally closed units. In order to ensure the balance of power and economic operation, the operating status of such units will not change during the entire scheduling cycle; limited by the physical characteristics of the unit itself, the operation of the unit must meet the minimum start and stop time constraint, that is, there is a time coupling relationship between the operating status of the unit. The present invention fully considers the physical characteristics of the operation of the above-mentioned power system, proposes a method for identifying normally open and normally closed units and a method for predicting the start and stop period of the start and stop unit, constructs a small-scale auxiliary unit combination model by predicting the start and stop state variables of some units, and realizes the efficient solution of the unit combination problem in combination with the proposed accelerated solution framework. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is the basic framework of the power system unit commitment acceleration method based on dynamic interaction of main and sub-problems of the present invention;

[0052] Figure 2 This is a schematic diagram of the principle of accelerating the branch and bound search process according to the method proposed in the present invention. DETAILED DESCRIPTION

[0053] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0054] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0055] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0056] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, or a communication; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0057] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0058] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0059] 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.

[0060] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0061] 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.

[0062] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0063] Example 1

[0064] See also Figure 1 , a power system unit commitment acceleration method based on dynamic interaction of master and sub-problems includes the following steps:

[0065] Step 1: Establish a mathematical model of the power system unit combination and obtain the main problem;

[0066] Step 2: Solve the model based on the branch and bound method and collect N relaxed solutions in the initial stage of the solution;

[0067] Step 3: Evaluate the relaxed solution based on domain knowledge and predict the values ​​of the unit start-stop state variables;

[0068] Step 4: Fix the start and stop status of some units, build a small-scale auxiliary unit combination model, and obtain sub-problems;

[0069] Step 5: Solve the sub-problem and send the obtained suboptimal solution to the main problem solving process. By updating the global upper bound, the branch and bound convergence process is accelerated, and finally the optimal solution to the main problem is obtained.

[0070] The power system unit combination mathematical model described in step 1 aims to minimize the system operation cost, and the objective function is specifically expressed as:

[0071]

[0072] In the formula, set T represents the dispatch period; set G represents the dispatch unit; constant C g represents the unit coal consumption cost of power generation of unit g; variable p g, t represents the output of unit g in period t; constant and Respectively represent the startup cost and shutdown cost of unit g; variable y g, t and z g, t represents the start command and shutdown command of unit g at time t respectively.

[0073] The constraints of the power system unit combination mathematical model described in step 1 are as follows:

[0074]

[0075] In the formula, set D represents the system load; set L represents the network line; constant D d,t represents the load size of load node d at time t; constant r t represents the backup demand of the system at time t; the constant F l max represents the transmission power limit of line l; H PTDF is the line power flow transfer distribution factor matrix; variable u g,t Indicates the start and stop status of unit g at time t; constant and Indicates the upper and lower limits of the output of unit g; constant and Indicates the minimum start and stop time of unit g; constant S g and R g Indicates the start / stop rate and ramp rate of the unit;

[0076] Formula (2) is the power balance constraint, indicating that the system operation needs to meet the balance of power supply and demand at all times; Formula (3) is the spinning reserve constraint, indicating that the system operation needs to reserve sufficient reserve capacity; Formula (4) is the line flow safety constraint, indicating that the system operation needs to ensure that the flow of each line is within the safety limit; Formula (5) is the variable logic constraint, indicating the logical relationship between the unit operating state and state transition; Formula (6) is the output constraint, which limits the output range of the unit; Formulas (7)-(8) are the minimum start-stop time constraints, which limit the unit from frequent start-stop in a short time; Formula (9) is the ramp constraint, which limits the range of unit output changes between adjacent time periods; Formula (10) gives the value range of the decision variable.

[0077] As described in step 2, a general solver with branch and bound method as the core algorithm is used to solve the power system unit combination model, and N relaxation solutions of the unit start and stop state variables are collected at the beginning of the solution process, and the i-th relaxation solution is recorded as

[0078] As described in step 3, the relaxed solution is analyzed and evaluated in combination with the power system domain knowledge to predict the values ​​of the unit start-stop state variables;

[0079] First, considering that a large number of units in the actual system are usually in the full-time on or off state, the threshold method is used to identify the normally open and normally closed units, and determine the values ​​of the start and stop state variables of the units. The judgment criteria are specifically expressed as:

[0080] for like but

[0081] for like but

[0082] In the formula, the parameter α 1 and β 1 is the judgment threshold, usually 0≤β 1 <α 1 ≤1; represents the i-th predicted value of the start / stop state variable of unit g in period t;

[0083] Secondly, considering that the unit operation needs to meet the minimum start-stop time limit, the minimum start-stop time is used as the time window, the threshold method is used to identify the start-stop period of the start-stop unit, and the start-stop state variable value of the unit is determined. The judgment criterion is specifically expressed as:

[0084] for like but

[0085] for like but In the formula, the parameter α 2 and β 2 is the judgment threshold, usually 0≤β 2 <α 2 ≤1;

[0086] Thirdly, the current prediction results are corrected according to the minimum start-stop time constraint to determine the values ​​of more unit start-stop state variables. The correction criteria are specifically expressed as:

[0087] for like but

[0088] for like but

[0089] Finally, in order to improve the accuracy of variable prediction, the prediction results of N relaxed solutions are intersected to obtain the final prediction values ​​of the start-stop state variables of some units, which are recorded as

[0090] for like but

[0091] for like but

[0092] The objective function of the small-scale auxiliary unit combination model described in step 4 is the same as equation (1), and the constraints include the following variable fixed constraints in addition to equations (2)-(10):

[0093]

[0094] The feasible domain of the small-scale auxiliary unit combination model is included in the feasible domain of the original unit combination model. The optimal solution of the sub-problem must be a feasible solution to the main problem, but not necessarily the optimal solution. Therefore, it is called a suboptimal solution to the main problem.

[0095] As described in step 5, the suboptimal solution obtained from the subproblem is sent to the main problem solving process, and the global upper bound of the main problem is updated. Nodes whose local lower bounds are greater than the global upper bound will be pruned, thereby accelerating the branch and bound search process (see the principle for details). Figure 2 ), and finally quickly obtain the optimal solution to the main problem.

[0096] Test systems of different sizes are used as research objects. The system load data comes from the actual operation data of a province in China. The system parameters and solution parameters are shown in Table 1:

[0097] Table 1 Test system parameters and solution parameters

[0098]

[0099] The above test system was solved by using Gurobi commercial solver and the method proposed in the present invention, and the solution results are shown in Table 2:

[0100] Table 2 Test system solution results

[0101]

[0102]

[0103] The results of the case study show that the proposed variable prediction method can accurately predict the values ​​of more than 90% of the unit start-stop state variables, and achieve rapid solution of sub-problems by reducing the model scale, thereby accelerating the branch and bound search process of the main problem; the proposed unit combination acceleration method embeds the heuristic method into the general solver solution process, which can achieve customized and efficient solution of unit combination while ensuring the solution accuracy.

[0104] Example 2

[0105] An embodiment of the present invention provides a power system unit combination acceleration system based on dynamic interaction of main and sub-problems, including a power system unit combination mathematical model module, a relaxation solution solving module, a prediction module, an auxiliary unit combination model module, and an optimal solution module.

[0106] The power system unit combination mathematical model module is used to construct the main problem.

[0107] The relaxation solution solving module is used to solve the mathematical model of the power system unit combination, and output the solution of the mathematical model of the power system unit combination, and collect N relaxation solutions in the initial stage of the solution.

[0108] The prediction module is used to evaluate the N relaxation solutions in the relaxation solution solving module and predict the values ​​of the start-stop state variables of the unit.

[0109] The auxiliary unit combination model module is used to construct a small-scale auxiliary unit combination model according to the values ​​of the unit start-stop state variables to obtain a sub-problem.

[0110] The optimal solution module is used to solve the sub-problem and send the obtained sub-optimal solution to the main problem solving process, and accelerate the branch and bound convergence process by updating the global upper bound to obtain the optimal solution to the main problem.

[0111] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0112] In addition, it should be understood that although this specification is described in accordance with the implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art. The above content is only to illustrate 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 according to the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for accelerating the unit commitment of a power system based on dynamic interaction of master and sub-problems, characterized in that: include: Step 1, establish a mathematical model of the power system unit combination to obtain the main problem; Step 2, using the branch and bound method to solve the mathematical model of the power system unit combination, and collecting N relaxed solutions in the initial stage of the solution; Step 3, evaluate N relaxation solutions and predict the values ​​of the start-stop state variables of the unit; Step 4, fix the start and stop status of some units, build a small-scale auxiliary unit combination model, and obtain the sub-problem; Step 5, solve the sub-problem and send the obtained sub-optimal solution to the main problem solving process, accelerate the branch and bound convergence process by updating the global upper bound, and finally obtain the optimal solution to the main problem.

2. A method for accelerating the unit commitment of a power system based on dynamic interaction of main and sub-problems according to claim 1, characterized in that: In step 1, the power system unit combination mathematical model aims to minimize the system operation cost, and the objective function is specifically: In the formula, set T represents the dispatch period; set G represents the dispatch unit; constant C g represents the unit coal consumption cost of power generation of unit g; variable p g, t represents the output of unit g in period t; constant and Respectively represent the startup cost and shutdown cost of unit g; variable y g, t and z g, t represents the start command and shutdown command of unit g at time t respectively.

3. The method for accelerating the unit commitment of a power system based on dynamic interaction of main and sub-problems according to claim 1, characterized in that: In step 1, the constraints of the power system unit combination mathematical model are specifically: In the formula, set D represents the system load; set L represents the network line; constant D d,t represents the load size of load node d at time t; constant r t represents the backup demand of the system at time t; the constant F l max represents the transmission power limit of line l; H PTDF is the line power flow transfer distribution factor matrix; variable u g,t Indicates the start and stop status of unit g at time t; constant and Indicates the upper and lower limits of the output of unit g; constant and Indicates the minimum start and stop time of unit g; constant S g and R g Indicates the start / stop rate and ramp rate of the unit.

4. The method for accelerating the power system unit commitment based on dynamic interaction of master and sub-problems according to claim 1 is characterized in that: In step 2, a general solver of the branch-and-bound method is used to solve the mathematical model of the power system unit combination, and N relaxation solutions of the unit start-stop state variables are collected at the beginning of the solution process, and the i-th relaxation solution is recorded as 5. The method for accelerating the unit commitment of a power system based on dynamic interaction of master and sub-problems according to claim 1, characterized in that: In step 3, N relaxation solutions are evaluated to predict the values ​​of the start-stop state variables of the unit, including: The threshold method is used to identify the normally open and normally closed units, and determine the values ​​of the start and stop state variables of the units. The specific judgment criteria are: for like but for like but In the formula, parameters α1 and β1 are judgment thresholds, usually 0≤β1<α1≤1; represents the i-th predicted value of the start / stop state variable of unit g in period t; Taking the minimum start-stop time as the time window, the threshold method is used to identify the start-stop period of the start-stop unit, and the value of the start-stop state variable of the unit is determined. The specific judgment criteria are: for like but for like but In the formula, parameters α2 and β2 are judgment thresholds, usually 0≤β2<α2≤1; The current prediction results are corrected according to the minimum start-stop time constraint to determine the values ​​of more unit start-stop state variables. The correction criteria are specifically expressed as: for like but for like but The prediction results of N relaxed solutions are intersected to obtain the final prediction values ​​of the start-stop state variables of some units, which are recorded as for like but for like but 6. The method for accelerating the unit commitment of a power system based on dynamic interaction of main and sub-problems according to claim 1, characterized in that: In step 4, the small-scale auxiliary unit combination model aims to minimize the system operation cost, and the objective function is specifically: In the formula, set T represents the dispatch period; set G represents the dispatch unit; constant C g represents the unit coal consumption cost of power generation of unit g; variable p g,t Indicates the output of unit g during period t; constant and Respectively represent the startup cost and shutdown cost of unit g; variable y g,t and z g,t They respectively represent the start command and shutdown command of unit g at time t.

7. The method for accelerating the unit commitment of a power system based on dynamic interaction of main and sub-problems according to claim 1, characterized in that: In step 4, the constraints of the small-scale auxiliary unit combination model are specifically: In the formula, set D represents the system load; set L represents the network line; constant D d,t represents the load size of load node d at time t; constant r t represents the backup demand of the system at time t; the constant F l max represents the transmission power limit of line l; H PTDF is the line power flow transfer distribution factor matrix; variable u g,t Indicates the start and stop status of unit g at time t; constant and Indicates the upper and lower limits of the output of unit g; constant and Indicates the minimum start and stop time of unit g; constant S g and R g Indicates the start / stop rate and ramp rate of the unit.

8. A power system unit combination acceleration system based on dynamic interaction of master and sub-problems, characterized in that: It includes a power system unit combination mathematical model module, a relaxation solution solving module, a prediction module, an auxiliary unit combination model module, and an optimal solution module; The power system unit combination mathematical model module is used to construct the main problem; The relaxation solution solving module is used to solve the power system unit combination mathematical model, and output the solution to the power system unit combination mathematical model, and collect N relaxation solutions in the initial stage of the solution; The prediction module is used to evaluate the N relaxation solutions in the relaxation solution solving module and predict the values ​​of the start-stop state variables of the unit; The auxiliary unit combination model module is used to construct a small-scale auxiliary unit combination model according to the unit start-stop state variable values ​​to obtain a sub-problem; The optimal solution module is used to solve the sub-problem and send the obtained sub-optimal solution to the main problem solving process, and accelerate the branch and bound convergence process by updating the global upper bound to obtain the optimal solution to the main problem.

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