A power system unit commitment acceleration method and system based on main sub-problem dynamic interaction

Abstract

The application discloses a kind of based on main sub-problem dynamic interaction's power system unit commitment acceleration method and system, method includes: step 1, establish power system unit commitment mathematical model, obtain main problem;Step 2, using branch and bound method to solve power system unit commitment mathematical model, collect N slack solution of initial solution;Step 3, N slack solution is evaluated, predicts the unit start-stop state variable value;Step 4, the start-stop state of fixed part unit is constructed small-scale auxiliary unit commitment model, obtain sub-problem;Step 5, solve sub-problem and send the sub-optimal solution obtained to main problem solving process, accelerate branch and bound convergence process by updating global upper bound, finally obtain the optimal solution of main problem.The heuristic method based on the physical characteristics of power system is deeply embedded in the general solver solving process with branch and bound as the core algorithm, and customized efficient and accurate solution of power system unit commitment is realized.

Description

Technical Field

[0001] This invention belongs to the field of power system operation optimization, specifically to a method and system for accelerating power system unit combination based on dynamic interaction of master and sub-problems. Background Technology

[0002] Unit combination in power systems is one of the most important technologies for formulating short-term power dispatch plans and a key step in day-ahead market clearing in the electricity spot market environment. Unit combination generally aims to minimize total system operating costs or maximize social welfare, arranging the start-up and shutdown schemes and generation plans of units in the system to meet system load and reserve requirements while ensuring operational safety. Mathematically, the unit combination problem is a non-convex large-scale mixed-integer programming problem. With the development of commercial mixed-integer linear programming (MILP) solvers, MILP solvers based on the branch-and-bound method have become the mainstream tool for solving unit combination problems and are widely used in practical provincial dispatch systems.

[0003] With the expansion of power grid scale and the increase in the types and quantities of resources in the system, the model size of unit combination has increased significantly. Under the constraints of practical engineering requirements such as electricity market clearing, unit combination faces the challenge of balancing solution accuracy and efficiency. Among 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 exact algorithm, the branch and bound method can guarantee the optimality of the calculation results, but its convergence efficiency is low for large-scale problems. A major idea for accelerating unit combination is to reduce the problem size based on knowledge of the power system domain, for example, by fixing some decision variables or removing ineffective constraints to reduce the model size, thereby improving solution efficiency. However, such heuristic methods can only obtain feasible solutions to the original problem, but cannot guarantee the optimality of the solution.

[0004] In summary, among the existing methods for solving unit combination problems, the branch and bound method can guarantee the optimality of the solution, but it has low convergence efficiency for large-scale problems and cannot meet the efficiency requirements of practical engineering. Heuristic methods utilize domain knowledge to achieve efficient problem solving, but cannot guarantee the accuracy of the solution. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a method and system for accelerating power system unit combination based on dynamic interaction of master and subproblems. This 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 solutions for power system unit combination.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for accelerating power system unit combination based on dynamic interaction of master and subproblems includes:

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

[0009] Step 2: Solve the mathematical model of the power system unit combination using the branch and bound method, and collect N relaxed solutions in the initial stage of the solution.

[0010] Step 3: Evaluate the N relaxed solutions and predict the values ​​of the unit start-up and shutdown state variables;

[0011] Step 4: Fix the start-up and shutdown status of some units, construct a small-scale auxiliary unit combination model, and obtain sub-problems;

[0012] Step 5: Solve the subproblem and send the obtained suboptimal solution to the main problem solving process. By updating the global upper bound, the branch-bound convergence process is accelerated, and finally the optimal solution to the main problem is obtained.

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

[0014]

[0015] In the formula, set T represents the scheduling period; set G represents the scheduling unit; and the constant C g The variable p represents the unit coal consumption cost for generating electricity from unit g; g,t This represents the output of unit g during time period t; a constant. and These represent the start-up cost and shutdown cost of unit g, respectively; variable y g, t and z g, t represents the start-up 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 as follows:

[0017]

[0018]

[0019] In the formula, set D represents the system load; set L represents the network lines; and the constant D d,t This represents the load magnitude at load node d at time t; the constant r t Represents the system's reserve requirement at time t; constant F l max H represents the transmission power limit of line l; PTDFThe power flow transfer distribution factor matrix of the line; variable u g,t This represents the start-up and shutdown state of unit g at time t; a constant. and Represents the upper and lower limits of the output of unit g; a constant. and Represents the minimum start-up and shutdown time of unit g; constant S g and R g This indicates the unit's start-up and shutdown rate and ramp-up rate.

[0020] Preferably, in step 2, a general solver using the branch and bound method is employed to solve the mathematical model of the power system unit combination. In the initial stage of the solution process, N relaxed solutions for the unit start-up and shutdown state variables are collected, and the i-th relaxed solution is denoted as...

[0021] Preferably, in step 3, the evaluation of N relaxed solutions and the prediction of the values ​​of the unit start-up and shutdown state variables specifically include,

[0022] The threshold method is used to identify normally open and normally closed generating units and determine the values ​​of the unit start-up and shutdown state variables. The specific judgment criteria are as follows:

[0023] for like but

[0024] for like but

[0025] In the formula, the parameters α1 and β1 are the judgment thresholds, usually 0≤β1<α1≤1; This represents the i-th predicted value of the start-up / shutdown state variable of unit g during time period t;

[0026] Using the minimum start-up and shutdown time as the time window, a threshold method is employed to identify the start-up and shutdown periods of the units and determine the values ​​of the unit start-up and shutdown status variables. The specific judgment criteria are as follows:

[0027] for like but

[0028] for like but

[0029] In the formula, the parameters α2 and β2 are the judgment thresholds, usually 0≤β2<α2≤1;

[0030] The current forecast results are revised based on the minimum start-up and shutdown time constraint to determine more values ​​for unit start-up and shutdown state variables. The specific revision criteria are as follows:

[0031] for like but

[0032] for like but

[0033] Taking the intersection of the prediction results of N relaxed solutions yields the final predicted values ​​of some unit start-up and shutdown state variables, denoted 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 operating cost, and the objective function is as follows:

[0037]

[0038] In the formula, set T represents the scheduling period; set G represents the scheduling unit; and the constant C g The variable p represents the unit coal consumption cost for generating electricity from unit g; g,t This represents the output of unit g during time period t; a constant. and These represent the start-up cost and shutdown cost of unit g, respectively; variable y g,t and z g, t represents the start-up 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 as follows:

[0040]

[0041]

[0042] In the formula, set D represents the system load; set L represents the network lines; and the constant D d,t This represents the load magnitude at load node d at time t; the constant r t Represents the system's reserve requirement at time t; constant F l max H represents the transmission power limit of line l; PTDF The power flow transfer distribution factor matrix of the line; variable u g,tThis represents the start-up and shutdown state of unit g at time t; a constant. and Represents the upper and lower limits of the output of unit g; a constant. and Represents the minimum start-up and shutdown time of unit g; constant S g and R g This indicates the unit's start-up and shutdown rate and ramp-up rate.

[0043] A power system unit combination acceleration system based on dynamic interaction of master and subproblems 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.

[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 mathematical model of power system unit combination and output the solution of the mathematical model of power system unit combination, and collect N relaxation solutions in the initial stage of the solution.

[0046] The prediction module is used to evaluate the N relaxed solutions in the relaxation solution solution module and predict the values ​​of the unit start-up and shutdown state variables.

[0047] The auxiliary unit combination model module is used to construct a small-scale auxiliary unit combination model based on the values ​​of the unit start-stop state variables, and obtain sub-problems;

[0048] The optimal solution module is used to solve subproblems and send the obtained suboptimal solutions to the main problem solving process. By updating the global upper bound, the branch-bound convergence process is accelerated, and the optimal solution to the main problem is obtained.

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

[0050] This invention provides an accelerated power system unit combination method based on dynamic interaction between the master problem and subproblems. Through the dynamic interaction between the master problem (original unit combination problem) and subproblems (small-scale auxiliary unit combination problem), a heuristic method is deeply embedded in the solution process of a general solver with branch and bound as the core algorithm, achieving customized, efficient, and accurate solutions to the problem. This invention is a heuristic variable prediction method based on the physical characteristics of power systems. In actual large-scale power systems, there are a large number of normally open and normally closed generating units. To ensure power balance and operational economy, the operating state of these units does not change throughout the entire scheduling cycle. Due to the limitations of the units' own physical characteristics, unit operation must meet minimum start-up and shutdown time constraints, meaning that the unit operating states have a temporal coupling relationship. This invention fully considers the above-mentioned physical characteristics of power system operation, proposing a normally open and normally closed generating unit identification method and a start-up and shutdown time prediction method for starting and stopping units. By predicting the values ​​of some unit start-up and shutdown state variables, a small-scale auxiliary unit combination model is constructed, and combined with the proposed accelerated solution framework, an efficient solution to the unit combination problem is achieved. Attached Figure Description

[0051] Figure 1 This forms the basic framework of the power system unit combination acceleration method based on dynamic interaction of master and sub-problems in this invention.

[0052] Figure 2 This diagram illustrates the principle of the method proposed in this invention for accelerating the branch and bound search process. Detailed Implementation

[0053] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0054] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0055] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0056] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a communication connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0057] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

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

[0059] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0060] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0061] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0062] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0063] Example 1

[0064] See Figure 1 A method for accelerating power system unit combination based on dynamic interaction of master and subproblems includes the following steps:

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

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

[0067] Step 3: Evaluate the relaxation solution by combining domain knowledge and predict the values ​​of unit start-up and shutdown state variables;

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

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

[0070] The mathematical model for power system unit combination described in step 1 aims to minimize system operating costs. The objective function is specifically expressed as follows:

[0071]

[0072] In the formula, set T represents the scheduling period; set G represents the scheduling unit; and the constant C g The variable p represents the unit coal consumption cost for generating electricity from unit g; g, t represents the output of unit g during time period t; a constant. and These represent the start-up cost and shutdown cost of unit g, respectively; variable y g, t and z g, t represents the start-up 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 lines; and the constant D d,t This represents the load magnitude at load node d at time t; the constant r t Represents the system's reserve requirement at time t; constant F l max H represents the transmission power limit of line l; PTDF The power flow transfer distribution factor matrix of the line; variable u g,t This represents the start-up and shutdown state of unit g at time t; a constant. and Represents the upper and lower limits of the output of unit g; a constant. and Represents the minimum start-up and shutdown time of unit g; constant S g and R g This indicates the unit's start-up and shutdown rate and ramp-up rate;

[0076] Equation (2) is a power balance constraint, indicating that the system operation needs to meet the power supply and demand balance at all times; Equation (3) is a spinning reserve constraint, indicating that the system operation needs to reserve sufficient reserve capacity; Equation (4) is a line power flow safety constraint, indicating that the system operation needs to ensure that the power flow of each line is within the safety limit; Equation (5) is a variable logic constraint, indicating the logical relationship between the unit's operating state and state transition; Equation (6) is an output constraint, limiting the output range of the unit; Equations (7)-(8) are minimum start-up and shutdown time constraints, limiting the unit from frequent start-up and shutdown in a short period of time; Equation (9) is a ramp constraint, limiting the range of changes in the unit's output between adjacent time periods; Equation (10) gives the range of values ​​for the decision variables.

[0077] As described in step 2, a general solver with the branch-and-bound method as its core algorithm is used to solve the power system unit combination model. In the initial stage of the solution process, N relaxed solutions for the unit start-up and shutdown state variables are collected, and the i-th relaxed solution is denoted as...

[0078] As described in step 3, the relaxation solution is analyzed and evaluated using knowledge of the power system domain to predict the values ​​of the unit start-up and shutdown state variables.

[0079] First, considering that a large number of units in the actual system are usually in a state of constant operation or shutdown, a threshold method is used to identify normally open and normally closed units and determine the values ​​of the unit start-up and shutdown status variables. The judgment criteria are specifically expressed as follows:

[0080] for like but

[0081] for like but

[0082] In the formula, the parameters α1 and β1 are the judgment thresholds, usually 0≤β1<α1≤1; This represents the i-th predicted value of the start-up / shutdown state variable of unit g during time period t;

[0083] Secondly, considering the requirement that unit operation must meet the minimum start-up and shutdown time limit, the minimum start-up and shutdown time is used as the time window. A threshold method is employed to identify the start-up and shutdown periods of the units and determine the values ​​of the unit start-up and shutdown status variables. The specific judgment criteria are expressed as follows:

[0084] for like but

[0085] for like but In the formula, the parameters α2 and β2 are the judgment thresholds, usually 0≤β2<α2≤1;

[0086] Next, the current forecast results are revised based on the minimum start-up and shutdown time constraints to determine the values ​​of more unit start-up and shutdown state variables. The revision criteria are specifically expressed as follows:

[0087] for like but

[0088] for like but

[0089] Finally, to improve the accuracy of variable prediction, the intersection of the prediction results of the N relaxed solutions is taken to obtain the final predicted values ​​of some unit start-up and shutdown state variables, denoted as .

[0090] for like but

[0091] for like but

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

[0093]

[0094] The feasible region of the small-scale auxiliary unit combination model is contained within the feasible region of the original unit combination model. The optimal solution of the subproblem is always a feasible solution of the main problem, but it is not necessarily the optimal solution. Therefore, it is called the suboptimal solution of the main problem.

[0095] As described in step 5, the suboptimal solutions obtained from the subproblems are sent to the main problem solving process to update the global upper bound of the main problem. Nodes with local lower bounds greater than the global upper bound will be pruned, thereby accelerating the branch and bound search process (see [link to principle] for details). Figure 2 This allows us to quickly obtain the optimal solution to the main problem.

[0096] Test systems of different sizes were used as the research objects. The system load data came from actual operating data in a province of 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 using the commercial Gurobi solver and the method proposed in this invention, respectively. The solution results are shown in Table 2.

[0100] Table 2. Solution results of the test system

[0101]

[0102]

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

[0104] Example 2

[0105] This invention provides a power system unit combination acceleration system based on dynamic interaction of master and subproblems, 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 mathematical model module for power system unit combination is used to construct the main problem.

[0107] The relaxation solution solving module is used to solve the mathematical model of power system unit combination and output the solution of the mathematical model of 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 solution module and predict the values ​​of the unit start-up and shutdown state variables.

[0109] The auxiliary unit combination model module is used to construct a small-scale auxiliary unit combination model based on the values ​​of the unit start-up and shutdown state variables, thereby obtaining sub-problems.

[0110] The optimal solution module is used to solve subproblems and send the obtained suboptimal solutions to the main problem solving process. By updating the global upper bound, the branch-bound convergence process is accelerated, and the optimal solution to the main problem is obtained.

[0111] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0112] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for accelerating the combination of generating units in a power system based on dynamic interaction of master and subproblems, characterized in that, include: Step 1: Establish a mathematical model of the power system unit combination to obtain the main problem; Step 2: Solve the mathematical model of the power system unit combination using the branch and bound method, and collect data from the initial solution process. N A relaxed solution; Specifically: A general solver using the branch and bound method is employed to solve the mathematical model of power system unit combination, and the start-up and shutdown state variables of the units are collected in the early stages of the solution process. N The relaxation solution, and the first... i The relaxation solution is denoted as ; Step 3, for N The relaxation solution is evaluated to predict the values ​​of the unit start-up and shutdown state variables; right N The relaxation solutions are evaluated to predict the values ​​of the unit start-up and shutdown state variables, specifically including... The threshold method is used to identify normally open and normally closed generating units and determine the values ​​of the unit start-up and shutdown state variables. The specific judgment criteria are as follows: In the formula, the parameters and To determine the threshold, typically ; Indicates the unit g exist t The first time period of the unit start-up and shutdown state variables i One predicted value; Using the minimum start-up and shutdown time as the time window, a threshold method is employed to identify the start-up and shutdown periods of the units and determine the values ​​of the unit start-up and shutdown status variables. The specific judgment criteria are as follows: In the formula, the parameters and To determine the threshold, typically ; The current forecast results are revised based on the minimum start-up and shutdown time constraint to determine more values ​​for unit start-up and shutdown state variables. The specific revision criteria are as follows: right N The intersection of the prediction results of each relaxation solution yields the final predicted values ​​of some unit start-up and shutdown state variables, denoted as . : Step 4: Fix the start-up and shutdown status of some units, construct a small-scale auxiliary unit combination model, and obtain sub-problems; Step 5: Solve the subproblem and send the obtained suboptimal solution to the main problem solving process. By updating the global upper bound, the branch-bound convergence process is accelerated, and finally the optimal solution to the main problem is obtained.

2. The method for accelerating power system unit combination based on dynamic interaction of master and sub-problems according to claim 1, characterized in that, In step 1, the mathematical model for the power system unit combination aims to minimize the system operating cost. The specific objective function is as follows: In the formula, the set T Indicates a scheduling period; set G Represents the dispatching unit; constant Indicates the unit g Unit coal consumption cost for power generation; variables Indicates the unit g exist t Output over a period of time; constant and They represent the generating units. g Startup and shutdown costs; variables and They represent the generating units. g exist t Start and stop commands at specific times.

3. The method for accelerating power system unit combination based on dynamic interaction of master and subproblems according to claim 1, characterized in that, In step 1, the constraints of the power system unit combination mathematical model are specifically as follows: In the formula, the set D Represents system load; set Represents network lines; constants Indicates load node d exist t Load magnitude at any given time; constant Indicates that the system is in t Backup requirements at any time; constant Indicates the line l The transmission power limit; The power flow transfer distribution factor matrix of the line; variables Indicates the unit g exist t Start and stop states at any given time; constant and Indicates the unit g The upper and lower limits of output; constant and Indicates the unit g Minimum start / stop time; constant and This indicates the unit's start-up and shutdown rate and ramp-up rate.

4. The method for accelerating power system unit combination based on dynamic interaction of master and subproblems according to claim 1, characterized in that, In step 4, the small-scale auxiliary unit combination model aims to minimize the system operating cost, and the specific objective function is as follows: In the formula, the set T Indicates a scheduling period; set G Represents the dispatching unit; constant Indicates the unit g Unit coal consumption cost for power generation; variables Indicates the unit g exist t Output over a period of time; constant and They represent the generating units. g Startup and shutdown costs; variables and They represent the generating units. g exist t Start and stop commands at specific times.

5. The method for accelerating power system unit combination based on dynamic interaction of master and subproblems according to claim 1, characterized in that, In step 4, the specific constraints of the small-scale auxiliary unit combination model are as follows: In the formula, the set D Represents system load; set Represents network lines; constants Indicates load node d exist t Load magnitude at any given time; constant Indicates that the system is in t Backup requirements at any time; constant Indicates the line l The transmission power limit; The power flow transfer distribution factor matrix of the line; variables Indicates the unit g exist t Start and stop states at any given time; constant and Indicates the unit g The upper and lower limits of output; constant and Indicates the unit g Minimum start / stop time; constant and This indicates the unit's start-up and shutdown rate and ramp-up rate.

6. A power system unit combination acceleration system based on dynamic interaction of master and subproblems, characterized in that, It includes a power system unit combination mathematical model module, a relaxation solution solution 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 mathematical model of power system unit combination and output the solution of the mathematical model of power system unit combination, and collect the initial solution data. N A relaxed solution; Specifically: A general solver using the branch and bound method is employed to solve the mathematical model of power system unit combination, and the start-up and shutdown state variables of the units are collected in the early stages of the solution process. N The relaxation solution, and the first... i The relaxation solution is denoted as ; The prediction module is used to perform calculations in the relaxation solution module. N The relaxation solution is evaluated to predict the values ​​of the unit start-up and shutdown state variables; right N The relaxation solutions are evaluated to predict the values ​​of the unit start-up and shutdown state variables, specifically including... The threshold method is used to identify normally open and normally closed generating units and determine the values ​​of the unit start-up and shutdown state variables. The specific judgment criteria are as follows: In the formula, the parameters and To determine the threshold, typically ; Indicates the unit g exist t The first time period of the unit start-up and shutdown state variables i One predicted value; Using the minimum start-up and shutdown time as the time window, a threshold method is employed to identify the start-up and shutdown periods of the units and determine the values ​​of the unit start-up and shutdown status variables. The specific judgment criteria are as follows: In the formula, the parameters and To determine the threshold, typically ; The current forecast results are revised based on the minimum start-up and shutdown time constraint to determine more values ​​for unit start-up and shutdown state variables. The specific revision criteria are as follows: right N The intersection of the prediction results of each relaxation solution yields the final predicted values ​​of some unit start-up and shutdown state variables, denoted as . : The auxiliary unit combination model module is used to construct a small-scale auxiliary unit combination model based on the values ​​of the unit start-stop state variables, and obtain sub-problems; The optimal solution module is used to solve subproblems and send the obtained suboptimal solutions to the main problem solving process. By updating the global upper bound, the branch-bound convergence process is accelerated, and the optimal solution to the main problem is obtained.

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